esa-sceva/satcom-chunk-collection huggingface dataset:
# A Form-Finding Method for Branching Structures Based on Dynamic Relaxation
## 2 Method
### Governing Equation  
The Governing equation of particle movement of dynamic relaxation method is  
\[\mathbf{M}\cdot\dot{\mathbf{v}}^{t}+\mathbf{C}\cdot\mathbf{v}^{t}=\mathbf{R}^{t} \tag{1}\]  
\[\mathbf{R}^{t}=\mathbf{F}_{\text{ext}}^{t}-\mathbf{F}_{\text{int}}^{t} \tag{2}\]  
where \(\mathbf{M}\) is the mass matrix, \(\mathbf{v}^{t}\) is the particle velocity vector at time \(t\), \(\mathbf{C}\) is the damping vector, \(\mathbf{R}^{t}\) is the residual force vector, \(\mathbf{F}_{\text{int}}^{t}\) is the internal force vector and \(\mathbf{F}_{\text{ext}}^{t}\) the external force vector.  
Assuming that the velocity varies linearly in time step \(\Delta t\), so the acceleration of the particle in \(\Delta t\) is constant. The velocity and acceleration of particle \(i\) at the time \(t\) can be expressed as  
\[v_{i,j}^{t}=\frac{v_{i,j}^{(t-\Delta t/2)}+v_{i,j}^{(t+\Delta t/2)}}{2},\dot{ v}_{i,j}^{t}=\frac{v_{i,j}^{(t+\Delta t/2)}-v_{i,j}^{(t-\Delta t/2)}}{\Delta t} \tag{3}\]  
where \(v_{i,j}^{t}\) is the velocity of particle \(i\) in direction \(j\) (\(j\) represents \(x,y\) or \(z\)) at time \(t\). According to Equations (1)-(3), the velocity of particle \(i\) at the time \(t+\Delta t/2\) can be obtained as  
\[v_{i,j}^{(t+\Delta t/2)}=v_{i,j}^{(t-\Delta t/2)}\frac{m_{i}/\Delta t-c_{i,j }/2}{m_{i}/\Delta t+c_{i,j}/2}+\frac{r_{i,j}^{t}}{m_{i}/\Delta t+c_{i,j}/2} \tag{4}\]where \(j\) represents the direction of \(x\), \(y\) or \(z\), \(c_{ij}\) is the damping coefficient of particle \(i\) in the direction \(j\), \(r_{i,j}^{t}\) is the residual force of particle \(i\) in \(j\) direction. Thus, the updated geometry at time \(t+\Delta t\) is  
\[\mathbf{P}^{(t+\Delta t)}=\mathbf{P}^{t}+\mathbf{v}^{(t+\Delta t/2)}\cdot \Delta t \tag{5}\]  
where \(\mathbf{P}^{t}\) is the nodal position vector at time \(t\). The kinetic energy at \(t+\Delta t\) is  
\[KE^{t+\Delta t}=\frac{1}{2}{\sum_{i,j}{m_{i}(v_{i,j}^{t+\Delta t})}^{2}} \tag{6}\]  
where \(KE^{t+\Delta t}\) is the kinetic energy at \(t+\Delta t\).  
The convergence of the computation is affected by the mass matrix \(\mathbf{M}\), damping coefficient, and time increment \(\Delta t\). In the following numerical examples, the mass setting is based on the method given in the literature [15], and the time increment \(\Delta t\) is determined by trial calculation. Kinetic damping proved to be stable and rapidly convergent [15]. We adopt kinetic damping in which viscous damping coefficients in Equation (4) are no longer needed but instead all the nodal velocities are set to zero when a kinetic energy peak is detected.
