esa-sceva/satcom-chunk-collection huggingface dataset:
# LEACH-MTC: A Network Energy Optimization Algorithm Constraint as Moving Target Prediction
## 4 Simulation Results and Discussion
### Verification of Proposed Algorithm
#### 4.2.1 Working Node Selection and Monitoring Accuracy  
As the target moves, the network nodes that are working are constantly changing. Figure 5 shows the change of the target position and the working node when the LEACH-MTC algorithm is running in the 1 th round, 300 th round, 600 th round, and 800 th round, respectively.  
In Figure 5, \(\square\) stands for base station, \(\vartriangle\) stands for moving target, \(\diamondsuit\) stands for working node, \(\ast\) stands for sleep node, curve stands for target trajectory. In Figure 5a,c, when the number of network running rounds is in the 1 th round and the 600 th round, the number of nodes that are in the working state is 15, and the remaining nodes are in a dormant state. In Figure 5b,d, when the number of network running rounds is 300 th round and 800 th  
Figure 4: Node distribution of four algorithms.  
round, the number of nodes that are in working state is 16. The above results indicate that as the target position changes, the long axis, the short axis, and the shape of the ellipse also change, besides, the number of working nodes that are in the ellipse area is constantly changing.  
Assume that the elliptical area are formed by the working node is \(S_{ab}=14,400m^{2}\), and all the working nodes in this area can meet the monitoring accuracy requirements. According to Equations (18)-(21), the long semi-axis \(a\), the short semi-axis \(b\), and the half-focal length \(c\) of the ellipse area can be obtained. The network monitoring accuracy is shown in Table 2.  
From Table 2, it can be seen that the number of running rounds is the 1 th round, the 300 th round, the 600 th round, and the 800 th round, respectively; the number of working nodes in the elliptical area is not the same, which indicates that as the target position  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Run Time Parameter** & **Round 1** & **Round 300** & **Round 600** & **Round 800** \\ \hline Number of working nodes & 15 & 16 & 15 & 16 \\ long semi-axes \(a\) & 79.1984 m & 96.6545 m & 86.9920 m & 82.9734 m \\ short semi-axes \(b\) & 57.8757 m & 47.4232 m & 49.3975 m & 51.7899 m \\ Half focal length \(c\) & 54.0629 m & 84.2208 m & 71.6066 m & 64.8259 m \\ Network monitoring error & 1.85\% & 2.10\% & 1.73\% & 1.65\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ellipse parameters and network monitoring error.  
Figure 5: Change of target position and working node selection.  
changes; and the ellipse area also changes. It can be seen from the changes of the long semi-axes, the short semi-axes, and the semi-focal lengths in Table 2. This indicates that as the target position changes constantly, the shape of the ellipse area also changes. The analysis of the monitoring accuracy of the network at four moments shows that the monitoring error is less than, which can meet the requirements of network monitoring accuracy.
## 5 Simulation Results and Analysis
### Comparison of Network Performance  
In order to achieve better detection performance when 5% and 20% of the malicious nodes exist in the network, MinPts = 9 and Eps = 1.2 were taken as parameters by analyzing and comparing the influence of parameters MinPts and Eps on DOIDS detection performance. This part compares the average hop count, packet delivery ratio, and energy tax of UWSN packets transmitted to the sink node under normal conditions, malicious attack without DOIDS, and malicious attack with DOIDS, respectively. In this section, the malicious attack is the sinkhole attack.  
* Average Hop Count: The average hop count is the average number of relay nodes needed to route a packet from the source node to the sink node. Ideally, packets should always be able to route to the sink node using the shortest path with the fewest relay nodes.
* Packet Delivery Ratio: PDR is defined by the ratio of the number of packets received by the Sink node to the number of packets sent by the source node: \[PDR=\frac{R_{packets}}{S_{packets}}\] (16) where \(R_{packets}\) represents the number of packets received by the sink node, and \(S_{packets}\) represents the number of packets sent by the source node.
* Energy Tax: The energy tax is the average energy consumed by each node to route a packet towards the sink node, including the energy consumption of receiving and sending packets as well as the energy consumption in an idle state.  
\[EnergyTax=\frac{E_{consumed}}{m\times R_{packets}} \tag{17}\]  
where \(E_{consumed}\) and \(m\) represent the total energy consumed during a round of working and the number of deployed nodes, respectively.  
