esa-sceva/satcom-chunk-collection huggingface dataset:
# Underwater Wireless Sensor Networks Performance Comparison Utilizing Telnet and Superframe
###### Abstract  
Underwater Wireless Sensor Networks (UWSNs) have recently established themselves as an extremely interesting area of research thanks to the mysterious qualities of the ocean. The UWSN consists of sensor nodes and vehicles working to collect data and complete tasks. The battery capacity of sensor nodes is quite limited, which means that the UWSN network needs to be as efficient as it can possibly be. It is difficult to connect with or update a communication that is taking place underwater due to the high latency in propagation, the dynamic nature of the network, and the likelihood of introducing errors. This makes it difficult to communicate with or update a communication. Cluster-based underwater wireless sensor networks (CB-UWSNs) are proposed in this article. These networks would be deployed via Superframe and Telnet applications. In addition, routing protocols, such as Ad hoc On-demand Distance Vector (AODV), Fisheye State Routing (FSR), Location-Aided Routing 1 (LAR1), Optimized Link State Routing Protocol (OLSR), and Source Tree Adaptive Routing--Least Overhead Routing Approach (STAR-LORA), were evaluated based on the criteria of their energy consumption in a range of various nodes of operation with QualNet Simulator using Telnet and Superframe applications. STAR-LORA surpasses the AODV, LARI, OLSR, and FSR routing protocols in the evaluation report's simulations, with a Receive Energy of 0.1 mWh in a Telnet deployment and 0.021 mWh in a Superframe deployment. The Telnet and Superframe deployments consume 0.05 mWh transmit power, but the Superframe deployment only needs 0.009 mWh. As a result, the simulation results show that the STAR-LORA routing protocol outperforms the alternatives.  
Keywords:CB-UWSN; energy; telnet; routing protocols; UWSN; superframe +
Footnote †: journal: _Sensors_**2023**, 23, 4844. [[https://doi.org/10.3390/s23104844](https://doi.org/10.3390/s23104844)]([https://doi.org/10.3390/s23104844](https://doi.org/10.3390/s23104844))
# QoS-Based Pattern Recognition Approach for Web Service Discovery: Ar_WSDS
## 6 Evaluation and Results
### PS Recognition Scenarios  
We used the exact scenario definition of KS recognition, as this type of recognition had a more significant WS; therefore, the number of solution pathways may increase. The \(\beta(\varphi)\) function depended on the average value for each \(QoS_{i,j}\) to be recognized. As such, the processing required by Ar_WSDS was considerably increased.  
Table 3 presents the results of the partial signal recognition phase, showing how the number of candidate services increases. This condition arose as some WSs could  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter Name** & **Description** & **Units** & **Max (+)/Min (\(-\))** \\ \hline Response Time (\(P_{1}\)) & \begin{tabular}{c} Time taken to send a request and \\ to receive the response. \\ \end{tabular} & ms & \(-\) \\ \hline Availability (\(P_{2}\)) & \begin{tabular}{c} Time period in which a web \\ service exists or is ready for use. \\ \end{tabular} & \% & + \\ \hline Throughput (\(P_{3}\)) & \begin{tabular}{c} Number of services that a platform \\ providing web services can \\ process for a unit time. \\ \end{tabular} & invokes/second & + \\ \hline Successability (\(P_{4}\)) & \begin{tabular}{c} Message success ratio after \\ invocation of a service. \\ \end{tabular} & \% & + \\ \hline Reliability (\(P_{5}\)) & \begin{tabular}{c} Rate of failures over a period of \\ time \\ \end{tabular} & \% & \(-\) \\ \hline Compliance (\(P_{6}\)) & \
\begin{tabular}{c} WSDL compliance rate based on \\ its formal W3C specification. \\ \end{tabular} & \% & + \\ \hline Latency (\(P_{7}\)) & Delay time to process a request. & \% & \(-\) \\ \hline Best Practices (\(P_{8}\)) &
\begin{tabular}{c} Service specification compliance \\ ratio using WS-I Basic Profile. \\ \end{tabular} & \% & + \\ \hline Documentation (\(P_{9}\)) & Compliance ratio the WSDL. & \% & + \\ \hline Adapted from [28]. & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: QoS parameter description for WS selection.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(WS_{T}\) & **KS** & **y** & **Run Time (s)** & **SP** \\ \hline
10 & 2 & 17 & 0.5 & 8 \\
50 & 2 & 129 & 5.82 & 34 \\
100 & 2 & 1047 & 122.3 & 166 \\
10 & 4 & 10 & 0.3 & 1 \\
50 & 4 & 81 & 3.21 & 21 \\
100 & 4 & 421 & 39 & 58 \\
10 & 9 & 10 & 0.17 & 1 \\
50 & 9 & 50 & 2.34 & 1 \\
100 & 9 & 100 & 6.02 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Runtime results using KS.  
