esa-sceva/satcom-chunk-collection huggingface dataset:
# FANET Routing Protocol Analysis for Multi-UAV-Based Reconnaissance Mobility Models
###### Abstract  
Different from mobile ad hoc networks (MANETs) and vehicular ad hoc networks (VANETs), a flying ad hoc network (FANET) is a very low-density network where node topology changes rapidly and irregularly. These characteristics, the density, mobility, and speed of flight nodes, affect the performance of FANET. Furthermore, application scenarios and environmental settings could affect the performance of FANETs. In this paper, we analyzed the representative FANET protocols, AODV, DSDV, and OLSR, according to mobility models, SRWP, MP, RDPZ, EGM, and DPR, under the multi-UAV-based reconnaissance scenario. We evaluated them in terms of the number of nodes, network connectivity, mobility model's reconnaissance rate, speed of nodes, and ground control station (GCS) location. As a result, we found that AODV showed the highest PDR performance (81%) with SRWP in multiple UAV-based reconnaissance scenarios. As for a mobility model under the consideration of reconnaissance rate, SRWP was excellent at 76%, and RDPZ and EGM mobility models were reasonable at 62% and 60%, respectively. We also made several interesting observations such as how when the number of nodes increases, the connectivity of the network increases, but the performance of the routing protocol decreases, and how the GCS location affects the PDR performance of the combination of routing protocols and mobility models.  
FANET; routing protocol; NS-3; multi-UAVs; reconnaissance; mobility models +
Footnote †: journal: Advances in Mobile Networks
## 4 Performance Evaluation
### Performance Evaluation
#### 4.4.2 End-To-End Delay of Routing Protocols  
Next, we look at the E2E simulation results of the three protocols as shown in Figure 5(a). The OSPF routing algorithm typically aims for a low routing delay by selecting the shortest path which is expected to perform well under normal conditions; however, the results state otherwise. From the results, it can be seen that the OSPF has the worst performance. This is due to the fact that most of the initial routes selected by the OSPF algorithm do not change and are subject to being overused and fail to respond to real-time network changes. As a result, the RNs along such paths receive a larger amount of traffic than they can handle and process in a minimum time. The DOS-RL algorithm outperforms the SDN-Q not only due to its dynamic adaptability to changes, but because it can intelligently route traffic while considering different objectives such as prioritizing latency-sensitive states, optimizing energy efficiency or maximizing reliability, and making decisions to meet the specific needs of the network at a given time. Unlike the DOS-RL, traditional Q-routing is subjected to struggle to adapt to the dynamic changes in the network environment or to optimize objectives beyond what is initially defined. While the number of the RNs was varied, as shown in Figure 5(b), the performance of all schemes in terms of delivery delay falls progressively. As more nodes are added to the network, the search space and hence path length increases and this affects the performance of the networks, especially during the early exploration stages for the SDN-Q and DOS-RL algorithms. However, our proposed scheme adjusts well by adjusting learning objectives in real-time. The SDN-Q fails to perform well because the paths it selects last longer and hence, increase the probability for congested paths to occur.
## 6 Application  
The material's superior transmission efficiency and minimal reflection losses make it ideal for antenna substrates, enhancing signal clarity and strength in wireless communication systems. It is also applicable in electromagnetic shielding, protecting sensitive electronic equipment from interference and improving overall device performance and reliability. Additionally, its configurable band gap optimizes sunlight absorption in solar cells, making it suitable for advanced photovoltaic systems. The material's ability to effectively interact with electromagnetic waves enables its use in sensors for detecting various signals, supporting IoT and cutting-edge technological applications. Moreover, its  
Figure 16: S21 transmission coefficient (**a**) with block; (**b**) without block.  
high UV absorption capacity makes it valuable for creating UV sensors and devices for environmental monitoring and safety applications.
# Estimation of Age in Humans Using Dental Translucency of Permanent Teeh: An Experimental Study
## 2 Materials and Methods  
### Sample  
In this study, we investigated a sample of 155 permanent teeth which were extracted for therapeutic needs (in 2015-2019). The teeth have been stored in the refrigerator and were not modified or processed in any way after the extraction. The initial sample size was 174 teeth, but 19 of them were excluded from the sample for the impossibility of measuring Root Height (RH) and/or Root Height Translucency (RHT). The sample, in the age range 18-84 years, included both males (n = 63) and females (n = 92). While respecting anonymity, data on the person's sex and age were collected at the time of dental extraction. In addition, we provided a numerical code for each tooth specimen. Having decided not to give any preference regarding tooth type, we used both multi-rooted and single-rooted teeth for this study [1; 19]. As single-rooted, we considered: incisors, canines, inferior premolars, and the second superior premolars. As multi-rooted, we considered: all the molars and the first superior premolars. Teeth with root caries or with any alteration in the cervical apex (ankylosis or reabsorption) were excluded.
