esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 FPGA-Based Optimization of Deployment  
### Image Input Channel Optimization  
The number of image channels refers to the number of values required to describe a pixel. Generally, the grayscale image's channel number is 1; the color image's channel number is 3, representing the red, green, and blue (RGB) values of each pixel. In addition, there are some special four-channel images, such as color images with RGB and A channel values, where A represents transparency. The proposed LSDF-Net is input by four-channel infrared sequential images.  
Most embedded platforms or AI solutions are designed for color images. In the case of the four-channel input, taking the RGBA format as an example, the R, G, B, and A values of  
Figure 7: Target allocation schematic of multiple anchor boxes.  
each pixel are stored, and the image is arranged pixel by pixel. Some FPGA platforms only support a pixel-by-pixel four-channel arrangement, while sequential single-band infrared images are arranged channel by channel, as shown in Figure 8.  
Therefore, during network deployment, it is necessary to rearrange the dataset format before inputting the images, converting the sequential infrared images from a channel-by-channel layout to a pixel-by-pixel layout. The rearrangement of the data format will consume much time, resulting in a poor real-time performance of the detection system. To avoid the rearrangement of the data format and accelerate the differential operation, the differential algorithm is optimized. The original differential operation subtracts the last three frames from the first frame, and the improved differential operation subtracts the mean of the last three frames from the first frame, as shown in Figure 9. Consequently, the differential output is a single channel, so data rearrangement is not required.  
Figure 8: (**a**) Arrangement of four-channel color images; (**b**) four-channel grayscale image arranged by pixels; (**c**) four-channel grayscale image arranged by channels.  
Figure 9: New differential layer.  
The merging process of the differential layer and adjacent convolution layer is updated as follows:  
\[[X1,X2,X3,X4]\begin{bmatrix}1\\ -1/3\\ -1/3\\ -1/3\end{bmatrix}*\omega^{\prime}+b^{\prime}=[X1,X2,X3,X4]*\begin{bmatrix}\omega^ {\prime}/3\\ -\omega^{\prime}/3\\ -\omega^{\prime}/3\\ -\omega^{\prime}/3\end{bmatrix}+b^{\prime} \tag{7}\]  
where X1, X2, X3, and X4 are four time-sequential image inputs, \(\omega^{\prime}\) represents the weight of the convolution kernel, with a size of \(3\times 3\times 1\times 16\), and \(b^{\prime}\) represents the bias, with a size of \(1\times 1\times 16\). The schematic diagram is shown in Figure 10.
# MLKD-Net: Lightweight Single Image Dehazing via Multi-Head Large Kernel Attention
## 5 Ablation Study
### Experiment on the Channel Shuffle Block  
The Channel Shuffle block within MLKA is designed to establish indirect interactions between attention heads, thereby enhancing the model's ability to learn spatial representations. To evaluate its contribution, we conducted an ablation study by removing the Channel Shuffle block from MLKD-Net-T under various head configurations: Split-H1 (single head), Split-H2 (two heads), and Split-H4 (four heads). The results, summarized in Table 4, demonstrate that the absence of the Channel Shuffle block led to consistent performance degradation across all configurations.  
Specifically, in the Split-H1-T setting, the PSNR dropped slightly from 35.71 dB to 35.67 dB, indicating only a marginal impact when using a single head. In contrast, Split-H2-T showed a more noticeable drop from 36.03 dB to 35.54 dB, and Split-H4-T exhibited the most significant decline--from 35.49 dB to 35.07 dB. These results indicate that in multi-head mechanisms, the indirect connections between attention heads enabled by the Channel Shuffle block play a critical role in effectively learning spatial representations.
# A Review of Edge Computing Technology and Its Applications in Power Systems
## 3 Review of Edge Computing Technologies
### Energy Consumption  
Edge computing reduces latency, enhances security, and offloads heavy computation from the cloud to the network edge. While energy consumption in cloud centers has been extensively studied, research on the energy efficiency of edge computing is still needed. Communication among edge devices, servers, and cloud centers is complex, making energy efficiency a hot topic in edge computing.  