Figure 10 compares the influence of DOIDS on the average hop count, packet delivery ratio, and energy tax of network packet routing to the sink node when there are different proportions of malicious nodes in the network. As can be seen from the figure, the existence of malicious nodes increases the average hop count and energy tax, and reduces the packet delivery ratio. By embedding IDS for each node in the network and isolating the malicious node after it is detected by the system, the impact of malicious attacks on network performance can be reduced to some extent; although, the performance cannot be completely restored to the network state without malicious nodes. The reason is that isolating a node is equivalent to no longer using it as a routing relay node. The larger the proportion of malicious nodes, the fewer nodes can be used as relay nodes in the network. As a result, the network cannot be restored to the no-attack state. However, the overall performance of the isolated response is still better than that of the malicious node, indicating that the method in this paper is effective in defending against routing attacks.  
Figure 10: (**a**) The influence of DOIDS on the average hop count; (**b**) The influence of DOIDS on the packet delivery ratio; (**c**) The influence of DOIDS on the energy tax.
# Architectural Design for Digital Twin Networks
## 3 Digital Twin Network
### Reference Architecture
#### 3.2.3 Application Layer  
The top layer supports various network applications, such as Operations, Administration, and Maintenance (OAM), which leverage the DTN to perform network operations with minimal impact on the real network. Applications interact with the DTN through the northbound interface, making requests that are processed by the appropriate twin instances. This interaction allows for the execution of both conventional and innovative network functions in a cost-effective and non-disruptive manner.  
Figure 1: Graphical representation of the reference architecture for a Digital Twin Network
# Scanning Electron Microscopy Techniques in the Analysis of Gunshot Residues: A Literature Review
## 3 Results
### Gunshot Residue Analysis: Detection, Persistence, and Emerging Forensic Solutions
Despite these technological advancements, challenges persist in ensuring the reliability and accuracy of GSR analysis. One of the most pressing concerns is the differentiation of genuine GSR particles from environmental contaminants that may share similar elemental profiles. Non-firearm sources such as fireworks, brake linings, and certain industrial processes can produce particles that resemble GSRs in both morphology and composition. Addressing this issue requires strict particle classification criteria and the continuous refinement of detection algorithms. The application of machine learning techniques for automated particle classification has shown promise in reducing false positives and improving detection efficiency, thereby enhancing the overall reliability of forensic interpretations [42]. However, the variability in GSR classification practices across different forensic laboratories remains a significant obstacle to achieving standardized protocols. Studies have highlighted inconsistencies in particle classification, particularly regarding borderline particles containing combinations of barium, lead, and antimony. This variability underscores the need for rigorous inter-laboratory validation and the adoption of universally accepted standards to ensure consistency in forensic reporting [43]. Such efforts are essential not only for the scientific robustness of GSR analysis but also for maintaining its credibility in judicial proceedings, where even minor analytical discrepancies can have profound legal implications.
## 3 Results: Optimization Workflow  
### Workflow Architecture  
The architecture of the optimization workflow describes the hierarchical relationships of its elements and is, therefore, part of the formalization process in Figure 2. The workflow architecture is modeled as a block definition diagram (bdd) and is shown in Figure 10. Similar to the design and test workflows, the optimization workflow consists of the sections _get_, _calculate_ and _set_. _Calculate_ consists of the parts _optimize_ and _select_. The block _select_ is used to select the most suitable tradeoff if multiple optimal variants are available. _Optimize_ consists of an _analysis_ and an _optimization algorithm_ (corresponding Figure 9). [In the _analysis_, _objectives_, and _constraints_ (implemented as functions) are evaluated. The _optimization algorithm_ performs the optimization task.