## 2 Design of Detector Parameters  
### Detector Operation  
The developed XS detector consists of a Kovar-alumina brazed body assembly containing MCP stacks, resistive layer, and XS anode. The schematic diagram of XS detector operation is depicted in Figure 1. First, the incident particle is converted into an electron cloud of 10\({}^{6}\)-10\({}^{7}\) electrons by the MCP stack. Then, these electrons are accelerated by the electric field to the resistive layer, and induced charge pulses are generated on the XS anode strips because of the change of the electromagnetic field. Finally, the position of incident photons can be inversed by calculating the charge collected by the anode. The MCP stack is formed by using two MCPs with one placed on top of another with a bias angle of 13\({}^{\circ}\). The resistive layer is a ceramic substrate coated with high-resistance germanium film; a \(-\)200 V is applied across the MCP-to-resistive layer gap by using a resistor mounted on the external wall of the brazed body.  
Figure 1: Schematic diagram of XS detector operation.
## 5 Simulation Study
### Key Findings  
The simulation results presented in Section 5.3 are summarised in Table 5. For each application traffic type, the table shows the reliability and delay performance for each link metric version. The first set of results obtained are from a 25-node grid cluster NAN topology, which analyses the PDR of each application traffic type. The average PDR and average end-to-end delay of all applications in the 25-node grid cluster scenario is presented.  
Figure 14: Average end-to-end delay for SG application on grid clusters with a central placement of the data concentrator.  
Figure 15: Average end-to-end delay for SG application on grid clusters with the data concentrator placed at the top corner.  
The following observations can be drawn from Table 5:  
1. The routing decision made by OLSR-MM provided the highest PDR for reliability and delay-sensitive application traffic, such as EV and DR.
2. Similarly, for power quality and periodic smart metering traffic types, the PDR for \(OLSR_{MM}\) was highest.
3. The average end-to-end delay was also improved in the case of \(OLSR_{MM}\), compared with other OLSR versions.  
Additional WMN-based NAN scenarios were explored by using two other network topologies to evaluate the performance of the proposed \(OLSR_{MM}\). The topologies evaluated are:  
* Different grid cluster topologies with 25, 36, 49 and 64 smart meter nodes.
* Data concentrator placement, at the centre of the grid and at the top right corner of the grid.  
The results show that data concentrator placement at the centre performed better in all grid clusters than data concentrator placement at the top right corner. This indicates that in future NAN deployments, placing the data concentrator closer to the smart meters will improve the network reliability and delay. However, there was no significant difference in the performance of \(OLSR_{MM}\) in both scenarios. From the results presented in this paper, the \(OLSR_{MM}\) outperformed the other OLSR versions. The proposed protocol can find a route that best improves the reliability of all of the application traffic types transmitted to the data concentrator simultaneously. The multipath routing provided by \(OLSR_{MM}\) using multiple metrics and AHP has increased the PDR and reduced the delay of all of the application traffic types, compared with single-metric OLSR versions. Consequently, the multiple metrics routing approach can be used to provide QoS routing for different SG traffic types. All of the application traffic types transmitted in the simulation used a required parameter provided from the link metrics to make decisions on their path to the data concentrator. The simulation results have shown that with the use of multiple metrics, the QoS requirements of each traffic type can be better satisfied. It is also important to state that the use of multiple metrics is not only limited to the routing protocol and link metric types used in our simulations.
## 5 Conclusions and Future Work  
In this study, we aimed to address the segmentation problems in transparent images by cropping the image into patches and classifying their foreground and background. We used CNNs and ViT deep learning methods to compare the patch-level and pixel-level performances of the transparent image segmentation. In segmenting transparent microorganism images, we found that the pixel-level generally outperforms the patch-level segmentation. However, the patch-level method works better in multiobject segmentation. Moreover, in the patch-level segmentation experiment, CNNs were better than the ViT models, but in the pixel-level experiment, the ViT model segmentation performed better than that of most CNNs. The smaller the patch pixel is, the more the regions perceived by the ViT model, and the stronger the ability to combine contextual information. Furthermore,the loss convergence and stability of the ViT model during training were better than those of the CNN model. In conclusion, the CNN and ViT models have more advantages in image classification. CNN is better at extracting the local features of images, whereas ViT is better at extracting the global features of images combined with contextual information. The ViT model has great potential for the future.  
In the future, we plan to increase the amount of data to improve the stability of the comparisons. Meanwhile, images reconstructed by deep learning classification can be extended to the positioning, recognition, and detection of transparent images. We will further strengthen the applicability of these results.  
