esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analysis
### Result Analysis  
To enhance the performance of the STL-DCSInformer-ETS model, hyperparameter optimization was carried out using Grid Search and Bayesian Optimization methods. Initially, Grid Search was employed to exhaustively explore a predefined hyperparameter space, adjusting parameters such as the learning rate, number of epochs, and batch size. The learning rate was tested across values of [0.0001, 0.001, 0.01], batch size was varied between [16; 32; 64; 128], and the number of epochs was set to [50; 100; 150]. Each combination was evaluated based on the Mean Squared Error (MSE) on the validation set, ensuring a thorough initial search that captured a wide range of possibilities. Following this, Bayesian Optimization was applied to refine the results obtained from Grid Search. This method allowed for a more efficient exploration of the hyperparameter space by leveraging a probabilistic model (Gaussian Process) to prioritize the most promising parameter combinations. The learning rate was narrowed to [0.0005, 0.001] based on prior performance, while the batch size was fine-tuned between 32 and 64 to strike a balance between training stability and efficiency. The objective of this optimization was to minimize MSE on the validation set, ultimately converging on the optimal parameter settings, as presented in Table 4. These optimizations led to a significant improvement in the model's predictive accuracy, enhancing both its robustness and performance across various data scenarios.  
For the forecasting task, the window size was set to 12 months. The forecasting method used in this study is a direct multi-step prediction approach, where the model predicts  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Hyperparameters** & **Numeric** \\ \hline Learning Rate & 0.001 \\ Batch size & 32 \\ Number of training rounds & 100 \\ Model size & 512 \\ Encoder input step size & 72 \\ Decoder input step size & 36 \\ Early Stopping Strategy & 3 \\ Number of attention heads & 8 \\ Optimizer & Adam \\ Activation Function & Leaky ReLU \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameter settings.  
multiple time steps at once, without relying on recursive predictions. Each prediction step is independent, and the model generates all predictions for the forecast horizon simultaneously. To further prevent overfitting, early stopping is used during training. The process is halted once the validation error stops improving.
# Recent Progress on Underwater Wireless Communication Methods and Applications
## 4 Future Trends
### Intelligent and Sustainable Development  
Artificial intelligence and ML will play a transformative role in addressing dynamic underwater challenges. Deep reinforcement learning (DRL) could empower AUVs to dynamically select communication modes based on real-time environmental parameters such as turbidity, salinity. GANs and attention-based models, already proven effective in denoising acoustic signals, may evolve into real-time turbulence compensation tools for optical systems. Federated learning frameworks could further optimize collaborative decision-making in AUV clusters while minimizing energy consumption. Sustainability will drive the design of eco-adaptive technologies. Salinity- and pressure-resistant optical transceivers, biodegradable sensor nodes, and energy-harvesting systems like ocean thermal gradient converters will reduce the environmental footprint of underwater infrastructure. Orbital angular momentum (OAM) multiplexing shows promise for stabilizing optical signals in polluted waters, while bio-inspired electric field communication mimics weakly electric fish to ensure robust short-range links in murky environments. Standardization efforts, including unified modulation protocols and blockchain-based authentication, will enhance interoperability and cybersecurity in heterogeneous networks.  
Recent efforts have translated AI-based methods from simulations into real deployments. For example, [PERSON] et al. [34] demonstrated a real-time GAN-based denoising system on an autonomous surface vehicle operating in the East China Sea, successfully improving the signal-to-noise ratio by over 8 dB in high-bioactivity zones.  
In another instance, the DRL-based adaptive protocol in the iLab-MarineNet framework allows a fleet of AUVs to switch between acoustic and optical links in real-time, guided by salinity and turbidity sensors. This has led to a 40% reduction in average latency across mission-critical segments [90].  
Additionally, federated learning has been applied in the DeepOceanSense platform to optimize acoustic equalization models across distributed nodes without centralized retraining, significantly lowering communication overhead and improving robustness.
