esa-sceva/satcom-chunk-collection huggingface dataset:
# Gridded Visibility Products over Marine Environments Based on Artificial Neural Network Analysis
###### Abstract  
The reconstruction and monitoring of visibility over marine environments is critically important because of a lack of observations. To travel safely in marine environments, a high quality of visibility data is needed to evaluate navigation risk. Currently, although visibility is available through numerical weather prediction models as well as ground and spaceborne remote sensing platforms and ship measurements, issues still exist over the remote marine environments and northern latitudes. To improve visibility prediction and reduce navigational risks, gridded visibility data based on artificial neural network analysis can be used over marine environments, and the problem can be regarded as an air quality prediction problem based on machine learning algorithms. This new method based on artificial intelligence techniques developed here is tested over the Indian Ocean. The mean error of the inferred visibility from the artificial neural network analysis is found to be less than 8.0%. The results suggested that satellite-based optical thickness and numerical model-based reanalysis data can be used to infer gridded visibility values based on artificial neural network analysis, and that could help us reconstruct and monitor surface gridded visibility values over marine and remote environments.  
marine visibility; aerosol optical depth; artificial neural network +
Footnote †: journal: Applied Sciences
## 3 Problem Formulation and Solutions
### Solution: Power Allocation (PA) and Power Splitting (PS)
\(\left(-\frac{a_{d,k}\,e_{d,k}}{ln2}+log_{2}\,a_{d,k}+\frac{1}{ln2}\right)\geq \tau_{d,k}\). To make problem (31) solvable, another slack variable \(\theta_{d,k}=\frac{P_{\theta,k}^{\min}}{(\eta(1-\rho_{d,k}))}\) is introduced, and constraint (17e) in (31) can be conveyed as  
\[\sum_{d=1}^{D}\sum_{k=1}^{\Xi_{d}}\
orm{\mathbf{h}_{d,k}^{H}\mathbf{\hat{ \mathbf{\theta}}}^{B}}_{2}p_{d,k}+\sigma_{u}^{2}\geq\theta_{d,k} \tag{32}\]  
Now, to deal with the constraints relate to \(\tau_{d,k}\) and \(\vartheta_{d,k}\), we transform them into matrix form as in [3]  
\[ \begin{bmatrix}\tau_{d,k}&1\\ 1&\rho_{d,k}\end{bmatrix} \geq 0,\,\forall\,d,k \tag{33}\]  
and  
\[ \begin{bmatrix}\vartheta_{d,k}&\sqrt{P_{d,k}^{min}/\eta}\\ \sqrt{P_{d,k}^{min}/\eta}&1-\rho_{d,k}\end{bmatrix} \geq 0,\,\forall\,d,k \tag{34}\]  
Finally, after having the optimal values corresponding to \(a_{d,k}\), \(c_{d,k}\), and \(\rho_{d,k}\), we move towards solving the optimization problem as follows:  
\[\max_{\rho_{d,k}}\sum_{d=1}^{D}\sum_{k=1}^{\Xi_{d}}\tau_{d,k} \tag{35}\]  
s.t. (17c), (31), (33) and (34). The solution of (35) produces an optimal value of \(p_{d,k}\) in the \(i\)th iteration. Thus, through the iterative AO algorithm, the resulted optimal values for \(a_{d,k}\), \(c_{d,k}\), \(p_{d,k}\), and \(\rho_{d,k}\) will preserve or increase the solution for the objective function in (26).
