esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
The digital factory is a crucial enabler for Industry 4.0 to continuously innovate to create new revenue streams, reduce costs, increase efficiency, and build eco-friendly industries. The integration of a private 5G network with sensor networks represents a significant advancement in wireless communication, offering new opportunities for various applications. The solution proposed in this research achieves interoperability between YANG and OPC UA, the two significant technologies utilized in network management and inter-device communications. A substantial contribution of this work is that it offers thorough reasoning for the defined mapping through a semantic analysis of the involved elements and the development of a pyang plugin to support interoperability. The OPC UA pyang plugin has been released in the GitHub repository and is freely available. The proposed mapping method in the research allows YANG data model-based network devices to operate independently without the need for modification, making it easy to integrate them with any OPC UA industrial applications for network management. This research focuses on transforming the YANG data model to an OPC UA design model file. It is also feasible to go in the opposite direction from the OPC UA to the YANG data model. However, this would require the development of an extensive metadata model covering all OPC UA nodes. The authors are currently developing a YANG/NETCONF to OPC UA adapter to ensure interoperability at runtime. This adapter will support the NETCONF protocol for communication with devices on the southbound side and the OPC UA protocol for communication with OPC UA applications on the northbound side. This enables seamless interoperability without the need for modifications to either the network devices or OPC UA client applications. This research is original, as no other contributions are present in the current literature on the same subject, and it paves the way for the widespread adoption of 5G sensor networks in industrial automation.  
Conceptualization, D.S. and D.T.; methodology, D.S.; software, D.S.; validation, D.S.; formal analysis, D.S.; writing--original draft preparation, D.S. and D.T.; writing--review and editing, D.S. and D.T. All authors have read and agreed to the published version of the manuscript.  
This research received no funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
**Abbreviations**  
**Abbreviation**  
**Description**  
3 GPP 3 rd-Generation Partnership Project  
5G  Fifth-generation mobile networks  
5G-ACIA 5G Alliance for Connected Industries and Automation  
5 GC 5G Core  
API  Application programming interface  
AutomationML  Automation Markup Language  
CAGR  Compound annual growth rate  
CIM  Common Information Model  
CIO  Chief information officer  
CU  Centralized unit  
DTDL  Digital Twin Definitions Language
# Single-Pixel Imaging Based on Enhanced Multi-Network Prior
## 4 Result and Discussion  
In order to verify the effectiveness of EMNP-SPI, we conducted both simulation and practical experiments. The generalization of the proposed method cannot be verified using only one dataset. In this section, we train the EMNP with the flower dataset and natural image dataset (set91 and set5), respectively. Since our method aims to explore the reconstruction accuracy of the image pixel intensity, in order to evaluate the experimental results more intuitively and also to facilitate the comparison with existing methods, we preprocess the images in these datasets, converting the color images into grayscale images for training and testing. In addition, we also performed an ablation experiment, using different structures to reconstruct images at the same MR, proving the necessity of the combination of SAE and Unet.
###### Abstract  
Mobile ad hoc networks (MANETs) are autonomous systems composed of multiple mobile nodes that communicate wirelessly without relying on any pre-established infrastructure. These networks operate in highly dynamic environments, which can compromise their ability to guarantee consistent link lifetimes, security, reliability, and overall stability. Factors such as mobility, energy availability, and security critically influence network performance. Consequently, the selection of paths and relay nodes that ensure stability, security, and extended network lifetimes is fundamental in designing routing protocols for MANETs. This selection is pivotal in maintaining robust network operations and optimizing communication efficiency. This paper introduces a sophisticated algorithm for selecting multipoint relays (MPRs) in MANETs, addressing the challenges posed by node mobility, energy constraints, and security vulnerabilities. By employing a multicriteria-weighted technique that assesses the mobility, energy levels, and trustworthiness of mobile nodes, the proposed approach enhances network stability, reachability, and longevity. The enhanced algorithm is integrated into the Optimized Link State Routing Protocol (OLSR) and validated through NS3 simulations, using the Random Waypoint and ManhattanGrid mobility models. The results indicate superior performance of the enhanced algorithm over traditional OLSR, particularly in terms of packet delivery, delay reduction, and throughput in dynamic network conditions. This study not only advances the design of routing protocols for MANETs but also significantly contributes to the development of robust communication frameworks within the realm of smart mobile communications.  
mobile ad hoc networks; optimized link state routing protocol; multipoint relays selection; mobility; security; energy; NS3; IoT +
Footnote †: journal: Information  
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# Multi-Gateway-Based Energy Holes Avoidance Routing Protocol for WSN
## 3 Node Deployment Model  
We have considered various scenarios of the MGBEHA protocol according to the size of the area; e.g., one with two gateway nodes and another with four gateway nodes as shown in Figure 1a,b. Figure 2a-c shows scenarios with 6 GW, 10 GW, and 20 GW. There are four types of nodes in each scenario; normal sensor nodes, cluster head, cluster members, and the gateway node. In the beginning, other than gateway nodes, all the nodes are normal, but after the clustering process, some nodes are selected as the cluster heads and some as the cluster members. If the node is a cluster member,it consumes power in transmission only, while if the node is a cluster head, it consumes power in transmission, receiving, and aggregating the received data. Basic assumptions are as follows:  
* The Surveillance field is a two dimensional area.
