esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this paper, we proposed a distributed 3-D path planning for multiple UAVs, based on Particle Swarm Optimization with Bresenham Algorithm, to make an optimal trajectory for multiple UAVs. We introduced a multi-dynamic fitness function that has optimization indexes, such as energy consumption, flying risk, surveillance area importance (SAI), and UAV maneuverability. Moreover, we also obtained the optimal weight of SAI for an objective value to obtain dynamic fitness to generate a collision-free trajectory for multiple UAVs. To analyze the performance of the proposed optimal trajectory planner, we designed a dynamic fitness function mechanism with a cost function. The numerical results of experiments carried out in this research work show that the PSO, with Bresenham Algorithm, can be applied for multi-UAVs to surveillance the whole area of interest by generating an optimal path. Currently, we carry out experiments for the swarm of four UAVs, and we evaluate feasibility, robustness, and dynamic environment adaptability for our three dimensions distributed trajectory planner to analyze the performance and effectiveness of the system. The simulation results prove that our proposals can perform a collision-free distributed trajectory planning for multiple UAVs to surveillance the whole area of interest by flight time and flight distance optimized manner. For future work, we will consider an unstructured dynamic environment to perform three dimensions of distributed trajectory planning. We may apply our developed distributed trajectory planner to interface with the drone model for security purposes, to make a real-time application of multiple UAVs for full area surveillance under a dynamic environment.  
**Author Contributions:** Supervision and investigation K.C.; N.A. and C.J.P. are contributed equally in this paper for methodology and writing original draft preparation; writing-review and editing K.C., N.A. and C.J.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. NRF-2019R1F1A1061696).  
**Institutional Review Board Statement:** Not Applicable.  
**Informed Consent Statement:** Not Applicable.  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing does not apply to this article.  
Figure 9: Distributed Path Planning for Multi-UAVs for larger size in (**a**) 2-D environment, (**b**) 3-D environment.
## 3 Model Parameter Identification and Comparison
### Results and Discussion  
Model parameters of eight models are identified by the above method in the whole SOC area, and results are shown in Figures 4 and 5. Figure 4 shows identification results under NEDC working condition. As shown in Figure 3(a), the accuracy of the E\(n\)RC model is obviously higher than that of the \(n\)RC model with the same order in the low SOC area, indicating that the proposed model provide satisfactory accuracy in the whole SOC area due to the consideration of the solid-phase diffusion process. Moreover, the model accuracy increases with the increase of the order of RC network, but it will not increase continuously. The accuracy of E2 RC and E3 RC models is almost the same. Therefore,  
Figure 5 shows RMSE and terminal voltage comparisons of various models under DST working condition. We can see that the E2 RC model is the best choice under DST. Based on the comparative study of the above eight models under two working conditions, we can conclude that the model error in the low SOC are is only half of that for traditional ECM, and the E2 RC model is the most suitable model. Therefore, the battery model used in this study for SOC estimation is the E2 RC model.  
It is noted that the \(n\) in E_n_RC model represents the number of double layers in the SPM. It can be seen that the models with two double layers have the highest accuracy, which is consistent with the model shown in Figure 1. Moreover, the proposed model uses only one solid-phase diffusion equation in the SPM to achieve satisfactory model accuracy, which is different with the model proposed in Reference [14].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model Name** & **Identification Time (s)** \\ \hline
0 RC & 1.7796 \\ E0 RC & 4.0983 \\
1 RC & 5.2645 \\ E1 RC & 5.9482 \\
2 RC & 5.9897 \\ E2 RC & 6.1520 \\
3 RC & 8.5727 \\ E3 RC & 11.3445 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Identification time of eight battery models (Cell 1@ European Driving Cycle (NEDC)) in the entire state-of-charge (SOC) area.  
Figure 4: Parameter identification results under NEDC working condition. (**a**) Root-mean-square error (RMSE) comparison for eight models; (**b**) terminal voltage profiles for 2 RC and E2 RC.
