esa-sceva/satcom-chunk-collection huggingface dataset:
# A Semi-Random Elliptical Movement Model for Relay Nodes in Flying Ad Hoc Networks
## 3 SREM Model
### Application of SREM  
SREM is a mobility model optimized for scenarios where relay-based communication is essential. By aligning the major axis of an ellipse with the straight line connecting the source and destination and guiding UAVs to move semi-randomly along the elliptical path, SREM naturally concentrates relay nodes along the relay route. This enables SREM to provide more structured node alignment than random models and greater flexibility than path-based models. Such a configuration enhances the probability of maintaining relay paths, making it well-suited for realistic relay-based FANET experiments.  
Key parameters of SREM--such as the semi-major and semi-minor axes \((a,b)\), angular range \(\theta\), and velocity \(v\)--can be tuned to flexibly control the degree of concentration along the communication path, the spatial distribution range, and the frequency of trajectory shifts. These structural features distinguish SREM from the SRCM model, which was more suitable for coverage-oriented uniform distribution analysis, and they position SREM as a more appropriate model for controlling relay node distribution and optimizing communication performance in FANET simulations.  
Beyond its application in relay-centric FANET simulations, the SREM model is also highly effective for adaptive modeling in irregular spatial diffusion scenarios. For instance, in maritime search-and-rescue operations where the estimated location of a distress target expands elliptically due to currents and wind, SREM can be used to align the major axis of the ellipse with the central search line (e.g., between the last known and predicted locations). By deploying FANET drones along the elliptical trajectory, the search effort can be concentrated along the most probable path, maximizing coverage efficiency with limited resources.  
Similarly, in disaster situations such as rapidly spreading wildfires or toxic gas leaks, SREM enables strategic deployment by aligning the major axis of the ellipse with the primary direction of hazard propagation. FANET drones can be densely positioned at the forward edge of the spread to ensure fast data collection and real-time monitoring while maintaining a naturally dispersed layout in the rear area to optimize resource allocation.  
In summary, SREM serves not only as a mobility model optimized for evaluating communication-path alignment in FANETs but also as a flexible tool for adaptive FANET coverage planning that reflects physical environmental changes. By leveraging the geometric properties and mathematical tractability of ellipses, SREM enhances both the scalability and precision of FANET deployment strategies across various application domains, including maritime search, disaster response, and perimeter surveillance.
## 2 State of the Art and Related Work
### Workpiece-Based Referencing  
An approach for avoiding a separate calibration fixture by utilizing reference points on the workpiece itself for pose estimation and orientation correction has been demonstrated for aircraft wing assembly [37]. The specified goal was to increase the precision and speed of the wing-fuselage connection process in aircraft assembly. This was achieved by the continuous measuring of reference points on the aircraft wing with a laser tracker while its pose and position were manipulated by computer-controlled actors. The wing's CAD model was used to find the workpiece coordinate system relative to the reference points. For the pose estimation, the Newton-Euler method, in combination with quaternions, was used.  
Unlike the distinctly precise and immoderately priced laser trackers used in the paper, a system with low-cost moving head spots loses depth information, resulting in the light beam being represented by a vector from the moving head to the reference point with an unknown length. In order to use the remaining information, a different mathematical approach is necessary. This approach will be presented in the following chapters after an overview of the moving head control.
## 3 Results and Discussion  
### Characterization of the EGDMA + St Microspheres with a Lignin Component  
Figure 2 shows the possible structure of the copolymer EGDMA + St with lignin. Three components were used to prepare polymeric microspheres. Each of the monomers performed a definite function during the formation of polymer microspheres. Ethylene glycol dimethylacrylate was used as a crosslinking monomer. This process makes microspheres resistant to the mechanical damages and action of solvents. EGDMA is a popular monomer used for the preparation of methacrylate composites. Styrene is an aromatic, very reactive, active solvent. The last component is the commercial kraft lignin. The presence of polar groups in the lignin structure increases the chemical affinity of this biopolymer for the EGDMA. In this way, a high percentage of lignin in the copolymer structure reaches the 90% yield (Table 1). Moreover, the presence of the functional groups in its structure modifies the surfaces of the microspheres. Unfortunately, lignin attaches to the microspheres on their outer part and thus blocks the pores. Therefore, the obtained microspheres are characterized by a low porosity. Due to the fact that lignin is a biopolymer that contains a lot of aromatic parts in its structure, the carbonization process of the microspheres was induced in order to increase their potential applications. After the carbonization process, the porosity of polymeric microspheres expanded. Characterization of the carbonized materials was made as compared to the polymeric precursor EGDMA + St + 4L, which will be discussed in the next part of the article. In Figure 3, photos of the obtained microspheres are presented. As one can see, as the amount of lignin increases, the microspheres become darker.  
