esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Basic Theoretical Concepts
### Virtual Local Area Network  
Recently, the world has witnessed remarkable development in the field of wireless communication, including many different portable devices that do not have a traditional (wired) connection. Fixed stations for wireless networks have been established using mobile nodes and computers. These are called wireless roaming networks [8]. The mobility and flexibility achieved by portable wireless networks increases connectivity. This has allowed researchers to build a mobile communications engineering computing platform that is compatible with current architectures, in addition to considering the wireless and mobility characteristics [9].  
Recently, virtual LANs have expanded from each major LAN to become an integral property of switched LAN solutions. One of the reasons for the attention now being given to VLAN features is the rapid deployment of LAN switching, which started two decades ago. A VLAN provides an alternative solution to broadcast containment routers because it makes it possible to switch to broadcast traffic. A VLAN makes it possible to split a physical network into two layers [6].  
Virtual LAN technology is used in the development of university internal networks, companies, and business networks. A VLAN is used for data link layers to create multiple networks. The Local Area Network is subdivided into different broadcast domains. The division of workstations is focused on virtually linked platforms and software teams that may or may not be physically related. A VLAN enables a network administrator to create a logical network from a physical network. By dividing a large network into smaller networks, VLAN technology improves network efficiency, management, and security [10]. That is, when using VLAN, in addition to the possibility of configuring the physical network, the structure is logical and impeding. VLAN isolates computers with better shape and performance from WLAN networks. This can be accomplished by configuring users and switching to a VLAN so that their department networks are accessible in a shared space [11; 12]. Therefore, logical connectivity between switch ports can be made available in VLAN. Thus, communication is performed between nodes within a similar VLAN to supply the necessary security, admission control, and to meet the overall network performance and scalability. VLAN's minimize broadcast traffic and mend traffic management by providing flexibility in the IP network location and relocation of devices [13]. There is always a need for monitoring tools to enable network administrators to maintain effective communication over a swapped infrastructure. Efficient routing algorithms must be applied to ensure effective communication paths [14].
## 3 Experimental Test Procedure
### Displacement Calculation  
The outputs of image matching are identical feature pixels between two images, as shown in example in Figures 11 and 12. Each image represents one measurement epoch. Based on the RGB+D data, 3D coordinates of each pixel in each image can be calculated. As stated before, for each image pixel in the ROI theodolite angles (\(Hz\) and \(V\)) and 3D distance (\(d_{3D}\)) can be known.  
3D coordinates of a pixel are defined as:  
\[\begin{split} Pix_{coo}=Cam\ _{coo}+\begin{bmatrix}\Delta_{x}\\ \Delta_{y}\\ \Delta_{h}\end{bmatrix}\ \rightarrow\begin{bmatrix}x_{pix}\\ y_{pix}\\ h_{pix}\end{bmatrix}=\begin{bmatrix}x_{cc}\\ y_{cc}\\ h_{cc}\end{bmatrix}+\begin{bmatrix}d_{h}*\sin Hz\\ d_{h}*\cos Hz\\ d_{3D}*\cos V\end{bmatrix},\end{split} \tag{6}\]  
where \(Cam\ _{coo}\) are the 3D coordinates of the camera centre, and \(d_{h}\) is defined as:  
\[\begin{split} d_{h}=d_{3D}*\sin V.\end{split} \tag{7}\]  
2D and 3D displacements of matched features can be calculated based on the calculated 3D coordinates of pixels in images from different measurement epochs. 2D and 3D distances were calculated from 3D coordinates of matched points in different epochs using the formula for Euclidean distance between two points in Cartesian space.  
Displacements were calculated between four epochs for BEAM 1, and five epochs for BEAM 2. The wooden beams are shown later in the paper in Section 4.1.  
Figure 11: Example of plotted identical points and image displacement vectors between two epochs (Source: Own compilation).
# Mitigating Class Imbalance in Network Intrusion Detection with Feature-Regularized GANs
## 2 Related Work
### GANs with Modified Loss Functions or Network Architectures  
While many studies adopt standard GAN frameworks, others have sought to enhance sample quality and training stability by modifying GAN loss functions or network architectures. [PERSON] [13] proposed a BiGAN that replaces Jensen-Shannon (JS) divergence with Wasserstein distance, incorporating feature matching and residual loss to mitigate mode collapse and improve sample realism. [PERSON] et al. [14] further refined BiGAN by introducinglabel flipping and a one-class classifier, optimizing the generator's ability to reconstruct and synthesize rare attack samples.  
