esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Cooperative Decision-Making in a Multi-Vehicle Cooperative Task
### Wighted Reward Assignment  
In the fully cooperative MARL setting, each agent was provided with the same goal and assigned the same reward after executing the action at every step. The same reward can be represented by the average global reward as \(r_{t}=\frac{1}{N}\sum_{i=1}^{N}r_{i,t}\). However, the shared rewards scheme is intricate in order to infer each vehicle's contributions to the system cooperation. Further, instead of embracing a global reward, a local reward assignment strategy could alleviate the issue of the credit assignment problem. Specifically, each host vehicle only focuses on its surrounding vehicles, which considerably impacts the smooth interaction between the vehicles. Thus, the reward for the ego vehicle \(v_{i}\) at the step \(t\) is defined as follows.  
\[r_{i,t}=\frac{1}{|T_{i}|}\sum_{j\in T_{i}}r_{j,t}, \tag{6}\]  
where \(T_{i}=i\cup N_{i}\) is a set whose elements include the ego vehicle \(v_{i}\) and its neighbor vehicles and \(|\cdot|\) represents the cardinality operator of the set. The local reward assignment has two advantages. First, the communication burden can be reduced by focusing only on the nearby vehicles, establishing a more real-time system. Second, the contribution of the vehicles to their cooperation can be indicated more accurately.  
Nevertheless, the regional reward assignment strategy still doesn't accurately differentiate between the contributions of each ego vehicle at different positions on the road. Collisions are more likely to occur inside the intersection than outside it, meaning that a vehicle closer to the intersection should be assigned more rewards, i.e., a weighted reward assignment according to the vehicle's position. A weight factor \(\eta\) was defined to measure the contribution for crossing the intersection safely and efficiently, as shown in Equation (7).  
\[\eta_{i}=\frac{L_{s}-d_{i}}{\sum_{j\in T_{i}}\left(L_{s}-d_{j}\right)}, \tag{7}\]  
where \(d_{i}\) denotes the distance from the vehicle \(v_{i}\) not entering the intersection to the entrance or from \(v_{i}\) not exiting the intersection to the exit, and \(L_{s}\) is the length of the entrance or exit straight lane. When \(v_{i}\) enters the intersection, \(d_{i}=0\). Thus, the reward for the ego vehicle \(v_{i}\) at the step \(t\) is as follows.  
\[r_{i,t}=\sum_{j\in T_{t}}\eta_{i}r_{j,t}. \tag{8}\]  
When a vehicle and its neighbors are in the intersection, Equation (8) will degenerate to the local reward assignment strategy, i.e., Equation (6).
# Reversible Data Hiding in Encryppted Images with Extended Parametric Binary Tree Labeling
## 4 Experimental Results
### Image Quality Assessment  
It is important to assess the quality of the recovered image compared to the original image and the encrypted image. If the recovered image is different from the original cover image, full reversibility is not ensured. If the quality of the encrypted image is high, the scheme is poorly encrypted and there is a risk of information leakage.  
The PSNR and the SSIM are often used to evaluate the quality of an image after it has been processed compared to the original image. the PSNR is the peak signal to noise ratio, which is the ratio between the maximum possible power of the signal and the power of the corrupted noise that affects the fidelity of its representation. The unit is decibels (dB). The higher the PSNR, the closer the image is to the original. The SSIM is the Structural Similarity Index. It is a measure of the quality of images based on visual perception and takes a value from 0 to 1. The SSIM value is 1 when the two images are structurally identical.  