[PERSON] et al. [73] presented an energy-aware offloading scheme for optimizing resource allocation. The core technology of this scheme is an iterative search algorithm combining interior penalty functions. This algorithm takes the residual energy of smart devices' batteries as the weighting factor of power consumption and latency. Results demonstrate that the proposed algorithm can find the optimal solution for energy consumption.  
In [74], [PERSON] et al. studied the energy consumption of energy-aware edge server placement. This problem is formulated as a multi-objective optimization problem. An energy-aware edge server placement algorithm involving particle swarm optimization is investigated to find the optimal solution. Experiments indicate that this algorithm can improve the computing resource utilization and minimize the energy consumption.  
[PERSON] et al. [75] introduced green energy sources such as wind energy into edge devices. Nodes are divided into clusters, each containing several edge nodes to ensure a minimum distance between nodes in the cluster. A scheduling heuristic algorithm is implemented to schedule tasks and transfer energy based on the clustering method. Experiment results show that the proposed method reduces total energy consumption both in and out of the system.
# Security in Wireless Sensor Networks: A Cryptography Performance Analysis at MAC Layer
## 3 Analyzed Cryptographic Methods and Primitives
### [PERSON] (RSA)
#### 3.2.1 Confidentiality in RSA  
We ensure confidentiality with RSA by ciphering the message with the public key of the target server, which upon the receipt of the message will decipher it using its own private key see Figure 4. This is the traditional schema used to ensure confidentiality using the public key cryptography. This will guarantee that only those who know the private key, i.e., the server entity, will be able to decipher the message. Of course, the security of public key cryptography is based on the fact that no one except the possessor of the private key is able to get information about this key.
# Modeling and Analysis of New Hybrid Clustering Technique for Vehicular Ad Hoc Network
## 2 Literature Study
That is, the entropy K-Means algorithm can get rid of irrelevant features through feature reductions free of initializations by automatically finding the optimal number of clusters. The current paper proposes a model that uses more parameters, resulting in a more efficient clustering technique. The comparison results showed that the entropy K-Means algorithm can simultaneously find the optimal number of clusters and perform feature reduction. This paper presents simulations and a comparison of results obtained for RK-Means, K-Means++ [22], and OK-Means [23]. RK-Means involves new criteria for selecting the initial number of clusters that are not included in K-Means++ and OK-Means. Furthermore, the use of a CRS in RK-Means increases the cluster stability compared with the other algorithms, because all cluster nodes have a minimum speed variant. The modified K-Means in the proposed RK-Means model attempts to collect the nodes with a minimum error rate. In low-density scenarios, there are large spaces between the clusters, so the proposed model provides high dissimilarity between clusters compared with other models. The challenge lies in high-density scenarios, which have small distances between clusters, and the proposed model was more successful at creating optimal clusters based on several parameters compared with the K-Means++ and OK-Means algorithms. Table 2 summarizes the effective parameters used in the discussed clustering schemes. From Table 2, we can observe that the proposed RK-Means scheme uses more parameters to create an efficient clustering technique and increase the cluster stability. The effective parameters considered were: node location and effectiveness in the clustering model; node speed; the direction of the node (because the direction parameter is more important in terms of cluster lifetime); node distance from reference location; and node transmission range (which indicates a node's coverage in relation to other nodes and how it affects the cluster number). RK-Means includes all the above parameters, so the similarity within the clusters and the divergence between clusters are high.