Some studies have focused on multi-task learning within GAN architectures. [PERSON] et al. [15] introduced TMG-GAN, a multi-generator WGAN where each generator specializes in different attack types, while the discriminator performs both real/fake discrimination and multi-class classification using cosine similarity loss and spectral normalization. [PERSON] and [PERSON] [16] proposed GAN-AE, an autoencoder-based GAN architecture that leverages reconstruction error as an anomaly detection mechanism. Similarly, [PERSON] et al. [17] employed a BiGAN with a denoising generator to detect and restore adversarial examples in IDS.  
Optimizing decision boundaries has been another key research area. [PERSON] et al. [18] developed a Boundary-Seeking GAN (BGAN), encouraging the generator to produce attack samples near decision boundaries, thereby improving minority-class detection. To handle the complexity of time-dependent traffic data, [PERSON] et al. [19] replaced traditional CNN-based generators with Transformer-based architectures, combining residual and discriminative loss functions.  
Other studies have explored adaptive GAN training mechanisms. [PERSON] et al. [20] proposed SC-WGAN, where the generator receives synchronous feedback from both the classifier and discriminator, explicitly improving the quality of synthetic samples for under-represented attack types. [PERSON] et al. [21] introduced an MLP encoder and U-Net generator to capture fine-grained traffic patterns, enhancing anomaly detection. Finally, [PERSON] et al. [22] combined CopulaGAN with Deep Reinforcement Learning (DRL), optimizing both binary and multi-class intrusion detection through a reward-based mechanism.
The string contains all possible situations in which a receiver can face difficulty to verify and check the strength of the signal. The expected outcomes of the work are the development of a digital encoding scheme for IoT devices to communicate at a low power spectrum density. The SES enables effective communication for complex networks with lightweight signal modulation. The SES reduces unnecessary bandwidth consumption, maximizes the utilization of the power spectrum and helps to control direct current components. It has been observed from the results that SES is around 50% more effective in PSD against the other well-known digitizing schemes. SES enables lightweight communication for the IoT and sensor-based communication for smart cities. The rest of the paper is organized as follows. Section 2 describes comprehensive related work. The SES and the details of PSD are discussed in Section 3. Section 4 presents the mathematical equations to calculate PSD. The core structure of the SES is described in Section 5. Section 6 covers the analytical assessment of the SES. Section 7 elaborates on the results and discussion. Finally, Section 8 summarizes the paper as a conclusion.
## Materials and Methods
### Fracture Toughness and Notch Fracture Toughness Values  
The experimental values of the fracture toughness (K\({}_{\rm lc}\)) of GPPS and PMMA materials may be derived from the CBD and the SCB (cracked) samples. In the case of the SCB samples, K\({}_{\rm lc}\) values will be obtained by using the relationship proposed by ISRM standard [51]:  
\[{\rm K_{\rm lc}=Y_{\rm I}\cdot P_{\rm max}\cdot(\tau a)^{1/2}/(2 Rt)}, \tag{1}\]  
where S is the half distance between bottom supports, R is the radius of the SCB sample, t is the sample thickness, a is the crack length, P\({}_{\rm max}\) is the fracture load and Y\({}_{\rm I}\) is the geometrical factor, which follows Equation (2) [51]:  
\[{\rm Y_{I}=-1.297+9.516\cdot(S/2R)-[0.47+16.457\cdot(S/2R)]\cdot(a/R)+[1.071+34. 401\cdot(S/2R)\cdot(a/R)^{2}}, \tag{2}\]  
Concerning the CBD samples, the equation suggested by [PERSON] and [PERSON] [52] will be used for determining the value of K\({}_{\rm lc}\):  
\[{\rm K_{\rm lc}=K_{\rm I}}^{*}\cdot P_{\rm max}\cdot(2\pi R)^{1/2}/(Rt), \tag{3}\]  
where R and t are, respectively, the radius and the thickness of the CBD sample, P\({}_{\rm max}\) is the fracture load, and K\({}_{\rm I}^{*}\) is the dimensionless geometry factor, equal to 0.2208 when \(a/R=0.5\)[52].  
