esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model and Description
### System Models of Existing and Proposed Protocols
#### 3.3.3 Neighbors Selection  
In the proposed protocols, neighbors list is maintained by every node in the transmission range. They broadcast the message in the network. This message includes the source node, destination node, location, and depth 'yd' of the source node. Receiving node calculates the distance from the source to the destination node using Euclidean distance which is expressed as Equation (13);  
\[Distance_{(i,j)}=\sqrt{(x_{1}-x_{2})^{2}+(y_{1}-y_{2})^{2}}, \tag{13}\]  
where \((x_{1},x_{2})\) and \((y_{1},y_{2})\) are the coordinates of source and receiver node. Every sensor node maintains a table of their source ID, location, depth, and distance from each neighbor. Coordinates \((x,y)\) are obtained from the control message, while distance is calculated using Equation (13) and the depth 'yd' is obtained by calculating the depth of the node from the sea surface. After getting the required tuple, following constraints help the proposed protocols to find the optimum neighbor as forwarders.  
\(Distance_{(i,j)}\leq TransmissionRange(R_{t})\)  
\(Depth_{i}>Depth_{j}\)  
This depth of sensor nodes helps to find the current forwarder which is closer to the sea surface. Information regarding higher depth node is not required in the proposed protocols to eliminate the extra EC and communication overhead.
# Device-Free Localization and Identification Using Sub-GHz Passive Radio Mapping
## 4 Sub-GHz Passive Identification
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Estimate**} \\ \hline  & **Participant \#** & **1** & **2** & **3** & **4** \\ \hline \multirow{5}{*}{Ground Truth} & 1 & 0.93 & 0.07 & 0 & 0 \\ \cline{2-6}  & 2 & 0 & 0.58 & 0.42 & 0.01 \\ \cline{2-6}  & 3 & 0 & 0.05 & 0.23 & 0.72 \\ \cline{2-6}  & 4 & 0 & 0.23 & 0.18 & 0.59 \\ \cline{2-6}  & 5 & 0 & 0.36 & 0.06 & 0.58 \\ \cline{2-6}  & 6 & 0 & 0.70 & 0.10 & 0.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Confusion matrix for identification based on a naive Bayesian classifier approach applied to RSS data from both the 433 MHz and 868 MHz network. Second, in contrast to localization, it appears that the combination of data from two frequency bands does not seem to have any significantly positive effects in regards to identification accuracy. The total accuracy rates for 433 MHz, 868 MHz and the two of them combined are provided in Table 16 where this can clearly be seen. Only for the Euclidean approach does the frequency band have a minor impact on the total identification accuracy rates: 0.47 for 433 MHz, 0.52 for 868 MHz and 0.52 as well for a combination of the two. For the Bayesian method, the total identification accuracy rate is 0.58 regardless of frequency. Finally, in the schematic overview images shown in Figures 10-12 we can see that differences in accuracy rate between different locations are truly vast. As an example, the rates vary between 0.07 and 0.75 for the Bayesian approach using 433 MHz and 868 MHz data. A potential explanation for this would be that certain links which are affected most strongly when a target is present in a specific location are much more sensitive to differences between individuals. Determining whether this is true and if so, developing a method to identify and make use of these links are interesting topics for future research. \begin{table}
\begin{tabular}{c c c c} \hline  & **433 MHz** & **868 MHz** & **433 MHz and 868 MHz** \\ \hline Euclidean & 0.47 & 0.52 & 0.52 \\ \hline Bayesian & 0.58 & 0.58 & 0.58 \\ \hline \end{tabular}
\end{table} 
Table 16: Overview of total identification accuracy rates for the entire experimental environment. Figure 11: Identification accuracy rates for each location for both the (**a**) Euclidean distance minimization and (**b**) naive Bayesian classifier approach applied to 868 MHz RSS data. Figure 12: Identification accuracy rates for each location for both the (**a**) Euclidean distance minimization and (**b**) naive Bayesian classifier approach applied to 433 MHz and 868 MHz RSS data.
