esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Fifth-Order Korteweg-de Vries (fKdV) Equations  
In this section, we consider the generalized form of the fifth-order KdV (fKdV) equation:  
\[u_{t}+au^{2}u_{x}+bu_{x}u_{xx}+cuu_{xxx}+du_{xxxxx}=0 \tag{21}\]  
where \(a\), \(b\), \(c\), and \(d\) are constants. Two types of the fKdV equation, which have a remarkable importance in the description of wave phenomena are the Sawada-Kotera (SK) equation [58] with the coefficients \(a=45\), \(b=15\), \(c=15\), and \(d=1\), and the Lax equation [59, 60], with \(a=30\), \(b=30\), \(c=10\), and \(d=1\). The analytical solutions of the SK and Lax equations have the form of the traveling wave. For the initial condition \(u(x,0)=2k^{2}\text{sech}^{2}[k(x-c)]\)  
Figure 9: Kuramoto–Sivashinsky equation: the solution errors calculated based on the results obtained applying CD schemes with various combinations of the computational stencils \(M\) and \(N\). (**a**) Exact (lines) and numerical (symbols) solutions, (**b**) \(M=1\) and various \(N\), (**c**) \(M=2\) and various \(N\), (**d**) \(M=4\) and various \(N\).  
the analytical solution of the SK equation is \(u(x,t)=2k^{2}\text{sech}^{2}[k(x-16k^{4}t-c)]\), and the one of the Lax equation with the initial condition \(u(x,0)=2k^{2}[2-3\tanh^{2}(k(x-c))]\) is given by \(u(x,t)=2k^{2}[2-3\tanh^{2}(k(x-56k^{4}t-c))]\). In this paper, we consider the SK and Lax equations with the coefficients \(k=0.01\) and \(c=0.0\), as used in [PERSON] [61] and [PERSON] et al. [62]. These authors applied rather very complex methods, i.e., the modified Adomian decomposition method and the modified variational iteration algorithm-II, but obtained very accurate results. With the above values of \(k\) and \(c\), the period of the solution, although imaginary, was very large (\(100i\pi\)), but more importantly, the solution at small times moved only slightly from its initial position, with the peak value centered at \(x=0.0\). Figure 10a shows the analytical and numerical solutions of the SK and Lax equations on the mesh consisting of \(K=257\) points obtained using the 6 th-order CD scheme (\(M=1\), \(N=4\)) at time \(t=1.0\), at which we analyze the solution accuracy, and, for comparison, at time \(t=50\times 10^{6}\) (dashed lines). The fact that the peak at \(t=1.0\) stays far from the boundary means that the lack of periodicity in real space should not have a significant destructive impact on the solution accuracy. Indeed, the solution error reached the machine precision, i.e., it dropped to \(1.45\times 10^{-14}\) for the SK equation and \(6.37\times 10^{-13}\) for the Lax equation.
## 7 Design of a Crossed-Dipole Multi-GNSS Antenna
At 1.1 GHz, AR\({}_{\max}\) = 5.94 dB at \(\varphi\) = 119\({}^{\circ}\) and AR\({}_{\min}\) = 2.52 dB at \(\varphi\) = 179\({}^{\circ}\); At 1.3 GHz, AR\({}_{\max}\) = 5.67 dB at \(\varphi\) = 45\({}^{\circ}\) and AR\({}_{\min}\) = 0.35 dB at \(\varphi\) = 76\({}^{\circ}\); At 1.6 GHz, AR\({}_{\max}\) = 4.88 dB at \(\varphi\) = 121\({}^{\circ}\) and AR\({}_{\min}\) = 0.59 dB at \(\varphi\) = 159\({}^{\circ}\). The performance of the proposed antenna is compared with previous works in Table 5, focusing on wide beamwidth performance such as the 6 dB axial ratio (AR) beamwidth, the gain, and the AR in the horizontal plane. For the sake of fairness, we compare our design with existing works that employ crossed dipoles in Table 5. The beamwidth for AR < 6 dB is in the elevation direction for all 360-degree azimuth angles in the case of our work, while existing works seldom show the axial ratio for all azimuth angles. The frequency range is not considered critical since we focus on wide-beamwidth performance. The gain and axial ratio (AR) at \(\theta\) = 90\({}^{\circ}\) are critical parameters for wide-beamwidth antenna comparison. The antenna dimension is also considered non-critical. Dual-circularly polarized antenna design is not studied in the works compared in Table 5, while our design offers an advantage of dual-polarized operation for \(L\)-band satellite phones as well as multi-GNSS signal reception. The most challenging design task is to obtain a wide AR beamwidth over a large frequency range. Most of the existing work focuses on wide-beam AR design over a narrow frequency range. The work in [22] is comparable to our design in bandwidth and beamwidth performance with a smaller size. But their structure is significantly more complicated than the proposed design. The result in [23] boasts impressive beamwidth performance and gain/AR in the horizontal plane with narrower bandwidth and larger dimensions than our design.
