esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning Application in Dental Caries Detection Using Intraoral Photos Taken by Smartphones
## 2 Materials and Methods
### Reference Standard and Labeling Dataset  
Smartphone photos were visually inspected on a laptop (MacBook Air, Apple) by a single experienced dentist (V.T.N.N, >20-year experience) to detect any caries lesions based on the criteria of the International Caries Classification and Management System (ICCMS) [19]. All photos were diagnosed and labeled as follows: Class 0-Sound: \"No surface change\" (NSC); Class 1-Initial: \"Visually Non-Cavitated (VNC), a white spot lesion or brown carious discoloration\"; Class 2-Moderate: \"Cavitated, a white or brown spot lesion with localized enamel breakdown, or an underlying dentine shadow\"; Class 3-Extensive: \"Late Cavitated, a distinct cavity in opaque or discolored enamel with visible dentine\". In total, 1902 teeth were labeled as class 1, 1598 teeth were labeled as class 2, and 2127 teeth were labeled as class 3 (Figure 2).  
Figure 1: Central view (**a**,**c**) and lateral view (**b**,**d**) photos with non-cavitated (in green boxes) and cavitated lesions (in blue boxes) diagnosed by deep learning models (YOLOv3, Faster R-CNN).  
Figure 2: The flow chart of the method.  
To test the reliability of the reference standard, a test-retest reliability was performed. An experienced dentist relabeled 30% of photos, randomly selected from the first 500 photos one week after the first diagnosis. Intraclass correlation coefficients (ICCs) were calculated to assess the test and retest the reliability. The ICC values were 0.917 (\(p<0.05\)), showing excellent reliability.
# Hybrid Beamforming in Massive MIMO for Next-Generation Communication Technology
## 7 Conclusions  
This paper briefly discusses the advancement in wireless telecommunications from 1G to 6G. Secondly, the concept of beamforming is introduced, and its architectural types like analog, digital, and hybrid types, along with some digital linear precoding techniques, the concept of MIMO, and its reason for introduction in wireless communication technology, is explained. Finally, the concept of hybrid beamforming in MIMO with a hybrid algorithm is described. The proposed technique shows promising results as compared to the conventional techniques used.  
The configuration of the antenna array, which has been employed for the proposed beamforming, can significantly affect the performance and functionality of the system. The spatial distribution of the hybrid antenna array in the horizontal and vertical directions has  
Figure 8: Achievable rate vs. Average SNR at \(\xi_{k}\), \(\xi_{x}=4\).  
Figure 10: Achievable rate vs. Average SNR at \(\xi_{k}\), \(\xi_{x}=64\).  
further enhanced its capability to control reception signals. In the following, the software and hardware implementations of the hybrid beamforming have been fulfilled in the MathWorks Simulink visual programming environment and FPGA board, respectively. Details of the hardware implementation have been fully demonstrated on the FPGA board. The proposed optimization techniques incorporated with the hybrid antenna array have resulted in a very strong beamformer with superior capabilities and high-performance efficiency.  
Conceptualization: S.R.C., S.H., B.C.F., D.D.T. and A.M.S.; writing--original draft preparation: S.R.C., S.H., S.T. and A.G.; methodology: S.H., S.R.C., A.G., O.A. and S.T.; writing--review and editing: S.R.C., A.G., S.T. and A.M.S.; Software: S.H., A.G., B.C.F., D.D.T. and A.M.S.; Visualization: B.C.F., S.R.C., A.G., O.A. and S.T.; Investigation: S.T., B.C.F., D.D.T. and O.A. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Researchers Supporting Project Number (RSP2023R102) King Saud University, Riyadh, Saudi Arabia, and also supported by a grant of the Ministry of Research, Innovation and Digitization, Romania, CNCS/CCCDI-UEFISCDI, project number PN-III-P1-1.1-TE-2021-0393, within PNCDI III.  
Not applicable.  
The authors declare no conflict of interest.
# A Survey on Emerging Blockchain Technology Platforms for Securing the Internet of Things
## 5 Effect of Blockchain  
In an IoT network, devices can communicate and transfer data autonomously. Transferly data may be private and highly confidential in many applications, e.g., healthcare, defence, smart buildings, etc., which amplifies the security risks of such systems. Blockchain technology has revolutionised the IoT system by addressing these security risks (such as weak authentication, insecure communication, and unreliable updates). Through decentralised identity, encrypted transactions, immutable records, and data distribution, blockchain ensures the integrity, authenticity, and protection of IoT systems. Its resistant nature to manipulation further solidifies its role in safeguarding IoT ecosystems. The adoption of blockchain also has both security and performance effects on IoT systems.