have high or equal values in their QoS, generating a higher number of WS belonging to that interval of their average value, which is a different result from that considering the limitation imposed by KS recognition. Moreover, the results showed a tendency to find fewer solution paths when all nine signals were active for recognition. For very high values of candidate WSs, the processing time to find the routes increased. The measured \(\beta(\varphi)\) was one and two, in order to increase the coverage of the solution space.
## 5 Experimental Results
### Experiment 2: Ensemble Learning via Combining Different GNN Structures
It is worth to mention that, as is known in the area of ensemble learning, the ensemble learning methods and models possess the advantage of tending to have high robustness. Ensemble learning can significantly improve the prediction accuracy compared to using a single model. This technique, by combining diverse models, effectively captures different aspects of the data and aims to reduce model bias and errors. Additionally, since this technique involves multiple models trained on different subsets of the data and utilizes different algorithms, it is less affected by outliers or noise in the data, enabling the creation of more accurate and robust predictions. It is important to note that ensemble learning also has potential drawbacks, such as increased computational complexity, longer training time, and the need for more resources. Moreover, the effectiveness of the employed methods depends on the diversity and quality of each model. However, the advantages of ensemble learning make it a powerful technique for improving prediction accuracy and generalization in various machine learning scenarios. Comparing the results of the ensemble method proposed here from Table 4 with the results of Table 3 (last two rows) shows that this method gives better results than applying only BERT or BERT+GCN, which was also considered in [21; 22] (in the context of English language). It is worth mentioning that other frameworks such as dependency grammar-based rules, LSTM, and CNN have been considered for sentiment analysis on Persian datasets in papers such as [25; 26]. However, to the best of our knowledge, before the present paper there was no other work on the sentiment analysis of Persian texts using a combination of ensemble learning methods with GNN structures.
# An Improved Method for Ship Target Detection Based on YOLOv4
## 4 Experiments and Results
### Detection Effect of the Improved Model  
#### 4.5.1 Detection Effect of Small Targets  
As shown in Figure 9, there are multi-scale ships in the image, and when the YOLOv4 model performs detection, the small ships are not detected, or not all of them can be detected (the first column of Figure 9). When the improved model detects, all the small ships are detected (the second column of Figure 9). When the YOLOv7 model detects, the detection effect of small ships is improved compared with the YOLOv4 model (the first and third rows of the third column of Figure 9), but there are still small ships missed (the second and fourth rows of the third column of Figure 9). Based on our tests, our method outperforms all other methods when it comes to detecting small ships.
# Color-Space-Based Visual-MIMO for V2X Communication +
## 4 Conclusions  
In this paper, we showed the applicability of GCM-based visual-MIMO for V2X. By using the proposed visual-MIMO scheme, while performing seamless communication, we can maintain the original color and brightness in addition to increasing the capacity by using color encoding. First, by performing a numerical example with a scenario, we showed that the choice of multiplexing and/or diversity depends largely on the distance between the source and destination node. Moreover, by performing a simultaneous transmission through the relay and on the direct link, we could obtain the additional SNR gain because of the path-diversity effect. Secondly, by dividing the node-specific communication area, we could achieve the simultaneous multiple access communication without inter-channel (inter-node) interferences. Finally, we demonstrated numerically that improvements in the SER performance through noise compensation could be achieved by sending the reference color (symbol) in the transmitted networking information. This improvement effect was represented by the expected distance gain, which extended the multiplexing mode period.  
