esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Image Inpainting Methods Based on Deep Learning
## 5 Applications
### Image Inpainting  
Images, especially older ones, often have various imperfections. Different scenes from different eras, due to varying photography conditions and technologies, exhibit different levels of damage and distortions. Addressing these image defects has become a crucial research topic.  
In particular, restoring images of ancient murals and artifacts is a significant application of image Inpainting. [PERSON] et al. [35; 36; 37; 38; 39; 40; 41; 42] applied image inpainting techniques to various scenes and degrees of damage in ancient murals and artifacts. This is vital for the revival and study of ancient cultures.  
In modern life and industrial sectors, image inpainting remains crucial. [PERSON] et al. [26] restored dirtied license plates, [PERSON] et al. [27] repaired scratches in coal rock microimages, [PERSON] et al. [28] quickly fixed blank areas in borehole electric imaging, and [PERSON] et al. [29] repaired areas in digital images affected by mirror reflections. Mobile robot semantic map building faces many challenges. Depth images directly obtained from depth cameras have many invalid points that affect the detection and calculation of object positions. [PERSON] et al. [30] applied image inpainting techniques to depth images in mobile robot simulation scenarios. In real scenarios, strong light spots sometimes appear in the front-view road images captured by small robots, which severely distort the images. Before processing such images, [PERSON] et al. [31] effectively restored them.
## 2 Background

Table 1: The most referenced survey papers in federated learning research. The integration of federated learning (FL) and blockchain introduces intricate computational and operational challenges, potentially amplifying the costs associated with deployment and maintenance. This complexity arises due to various factors, including the following [53; 54; 55; 56]:  
* Computational Overhead: Integrating these two technologies may impose significant computational demands, particularly in handling large-scale data. This arises from the necessity of coordinating multiple devices or servers for model training, the cryptographic operations required for transaction validation and the consensus mechanisms inherent in blockchain applications. * Data Synchronization: Employing federated learning involves utilizing distributed data sources, with each node training a local model using its respective data. However, this introduces an additional layer of complexity, necessitating the maintenance of data consistency and synchronization across multiple nodes within a blockchain network. * Scalability Challenges: While federated learning (FL) and blockchain systems are scalable to handle large volumes of data and network devices and nodes, their scalability can lead to increased communication overhead and consensus latency in FL and blockchain networks. These factors can result in performance bottlenecks and elevated operational costs. * Security and Privacy Concerns: Federated learning (FL) relies on sharing model parameter updates and aggregating information among nodes, raising concerns about data privacy and confidentiality. Integrating FL with blockchain introduces additional privacy challenges due to the inherent transparency of blockchain technology. Furthermore, blockchain presents complex cryptographic challenges in safeguarding sensitive information while ensuring integrity. * Regulatory Compliance: The intersection of federated learning (FL) and blockchain introduces challenges regarding regulatory compliance. Ensuring compliance with data protection regulations, privacy laws, and industry standards adds complexity and may incur additional compliance costs. Compliance with these regulations and standards is essential but may necessitate additional resources and considerations.