## 2 Overview of SMOTE Schemes and Ensemble Learning Schemes
### SMOTE Techniques for Imbalanced Data
Given a training data set (\(X\), \(Y\)) in which \(X\) represents a data sample in minority class samples, \(X=x_{1}\),..., \(x_{n}\), \(Y\) is the over-sampling target from \(X\), and \(Y=y_{1}\),..., \(y_{N}\), \(y_{i}\) represents the i\({}^{\text{th}}\) of the \(N\) nearest neighbors of the \(X\). Notably \(N\) synthetic samples (i.e., \(y_{1}\),..., \(y_{N}\)) are randomly selected from \(K\) nearest neighbors as shown in Figure 2. To increase the number of minority class samples, the random interpolation operation between \(X\) and \(y_{i}\) (\(i=1\), \(2\),..., \(N\)) is performed by the following formula to obtain the interpolated sample \(p_{i}\), [21]  
\[p_{i}=X+rand(0,1)\times(y_{i}-X), \tag{2}\]  
where \(rand(0,1)\) represents a random number in the range (0, 1). Obviously, the number of sampling augmentation \(N\) depends on the IR of the dataset used. In further, \(N=\) round(IR), where round(IR) represents the value obtained by rounding up the IR. Once achieved the above over-sampling operation, new synthetic samples are generated randomly from minority class samples and their neighbors, and the majority class samples and the minority class samples became balanced. Researchers have focused on using the SMOTE method as a data preprocessing mechanism for balancing data sets in intrusion detection (Table 3).
# Wideband Versatile Receiver for CubeSat Microwave Front-Ends
## 3 Receiver Design, Simulation and Components Characterization
The up-converted signal was amplified by means of the AMM 6702 amplifier by Marki Microwave, Inc., in order to compensate for the first mixer stage losses. The measured Scattering parameters are reported in Figure 4, where it is worth noting that all the parameters fully comply with the specifications reported in the datasheet. Moreover, different fixed attenuator stages were employed to better equalize the system power budget. In order to limit the bandwidth to 1 GHz, thus properly fulfilling the goal of spurious rejection, a PB1182 WB bandpass filter by A1 Microwave Ltd. was employed. This is a critical component of the chain, and unfortunately, Microwave Ltd. did not provide the related scattering (S-) parameters. Therefore, the filter performance was modeled by designing an equivalent circuital network. To this purpose, a 7-elements Chebyshev lumped-element topology was designed. In Figure 5a, the schematic of the filter model is shown. Figure 4: Measured S\({}_{21}\) (black solid line), S\({}_{11}\) (blue solid line), and S\({}_{22}\) (red solid line) of the APM-6702 amplifier. Figure 3: Measured S\({}_{21}\) (black solid line), S\({}_{11}\) (blue solid line), and S\({}_{22}\) (red solid line) of the APM-6849 amplifier. Thereafter, the filter was tested in order to validate the extracted model. The good matching between the real and reproduced curves might be verified by observing Figure 5b, where the measured S\({}_{21}\) is reported along with the simulated one. The values of the filter elements are reported in Table 1.  
Thereafter, the MM1-1467H mixer was exploited to down-convert the signal down to the frequency of 1 GHz in the down-conversion stage. A 28 GHz signal, synchronized with the LO of the first mixer, was injected into the second LO input. Such a LO value was selected to obtain the central frequency of 1 GHz at the mixer output. The signal is finally amplified and filtered by means of the ZFL-1500 VH+ and the VLF-1500+ by MiniCircuits, respectively. The S-parameters of both the amplifier and filter are reported in Figure 6a,b. At this stage, the IF signal can be easily processed by employing currently available analog-to-digital converters. Finally, the power budget of the receiver was simulated to verify the compliance of the components' dynamic ranges within the entire chain and reported in Figure 7. To this purpose, the expected maximum input power of \(-20\) dBm was considered. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Element Type** & **Element ID** & **Value** \\ \hline Resistor & R1 & 5 \(\Omega\) \\ Inductor & L1 & 10.92 nH \\ Inductor & L2 & 6.77 pH \\ Inductor & L3 & 18.11 nH \\ Inductor & L4 & 6.22 pH \\ Capacitor & C1 & 2.72 fF \\ Capacitor & C2 & 4.39 pF \\ Capacitor & C3 & 1.64 fF \\ Capacitor & C4 & 4.78 pF \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Values of the simulated filter elements. Figure 5: (**a**) Schematic and (**b**) insertion loss of the measured (red solid line) and simulated (blue dashed line) S\({}_{21}\) for the PB1182 WB filter.
