esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Background  
#### 2.2.1 Ica  
ICA is a statistical method that seeks to recover latent sources from a set of observed data with the assumption that the latent sources are statistically independent of one another [50]. Since it places few assumptions on data, ICA has been widely used in brain imaging studies [51; 52; 53; 54].  
Given a feature dataset \(\mathbf{X}\in\mathbb{R}^{M\times V}\) comprised of \(M\) subjects and \(V\) samples (e.g., voxels), the generative model for noiseless ICA can be written as  
\[\mathbf{X}=\mathbf{A}\mathbf{S}, \tag{1}\]  
where \(\mathbf{A}\in\mathbb{R}^{M\times M}\) is a full rank square mixing matrix and \(\mathbf{S}\in\mathbb{R}^{M\times V}\) is the latent sources. The goal of ICA is to estimate a demixing matrix \(\mathbf{W}\in\mathbb{R}^{M\times M}\) such that the estimated source matrix \(\mathbf{\hat{S}}\) can be computed as  
\[\mathbf{\hat{S}}=\mathbf{W}\mathbf{X}. \tag{2}\]  
This can be achieved by minimizing the following cost function:  
\[\mathcal{J}_{\text{ICA}}(\mathbf{W})=\sum_{m=1}^{M}\mathcal{H}(\mathbf{\hat{s} }_{m})-\log|\det(\mathbf{W})|, \tag{3}\]  
where \(\mathcal{H}(\cdot)\) is the differential entropy, \(\mathbf{\hat{s}}_{m}\) denotes the \(m\)th row of \(\mathbf{\hat{S}}\), and \(\det(\mathbf{W})\) is the determinant of \(\mathbf{W}\)[21]. Since both \(\mathbf{A}\) and \(\mathbf{S}\) are unknown, the solution of ICA decomposition is subject to permutation and scaling ambiguities. The scaling ambiguity can be resolved by normalizing the estimated ICs to have unit variance, as this is not information we can retain. Hence, the inverse of \(\mathbf{W}\) can be considered to be an estimate of mixing matrix \(\mathbf{A}\), subject to permutation ambiguity, which is fortunately not a serious problem in most applications. The columns of the estimated mixing matrix \(\mathbf{\hat{A}}\)--i.e., the inverse of \(\mathbf{W}\)--contain the weights for the estimated sources across subjects. We refer to these columns as the subject covariations or profiles and they can be used to explore the associations between different modalities.
# Conceptual Framework of an Intelligent Decision Support System for Smart City Disaster Management
## 3 Methodology
### Issues and Scenarios of Heatwave/Cold Wave Disasters  
The fund for disaster support money paid to the families of those who are killed by heat is limited between 5000 and 10,000 dollars (in the case of household heads) based on the South Korea disaster relief regulations and costs (DRRCs) for disaster recovery. However, no system or technology has been established to objectively determine the causes of deaths during the summer in South Korea. In addition, there have been numerous difficulties in diagnosing the causes of deaths owing to victim interventions and ethical issues that may have arisen. Furthermore, while heatwave damage occurs in almost all areas during the summer, the extent of the damage varies depending on the local scenarios and personal health conditions, which can lead to legal repercussions.  
A domestic cold wave alarm is issued based on three criteria. It is issued during the winter (October to April), when the minimum temperature the following morning is expected to drop by more than 10 \({}^{\circ}\)C compared to that of the previous day and is below 3 \({}^{\circ}\)C, or when the temperature is lower than the average winter temperature values of previous years by 3 \({}^{\circ}\)C. The warning is also issued when the minimum morning temperature is expected to be below 12 \({}^{\circ}\)C for two days or more, or when there is a significant loss of life and property due to a sudden fall in the temperature. If there is a specific list of victims or temperature damages, it is possible to set an absolute standard for facility damages or loss of life. However, as such a database has not yet been established, cold weather management will need to be implemented and systematically managed by generating the associated database.  