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Objective** \\ **Function** \\ \end{tabular} } & \multirow{2}{*}{**Estimate**} & \multirow{2}{*}{**SR**} & \multirow{2}{*}{**MCS**} & **Error SR and** & **Adjusted \(R^{2}\)** & **Predicted \(R^{2}\)** \\  & & & & **MCS (\%)** & **(\%)** & **(\%)** \\ \cline{3-6}  & & **600 Runs** & **600 Runs** & **600 Runs** & **600 Runs** & **600 Runs** \\ \hline \multirow{2}{*}{\begin{tabular}{c} \"\(ll^{\prime}\) \\ (m\({}^{3}\)/d.kPa) \\ \end{tabular} } & P10 & 0.0025560 & 0.0025560 & 0.0 & & \\  & P50 & 0.0025550 & 0.0025550 & 0.0 & 99.7 & 86.1 \\ \multirow{2}{*}{\begin{tabular}{c} \"Mineralization\_1000 yr\" \\ (gmole) \\ \end{tabular} } & P10 & \(-2.286\times 10^{6}\) & \(-2.346\times 10^{6}\) & 2.6 & & \\  & P50 & \(-2.377\times 10^{6}\) & \(-2.420\times 10^{6}\) & 1.8 & 91.3 & 90.5 \\ \multirow{2}{*}{\begin{tabular}{c} \"Calcite\_1000 yr\" \\ (gmole) \\ \end{tabular} } & P10 & \(-2.286\times 10^{6}\) & \(-2.346\times 10^{6}\) & 2.6 & & \\  & P50 & \(-2.377\times 10^{6}\) & \(-2.420\times 10^{6}\) & 1.8 & 91.3 & 90.5 \\ \multirow{2}{*}{\begin{tabular}{c} \"Dolomite\_1000 yr\" \\ (gmole) \\ \end{tabular} } & P10 & 57.70 & 55.2 & 4.3 & & \\  & P50 & 53.45 & 53.0 & 0.8 & 82.0 & 80.0 \\ \multirow{2}{*}{\
\begin{tabular}{c} \"Quartz\_1000 yr\" \\ (gmole) \\ \end{tabular} } & P10 & 51.71 & 50.4 & 2.5 & & \\ \hline \multirow{2}{*}{
\begin{tabular}{c} \"Quartz\_1000 yr\" \\ (gmole) \\ \end{tabular} } & P10 & It was not possible to calibrate a response surface because no changes were observed in relation to the initial content set for quartz throughout the simulation period. \\ \cline{1-1}  & P50 & It was not possible to calibrate a response surface because no changes were observed in relation to the initial content set for quartz throughout the simulation period. \\ \cline{1-1}  & P90 & It was not possible to calibrate a response surface because no changes were observed in relation to the initial content set for quartz throughout the simulation period. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Stochastics estimates for the objective functions using SR and MCS, associated errors, and \(R^{2}\) (adjusted and predicted).
# A Blockchain Protocol for Authenticating Space Communications between Satellites Constellations
## 5 Simulation Results
### PoST Authenticity and Reliability Results  
The next investigation is assessing PoST authenticity and reliability in processing satellite transactions. To do that we reconfigured the used dataset to consist of trusted transactions and fake transactions within an individual swarm and in two different swarms, then we applied the PoST protocol on the reconfigured dataset to test the efficiency of the proposed protocol in accepting trusted satellite transactions and rejecting the fake ones. This can be measured by creating the confusion matrix parameters, then calculating True Positive Rate (TPR), True Negative Rate (TNR), and Accuracy as in Equations (5)-(7). Table 7 summarizes the obtained results.  
\[\text{TPR}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{5}\]  
\[\text{TNR}=\frac{\text{TN}}{\text{TN}+\text{FP}} \tag{6}\]  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{7}\]
###### Abstract  
The presence of CO\({}_{2}\), sand, and water in oil and gas reservoirs causes erosion-corrosion leading to material degradation in pipelines and fluid handling equipment that results in increasing maintenance and repair costs and a decrease in production. While the weight loss caused by erosion-corrosion is known to depend on flow velocity, angle of impact, sand loading and size and target material properties, field operators often limit the flow rate based on a critical corrosion velocity to protect the equipment. This study investigates the effects of sand loading and flow velocity on weight loss associated with erosion-corrosion in a mild steel sample using a submerged impingement jet. The weight loss by erosion, corrosion and their interaction for a flow velocity range of 10 m/s to 20 m/s and sand loading range of 300 mg/L to 600 mg/L, in a seawater environment, are presented. The results showed that the weight loss by pure erosion and erosion-corrosion interaction increases linearly with jet velocity and sand loading, and that erosion is dominant in all cases except at low velocity and sand loading. The scanning electron microscope (SEM) images after impingement tests were analyzed. In addition, correlations for the velocity and sand loading were derived using the design of experiment method (DOE).  