###### Abstract  
Accurate predictions of wind and other weather phenomena are essential for making informed strategic and tactical decisions in sailing. Saliors worldwide utilize current state-of-the-art forecasts, yet such forecasts are often insufficient because they do not offer the high temporal and geographic resolution required by sailors. This paper examines wind forecasting in competitive sailing and demonstrates that traditional wind forecasts can be improved for sailing events by using an integration of traditional numerical modeling and machine learning (ML) methods. Our primary objective is to provide practical and more precise wind forecasts that will give sailors a competitive edge. As a case study, we demonstrate the capabilities of our proposed methods to improve wind forecasting at Lake Kinneret, a popular sailing site. The lake wind pattern is highly influenced by the area's topographic features and is characterized by unique local and mesoscale phenomena at different times of the day. In this research, we simulate the Kinneret wind during the summers of 2015-2021 in up to one-kilometer resolution using the Weather Research and Forecasting (WRF) atmospheric model. The results are used as input for convolutional neural network (CNN) and multilayer perceptron (MLP) ML models to postprocess and improve the WRF model accuracy. These advanced ML models are trained using training datasets based on the WRF data as well as real data measured by the meteorological service, and subsequently, a validation process of the trained ML model is performed on unseen datasets against site-specific meteorological service observations. Through our experimental analysis, we demonstrate the limitations of the WRF model. It uncovers notable biases in wind direction and velocity, particularly a persistent northern bias in direction and an overestimation of wind strength. Despite its inherent limitations, this study demonstrates that the integration of ML models can potentially improve wind forecasting due to the remarkable prediction accuracy rate achieved by the CNN model, surpassing 95%, while achieving partial success for the MLP model. Furthermore, a successful CNN-based preliminary forecast was effectively generated, suggesting its potential contribution to the future development of a user-friendly tool for sailors.  
weather forecasting; machine learning; convolutional neural networks; multi-layer perceptron +
Footnote †: journal: Applied Sciences  
1
## 2 Forward Modeling Method
### Solving the Discretization of the Target Region  
To solve [PERSON]'s equations numerically, the modeling domain is discretized in a three-dimensional Cartesian coordinate system using hexahedral grids (Yee cells) with variable intervals [27]. The Yee cell discretization places electric fields at the midpoints of grid cell edges and magnetic fields on grid cell faces (Figure 3a), defining conductivity and magnetic permeability at the center of each cell. This discretization method ensures that the tangential component of the electric field is continuous along each edge of the grid cell, and the normal component of the field is continuous across each face of the grid cell. The curl operator can be approximated by integration using the finite volume method. In four adjacent cells, the magnetic field \({\mathbf{H}}\) defined at the face center is surrounded by the electric field \({\mathbf{E}}\) defined at the edge center (Figure 3b). Using Equation (7) and [PERSON]'s theorem, the weak solution forms of Equations (1) and (2) can be obtained:  
\[\oint_{\Gamma}{\mathbf{E}}\cdot d{\mathbf{\Gamma}}=-i\omega\iint_{\mathbf{S}}{\mathbf{B}} \cdot d\mathbf{S}, \tag{11}\]  
\[\oint_{\Gamma}{\mathbf{H}}\cdot d{\mathbf{\Gamma}}=\iint_{\mathbf{S}}\
abla\times\mathbf{{H}}\cdot d\mathbf{S}=\iint_{\mathbf{S}}(\sigma{\mathbf{E}}+{\mathbf{J_{s}}})\cdot d \mathbf{S}, \tag{12}\]  
where \(\mathbf{S}\) denotes the cross-sectional area of the control volume in the \(\mathbf{XOZ}\) plane. By using the first equation in (12), the curl operator can be approximated as the line integral of the field along a closed path normalized by the area \(\mathbf{S}\). It can be seen from the second equation in (12) that the line integral of \({\mathbf{H}}\) along the closed loop is equal to the surface integral of the current density within the loop [7; 27]. Assuming that the electric field in the Y-direction within the control volume is uniform, similar to a parallel circuit, the total current density in the Y-direction excluding external sources is equal to the sum of the surrounding four cells:  