Four test images in Figure 13 were used for the experiment. Table 3 shows the PSNR and SSIM for the original and recovered images, where PSNR is \(\infty\) and SSIM is 1. This indicates that the proposed scheme can be made fully reversible. Table 4 shows a comparison of the original image and embedded encrypted image, where the PSNR does not exceed 10 and the SSIM is close to 0. This shows that the two images are so different that the proposed scheme cannot derive the original image from the embedded encrypted image.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Image** & **Original Image** & **Encrypted Image** & **Embedded Encrypted Image** \\ \hline Lena & 7.4455 & 7.9985 & 7.9865 \\ Man & 7.1926 & 7.9862 & 7.9952 \\ Jetplane & 6.6776 & 7.9923 & 7.9912 \\ Baboon & 7.3899 & 7.9965 & 7.9936 \\ \hline \end{tabular}
\end{table}
Table 2: Information entropy of the image at different stages.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Image** & **Lena** & **Man** & **Jetplane** & **Baboon** \\ \hline PSNR(dB) & \(\infty\) & \(\infty\) & \(\infty\) & \(\infty\) \\ SSIM & \(1\) & \(1\) & \(1\) & \(1\) \\ \hline \end{tabular}
\end{table}
Table 3: PSNR and SSIM between the original image and recovered image
# Dynamic Multi-Target Self-Organization Hunting Control of Multi-Agent Systems
## 2 Preliminary and Problem Formulation  
Assuming that all agents are isomorphic--that is, each agent has the same dynamic model and functional attributes--the communication connection of multi-agent systems can be represented by a graph: \(G(V,\,E,\,A)\). \(V=\{V_{1},\,V_{2},\ldots,V_{N}\}\) represents the set composed of \(N\) agent nodes in \(G\), and \(V_{i}\) represents agent \(i\). \(E\subseteq V_{i}\times V_{i}\) is the edge set, representing the node connections in graph \(G\). \(A=\left[a_{ij}\right]\in\mathbb{R}^{N\times N}\) is the adjacency matrix, and \(a_{ij}\) represents the connection weight between agent \(i\) and agent \(j\). To ensure connectivity, assume that \(G\) is a connected graph.  
Considering the limited detection distance of any actual sensor, the detection range refers to the maximum distance at which an agent can detect other agents and respond to a target, and the detection radius of the intelligent agent is set to \(R_{\text{det}}\).  
\[a_{ij}=\left\{\begin{array}{c}1,d_{ij}\leq R_{det}\\ 0,d_{ij}>R_{det}\end{array}\right. \tag{1}\]  
Here, \(d_{ij}\) is the distance between agent \(i\) and agent \(j\). Equation (1) shows that if mutual detection is not possible, a connection cannot be established. This is in line with reality. Agents establish mutual connections based on the detection distance. This paper only considers that the topology of the multi-agent system is connected, based on the actual situation.  
\(d_{\text{min}}\) is a safe distance, which is the minimum distance that prevents collisions between any two agents  
\[d_{\text{min}}-d_{ij}\leq 0 \tag{2}\]
# A Comprehensive Review of Acoustic Methods for Locating Underground Pipelines
## 3 Acoustic Methods for Locating Buried Pipelines
### Seismic Wave Methods
#### 3.4.1 Compressional Wave Method  
A compressional wave method has been proposed to detect objects buried at a shallow depth [101]. The experimental set-up is shown in Figure 12a. In this set-up, the shaker was placed on the ground surface as the acoustic source, using a specially designed platform (see Figure 12b) to achieve well coupling with the soil and to generate a compressional wave. This method is a modified work based on [103], where the CDP method was adapted to locate buried point-like objects. One modification of the compressional wave method is that a time extended signal, instead of impulses, was used to drive the shaker vibration. In this way, the excitation signal can be constructed to input the desired frequency content. In addition, the energy can also be supplied to the ground over a sufficient period without resorting to input amplitudes that produce a non-linear response in the ground [101]. However, the employment of time-extended signal renders the time-domain approach inapplicable. Consequently, the cross-correlation function with additional signal processing has been used in the imaging algorithm to produce a cross-sectional image of the buried object.  
Figure 11: Principle of seismic wave methods, modified from [99].  
Field tests have been undertaken to validate the efficiency of the compressional wave method, where a concrete pipe with diameter of 0.6 m buried at a depth of 1 m was selected as a target. The time-extended signal was a linear chirp with the frequency range of the input varying from 50 Hz to 2 kHz. The imaging result displayed clear location of the pipe, as illustrated in Figure 13, despite the limited resolution.
## 2 Materials and Methods
### Modeling and Simulating of the Solar Collector
#### 2.1.3 Simulation Results  
Using carbon-based and water-based nanofluid, the efficiency results of a direct absorption solar collector were obtained. Figure 2 shows the efficiency of the LTSC with respect to the volume fraction for 3 different sizes of nanoparticles at a fixed radiation of 800 W/m\({}^{2}\) and a mass flow rate of 9 \(\times\) 10\({}^{-5}\) kg/s. From the figure, it can be seen that the efficiency increases with a higher volume fraction. Likewise, it can be seen that the efficiency improves by reducing the size of the nanoparticles.  