There is no big data AI study case for heat and cold waves. In 2018, the Ministry of Public Administration and Security identified heat and cold waves as new types of disasters. Currently, there is no specific management standard to deal with crises caused by heat and cold waves in Korea. It is also necessary to improve the speed and accuracy of the system that predicts heat and cold waves. For example, it is possible to predict pre-heat phenomena two days in advance; however, in developed regions such as Europe or the U.S., this can be done three to five days in advance. Through this study, by collecting data to prevent and be prepared for disasters, an AI-based support system was developed to enable more rapid and more accurate decision-making than is currently achievable.
## 1 Introduction
### Motivation and Contributions
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Ref.** & **Year** & **RP 1** & **SC 2** & **PP 3** & **RM 4** & **EM 5** & **DP 6** & **Contribution** \\ \hline
[16] & 2018 & ✓ & & & ✓ & & & Review of SAGIN research, covering design, resource allocation, and performance optimization, with future directions. \\ \hline
[13] & 2019 & ✓ & & & & & & Analysis of routing protocols in UAV networks, comparing features and discussing design challenges. \\ \hline
[17] & 2019 & & & & & & ✓ & Examination of power configurations and energy management in electric UAVs, revealing research gaps. \\ \hline
[9] & 2021 & ✓ & ✓ & & & & & Review of security in Space-Air-Ground-Sea Integrated Networks (SAGINs), addressing threats and defense strategies. \\ \hline
[15] & 2021 & & & & & & ✓ & Evaluation of UAV-based LoRa networks, highlighting low-bit-rate connection reliability for long-distance transmission. \\ \hline
[12] & 2021 & & & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline
[2] & 2022 & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline
[10] & 2022 & ✓ & ✓ & ✓ & ✓ & & ✓ \\ \hline
[14] & 2022 & & ✓ & & ✓ & & ✓ & Review of UAV deployment and trajectory planning for enhancing 5G networks and beyond in coverage, throughput, and resource management. \\ \hline
[6] & 2024 & & & ✓ & & & ✓ & Study of learning algorithms in UAV-assisted SAGINs, proposing 3D satisfaction learning model. \\ \hline
[11] & 2024 & & ✓ & & ✓ & & Review of resource management strategies in 6G-era JCC-SAGINs. \\ \hline This survey & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}  
* Routing protocols. 2 Security control. 3 Path planning. 4 Resource management. 5 Energy management. 6 Deployment of UAVs.  
\end{table} 
Table 1: Analysis of recent survey studies on SAGINs. 2. We summarize the key technologies for UAV-assisted SAGINs, including routing protocols, path planning, resource allocation, security control, and aerial deployment. 3. We summarize the advantages of UAV-assisted SAGINs and their applications in various fields. In addition, we summarize future research directions based on the above technical analysis and application results. Figure 2 shows the overall structure of this article. The remainder of this article is divided into five sections. Section 2 introduces the background, including SAGIN and UAV communication. Section 3 reviews the significant research on UAV-assisted SAGINs, including routing protocols, security control, path planning, resource management, and UAV deployment. Building on this, we elaborate on the applications of UAV-assisted SAGINs in Section 4. In Section 5, we summarize the existing challenges and look forward to future research directions. Finally, Section 6 summarizes the work presented in this paper. The main terminology abbreviations used in this paper are listed in Abbreviations. Figure 2: A preview of the overall structure of this paper.
# A 3D-Printed Enclosed Twist Dielectric Resonator Antenna with Circular Polarization
## 1 Introduction  
In recent years, additive manufacturing has gained huge interest in microwave and antenna research [1]. This is mainly due to the possibility of manufacturing prototypes or functional parts that were either too expensive or difficult to implement using standard fabrication processes. This is also possible due to the introduction of conductive filaments and low-loss dielectric filaments [2] and the lower cost of high-resolution 3D printers; this combination makes these solutions appear cost-effective for several applications [3]. Some topologies that have been implemented are metamaterials [4, 5], horn antennas [6], dielectric lenses [7], and other structures [8, 9].  
One particular kind of antenna that has been directly taking advantage of 3D printing is dielectric resonator antennas (DRAs). These antennas, which consist of a dielectric slab that that can radiate depending on how it is excited, offer significant versatility in terms of radiation patterns and operational frequencies [10]. This is thanks to the possibility of exciting different cavity modes in the dielectric structure, which are dependent on the shape of the slab, its dimensions and its permittivity. One of the drawbacks of this technology is the possible choice of materials for the dielectric slab, in that the permittivities are basically set by the ceramic or dielectric in use. In addition, difficult shapes can be expensive or difficult to implement, for example, when using ceramic materials for high-permittivity values, which can limit the kind of topologies that can be implemented. Both issues can be solved by using 3D printing and the aforementioned dielectric filaments [11].  