\[\iint_{\mathbf{S}}\overline{\sigma}{\mathbf{E}}\cdot d\mathbf{S}=\sum_{i=1}^{4} \iint_{S_{i}}\sigma_{i}{\mathbf{E}}\cdot d\mathbf{S}_{i}, \tag{13}\]  
where \(\overline{\sigma}\) is the average electrical conductivity, \(\sigma_{i}\) is the electrical conductivity in the \(i\)-th cell, and \(\mathbf{S}_{i}\) is the cross-sectional area of the \(i\)-th cell. From Equation (13), the average electrical conductivity defined on the grid edge can be calculated as the area-weighted average of the adjacent four conductivities:  
\[\overline{\sigma}=\sum_{i=1}^{4}\frac{S_{i}}{S}\sigma_{i}, \tag{14}\]  
The average conductivity \(\overline{\sigma}\) in Equation (14) can be computed using the averaging matrix \(\mathbf{A}\). The averaging matrix \(\mathbf{A}_{E2C}\) maps variables from the midpoints of grid cell edges to the centers of cell volumes. Another averaging matrix \(\mathbf{A}_{F2C}\) for staggered grids maps variables from the centers of cell faces to the centers of cell volumes.
# Research on Path and Sequence Planning for Multi-Cabin Onboard Transportation of Large Cruise Ships
## 4 Multi-Transportation Path Planning Based on the Improved A-Star Algorithm
### Transport Path Planning  
This study combines the improved A-Star algorithm with the reverse sequence planning strategy, using the grid map from the previous section, to design a planning scheme for the transport sequence and path of the cabins on the cruise ship.  
In the grid, the transport direction of modular cabins typically offers eight possible directions, as shown in Figure 11, namely OA, OB, OC, OD, OE, OF, OG, and OH. However, in practical working environments, due to the large volume of modular cabins and the numerous pillars within the ship's cabins, cabins are prone to collisions with bulkheads and other obstacles during transport. Moreover, diagonal transport is not supported within the ship's aisles. Therefore, this study only considers movement in the four cardinal directions: OA, OB, OC, and OD. Even though this may result in a slight increase in the planned path length, this approach is more consistent with the practical conditions of the construction environment, and is therefore both reasonable and necessary.  
In the optimization of the transport path for each cabin, the grid map allows the transport time of each cabin to be equated to the path length of the movement. However, due to the rectangular shape of the cabins, the transport times along the length and width directions are not exactly the same. For the problem discussed in this study, let the time required to move the cabin by one meter be denoted as the unit time t. Thus, the transport time in the OA and OC directions is denoted as, and the transport time in the OB and OD directions is denoted as.  
The specific process for large cruise ship push path planning based on the reverse sequencing strategy integrated with the improved A-Star algorithm is shown in Figure 12. The main steps are as follows:  
1. Establish the Grid Map: Input the coordinates of the start and end points, and place all cabins inside the ship.
2. Select Movable Cabins: Identify all currently movable cabins, and use the A-Star algorithm to compute the shortest transport time for these cabins.  
Figure 10: Simplified raster map.  
3. Select Installable Cabins: Based on the installation capacity requirements, select the cabins that can be installed. Once all installations are complete, sequentially select cabins from these to perform the transportation.
4. Check for Completion of All Cabin Transportation: Determine whether all cabins have been transported. If any cabins remain, repeat steps (2)-(3). If all cabins have been transported, proceed to the next step.
5. Calculate Entry Times: Compute the entry time for each cabin, and determine the actual sequence of entries.
6. Output the Optimal Path: Using the improved A-Star algorithm, output the optimal path for each cabin based on the actual entry sequence.  
Figure 11: Transport direction diagram.  
Figure 12: Path planning flow chart.