Figure 1: Chemical structure of monomers. (**a**) styrene, (**b**) ethylene glycol dimethacrylate, (**c**) lignin.
# Comparative Impact Analysis of Cyclone Ana in the Mozambique Channel Using Satellite Data
## 5 Conclusions  
This study aimed to map the impacts of TC Ana following its passage over Mozambique and Madagascar, two countries that have been most affected by the said tropical depression. It was found that there were differences in both environmental and anthropogenic impacts across all four ROIs, with the regions of Zambezia, Mozambique and Boeny, Madagascar experiencing the majority of flooding and flood-related impacts. In addition, urban areas were found to have been less likely to be flooded across all countries. Ecologically, this study established a difference in region-wide vegetation health compared to after the cyclone season. Despite this, mangrove forests across all ROIs were considered to be generally healthy in 2022. Finally, it was found that, in terms of the exposed population, areas in the vicinity of mangroves fared better than non-mangrove areas further inland. As such, this study further supports the global and local push for mangrove reforestation in tropical-coastal ecosystems to protect vulnerable communities from natural hazards worldwide.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13074519/s1](https://www.mdpi.com/article/10.3390/app13074519/s1)]([https://www.mdpi.com/article/10.3390/app13074519/s1](https://www.mdpi.com/article/10.3390/app13074519/s1)), Supplementary S1: Supplementary for Madagascar Channel. Supplementary S2: Pre-Processing. Supplementary S3: Post-Processing.  
**Author Contributions:** M.S.: Conceptualization, Methodology, Software Support, Writing; Oversight, Analysis, Support and Guidance, Writing, Editing; E.S.: Data Sourcing, Data Curation, Analysis, Draft Preparation. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data used can be obtained here: [[https://github.com/emmajchoemmakers/comparative_impact_irp-22](https://github.com/emmajchoemmakers/comparative_impact_irp-22)]([https://github.com/emmajchoemmakers/comparative_impact_irp-22](https://github.com/emmajchoemmakers/comparative_impact_irp-22)) (accessed on 23 September 2022).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Electromagnetic Characteristics Analysis of a Tubular Moving Magnet Linear Generator System
## 3 Electromagnetic Analysis
### Efficiency Characteristics  
The linear generator is designed for the rated reciprocating motion frequency of 50 Hz with the rated stroke of 36 mm, which is equal to 3000 turns per minute of rotary machines. The generating current of the coil is controlled according to a standard sine ware, and the rated amplitude is 15 A. Meanwhile, due to the limitations of the test conditions, the generator will be tested in the next section at the frequency of 35 Hz with the current amplitude of 25 A.  
Thus, Figure 8 shows the efficiency characteristics of the linear generator simulated at the reciprocating motion frequency of 50 Hz and 35 Hz. The rated operation point and the tested point are also shown in the figure. The generating power of 1.4 kW with the efficiency of 91.4% can be obtained as working at the rated operation point. Meanwhile, the generating efficiency will be reduced to 84% when the working frequency is reduced to 35 Hz.  
Figure 8: Efficiency characteristics of the linear generator.  
Figure 7: Electromotive force (EMF) characteristics of the linear generator.
## 4 Experiments
### Ablation Study
#### 4.4.2 The Effects of the Concatenation in the CMFFM  
Figure 4 illustrates the structure of the CMFFM, which incorporates two concatenations, C1 and C2. C1 represents the feature map after the concatenation of PAN and MS features on the channel (\(H\times W\times 2C\)); C2 represents the Cross-modality Feature (\(H\times W\times 3C\)) obtained after passing through our CMFFM. The effectiveness of these concatenations was evaluated separately on the WV4 dataset, with results presented in Table 5. The performance metrics indicate that utilizing either concatenation C1 or C2 yields improvements over the method that excludes both. However, the optimal pansharpening results are achieved when both C1 and C2 are employed simultaneously within the network.