## 3 Experiments
### Comparative Methods and Metric  
Pooling operations applied to values in each grid of an original traffic congestion matrix determine the effectiveness of our proposed representation framework, as discussed in Section 2.1. We compare our proposed MAV pooling operation with three others used in the existing literature:  
* The nearest neighbor value (NNV), as defined by Equation (2), is based on a common image resampling algorithm [51]. In our experiment, this operation returns the value in the upper left corner of a grid [52];
* The average of the maximum and minimum values (AMM) which is inspired by weighted median filter [53; 54]. In our experiment, this operation returns the mean of the maximum value and minimum value in a grid, as defined by Equation (3);
* The nonzero average (ANZ) of the values in each grid used in the existing literature for the prediction of traffic flow variables such as speed or congestion [5; 28], as defined by Equation (4). \[c_{i,j}^{t}=p_{t,i,j}^{0}\] (2) \[c_{i,j}^{t}=\frac{max(\left\{p_{t,i,j}^{k}\left|\right.k=1,2,\ldots,g^{2} \right\})+min(\left.\left\{p_{t,i,j}^{k}\left|\right.k=1,2,\ldots,g^{2}\right\} )\right.}{2}\] (3) \[c_{i,j}^{t}=\frac{\sum_{k=1}^{g^{2}}p_{t,i,j}^{k}}{\sum_{k=1}^{g^{2}}\left[p_ {t,i,j}^{k}>0\right]}\] (4) in which \(p_{t,i,j}^{k}\) is a certain element in a grid indexed by \(i\) and \(j\) of \(P_{t}\) before this grid is transformed.
## 3 Results and Discussion
### Accumulation Characteristics in the Coking Chamber after Coal Charging
#### 3.4.4 Analysis of Range  
In Table 4, it can be seen that the range values of factors A, B, C, and D of coal bulk density are all greater than the blank column, so the four factors had a significant influence on the bulk density. However, the factor blanking height of the repose angle was smaller than the blank column, indicating that the blanking height had no significant influence on the repose angle. According to the comparison and analysis of the horizontal range of each factor, the optimal combination of control conditions corresponding to the bulk density was A\({}_{5}\)B\({}_{1}\)C\({}_{1}\)D\({}_{2}\) (particle size distribution was 0.9-2 mm; coal moisture content was 3%; cylinder lifting speed was 0.02 m/s, and blanking height was 0.2 m). Optimal combination of the repose angle A\({}_{5}\)B\({}_{5}\)C\({}_{2}\)D\({}_{1}\) (particle size distribution was 0.9-2 mm; moisture content was 11%; cylinder lifting speed was 0.04 m/s, and blanking height was 0.1 m). However, the range analysis method had some limitations, it was impossible to estimate the errors in the test process and the test results. Therefore, it was impossible to distinguish whether the difference in the test results corresponding to each level of a certain factor was caused by  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Factors**} & \multicolumn{1}{c}{**A**} & \multicolumn{1}{c}{**B**} & \multicolumn{1}{c}{**C**} & \multicolumn{1}{c}{**D**} & \multicolumn{1}{c}{**Blank**} & \multicolumn{1}{c}{**Blank**} \\ \hline \multirow{5}{*}{\(\rho\)} & K1 & 707.43 & 789.18 & 783.75 & 758.83 & 745.32 & 745.06 \\  & K2 & 754.05 & 769.86 & 755.60 & 774.36 & 745.37 & 748.22 \\  & K3 & 756.66 & 745.11 & 747.65 & 749.32 & 765.17 & 742.61 \\  & K4 & 762.17 & 717.88 & 735.52 & 738.06 & 743.82 & 765.95 \\  & K5 & 763.01 & 721.28 & 720.81 & 722.74 & 743.63 & 742.60 \\  & R & 55.58 & 71.30 & 62.94 & 51.62 & 21.54 & 23.35 \\ \hline \multirow{5}{*}{a} & K1 & 23.29 & 23.75 & 27.10 & 26.53 & 25.57 & 25.57 \\  & K2 & 25.30 & 25.00 & 27.75 & 26.36 & 26.16 & 25.01 \\ \cline{1-1}  & K3 & 26.40 & 26.12 & 26.89 & 26.01 & 26.16 & 26.93 \\ \cline{1-1}  & K4 & 27.00 & 27.12 & 24.94 & 25.82 & 26.17 & 26.36 \\ \cline{1-1}  & K5 & 28.43 & 28.44 & 23.74 & 25.70 & 26.36 & 26.16 \\ \cline{1-1}  & R & 5.14 & 4.70 & 4.00 & 0.83 & 0.79 & 1.92 \\ \hline \hline \end{tabular} Note: \(\rho\)—Bulk density; a—repose angle.  
\end{table}
Table 4: Orthogonal test L\({}_{25}\) (5\({}^{\circ}\)).  