Conceptualization, C.L.; methodology, H.Y. and C.L.; software, H.Y.; validation, P.Z., A.C. and H.Y.; formal analysis, Y.T. and H.Y.; investigation, M.G. and T.J.; resources, X.Z.; data curation, C.L. and H.Y.; writing--original draft preparation, H.Y. and C.L.; writing--review and editing, C.L., J.Z., S.Q. and H.Y.; visualization, H.Y.; supervision, C.L.; project administration, C.L.; funding acquisition, C.L. and T.J. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
We thank [PERSON] and [PERSON] for their important discussion.  
The authors declare no conflict of interest.
## 3 Materials and Methods
### Satellite Network Architecture
#### 3.2.2 Space Segment  
The space segment represents the satellite communication layer within the network and consists of \(MEO\) and \(LEO\) satellites (Figure 2). \(MEO\) satellites transmit high-precision positioning and timing data to GNSS user terminals using GPS L1 and L2 signals. Upon reception, the \(GNSS\) converts these signals to the NMEA format and delivers the data to users' devices. Additionally, \(MEO\) satellites exchange communication data, including GNSS information, with \(GW_{2}\). Through this connection, \(MEO\) satellites relay the GNSS data to the ground network and process the command data received from ground stations for satellite operations. \(LEO\) satellites primarily function as data relays, collecting information from the \(IoT\) and \(UAV\) and transmitting it to the ground. The data are first transferred to \(GW_{1}\) via the MIOTY and subsequently sent to the _GCS_ via the CCSDS.  
In satellite networks, inter-satellite links (ISLs) are typically used for communication between satellites, primarily employing radio-frequency (RF) or laser communication methods. However, this study focuses on security vulnerability analysis and threat modeling to assess the security of satellite networks, with a particular emphasis on threat analysis at the node and protocol levels.
## 4 Experiments
### Qualitative Results  
Since we aim to provide the interpretable representation, we measure how much the representation describes the decision of the target model given the input well. Therefore, we remove the most representative region searched by our work as well as LIMESegment, the baseline. Then, we compute the difference of the logit. The experiment is based on our assumption as follows. A modified input that is removed from the most representative region is given to the target model. Then, we assume that the logit will be significantly changed.  
We remove 25% of the most representative region according to each method and calculate the difference of the logit between the modified input and associated original input. For example, let a signal of wakefulness given to the target model produce the logit 0.85. If we remove 25% of the most representative region in the signal and it is given to the target model, the logit will be decreased to some value such as 0.35. Then, the difference of the logit is 0.5. In other words, the representative region provides probable decision criteria of the target model given the signal. We list the mean of the difference for the sleep stages, which are wakefulness, non-REM 1-3 and REM in Table 1. In the table, the five sleep stages are annotated as \"W\", \"N1\", \"N2\", \"N3\" and \"REM\". In the table, the mean of the difference of logits shows that our work finds a more representative region than the baseline.  
Subsequently, we exploit \(t\)-test for each class, which compares the means of two groups and whether they are significantly different or not. We set the null hypothesis and the alternative hypothesis for the test as below.  
* **Null hypothesis**: The representations obtained by ours and LIMESegment are same.
* **Alternative hypothesis:** The representation obtained by ours is more critical for the model decision than the baseline.  
Because the standard deviations are not same between ours and the baseline, we apply [PERSON]'s \(t\)-test and set alpha 10%. Finally, we obtain \(p\)-values for each sleep stage less than \(1\times 10^{-5}\). Therefore, we reject the null hypothesis and statistically prove that our method is significantly critical to describe the model decision.
## 5 Opportunities and Alternatives  
### Some General Applications  
Nowadays, unmanned aircraft vehicles are present in many human activities, helping in difficult situations [74; 75], bringing innovative ideas [76] and new possibilities in many other industry applications [77]. UASs have the ability to do things that were not possible before [78]. With their help, we can reach places where piloted aircraft cannot [79], and even futuristic applications become reality [80]. Two technologies are mixed in [81], using a facial recognition UAS with the aim of helping reduce violence and crime in Latin America. Researchers used a handmade UAV and a commercial embedded system board, based on a convolutional neural network to build a database implemented on the board. Furthermore, research has been carried out to achieve a forest inventory [82], and maximum power tracking for UAVs [83].  
Electrical mobility has been a world revolution. UAVs are part of this e-mobility, and all the systems involved can be improved [84; 85], giving them more autonomy by improving their beyond visual line of sight (BVLOS) [86], such as certain applications, which need accuracy to reach intricate places and precision tasks [87], and ecosystems in industry 4.0, where data traffic digitization is necessary and the use of drone networks is expanding [88]. In applications such as deliveries, large companies are testing UAS to complete these tasks, but the performance of drone deliveries must be optimized and human harm avoided [89].  