# Object Detection in Drone Imagery via Sample Balance Strategies and Local Feature Enhancement
## 2 Related Work
### Datasets for Drone Imagery Object Detection  
While ground-based datasets, such as MSCOCO [47], PASCAL VOC [48], and ImageNet [49] have achieved great success, when these datasets are used for object detection in drone images, there is a massive performance degradation. To date, there are not many datasets that can be applied to object detection from drones because it requires a significant amount of data annotation. COWC [50] is an aerial-based dataset which consist of 32.7 annotated vehicles and 5.8 useful negative samples, (i.e., boats, trailers, bushes, and A/C units). The quality, appearance, or rotation of annotated targets are all uncontrollable however. Meanwhile, the size of a vehicle in this dataset is between 24 to 48 pixels. CARPK [51] is a drone-based dataset that mainly focuses on car counting and includes 1448 images that were captured by drones in parking lots. DOTA [52] is an aerial-based dataset that contains 2806 aerial photos that are in 15 categories and 188,282 instances. Vishrone [12] is a drone-based dataset and a large-scale benchmark that facilitates object detection from drone imagery. The Vishrone datasets contain ten object categories, including pedestrian, person, car, van, bus, truck, motor, bicycle, awning-tricycle, and tricycle. In daily life, vehicles and people are the highest frequencies detected. In this paper, we mainly apply the Vishrone datasets to train and detect objects in the cars and people categories.
# Deep Learning in Financial Modeling: Predicting European Put Option Prices with Neural Networks
## 3 Materials and Methods
### Training and Evaluation
#### 3.7.3 Scalability and Limitations  
While the proposed model demonstrated high accuracy when trained on synthetic data, several challenges remain for real-world implementation. One of the primary concerns is the model's sensitivity to boundary conditions. Since the training dataset was generated within predefined parameter ranges, the network's ability to generalize beyond these limits remains uncertain. Future research should investigate the robustness of predictions in extreme market conditions, particularly for deep out-of-the-money options or high-volatility environments.  
Another limitation arises from the use of synthetic data, which, while effective for model validation, lack the stochastic volatility, liquidity constraints, and market inefficiencies observed in real financial markets. The absence of these factors may cause discrepancies between model predictions and actual market behavior. Integrating historical market data into the training process will be crucial to improving model adaptability and ensuring practical relevance.  
A final consideration is the scalability to more complex financial instruments. The current architecture is optimized for single-asset European put options. However, many real-world applications require pricing exotic derivatives, multi-asset portfolios, or options under stochastic volatility models. Expanding the model to accommodate these complexities will likely necessitate additional layers, alternative architectures such as recurrent or transformer-based networks, and an increased computational capacity.  
Despite these limitations, the proposed DNN-based pricing approach offers several advantages over traditional analytical methods. Its ability to capture nonlinear relationships, adapt to diverse datasets, and efficiently process large volumes of financial data makes it a promising tool for risk management, algorithmic trading, and derivative pricing in dynamic market conditions.
# Image-Level Anti-Personnel Landmine Detection Using Deep Learning in Long-Wave Infrared Images
## 2 Dataset
### AP Landmine Dataset
To address the problem of performance variations and evaluate the generalization performance of the trained model, we employ \(k\)-fold cross-validation, a widely adopted technique in the literature [23; 24] when working with small-sized datasets. In \(k\)-fold cross-validation, the dataset is partitioned into \(k\) equally sized subsets or folds. The model is then trained and validated \(k\) times, each time using a different fold as the test set while the remaining \(k-1\) folds are used for training. The final evaluation results are obtained by averaging the \(k\) individual results or accumulating those. This ensures that a performance measure of the trained model is less sensitive to the variability of a single train-test split. In our work, \(k\) is set to 5. It is important to note, however, that \(k\)-fold cross-validation cannot fully mitigate the limitations imposed by the small size of the dataset. In other words, the restricted dataset size may still hinder the model from achieving optimal classification performance. Another distinguishing characteristic of the dataset is the relatively small size of AP landmines within the images. Unlike conventional image classification datasets such as ImageNet [25] where objects of interest typically occupy a significant portion of the image, AP landmines cover only a small area, as shown in Figure 1. As a result, when a global pooling operation--commonly used to obtain image-level features--is applied to the extracted feature maps, the resulting feature tends to be dominated by background information. This dominance can suppress the discriminative features associated with AP landmines. In Section 3, we present our approach to addressing the key challenges that hinder model performance, namely, the limited size of the dataset and the small spatial footprint of AP landmines within the images.