* Total N homogeneous sensor nodes equipped with equal initial energy have been deployed randomly and uniformly in the area. All nodes are assigned with a unique identifier (ID) and these are capable of information collection. The sink node is at the center of the area and surrounded by a predefined area.
* One gateway node is deployed between every two regions and it is surrounded by a pre-specified area.
* Sensor nodes, sink node, and gateway nodes are stationary after deployment.
* Sink node and gateway nodes have an unconstrained power supply.
* Location information is accessible to all sensors and gateway nodes either through an embedded GPS device or through some location-finding algorithm.  
Figure 1: MATLAB view of the MGBEHA protocol with (**a**) two gateways; and (**b**) four gateways.
# Design and Simulation of Electromagnetic Linear Actuators for Jet Dispensers
## 6 Conclusions  
This study has utilized the finite element method (FEM) simulation approach to design, modify and characterize the performance of three electromagnetic actuators (namely a solenoid actuator (SA), a moving coil actuator (MCA) and a moving magnet actuator (MMA)) for jet dispensing applications. The total actuation force required to drive the needle in the jet dispenser has been determined using an experimental model. FEM simulations have been performed to modify the geometries of the three proposal actuators in such a way as to achieve the thrust force required to actuate the needle in the jet dispenser with the minimum excitation current possible. The average percentage improvements of the thrust force for the SA, MCA, and MMA after modification are about 27.5%, 36%, and 48%, respectively. Comparing the three actuator mechanisms, the SA generates a high thrust force in the initial position. However, the thrust force drops by approximately 61% when the plunger moves through a stroke length of 0.5 mm. The thrust force drop produced by the MCA and MMA models is far lower; with drops of just 0.7% and 1.8%, respectively, as the moving parts (i.e., the coil and magnet) move through the same stroke distance of 0.5 mm.  
In the absence of a return spring, the excitation current required to achieve the necessary thrust force is equal to 1.8 A and 1.9 A for the MCA and MMA models, respectively. When a return spring is optionally fitted to the actuator mechanism, the minimum excitation current increases to 2.2 A, 3.8 A, and 4.1 A for the SA, MCA, and MMA models, respectively. In the two cases without and with a return spring, MCA and SA are the best choices for jet dispenser applications, respectively. Based on the modified simulation models, three preliminary designs for jet dispenser structures integrated EMAs have been proposed for reference purposes. In future research, an experimental model consisting of both the proposed actuators and the EMAs-based jet dispenser will be fabricated in order to verify the accuracy of the simulation results and serve as the basis for further modifications and improvements.  
Methodology, M.-S.T. and S.-J.H.; investigation, M.-S.T.; formal analysis, M.-S.T.; validation, S.-J.H.; writing--original draft preparation, M.-S.T.; writing--review and editing, M.-S.T. and S.-J.H.; and supervision, S.-J.H. All authors have read and agreed to the published version of the manuscript.  
This research received no extend funding.  
The authors declare no conflict of interest.  
Figure 16: Dispenser configuration of jet dispensers based on proposed actuators.
## 3 Impact of Rainfall on Slope Stability
### Analysis of Calculation Results  
Through finite element calculation, the saturation distribution and pore water pressure distribution of the slope before and after rainfall are shown in Figures 6 and 7, and the rainfall flow velocity distribution at the beginning and end of rainfall is shown in Figure 8.  
According to the above analysis results:  
1. Considering that the pore water pressure distribution after rainfall infiltration differs from the initial state, the suction area below the top of the slope decreases, and the matrix suction also decreases. Comparing the results at different times, it can be found that with the extension of rainfall time, the saturation increases, the pore water pressure increases, and the matrix suction in shallow soil decreases or disappears. After rainfall decreases or stops, saturation decreases gradually with the extension of time, saturation decreases, pore water pressure drops, and matrix suction in the shallow soil layer increases slowly.