# On the Use of Bright Scatterers for Monitoring Doppler, Dual-Polarization Weather Radars
## 2 Materials and Methods
### The Dual-Polarization, Doppler Radar Located on Monte Lema at an Altitude of 1625 m
#### 2.2.6 The Differential Phase Shift between the Two Orthogonal Polarizations  
A third polarimetric quantity measured by the MeteoSwiss radars is the differential phase shift, \(\Psi_{\rm dp}\), between the phase of the copolar signal at the horizontal and vertical polarizations, respectively. This difference between the two orthogonal polarization phases arises from two sources:  
* a difference in the delay introduced by the scattering of the transmitted wave, which is known as the backscattering phase delay, \(\delta_{\rm co}\);
* a difference in the forward propagation velocity of the two polarizations, which is known as the differential propagation phase, \(\Phi_{\rm dp}\). In formula form, the differential phase shift, \(\Psi_{\rm dp}\), at any given range, is: \[\Psi_{\rm dp}=\delta_{\rm co}+\Phi_{\rm dp}+\Psi_{0},\] (7) which is the sum of the backscattering phase delay of the targets at that range; the two-way differential propagation phase that occurred when propagating from the radar to the observed target and then back to the radar; an offset value \(\Psi_{0}\), which is the phase difference between the transmitted and received vertically and horizontally polarized waves at the zero range. This constant value depends on the hardware components and design of the radar; at MeteoSwiss, this value was set close to \(0^{\circ}\) through a post-facto software adjustment (and it is monitored to ensure it remains constant).  
As stated in Section 2.2, the differential phase shift is the only radar observable that is quantized using two bytes. The DN in binary form ranges from 1 to 65,535; DN = 0 is not used. The conversion formula from DN to the value of \(\Psi_{\rm dp}\), which is expressed in degrees, is:  
\[\Psi_{\rm dp}=({\rm DN}-32768)/32767\times 180^{\circ}. \tag{8}\]
# Formal Verification and Analysis of 5G AKA Protocol Using Mixed Strand Space Model
###### Abstract  
The 5 th generation mobile communication technology (5G) authentication and key management (AKA) protocol specified by the 3 rd generation partnership project (3 GPP) includes three cases because it introduces synchronization failure and message authentication code (MAC) failure procedures. Thus, there may be interactions between these cases, forming vulnerabilities that do not exist in any single case. However, this is not fully considered in the existing formal analysis and improvement of the 5G AKA protocol. To solve this problem, this paper formally analyzes the security of the latest version of the 5G AKA protocol based on the mixed strand space model for mixed protocols and finds many new attacks, including cross attacks for mixed cases. Then, a secure and efficient primary authentication and key agreement protocol for 5G networks is proposed, which is named the 5G-AKA'. In the 5G-AKA' protocol, the pre-shared key between the user equipment (UE) and the home network (HN) is replaced with a derivation key of the pre-shared key, the challenge-response mechanism between the serving network (SN) and the HN is added, the subscription permanent identifier (SUPI) of the UE is added to the second message between the SN and the HN, and the MAC failure is replaced with a timeout mechanism on the HN. Finally, the 5G-AKA' protocol is proved secure in the mixed strand space model and can overcome these attacks of the latest version of the 5G AKA protocol. Additionally, the comparative analysis shows that the 5G-AKA' protocol is better than the recently improved 5G AKA protocols in security, and the 5G-AKA' protocol is efficient and is backward compatible with the 5G AKA protocol.  
5G-AKA; mutual authentication; weak agreement; attack scenarios; location privacy; pre-shared key; perfect forward secrecy; challenge-response; timeout mechanism Article
# Quality of Service Based NOMA Group D2D Communications
## 4 Numerical Results and Discussion
### Impact of \(R_{d}\) on Outage Probability  
Figure 2 presents the impact of varying coverage radius \(R_{D}\) of test GT on the outage probability of ordered DRs as a function of SNR. The derived outage results in Equation (8) are shown to be in good agreement with the Monte Carlo simulations.  