## 3 Proposed Methods
### Feature Distillation Mechanism  
To further refine the feature reconstruction process, this study introduces a feature distillation mechanism. As shown in Figure 3, after each residual block, the \(L_{2}\) loss between the feature maps of the teacher and student models is computed to regularize the student model's learning process:  
\[\mathcal{L}_{\text{distill}}^{l}=\frac{1}{H_{l}\times W_{l}\times\mathcal{C}_{l }}\sum_{i=1}^{H_{l}}\sum_{j=1}^{W_{l}}\sum_{k=1}^{\mathcal{C}_{l}}\Big{\|} \mathbf{F}_{\text{teacher}}^{l}(i,j,k)-\mathbf{F}_{\text{student}}^{l}(i,j,k) \Big{\|}_{2}^{2},\]  
where \(H_{l}\times W_{l}\times\mathcal{C}_{l}\) denotes the dimensions of the feature map at layer \(l\). Minimizing the feature distillation loss allows the student model to learn more effectively from the teacher model's feature representations, thereby enhancing the reconstruction accuracy of the occluded regions.
## 1 Introduction  
Porous asphalt concrete (PAC) has been widely used as a road surfacing material because of its various beneficial functions, such as low traffic noise, good visibility and better friction, obtained by the drainage capability of the mixture and by the macro-texture that is created due to the gradation in rainy days [1; 2; 3; 4; 5; 6; 7]. These attractive functions are mainly attributed to its high air void content (typically 15-20%) that allows water to penetrate into the material and absorbs noise. However, the high air void content also decreases durability of PAC, because water and oxygen have larger contact surface area with the mixture [8; 9; 10]. Thus, it is important to balance the functional performance (i.e., permeability) and structural performance of PAC in mixture design [11].  
Many researches have been carried out to improve the overall performance of PAC. It has been reported that some measures were effective in improving the structural performance of PAC, such as using high-viscosity binder, fiber and so on [12; 13], while aggregate gradation plays an important role in both the structural and functional performance of PAC [14; 15]. Aggregate gradation is very important for PAC, because it controls the porosity of the aggregate structure, which in turn affects the permeability of PAC.  
PAC13 which has a nominal maximum aggregate size (NMAS) of 13.2 mm is now widely used in the permeable surface layer, which is typically 40 mm thick, in China [16]. Because of its excellent surface characteristics, there is increasing demand on using PAC in overlays for highway maintenance projects in recent years, and PAC with smaller NMAS is desired for thin overlays. However, a smaller NMAS may reduce the permeability of PAC [17]. In China, the asphalt mixtures used in the surface layer are typically composed of aggregate with four aggregate size ranges, including 1#: 10 to 15 mm, 2#: 5 to 10 mm, 3#: 3 to 5 mm, and 4#: 0.075-3 mm, and the 3# aggregate is typically excluded in PAC to meet the requirement of open-graded gradation. In this study, to improve the permeability of the small NMAS PAC, a novel PAC with 10 mm aggregate size (PAC10) was proposed by excluding the 4# aggregate, instead of 3#. However, removing 4# aggregate may negatively affect the durability of PAC, and using more mineral filler to create more mastic coating the coarse aggregate might be a solution to compensate such effect. To this end, this study aims to evaluate the effects of gradation composition, especially the mineral filler content, on both the structural and functional performances of PAC10. For comparison, two conventional PACs, PAC10-C (conventional PAC10) and PAC13 (PAC with 13 mm NMAS) were also evaluated as reference.
# A Versatile Shared-Aperture Antenna for Vehicle Communications
## 2 Antenna Configuration  
The basic structure of the proposed antenna is illustrated in Figure 2. It primarily consists of an E-shaped microstrip antenna, a cloverleaf-shaped circularly polarized cross-dipole antenna, and a pattern reconfigurable antenna with added PIN diodes. The antenna is designed with a two-layer substrate structure, both using Taconic RF35 (UNICONIC, Shenzhen, China) as the substrate material with a dielectric constant of 3.5 and a thickness of 1 mm. Additionally, the four cross-dipole circularly polarized antennas are affixed to the lower substrate structure with a metal framework, securely fastened using screws to ensure structural stability.  
Figure 2: Configuration of tri-band shared-aperture antenna. (**a**) Perspective view; (**b**) top view; (**c**) bottom view.  
This design starts from the traditional rotating feed microstrip antenna, as shown in Ant 1 in Figure 3, by adding slot antennas around it, as shown in Ant 2, and adding a feed structure in the middle free area, introducing a pattern reconfigurable antenna. Through parameter adjustment, the antennas complement each other, and the directional pattern is not distorted. Finally, the dipole array is added. Due to the symmetrical structure, the axial ratio performance of the dipole array will not be significantly deteriorated. After parameter adjustment, a design model for the proposed three-frequency shared aperture is obtained.