The versatility of DRAs is also important when implementing circular polarized (CP) antennas. This characteristic has been used in wireless communication applications, such assatellite communications and unmanned vehicles [12], for the advantages that are offered regarding the reduction in the delay spread [13] and its robustness in the face of multipath interference [14,15]. From that perspective, few works have explored implementing CP 3D-printed DRA. Amongst those works, we can find a cylindrical DRA that includes a parasitic helix to achieve CP [16], and others that use an integrated polarizer [17,18].  
This manuscript presents an enclosed twist DRA that generates a CP depending on the turning sense of the twist. The antenna operates at 5.8 GHz and is a single-fed structure, which makes it easier for implementation. Preliminary simulation studies were presented in [19], and this work shows a final design, modal analysis, and the manufacturing and measurement results of the antenna.
# Microstructure-Based Prediction of the Elastic Behaviour of Hydrating Cement Pastes
## 6 Results and Discussion
### Parametric Study
#### 6.3.2 Influence of Voxel Size  
Most of the hydration models, including the HYMOSTRUC3D, are based on the vector approach [11] which assumes cement grains as spheres, being able to grow inwards and outwards, or to nuclei inside the REV. The precision of the numerical model is dependent on the resolution of voxels that discretises these spheres. The results of different voxel sizes implemented for discretisation  
Figure 14: (**a**) Three adopted PSD curves in the hydration model by HYMOSTRUC3D; (**b**) Young’s moduli obtained for the three different PSD curves.  
of REV are plotted in Figure 15 for \(w/c\) = 0.40. As shown in this figure, by decreasing the voxel size the effective Young's modulus of the cement paste also decreases. However, there is no significant change when the voxel size is considered less than about 70% of the minimum particle size (1.0 \(\upmu\)m)--see the results for the voxel size of 0.57 \(\times\) 0.57 \(\times\) 0.57 \(\upmu\)m\({}^{3}\). In fact, the voxel size of 0.67 \(\times\) 0.67 \(\times\) 0.67 \(\upmu\)m\({}^{3}\) can be an optimised voxel size for discretisation of the REV. This can be explained by the fact that the artificial solid phase connection becomes a minimum when the voxel size decreases. When the artificial solid phase connection decreases, it reduces the total solid part connectivity in REV, leading to a decrease of the effective Young's modulus. This resolution is important to capture properly the effect due to the solid percolation threshold of the cement paste at very early ages. As shown in Figure 15, at the age of \(\sim\)5 h, and decreasing the voxel size from 1.25 \(\upmu\)m to 0.57 \(\upmu\)m, the accuracy of the model to estimate the experimental value increases.  
The influence of voxel resolution on the effective Young's modulus can be considered as a modelling weakness as far as predicting mechanical properties of the microstructure is concerned. The choice of the voxel resolution is directly limited by the available computational power, and sometimes by algorithmic problems (such as convergence).
# Enhancing Vibration Isolation Performance by Exploiting Novel Spring-Bar Mechanism
## 4 Conclusions  
This study investigated a vibration isolation system consisting of a vertical spring-damper linear isolator unit and a spring-bar mechanism (SBM) created by a pair of bars linked with horizontal springs. The system model was first introduced and was then analysed, both by analytical approximations using the averaging method and by numerical integrations. The performance of the isolator was examined by using both nonlinear force transmissibility as well as the time-averaged power flows and the maximum kinetic energy of the mass. It was shown that the stiffness characteristics of the system can be controlled by two parameters describing the initial compression of the horizontal springs as well as the stiffness ratio of the horizontal and vertical springs. It was found that adding SBM can reduce the natural frequency of the linearized system so as to provide a larger functioning frequency range. Compared to the traditional linear spring-damper isolator and the previous NSM isolator design, the proposed SBM can reduce the force transmission level near the resonance and has a wider effective isolation range. It was also found that different combinations of these parameters may yield different nonlinear effects for enhanced vibration isolation. By adjusting these two parameters, the SBM can also provide a purely linear negative restoring force and thus negative stiffness without introducing possibly harmful nonlinear effects into the system. It was shown that adding the SBM was shown to be beneficial for vibration isolators as both force transmission and the amount of power input were reduced in the high-frequency range. It was found that the system can exhibit chaotic motions, for which the associated time-averaged input power tended to an asymptotic value with increasing averaging time. Thus, time-averaged power flow variables can be used as a uniform index to assess and compare the performance of nonlinear vibration isolation systems exhibiting either periodic or chaotic motions.  