## 2 Materials and Methods
### Proposed Wireless Network
[CrossRef]
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* [16] [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Adaptive filtering methods for RSSI signals in a device-free human detection and tracking system. _IEEE Syst. J._**2019**, _13_, 2998-3009. [CrossRef]
* [17] [PERSON]; [PERSON]; [PERSON]; [PERSON] Received signal strength indicator and its analysis in a typical WLAN system (short paper). In Proceedings of the 38 th Annual IEEE Conference on Local Computer Networks, Sydney, Australia, 21-24 October 2013; IEEE: New York, NY, USA, 2013; pp. 304-307. * [18] [PERSON]; [PERSON]; [PERSON]; [PERSON] Evaluation of IEEE802. 15.4 g for Environmental Observations. _Sensors_**2018**, _18_, 3468. [CrossRef] [PubMed]
* [19] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] Low-cost sensor-based and LoRaWAN opportunities for landslide monitoring systems on IoT platform: A review. _IEEE Access_**2021**, _10_,710-7127. [CrossRef]
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# Authentication Securing Methods for Mobile Identity: Issues, Solutions and Challenges
## 2 Mobile Authentication Method and Issues
### Dynamic Password Authentication  
The SMS verification code is one of the most commons dynamic password authentications. Like the static password, the system will require identification information from the user, paired with the server's generated PIN or token for access authentication [7].  
Additionally, users need to register themselves and provide a username and mobile phone number as a primary key tied to their information, as shown in Figure 2. During the login, the user will need to enter the username, and once the server validates that their profile exists, an SMS of the generated password will be sent to the correlated mobile number. The mobile phone user will then use the generated password for authenticating their access. SMS authentication codes are sent in plain text. This presents some vulnerability that is constantly exposed to high-risk threats.
## 5 Conclusions  
In this paper, we investigated a novel mobility-assisted firework routing mechanism named Mobility Tolerant Firework Routing to improve packet reachability, and we evaluated it by simulations with the random walk mobility model to study the effect of its parameters on reachability, end-to-end delay and overhead. In addition, we discussed some related theoretical models for random geometric graphs and showed that the simulation results match well with the analytical values regarding the critical transmission radius at which global connectivity can be maintained.  
First, we studied the performance of our method from the wireless coverage viewpoint. It turned out that our method showed a good improvement in reachability by the firework effect, and the increase in wireless coverage resulted in general in a higher reachability. Second, we analyzed the tendency of additional end-to-end delay and traffic overhead with our method. Both results showed that the better reachability came at the expense of a higher delay and overhead. However, this may be overcome by the appropriate choice of parameters to make our method feasible enough for implementation in an actual system. Third, we analyzed two parameters, the firework threshold and hop limit, against packet reachability. We also confirmed that optimal values for both parameters exist that vary depending on the system conditions, such as the wireless transmission environment and the number of nodes. We believe that MTFR has the ability to improve reachability in MANETs for providing a robust and adaptive social communication network infrastructure.
# Deep Learning Based Lithology Classification Using Dual-Frequency Pol-SAR Data
## 1 Introduction
In References [17; 18], [PERSON] and [PERSON] proposed improved land cover classification methods based on superpixels to use the local spatial information more properly. In this study, SSAE is selected due to several advantages compared to other methods. First, unlike the convolutional neural network, SSAE is pixel-based, thus, it can classify any pixel including boundary pixels. Second, SSAE can easily handle large and multi-dimension remote sensing datasets, which is especially suitable for this study. Finally, it has a flexible and straightforward structure and has shown good results in various remote sensing applications. In the processing of classifying different vegetation covers or different urban land covers using Pol-SAR, effective features extraction is the foundation to obtain good results. However, it is difficult to get satisfactory classification features just by using one band Pol-SAR data for lithology, since lithologies are affected by surface weathering over long periods of time, and the surface characters thus become similar among some classes [1]. To classify different lithologies, dual-frequency Pol-SAR will play a role because of the different wavelengths and varying penetrability to vegetation cover. L-frequency has the longer wavelength and better penetrability. SSAE can extract some features according to its own rules, but it cannot extract all possible features, especially those with specific physical meanings. Combining the effective handcrafted features with raw data may improve the lithology classification performance of SSAE. In this paper, new polarimetric features were proposed based on dual-frequency Pol-SAR data for the surface fluctuations of different lithologies, and then SSAE was used to extract deep features and classify different lithologies. To the best of our knowledge, this is the first attempt at the classification of lithology by combining SSAE and dual-frequency Pol-SAR data. The paper is organized as follows: in Section 2, we explain the polarimetric features extraction and SSAE. This section also presents the new lithology classification method and describes the dataset used in the experiments. The proposed method is verified and analyzed in Section 3, at the same time, the proposed method is also compared with other methods in this section. A discussion is found in Section 4. And some useful conclusions are drawn in Section 5.