# Dual-Energy CT Applications in Urological Diseases
## 1 Introduction  
Computed tomography (CT) is a non-invasive diagnostic imaging technique that uses X-rays to obtain detailed images of internal structures in the body.  
By accurately detecting and characterizing the wide variety of genitourinary disorders, such as renal stones or aberrant renal parenchyma and renal lesions, CT scanning can guide clinicians to the most suitable treatment [1]. However, there are some drawbacks to the use of traditional CT imaging techniques to assess urological disorders [2].  
Dual-energy CT (DECT) is a relatively new technology that improves the visualization and characterization of tissues by utilizing two different energy levels of X-rays [3]. DECT has emerged as a promising tool for the evaluation of urinary tract diseases, providing accurate information about the morphology, function, and composition of the urinary system and able to overcome some of the limitations of traditional CT in this field [4].  
In this article, we discuss the principles and advantages of DECT in the study of urinary tract disorders and its potential applications in clinical practice.
# Underlay Loosely Coupled Model for Public Safety Networks Based on Device-to-Device Communication
## 3 Underlay D2D Network Based on ProSe Service
### ProSe Functions and Communication Scenarios in Public Safety Networks  
In a public safety context and following any eventual damage caused by a disaster, three situations can result: (1) cellular antennae continue to operate normally, (2) part of the antennae is available while others are paralyzed, and (3) the entire access network is paralyzed. To support these cases, D2D communication can be divided according to the following three scenarios, as shown in Figure 3:  
* In-coverage: when all UEs are within the coverage of the eNB(s).
* Partial-coverage: when at least one in-coverage participant acts as a UE-to-Network relay, while other UE-to-UE relays can provide network access to distant users.
* Out-of-coverage: when all participants are out of the eNB.  
Figure 4 illustrates all supported ProSe functions and scenarios for both commercial and public safety use cases. While only ProSe discovery operations are supported in a commercial context, public safety can benefit from all available ProSe functions including direct and relay-based communication through the PC5 interface. We note that WLAN integration is defined only in ProSe based on LTE, while the UE-to-UE relay function is defined only in ProSe based on a 5G network.  
Figure 4: Overview of ProSe functions.  
Figure 3: D2D communication scenarios.
## 4 Experimental Analysis
### Results and Discussion
#### 4.4.1 Quantitative Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Methods** & **1 View** & **2 Views** & **3 Views** & **4 Views** & **5 Views** & **8 Views** & **12 Views** & **16 Views** & **20 Views** \\ \hline
3D-R2N2 [5] & 0.351 & 0.372 & 0.372 & 0.378 & 0.382 & 0.383 & 0.382 & 0.382 & 0.383 \\ AttSets [6] & 0.395 & 0.418 & 0.426 & 0.430 & 0.432 & 0.444 & 0.445 & 0.447 & 0.448 \\ Pix2 Vox++ [9] & 0.436 & 0.452 & 0.455 & 0.457 & 0.458 & 0.459 & 0.460 & 0.461 & 0.462 \\ GARNet [10] & 0.418 & 0.455 & 0.468 & 0.475 & 0.479 & 0.486 & 0.489 & 0.491 & 0.492 \\ GARNet++ & 0.399 & 0.446 & 0.465 & 0.475 & 0.481 & 0.491 & 0.498 & 0.501 & 0.504 \\ EVoIT [12] & - & - & - & 0.358 & - & 0.448 & 0.475 & 0.486 & 0.492 \\ LegoFormer [15] & 0.282 & 0.392 & 0.428 & 0.444 & 0.453 & 0.464 & 0.470 & 0.472 & 0.472 \\
3D-RETR [14] & - & - & - & - & - & - & - & - & - \\ UMIFormer [13] & 0.4281 & 0.4919 & 0.5067 & 0.5127 & 0.5168 & 0.5213 & 0.5232 & 0.5245 & 0.5251 \\ UMIFormer+ & 0.3177 & 0.4568 & 0.4947 & 0.5104 & 0.5216 & 0.5348 & 0.5415 & 0.5451 & 0.5466 \\ LRGT [11] & 0.4461 & 0.5005 & 0.5148 & 0.5214 & 0.5257 & 0.5311 & 0.5337 & 0.5347 & 0.5353 \\ LRGT+ & 0.3378 & 0.4618 & 0.4989 & 0.5161 & 0.5271 & 0.5403 & 0.5467 & 0.5497 & 0.5510 \\ \hline
**WGA-SWIN** & **0.4596** & **0.5095** & **0.5261** & **0.5312** & 0.5354 & 0.5408 & 0.5427 & 0.5442 & 0.5457 \\
**WGA-SWIN+** & 0.3492 & 0.4723 & 0.5109 & 0.5297 & **0.5379** & **0.5492** & **0.5565** & **0.5594** & **0.5617** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluations of performance comparison on ShapeNet using F-Score@1% for the multi-view 3D reconstruction approach. The best results are highlighted in bold. significant improvements over LRGT and other SOTA methods as the number of views rises, with the difference expanding as the view count increases. These results demonstrate how well our WGA-SWIN captures multi-view dependencies and provides accurate 3D reconstructions. For multi-view reconstruction, we emphasize how well WGA-SWIN and WGA-SWIN+ handle different numbers of input views.