# The Interaction between the LEO Satellite Constellation and the Space Debris Environment
## 5 The Influence of Constellation on Space Environment
### Objects Number Influenced by Constellation  
Based on the collision simulation of a single satellite, the collision influence of the constellation is analyzed comprehensively. Figure 16 shows the evolution of the number of space objects larger or equal to 10 cm with and without constellations. After joining constellations, the number of objects increases obviously at the beginning and then fluctuates. The blue line represents the proportion of space objects with and without constellations, which is on the rise, indicating that the existence of constellations has accelerated the growth of the number of objects, and by 2040, the number of space objects will be nearly 1.3 times higher than when there are none constellations. This is only the impact of some satellites in the Starlink constellation plan. In the later stage, after all 38,000 satellites are deployed, the impact on the number of objects will increase exponentially, and the collision risk will increase step by step. The high requirements for configuration and accuracy of constellation satellites may even lead to cascade collisions.
# Toward Vulnerability Detection for Ethereum Smart Contracts Using Graph-Matching Network
## 4 Methodology
### Framework  
The underlying idea of our approaches is that ASG, extended syntactic information with more code structural features, is expected to preserve more syntactic and semantic information. Furthermore, GMN has been proved to be an effective model for matching graph-structured objects [33]. Figure 3 illustrates the general framework of the proposedapproaches, which is comprised of two main phases, including model training and vulnerability prediction.  
As shown in Figure 3a, given a dataset that contains both benign and malicious contracts, the workflow of model training can be further divided into two steps. The first step is to generate ASG from the code. When the source code is available, ASG is based on the AST but enriched by control flow and data flow. Otherwise, it consists of decompiling and augmenting basic block sequences with control flow. The second step is to calculate vector representations for smart contracts using GNN, then utilize GMN to learn similarities between the graph pairs. Note that our two proposed tools _ASGVulDetector_ and _BASGVulDetector_ only differ in the graph generation step; the other following procedures are performed in the same manner.  
To reduce the false positive rate of detection, each type of vulnerability can be isolated by an independent trained model. Nevertheless, each type of vulnerability is predicted through the same procedure, as shown in Figure 3b. First, suspicious vulnerability types of trained models should be selected before the sample contract is fed to the tool. Then, the sample contract is processed as a fresh input to the training model, which means procedures of ASG generation, graph embedding will be conducted to obtain a vector representation of the sample contract. Finally, such a vector is evaluated in the trained model in terms of similarity with known vulnerabilities. In order to detect different types of vulnerabilities, the sample contract will be evaluated through different trained models in series, parallel, or any combination thereof.
## 2 Data and Methods
### MERRA-2 Reanalysis Data  
MERRA-2 is the follow-on to the original MERRA reanalysis developed by the National Aeronautics and Space Administration (NASA) Global Modeling and Assimilation Office (GMAO). It is intended as an intermediate reanalysis with some important updates,but many basic aspects of the MERRA-2 system, such as the variational analysis algorithm and observation handling, are largely unchanged since MERRA [33]. MERRA-2 provides three-hourly temporal resolution, a horizontal resolution of approximately 50 km (0.5\({}^{\circ}\)\(\times\) 0.625\({}^{\circ}\)), and a vertical resolution of 72 levels from the surface to 10 Pa [33]. Again, note that COSMIC-2 RO soundings had not been operationally assimilated into MERRA-2 during the study period.