# Cybersecurity Challenges in the Maritime Sector
## 3 Cyberattacks on the Ship Automation Systems
### Global Position System  
GPS and navigational technologies, which are actively used in the maritime sector, are specific goals of various cyberattack that aim to exploit design flaws to destabilise services that depend on these technologies [29]. Such attacks pose medium to high risks because, in addition to data and service protocol violations, there is the possibility of physical damage. Several attacks have been reported that attempted to exploit this set of technologies [30; 31; 32]. For instance, spoofed GPS signals enabled attackers to reroute a vessel without triggering an alarm or alert from system handling [30]. In a similar incident, GPS signal jamming in South Korea affected the signal reception of more than 1000 aircraft and 700 ships for more than a week. Such cyberattacks can be classified as medium to high difficulty and are the result of the designs and standards of GPS and navigation systems. According to [33], Satellite Communication Systems (SATCOMs), including those connecting vessels via the Internet with each other and with the mainland, contain a large number of vulnerabilities and critical security holes such as devices using unsecured or even undocumented protocols, factory-set-up accounts, the ability to exploit the password reset function, and backdoors.
## 2 Integration of Artificial Intelligence (AI) in Robotic Surgery
### Addressing Limitations in Haptic Feedback  
A persistent challenge in robot-assisted surgery is the absence of haptic (tactile) feedback, which traditionally enables surgeons to assess tissue stiffness, tension, and other physical properties during procedures. This limitation can hinder precision and elevate the risk of intraoperative errors, such as excessive force application or unintentional tissue damage. Recent innovations leveraging artificial intelligence (AI) and advanced imaging technologies aim to bridge this gap by interpreting visual and sensory data to simulate feedback. Machine learning algorithms have been trained to analyze organ characteristics and predict tissue responses, thereby offering surgeons real-time virtual feedback to support more informed intraoperative decisions. In a notable example, [PERSON] et al. developed a biaxial shear sensing system integrated into a robotic grasper, designed to detect excessive tension and provide haptic warnings prior to suture breakage [9]. Their study showed that this system significantly reduced suture failures by 59%, and lowered the average applied force by 25%, without compromising the quality of knot tying. This indicates that sensor-assisted feedback mechanisms can enhance safety and consistency in delicate surgical tasks. Similarly, in the context of robotic-assisted kidney transplantation (RAKT), [PERSON] et al. employed three-dimensional augmented reality (3D AR) to address the inability to palpate atherosclerotic plaques in iliac vessels--an important limitation in minimally invasive vascular procedures [10]. By superimposing high-resolution CT-based virtual models onto the surgical field, the AR system enabled accurate intraoperative localization of vascular plaques, improving surgical navigation and procedural safety. These advancements highlight the critical role of AI and immersive technologies in compensating for the lack of tactile feedback, paving the way for safer and more precise robotic interventions. Figure 1 illustrates the integration of AI into robotic surgery workflows, including its applications in surgical skill evaluation, intraoperative guidance, and postoperative prediction.
# Biological Effect of Gamma Rays According to Exposure Time on Germination and Plant Growth in Wheat
## 3 Results
### Gamma Radiation Effect on the Expression of DNA-Repair-Related Genes  
The effect of gamma irradiation on the expression of DNA-repair-related genes was analyzed using RT-qPCR (Figure 5). After gamma irradiation, most DNA-repair-related gene transcripts showed similar expression patterns, exhibiting a gradual decrease with increasing exposure doses. The expression of _X-ray repair cross-complementing protein_ (_XRCC_), _KUI70_, and _DNA methyltransferase 2_ (_DnMT2_) was slightly upregulated by short-term irradiation at 100 Gy (Figure 5A-C). The _MSH6_ transcript levels decreased with increasing gamma radiation doses (Figure 5D). The RT-qPCR results show that DNA-repair-related gene transcripts were drastically reduced upon long-term irradiation compared with short-term irradiation. These results indicate that long-term irradiation negatively impacts the DNA repair system in plants compared with short-term irradiation even at equal gamma-ray doses.  
Figure 4: Determination of total chlorophyll, chlorophyll \(a\), and chlorophyll \(b\) contents upon gamma irradiation. Each bar represents the mean \(\pm\) SD (_n_ = 3). Values with different letters are significantly different using [PERSON]’s multiple range test (_p_ < 0.05).