Figure 14: Influence of coking coal characteristics on repose angle. (**a**) The effect of particle size distribution on the bulk density; (**b**) The effect of moisture content on the bulk density.  
the change in the level or experimental error. Therefore, the variance analysis method was adopted below to make up for the deficiency of the range analysis method.
## 3 Electromagnetic Characteristic Analysis
### Transient Analysis
#### 3.2.1 Rated Load Characteristics  
When the rated torque is loaded, the RMS current flowing out from the power supply should be 14 A. Therefore, sinusoidal current source excitations with an RMS value of 14 A are applied to each of the three-phase windings of the axial flux permanent magnet motor, respectively, as shown in Figure 14. The simulated back electromotive force (EMF) under these conditions is presented in Figure 15, while the simulated output torque is shown in Figure 16. Both the motor's speed and current remain stable during operation, and the back EMF and torque output are steady, meeting the motor's design requirements.  
In order to obtain the power calculation formula, the following formulas are solved simultaneously.  
\[P=F\cdot v \tag{2}\]  
\[F=\frac{T}{R} \tag{3}\]  
\[v=2\pi R\cdot n \tag{4}\]  
Figure 13: External characteristic curve of axial flux permanent magnet motor.  
where \(P\) is the output power (W), \(F\) is the force (N), \(v\) is the speed (m/s), \(T\) is the rated torque (N\(\cdot\)m), \(R\) is the radius (m), and \(n\) is the rated speed (r/s). The output power is shown in the following formula:  
\[P=\frac{Tn}{9550} \tag{5}\]  
where \(P\) is the output power (kW), \(T\) is the rated torque (N\(\cdot\)m), \(n\) is the rated speed (rpm), and 9550 is a constant used to convert power (kW) and speed (r/min) into torque (N\(\cdot\)m), which is the result of unit conversion and formula derivation. As shown in Figure 16, the rated torque reaches 2.7 N\(\cdot\)m, corresponding to a rated speed of 1080 rpm. Based on the calculation, the motor's output power under rated load conditions is approximately 0.305 kW, which satisfies the design requirements.  
Figure 16: The output torque of the motor under rated load conditions.  
Figure 14: The three-phase winding current source of the motor is excited under the rated load condition.  
Figure 15: The opposite electromotive force of the motor under rated load conditions.
# BBSF: Blockchain-Based Secure Weather Forecasting Information through Routing Protocol in Vanet
## 3 Methodology
### Routing Strategy
#### 3.3.3 Multi-Hop Routing Method  
To achieve a high level of network connectivity with efficient weather data broadcasting, in terms of supersession and network connectivity, we selected the minimum number of intermediate nodes for effective network connectivity and reliability node values. The network coverage also decreased whenever the number of relay messages decreased inside the network. To increase the speed and reliability of the network connectivity, we set up five hops in each routing for network connectivity values and provided efficient network services. Each weather forecasting information packet had several relay nodes attached with a counter to select and count the number of intermediate nodes. Every intermediate node checked the hop counter threshold. If it became lower, the message was received or forwarded to the next hop for the destination node. The protocol worked with efficient routing information when this number of incident nodes occurred. The sources and destination nodes only shared the blockchain-based encrypted data using the public and private keys, and kept secure the number of relay packets it received with the highest accuracy values. Figure 3 shows the routing node. Intermediate nodes, from sender to recipient nodes, were selected based on their abilities to participate in the data processing, the number of tasks currently processing, communication, and, more specifically, the closest range in the sending device. When the sender node sent the device, it became the predecessor, and the next node became the sending node. Then, the new sending node searched for the next hop. The proposed weather forecasting data and information flow technique determined the minimum number of hop counts.