## 2 Results and Discussion
### Analysis of Volatiles in Cooked Sorghum Samples by Using GC\(\times\)GC-TOFMS  
Figure 2 shows the bubble plot obtained by GC\(\times\)GC-TOFMS analysis of the volatile components of two cooked sorghum samples. Tentative compound identification was performed by firstly comparing database mass spectra with experimental ones, where database matches were considered with a spectral similarity of higher than 80%, and secondly, comparing between calculated and literature retention values, where a difference value within 50 is desirable. Thirdly, the positive identification of 98 compounds (\(\sim\)67% of the total number) was performed with commercially available pure standard compounds injected for qualitative analysis. Finally, a total of 145 volatile compounds were retained in this study, among which aldehydes were present in the highest number (34), followed by alcohol (25), ketones (19), esters (16), terpenes (14), acids (14), heterocyclics (12), sulfides (4), aromatics (3), pyrazines (2), and lactones (2) (Table 1). As shown in Figure 3, aldehydes were the most abundant class of volatile compounds in the cooked sorghum samples and both saturated and unsaturated linear chain compounds were representative of this class [5]. Aldehydes usually derive from lipid oxidation, and these results could be related to the larger amount of lipids present in the bran layers. Saturated fatty aldehydes which are aromatics, have a greater contribution to the aroma of a cereal product because of their low odor threshold values (OAVs), and provide almond, malt, pungent (pentanal), grassy, green and fatty (hexanal), fatty, citrus and rancid (heptanal), fatty, soapy and green (octanal), fatty, citrus and green (nonanal) characteristics. (_E_)-2-enol is an important kind of aldehyde, there are (_E_)-2-heptenal, (_E_)-2-ocneal, (_E_)-2-nonenal and (_E_)-2-decneal, respectively. Most of them show green, fruity, and fatty odors and the odor characters change with increases of the carbon chain length (Table 2). (_E_)-2-enals were not detected in the free aroma of original sorghum, but were detected in various  
Figure 2: The 2D bubble plot of glutinous and non-glutinous sorghum samples.
## 3 Proposed Methodology  
The primary methodology of this study involves implementing multi-level compression using the JPEG algorithm, followed by managing the compressed data using a tree-based structure. Subsequently, a deep neural network model is employed to enhance the restoration quality of the compressed data. Figure 1 illustrates the process, where multi-level compression is achieved through iterative downsampling and merging at varying JPEG quality settings. The quadtree structure is utilized to store pertinent information for each level of compressed data, including the compressed data themselves, coordinate details, peak signal-to-noise ratio (PSNR), mean absolute error (MAE), and other relevant metrics. The data restoration enhancement module is based on the ConvNeXt network, trained on a dataset comprising ocean data from the previous five years. Parameters such as learning rate and network depth are fine-tuned to optimize the network's performance.
## 3 Results and Discussion
### Performance of Low-Temperature Solar Collectors with Antifreez Fluids  
Figure 9 presents the efficiency and useful heat concerning fluid temperature for three types of solar collectors and five different antifreeze working fluids, under fixed mass flow and radiation conditions. As shown in Figure 9a, when comparing the heat pipe flat plate collector (HPFPC) with other common collectors, the efficiency of the flat plate collector (FPC) decreases significantly from 70% to 8% at a rate of 1.2% for each degree Celsius increase in temperature. By comparison, the efficiency of the evacuated tube collector (HPETC) drops from 83% to 58%, decreasing at a rate of 0.5% per degree Celsius. Conversely, for the HPFPC--with water as the working fluid--the efficiency declines more steeply from 83% to 29% at a rate of 1% for each degree Celsius. When evaluating a 30% antifreeze mixture, the efficiency reduces from 60% to 21.5% at a rate of 0.77% per degree Celsius.  
The figure further indicates that, for fluid temperatures below 50 \({}^{\circ}\)C, the efficiency of the HPFPC is lower than that of the standard flat plate collector. However, above 50 \({}^{\circ}\)C, the HPFPC outperforms the flat plate collector. Specifically, at temperatures below 50 \({}^{\circ}\)C, using water can result in a 19% efficiency increase, while the use of antifreeze mixtures leads to up to a 13% reduction in efficiency. At a fluid temperature of 50 \({}^{\circ}\)C, both water and antifreeze mixtures maintain the same efficiency--19% for water and a similar performance for antifreeze. As the temperature rises to 65 \({}^{\circ}\)C, the efficiency increases by 19% for water and 7% for antifreeze mixtures. At 80 \({}^{\circ}\)C, these increases are 21% for water and 13% for antifreeze mixtures. The trends in useful heat, as shown in Figure 9b, mirror those of efficiency, indicating that the use of heat pipes in flat plate solar collectors can enhance efficiency by up to 21% when operating at fluid temperatures above 50 \({}^{\circ}\)C. According to [51; 52], and the trend presented in Figure 8, it can be expected that, with mixtures of 20% ethylene glycol and/or propylene glycol, the efficiency of the heat pipe flat plate solar collector can improve compared to the use of 30% of these antifreeze, and that it still protects the equipment against freezing.  
