esa-sceva/satcom-chunk-collection huggingface dataset:
# NFT Image Plagiarism Check Using EfficientNet-Based Deep Neural Network with Triplet Semi-Hard Loss
## 2 System Overview  
### NFTs Concept  
NFTs are cryptographic tokens originally developed on the Ethereum blockchain and later adopted by other blockchains. NFTs are distinguishable from fungible tokens, such as bitcoins, where each token is equivalent and imperceptible. Because NFTs are non-fungible, each token is guaranteed to be distinctive, conferring right privileges over a digital treasure to its bearer [10]. Specifically, producers can easily prove ownership of their digital goods, such as artwork, images, and films, by using NFTs. Moreover, by definition, each non-fungible token (NFT) is distinctive and non-divisible; there should be an infinite number of NFT types. Academics generally categorize NFTs into six main groups based on their common applications: art, metaverse, gaming, collectibles, utility, and others [11].  
In addition, [PERSON] et al. [12] explained the NFT as a token, as a unit of account, is a form in a dispersed blockchain controlled by a computer algorithm of a smart contract, in which the amount of the equity on the accounts of token holders are documented, allowing them to be transferred from one wallet to another. The idea of the non-fungible token was first introduced in the Ethereum Improvement Proposal (EIP) 721 [13] and further developed in EIP-1155 [14; 15]. ERC-721 is a non-fungible token standard, which is different from fungible tokens. It can be unique because every token has a uint256 variable called tokenID that is globally distinctive. The ERC-721 standard includes basic capabilities such as _transferFrom_ and _ownerOf_ for tracking and transmitting NFTs in its smart contract API. ERC-1155 is a standard extension that can represent both non-fungible and fungible tokens. It delivers a representational interface for many tokens. Algorithms 1 and 2 demonstrate standard interfaces for ERC-721 and ERC-1155, respectively.  
```
interface ERC721 {  function transferFrom(address_from, address_to, uint256_tokenId) external  payable; function ownerOf(uint256_tokenId) external view returns (address); function balanceOf(address_owner) external view returns (uint256);... }
```  
**Algorithm 1** ERC-721 Standard Interface  
There are some differences between ERC-721 and ERC-1155. To begin with, each ERC-721 transaction requires a single operation. Meanwhile, with ERC-1155, a single transaction can contain several operations. Second, we may employ the ERC-1155 token standard as a vital infrastructure to enable batch token transfers. According to the ERC 721 token standard, a separate smart contract must be created for each NFT or token transfer. To transmit many NFTs through an ERC-721 system, transactions for each one needed to be created. Third, when assets are transferred via the ERC-721 standard to the incorrect address, they cannot be reverted. On the other hand, the ERC-1155 token standard provides a special function called the \"safe transfer\" function [16].
## 3 Results
### Cell Proliferation (MTS) Assay  
We established the MTS standard curve using different numbers of MC3T3 (R\({}^{2}\) = 0.9981, data not shown) to validate the assay protocol. The most obvious inference from Figures 7 and 8 is that there is a significant increase in cell numbers from day 1 to day 7 in all groups with the exception of PA-ePCL group. This is consistent with the previous observation from Live/Dead assay and confirms the cytotoxicity of PA treatment. Importantly, there is no statistically significant difference in the day 1 cell viability between Ctrl-ePCL and PA-ePCL+EtOH groups (\(p\) = 0.9079). This indicates the effectiveness of ethanol quench in overcoming the negative effects of PA. We further notice that despite having similar number of cells at day 1, Ctrl-ePCL and PA-ePCL+EtOH were significantly different in their ability to support cell proliferation at day 7 (\(p\) = 0.013). In both cases, the increase in cell numbers was moderate (ranging from 25-60%).  
Figure 6: Scanning electron microscopy (SEM) images of MC3T3 after 24-h culture on Ctrl ePCL (**A**) and PA-ePCL treated with ethanol (**B**) show similar density and spreading behavior. This illustrates successful removal of PA residuals from ePCL by ethanol wash.  