## 2 Circuit Descriptions
### Proposed Electronically-Tunable Mixed-Mode Biquadratic Filter
#### 2.2.2 VM and TAM  
According to Equations (2)-(10), VM and TAM can be obtained by setting the input voltage I\({}_{\text{in}}\) = 0 (opened) and getting V\({}_{\text{in}}\) as input signal. The four voltage transfer functions obtained are:  
\[\frac{\text{V}_{01}}{\text{V}_{\text{in}}}=\frac{\text{G}_{\text{X}1}\text{G} _{\text{X}2}\text{G}_{\text{X}3}}{\text{D}(\text{s})} \tag{23}\]  
\[\frac{\text{V}_{02}}{\text{V}_{\text{in}}}=\frac{\text{s}\text{C}_{1}\text{G} _{\text{X}1}\text{G}_{\text{X}3}}{\text{D}(\text{s})} \tag{24}\]  
\[\frac{\text{V}_{03}}{\text{V}_{\text{in}}}=\frac{-\text{s}^{2}\text{C}_{1} \text{C}_{2}\text{G}_{\text{X}3}}{\text{D}(\text{s})} \tag{25}\]  
\[\frac{\text{V}_{04}}{\text{V}_{\text{in}}}=(\frac{\text{G}_{\text{X}3}}{\text {g}_{\text{m}3}})(\frac{\text{s}^{2}\text{C}_{1}\text{C}_{2}\text{G}_{\text{ X}1}+\text{g}_{\text{m}2}\text{G}_{\text{X}1}\text{G}_{\text{X}2}}{\text{D}( \text{s})}) \tag{26}\]  
As indicated by Equations (23)-(26), a non-inverting LP filtering response is obtained from V\({}_{01}\), a non-inverting BP filtering response is obtained from V\({}_{02}\), an inverting HP filtering response is obtained from V\({}_{03}\), and a non-inverting BS filtering response is obtained from V\({}_{04}\). Notably, connecting the output current signal I\({}_{05}\) to the output voltage node \(V_{04}\) also obtains a non-inverting AP transfer function in VM as follows:  
\[\frac{\text{V}_{04}}{\text{V}_{\text{in}}}=(\frac{\text{G}_{\text{X}3}}{ \text{g}_{\text{m}3}})(\frac{\text{s}^{2}\text{C}_{1}\text{C}_{2}\text{G}_{ \text{X}1}-\text{s}\text{C}_{1}\text{g}_{\text{m}1}\text{G}_{\text{X}1}+ \text{g}_{\text{m}2}\text{G}_{\text{X}1}\text{G}_{\text{X}2}}{\text{D}(\text{ s})}) \tag{27}\]  
The gain constants in Equations (23)-(26) are  
\[\text{H}_{01}=\frac{\text{G}_{\text{X}3}}{\text{g}_{\text{m}2}},\;\text{H}_{ 02}=\frac{\text{G}_{\text{X}3}}{\text{g}_{\text{m}1}},\;\text{H}_{03}=-\frac{ \text{G}_{\text{X}3}}{\text{G}_{\text{X}1}},\;\text{H}_{04}=\frac{\text{G}_{ \text{X}3}}{\text{g}_{\text{m}3}} \tag{28}\]  
In Equation (18), the proposed filter orthogonally controls \(\omega_{0}\) and Q by tuning conductance G\({}_{\text{X}2}\) for \(\omega_{0}\) and then tuning transconductance gain g\({}_{\text{m}1}\) for Q without disturbing parameter \(\omega_{0}\). According to Equation (28), the VM filter transfer gains can be independently controlled by changing G\({}_{\text{X}3}\) without affecting \(\omega_{0}\) and Q, and the finite input conductance G\({}_{\text{X}3}\) at the X-terminal of the CCCCTA(3) is tunable. Therefore, the VM filter provides orthogonal tunability of all three filter parameters (\(\omega_{0}\), Q and H\({}_{0}\)) in all five responses. This is because the three output voltages V\({}_{01}\), V\({}_{02}\) and V\({}_{04}\) are not in low-output impedance terminals. Voltage followers are needed for the proposed circuit to drive low impedance loads or to be directly connected to the next stages.
# Modeling the Directional Clumping Index of Crop and Forest
## 4 Sensitivity Analysis
### Factors Influencing Crop Clumping Index Variation
#### 4.1.3 Row Structure  
Row structure is characterized by canopy height, \(H\), and row period width, \(A\). As discussed above, \(\Omega(\theta)\) increases with zenith angle until the range angle, which is controlled by the row structure. Figure 14 shows calculated \(\Omega(\theta)\) for various row structures that are discriminated by \(A/H\), where \(H=1\), and \(a_{1}/a_{2}=2.3\). Thus, when \(A/H\) increases with growing \(A\), the range angle grows, which is consistent with (5).  
Generally, the LAI and \(A/H\) both have apparent influence on \(\Omega(\theta)\), while \(G_{\theta}\) has less influence than the other two factors. However, LAI and \(A/H\) are inter-correlative, as we assume that the leaf volume density keeps constant [41], and their influence will partly compensate each other. The influence of all three factors are less obvious at large zenith angles, because \(\Omega(\theta)\) reaches a relatively stable value close to 1 after the zenith angle exceeds the range angle, when the discontinuous crop canopy can be approximately regarded as continuous vegetation canopy.  