Figure 8: Heat transfer coefficient in the condenser, on the manifold side, with respect to the inlet temperature to the manifold, of the two collectors with heat pipes.
## 5 Numerical Simulations and Comparison
### Radiometric Sensitivity  
The real and the imaginary parts of the complex visibilities \(V\) and \(\widetilde{V}\) have been blurred by an additive Gaussian noise \(\Delta V\) and \(\Delta\widetilde{V}\) with standard deviation \(\sigma_{\Delta V}=\sigma_{\Delta\widetilde{V}}=0.1\) K. This is a typical value for SMOS, which should translate in the brightness temperature domain with a radiometric sensitivity on the order of 2.5 K [10]. This is what is observed in Figure 24 where the radiometric sensitivity in the boresight direction \(\xi=(0,0)\) is equal to 2.55 K in far-field imaging conditions and to 2.57 K in near-field ones. Moreover, the radiometric sensitivity map does not depend on the distance between the antenna array  
Figure 23: Root Mean Square Error (RMSE) in the alias-free field of view as a function of the distance \(h\) between the source and the antenna array when observing an extended source with a constant temperature set to 100 K in the whole space in front of the instrument (red). Attention should focus on the decrease towards the floor error obtained in far-field conditions (0.14 K, green) as \(h\) increases. For the Fraunhofer distance \(2D^{2}/\lambda=430\) m, this floor error is about 0.19 K, i.e., 35% larger than the far-field value. This excess remains below 5% as soon as \(h\) exceeds 2 Km \(\simeq 10D^{2}/\lambda\).  
Figure 22: Brightness temperature maps \(\widetilde{\mathcal{T}}_{r}\) (far-field, **left)** and \(\mathcal{T}_{r}\) (near-field, **right)** retrieved after inverting the complex visibilities \(\widetilde{V}\) and \(V\) shown in Figure 21. In both cases, a Blackman windowing function \(W\) has been used to dampen the reconstructed maps \(\widetilde{T}_{r}\) and \(T_{r}\). Shown in dashed brown are the horizons from the six neighbors to the hexagonal field of view in red, which are responsible for field-aliasing. To facilitate the comparison, the color scale is the same for these two images and for the brightness temperature \(T\) shown in Figure 19. In the central part of the field of view free from field-aliasing, the distribution of the retrieved brightness temperature is 100.04 K \(\pm\) 0.14 K (far-field, **left)** and 100.21 K \(\pm\) 0.49 K (near-field, **right); these numbers have to be compared to the expected 100 K.  
and the scene observed by the instrument. Referring back to Figure 4, this is an expected behavior when comparing the singular values of \(\mathbf{G}\) and \(\widetilde{\mathbf{G}}\), and especially the role played by those kept in the pseudo-inversion, which are almost identical.
# Semantic Segmentation Based on Depth Background Blur
## 2 Related Work
### A Change in Thinking  
In recent studies, scholars have attempted to solve semantic segmentation with a structure different from FCN (a standard encoder-decoder architecture). [PERSON] et al. [21] introduced hypernetworks into semantic segmentation for the first time and proposed a novel network model, HyperSeg. Hypernetworks are networks that generate weight for another network. The crucial role of their HyperSeg is to have the encoder generate weights for the decoder with an advanced features encoded. [PERSON] et al. [25] serialized the input image into a sequence of image patches and adopted a pure transformer structure,  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Backbone**} & \multicolumn{2}{c}{**Mean IOU**} \\  & & **PASCAL VOC** & **CamVid** \\ \hline FCN [9] & VGG-16 & 62.2 & - \\ \hline DFANet [16] & Xception-A & - & 64.7 \\ \hline BiSeNet [14] & ResNet-18 & - & 68.7 \\ \hline DeepLabV3 [12] & ResNet-101 & 78.5 & - \\ \hline Hyperseg-s [21] & EfficientNet-B1 & - & 78.4 \\ \hline DeepLabV3+ [13] & Xception-71 & 80.0 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Accuracies of segmentation models on the PASCAL VOC [19] 2012 val and CamVid [20] test dataset.  
called SETR, to solve semantic segmentation. Based on the idea of mathematical statistics, we explore the inherent characteristics of the dataset itself and use the proposed bokeh method to improve the performance of existing semantic segmentation models.