Figure 7: Cell proliferation data from MTS assay. Raw OD from 1-day and 7-day culture of MC3T3 osteoblasts across different substrates. All substrates show positive cell proliferation from day-1 to day-7 with the exception of ePCL treated with PA. There was no detectable signal at day-1 and day 7 indicating complete cytotoxicity of these scaffolds following PA exposure. Alvetex on the other hand showed increased cell proliferation after PA exposure. PA effects were effectively neutralized following ethanol wash.  
In complete contrast to ePCL, Alvetex scaffolds were not cytotoxic after PA treatment at day 1. This reduced adsorption of PA components can be a result of different material chemistry (PCL vs. polystyrene), scaffold architecture and specific surface area (nanofibrous PCL vs. macroporous Alvetex). In addition, cells seeded on Alvetex demonstrated robust cell proliferation that was significantly higher than ePCL (100-200%). Interestingly, PA-Alv showed maximum proliferation and if PA-Alv were washed with ethanol, the proliferation rates return back to baseline (Ctrl-Alv). This again indicates that ethanol was effective in reversing the PA-induced change in Alvetex polystyrene scaffolds.
## 4 Results and Discussion
### Summer Maize Recognition in Huanghuaihai Plain  
#### 4.3.1 The MAD between Standard Summer Maize EVI and Actual MODIS EVI  
The MAD map of the study area is shown in Figure 9, where the minimum value is 0 and the maximum is 6. The lower the MAD value, the greater the similarity between the actual EVI value and the simulated EVI value, which indicated that the summer maize planting area is more likely. On the contrary, the summer maize planting area is less likely. According to the p-tile algorithm, the optimal thresholds of five provinces were determined: 0.85 for Beijing, 0.89 for Tianjin, 0.71 for Hebei, 0.59 for Henan, and 0.74 for Shandong. The value of MAD that was smaller than the optimal threshold was considered to be the summer maize area by comparing every raster value of the MAD map with the optimal threshold value, which was used to extract the spatial distribution of summer maize in Huanghuaihai Plain.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & **Bao-di** & **Dong-ng** & **Gao-Mi** & **Gu-Cheng** & **Kai-Feng** & **Lu-Shan** & **Meng-Hou** & **Shou-Uang** & **Xi-Fing** & **Xu-Shui** \\ \hline R & 0.905 ** & 0.917 ** & 0.907 ** & 0.926 ** & 0.989 ** & 0.955 ** & 0.989 ** & 0.922 ** & 0.968 ** & 0.912 ** \\ Sig. & 0.002 & 0.001 & 0.002 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.001 \\ N & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 \\ \hline \end{tabular} Note: ** Indicates the correlation is significant with \(p<0.01\).  
\end{table}
Table 4: Correlation analysis between the actual MODIS EVI curve and the simulation standard EVI curve of maize.  
Figure 9: The mean absolute distance (MAD) map in Huanghuaihai Plain in 2012.
# Efficient Path Planning Based on Dynamic Bridging Rapidly Exploring Random Tree
## 5 Simulation and Experimental Results
### Simulation I
The experimental results show that the RRT-Connect algorithm takes the least amount of time among all the reference algorithms, this is due to the bidirectional search strategy adopted by the RRT-Connect algorithm, which can quickly discover effective paths from the starting point to the end point. However, its standard deviation is large, and the speed profile shown in Figure 10 is rugged, and the excessively fast search speed sacrifices some stability. In addition, the proposed DBR-RRT algorithm inherits the bidirectional search strategy of RRT-Connect, and although the average running time is slightly longer than that of the initial RRT-Connect algorithm, the speed has a smaller standard deviation and possesses the largest maximum speed, which indicates that the speed fluctuation be \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Planner**} & \multicolumn{2}{c}{**Vel. (m/s)**} & \multirow{2}{*}{**STD Vel.**} & \multirow{2}{*}{**Time (s)**} \\ \cline{2-2} \cline{5-5}  & **Avg** & & & \\ \hline Informed-RRT* & 0.163 & 0.253 & 0.051 & 25.03 \\ RRT-Connect & 0.154 & 0.276 & 0.043 & 21.83 \\ RRT* & 0.157 & 0.256 & 0.039 & 24.16 \\ DBR-RRT & 0.189 & 0.301 & 0.037 & 22.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between Informed-RRT*, RRT-Connect, RRT*, and proposed method in terms of time, average velocity, maximum velocity, and standard deviation of executed velocities. Figure 10: Speed curves generated by the Informed-RRT*, RRT-Connect, RRT*, and DBR-RRT algorithms in simulation I.  