## 1 Introduction  
Modern mobile devices are equipped with a number of both heterogeneous Network Interface Cards (NICs) and multimedia sensors that allow them to host emerging perception-related applications, such as face/gesture detection/classification, visual text translation, fusion of sensed data, and video image processing, just to cite a few. Typically, these applications are delay-sensitive and computation-intensive, while the computing and battery capacities of current mobile devices are still limited. In order to cope with these limitations, the so-called Mobile Cloud Computing (MCC)paradigm proposes to offload computation-intensive tasks to the remote Cloud for the execution [1]. However, task offloading towards remote large-size cloud-based data centers undergoes large network latencies, mainly induced by the limited bandwidth still offered by multi-hop cellular Wide Area Networks (WANs) [1]. In principle, a more appealing approach could be, indeed, to allow mobile devices to exploit both the simultaneous utilization of the hosted NICs and the sub-millisecond network latencies featuring the forthcoming _Fifth-Generation_ (5G) communication technology, in order to distribute task offloading suitably to _both_ the remote Cloud and _nearby_ small-sized virtualized data centers, generally referred to as _Fog nodes_[2]. The reason why the convergence of the three-pillar paradigm of Fog Computing (FC) [3], Cloud Computing (CC) [4] and 5G [5,6] is expected to improve both the energy and delay performance of task offloading of computing-intensive mobile applications relies on their complementary native features (see Table 1 for a synoptic comparison). Overall, it is expected that the forthcoming 5G paradigm will be capable of simultaneously exploiting the benefits of centralized CC infrastructures, distributed FC infrastructures, and heterogeneous radio technologies, in order to provide seamless resource-augmentation to resource-limited mobile devices. The pivotal idea is to take full advantage of the cooperative radio resource management and distributed computing capability at nearby Fog nodes, in order to: (i) shorten the communication latencies for the execution of delay-sensitive light tasks; and (ii) resort to centralized remote cloud data centers only for the execution of delay-tolerant workload-intensive tasks.
# Face Spoofing Detection with Stacking Ensembles in Work Time Registration System
## Appendix A \begin{table}
\begin{tabular}{c c c c c c} \hline
**LR** & **BS** & **Epochs** & **Dropout** & **Smartphone CNN** & **Image CNN** \\ \hline \multicolumn{6}{c}{Small dataset} \\ \hline  
\end{tabular}
\end{table} 
Table 1: Obtained accuracies [%] in a 5-fold cross-validation for two proposed convolutional neural networks (Smartphone Detection CNN and Image Analysis CNN) with different hyperparameter combinations (learning rate (LR), batch size (BS), epochs, and dropout rate). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Combination** & **Default Parameters** & **Optimal Parameters** \\ \hline \hline \multicolumn{3}{c}{**Small dataset**} \\ \hline C,N,E & 89.20 \(\pm\) 1.71 & 91.78 \(\pm\) 1.29 \\ \hline S,C,N,E & 91.31 \(\pm\) 2.54 & 91.02 \(\pm\) 3.60 \\ \hline S,C,N,X,E & 90.84 \(\pm\) 3.28 & 91.55 \(\pm\) 3.44 \\ \hline B,S,C,N,X,E & 91.78 \(\pm\) 2.69 & 91.55 \(\pm\) 3.61 \\ \hline B,S,C,N,X,E,V & 91.78 \(\pm\) 2.36 & 91.55 \(\pm\) 3.61 \\ \hline \multicolumn{3}{c}{**Spoofing Dataset**} \\ \hline C,N,E & 95.56 \(\pm\) 0.56 & 96.27 \(\pm\) 1.30 \\ \hline S,C,N,E & 96.36 \(\pm\) 0.83 & 96.84 \(\pm\) 0.23 \\ \hline S,C,N,X,E & 96.46 \(\pm\) 0.78 & 97.22 \(\pm\) 0.36 \\ \hline B,S,C,N,X,E & 96.75 \(\pm\) 0.98 & 97.03 \(\pm\) 0.36 \\ \hline B,S,C,N,X,E,V & 96.56 \(\pm\) 1.26 & 97.13 \(\pm\) 0.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Obtained accuracies [%] in a 5-fold cross-validation for the stacking ensembles from Table 4 with default and optimal hyperparameters for our models-Smartphone Detection CNN (S) and Image Analysis CNN (N). The other models used in the ensembles: Bezel (B), Context (C), EfficientNetV2 (E), ConvNeXt (X), and Vision Transformer (V).