## 5 Simulation Analysis  
A MATLAB 2020 simulation was conducted to evaluate the S21 transmission coefficient and S11 reflection coefficient for a rectangular microstrip antenna designed for dual-band  
Figure 10: S21 transmission coefficient with and without block.  
functionality between 1 and 15 GHz. The antenna, fabricated using a FR4 substrate (\(\epsilon_{r}=4.4\)) and with a thickness of 1.6 mm, is designed for frequencies of 4 GHz (C-band) and 13.7 GHz (Ku-band). The simulation assessed the S21 and S11 parameters in both the presence and absence of obstructions to analyze the antenna's transmission and reflection characteristics. The S21 transmission coefficient without the block demonstrated superior transmission efficiency. However, the inclusion of the block resulted in considerable attenuation. The S11 reflection coefficient exhibited increased reflection with the introduction of the block, affecting overall impedance matching and efficiency. The experimental findings closely corresponded with the predicted data, confirming that the engineered antenna sustains efficient performance over the specified bands. Minor discrepancies between experimental and simulated values were observed due to manufacturing tolerances and material losses at high frequencies.
## 2 Standard Scalar Gaussian Beams
### Gaussian Beam Propagation
Furthermore, it is also worth noting that the expression for the expansion rate (26) is also the same as the wavefront curvature of the Gaussian beam, with its inverse providing the radius of curvature of the latter. In this regard, we have that wavefronts are flat (infinite radius of curvature) at the input plane \(z=0\) (i.e., at the beam waist) and also as \(z\) becomes large compared to \(z_{R}\) (i.e., in the Fraunhofer regime). On the contrary, they undergo maximal curvature when \(z=z_{R}\) (with the radius of curvature being \(2z_{R}\)), just at the plane where the beam expansion rate (26) gets its maximum value, \(1/2z_{R}\).
# A Selective Dynamic Sampling Back-Propagation Approach for Handling the Two-Class Imbalance Problem
## 3 State-of-the-Art of the Class Imbalance Approaches
### Under-Sampling Approaches  
**TL**: Tomek links are pairs of samples \(a\) and \(b\) from different classes, and there does not exist a sample \(c\), such that \(d(a,c)<d(a,b)\) or \(d(b,c)<d(a,b)\), where \(d\) is the distance between pairs of samples [22]. Samples in TL are noisy or lie in the decision border. This method removes those majority class samples belonging to TL [9].  
**CNN**: The main goal of the condensed nearest neighbor algorithm is the reduction of the size of the stored dataset of training samples while trying to maintain (or even improve) generalization accuracy. In this method, every member of \(X\) (the original training dataset) must be closer to a member of \(S\) (the pruned set) of the same class than any other member of \(S\) from a different class [23].  
**CNNTL**: combines the CNN with TL [9].
**NCL**: The Neighborhood Cleaning Rule uses the Editing Nearest Neighbor (ENN) rule, but only eliminates the majority class samples. ENN uses the \(k-NN\)\((k>1)\) classifier to estimate the class label of every sample in the dataset and discards those samples whose class labels disagree with the class associated with the majority of the \(k\) neighbors [20].  
**OSS**: The One-Sided Selection method performs TL, then CNN on the training dataset [21].  
**RUS** The Random Under-Sampling randomly eliminates samples from the majority class and biases the discrimination process to compensate for the class imbalance.