Several observations can be made from the results in Figure 2: (1) increasing the coverage radius of GT results in a higher outage probability because of a larger path loss; (2) different ordered users have distinct decreasing slopes of outage probability because of different targeted rates; (3) the higher order DRs in our proposed Q-NOMA group D2D communications achieve better outage performance because they have smaller targeted rates. This is different from conventional NOMA that orders users  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Description** & **Value** \\ \hline \(M\) & Total users & 3 \\ \(\{R_{m}\}_{m=1}^{M}\) & Users’ targeted rates & \(\{0.7,1.1,2\}\) \\ \(R_{D}\) & Coverage of GT & 10 m \\ \(\alpha\) & Path loss exponent & 4 \\ \(\lambda_{\text{GT}}\) & Intensity of GTs & \(10^{-4}\) \\ \(\rho_{t}\) & SNR range & (5-40) dB \\ \(L,N,V,Q,S\) & Gaussian-Chebyshev parameters & 5 \\ \(P\) & Degree of Gauss-Laguerre polynomial & 5 \\ \(d\) & Distance between probe DR and GT & 5 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
Figure 2: Impact of \(R_{D}\) on outage probability.  
based on channel conditions, and where the higher ordered users have larger outage probabilities due to poor channel conditions [27; 28].
## 2 Background--Related Work
### Clustering and Routing Algorithms for WSNs
EDAC [32] is an improvement of LEACH for heterogeneous environments that supports energy-driven CHs rotation and extends the node lifetimes, while LENO [33] proposes a dynamic CH rotation algorithm that outperforms both LEACH and EDAC. PEGASIS [34], LBCS [9] and EECS [35] are various enhancements of LEACH, where energy dissipation is balanced among sensor nodes under special conditions or assumptions. PEGASIS and LBCS assume that all nodes have location information about all other nodes, while EECS supposes that all nodes are stationary and uniformly dispersed within a sensor field. In all the above cases, the sensors are directly connected to their CHs. Furthermore, layered approaches exist such as SOHS [36] and EECF [15] in which sensors are organized into clusters creating a hierarchical topology and HMPR [37] where the WSN is initially constructed as a layered network. These protocols necessitate the existence of fixed centralized nodes or fixed lifetimes and identifiers for the sensor nodes. Finally, TinyHop [38] is proposed as a reactive routing protocol that aims to minimize the number of messages necessary to perform routing. Therefore, this protocol avoids the energy consuming periodic beacon messages generated by other proactive routing protocols. TinyHop may also be configured to limit the flooding of messages within the scope of the cluster. In our approach, the routing among the CHs is designed in a reactive manner, taking into consideration existing reactive protocols that are already designed for mobile _ad hoc_ networks [39] such as DSR [40]. The NEURON protocol is designed as a new approach for providing autonomic and energy efficient clustering and routing in WSNs without assuming specific pre-conditions for the nodes functionality and their knowledge. As described in detail in Section 3, NEURON does not make any assumptions about the location of the sensors, their heterogeneity, their capabilities, the pre-definition of CHs or centralized nodes and their unique identifiers. This is achieved since the clustering and routing mechanisms are self-organized and are based on the awareness of the neighbouring environment of each node (_self-awareness_). Furthermore, in order to maximise network lifetime, the self-organization phase is designed in order to be short and energy efficient, characteristic that is not adequately addressed from the existing protocols [27].
# A Power Control and Intervention Algorithm for Co-Existing IMT Base Stations and Satellite Services
## 2 System Model
### IMT-2020 Antenna Model  
Active Antenna System (AAS) technology is crucial for achieving high transmitter gain in the IMT-2020 network. ITU-R recommendation M.2101 typically provides an IMT-AAS  
Figure 1: The interference scenario.  
antenna model for IMT-2020 spectrum sharing research. The antenna array consists \(N_{h}\) of and \(N_{v}\) elements placed along the y-axis and z-axis respectively, as shown in Figure 2.  