Similarly, notch fracture toughness (K\({}_{\rm lc}\)\({}^{\rm V,\rho}\)) values may be derived from the tests performed on notched specimens. [PERSON] and [PERSON] [53] proposed the following equation to determine the notch stress intensity factor:  
\[{\rm K_{\rm I}}^{\rm V,\rho}=(2\pi)^{1/2}\cdot\sigma_{\Theta\Theta}(r_{0},0) \cdot r_{0}{}^{1-\lambda 1}\ /(1+\omega_{1}), \tag{4}\]  
where \(r_{0}\) is the distance from the origin of the coordinate system to the notch tip [53], \(\lambda_{1}\) is a [PERSON]'s mode I eigenvalue [53], \(\omega_{1}\) is an auxiliary parameter depending on the opening angle (\(\alpha\)) [53; 54], and \(\sigma_{\Theta\Theta}(r_{0},0)\) is the tangential stress at the notch tip, usually obtained from finite element (FE) analyses. The calculation of the notch fracture toughness (K\({}_{\rm lc}\)\({}^{\rm V,\rho}\)) requires, firstly, to develop a (linear-elastic) FE analysis of the corresponding sample and to subject the virtual specimen to the averaged fracture load measured in the experiments, obtaining the tangential stress component along the notch bisector. Then, the value of \(\sigma_{\Theta\Theta}(r_{0},0)\) calculated from FE analysis is substituted into Equation (4). The details of the  
Figure 4: (**a**) RVNBD sample made of PMMA with R = 10 mm and 2\(\alpha\) = 30\({}^{\circ}\) (mode I loading); (**b**) during test procedure, (**b**) RVCSB sample made of GPPS with R = 5 mm and 2\(\alpha\) = 45\({}^{\circ}\) (mode I loading).  
finite element analyses performed in this research to determine the different values of notch fracture toughness are summarized in Appendix A.
# Data Access Based on a Guide Map of the Underwater Wireless Sensor Network
## 4 The DAGM Framework and Method of UWSN Data Storage and Query
### Data Storage Strategy  
When the sensor nodes generate data, the location of the storage node is determined based on GHT according to the network area partition. According to the system model in the second section, the location of the \(k\)-th storage node is shown as Equation (5):  
\[H(X,\;k)=X_{0}+G_{X}\times g+\frac{g}{k},\;H(Y,\;k)=Y_{0}+G_{Y}\times g+\frac{ g}{k} \tag{5}\]  
where \(G_{X}=\lfloor\frac{X-X_{0}}{g}\rfloor\), \(G_{Y}=\lfloor\frac{Y-Y_{0}}{g}\rfloor\) represent the lattice ID numbers of nodes.  
The data transmission routing is calculated out by GPSR routing protocol when the sensor node gets the location of the storage node. Then, the data is transmitted to the storage node. In processing data transmission, each node will send data to the neighbor node that is the nearest from the storage node. When a node's next hop is itself, this node can be considered a storage node, which stores data locally and generates metadata. The metadata is composed of data content abstract (\(CA\)) and data storage location abstract (\(SA\)). The \(CA\) (\(VN\), \(VD\), \(SN\), \(GT\)) indicates the data attribute name, attribute values, generation node identification number and generation time, respectively; and \(SA\) (\(GN\), \(VN\), \(SL\)) indicates lattice ID numbers of storage node, data attribute names and storage node identification number, respectively. After that, the metadata is sent to the center ring node that is the nearest from the storage node. When the metadata is transmitted to the center ring node, the transmission is stopped. The data storage process is described as Algorithm 3:
# Detecting AI-Generated Images Using a Hybrid ResNet-SE Attention Model
## 4 Results and Discussion
### Limitations and Areas for Caution  
Despite its high performance, the SE-ResNet-50 model presents certain limitations that warrant further research and engineering optimization:  
* Computational overhead: Although more efficient than DenseNet, the hybrid model still introduces additional parameters due to the SE blocks. This could be a constraint for ultra-low-power devices or latency-sensitive real-time systems.
* Data sensitivity: The model's performance is closely tied to the quality and diversity of training data. Biases or limitations within the CIFAKE dataset could influence its generalization ability across unseen generative models or content types.
* Overfitting to visual artifacts: There is a risk that the model may be overfit to the generative patterns present in the current dataset (e.g., GAN fingerprints or compression artifacts), reducing its adaptability to emerging or more advanced synthetic generation techniques that minimize such artifacts [29; 31].
* Adversarial vulnerability: Like most deep learning models, SE-ResNet-50 may remain vulnerable to adversarial attacks, where carefully crafted perturbations can manipulate outputs. Adversarial robustness was not the focus of this study and remains a critical direction for future investigation [17].  