## Appendix A : Pre-train DNN and the Auxiliary Dataset  
Considering that transfer learning enables leveraging knowledge from similar tasks, we designed an auxiliary dataset in order to pre-train the DNN to obtain the optimal neural network architecture that is suitable for our targeted task, i.e., predicting crop yield. We used the net primary production (NPP) product derived from the Landsat [71] as an approximate of the crop yield, the dependent variable in our targeted task; and the independent variables include EVI, VPD, precipitation, and temperature, which are the same as our targeted dataset. The auxiliary dataset from 2013 to 2015 was processed in the GEE using the same method described in Section 2.2.3 to obtain county-level mean values. Then we split the auxiliary dataset into two parts. Specifically, the first part of the data was from 2013 to 2014, and the second set, or the testing set, included data in 2015. Next, the 5-fold CV was employed on the first set to select training and validation data and to optimize hyperparameters. The hyperparameters obtained mainly included the number of layers of the neuron network, the number of neurons in each layer, the activation function, the dropout rate, and the optimize algorithm. The best hyperparameters were selected based on the validation dataset by choosing the ones with the highest averaged R\({}^{2}\). Last, the DNN with best hyperparameters was tested on the second set. Part of the testing results for model architecture selection was presented in Table S1, and the model structure was shown in Figure S3. It should also be noted that the weights from the pre-trained model of which the NN architecture was selected are not used for initialization. Instead, the DNN models in Section 3.2 were all randomly initialized in order to prevent the information from EVI as the pre-trained dataset contaminate the 'SIF' models and to provide a relatively fair comparisons among models that input EVI and the ones that input SIF data.
## 3 Results and Discussion
### Leading Process
#### 3.1.5 Effect of Liquid-To-Solid Ratio  
The liquid-to-solid ratio showed a significant effect on the volume of filtrate and concentration of purpose product in the leaching process. A series of experiments were conducted to evaluate an optimal liquid-to-solid ratio to achieve high leaching efficiency of vanadium and chromium. The liquid-to-solid ratio was set as 3:1 mL/g, 4:1 mL/g, 5:1 mL/g, 6:1 mL/g, and 7:1 mL/g, and other reaction conditions were kept as constant: reaction temperature of 90 \({}^{\circ}\)C, reaction time of 180 min, dosage of MnO\({}_{2}\) at 50 wt.%, and concentration of H\({}_{2}\)SO\({}_{4}\) at 30 wt.%. The results are shown in Figure 8.  
Figure 6: Effect of reaction temperature on the leaching efficiency of vanadium and chromium.  
Figure 7: Effect of reaction time on the leaching efficiency of vanadium.  
The leaching process was inefficient for low liquid-to-solid ratio as the viscosity of reaction medium was large, just 88.64% of chromium and 80.45% of vanadium were leached out in liquid-to-solid at 3:1 mL/g. The leaching process favored higher liquid-to-solid ratio, where the chromium and vanadium of 99.80% and 97.93% were obtained at a liquid-to-solid ratio of 5:1 mL/g. Further increase of the liquid-to-solid ratio only improved the leaching efficiency slightly, therefore, a liquid-to-solid of 5:1 mL/g was recommended for future use.  
Based upon the above experimental results, the optimal reaction conditions for vanadium and chromium leaching out were reaction temperature of 90 \({}^{\circ}\)C, reaction time of 180 min, dosage of MnO\({}_{2}\) at 50 wt.%, concentration of H\({}_{2}\)SO\({}_{4}\) at 30 wt.%, and liquid-to-solid at 5:1 mL/g. The leaching efficiency of vanadium and chromium was up to 99.80% and 97.93% under optimal reaction conditions.