tween each run is small, and it achieves a balance between increasing the average speed and maintaining less speed fluctuations. Although the RRT algorithm needs to consider more candidate paths in the process of finding the optimal path, and thus consumes the longest time, it is clearly unnecessary to sacrifice time to find the optimal path in a simple L-shaped environment. Compared with DBR-RRT, Informed RRT* can theoretically improve the efficiency of path planning through conducting a more informed search, but in this test environment, its efficiency does not exceed that of the other algorithms, and the large fluctuations in the velocity curves shown in Figure 10. indicate that the algorithm has a large variation in the robot's velocity when planning paths.
## 3 Market Research: Current State of Immersive Technologies in Industrial Design
### Available Resources of Immersive Tools for Industrial Design
#### 3.2.2 Analysis of Software for Immersive Design
In contrast, more advanced tools such as Mindesk, Alias Create VR, Unity, or Unreal Engine require PC-connected headsets to fully leverage their technical and graphical capabilities. Hardware has recently evolved toward models with advanced passthrough and mixed reality capabilities. This development has also driven the creation of specific software functions, enabling smoother integration between physical and virtual environments. This technical compatibility between software platforms and immersive hardware is essential to ensure efficient, realistic workflows that are adaptable to different stages of the product design process. To conclude the analysis, immersive software is changing the industrial design process. Its adoption does not rely solely on technology, but on its ability to integrate into existing workflows, add value at various stages, and enable more effective communication among teams. Platforms such as 'Gravity Sketch' and 'ShapesXR' stand out for providing a balanced combination of functionality, accessibility, and scalability, while others such as 'Mindesk', 'Alias Create VR', and 'Unity' allow for addressing specific tasks that require high precision or greater functional complexity. The interoperability and gradual integration with CAD environments point to a smoother transition between visual thinking, technical modelling, and immersive experiences. The choice of software should be aligned with the specific project needs, design phase, product type, required immersion level, and available hardware.
# Smart Bandwidth Assignation in an Underlay Cellular Network for Internet of Vehicles
## 3 Problem Definition
### System Model
This \(RB_{t}\) set of orthogonal RBs is divided into different resource pools, \(RP_{t}:=\{rp_{1},rp_{2},...,rp_{k}\}\), where each \(rp_{x}\) is formed by a variable number of RBs depending on the existing transmission requirements. Formally, the set of C-UEs, V-UEs and clusters in the scenario at time interval \(t\) can be define as \(C_{t}:=\{c_{1},c_{2},...,c_{n}\}\), \(V_{t}:=\{v_{1},v_{2},...,v_{m}\}\) and \(CLU_{t}:=\{clu_{1},clu_{2},...,clu_{j}\}\), respectively. During the observation or simulation period, additional users may join or leave the scenario, subject to their routes, which may vary with V-UEs' and C-UE' densities over time and space. In addition, the properties of the scenario impact users' mobility and communications' reliability. Furthermore, the network system comprises the cellular infrastructure and GeoServer which manages the creation and maintenance of clusters and where some ITS applications are implemented, e.g., congestion prediction, planning of routes, etc.
# FANET Routing Protocol Analysis for Multi-UAV-Based Reconnaissance Mobility Models
## 3 FANET Routing Protocols, Mobility Models, and Reconnaissance Scenarios
### Mobility Model Descriptions  
In reconnaissance scenarios, UAVs need to move irregularly. If a UAV moves regularly, the enemy can easily predict the UAV path. Therefore, in a reconnaissance scenario, a mobility model is required to ensure irregular movement. In this section, we introduce five mobility models.  
The random waypoint model has been used as a default model due to its simplicity [32]. In the model, a UAV randomly generates a new destination in the reconnaissance area and then flies to the destination. When a UAV reaches the destination, it repeats this process during a period of reconnaissance time. Smooth Random WayPoint (SRWP) operates in the same way as the random waypoint model. The difference is that it considers a smooth turn by considering the UAV speed [33].  