Figure 11: Compensation of channel noise by sending a reference color in the color space of CIE1931.  
**Supplementary Materials:** Figure S1: The illustration of V2V communication using color-independent visual-MIMO.  
**Acknowledgments:** This research was supported by Basic Science Research Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Education [2015R1D1A1A01061396] and was also supported by the National Research Foundation of Korea Grant funded by the Ministry of Science, ICT, Future Planning [2015R1A5A7037615].  
**Author Contributions:** [PERSON] designed and performed the main experiments and wrote the manuscript. [PERSON] analyzed and improved the experimental results. [PERSON] mathematically demonstrated the simulations and reviewed the manuscript. [PERSON] as corresponding author initiated the idea and supervised the whole process of this research and wrote the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Problem Description and System Models  
### Problem Formulation  
In the context of reactive power and voltage security control in distribution networks with high penetration of renewable energy sources, the various nodes are equipped with PV units and capacitor banks. The PV units are integrated into the grid through controllable smart inverters, which, like conventional capacitors, provide reactive power support to regulate the bus voltage at each node within a stable threshold.  
The structure of reactive power and voltage security control in the distribution network is illustrated in Figure 1. Under the control of the trained agent, the reactive power compensation devices are coordinated to ensure the voltage security of the distribution network, while simultaneously minimizing device regulation costs and active power losses. Therefore, in a distribution network consisting of \(N+1\) nodes, including \(m\) PV units and \(k\) capacitor banks, the objective of reactive power-based voltage security optimization over \(T\) time steps is to  
\[\min\sum_{t=1}^{T}\left(\frac{1}{N}\sum_{i}^{N}\lvert v_{i,t}-v_{\text{ref}} \rvert+\sum_{(i,j)\in\Omega}r_{ij}I_{ij}^{2}\ +\sum_{a}c_{a}1(d_{t}^{a})\right) \tag{1}\]  
In the above formulation, \(v_{i,t}\) denotes the voltage magnitude at node \(i\) at time \(t\), and \(v_{\text{ref}}\) represents the reference voltage value. The pair \((i,j)\) denote a branch connecting node \(i\) and node \(j\), with \(r_{ij}\) and \(I_{ij}\) representing the resistance and current magnitude of the branch, respectively. \(C_{a}\) refers to the switching cost of the \(a\)-th mechanical device. The indicator function \(1(d_{t}^{a})\) equals \(1\) if the \(a\)-th device is actuated at time \(t\), and \(0\) otherwise.
## 3 Results and Discussion
### In Vivo  
The in vivo antidiabetic investigations were carried out in diabetic-induced Wistar rats and the plasma glucose level was measured over a period of 6 h. Optimized insulin niosome emulgel (F6) was selected for in vivo studies because this formulation shown the highest permeation (90.78 mg/cm\({}^{2}\)/h) in the in vitro studies (Table 6) and was compared with other formulations prepared. The blood glucose level of all animals was measured before the study and was over 250 mg/dL. The requirement of insulin in human varies as the dose is based on individual's glucose level, which range between 0.5 and 1 unit/kg/day in multiple doses. In rats (250-300 g), the requirement of insulin is 5-5.5 unit/kg/day, according to the equation described in the literature [41]. However, this dose is administered by subcutaneous route. On the other hand, the dose of transdermal system primarily depends on the skin in put rate (flux), which is also influenced by the drug concentration in the formulation [42]. Therefore, a higher dose is generally used in transdermal formulations to achieve adequate skin in put rate. Hence, a higher dose of 0.54 mg (equals 15.56 IU of insulin) was incorporated in all formulations and were applied into animals. Figure 9 showed the results of the percentage of plasma glucose level reduction. Significant reduction (\(p<0.05\)) of plasma glucose level was observed in insulin niosome emulgel preparation during 6 h investigation compared to all formulation under investigation except with insulin niosome gel, the plasma glucose level was only significant at 4 and 6 h (\(p<0.05\)). Indeed, the blood glucose monitoring showed that the applied dose effectively reduced blood glucose by more than 30% for inulin noisome emulgel formulation. On the other hand, the percentage of plasma glucose level remains same in untreated control group. The results suggested that, the insulin niosome emulgel preparation could be considered as efficient transdermal delivery system for insulin.  