e 2022 2022  
Flow Velocity and Sand Loading Effect on Erosion-Corrosion during Liquid-Solid Impingement on Mild Steel  
1 Department of Aerospace Engineering, Khalifa University of Science and Technology, Abu Dhabi 127788, United Arab Emirates; [EMAIL_ADDRESS]  
2 Department of Mechanical Engineering, Khalifa University of Science and Technology, Abu Dhabi 127788, United Arab Emirates; [EMAIL_ADDRESS]  
3 Center for Engineering Concepts Development, Department of Mechanical Engineering, University of Maryland, 2140 Glenn L, Martin Hall, MD 20742, USA  
4 Centre for Defense Chemistry, Cranfield University, Defense Academy of the United Kingdom, Shrivenham SN6 8 LA, UK; [EMAIL_ADDRESS]  
5
# Halloysite and Laponite Hybrid Pigments Synthesis with Copper Chlorophyll
## 4 Conclusions  
In the present study, the adsorption of copper chlorophyll in two types of nanoclay structures, halloysite nanotubes and laponite sheets, was successfully achieved, which was found in the XRD pattern modifications and the DTA thermal analysis. By using DoE, we found interactions in different synthesis factors, which affected the final hybrid pigments. First, adsorption behavior was affected by pH and ionic strength from adding mordant salt. Maximum dye adsorption took place at an acidic pH with or without mordant salt, or at a natural pH in water dispersion when mordant salt was added. Optical properties were also affected by the modifiers during synthesis. TST (%) improved without silane under acidic conditions or with silane at natural pH. The halloysite hybrid pigments obtained higher TSR(%) values with no surfactant.  
Color perceptions were affected, and darker samples were obtained with the laponite sample, the surfactant and at a natural pH, while the Chroma Cab* value increased. The highest values were obtained with the surfactant and halloysite samples. These new hybrid halloysite/laponite pigments can be optimized following the results of the DoE analysis to be used for different industrial applications, such as thermoplastic polymers for 3D printing filaments, packaging, coatings, inks, etc., and by putting to good use a wide natural color range and improved TSR (%) properties.  
The color fastens was also improved in the biocomposites application. The samples generated with 10% of hybrid pigments (from HA or LAP clay), show higher colorfastness than the sample with the natural chlorophyll, due to the nanoclays-dye interaction and protection.  
Figure 16: TGA–DTA diagram of halloysite (HA), chlorophyll (G) and the synthesized hybrid pigments GHA for the different conditions [1,8].  
**Author Contributions:** Writing--original draft preparation, [PERSON]-V.; methodology, [PERSON]-[PERSON] and [PERSON]-V.; software, [PERSON]-[PERSON] and B.M.-V.; validation, E.P.R. and V.V.; formal analysis, [PERSON]-[PERSON] and B.M.-V.; investigation, [PERSON]-N. and B.M.-V.; resources, E.P.R. and V.V.; data curation, B.M.-V. and [PERSON]-N.; writing--review and editing, E.P.R. and V.V.; supervision, E.P.R. and V.V.; project administration, V.V., funding acquisition, V.V. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Spanish Ministry of Economy and Competitiveness (project RTI2018-096000-B-100).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Acknowledgments:** To the Spanish Ministry of Economy and Competitiveness for funding the project entitled: \"Diseho y caracterizacion de la apariencia visual de productos\" (REF: RTI2018-096000-B-100).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Proposed Solution to Predict the AoA
### Beamforming and Precoding Strategy
#### 3.4.4 Practical Considerations for O-RAN Integration  
The beamforming logic in the proposed framework is architecturally decoupled and mapped onto the O-RAN functional split as follows:  
* At the O-RU, a rapid generation of beamforming weights using LR-based AoA predictions, jump-detection clustering, and enhanced MUSIC (with window constraints) allows for near-instantaneous adaptation at the network edge.
* At the O-DU, longer-term refinement based on curvature-adaptive SVR, confidence-weighted fusion, and user classification can be sent to the O-RU for synchronised future-slot updates over eCPRI.
* Slot-to-Slot Update Capability offers beam weight updates at each TTI based on the most recent DE-ACSW-R prediction or MUSIC result, maintaining a rapid response in mobile scenarios.