## 4 Summary and Conclusions  
In this paper, we first used the fuzzy C-means clustering method to perform fuzzy clustering on the surface sound speed of the Kuroshio extension study area and obtained the range and location of the frontal zone. The result of cluster analysis can be consistent with the result of surface temperature gradient judgment. Next, according to the position and range of the frontal zone at different times, we selected the corresponding melt parameters to construct a two-dimensional parametric model of ocean fronts based on the sound speed profile. The results showed that it had a good similarity with the actual two-dimensional sound speed distribution of the ocean front. In order to verify the effectiveness of the ocean front model in acoustic forecasting, we selected a characteristic section and used the BELLHOP ray model to obtain the comparison between the model and the actual transmission loss by setting different sound source depths and emission frequencies. It was found that the root means square error between the calculated results of the model and the actual results was mostly controlled below 6 dB. As far as the percentage with the difference exceeding 10 dB is concerned, this level was basically maintained at around 10%. In terms of the transmission loss, the calculation results of the model were in good agreement with the actual situation. Finally, we proposed a simple combination of the melt function and the depth of the convergence area to verify the effectiveness of applying the melt function to the rapid forecasting of the depth of the convergence area in the ocean front environment. After calculation, the root mean square error between the forecasting result and the actual calculation result in the first detection convergence area was 82.3 m, and the second detection convergence area was 43.3 m, the forecasting effect was better. It is hoped that this research may be to some degree of referential significance to acoustic detection in the ocean front environment. As one of the most common mesoscale phenomena in the ocean, the impact of ocean fronts on underwater acoustic detection has become a hot spot for hydroacoustics. Because experimental data with high spatial resolution in the ocean front area can not be quickly and effectively obtained by oceanographic surveys, it is feasible to use theoretical models combined with typical data to reconstruct ocean fronts and analyze their impact on acoustic detection. Therefore, according to our method, future oceanographic surveys only require surface sound speed data (of course, this part of the data can also be obtained through the satellite remote sensing data of high spatial resolution) and sound speed profiles representing the typical properties of water masses (this part of the data can be obtained through the actual ocean surveys). Then we can construct the two-dimensional sound speed field distribution model in the ocean front environment. This method was proven effective in our research. This method can effectively predict the depth of the convergence
## 3 Results
### 1 h and 24 h Binary Mixture of Cd + ZnO--MixTOX Analysis
The results are \(\pm\) SD of three parallel experiments. Cd + Zn, strong correlations (\(R\) = 0.93) were found between DPPH and HRSA, as well as between DPPH and TPC. However, lower correlations were identified between the antioxidant assays in the chronic condition of this mixture (Table 4). At the same time, in the bimetallic mixture of Cd + ZnO during chronic exposure, DPPH and HRSA as well as DPPH and TPC showed a strong correlation R = 0.91 and R = 0.95 at 24 h, respectively (Table 4). The hydroxyl radical is a highly important and reactive free radical formed in biological systems, capable of interacting with all molecules within living cells and causing severe damage. The ability of _E. acidiculatus_ cell extracts to scavenge hydroxyl radicals is expressed as the percentage of scavenged hydroxyl radicals, using ascorbic acid used as a standard. As shown in Figure 3, the cell extracts showed varying levels of radical scavenging ability in single-metal treatments, ranging from 31.20% to 68.98% at 1 h and from 48.83% to 71.92% at 24 h.  
In the case of binary mixtures, the hydroxyl radical scavenging activity ranged from 39.39% to 71.88% (Figure 3). The minimum and maximum hydroxyl radical scavenging activity was observed in the 0.5 + 1 mixture at 1 h and 24 h of Cd + Zn, respectively. In the binary mixture of Cd + ZnO, higher activity was observed in the mixture of 0.5 + 0.25 (55.74%), and in Cd + Zn at 24 h, higher scavenging activity was observed in the binary mixture of 0.5 + 1 (71.88%) (Figure 3) with a significant \(p\)-value of < 0.001. Generally, Cd + Zn at 1 h and Cd + ZnO at 24 h showed a wide range of scavenging abilities (39.39-61.38% and 46.31-55.74%, respectively), with Cd + Zn at 24 h consistently showing higher scavenging activity (49-71.88%). In the present study, a significant and positive correlation was observed between HRSA and TPC assays in single-metal treatments at both 1 h (\(R\) = 0.85) and 24 h (\(R\) = 0.86). The correlation was stronger in the bimetallic mixture compared to single-metal exposure, with the lowest correlations found between the antioxidant assays of HRSA and TPC at 1 h in the binary mixture of Cd + Zn (Table 4). However, strong correlations were found in Cd + Zn and Cd + ZnO at 24 h, with \(R\) values of 0.89 and 0.81, respectively.