Figure 3 shows the efficiency of LTSC with respect to solar irradiation at different volume fractions using a nanoparticle diameter of 5 nm and a mass flow rate of 9 \(\times\) 10\({}^{-5}\) kg/s. From the figure, it can be seen that as solar irradiation increases, the efficiency decreases linearly. Likewise, it can be seen that as the volume fraction increases, the efficiency of the LTSC increases.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Therophysical Property** & **Water**[45] & **Fullerene** \\ \hline Density, kg/m\({}^{3}\) & 997 & 1720 [46] \\ Specific heat, J/kg K & 4180 & 506.1 [47; 48] \\ Thermal conductivity, W/m K & 0.607 & 0.4 [46] \\ Viscosity, kg/m s & 0.00798 & 0.133231 [49] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Thermophysical properties of water and nanoparticles at 300 K.  
Figure 3: Analysis of the efficiency of the LTSC with increasing solar irradiation.  
Figure 2: Simultaneous analysis of the effect of nanoparticle size and volume fraction on LTSC performance.  
In Figure 4, the intersection with the vertical axis, efficiency, is equal to F\({}_{\text{R}}\)(\(\tau\alpha\)) and at this point the temperature of the fluid entering the collector is equal to the ambient temperature. The slope of the curve is equal to F\({}_{\text{R}}\)U\({}_{\text{L}}\) and indicates how much energy is lost from the solar collector. The stagnation point occurs when there is no flow circulating through the collector. With this efficiency curve, the results obtained in the direct absorption collector (LTSC) are compared.  
From the simulation, the efficiency is negatively affected when the diameter of the nanoparticle increases because the global heat transfer area between the nanoparticle and the base fluid decreases. Further, the efficiency decreases when the solar radiation and the inlet temperature rise. Likewise, the efficiency enlarges when the volume fraction is higher.
# A Review of Hydrogen Sensors for ECLSS: Fundamentals, Recent Advances, and Challenges
## 2 Catalyst Combustion Hydrogen Sensors  
Hydrogen sensors based on the catalyst combustion principle became a research hotspot in hydrogen measurement technology due to their good stability and high sensitivity, and are widely used in the aerospace field [19; 20; 21]. The catalyst used in this type of sensor is metallic Palladium (Pd), which has two disadvantages. Firstly, the reaction temperature is high, and the catalyst needs to be heated, while the reaction temperature is as high as 500 degrees, and the product has high power consumption. Secondly, it is susceptible to humidity, and long-term operation under certain conditions can lead to phenomena such as agglomeration of catalyst grains, resulting in changes in the catalytic capacity of the catalyst and causing drift in the sensor output. In recent years, domestic and international research on the catalyst combustion principle of hydrogen sensors focused on the modification and preparation of catalyst materials to complement the two inherent disadvantages of the current conventional catalysts, which are high reaction temperature and interference by humidity [22; 23].
# BL0K: A New Stage of Privacy-Preserving Scope for Location-Based Services
## 3 Preliminaries
### Deficiency of BLOT  
The use of ZKP allows data to be transmitted cryptically between the user and the LBS server. The BLOT approach protects against eavesdroppers but does not provide perfect protection. The deficiency of this approach is not being able to directly detect the malicious servers. Hence, continuous ambiguity must be maintained, which may affect the performance and data leakage.
# Real-Time Lightweight Morphological Detection for Chinese Mitten Crab Origin Tracing
## 2 Materials and Methods
### Detection Model MC-RTLNet  
#### 2.2.1 YOLOv10n Model Introduction  
YOLOv10 is the latest technological achievement in the field of target detection and represents an important breakthrough in the balance of efficiency and accuracy for the YOLO family of algorithms. Through a consistent dual allocation strategy, YOLOv10 eliminates the reliance on non-maximal suppression training, thus optimizing the redundancy problem of traditional YOLO [24] models for multi-target detection. This strategy optimizes one-to-one and one-to-many detection heads in the training phase and uses only one-to-one detection heads in the inference phase, which significantly improves inference efficiency. The model adopts an overall efficiency-accuracy-driven design, including a lightweight classification head, spatial-channel-discrete downsampling (SCDown), and intrinsic rank-based modules. The SCDown module implements highly efficient feature downsampling, and the PSA module enhances the performance of global information extraction through the partial self-attention mechanism while reducing the computational overhead. YOLOv10 performs well on the COCO dataset and not only maintains high detection accuracy in complex scenarios, but also has lightweight features for embedded device deployment.  