The Markov Process model exhibits three types of mobility [34]. The three mobility decisions are left turn, straight ahead, and right turn. These decisions are determined probabilistically according to the current state. For example, if a UAV is currently turning left, the probability of continuing to turn left is 70%, and the probability of changing to go straight ahead is 30%. A right turn works in the same way. When a UAV is currently going straight, the probability of continuing to go straight is 80%, the probability of turning left is 10%, and the probability of turning right is 10%.  
The Enhanced Gauss-Markov (EGM) model applies the Gauss-Markov model for UAV reconnaissance [35]. This model accounts for smooth movement within the reconnaissance area and a smooth turn in the border area. When a UAV approaches the border area, the UAV avoids a collision by changing the mean directional deviation as well as the mean variance in the directional deviation.  
The Distributed Pheromone Repel (DPR) model was inspired by the pheromones of ants [34]. Ants share their local pheromone information and move toward areas with strong pheromones. The DPR model adopts the concept of ant movements but implements it in an opposite fashion. Since the DPR model is designed for reconnaissance, UAVs fly to areas with fewer pheromones. Each UAV has its own virtual map that accumulates the pheromone distribution and updates its map by communicating with nearby nodes.  
The Random Destination with Partitioned Zone (RDPZ) model reconnoiters by communicating with each UAV [36]. The RDPZ model divides the reconnaissance area into n * n zones and manages the number of reconnoitered destinations in each zone by communicating with the nearby UAVs. When a UAV flies to a destination, the UAV stochastically chooses the intermediate zone with fewer destinations.
## 3 Methodology
### Preprocessing  
In the preprocessing stage, delineated in Figure 1, we applied filtering techniques to the collected social media data to extract and eliminate noise and irrelevant information that could impair the embedding model's capacity to learn effectively. During this stage, the raw textual data harvested from social networking platforms undergo a cleaning process. This included the elimination of punctuation, newline characters, URLs, hashtags, mentions, and similar non-content elements. To enhance the LSTM-Attention-BiTCN model's performance, we substituted instances of emails, URLs, mentions, and punctuation with a single whitespace. In addition to these steps, we standardized the text by converting it to lowercase and removing newline characters to ensure uniformity. The filtration process also involved the exclusion of stop words to maintain focus on the content's substantive elements. After purging the text of all undesired characters, we tokenized the posts usingthe Natural Language Toolkit. This process broke down each sentence into a list of its tokens, facilitating further analysis. After tokenization, the cleaned dataset was ready for entry into the embedding layer. Here, it would be transformed into a structured vector format that the model can process to discern underlying emotional states and assess the risk of suicidality present in the text.
# Resilience in the Internet of Medical Things: A Review and Case Study
## 4 Key Capabilities for Resilience in IoMT
### Scalability  
A resilient network must be adaptable to accommodate a large increase in the number of nodes in the network. In large IoMT networks, as workload increases through the increasing number of nodes, the required resources also need to be scaled up to accommodate the increasing numbers of users and meet the performance requirement of the networks [30]. Hence, scalability is another important capability of resilient IoMT networks.
## 5 Evaluation Results  
### Case Study Setting  
To validate the speed and accuracy of the proposed method, simulations are conducted in the scene depicted in Figure 4. In this scene, eight household users participate in DR,and their load forecasts are illustrated in Figure 5, along with the baseline load set by the grid. The power grid sets the overall target load that users need to achieve.  
To emphasize the advantages of the two-layer optimization framework for the VPP proposed in this paper, four comparison scenes, as shown in Table 1, are established. The simulation is conducted separately in the aforementioned four scenes. It should be noted that, given that the conventional DR method is not inherently compatible with the household refinement modeling concept proposed in this paper, household users are assumed to distribute the superior instructions evenly while executing response decisions.  