## 3 Results and Discussion
### Quantitative Analysis of the Curing Process Using FTIR
Figure 4: Schematic of the fitting results of the characteristic absorbance peak area: (**a**) wavenumber 915 cm\({}^{-1}\) and (**b**) wavenumber 830 cm\({}^{-1}\). To correlate \(\alpha\) with the tensile strength of epoxy asphalt, \(G\alpha\) was compared to the curing degree of epoxy asphalt as represented by the tensile strength, \(G_{\mathrm{s}}\):  
\[G_{\mathrm{S}}=\frac{S_{t}}{S_{120}}\times 100\% \tag{3}\]  
where \(S_{t}\) and \(S_{120}\) are the tensile strengths of epoxy asphalt at curing times \(t\) and 120 h, respectively, where \(S_{120}\) is considered the ultimate stable tensile strength. The results for \(G_{\alpha}\) and \(G_{\mathrm{s}}\) are compared in Figure 5. Figure 5a shows similar increasing trends for \(G_{\alpha}\) and \(G_{\mathrm{s}}\), indicating that the strength of epoxy asphalt is mainly derived from the epoxy resin crosslinked network. Since there is no direct correspondence between \(\alpha\) and the curing time for the development of the tensile strength, polynomial regression was used to fit \(G_{\alpha}\), as shown in Figure 5b, and to obtain \(G_{\alpha}\) values at 12, 24, 36, 48, 72, 96, and 120 h (Table 5). A correlation analysis was conducted on \(G_{\mathrm{s}}\) and \(G_{\alpha}\). The results in Figure 5c show that \(G_{\alpha}\) and \(G_{\mathrm{s}}\) are linearly correlated, with an \(R^{2}\) greater than 0.99, indicating that \(G_{\alpha}\) and \(G_{\mathrm{s}}\) are highly correlated. Thus, \(\alpha\) can be used to indirectly evaluate the tensile strength and curing degree of epoxy resin. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Curing Time / h** & **(\(A^{{}^{\prime}}\)\({}_{915}\)/\(A^{{}^{\prime}}\)\({}_{830}\))\({}_{t}\)** & \(\frac{(A^{{}^{\prime}}\)\({}_{915}\)/\(A^{{}^{\prime}}\)\({}_{830}\))\({}_{t}}{(A_{915}\)/\(A_{830}\))\({}_{0}}\) & **Conversion Rate of** & **Curing Reaction** \\ \hline
0 & 0.109 & 1.000 & 0.000 & 0 \\
3 & 0.104 & 0.958 & 0.042 & 8 \\
7 & 0.100 & 0.915 & 0.085 & 15 \\
22 & 0.083 & 0.759 & 0.241 & 44 \\
29 & 0.085 & 0.782 & 0.318 & 58 \\
40 & 0.067 & 0.615 & 0.385 & 70 \\
46 & 0.065 & 0.592 & 0.408 & 74 \\
69 & 0.054 & 0.496 & 0.504 & 91 \\
93 & 0.050 & 0.456 & 0.544 & 98 \\
120 & 0.049 & 0.451 & 0.549 & 99 \\
144 & 0.049 & 0.448 & 0.552 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Calculated conversion rate of epoxy groups, \(\alpha\), and the curing reaction degree of epoxy asphalt, \(G_{\alpha}\).
## 1 Introduction
A novel Hi-DM method is proposed in [21]; this scheme combines multiple short DMs into a hierarchical structure to create a longer one. This approach effectively achieves a balance between the advantage of long DMs and the simplicity of short DMs. However, these two methods still face limitations in terms of high implementation complexity and insufficient flexibility due to their multilayer design. Inspired by existing Hi-DM methods, this work aims to investigate a simpler shaping structure that employs only a single-layer lookup table as a distribution matcher and offers advantages in terms of implementation simplicity. Nonlinear distortions caused by the nonlinear effects of optical fibers and optoelectronic devices represent another critical factor influencing transmission performance. Neural network-based nonlinear equalization algorithms, owing to their superior adaptive capability and nonlinear approximation performance, offer a novel approach to addressing complex nonlinear distortion problems [22; 23]. A variety of neural network-based schemes have been proposed. These include a multiple-input multiple-output (MIMO) deep neural network [24], complex-valued convolutional neural networks (CV-CNNs) [25] and a pruning I/Q-joint deep neural network [26]. However, radial basis function neural networks(RBFNNs) have received relatively little attention in existing research. Given their fast training speed, simple structure, and strong localized response characteristics, RBFNNs are particularly well-suited for nonlinear equalization tasks. Therefore, further investigation is warranted to explore their potential and effectiveness in this context. In [27], the authors completed the simulation-based verification of the joint optimization of probabilistic shaping and RBFNN nonlinear equalization in a photonic terahertz OFDM system, achieving a 61.9% reduction in bit error rate (BER). The main contributions of this paper are as follows:  
* We present a PS scheme based on a single-layer lookup table (LUT-PS) that adjusts the symbol distribution by using a specific LUT architecture and a flexible shaping proportion to realize probabilistic shaping. * We propose a joint scheme combining LUT-PS and RBFNN equalization to further mitigate nonlinear distortion. * We demonstrate the transmission of 16-QAM OFDM signals over a photonic terahertz-over-fiber system at 400 GHz. Section 2 explains the principle of LUT-PS and RBFNN nonlinear equalization. Section 3 describes in detail the setting of the simulation. Section 4 provides a detailed presentation and discussion of the simulation results. To conclude, we summarize the results of the above-described work in Section 5.