YOLOv10 consists of four parts: Input, Backbone, Neck, and Head. The input part improves the model's generalization ability through data enhancement (e.g., HSV enhancement, image panning, scaling, flipping, and mosaic enhancement). The backbone part adds new SCDown, C2 fUIB, and PSA modules to optimize feature extraction. The Neck part adopts the classic FPN and PANet structures to achieve multi-scale feature fusion. The Head part combines one-to-one and one-to-many detection heads for lightweight target detection tasks. YOLOv10 is available in six versions to meet the needs of different  
Figure 1: System’s overall framework.  
scenarios. Among them, YOLOv10n, as a lightweight version, is ideal for embedded device deployment and small target detection tasks.  
Based on the above advantages, this paper uses the target detection model based on YOLOv10n and improves it for the actual needs of crab traceability. The improved YOLOv10n network architecture is shown in Figure 2.
# Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques
## 4 ICIC in 3 GPP Standards  
LTE is a wireless communication standard introduced by 3 GPP on Release 8. It has an all-IP network architecture and high flexibility on spectrum allocation, since OFDMA is the multiple access scheme on DL. Consequently, the eNB can allocate a UE to any sub-carrier in the frequency domain. This allocation is based on Resource Blocks (RBs) and each RB has 12 sub-carriers and 180 kHz of minimal bandwidth [23]. This flexibility is important to enable fractional reuse of the spectrum.  
The X2 interface, introduced on LTE, is also important for ICIC. It enables signaling between eNBs, allowing for the ICIC algorithms to manage the RB and power allocation on neighboring cells [24].  
Moreover, 3 GPP's Release 8 also introduces implicit support to interference coordination [25], given that LTE has good control over time, frequency, and power resources. Release 9 presents studies that attempt to mitigate interference between macrocell and lower power nodes and, further on, Release 10 extends ICIC to the time domain as it introduces Enhanced ICIC (eICIC). Additionally, Release 11 presents the Further Enhanced ICIC (feICIC) in order to mitigate interference due to control signals.  
Given this history, eICIC and feIC are often considered as the evolution of ICIC. However, FFR-based techniques are still relevant based on recent studies, given that New Radio (NR), the new 3 GPP standard for 5G systems, demands a flexible and efficient use of the spectrum [26]. For example, the authors of [27] propose a FFR-based ICIC scheme for NR. The algorithm dynamically allocates users suffering from interference to a set of Physical Resource Blocks (PRBs) that are not accessible to interfering cells.
## 2 Related Work
### System Modelling
#### 3.3.3 Complexity Analysis  
The time complexity of the proposed green communication framework FRGNWS mainly depends upon three parameters, namely, initialization of the sensor node population size \(|SN_{set}|\) in the network, the fitness calculation time \(t_{max}\) for green route identification, and finally updating the disjoint routes in the sensor node population \(|SN_{set}|\). Therefore, the complexity of the FRGNWS framework can be represented as \(\text{O}(|SN_{set}|+t_{max}|SN_{set}|+|SN_{set}|xd)\). Here, \(|SN_{set}|\) denotes the total sensor population size, \(d\) presents the dimension of the maximum node-disjoint route problem, and \(t_{max}\) is the maximum number of iterations in the green route identification. Further, the time complexity of the proposed framework described in [35] is \(\text{O}(SN_{set}(|SN_{set}|+t_{max}|SN_{set}|+|SN_{set}|xd))\). From a comparison point of view, it can be clearly deduced that the complexity of the proposed framework is \(SN_{set}\) times lower than the considered literature technique.
## 3 Results and Discussion
### Changes in Electric Current and Energy Consumption
\[W^{\prime}=\frac{W\times 1000}{m(C_{0}-C_{t})} \tag{3}\]  
where \(W^{\prime}\) is unit energy consumption, kWh\(\cdot\)mg\({}^{-1}\), and \(m\) is the mass of dry contaminated soil, kg. Table 3 shows the removal efficiency, energy consumption, and unit energy consumption of copper in eight groups. The energy consumption of EK-PRB tests is generally higher, but the removal efficiencies are significantly improved. The unit energy consumption values, \(W^{\prime}\), of the tests with PRB under the conditions of four electrolytes are all smaller than those of EK repair tests alone, the \(W^{\prime}\) values decrease by 10.1-46.1%. Removal efficiency and unit energy consumption are different when various electrolytes are used. The order of removal efficiency of the four electrolytes is 0.1 m citric acid \(>\) 1% PASP \(>\) 0.1 m Lac \(>\) 0.1 m KCl, and the order of \(W^{\prime}\) values is 1% PASP \(<\) 0.1 m citric acid \(<\) 0.1 m AC \(<\) 0.1 m KCl. Therefore, 0.1 M citric acid and 1% PASP are identified as better effects on Cu-contaminated soil restoration by considering the removal efficiency and unit energy consumption comprehensively.