With all the opportunities, applications, and research presented above, it is necessary to consider the vulnerability of UAS due to their omnipresent operations on the internet of things (IoT) and industrial internet of things (IIoT) systems [90]. Furthermore, security and privacy are other issues to consider [22] and, even more, public safety, precisely because they are vulnerable to cyberattacks and can be used as signal sniffers, signal jammers, or in any other harmful way [91]. Taking into account the versatility of uses, these aspects of UAS must be reinforced to improve them [92], making them safer.
# Bioactive Glass as a Nanoporous Drug Delivery System for Teicoplanin
## 3 Results and Discussion
### Teicoplanin Release Test  
Figure 5 shows the cumulative release of teicoplanin of BG and B-doped BG (BG_B100). Both BG and BG_B100 with two different antibiotic contents showed a similar pharmacokinetic pattern with early rapid release and later slow and sustained release. The cumulative release of teicoplanin from BG and BG_B100 reached 20.09% and 3.17% on the first day, respectively. The drug release gradually slowed, reaching 29.43% and 4.83% after 7 days, respectively. The release reached a stable plateau period until the end of the observation period. The antibiotic release time of the two groups of samples reached 28 days, and the total cumulative release rates were 32.90% and 6.36%, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Samples** & **BG** & **BG\_B100** \\ \hline Average pore size (nm) & 19.43 & 20.68 \\ BET surface area (m\({}^{2}\)/g) & 23.95 & 49.42 \\ Total pore volume (cm\({}^{3}\)/g) & 0.12 & 0.26 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average pore size Brunauer–Emmett–Teller (BET) surface area and total pore volume of BG and B-doped BG.  
Figure 4: (a) N\({}_{2}\) adsorption–desorption isotherms and (b) pore size distribution of BG and B-doped BG.  
Figure 5: Cumulative release of teicoplanin from BG and B-doped BG (BG_B100).  
Teicoplanin is a water-soluble antibiotic. When PBS enters the drug carrier, it dissolves the drug in situ and has a high drug concentration. BG particles demonstrated the ability to deposit apatite, so the particle size increased. For example, the deposited layer upon Bioglass(r) powder increased in thickness to about 600 nm thick after 21 days of immersion in SBF [31]. In addition, from the first day to the 30 th day of immersion in SBF, the porosity percent decreased from 8.5% to 0.7% [28]. Due to the apatite formation on the surface of BG, the drug is limited to that in the drug carrier. The difference in drug concentration between the inside and the outside of the drug carrier is used as the driving force to make the drug diffuse through the surface barrier layer to the outside of the carrier. Drug delivery consists of two processes: drug dissolution and drug diffusion. Because the drug dissolves quickly, the diffusion rate is slower, so the drug release process is controlled by the diffusion effect. BG_B100 has a special pore size distribution characteristic combined with mesoporous and microporous features and provides a greater surface area to form the apatite shell. It can slow drug diffusion and maintain the lower release rate of teicoplanin. A lower release rate of teicoplanin could maintain the antibacterial ability but not induce a reduction in the cell number in osteoblasts and chondrocytes [32]. The proposed material BG_B100 has potential to be used as a drug delivery system, especially for dentistry and bone tissue engineering applications.
## 2 Materials and Methods  
### Structure of Thin Film Temperature Sensor  
To accurately measure the fast-changing ambient temperature during the high-speed drop of a radiosonde, the temperature sensor needs to have a fast response and high sensitivity. So, the response speed can be improved by reducing the size of the platinum resistor to reduce the thermal hysteresis. At the same time, to achieve high sensitivity and high accuracy measurement, the resistance value can be increased, and the initial design resistance value is around 100 \(\Omega\). In addition, due to the thin atmosphere in near space and the object's weak heat dissipation capability, the Pt resistor's surface area needs to be increased to improve its heat dissipation capability. By designing the platinum as a thin film shape, one can achieve the performance of fast response and high-accuracy measurement.  
In this scheme, we designed a Pt thin film microbridge temperature sensor, as shown in Figure 1. The sensor consists of three parts, from top to bottom: a thermistor, an insulating layer, and a matrix. The matrix was designed with a central hole to enhance the heat exchange between the thermistor and the air, allowing the insulator's central part to be suspended. The insulation layer with insulation slots reduces heat transfer from the insulation layer to the thermistor. The thermistor is a Pt thin film, the insulating layer is silicon dioxide, and the substrate is sapphire. First, the Pt thin film's small size and large surface area make the thermal hysteresis very small and allow for a fast response. Second, silica has good thermal insulation properties. Finally, the sensor has an anti-radiation coating that provides some protection against radiation. Therefore, for the temperature correction model of the radiosonde in this paper, there are three temperature measurement errors: aerodynamic heat, solar radiation, and Joule heat, while the other errors are negligible [23; 38].