# A New Defect Diagnosis Method for Wire Rope Based on CNN-Transformer and Transfer Learning
## 4 Case Studies
### Experiments
#### 4.4.2 Transfer Experiments  
To prove the effectiveness of the proposed TL architecture, transfer comparison experiments were conducted in this study. Furthermore, the defect sample usually is scarce and weak in real applications. Thus, the transfer efficiency analysis is meaningful for further research purposes. As shown in Table 10, domain adaptation comparison experiments were conducted for 12 groups. For example, A\(\rightarrow\)B means to train the DCNNT network using all the samples of source domain A. Then, the fine-tuning model used a part of the dataset obtained from target domain B. While 5%, 10%, 15%, and 20% target domain datasets were used for the transfer fine-tuning model, the comparison group presented the results without using TL architecture. It can be observed that the results present great fluctuations when the trained model predicts another domain sample directly. However, the TL method can be used to achieve a rapid increase in all the scenarios. As presented in Figure 13, the detection accuracy values can easily be improved even when using only a 5% target domain dataset, except for C\(\rightarrow\)A and D\(\rightarrow\)A, which is analyzed in Section 4.6.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Scenarios**} & \multicolumn{2}{c}{**Without TL**} & \multicolumn{3}{c}{**Target Domain Dataset with TL Architecture**} \\  & **Architecture** & **5\%** & **10\%** & **15\%** & **20\%** \\ \hline A\(\rightarrow\)B & 99.53 & 99.87 & 99.76 & 99.77 & 99.84 \\ A\(\rightarrow\)C & 98.12 & 98.31 & 98.34 & 98.53 & 98.73 \\ A\(\rightarrow\)D & 95.80 & 98.96 & 99.25 & 99.35 & 99.42 \\ B\(\rightarrow\)A & 95.60 & 95.95 & 96.21 & 96.72 & 98.88 \\ B\(\rightarrow\)C & 98.09 & 98.18 & 98.71 & 98.63 & 98.96 \\ B\(\rightarrow\)D & 98.32 & 99.39 & 99.58 & 99.42 & 99.60 \\ C\(\rightarrow\)A & 93.49 & 94.52 & 95.58 & 95.75 & 98.14 \\ C\(\rightarrow\)B & 98.99 & 99.07 & 99.33 & 99.52 & 99.57 \\ C\(\rightarrow\)D & 98.58 & 98.96 & 99.06 & 99.08 & 99.49 \\ D\(\rightarrow\)A & 94.73 & 96.27 & 96.86 & 97.08 & 97.15 \\ D\(\rightarrow\)B & 98.31 & 99.43 & 99.44 & 99.63 & 99.80 \\ D\(\rightarrow\)C & 97.53 & 97.96 & 98.47 & 98.70 & 98.81 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Detection accuracy (%) values of transfer fine-tuning method in different scenarios.  
Figure 13: The accuracy changes as the target domain dataset increases.
# FedCrow: Federated-Learning-Based Data Privacy Preservation in Crowd Sensing
## 2 Related Work
### Federated-Learning Data Security  
Although federated learning allows for data to be kept locally, there are still risks to data security. PrivLogit [24] proposed an algorithm that processes the horizontal distribution of data from different participants through logistic regression, integrates encrypted matrices and shared gradients, solves for optimal regression coefficients using [PERSON]'s iterative method, and improves the efficiency while addressing user privacy security. [PERSON] [25] introduced a vertical federated-learning algorithm suitable for vertically distributed datasets. The algorithm aligns samples and calculates results by generating CLK identifiers, requires both parties to create random masks, and leverages a third-party entity to ensure training accuracy, reducing the encryption computational costs while protecting privacy information. [PERSON] et al. [26] presented a federated-learning solution that combines adaptive gradient descent and differential privacy mechanisms, introduces differential privacy mechanisms to resist inference attacks, and provides more effective and quantifiable privacy protection for federated learning. A federated-learning-based IoT secure data-sharing mechanism FL2S [27] was proposed that employs a hierarchical asynchronous federated-learning framework. Utilizing deep reinforcement learning technology to select participants, reliable data sharing that protected the privacy of the source data was achieved. PFLM [28] introduced membership proof in federated learning, utilizing an encrypted accumulator to generate proof and publishing it on the blockchain. The PFLM joint-learning scheme was proposed, releasing the threshold assumption and designing a result verification algorithm based on the ElGamal encryption variant.  
[PERSON] et al. [29] focused on achieving secure federated learning in IoT communications. They proposed a solution based on fully homomorphic encryption to ensure the privacy and security of data during model training on edge devices. By leveraging fully homomorphic encryption technology, the solution enables computations on encrypted data, thereby avoiding the leakage of sensitive information. This research enhanced the efficiency and security of federated learning while protecting user privacy.  