# Rapid Measurement and Identification Method for the Geometric Errors of CNC Machine Tools
## 1 Introduction
However, in practical applications, it has some drawbacks, such as its heavy weight and complicated operation. According to the above-mentioned literature, experts and scholars have conducted a lot of research in the field of the geometric error measurement and identification of MTs in recent years. However, both direct and indirect measurement methods have various shortcomings in practical applications. Nowadays, it is difficult to develop a kind of geometric error measurement and identification system with a high precision and high detection efficiency, as well as easy installation and operation. In this study, a novel indirect measurement method, based on a stepwise identification method, is proposed. The measurement system was mainly composed of a self-developed micro-angle sensor and an L-shape standard piece. An integrated error model of the 3-axis MTs is established based on the MBS theory. In one measurement, a four degrees-of-freedom error, including two-dimensional displacement and two-dimensional angle of a linear guideway, can be obtained. Furthermore, the decoupling formulas of individual errors are deduced based on the integrated error model. The measurement paths are reasonably planned, and the L-shape standard piece is placed in XOY, XOZ and YOZ. Then, the measurement and identification of all 21 geometric errors can be completed. This study provides a significant basis for error compensation.
## 4 Results and Discussions  
### Determination of Significant Factors  
As mentioned above, SVR-model-based left-out sensitivity analysis was used to determine the relative importance of the input attributes in the prediction of BFP. However, machine-learning-based feature selection models such as decision tree and random forest were implemented in 4-fold cross-validation, and the determined feature importance was analyzed. These models produced different results in different folds. Even the age factor was determined by the models as the least significant factor; the WHR was also indicated as an attribute that has the least significant effect on BFP prediction by these models. Therefore, due to the inconsistent results of these models, only the SVR experiments are presented in this paper. The SVR model was trained and tested using all attributes as input variables to predict the BFP, and the model's prediction error (RMSE) was calculated for each training with a left-out approach. The results of the left-out sensitivity analysis are provided in Table 3.  
The RMSE values increased upon the removal of abdominal C, weight, height, WHR, and BFP, while they decreased when gender and age were removed from the input parameters. The results indicate that all of the parameters have relative importance in determining the BFP since the RMSE increased when any of the parameters were removed from the training data. Abdominal C, gender, height, WHR, BMI, weight, and age were determined as the most significant factors affecting BFP in descending order, respectively.  
Figure 5: Block diagram of the proposed SVR-EANN hybrid model.
## 3 Results and Discussion  
### Characterization of the Obtained GO-MIP Nanocomposites  
As FTIR spectroscopy is a powerful tool for identifying the chemical properties of carbon materials, the products of each reaction step were first characterized to ensure the successful preparation of GO-MIP nanocomposites. As shown in Figure 1a, the typical peaks of GO could be observed, such as the stretching vibrations of O-H, C=O, and C=C, centered at 3428, 1720, and 1615 cm\({}^{-1}\), respectively. Compared with that of the GO, the characteristic absorbance band of GO-RAFT appeared at 2928 cm\({}^{-1}\), which is related to the C-H stretching vibrations of the RAFT agent, while the intense bands at 1648, 1209, and 807 cm\({}^{-1}\) are attributed to the stretching of C=O, C=S, and C-S, respectively. The peak at 3230 cm\({}^{-1}\) in the GO-MIPs is ascribed to the -NH\({}_{2}\) stretching vibration, which confirms that the grafted polymers were covalently bound to the surface of the GO. The structures of GO, GO-RAFT, and GO-MIPs were further investigated by Raman spectroscopy (Figure 1b), and two pronounced peaks were clearly visible at around 1325 cm\({}^{-1}\) and 1583 cm\({}^{-1}\), corresponding to the so-called D and G bands, respectively. The G band is characteristic of sp\({}^{2}\)-hybridized carbon networks, and the D band is related to the disorder caused by structural imperfections. Compared with that of GO, the integrated intensity ratio of the D and G bands (\(I_{\mathrm{D}}/I_{\mathrm{G}}\)) of GO-MIPs was higher, which suggests that the formation of molecularly imprinted polymers increased the disorder of the nanocomposites, and this is consistent with other studies [31].  