## 4 Applications of GenAI-Driven CV in Smart Agriculture
### Precision Agriculture and Field Management
While challenges persist in model robustness and scalability, advancements in edge computing, synthetic data generation, and human-centric AI promise to further democratise intelligent farming solutions, aligning technical innovation with global sustainability goals [193; 157; 194]. Representative studies highlighting traditional CV and GAN-based methods in this domain are summarised in Table 3. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Study** & **Methodology** & **Task** & **Approach** & **Outcome** \\ \hline
[166] & CNN & \
\begin{tabular}{c} Weed and crop classification \\ \end{tabular} & Precision spraying &
\begin{tabular}{c} High accuracy in crop-specific \\ weed identification \\ \end{tabular} \\ \hline
[165] & CNN & \
\begin{tabular}{c} Weed detection \\ \end{tabular} & Real-time field analysis &
\begin{tabular}{c} Effective under diverse \\ field conditions \\ \end{tabular} \\ \hline
[169] & YOLOv10n & \
\begin{tabular}{c} Weed detection in rice \\ \end{tabular} & Object detection &
\begin{tabular}{c} Optimised for real-time \\ performance \\ \end{tabular} \\ \hline
[161] & UAV+AI pipeline & UAV data processing & Visualisation and analysis &
\begin{tabular}{c} Real-time analysis of \\ UAV imagery \\ \end{tabular} \\ \hline
[175] & Region-based CNN & \begin{tabular}{c} Weed type classification \\ \end{tabular} & \
\begin{tabular}{c} Dense environment \\ detection \\ \end{tabular} &
\begin{tabular}{c} Monocot/dicot separation in \\ complex scenes \\ \end{tabular} \\ \hline
[168] & GANs + CNN & \
\begin{tabular}{c} Weed image synthesis \\ \end{tabular} & Data augmentation &
\begin{tabular}{c} Enhanced training \\ dataset diversity \\ \end{tabular} \\ \hline
[185] & GAN & Vegetation monitoring & RGB-to-NDVI translation &
\begin{tabular}{c} Low-cost monitoring alternative \\ \end{tabular} \\ \hline
[173] & GANs + CNN & \
\begin{tabular}{c} Weed detection \\ \end{tabular} & Semi-supervised learning &
\begin{tabular}{c} Improved site-specific \\ model accuracy \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Representative studies of traditional CV and GAN-based methods in precision agriculture and field management.
## 4 Experiments and Results
### Comparison with SOTA Models
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Method** & _AP_ & _Recall_ & _Precision_ & _F1_ & _Pd_ & _Pm_ & _Pf_ & _FPS_ & **Param(M)** \\ \hline YOLOv2 & 90.10\% & 0.89 & 0.91 & 0.91 & 0.89 & 0.11 & 0.11 & 27 & 217 \\ \hline YOLOv3 & 91.84\% & 0.90 & 0.92 & 0.91 & 0.90 & 0.10 & 0.10 & 20.1 & 243.5 \\ \hline SSD & 80.77\% & 0.72 & 0.80 & 0.76 & 0.72 & 0.28 & 0.31 & 21.3 & 297.8 \\ \hline Fater R-cnn & 92.94\% & 0.71 & 0.92 & 0.76 & 0.71 & 0.29 & 0.38 & 13.3 & 482.4 \\ \hline Mask R-cnn & 92.43\% & 0.95 & 0.89 & 0.92 & 0.92 & 0.05 & 0.04 & 12.5 & 503.4 \\ \hline RetinaNet & 94.1\% & 0.94 & 0.92 & 0.93 & 0.94 & 0.06 & 0.05 & 12.8 & 442.3 \\ \hline YOLOv4 & 90.15\% & 0.86 & 0.94 & 0.90 & 0.86 & 0.14 & 0.15 & 24 & 244.2 \\ \hline Auto-T-YOLO & 96.3\% & 0.96 & 0.86 & 0.91 & 0.96 & 0.04 & 0.09 & 20.9 & 247.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Experimental results of SOTA models on SSDD dataset. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method** & _AP_ & _Recall_ & _Precision_ & _F1_ & _Pd_ & _Pm_ & _Pf_ & _FPS_ & **Param(M)** \\ \hline YOLOv3 & 95.39\% & 0.99 & 0.95 & 0.97 & 0.99 & 0.01 & 0.01 & 20.1 & 243.5 \\ \hline Faster R-cnn & 97.74\% & 0.96 & 0.97 & 0.97 & 0.96 & 0.04 & 0.03 & 13.3 & 482.4 \\ \hline RetinaNet & 97.81\% & 0.96 & 0.95 & 0.96 & 0.96 & 0.04 & 0.04 & 12.8 & 442.3 \\ \hline DAPN & 95.93\% & 0.99 & 0.96 & 0.98 & 0.99 & 0.01 & 0.01 & 24 & 244.2 \\ \hline HR-SDNet & 92.10 \% & 0.94 & 0.95 & 0.94 & 0.94 & 0.06 & 0.06 & 11 & 598.1 \\ \hline ARPN & 98.20 \% & 0.96 & 0.97 & 0.96 & 0.96 & 0.04 & 0.04 & 13 & 490.7 \\ \hline Auto-T-YOLO & 98.33\% & 0.98 & 0.96 & 0.97 & 0.98 & 0.02 & 0.02 & 20.9 & 247.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Experimental results of SOTA models on offshore scenes.