Limited architecture comparison: While our study compares with traditional CNN architectures, a comprehensive comparison with newer transformer-based models was not fully explored. Although our current results show competitive performance against selected transformer architectures, a more extensive evaluation across the full spectrum of vision transformers and diffusion-aware models would provide additional insights into relative strengths and weaknesses.
## 2 Materials and Methods
### Data Processing
#### 2.4.2 SIF Retrieval  
Gases in the terrestrial or solar atmosphere result in strong absorption in spectral regions [45]. The relative contributions between reflected radiance and SIF provide us with an approach to retrieving SIF based on the spectrum (FLD), especially in the O\({}_{2}\) absorption bands [46]. Although the retrieved SIF based on the three-band (3 FLD) algorithm is robust [47], it assumes that the reflectance in telluric O\({}_{2}\) absorption lines changes linearly and only uses limited bands. However, the spectral fitting method (SFM) takes advantage of the full features of wide-fitting windows [48]. Therefore, we used SFM to retrieve SIF from the vegetation radiation measurements. Here, we used a quadratic polynomial to parameterize SFM (Equation (7)), with an initial fitting window of 684.0-691.5 nm for the O\({}_{2}\)B band and 757.0-772.5 for the O\({}_{2}\)A band [49]. The quadratic formula is as follows [49; 50]:  
\[L(\lambda)=\left(b_{5}\Delta\lambda^{2}+b_{4}\Delta\lambda+b_{3}\right)\frac {E(\lambda)}{\pi}+b_{2}\Delta\lambda^{2}+b_{1}\Delta\lambda+b_{0} \tag{7}\]  
where \(b_{0-2}\) represents the fitted coefficients that can be used to model the fluorescence shape; \(b_{3-5}\) represents the fitted coefficients that can be used to model the reflectance shape; \(\lambda\) is the wavelengths in the fitting window, \(\Delta\lambda\) is the width of the fitting window; \(L(\lambda)\) is the radiance reflected from the canopy; and \(E(\lambda)\) is the irradiance downwelling from sky.
# A Period Training Method for Heterogeneous UUV Dynamic Task Allocation
## 3 MARL with the Attention Mechanism and Period Training Method
### Decoder with Attention Mechanisms  
The decoder sequentially allocates the tasks for each UUV until all tasks are finished. During the solving process, the model needs the high-level feature vectors extracted from the encoder to solve the task lists. The flow chart of the decoding process can be seen in Figure 3.  
The decoder uses an attention mechanism to output the task \(m_{i}^{t}\) for the UUV \(i\) at a specific time step \(t\). We aim to use one trained MARL model to solve the problem with different numbers of UUVs and tasks. This means that the dimension of data embedding in the decoder remains constant in changing task environments. Therefore, combining the  
Figure 3: The decoding process with the attention mechanism.  
high-level feature vectors, the context embedding \(c_{i}^{t}\) of the UUV \(i\) at step \(t\) is constructed as follows:  
\[c_{i}^{t}=[H_{i}^{U},H_{n_{i}^{t}}^{T},\frac{1}{N}{\sum_{i=1}^{N}H_{n_{i}^{t}}^{T }},\frac{1}{N}{\sum_{i=1}^{N}H_{i}^{U}},\frac{1}{M}{\sum_{m=1}^{M}H_{m}^{T}}] \tag{7}\]  
where \([\cdot,\cdot,\cdot]\) is the horizontal concatenation operator. The dimension of \(c_{i}^{t}\) is \((5\cdot dim)\), which can remain unchanged with differing numbers of UUVs and tasks. A single-head attention layer in the decoder is used to calculate the high embedding \(c_{i,m}^{t}\) of the UUV \(i\) in task \(m\), which denotes the matching degree between the UUV \(i\) and task \(m\) at decoding step \(t\).  
\[c_{i,m}^{t}=\tanh(\frac{(\mathbf{W}_{3}c_{i}^{t})^{T}(\mathbf{W}_{4}H_{m}^{T})}{\sqrt{ \text{dim}}}) \tag{8}\]  
where \(\mathbf{W}_{3}\) and \(\mathbf{W}_{4}\) are the parameter vectors of the linear projection networks. The probability that the UUV \(i\) selects task \(m\) at the time step \(t\) can be calculated as follows:  
\[p_{\mathbf{\theta}|i,m}^{t}=\frac{e^{c_{i,m}^{t}}}{\sum\limits_{m=i}^{M}e^{c_{i,m }^{t}}} \tag{9}\]  
where \(p_{\mathbf{\theta}|i,m}^{t}\) represents the selection probability and \(\mathbf{\theta}\) is the total parameter vector of the encoder and the decoder. According to \(p_{\mathbf{\theta}|i,m}^{t}\) and the maximum task load constraint in (4), the decoder circularly outputs the tasks for each UUV as the solution until all tasks are selected.  