## 1 Introduction
[9] proposed a small circle arc method that was optimized to achieve equal-area cell regions. However, the algorithms of some DGGSs are very complicated and have no analytical solution [1; 3; 9]. (In our approach, we have straightforward subdivision schemes and clear transformation laws between a spherical point's Cartesian coordinate \((x,y,z)\) and the SAC \((\lambda_{1},\lambda_{2},\lambda_{3})\).) Global Grid Systems are widely used in different areas of science and engineering. The cubed sphere, a well-known Cartesian-to-spherical mapping, was proposed by [PERSON] et al. [10] and has been used to solve partial differential equations on the sphere. On the basis of the same grid system, [PERSON] et al. [11] proposed a class of spherical wavelet bases for the analysis of geophysical models and the tomographic inversion of global seismic data. Furthermore, an axis-free grid named the Yin-Yang grid was used by [PERSON] et al. [12] to simulate 3D self-gravitating flows. In computer graphics, researchers have developed some discrete spherical wavelet bases that rely on geodesic sphere construction [13; 14; 15; 16; 17]. They have been used successfully for spherical data visualization texturing and the bidirectional reflectance distribution function (BRDF). In our approach, we adopt different strategies. First, we partition the globe into a series of spherical triangular cells without overlapping. Second, the local SACs on each triangular region are established, and then the global SACs can be constructed by gluing these local coordinate charts together along boundaries. Finally, we consider coordinate isolines to be grid cell edges and the intersection of isolines to be grid cell vertices, analogous to the Cartesian coordinate grid on a plane. The main properties of  
* The construction process is very simple;
* Since our grid system has a slower growth rate between levels, it is more flexible and can be used for many applications;
* The grid cells have a low distortion in shape and area;
* The grid cells exhaustively cover the globe without overlapping;
* The grid system has a congruent hierarchical structure;
* The grid system has a simple relationship with conventional coordinates. Our main contribution in this paper is the proposal of a new method to construct spherical area coordinates and a new kind of DGGS that is based on these coordinate systems.
## 2 Materials and Methods
### Input Data  
The landslide inventory consists of 650 landslide points sourced from Department of Mineral and Geoscience Malaysia (JMG). It is the sole official landslide repository that was established in 2014 through field mapping and includes only rainfall-triggered landslides. Although it contains only partial information on location and type of slope failure (mandade or natural), the original landslide points were refined through quality assessment to ensure the evidence of landslides location and its accuracy. The points were overlain on the DTM, orthorectified aerial photographs and satellite imageries from Google Earth, and repositioned at the crown of the landslides.  
The DTM was derived from LiDAR data from DBKL. The 2014 LiDAR data have a resolution of 1 m, and they were resampled to a pixel size of 5 m for the study. Aerial photographs were acquired from the Department of Survey and Mapping Malaysia (JUPEM) and DBKL. Topographic maps used were at the scales of 1:50,000, 1:25,000 and 1:10,000 published by JUPEM. Bedrock geology maps obtained from JMG were updated to incorporate information on surface geology and geological structures. The additional data were obtained from boreholes and field visits as well as interpretation from aerial photographs and topographic maps.
## 4 Experiments and Results
### Experimental Findings of Position-Independent HAR  
The model developed in this study demonstrated potent interpretation for position-independent HAR across multiple datasets, as shown in Table 5. Specifically, it achieved high accuracies and F1-scores, regardless of the on-body sensor position. The F1-scores ranged from 90.27% on the Opportunity dataset to 96.61% on the PAMAP2 dataset. The highest scores were observed on the PAMAP2 dataset, indicating it provides the most useful aggregated signals for position-independent HAR. In contrast, performance was lowest on the Opportunity dataset, indicating greater variability in informative signals across positions.  