Figure 9: Percentage of plasma glucose level of initial-time profile following transdermal applications of different formulations in diabetic rats. Data represented as mean \(\pm\) SD (n = 6).
## 2 Research Motivation and Related Work
### Collaborative Filtering  
The collaborative filtering method provides recommended items for a specific page (web page or application panel). The collaborative filtering method uses rating history information from all the users in the recommendation system. Explicit or implicit data collection methods can be used to achieve this [36; 37]. The explicit data collection method allows users to respond to the score or rating for items (e.g., movie review and rating system).  
On the other hand, the implicit data collection method uses various data, including item view history, online cart or purchase information, and likes and dislikes in social network services. There is a key issue in implementing the implicit data collection method: privacy. The operator of the recommendation system must obtain the users' agreement and specify the usage in the Terms and Conditions. To avoid this issue, we use the explicit data collection method in our recommendation system.  
The basic method to implement collaborative filtering is matrix factorization [38; 39; 40]. Mathematically, matrix factorization is able to represent information about users and items in a latent feature space or embedding space, which is similar to dimensionality reduction methods such as principal component analysis (PCA). This method is called latent-factor collaborative filtering. One potential problem with matrix factorization is selecting the proper number of columns in factor matrices. To deal with the issue, the authors of [41] proposed an automatic hyper-parameter learning method based on the Bayesian model.  
The authors of [42] introduced matrix factorization techniques for recommender systems, aiming to capture latent factors in user-item interactions and provide accurate predictions for personalized recommendations. The authors of [43] proposed neural collaborative filtering, a hybrid recommendation approach that combines matrix factorization and deep learning techniques to capture both user-item interactions and item-item correlations for enhanced recommendation performance. Although these state-of-the-art studies involve sophisticated techniques and methods, we focus on basic and straightforward methods for non-ICT major students.
## 3 Proposed MISO Converter
### Basic Operating Principles  
#### 3.2.1. State 1 [\(t_{0}\leq t\leq t_{1}\)]  
As shown in Figure 7, the switches \(S_{1a}\) and \(S_{2a}\) are on, the switches \(S_{1b}\) and \(S_{2b}\) are off, and the diodes \(D_{1}\), \(D_{2}\), \(D_{3}\), and \(D_{4}\) are off. The input voltages \(V_{d1}\) and \(V_{d2}\) excite the magnetizing inductances \(L_{m1}\) and \(L_{m2}\) and the leakage inductances \(L_{lkp1}\) and \(L_{lkp2}\), whose voltages on them are \(k_{1}V_{d1}\), \(k_{2}V_{d2}\), \((1-k_{1})V_{d1}\), and \((1-k_{2})V_{d2}\), respectively. The reverse-biased voltage of diode \(D_{1}\) is \(V_{C2}+n(k_{1}V_{d1}-k_{2}V_{d2})\); the reverse-biased voltage of diode \(D_{2}\) is \(V_{C1}-V_{C2}-n(k_{1}V_{d1}-k_{2}V_{d2})\); the reverse-biased voltage of diode \(D_{3}\) is \(V_{C2}+V_{C3}-V_{C1}+n(k_{1}V_{d1}-k_{2}V_{d2})\); the reverse-biased voltage of diode \(D_{4}\) is \(V_{o}-V_{C2}-V_{C3}+nk_{2}V_{d2}\). In addition, since all the four diodes are cut off, only the output capacitor \(C_{o}\) releases energy to the load in this state. Once the switch \(S_{2a}\) is turned off, state 1 comes to an end.
## 4 Remote Sensing Platforms Used in Peatland Research
### Airborne-Based Remote Sensing  
Among the various remote sensing platforms available, airborne-based remote sensing has gained considerable attention due to its ability to provide high-resolution data over large areas. This section evaluates the strengths and limitations of airborne-based remote sensing applications on peatlands, focusing on the field's key studies. However, at the study level, Table 4 shows the application and sensor used in all the airborne studies.
# On the Development of a Neural Network Architecture for Magnetometer-Based UXO Classification
## 5 Results
### Classical Machine Learning Methods for UXO/nonUXO Classification  
Experiments were conducted to establish reference performance benchmarks for classical machine learning methods. The results are presented in Table 4.  