Note that all parameters in the encoder and decoder do not depend on the UUVs and tasks. Hence, we can apply one MARLAM model to different numbers of UUVs and tasks without retraining.
###### Abstract
With the rapid advancement of 5G technology, the International Civil Aviation Organization (ICAO) released Circular 360 [16] in 2024, offering guidance on measures to protect radio altimeters from potential harmful interference. The circular addresses the risks of electromagnetic interference when C-band antennas coexist with 5G signals, with a particular emphasis on aviation safety. Some researchers have studied the IPL of small-sized fixed-wing aircraft within the radio altimeter frequency band [17]. Recent studies have also extensively investigated the effects of 5G electromagnetic interference on radio altimeters, as well as related protective measures [18; 19; 20; 21]. Meanwhile, many manufacturers have begun integrating satellite communication technology that operates without the need for base stations [22]. For example, a mobile phone with satellite communication capability can transmit at a peak power of up to 2 W (33 dBm) in the 1980-2010 MHz frequency band, which is higher than previous models. This increase in transmit power has raised concerns about possible electromagnetic interference with GNSS systems, including GPS, Galileo, and SBAS. The coexistence of mobile phones with satellite communication capabilities and aircraft GNSS systems has become a significant concern, especially with respect to modern GNSS frequencies such as GPS L1, Galileo E1/E5, and SBAS L5. The operating frequency band of GPS L1 is close to that used by mobile phone satellite communication systems. Currently, there is no standard for assessing whether mobile phone satellite communication functions can interfere with GPS antennas, and research on this topic is still limited. Although this study specifically focuses on the susceptibility of GPS L1, the results may also provide useful insights for analyzing interference with Galileo E1/E5 and SBAS L5. This paper presents the basic principles of electromagnetic compatibility assessment by analyzing the IPL loss mechanism. CST(2016) is used to construct an A320 cabin model for IPL calculation, and measurement experiments are conducted to validate the model and simulation approach. The purpose of this study is to explore the potential electromagnetic interference risks posed by these devices and to propose methods to evaluate their impact on GPS antennas.
## 4 Design and Analysis of TRA Metasurface
### Reflect Array Functionality  
Utilizing the distinctive attributes of the envisioned unit cell, a specialized array consisting of 23 \(\times\) 23 elements has been meticulously developed. This array operates as a reflective unit for distinct frequencies, contingent upon the incident wave's polarization. Specifically, in the case of TM mode, the array functions as a reflective entity at a frequency  
Figure 5: Reflection phase of the unit cell by varying the size (Wp) of the patch: (**a**) TE mode and (**b**) TM mode.  
Figure 6: Transmission phase of the unit cell by varying the size (Wp) of the patch: (**a**) TM mode and (**b**) TE mode.  
of 9.3 GHz. Conversely, when subjected to TE polarized waves, the same array operates as a reflective unit at 6.3 GHz. This versatile design showcases the capacity of the proposed unit cell to adapt to varying polarization conditions and frequencies, thus enhancing its applicability in multifaceted electromagnetic applications. Figure 7 presents the 2D reflection plot, providing a visual representation of the array's performance. The reflective behavior of the array for distinct modes is evident from the figure. In TE mode, the array operates as a reflective structure at 6.3 GHz, whereas for the TM mode, it functions as a reflect array at 9.3 GHz. Notably, for both TE and TM modes, the reflective array exhibits a gain exceeding 20 dB, signifying its pronounced amplification capability. Moreover, the array showcases minimized side lobe levels, further emphasizing its capacity to ensure enhanced directivity and improved electromagnetic propagation characteristics.  
Figure 8 showcases the 3D pattern of the proposed array, offering insights into its radiation characteristics. As depicted in Figure 8a, the array effectively operates as a reflector for the TE mode at 6.3 GHz. Similarly, the array serves as a reflect array for the TM mode at 9.3 GHz, indicating its versatile performance across different polarization states. This depiction highlights the array's capacity to manipulate and control electromagnetic waves, resulting in tailored and efficient radiation patterns that align with specific frequency and polarization requirements.