Nevertheless, there was a slight variance between trials on all datasets, with accuracy and F1-score standard deviations under 0.3%. This points to consistent model performance across multiple test runs. Although loss values were relatively modest, they were slightly higher than those observed under position dependence. Some decline is expected when aggregating signals across various positions. Given its uniformity, the model successfully enabled position-independent HAR by aggregating multi-position signals, especially on PAMAP2. The results demonstrate the viability of machine learning for both position-dependent and position-independent sensor-based HAR.
In the MEMAC (Adaptive Clustering of Marine Environmental Monitoring) algorithm proposed in this paper, the determination of pre-cluster heads is initially performed according to a distributed non-cooperative game of nodes, followed by the calculation of the overall remaining energy value of the whole network to ultimately determine CH nodes. This approach allows for a comprehensive optimization of the strategy for CH competition. 4. In the algorithm, the remaining energy and traffic are incorporated as different game conditions into the calculation of the payoff function, which effectively balances the energy consumption. Furthermore, CH nodes are rotated in special cases to ensure network stability. The rest of this paper is organized as follows: In Section 2, we mainly introduce the related research on clustering algorithms. In Section 3, we introduce the marine environment monitoring network model. In Section 4, we introduce the new MEMAC proposed in this paper in detail. Then, we compare the results through simulation experiments in Section 5 so as to verify the superiority of the algorithm in this paper, and finally, we share an outlook and summary in Section 6.
# A Novel Evolutionary Algorithm for Designing Robust Analog Filters
## 4 Experiments and Results
### Statistical Results of the Three Methods for Evolving Robust Filters  
#### 4.4.1 GA-RMS Improves the Robustness of Standard GP Results  
For the highpass filter problem, we did a t-test to compare the robustness of the evolved solutions by GPGARMS and standard GP in terms of variation (Equation (5)) at the 0.2 perturbation level. A significance level of \(p\leq 0.001\) is achieved, strongly indicating that GPGARMS improved the robustness of the filters evolved by standard GP. However, as shown in Figure 7, this improvement is at the cost of degraded performance. It is impressive that, in this highpass problem, GPGARMS achieves the highest robustness in terms of standard deviation for all 5 perturbation levels. However, as shown in Table 3, the GA could also potentially generate statistically significantly worse solutions or ruin the results evolved by standard GP. For the lowpass filter problem, we found that there is no significant difference in Type-I robustness between GPGARMS and GP. GA also decreased the Type-II robustness in this problem.  
4.2 Topological Innovation Using GP-Evolved Filters with Higher Robustness than Parametric Robust Design Using GA  
For the highpass problem, we applied the \(t\)-test to compare GPGARMS and GPRMS. The 95-percent confidence interval for differences in the means of Type-I robustness \(f_{robustI}\) ranges from \(-51.617\) to \(-39.841\), showing that GPGARMS degraded Type-I robustness. The difference in the mean values of the two groups is greater than would be expected by chance (\(p\leq 0.001\)). For GPGARMS and GPRPE, the difference is even more significant. The 95-percent confidence interval for difference of means ranges from \(-57.482\) to \(-44.846\) with \(p\leq 0.001\). In terms of \(f_{robustII}\), the 95-percent confidence interval for difference of means between GPRMS and GPGARMS ranges from \(-9.920\) to \(-2.538\), while there is no significant difference between GPRPE and GPGARMS.  
For the lowpass problem, the 95-percent confidence interval for the difference in the means of Type-I robustness \(f_{robustI}\) between GPRPE and GPGARMS ranges from \(-51.617\) to \(-39.841\) with \(p\leq 0.001\). There is no significant difference between GPRMS and GPGARMS in terms of \(f_{robustI}\). In terms of \(f_{robustIII}\), the 95 percent confidence interval for the difference in the means between GPRMS and GPGARMS ranges from \(-9.920\) to \(-2.538\), while there is no significant difference between GPRPE and GPGARMS.
# Internet of Things (IoT) and the Energy Sector
## 6 Future Trends
### Green IoT  
The energy consumption of IoT devices is an important challenge, especially in large-scale deployment of these technologies in near future. To run billions of devices that will be connected to the Internet significant amount of energy is required. The big number of IoT devices will also produce a great deal of electronic waste [161]. To tackle these challenges, a low-carbon and efficient communication networks are needed. Fortunately, these necessities has led to the appearance of the green IoT (G-IoT) [162; 163]. The key component of G-IoT is its energy-efficient characteristics throughout the life cycle, i.e., design, production, deployment, and ultimately disposal [129].  