Among the evaluated models, the best performance was achieved by the Random Forest classifier, which attained an accuracy of 86.39%, balanced accuracy of 83.34%, and an \(F1_{macro}\) score of 83.38%. The second-best model was the MLP neural network, with 85.26% accuracy, 80.94% balanced accuracy, and an \(F1_{macro}\) of 81.55%. The kNN classifier performed the worst, reaching 83.51% accuracy, 79.31% balanced accuracy, and 79.59% \(F1_{macro}\) score. All differences between models were statistically significant, based on a two-tailed, two-sample T-test with \(\alpha=0.1\).  
These results differ substantially from those reported in [9] due to two key distinctions between the datasets. As already mentioned, first, the earlier dataset included all three components of the magnetic field vector (X, Y, Z), while the current study relies solely on the magnitude of the magnetic field, a value typically returned by many commercial magnetometers such as optically pumped magnetometers (OPMs) [24]. More importantly, the previous dataset did not account for remanent magnetization, which plays a significant role in shaping the magnetic field distribution around ferromagnetic objects [25].
## 1 Introduction
### Atomic Layer Deposition  
Atomic layer deposition (ALD) is an attractive CVD (Chemical Vapor Deposition) method [24] that is well applicable to NiTi SMAs [25; 26; 27; 28; 29] due to the well-controllable coating thickness and outstanding conformality of the ALD coatings. No other thin-film technique can approach the conformality achieved by ALD on high-aspect-ratio structures, such as trench capacitors for DRAM (Dynamic Random-Access Memory) [30] or microcracks in an alloy. The ALD coating has the potential to improve the biocompatibility and corrosion resistance of NiTi [31], which is particularly important for medical implants and devices exposed to harsh physiological environments.  
The idea of using ALD coating on the NiTi surface was first realized 10 years ago [25]. However, the drawback of the ALD coatings applied to NiTi might be delamination due to large surface strains in NiTi (up to 10%, locally) [26], especially if the coating is made of ceramic material. Despite these challenges, ALD remains a promising technique for enhancing the surface properties of NiTi components. In the present study, the ALD method is used, for the first time, on a stretched NiTi thin wire to improve fatigue resistance. This innovative approach aims to explore the potential of ALD coatings to mitigate fatigue-related failures by providing a protective barrier that can withstand cyclic mechanical stresses.
## 3 Methodology
### Overview of YoloMal-XAI  
YoloMal-XAI is a deep learning framework designed to transform APK files into interpretable RGB images and classify them using a modified YOLO11 architecture. The process involves three key stages: (i) feature extraction and RGB image construction from the DEX, Manifest, and Resources components, (ii) image classification using YOLO11, and (iii) explanation generation through XAI methods. Each component is optimized for both performance and interpretability, forming a unified pipeline for accurate and explainable malware detection.  
* APK Feature Extraction and RGB Image Construction: Three essential components are extracted from APK files: DEX (Dalvik Executable), XML (Manifest), and ARSC (Resources). These components are mapped into an RGB image representation, where DEX, Manifest, and Resources are assigned to the red, green, and blue channels, respectively. This approach preserves the structural and semantic features of the APK while enhancing resilience against obfuscation.
* YOLO11-Based Classification: The generated RGB images are fed into the YOLO11 architecture, which consists of a Backbone for feature extraction, a Neck for multi-scale feature fusion, and a Classification Head for final predictions. The model performs both binary classification (Malware vs. Benign) and multi-class classification (Adware, Banking, Riskware, SMS, Benign), achieving high accuracy and real-time performance.
* Explainable AI (XAI) and Interpretability: To ensure transparency and trust, we integrate Grad-CAM-based visualization techniques (Grad-CAM, Grad-CAM++, Eigen-CAM, and HiRes-CAM). We further evaluate interpretability using metrics such as faithfulness, localization accuracy, robustness, and sparsity, ensuring the model's explanations are both accurate and reliable.  
To reinforce understanding, Table 2 summarizes the three main stages of the YoloMal-XAI framework, including their processes and key outputs.