The morphological structures of the prepared GO sheets and GO-MIPs were visually observed by SEM and TEM. Figure 2 shows SEM images of GO, GO-RAFT, and GO-MIPs, and the typical lamella structure of GO sheets with crumpled and wrinkled surfaces can be  
Figure 1: FTIR spectra (**a**) and Raman spectra (**b**) of GO, GO-RAFT, and GO-MIPs.  
clearly observed in Figure 2a. The GO-RAFT looked like rough pieces of wrinkled flakes, as can be seen in Figure 2b, showing that the covalent connection of the small molecule RAFT reagent does not cause obvious changes in the surface morphology of GO sheets. Compared with the GO sheets and GO-RAFT, the GO-MIP nanocomposites had a dense and rough surface (Figure 2c), which indicated the successful formation of MIPs on the GO surface with a high polymerization efficiency. TEM has proven to be a very effective way to examine particle size and morphology before and after modification. From the TEM image (Figure 3a), it can be seen that the GO sheets exhibit an ultrathin structure and are like a very thin piece of paper with some wrinkles. As can be noticed in Figure 3b, the MIP films were widely and uniformly dispersed on GO surface, indicating that the imprinted cavities were formed on the basal planes of GO sheets [31].
# Graph Convolutional Spectral Clustering for Electricity Market Data Clustering
## 4 Discussion
### Limitations and Future Research Directions  
While the isolation forest excels in anomaly localization, it may face challenges in handling anomalies within complex data distributions and multidimensional relationships. Additionally, the RBF neural network, known for its smooth interpolation, may encounter overfitting in extreme cases, necessitating careful parameter selection for practical applications to balance accuracy and stability. The GCN algorithm within the method exhibits sensitivity to changes in the graph structure, requiring retraining or parameter adjustments when significant changes occur. The spectral clustering algorithm, though effective, presents high computational complexity when processing large-scale data, demanding efficient computing resources.  
Future research avenues can concentrate on optimizing and integrating the algorithm, refining parameters of the isolation forest and RBF neural network to enhance overall applicability and robustness. The dynamic adaptability of the graph structure in GCN can be improved to better handle the dynamic changes in the power system's graph structure, ensuring real-time adaptability to the complexity of power system operations. Consideration may be given to integrating multiple algorithms to address more complex power data analysis scenarios. Attention to real-time and responsive application of algorithms is crucial, enhancing real-time power information processing capabilities for practical implementation. This improvement aims to make the technology more applicable and supportive of real-time monitoring and adjustment of power systems.
# Optimized Architecture for Efficient OFDMA Network Design
## 5 Simulation Results
### Validation Study  
#### 5.1.1 Conventional Scheduling Policies  
Figures 4-6 represent the average instantaneous throughput of active users during an ON period \(\overline{X}\), the average number of active users \(\overline{Q}\), and the average resource utilization \(\overline{U}\) for the three scheduling algorithms, respectively.  
For a number of \(N=10\) mobile stations, the validation study simulation lasted nearly 2 days for the scheduling algorithms. The performance parameter curves obtained from the analytical model closely resemble those obtained from the validation study, noting that for the analytical model, this operation took only _15 min_ for all scheduling algorithms simultaneously.  
Figure 4: The average instantaneous throughput for conventional scheduling algorithms (\(N=10\), \(x_{on}=3\) Mbits and \(t_{off}=3\) s).  
Figure 5: The average number of active users for conventional scheduling algorithms (\(N=10\), \(x_{on}=3\) Mbits, and \(t_{off}=3\) s).  
Figure 7 shows the stationary state probability \(\pi(n)\) for the three scheduling algorithms. It is evident that the \(\pi(n)\) curves from the validation study are similar to those obtained from the model for all three scheduling algorithms. Thus, this model has significantly reduced simulation time, bringing it down to a matter of hours instead of days.