Figure 14: Clumping index variation with zenith angle for different crop period/height (\(A/H\)) ratios (different curves). Simulation parameters: \(a_{1}/a_{2}=2:3,\ H=1\), intra-row LAI = 3.  
Figure 13: Leaf angle distribution effect for crop clumping index. Different curves refer to leaf angle distribution types (Erectophile, Plagiophile, Spherical, Extremophile, and Planophile). Simulation parameters: \(a_{1}=0.2,\ a_{2}=0.3,\ H=1,\ r_{g}^{h}=0.22,\ \rho_{1}=0.0999,\ T_{1}=0.1303\).
## 2 Materials and Methods
### Materials  
For this experimental investigation, a CFRP sheet from ECOTECH was used. The sheet was produced using unidirectional prepreg carbon fiber (CE 8201-200-45) aligned at 0\({}^{\circ}\)/90\({}^{\circ}\)  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Water jet pressure (P) & 100, 143.93, 250, 356.07, 400 MPa \\ Standoff distance (SOD) & 1, 2.32, 5.5, 8.68, 10 mm \\ Material thickness (t) & 2, 5 mm \\ Abrasive start synchronization (ASS) & On/Off \\ Abrasive mass flow (m\({}_{\text{a}}\)) & 0.45 kg/min \\ Abrasive size & 80 mesh \\ Focusing tube diameter & 0.76 mm \\ Orifice diameter & 0.35 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental parameters.  
angles and combined with a 364 g/m\({}^{2}\) epoxy resin. The important material mechanical properties included a Young's modulus of 230 to 294 GPa and a tensile strength of 3530 to 5490 MPa [50]. Hot pressing was used in production to ensure proper consolidation and material characteristics. The initial dimensions of the CFRP sheet were 350 mm \(\times\) 150 mm, making it ideal for machining and various application tests.  
The abrasive grain material used in this investigation was Barton garnet. It is a mineral material that has high performance and is widely used in AWJC due to its good cutting properties. This abrasive is known for its hardness, durability, and efficiency in cutting a variety of materials, including metals, composites, and ceramics. The material composition features almandine garnet (chemical formula Fe\({}_{3}\)Al\({}_{2}\)(SiO\({}_{4}\))), a hardness of 7.5-8.0 on the Mohs scale, and a specific gravity of 4.0-4.2 g/cm\({}^{3}\). Physically, Barton garnet exhibits an angular grain shape, which improves its cutting ability.
## 3 Data
### Geometry  
The application of a CNN in slope stability analysis is still in the preliminary stages. Therefore, this study adopted simple geometries to focus on understanding the concept and basics of such deep learning-based methods for slope stability analysis. As is shown in Figure 2, the slope images were produced on a 50 \(\times\) 50 canvas. To account for different geometries, the \(x\) and \(y\) coordinates of the four specified points in this figure were produced using random numbers.  
The equations for producing the coordinates of these four points are listed in Table 2, where the random variable \(\lambda\) is a uniformly distributed value between 0 and 1. In addition to the equation, some restrictions were adopted to obtain slope geometries that were significantly different and could be conveniently analyzed with LEMs. First, the \(y\) coordinates of Point 1 and Point 2 were the same, meaning that the bottom of the slope was always horizontal. Second, the crown of the slope was also horizontal. Third, the slip circles could not extend beyond the dimensions of the image data (50 \(\times\) 50). Fourth, the slip circles with radii of less than one were neglected, since they were susceptible to shallow failure. Fifth, in the calculation of the \(FS\), it was assumed that the center of the slip circle \((x_{c},y_{c})\) had the following attributes: \(0<x_{c}<x_{3}\) and \(y_{3}<y_{c}<y_{3}+15\). These boundaries were then divided to form 30 grids in each direction, resulting in 900 slip circle centers for each image. Next, for each of these centers, 30 potential radii were tested to find the slip circle that could yield the minimum \(FS\).  
Figure 2: An example of the slope image data used in this study.
# Dynamic Optimization Using Local Collocation Methods and Improved Multiresolution Technique
## 3 Improved Multiresolution Technique  
In this section an improved multiresolution technique is described. The description contains three parts. First, new generalized dyadic meshes are proposed. Next, a method based on slope analysis is developed to remove the redundant mesh points in the constant control regions. Finally, the improved multiresolution technique is developed to refine the mesh.