2. The maximum horizontal displacement occurs at the slope toe, and the full settlement occurs in the middle of the soil slope. This is because, after rainfall infiltration, suction decreases. The pore pressure increases, the effective stress decreases, and the unloading rebound phenomenon occurs. On the other hand, with rainfall infiltration, soil moisture content and unit weight will increase, leading to heave and stress development. The slope tends to cause sliding deformation due to rainfall infiltration, so it can be considered that slope stability is reduced.
3. As the rainfall intensity changes according to the law in Figure 2, it first increases from 0 to the maximum value and then gradually decreases to 0. Therefore, at the beginning of rainfall, rainwater infiltrates downward through the slope and slope top. As the rainfall intensity gradually decreases to 0, the overflow phenomenon occurs at the slope toe, and the flow velocity is largest at the middle elevation inside the slope.
4. For the slope toe element, the pore water pressure increases, and the effective average stress decreases under the action of rainfall infiltration. The effective stress path reaches the yield surface when it drops to a certain extent. Currently, the stress path moves to the left and down along the yield surface (Mohr-Coulomb strength envelope) until the rainfall gradually decreases, the suction increases, the pore water pressure drops, and the effective stress increases gradually shifts to the yield surface. For the internal unit of the soil slope, the unit weight above the unit increases after water absorption, increasing the average effective stress and partial stress. Until the later stage of rainfall, adequate moderate pressure and partial stress decrease near the end of the rains. This also confirms the view of some scholars that instability may occur mainly in the shallow layer of the slope under the action of rainfall infiltration [18; 19].
## 3 Experimental Results and Discussion
### Load-Deflection Curves  
Figure 7 shows the load-deflection curves of each measuring point for pre-cracking at mid-span. As Figure 7 shows, the stiffness of the pre-cracked test beam is reduced to different degrees, especially at the mid-span position. The load-deflection curves at the \(1/4\) and \(3/4\) measuring points are approximately symmetrical. When the prefabricated crack depth in the mid-span is small (20 mm), the load-deflection curves at the \(1/4\) and \(3/4\) measuring points of the ZSS1 beam are similar to that of the WS beam. The load-deflection curves of the ZSS2 and ZSS3 beams are significantly lower than that of the  
Figure 5: Crack-development distribution of test beams under hydraulic graded loading. (**a**) WS beam; (**b**) ZSS1 beam; (**c**) ZSS2 beam; (**d**) ZSS3 beam; (**e**) DSS1 beam; (**f**) DSS2 beam; (**g**) DSS3 beam.  
Figure 6: The failure mode of test beams. (**a**) Roof concrete crushing; (**b**) Main crack in mid-span.  
WS beam when the prefabricated crack depth in mid-span is larger (i.e., 60 mm), which indicates that the depth of the mid-span crack has significant influence on the section stiffness of the test beam. In addition, the load-deflection curves of beams ZSS2 and ZSS3 at each measuring point show a similar trend compared with the ZSS1 beam, and obviously lower than the ZSS1 beam. The results show that section damage significantly affects the stiffness of the test beam, while the local damage of the tensile steel bar section has little effect on the overall stiffness of the test beam.  
Figure 8 shows the load-deflection curves of each measuring point for pre-cracking at the \(l/4\) position. As Figure 8 shows, the stiffness degradation of the test beam after pre-cracking at the \(l/4\) position is not obvious. The load-deflection curve at each measuring point is in good agreement with that of the WS beam. Similar to beams ZSS1 to ZSS3, the load-deflection curves of beams DSS2 and DSS3 in the latter-half stage are significantly lower than those of WS beams when the prefabricated crack depth is larger (e.g., 60 mm), which indicates that the larger prefabricated crack depth at the \(l/4\) position also affects the overall stiffness of the test beam.