The composite gain of IMT-2020 AAS antenna is calculated by adding the component gain and array gain, and its mathematical formula is:  
\[A(\theta,\varphi)=A_{E}(\theta,\varphi)+10\log_{10}\left(\left|\sum_{m=1}^{Nh} \sum_{n=1}^{Nv}w_{i,n,m}\cdot\
u_{n,m}\right|^{2}\right), \tag{5}\]  
\(A_{E}(\theta,\varphi)\) represents the gain of a single element in an antenna array; more specifically, it depends on various factors such as the azimuth and elevation of the signal, the maximum gain of the element, and the horizontal and vertical aspect ratios of the antenna. The characteristics of IMT-2020 AAS are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **City** & **Suburban** \\ \hline Antenna pattern (m) & based on Recommendation ITU-R M.2101 & based on Recommendation ITU-R M.2101 \\  & Attachment 1 Section 5 & Attachment 1 Section 5 \\   Component gain (dBi) & 6.4 & 5.5 \\   Horizontal/vertical & 90\({}^{\circ}\) H & 90\({}^{\circ}\) H \\
3 dB beam width of a single element & 65\({}^{\circ}\) V & 90\({}^{\circ}\) V \\  (in degrees) & 30 & 30 \\   Horizontal/vertical front to back ratio & linear \(\pm\) 45\({}^{\circ}\) & linear \(\pm\) 45\({}^{\circ}\) \\   Antenna array configuration & \(16\times 8\) & \(16\times 8\) \\  (row \(\times\) column) & 2 & 2 \\   Array Ohmic Loss (dB) & \(\pm\)60 & \(\pm\)60 \\   Maximum coverage angle on the & \(\pm\)60 & \(\pm\)60 \\   horizontal plane of the base & \(\pm\)60 & \(\pm\)60 \\   station (degrees) & & \\   BS vertical coverage range & 90–120 & 90–120 \\   Mechanical Downtilt (Degrees) & 6 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: IMT-2020 AAS Characteristics.  
Figure 2: AAS antenna unit.
###### Abstract  
The use of multi-connectivity has become a useful tool to manage the traffic in heterogeneous cellular network deployments, since it allows a device to be simultaneously connected to multiple cells. The proper exploitation of this technique requires to adequately configure the traffic sent through each cell depending on the experienced conditions. This motivates this work, which tackles the problem of how to optimally split the traffic among the cells when the multi-connectivity feature is used. To this end, the paper proposes the use of a deep reinforcement learning solution based on a Deep Q-Network (DQN) in order to determine the amount of traffic of a device that needs to be delivered through each cell, making the decision as a function of the current traffic and radio conditions. The obtained results show a near-optimal performance of the DQN-based solution with an average difference of only 3.9% in terms of reward with respect to the optimum strategy. Moreover, the solution clearly outperforms a reference scheme based on Signal to Interference Noise Ratio (SINR) with differences of up to 50% in terms of reward and up to 166% in terms of throughput for certain situations. Overall, the presented results show the promising performance of the DQN-based approach that establishes a basis for further research in the topic of multi-connectivity and for the application of this type of techniques in other problems of the radio access network.  
Keywords:multi-connectivity; deep learning; Deep Q-Network; heterogeneous networks; cellular networks; 5G NR; LTE; O-RAN +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 4 Simulation Results and Analysis  
To validate the effectiveness of the proposed algorithm, its performance is evaluated using three indicators: detection probability, average false alarm ratio, and algorithm complexity. The proposed method is compared with the classical CME and FCME algorithms through Monte Carlo simulations to evaluate the performance of the three algorithms under single-tone interference, multi-tone interference, and partial-band interference scenarios. The simulation parameters are set as follows: FFT points \(N=2048\); sampling frequency \(f_{s}=5\) MHz; single-tone interference frequency, 1.745 MHz; multi-tone interference frequencies, 0.325 MHz, 0.7125 MHz, 1 MHz, 1.25 MHz, 1.625 MHz, and 2 MHz; the relative bandwidth (RBW) of partial-band interference is set to 10%, 50%, and 80%; and the number of Monte Carlo simulations is set to 5000. For the proposed algorithm, the two-step segmentation sizes are set as \(p=64\) and \(n=2\), and false alarm probability \(P_{f}=0.00001\). The jammer-to-noise ratio (JNR) ranges from \(-35\) dB to \(5\) dB and \(-10\) dB to \(20\) dB. In this context, the relative bandwidth (RBW) is defined as the ratio of the interference signal  
Figure 4: Flowchart of iterations.  
bandwidth to the sampling signal bandwidth, while the jammer-to-noise ratio (JNR) is defined as the ratio of the interference signal power to the noise signal power.