G-IoT cycle can be applied in different IoT technologies. For example, in radio frequency identification (RFID) tags. To decrease the amount of material in each RFID tag, which is difficult to be recycled, the size of RFID tags are reduced [164; 165; 166; 167; 168]. Green M2M communications is another example, which enables adjusting power transmission the minimum level, facilitates more efficient communication protocols using algorithmic and distributed computing techniques [129]. In wireless sensor networks also the sensors nodes can be in the sleep mode and just work when necessary. In addition, radio optimization techniques, such as, modulation optimization or cooperative communication can be applied to reduce the power consumption of the nodes. Moreover, energy-efficient routing techniques, such as, cluster architectures or multi-path routing can provide efficient solutions [130; 131]. In conclusion, the above-mentioned approaches and examples can reduce the energy needs of IoT systems.
# The Effect of Slow-Release Fertilizer on the Growth of Garlic Sports and the Soil Environment
## 3 Results and Analysis  
### The Effects of Different Treatment Groups on the Yield per Unit Area of Garlic Sprouts  
During vegetable cultivation, excessive fertilization not only fails to increase crop yield but may also reduce it. Therefore, replacing conventional fertilizers with new types of fertilizer is considered an effective approach to improving crop yield. As shown in Figure 2, the T3 treatment group, which received slow-release fertilizer with an equivalent nutrient input, exhibited the highest garlic seedling yield. The yields of garlic sprouts in the CK, T1, T2, and T3 groups were 1.08, 1.30, 1.29, and 1.38 kg/m\({}^{2}\), respectively. All of the fertilizer-treated groups had significantly increased garlic sprout yields compared to that in the CK group (\(p<0.05\)). Among them, the T3 treatment yielded the highest increase, improving by 6.15% relative to T1 and exhibiting the highest input-output ratio. This result indicates that replacing conventional fertilizers with slow-release fertilizers can significantly enhance garlic seedling yields and economic efficiency. The improved yield is attributed to the controlled and prolonged nutrient release from slow-release fertilizers, which aligns more effectively with crop nutrient uptake patterns during various growth stages. These findings are consistent with those of [PERSON] et al. [49], who reported an increase of 4600 kg/ha in the tomato yield with the application of slow-release fertilizers. Similarly, a study by [PERSON] et al. [50] demonstrated that the use of sulfur-coated urea increased the potato yield by 49.76%, outperforming traditional urea fertilization. These results collectively support the conclusion that substituting conventional fertilizers with slow-release formulations can enhance crop productivity.
# Impedance-Based Stability Analysis of Grid-Connected Inverters under the Unbalanced Grid Condition
## 2 Impedance Modeling of the DSOGI-PLL-Based Inverter and Verification
### Impedance Modeling under the Three-Phase Balanced Condition  
The frequency-coupled impedance model of the DSOGI-PLL-based grid-connected inverter is derived in this subsection. In the time domain, the \(a\)-phase voltage at the PCC is (taking the \(a\)-phase as an example, the other two phases lag the \(a\)-phase by \(120^{\circ}\) and \(240^{\circ}\), respectively)  
\[v_{\text{ta}}(t)=V_{1}\cos(\omega_{1}t+\varphi_{\text{ta}}) \tag{1}\]  
where \(v_{\text{ta}}\) represents the \(a\)-phase voltage, \(V_{1}\) represents the amplitude of the fundamental voltage, \(\omega_{1}=2\pi f_{1}\) represents the fundamental angular frequency, and \(\varphi_{\text{ta}}\) represents the initial phase of the \(a\)-phase voltage.  
For the DSOGI-PLL-based grid-connected inverter system, the small-signal harmonic voltage is injected into the PCC as the perturbation. Therefore, the terminal voltage and output current at the PCC are  
\[\mathbf{V}_{\text{ta}}[f]=\left\{\begin{array}{l}\mathbf{V}_{1},f=f_{1}\\ \mathbf{V}_{\text{p}},f=f_{\text{p}}\\ \mathbf{V}_{\text{c}},f=f_{\text{c}}\end{array}\right. \tag{2}\]  
\[\mathbf{I}_{\text{ta}}[f]=\left\{\begin{array}{l}\mathbf{I}_{1},f=f_{1}\\ \mathbf{I}_{\text{p}},f=f_{\text{p}}\\ \mathbf{I}_{\text{c}},f=f_{\text{c}}\end{array}\right. \tag{3}\]  
where \(\mathbf{V}_{1}\) and \(\mathbf{I}_{1}\) represent the voltage and current at the fundamental frequency, respectively. \(\mathbf{V}_{\text{p}}\) and \(\mathbf{I}_{\text{p}}\) represent the perturbation voltage and current, respectively. \(\mathbf{V}_{\text{c}}\) and \(\mathbf{I}_{\text{c}}\) represent the coupled voltage and current, respectively. \(f_{\text{p}}\) and \(f_{\text{c}}\) represent the coupled perturbation frequencies,\(f_{\text{p}}\)\(+f_{\text{c}}\)\(=\)\(2f_{1}\).