# A Virtualization Infrastructure Cost Model for 5G Network Slice Provisioning in a Smart Factory
## 4 Results and Discussion
### Cost Calculation and Breakdown
The MNOs, slice providers, virtualization infrastructure providers, or a joint venture between multiple stakeholders and factories can be responsible for the TCO in scenario A and the CP-related infrastructure in scenario B. The factories themselves are most suitable for the TCO in scenario C and the UP part in scenario B. Alternatively, the UP part in scenario B can be deployed by an MNO, a slice provider of virtualization infrastructure in an edge cloud strategically located near the factory premises. The cost allocation in scenario A and B can be based on the business model exercised. For example, if a slice provider provides services to a factory by using a virtualization infrastructure deployed by the MNO, then the slice provider charges the factory for the services provided, and the MNO charges the slice provider for the resources used. Alternatively, a large virtualization infrastructure provider can be responsible for the owning and operating the data centers, and offer different options as a service to MNOs, slice providers, factories, and application developers through infrastructure as well as function as a service model. Additionally, the costs can change significantly based on the number of devices, factories, and the device growth. Likewise, there can be significant differences arising from physical location costs; a fully centralized scenario requires one large data center for multiple factories, while a fully local scenario requires smaller data centers in each factory. A fully centralized deployment can offer higher scalability for a larger customer base with significant cost savings. A fully local scenario is expensive but can offer better latency and reliability. Cost savings and technical benefits can be optimized by selecting the optimum deployment scenario for each case. The results above are calculated for one specified scenario of input requirements per slice type and can change for different inputs as dictated by each case. Furthermore, there can be a different level of isolation instead of the partial isolation assumed in the calculation when some VNFs are dedicated to each slice, while some are common between slices. These uncertainties are tackled in the following sensitivity analysis.
## 3 Fused Filament Fabrication  
### Overview of the FFF Process  
FFF is one of the most widely adopted extrusion-based additive manufacturing techniques, known for its affordability, accessibility, and material flexibility [17]. The process involves feeding a thermoplastic filament through an extruder, where it is heated, melted, and deposited layer by layer onto a build plate. A schematic of this setup is shown in Figure 4. This material deposition follows a digitally defined toolpath to construct three-dimensional parts with varying complexity. A computer-controlled motion system ensures the precise coordination of extrusion and movement, enabling consistent geometric accuracy.  
The quality and mechanical performance of printed parts depend on process parameters, including extrusion temperature, layer height, print speed, raster orientation, and infill density. These variables directly influence outcomes such as interlayer bonding, dimensional accuracy, thermal gradients, and final part strength [18]. Commonly used FFF thermoplastics include Polylactic Acid (PLA), Acrylonitrile Butadiene Styrene (ABS), Polyethylene Terephthalate Glycol (PETG), and Nylon, each with distinct thermal and mechanical profiles [19]. In addition to these materials, recent studies have highlighted the growing relevance of semi-crystalline polymers such as polypropylene (PP), especially due to their attractive mechanical and chemical properties. However, their application in FFF presents distinct challenges, including high crystallization-induced shrinkage, reduced  
Figure 3: Density of co-occurring keywords in FFF simulation research (self-developed based on Scopus).  
adhesion to the build surface, and a strong dependence of final properties on thermal gradients. To address these limitations, current research has proposed strategies such as optimizing the thermal environment, applying surface treatments, and incorporating crystallization kinetics into numerical simulation models [20; 21].  
To accurately simulate or optimize this process, it is necessary to analyze the FFF workflow in discrete stages. Each phase involves distinct thermal, mechanical, and rheological phenomena that directly influence material behavior and part quality. In addition to polymer-based applications, FFF has also found widespread use in metal casting through the Lost-PLA technique. In this approach, printed PLA parts serve as sacrificial patterns in investment casting, enabling the production of intricate lattice and cellular structures that would be challenging to fabricate using conventional methods. Recent studies have demonstrated the effectiveness of this method for creating complex metallic components in aerospace and structural engineering contexts [22; 23]. Owing to its low cost and geometric flexibility, FFF is increasingly employed for prototyping and small-batch production in foundry environments.