## 5 Discussions  
This paper proposes the method to design the SAR operational parameters for KOMPSAT-6. Among those parameters, PRF is an important one because it determines the ambiguity ratios including RAR and AAR. PRF is determined using a nominal altitude but should be optimized as the in-orbit altitude changes from the nominal altitude. The proposed method uses the traveling return time to optimize PRF. Utilization of the traveling return time enables PRF against the altitude variation to be corrected, and consequently, the altitude variation does not degrade the SAR performances including the PRF and slant range margins. In addition, the proposed method does not cause any overload for the SAR controller, so the on-board optimization for PRF makes it possible to implement real-time operation of a SAR satellite. Instead of the incidence angle, the proposed method uses the look angle for the reference angle to improve the SAR performances. Numerical simulation shows that the proposed method achieves higher SAR performances even for the altitude variation in comparison with the previous methods, which include the one used for KOMPSAT-5. The proposed method is based on the mathematical equations that have been derived in the theoretical way. Along with the numerical simulation, such fact indicates that the proposed method is general one applicable to other satellites.  
As a further research, the impact of the real-world surface on SAR images will be studied by simulating the SAR imaging on real-world surface with various PRF selection approached. After launch of the KOMPSAT-6, the SAR performances will be evaluated using acquired SAR images, and the results will be presented in this journal.  
Figure 18: Procedure for real-time operation using proposed method.
# A Multimodal Framework for Advanced Cybersecurity Threat Detection Using GAN-Driven Data Synthesis
## 4 Experimental Analysis and Results  
### Evaluation Process  
Section 4 provides an extensive analysis and evaluation of the experimental results aimed at validating the effectiveness, robustness, and generalizability of the proposed integrative threat detection framework under diverse cybersecurity scenarios. Specifically, the extended evaluation presented in this chapter has been structured to assess how each component, including the input-type recognition layer, synthetic data generation modules, data classifiers, and final decision-level fusion module, contributes individually and in an integrated manner to improve the overall threat detection performance.  
The key objectives of this evaluation process include assessing the accuracy of the input-type recognition module, which ensures proper routing of image and tabular-based data to the appropriate pipeline branches. Additionally, the evaluation procedure aims to quantify the detection performance of each individual branch-the image-based CNN classifier and the tabular-based ensemble model-across the corresponding representative datasets. Another objective is to examine the contribution of GAN-based synthetic data augmentation in improving generalization, particularly for rare threat types such as zero-day and botnet attacks, ensuring the extensibility of this layer to further types of cybersecurity threat datasets. The comparative effectiveness of multiple decision-level fusion strategies is also investigated, in order to determine the most reliable method for combining multimodal outputs when available. Lastly, the evaluation considers performance across specific threat categories, such as zero-day exploits, botnet intrusions, and multi-source evasive threats, to understand the system's adaptability in realistic deployment scenarios.  
To support these objectives, multiple experiments are conducted using publicly available datasets augmented with synthetic data, with evaluation metrics such as accuracy, precision, recall, F1-score, and false positive rate. This chapter presents both quantitative and qualitative insights into the system's behavior under varying operational conditions and scenarios.
# Non-Orthogonal eMBB-URLLC Radio Access for Cloud Radio Access Networks with Analog Fronthauling
## 2 System Model
### Performance Metrics  
The performance metrics used to evaluate the interaction between eMBB and URLLC services in the considered A-RoC-based C-RAN architecture are detailed in the following.