## 4 Example Verification
### Model Evaluation Metrics  
To assess the performance of the model more effectively, four evaluation metrics were used: precision, recall, average precision (AP), and mean average precision (mAP).  
The quantitative metrics of precision, recall, and mAP directly translate to critical operational advantages in construction safety compliance monitoring. Higher recall ensures minimal missed detections of life-threatening violations, like absent fall-arrest systems. Concurrently, higher precision means fewer false positives, resulting in less lost productive time. The mAP metric values reflect strong consistency in multi-class detection of key safety checkpoints, enabling the vast majority of OSHA-mandated visual inspection standards to be monitored simultaneously through a unified system.  
\[Precision=\frac{TP}{TP+FP} \tag{8}\]  
\[Recall=\frac{TP}{TP+FN} \tag{9}\]  
\[AP=\int_{0}^{1}p(r)dr \tag{10}\]  
\[mAP=\frac{\sum_{i=1}^{k}AP_{i}}{k} \tag{11}\]  
where: (true positive) is the number of positive samples correctly predicted as positive.  
FP (false positive) is the number of negative samples incorrectly predicted as positive.  
FN (false negative) is the number of positive samples incorrectly predicted as negative.  
AP (average precision) is calculated from the precision-recall (P-R) curve, where recall (R) is plotted on the horizontal axis and precision (P) is on the vertical axis. The area enclosed by the P-R curve and the coordinate axes represents the AP.  
Recall (r) is the proportion of true positives correctly identified by the model.  
Precision (p(r)) is the precision value corresponding to a given recall value.  
k is the total number of classes or labels in the sample.  
\(\mathrm{AP_{i}}\) is the average precision for the i-th class or label.
## 8 Relevant Applications of Melt Electrospinning
### Food Packaging  
Developing new food packaging systems is always interesting to support physicochemical, optical, mechanical and catalytic properties in food industries. Active food packaging provides the ability to control moisture, ethylene and CO\({}_{2}\) absorbers, oxygen scavengers and antimicrobial and antifungal agents, while the smart packaging act as a sensor to show temperature, freshness and gas indication [181; 182]. There is a growing demand in the production of food packaging materials with a high loading of active agents and highly responsive nature so that materials can release active molecules as a response to environmental conditions. This can be achieved by melt electrospinning. In addition, the small fiber spacings act as a barrier against the bacterial entrance [183; 184]. Different active agents including antioxidant, antimicrobial and antifungal agents are shown in Figure 13 together with different electrospinninging techniques. Hybrid electrospinninging or co and coaxial electrospinning are effective processes to incorporate active agents in polymeric media according an in-situ process [183]. Nonetheless, one of the drawbacks of solution electrospinninging for food packaging applications is again the use of toxic solvents, which limits the compatibility of the obtained fibers with food.  
Potential of melt electrospinninging to prepare active/smart packaging is currently relevant [184]. Moreover, several agents have been effectively incorporated by means of melt electrospinninging. Relevant examples correspond to phenolic compounds with antioxidant properties such as carnosol, carnosoic acid, rosamol, rosmadial, epirosmanol, rosmadiphenyl and rosmarinic acid. Essential oils, such as \(\alpha\)-pinene, borny acetate, camphor and 1,8-cineole have been applied due to their strong antimicrobial properties that are fundamental in packaging industry. [PERSON] [184] developed for example a bioactive antimicrobial polymeric microfibrous structure using PCL and a natural extract from the rosmary plant, which is a popular antimicrobial agent. It was found that physical properties such as morphology and thermal stability of pure PCL remained unchanged and that logically the antimicrobial efficacy was increased. Although, challenges of melt electrospinninging (e.g., degradation of polymer or added agents during melt processing) limit this eco-friendly technique, researchers provided possible ways to customize the polymer fibers for food packaging applications as discussed in this review.