# Optical Wireless Fronthaul-Enhanced High-Throughput FC-AE-1553 Space Networks
## 1 Introduction
The secondary objective is that upon the foundation of this new hybrid network architecture, we aim to identify strategies to optimize system operations. Our focus is on increasing network bandwidth, reducing transmission latency, and delivering superior quality of service (QoS). The tertiary objective is that through comprehensive simulation experiments, we aim to demonstrate that our proposed HSN exhibits noticeably superior network performance when compared to the pre-existing FC-AE-1553 network. To overcome the constraints inherent to the current space information system, this paper delves into an in-depth study of the state-of-the-art FC-AE-1553 technology, the performance of optical wireless communication, and the mechanisms of wireless hybrid networks (Table 1). The contributions of this paper are as follows:  
* Proposal of a novel hybrid networking architecture that enhances the FC-AE-1553 network with an optical wireless network, leading to significant performance enhancements compared to the original network. * Introduction of a multi-level priority traffic allocation strategy for the hybrid FC-AE-1553 and Li-Fi network, demonstrating its capability to adapt to diverse traffic scenarios and dynamic network changes. * Development of a comprehensive simulation model for the hybrid network, showcasing improvements in throughput and latency, as well as an analysis of factors influencing network bandwidth, latency, and packet loss in the hybrid network. * Validation of the multi-level priority scheduling algorithm through simulations, highlighting its ability to provide differentiated quality of service for various priority levels. The algorithm allows for the configuration of weight coefficients based on transmission requirements, enabling adaptation to more suitable transmission paths.
## 8 Simulation and Results Analysis  
In this section, we present the simulation results of the proposed system's \(M/M/S/K\) queuing model and objective function using a genetic algorithm.  
We have developed and simulated the queuing model in MATLAB. In the simulation, the EVs arrival flow fluctuation is modeled using a Poisson distribution. We presume that all EVPSS have a level 3 plug-in, which is the fastest type of EV charger. We have assumed that the time it takes for the smart grid to respond to each EV is zero and all vehicles must be satisfied once the total charge time has expired. In addition, the charging station's number of charging kiosks (servers) is expected to be 20.  
The simulation demonstrates the performance of the queuing model with varying performance parameters. The blocking probability of the EVs with varying arrival rates, service rates, and the number of charging points available. The number of charging points = NC = {3, 4, 5} for DC fast charging infrastructure (DFCI), On-Road Charging Infrastructure (ORCI), and Multi-level Charging Infrastructure (MLCI) with arrival rates \(\lambda=\{7,5,3\}\) and service rates \(\mu=\{1,1.5,2\}\). As shown in Figure 11, increasing the arrival of DFCI leads to an increase in the probability of blocking and vice versa. We can also notice that the variations are more in the ORCI than in the MLCI. Figures 12 and 13 shows the similar effects. We have solved the objective function using the Genetic Algorithm, the result is shown in Figure 14. The Figure 14 shows that convergence is quick and the solution is optimal.  
Figure 11: EVs Blocking Probability at Charging Station with Varying Arrival Rates. (**a**) DC Fast Charging Infrastructure. (**b**) On Road Charging Infrastructure. (**c**) Multi-level Charging Infrastructure.  
Figure 14: GA-based optimal solution.  
Figure 12: EVs Blocking Probability at Charging Station with Varying Service Rates. (**a**) DC Fast Charging Infrastructure. (**b**) On Road Charging Infrastructure. (**c**) Multi-level Charging Infrastructure.  
Figure 13: EVs Blocking Probability at Charging Station with Varying Number of chargers. (**a**) DC Fast Charging Infrastructure. (**b**) On Road Charging Infrastructure. (**c**) Multi-level Charging Infrastructure.