Existing data security protection methods in federated learning based on differential privacy [30; 31; 32] and homomorphic encryption [33; 34] mainly focus on safeguarding data security during transmission. However, they lack effective identification and screening of malicious users and their data before the encrypted transmission, making them insufficient to resist man-in-the-middle attacks. Moreover, these methods incur a significant system overhead. Additionally, existing approaches lack flexibility in selecting suitable encryption methods according to specific application scenarios.
## 2 Proposed Model
### Physical-Based AVS Model  
Since the energy gap in graphene is zero, the current \(I_{D}\) in the AVS model for GFET has two parts due to electron \(Q_{x0e}\) and hole \(Q_{x0h}\) concentrations:  
\[\frac{I_{D}}{W}=(Q_{x0e}+Q_{x0h})v_{x0}F_{sat} \tag{9}\]  
Figure 2: Self-consistent solution of NEGF and Poisson’s Equation.  
Injection velocity \(v_{x0}\) was computed using the least-squared curve-fitting routine [18]. \(F_{sat}\) is empirically derived to conduct the transition from the triode to the active region of transfer and output characteristics and is computed as:  
\[F_{sat} =\frac{V_{dsi/V_{dat}}}{\left(1+\left(\frac{V_{dsi}}{V_{dat}}\right)^{ \beta}\right)^{1/\beta}} \tag{10}\]  
\[V_{dsat} =\ \frac{V_{x0}\ L_{G}}{\mu} \tag{11}\]  
where \(\mu\) is mobility, \(L_{G}\) is the GFET channel length, \(V_{dsi}\), \(V_{gsi}\), and \(V_{dsi}\) are the intrinsic drain-source, gate-source, and drain-gate bias, respectively, and computed as voltage drop of the circuit shown in Figure 3.  
\[V_{dsi} =V_{ds}-2(R_{elec}L_{dec}+R_{hole}I_{hole}) \tag{12a}\] \[V_{gsi} =V_{gs}-(R_{elec}L_{elec}+R_{hole}I_{hole})\] (12b) \[V_{gdi} =V_{gd}+(R_{elec}L_{dec}+R_{hole}I_{hole}) \tag{12c}\]  
The electron and hole concentrations are given as:  
\[Q_{x0} =C_{g}\ nO_{t}\text{log}(1+\exp(\frac{V_{gs}\ -V_{tu}-\alpha O_{t}FF_{t}}{ n}))+Q_{min} \tag{13}\] \[Q_{x0 th} =aC_{g}\ nO_{t}\text{log}(1+\exp(\frac{V_{dg}\ -V_{tp+\alpha O_{t}FF_{t}}}{ nO_{t}}))+Q_{min} \tag{14}\]  
where \(Q_{min}\), is minimum background doping, \(n\) is the subthreshold slope of GFET, and \(V_{tn}\), \(V_{tp}\), electron and hole threshold, respectively, are computed by:  
\[V_{tn} =V_{min0}+\Delta\text{V} \tag{15}\] \[V_{tp} =V_{min0}-\Delta\text{V} \tag{16}\]  
where \(V_{min0}\) shows the Diac-point voltage.  
\[FF_{e} =\frac{1}{1+\exp(\frac{V_{gsi}-(V_{tn}-\frac{\alpha O_{t}}{2})}{ \frac{aC_{t}}{2}})} \tag{17}\] \[FF_{h} =\frac{1}{1+\exp(\frac{V_{dg}+(V_{tp}+\frac{aC_{t}}{2})}{\frac{aC_{t}}{2} })} \tag{18}\]  
Here, \(\alpha\) gives the shift in the threshold voltage, \(O_{t}=K_{B}T/q=0.0258^{*}T_{jun}/298\), is the thermal voltage, \(\Delta\text{V}\) shows the trap charging on the graphene channel, and \(Q_{ref}\) is the electron and hole inhomogeneity near the Dirac point in graphene, as:  
\[Q_{ref}=C_{g}n_{0}O_{t} \tag{19}\]  
Figure 3: A phenomenological AVS circuit model.  
where \(C_{g}\) is gate capacitance, and \(n_{0}\) is the non-ideality factor. In the proposed method, some of these parameters are optimized according to the NEGF algorithm, and special features compatible with graphene/DNA effects are chosen for biosensor modeling.