The comparative results of the scenes are presented in Table 2. From Table 2, compared to Scene 1, Scene 4 exhibits an increase in VPP aggregator benefits by USD 187.00, a reduction in household energy costs by USD 222.01, and a decrease in EST benefits by USD 21.30. The increase in combined benefits and the reduction in household costs are mainly due to the grid's DR policy subsidy and the adjustment of customers' electricity  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Scene** & **Scene Description** \\ \hline
1 & No consideration of DR behaviors \\
2 & Using conventional DR methods [30] \\
3 & Using the proposed method, while EST is not involved in gaming \\
4 & Using the proposed method in this paper \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison scene.  
Figure 4: Simulation scene with eight household users.  
Figure 5: Household forecast loads and baseline loads.  
use hours. Since Scene 1 is not included in DR, the energy supply during peak load hours is mainly supplemented by EST, and thus Scene 1 has the highest EST benefits compared to the other scenes.  
In comparison to Scene 2, the model proposed in this paper intricately considers household energy preferences, enhancing household satisfaction and DR program feasibility. The conventional DR method exhibits limited load regulation capabilities, resulting in weaker performance across all metrics than in Scene 4.  
In Scene 3, the EST exits the game, losing the subsidy from DR compliance. It only needs to meet the household's load-adjusted shortfall, significantly reducing its benefits. Meanwhile, the VPP aggregator and households take on more load-adjusted tasks, leading to slightly worse relative benefits and energy usage costs than in Scene 4.
## 4 Dynamic Analysis of REJ on Suspension Bridge
### Under Train Load  
To highlight the dynamic effect of REJ irregularity, the vehicle-track-bridge coupling dynamic model only considers the influence of REJ irregularity without the random track irregularity. Measured irregularity in the REJ area is shown in Figure 15.  
Figure 16a shows the vertical acceleration curves of the switch rail and sleeper when the vehicle is traveling at 250 km/h. The peak vertical accelerations of rail corresponding to three schemes are 1210.00 m/s\({}^{2}\), 880.49 m/s\({}^{2}\), and 1028.50 m/s\({}^{2}\), respectively. The peak accelerations of sleeper are 27.52 m/s\({}^{2}\), 23.33 m/s\({}^{2}\), and 20.38 m/s\({}^{2}\), respectively. As the REJ in Scheme 1 is close to the beam end, the vertical vibration of the rail and sleeper will increase.  
Figure 14: Peak dynamic response.  
Figure 15: Measured irregularity in REJ area.  
Figure 16b shows the time-history curves of vertical bridge deck acceleration and displacement in the REJ area. The bridge deck vibration acceleration of Scheme 1 is significantly higher than that of Schemes 2 and 3. The peak vibration accelerations are 0.72 m/s\({}^{2}\), 0.05 m/s\({}^{2}\), and 0.193 m/s\({}^{2}\), respectively. The peak vertical bridge displacements of the three schemes are 0.46 mm, 1.12 mm, and 0.80 mm, respectively. Because the REJs in Schemes 2 and 3 are close to the mid-span, the vertical displacement is large.  
Figure 16c shows the time-history curves of vertical and lateral wheel-rail force. The peak vertical values are 94.67 kN, 96.73 kN, and 97.72 kN, respectively. The peak lateral values are 8.89 kN, 9.11 kN, and 9.20 kN, respectively. There is little difference between the wheel-rail vertical and lateral forces among the three schemes.  
Figure 16d shows the time-history curves of derailment coefficient and wheel unloading rate. The maximum derailment coefficients corresponding to the three schemes are respectively 0.0938, 0.0946, and 0.0947, which are all lower than the requirement in code [34]. The peak values of the wheel unloading rate are 0.33, 0.28, and 0.26, respectively. From the above analysis, when the REJs is laid close to the beam end, the wheel-rail force fluctuates more violently, and the wheel weight reduction rate increases.  
From the above analysis, it can be summarized that when the REJ laying position is closer to the beam end (Scheme 1) compared to laying Schemes 2 and 3, it is beneficial to reduce the vertical deformation of REJ, but this will increase the vibration acceleration of the rail, sleeper, bridge deck, and wheel unloading rate, affecting driving safety and comfort.  
Figure 16: Dynamic characteristics under train load: (**a**) Rail and sleeper; (**b**) Bridge deck; (**c**) Wheel-rail; (**d**) Security index.