## 3 State-of-the-Art Review on Energy Optimization Techniques in UIoT
### Wireless Power Transfer Techniques for UIoT
#### 3.1.2 Underwater Optical Wireless Power Transfer (Uo-WPT)  
The existing UO-WPT techniques are discussed in [162; 163; 164; 165; 166; 167; 168; 169; 170; 171; 172]. The UO-WPT is the subclause that originated from optical power transfer, it was first proposed for solar-based communication for satellites [162]. The concept of UO-WPT is similar to the idea of microwaves in the terrestrial environment. In addition, optical power transmission in a terrestrial environment deals with high-power transmission and long-distance communication. However, due to attenuation and scattering in UIoT environments, the energy  
Figure 5: (a) Design of AUV self-charging station. (b) Types of power transfer methods in UIoT networks.  
consumption is high and transmission coverage is low. In the case of UO-WPT, the optical power transmitter can be installed in UIoT devices that are capable of absorbing and transferring light waves to low-powered UIoT devices. Furthermore, compared to other underwater power transfer technologies, the UO-WPT can transfer high-power light waves to nearby sensor nodes. This can help to recharge the batteries of sensor nodes in UIoT networks.
###### Abstract  
In mobile ad hoc networks (MANETs), geographical routing provides a robust and scalable solution for the randomly distributed and unrestricted movement of nodes. Each node broadcasts beacon packets periodically to exchange its position with neighboring nodes. However, reliable beacons can negatively affect routing performance in dynamic environments, particularly when there is a sudden and rapid change in the nodes' mobility. Therefore, this paper suggests an improved Greedy Perimeter Stateless Routing Protocol, namely AFB-GPSR, to reduce routing overhead and increase network reliability by maintaining correct route selection. To this end, an adaptive beaconing strategy based on a fuzzy logic scheme (AFB) is utilized to choose more optimal routes for data forwarding. Instead of constant periodic beaconing, the AFB strategy can dynamically adjust beacon interval time with the variation of three network parameters: node speed, one-hop neighbors' density, and link quality of nodes. The routing evaluation of the proposed protocol is carried out using OMNeT++ simulation experiments. The results show that the AFB strategy within the GPSR protocol can effectively reduce the routing overhead and improve the packet-delivery ratio, throughput, average end-to-end delay, and normalized routing load as compared to traditional routing protocols (AODV and GPSR with fixed beaconing). An enhancement of the packet-delivery ratio of up to 14% is achieved, and the routing cost is reduced by 35%. Moreover, the AFB-GPSR protocol exhibits good performance versus the state-of-the-art protocols in MANET.  
adaptive beaconing: geographical routing; AFB-GPSR; fuzzy logic; MANET; OMNeT++
## 5 Results and Discussions
### PA Velocity Measurement  
In the PA velocity measurement, the detected time delay \(t_{p}\) was obtained as \(t_{p}=t_{2}-t_{1}=1.0\) ms using the PD. The peak of the CC coefficient acquired was determined at a time shift of \(t_{s}=0.14\) us. Using Equation (2), the velocity of the object oscillated by the LS was calculated as  
\[v=\frac{t_{s}c}{t_{p}\cos\theta}=\frac{1.5\times 0.14}{1.0\times 10^{-3}\times \cos 30^{\circ}}=241\text{ mm/s}. \tag{8}\]  
Figure 5 illustrates the results of the velocities obtained from the PA measurements ranging from \(-300\) to \(300\) mm/s. The black dots represent velocities measured at \(5^{\circ}\) intervals in the phase difference \(\varphi\). The green line indicates the moving average of the PA measurements. The maximum velocity \(v=316\) mm/s was recorded at \(\varphi=10^{\circ}\), corresponding to a time of 5.6 ms. The negative maximum velocity \(v=-292\) mm/s was obtained at \(\varphi=190^{\circ}\), which corresponds to a time of 0.11 s.  
The object oscillates sinusoidally at 5 Hz and completes one period every 0.20 s. The input sinusoidal signal undergoes a phase reversal every 180\({}^{\circ}\), which corresponds to a time of 0.10 s. The time difference between the two maximum velocities was 0.10 s, which is equivalent to half of a sinusoidal oscillation period. There is folding back of the LS oscillation at 0.055 s, resulting in significant velocity changes due to arm bending. The results indicate that the phase-resolved object motion is captured in the range of \(-300\) to \(300\) mm/s at a frequency of 5 Hz.