## 3 Results and Discussion
### Correlation between the Microbial Community and Flavor Substances during Fermentation
#### 3.5.2 Correlation between the Microbial Community and Nonvolatile  
In the CF (Figure 8a), there was a significant positive correlation between _Paucibacter_ and the three metabolites (\(p<0.05\)). In NF (Figure 8b), _Debaryomyces_ and _Meyerozyma_ were positively correlated with hydroxyurea, trans-3,5-dimethoxy-4-hydroxycinnamaldehyde and allose, and negatively correlated with the other 12 nonvolatile differential metabolites; furthermore, there was a significant negative correlation between _Pantoea_ and gluconic lactone, 3,6-anhydro-D-galactose, N-carbamylglutamate, dl-p-hydroxyphenyllactic acid, etc. The fungal genera in the NF group had a greater influence on differential nonvolatile metabolites.
## 5 Advanced MPPT Techniques
### Hybrid MPPT Techniques
#### 5.3.2 GWO-P&O Algorithm  
The GWO-P&O hybrid MPPT algorithm integrates the global optimization capabilities of GWO with the local search accuracy of P&O. In this hybrid approach, the GWO algorithm is initially employed to explore a broad range of potential MPPs by mimicking the hunting behavior of grey wolves. GWO effectively identifies a promising region for the MPP due to its strong global search ability. Once this region is determined, the P&O method takes over to fine-tune the operating point with high precision, adjusting the voltage to ensure that the system operates exactly at the MPP [34; 69]. Advantages of the GWO-P&O hybrid algorithm include enhanced performance in complex environmental conditions, as GWO's global search capabilities help avoid local maxima and improve convergence to a more accurate  
Figure 19: Flowchart of the GA MPPT.  
MPP. The P&O component then refines the power tracking, offering precise and efficient operation. Disadvantages involve increased computational requirements and complexity. GWO can be computationally intensive due to the need for multiple iterations to perform global optimization, which might demand more processing power and time [36].
# Information-Theoretic Analysis of a Family of Improper Discrete Constellations
## 1 Introduction
Is this rate reduction due only to a shaping loss? Should the channel encoder be modified for discrete improper constellations? In this paper, we address these questions and consider the design of improper discrete constellations for the additive white Gaussian noise channel (AWGN). We focus on coded communication systems, and therefore, our aim is the design of discrete improper constellations for the coded bits to approach the improper Gaussian capacity (i.e., maximize the throughput). Consequently, mutual information is the figure of merit to be optimized. The design of practical capacity-achieving encoders-decoders will be considered in a future work. Therefore, the main contribution of this paper is to propose a class of improper constellations and to show that, in terms of mutual information, this family of constellations behaves very similarly to the standard family of \(M\)-QAM proper constellations. Starting with a proper \(M\)-QAM constellation, we analyze the design of a widely linear transformation that generates a family of improper \(M\)-QAM constellations with a prescribed circularity coefficient \(\kappa\). The only free parameter of these improper \(M\)-ary constellations is a phase \(\phi\in[0,\pi/2]\). For uncoded communication systems, this phase should be optimized to maximize the minimum Euclidean distance between points of the improper constellation, so that the bit error rate (BER) is minimized. Note that for a given \(M\) (i.e., for a given spectral efficiency), maximizing \(d_{min}\) is equivalent, at high snr, to maximizing the mutual information (MI) between the transmit and the received signals [26]. On the other hand, for coded improper communication systems with a circularity coefficient \(\kappa\), one should find the \(\phi\) that maximizes, for each snr and \(M\), the envelope of the resulting family of mutual information values of these \(M\)-ary improper constellations. In this way, the rate gap compared to the improper Shannon capacity is minimized. One of the main contributions of this paper is to show that except for a shaping loss of 1.53 dB encountered at high snr, there is no rate loss with respect to the improper Gaussian Shannon capacity when using this family of constellations. We conclude that the proposed family of improper constellations behaves much the same as the standard family of proper \(M\)-QAM constellations. Finally,note that as in the case of proper QAM constellations, the shaping loss can be reduced by designing codes that result in a Gaussian-like rather than uniform distribution of the coded symbols. The rest of the paper is organized as follows. Section 2 provides preliminaries about improper random variables and introduces the considered model. The design of improper constellations is formulated in Section 3, and its information-theoretic analysis is done in Section 4. Section 5 provides some numerical examples. Finally, concluding remarks are presented in Section 6.