## 4 Simulation Results and Discussions
### Single-Objective OPF Problem  
The SOOPF problem considers only one objective to be optimized. Each objective including cost, emission, transmission loss, and VSI was individually selected to be the objective function. The results of the SOOPF problem solved by MaMPA for each considered system are presented in the following subsections.
## Appendix A Pad Tilting Angle, Pivot Deflection, and Pivot Maximum Stress  
Figure 11 shows the pad tilting angle versus supply pressure for each pad. The null pressure indicates hydrodynamic lubrication. As the supply pressure increases from 10 to 20 MPa, the pad tilt angle decreases, except for pad 3.  
**Pivot deflection, stiffness, and stress based on the Hertzian contact theory:**  
The pivot model in Refs. [28; 29; 30] calculates the pivot deflection in this study. In addition, Equation (A1) calculates the pivot maximum stress \(\sigma_{\max}\) based on Hertzian contact theory [40].  
\[\sigma_{\max}=0.798\sqrt{\frac{E\big{(}D_{h}-D_{p}\big{)}F_{p}}{2(1-\
u^{2})D_{ h}D_{p}}}\] (A1)  
where \(F_{p}\), \(\
u\), \(E\), \(D_{h}\), and \(D_{p}\) are the force concentrated on the pivot, Poisson's ratio, Young's modulus, housing diameter, and pivot diameter, respectively. During the design, the maximum stress under given pivot forces must be calculated to avoid surface damage.  
Figure A2 shows the pivot deflection and maximum pivot stress versus supply pressure for each pivot at a rotor speed of 60 krpm with a static load of 100 N. As the supply pressure increases, the pivot deflection increases. The loaded pads (pads 3 and 4) have pivot deflections larger than those for the unloaded pads (pads 1 and 2). Akin to pivot deflections, the maximum pivot stress increases with an increase in the supply pressure, and the highest pivot stress occurs at the loaded pads (pads 3 and 4). The highest value of the pivot stress for a supply pressure of 20 MPa is 6.4 MPa, which is well below the yield strength (240 MPa) of structural steel, indicating a high design safety factor.
## 3 Results and Discussion
### Regeneration Ability  
The mercury temperature-programmed desorption method was applied to further analyze the mercury species on Co\({}_{4}\)Fe\({}_{1}\)-ZSM-5, which is also suited to determining the conditions required for regeneration of the adsorbents. As shown in Figure 11a, the Hg-TPD profile of Co\({}_{4}\)Fe\({}_{1}\)-ZSM-5 fit well into three peaks, which was ascribed to the desorption of physically adsorbed Hg\({}^{0}\) and the decomposition of weakly adsorbed HgO (Hg-O) and strong bond HgO (Hg=O) [51; 52; 53]. It suggests the dominance of chemisorption over Co\({}_{4}\)Fe\({}_{1}\)-ZSM-5 in the Hg\({}^{0}\) adsorption process, which means the possibility to obtain a clean adsorbent surface after treatment at 500 \({}^{\circ}\)C and achieve a satisfactory performance in Hg\({}^{0}\) capture. To demonstrate this point, a test was conducted on the Hg\({}^{0}\) capture efficiency of the adsorbents regenerated at different temperatures, which revealed that the efficiency of the sample regenerated at 500 \({}^{\circ}\)C was higher than that regenerated at 300 and 400 \({}^{\circ}\)C, confirming that 500 \({}^{\circ}\)C is the optimal temperature for the regeneration of the spent adsorbents. Apart from that, the regeneration atmosphere is another important influencing factor in the recyclability of the adsorbents.  
As shown in Figure 11c, the Hg\({}^{0}\) removal efficiency of adsorbent was just around 85%. With the addition of 5% O\({}_{2}\), however, it improved to around 95%, suggesting that O\({}_{2}\) is conducive to improving the performance of the regenerated sample in Hg\({}^{0}\) adsorption. Despite a further increase in the level of O\({}_{2}\) concentration, there were no evident variations observed in Hg\({}^{0}\) removal efficiency, indicating that 5% O\({}_{2}\) is sufficient for restoring the Hg\({}^{0}\) capture performance of the adsorbent. After five cycles of recycling (Figure 11d), adsorbent Hg\({}^{0}\) removal efficiency remained basically unchanged above 90%, which could be accounted for by the insignificant variation in the physicochemical properties of the adsorbents after the adsorption and regeneration process (Figure S2, Table S3). It demonstrates the excellent recyclability of the Co\({}_{4}\)Fe\({}_{1}\)-ZSM-5 sample and its applicability as an adsorbent.