Conceptualization, J.Y.; methodology, B.S. and J.Y.; software, B.S. and J.Y.; validation, B.S. and J.Y.; formal analysis, B.S. and J.Y.; investigation, B.S. and J.Y.; resources, B.S., J.Y. and T.L.; data curation, B.S. and J.Y.; writing--original draft preparation, B.S. and J.Y.; writing--review and editing, J.Y. and T.L.; visualization, B.S. and J.Y.; supervision, J.Y.; project administration, J.Y. and T.L.; funding acquisition, J.Y. and T.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant numbers 51839005, 51879113, and 12172185.  
Not applicable.  
We would like to thank the reviewers and the Applied Sciences team for their support.  
The authors declare no conflict of interest.
# Speed Optimization for Incremental Updating of Grid-Based Distance Maps
## 4 The Methodology
### Algorithm Intuition
#### 4.1.2 Propagation in Canonical Ordering  
To speed up the construction efficiency, we introduce the concept of Canonical Ordering to guide the propagation of wavefronts. The application of Canonical Ordering has been recently discussed to speed up best-first search in grids-based pathfinding tasks in [24]. Different from the classical Dynamic Brushfire algorithm, which expands all the eight directions at every step, Canonical Ordering can eliminate redundant, symmetric paths from the source of state change, which accounts for the main part of the speed improvement.  
As shown in Figure 6a, in an open grid map without obstacles, a wavefront launched by a cell (denoted as \(s\)) on the left top can follow three different optimal paths to propagate its distance change to the traversable cell (denoted as \(g\)) on the right bottom. The only difference between these paths relies on the permutation of actions of moving right one cell and moving down-right two cells. It is clear to see that the number of possible paths grows exponentially if we enlarge the grid map and keep \(g\) in the right bottom corner. Since the existing Dynamic Brushfire algorithm generates the same cell  
Figure 4: Lower wavefront propagation. (**a**) Insert a new obstacle cell; (**b**) Launch a lower wavefront; (**c**) Propagate the distance change; (**d**) Reach edges of affected area; (**e**) New DM is reconstructed.  
Figure 5: Raise wavefront propagation. (**a**) Remove the center obstacle; (**b**) Launch a raise wavefront; (**c**) Propagate the resetting to the invalid edges; (**d**) Continue the lower wavefront from other sides; (**e**)The invalid region is again submerged.  
along different paths, such propagation would trigger large numbers of pushing and sort ascending operations in _OPEN_ list, which can be computationally expensive.  
To eliminate these redundancies, Canonical Ordering insists on a preference rule, i.e., distance changes first propagate diagonally before propagating cardinally whenever possible along a path (e.g., as shown in Figure 6a, by following this rule, only the gray path is tested.). With such a preference, a source cell in an open grid map returns a unique path from itself to each affected cell. As shown in Figure 6b a wavefront launched from the center cell (denoted as a yellow cell) draws lines diagonally and then branches vertically and horizontally, extending from the diagonals. Since every affected cell owns exactly one path from the source cell, the propagation is no longer a graph, but a tree.