# Minimizing Interference-to-Signal Ratios in Multi-Cell Telecommunication Networks
## 3 Dot-Product Clustering Algorithm for Total Interference Minimization Problem
### Phase 1: The Dot-Product Similarity Measure  
A superficial study of Equation (2) says that we want to decompose the graph in such a way that clusters contain _heavy_ (high weight) edges, and the cuts among the clusters consist of _light_ edges. The weight function is described via the matrix \(W\) where the rows correspond to the BSs, and the columns correspond to the users:  
\[W=\left(w_{i,j}\right)_{i\in\mathcal{I},j\in\mathcal{J}}\]  
Let \(w_{i,\bullet}\) denote the row vector of \(b_{i}\) (the \(i\)th BS), and let \(w_{\bullet,j}\) denote the column vector of \(u_{j}\) (the \(j\)th user). Hence, we can write \(W=\left[w_{i,\bullet}\right]_{i\in\mathcal{I}}=\left[w_{\bullet,j}\right]_{i \in\mathcal{J}}\). Our heuristic is that the higher the correlation between the weight distribution of two BSs, the more beneficial it is to include them in the same cluster. We define the _similarity function_ as  
\[\rho:\mathcal{I}\times\mathcal{I}\rightarrow\mathbb{R}^{\geq 0}\quad\text{ with}\quad\rho(i,k):=\frac{w_{i,\bullet}\cdot w_{k,\bullet}^{T}}{\|w_{i, \bullet}\|\cdot\|w_{k,\bullet}\|} \tag{3}\]  
among the BSs, where \(\|\cdot\|\) is the Euclidean norm. The name _dot-product_ is in reference to the enumerator of Equation (3). The similarity \(\rho\) depends only on the relations between the users and the BSs. We envisage that the larger the value of \(\rho\), the greater the similarity between the BSs.  
In the total interference minimization model, an ensemble of BSs in a cluster behave as one base station. Indeed, if the IF-cluster system \(\{P_{1},\ldots,P_{M}\}\) minimizes Equation (2), then replacing the ensemble of BSs in cluster \(I_{\ell}\) with just one new BS \(b_{\text{new}}\), whose weight to user \(j\) is \(\sum_{k\in I_{\ell}}w_{k,j}\), preserves the optimum, and the total interference metric takes this optimum on the partition pair \(\mathcal{I}^{\prime},\mathcal{J}\) where the \(\ell\)th class \(I_{\ell}\) is replaced with the index of \(b_{\text{new}}\) in \(\mathcal{I}^{\prime}\). Let us define  
\[\text{vec}(I_{\ell})=\sum_{i\in I_{\ell}}w_{i,\bullet} \tag{4}\]  
as the sum of the signal strength vectors of the base stations in \(B_{k}\). The similarity function \(\rho\) can be naturally extended to ensembles of BSs:  
\[\rho:2^{I}\times 2^{I}\rightarrow\mathbb{R}^{\geq 0}\quad\text{ with} \tag{5}\] \[\rho(I_{k},I_{m}):=\frac{\text{vec}(I_{k})\cdot\text{vec}(I_{m}) ^{T}}{\|\text{vec}(I_{k})\|\cdot\|\text{vec}(I_{m})\|}.\]