# A Review of Variable-Beam Divergence Angle FSO Communication Systems
## 3 Existing Methods for Vbda
### Vary Beam Divergence by Adjusting the Focal Length F of the Collimating Lens
#### 3.2.1 Axial Translation of Light Source or Collimating Lens  
Figure 16 is the schematic diagram of this method. (Note that the mode field diameter of a normal 1550 nm SMF is only about 10.4 \(\upmu\)m and is exaggerated for convenience in this and the following figures). The lens or light source is fixed on a linear electric stage, which adjusts the beam divergence angle by changing the distance between the light source and the collimating lens. This method has the advantages of a simple principle and device and could achieve a large range of divergence angle changes. The risk of introducing additional wavefront distortion is reduced because no extra zoom elements are inserted into the optical path. Despite its benefits, this method has a notable disadvantage; it demands an exceptionally high level of parallelism in the linear electric stage. In [58], it was suggested that the lateral motion (eccentricity) of the lens or light source should not exceed 3 \(\upmu\)m.  
Previous studies [59; 60; 61; 62; 63; 64] adjusted the beam divergence angle by moving the light source, while only [65] moved the lens. The light source, often a fiber head of a SMF, is smaller and easier to manipulate than collimating lenses, making it a more popular choice in experiments.  
By using this VBDA method, [PERSON] et al. [57] and [PERSON] et al. [60] achieved the beaconless acquisition function of an FSO system. [PERSON] [59] and [PERSON] et al. [61] mitigated the influence of different atmospheric attenuation on the received optical power. [PERSON] et al. [62] designed a satellite FSO communication terminal test device with VBDA. Refs. [63; 64; 65] improved optical tracking characteristics. The VBDA mechanism and its FSO terminal, as presented in [63], are shown in Figure 17.  
Figure 16: Schematic diagram of VBDA by axial translation light source or collimating lens (**a**) Moving light source. (**b**) Moving collimating lens.  
Figure 15: Schematic diagram of VBDA method by: (**a**) switching collimators with different focal lengths. (**b**) inserting different defocus lenses.
# Physical, Thermal, and Mechanical Characterization of PMMA Foils Fabricated by Solution Casting
## 1 Introduction
Other studies focused on the effect of dimethyl formamide and tetrahydrofuran (THF) on the miscibility in the liquid phase between PMMA and polyvinyl chloride at different relative concentrations [12] and the effect of solvents on the morphology of films prepared by either laser evaporation [16] or drop-casting [19]. However, it is very rare that information is reported in the literature about the thermal, structural behavior, and mechanical properties of PMMA obtained by solvent evaporation from solution casting methods. Although various types of solvents for casting PMMA foils have been discussed before [20; 21], various ratios of S/L (solid/liquid) of a specific solvent, such as the most common one, Toluene, to PMMA powder are rarely discussed. The novelty of this work is to tune the glass transition temperature of prepared PMMA foils by varying the concentration of solutions. The tune glass temperature will integrate with NiTi alloy for the formation of the composite. The composite could be able to deflect the behavior of sensors and actuators as the function of heating and cooling cycles with the combined effect of polymer-alloy behavior. The aim of the present work is to investigate the thermal, surface features, structural, and mechanical properties of samples obtained by casting PMMA prepared from solution cast method curing in dry, ambient air at room temperature. PMMA as-cast foils were prepared from various ratios of PMMA powders-to-toluene (solid-to-liquid) ratios and investigated experimentally. The as-cast PMMA foils were characterized by various S/L ratios by thermal, microstructural, structural, and mechanical properties. Different experimental techniques are used, such as differential scanning calorimetry (DSC), dynamic mechanical analysis (DMA), and Fourier-transformed infrared spectroscopy (FTIR), which allowed the examination of thermal (i.e., glass transition temperature), thermomechanical (i.e., storage modulus, loss modulus, tan delta), and structural properties of the samples (i.e., bonding type and occurrence), respectively. Simultaneously, the surface features were observed with an optical microscope with the following shape memory behavior of commercial and as-cast PMMA polymer foils.