# Joint Transmit Power Allocation and Splitting for SWIPT Aided OFDM-IDMA in Wireless Sensor Networks
## 4 Simulation Results and Discussions  
In this section, simulation results are provided to evaluate the performance of the proposed scheme. We consider a OFDM-IDMA system with 512 subcarriers, where the whole bandwidth of 80 MHz is evenly divided. The repetition code of length 16 is employed to spread the RNs' data over the subcarriers. The RNs are located to be 1 m away from the SN and the large scale fading is 30 dB with the path-loss exponent assumed to be 3. In addition, it also experiences the frequency selective multi-path Rayleigh fading with the number of fading paths \(L=6\), from SN to each RN. The power spectrum density of noise is \(\sigma^{2}=112\) dBm\(/\)Hz. For EH, the initial PS ratio and iteration number of all RNs are \(\rho=0.5\) and \(Q=30\), respectively. The EH circuit specifications of all RNs are \(a=0.14\), \(b=15\), and \(M=25.2\) mW.
## 3 Method
### Smote-Rsb  
SMOTE-RSB [22] employs the lower approximation principle of Rough Set Theory (RST) to filter out noisy data points in synthetic samples generated by the SMOTE algorithm. The process and logic behind SMOTE-RSB are detailed in the flowchart presented in Figure 2. First, minority class samples are identified from the unbalanced dataset, and new samples are created by interpolating between the minority class samples and their neighbors. Then, a similarity matrix is constructed for the newly generated samples, and the similarity values in the matrix are evaluated according to the lower approximation in RST. If the similarity value is below the lower approximation, the synthetic sample adds new information or changes to the dataset, so it is added to the dataset. Otherwise, the sample is considered too similar to the existing samples and is discarded.  
Figure 2: Flowchart of SMOTE-RSB algorithm.  
A crucial feature of SMOTE-RSB is its ability to reduce noise by leveraging the similarity relationships among synthetic instances and actual minority samples [49]. The lower approximation in RST captures elements definitively part of a set, while the upper approximation considers potential members. By applying the lower approximation, SMOTE-RSB effectively identifies and removes uninformative synthetic instances from the minority class, ensuring only beneficial data contribute to model training. The objective of this technique is to enhance the quality of the synthetic samples, thereby increasing the precision of models trained on balanced datasets.
# Deep-Learning-Based Framework for PET Image Reconstruction from Sinogram Domain
## 4 Results
### Simulation Experiments
#### 4.1.3 Accuracy  
Three frames of the Zubal thorax testing dataset were extracted to exhibit the reconstructed results, as shown in Figure 8. The method was also compared with the MLEM and TV algorithms. As shown in Figure 8, the reconstruction results of the proposed method are highly consistent with the ground truth in terms of the sharp edges and high pixel values of the ROIs. The results of the MLEM method contain excessive noise and artifacts because of the chessboard effect, even though the boundaries cannot be clearly observed. The TV method provides a clearer and sharper result than the MLEM method. However, the reconstruction effect of the small structure in the ROI3 area is poor, as indicated by the pink rectangular box. The detailed quantitative results are provided in Table 2. The images generated by cGANs had less than one tenth of the bias and less than one percent of the variance of the full image compared to the MLEM. Compared to TV, we still found much lower bias and variance using cGANs. However, for different frames, the performance of cGANs has a great difference. For the first few frames, such as the third frame and seventh frame, though the cGANs obtained better quantitative results on full image compared with TV, the bias values of ROI2--which is a tiny area in the Zubal thorax phantom--are very close. For the late frames, such as the 12 th and 18 th frames, the bias values of cGANs are almost one tenth of TV's. Even for the ROI2, cGANs also got surprised results. cGANs indeed has much stronger ability in the frames that own higher photons and less noise.  
Figure 8: Reconstruction results for Zubal thorax phantom with 40 min scanning and \(1\times 10^{7}\) counts. From left to right: MLEM results, TV results, cGAN results, and ground truth. From top to bottom: the 3 rd, 7 th, and 12 th frames.  
Figure 7: Convergence curves of the discriminator and generator. Both g-loss and d-loss converge quickly after approximately 1000 steps.  
Figure 9 shows the mean values of the bias and variance of all 18 frames of the Zubal thorax phantom. For the bias and variance, the cGAN framework obtained a satisfactory score. The TV method performs much better performance than the MLEM method because it can better suppress the noise in the images. However, compared with the other two areas, the reconstruction results of ROI2 were unsatisfactory, and the values of the bias and variance of the three methods were higher than those of the other areas. It is considered that this outcome was possible because ROI2 was extremely small for it to be distinguished from the other two parts.