# TopoSinGAN: Learning a Topology-Aware Generative Model from a Single Image
## 3 Results  
### NTC-Based Graph Classification  
The graph classification results, as depicted in Figure 5, illustrate the performance of different feature sets in categorizing graph structures using a Support Vector Machine (SVM) classifier. The \"Features\" set, which includes basic network attributes, achieved an accuracy of 71.35%. The addition of Degree Distribution Quantile Concentration (DDQC) features, labeled as \"Features + DDQC\", significantly improved the classification accuracy to 94.73%. The addition of Node Topology Clustering (NTC) features shown as \"Features + DDQC + NTC\" achieved the highest accuracy of 98.39%. These results suggest that incorporating NTC graph features enhances the model's ability to distinguish between different graph categories with disparate topological structures.
## 4 Experimental Results
### Discussion  
This study aims to construct a novel Autoencoder that can have both normal area reconstruction and defective-area repair capabilities. Hence, a new model training method with DEC and SSIM loss functions is proposed. The ablation studies for MSE and SSIM loss functions and weighting analysis between SSIM and DEC loss functions are provided in Sections 4.1 and 4.2. The simulation results show that using SSIM is better for texture repair in the defective area for nanofiber images. It is also obvious that the defective area can be clearly found using SSIM, but the defective area cannot be found using MSE. Furthermore, in Table 6, the weighting ratio between SSIM and DEC loss functions for the Simple and Inception Autoencoders is important for both normal area reconstruction and defect area repair capabilities. However, on more complex nanofiber images, the classification accuracy of normal images is reduced because the DEC loss function can reduce the reconstruction capability on normal areas, as listed in Table 8. It is interesting that SAE + DEC outperforms IAE + DEC on complex nanofiber images, and this result still needs to be further explored and discussed.  
In the following, the improvement of classifying defect and normal images is addressed by using the multi-thresholding method. The accuracy analysis for classifying normal and defect test images, subjective evaluation of defective area detection, and the comparison between our proposed method and the SOTA methods in [14] are conducted to verify the performance of the proposed method from Sections 4.3-4.5. The experimental results in Table 14 show that our proposed methods outperform the methods in [14] on both classifications of normal and defect images. The main reason for improving the accuracy of image classification is that our proposed Autoencoder can have both normal area reconstruction and defect area repair capabilities and the multi-thresholding image segmentation algorithm provides adaptive thresholding for each test image to distinguish between normal and defective images.
# Aggregation Dispatch and Control Strategies for Multi-Type Loads in Industrial Parks
## 4 Case Study
### Simulation Analysis
#### 4.2.3 Analysis of Scenario Three  
Building on scenario 2, a neural network model was trained based on the intra-day dispatch plan of controllable load to obtain real-time control commands for controlling the controllable load in real-time. The dispatch plan and the actual power output curve of the controllable load in industrial park are shown in Figure 10.  
It can be observed that there still exist significant deviations in the actual power output curve from the intra-day dispatch plan, and using a neural network model cannot make the actual power output curve follow the intra-day dispatch plan very well.  
Taking into account the ultra-short-term prediction error of photovoltaic and rigid loads, as well as the response execution deviation of the controllable loads, the expected intra-day power curve and the actual power curve of the industrial park interconnecting line are shown in Figure 11.  
Figure 10: Dispatch plan and the actual power output curve of the controllable load in industrial park based on neural network model.  
Figure 9: The day-ahead pre-dispatch plan curve(base), the optimized dispatch curve(expected), and the actual operating curve(actual) for the interconnection line of Scenario Two.  
Due to the actual power output curve of the controllable loads being unable to follow the intra-day dispatch plan very well, there exists a significant execution error between the expected power and actual power of the interconnecting line, leading to relatively high evaluation costs.  
After using a neural network model for real-time control of the controllable loads, the expected intra-day cost for the industrial park remains consistent with Scenario 2. The actual intra-day cost is S31 = CYN 386,169.39, and the evaluation cost is S32 = CYN 87,097.48, resulting in a total cost of S3 = CYN 473,266.87.