# Fibers by Electrospinning and Their Emerging Applications in Bone Tissue Engineering
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Method** & **Advantage** & **Ref.** \\ \hline \multirow{6}{*}{Fiber composition} & \multirow{3}{*}{Coaxial electrospinningning} & Beneficial for materials without ability to form fibers & \multirow{3}{*}{[11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22]} & \multirow{3}{*}{[23]} \\  & & as core; providing fibers abilities of sustained & \\  & & delivery; enabling surface functionalization without & & \\  & & affecting the core material. & & \\  & & Fiber blending & Fiber with the properties of two separate polymers; & \\  & & enhancing both mechanical and bioactive properties. & & \\  & & Fabricating porous fibers to enhance mesh porosity; & & \\  & & Emulsion electrospinning & retaining bioactivity and promoting sustained & & [24; 25; 26; 27] \\  & & release by encapsulating enzymes, growth factors, & & \\  & & Edge electrospinning & Enhancing collection efficiency by changing jet sites. & [28] \\ \hline \multirow{6}{*}{Fiber orientation} & \multirow{3}{*}{Rotating mandrel alignment} & With increasing rotational speed, the modulus and tensile stress of fibers in the direction of alignment & \multirow{3}{*}{[11; 13; 22; 27; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39]} & \\  & & Gap electrospinning & Producing highly aligned fibers; fabricating complex & \\  & & architecture by modulating electrode configuration. & & [40] \\  & & Leading to a reduction in branching; maintaining & & [41] \\  & & & alignment over time. & \\  & & Controlling deposition of electrospun fibers to & & \\  & & Pattern spinneret & produce specific architecture for generating specific & \\  & & cellular distributions and orientation. & & \\  & & H\(\ddot{\text{o}}\)wing spinneret & Enhancing flexibility through curly or wavy & \\  & & architectures. & & \\ \hline \multirow{6}{*}{Mesh architecture and composition} & \multirow{3}{*}{Co-electrospinning} & Controlling the mechanical properties and cellular & \multirow{3}{*}{[44; 45; 46]} \\  & & behavior by altering the weight ratio of each & & \\ \cline{1-1}  & & Layer-by-layer hydrospinning & & \\ \cline{1-1}  & & 3D electrospinning & Fabricating controlled specific 3D architecture. & [47; 48] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Examples of electrospinning methods. In this paper, different kinds of electrospinningning methods and main polymers recently employed in electrospinningning were summarized. Then, we reviewed the emerging application of electrospun nanofibers in BTE, focusing on three aspects including ECM simulation, mechanical support, and drug delivery system.
## 3 The Proposed Method
### CNN Predictor
#### 3.1.3 Training  
The proposed CNN-based predictor is trained using 10,000 randomly selected images from Kaggle. And all images were converted to 8-bits grayscale with a size of 512 \(\times\) 512 for training. The input and target are used, respectively, as the \"inner square\" and \"outer square\". For optimization, the Adam parameter is used for back-propagation and optimizer. Batch size is 4 and training loss function is defined by Equation (8):  
\[loss=\frac{1}{n}\sum_{i=1}^{n}\bigl{(}\overline{I_{o}}-I_{o}\bigr{)}^{2}+ \lambda\,||\omega\,||_{2}^{2} \tag{8}\]  
where \(n\) is number of training data, \(\overline{I}_{o}\) is the output image, \(I_{o}\) is the target image, \(\lambda\) is weight decay, and \(\omega\) denotes all the weight in the network.  
In this paper, the proposed predictor was tested on Google Colab pro T4 GPU with 51 GB RAM. To apply this algorithm, a predictor model (h5 file) generated from the training process is required, and the size of this model is 940 KB. When performing the entire embedding and extraction process, the memory usage is approximately 3900 MiB. This relatively high memory consumption is due to the use of a deep learning model.
## 3 Results  
### Characterization of Nanoemulsions  
#### 3.1.1 Particle Size and Homogeneity of Emulsions  
Particle size and size distribution of emulsions are important parameters to consider because of their effect on the activity and stability of loaded compounds and their release [22]. The average particle size and the polydispersity index (PDI) of the tested emulsions are shown in Table 2. Whatever the pH value, the emulsions stabilized using lecithin had a smaller particle size compared to those stabilized using pea protein. Lecithin resulted in an average size of about 0.11 \(\upmu\)m and 0.09 \(\upmu\)m at pH 3 and 7, respectively, whereas the average size of pea protein emulsions was 0.22 \(\upmu\)m at pH 3 and 0.62 \(\upmu\)m at pH 7. The smaller particle size of emulsions stabilized using lecithin could be attributed to the high efficiency of lecithin as an amphiphilic molecule to form water-in-oil (W/O) emulsions [23]. Furthermore, the same concentration of lecithin and pea protein was used in each emulsion. However, pea protein contains a non-soluble portion with no emulsifying effect that was separated before the emulsion was prepared. This reduced the final concentration of pea protein as an emulsifying agent compared to emulsions made with lecithin. Similar results were obtained by [24], who studied the effect of the emulsifier and obtained the smallest particle with lecithin compared to pea protein. A slight difference in particle size was observed depending on the pH. Among the four tested emulsions, the smallest particle size (0.09 \(\upmu\)m) was obtained with lecithin at pH 7. For emulsions prepared with pea protein, the smallest particle size (0.22 \(\upmu\)m) was obtained at pH 3, and particle size at pH 7 was significantly bigger (0.62 \(\upmu\)m). In fact, the pH could be decreased during the dispersion of TC to approach the isoelectric point of pea protein (PI = 4.3), leading to droplet aggregation and an increase in the measured size. Indeed, despite the pH adjustment to 7 after emulsification, the droplets might not completely dissociate [25].