esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Intrusion Detection Systems for Fog and Cloud Computing
## 3 Security in Fog Computing
### Security Challenges in Fog Computing  
Due to the above unique benefits and characteristics, the traditional data privacy and security approaches in cloud computing are no longer appropriate to protect huge data in FC. [PERSON] et al. [21] identified security matters as the crucial concerns in the fog environment within a comprehensive survey on the benefits and challenges of FC. By analyzing recent implements of fog computing and accessing their quality of security services, FC was found to be weak against security. The findings of this study point out some promising solutions and trends, including the desire for security protocols and new methods to detect and mitigate security risks in fog networks  
[PERSON] et al. [22] described security challenges in FC and proposed solutions with blockchain technology. Some security problems related to FC that need to be managed are authorization systems for the entire FC's node, authentication, data integrity, access control, DOS attacks, and attack detection, because it impacts the fog layer's working efficiency. The blockchain technique, a decentralized computation and information-providing medium, is applied to solve each security challenge. For example, the smart contract can strengthen authentication by creating a five-main-element scheme, including admins, fog nodes, smart contracts, cloud server providers, and end-users. In this case, a decentralized technique to manage access controls and authentication is followed. All the lodged fog nodes to connected devices are mapped by smart contracts, and users have to connect the respective fog node for authentication and obtain access.  
[PERSON] et al. [23] highlighted the significant security challenges in fog computing, including protecting computational data, access control, authentication, data storage, and privacy assurance problems. Services can be disrupted by any devices manipulated by an adversary as they are injected with false information or intrude with malicious actions. For example, even a tiny portion of compromised devices could potentially harm the entire ecosystem as these devices played as involved parties in the distributed fog environment at various layers. In order to address these issues raised in [24], privacy and security should be managed in the design process of FC.
# The Odyssey of Entropy: Cryptography
## 4 Entropy and Other Cryptographic Areas
### Secret Sharing  
Secret sharing is the science of dividing a secret into shares and distributing them among a number of participants in a way that the secret can be retrieved only when a predetermined minimum number of shares are collected from participants. For example, in [PERSON]'s \((k,n)\) secret sharing scheme, the secret \(s\) is distributed among \(n\) participants in a way that \(k\) of the participants can reconstruct \(s\). The procedure is as follows. The dealer (the party who owns the secret) chooses \(k-1\) random numbers \(a_{1},a_{2},\ldots,a_{k-1}\), and builds a polynomial \(S(x)=a_{0}+\sum_{i=1}^{k-1}a_{i}x^{i}\), where \(a_{0}=s\). Afterwards, the dealer calculates \(S_{i}=S(i)\) for all \(i\in\{1,2,\ldots,n\}\) and privately sends them to \(n\) participants \(P_{1},P_{2},\ldots,P_{n}\). The polynomial \(S(x)\) and consequently the secret \(s\) can be reconstructed using any subset \(S^{\prime}\) of \(S=\{S_{1},S_{2},\ldots,S_{n}\}\) via an interpolation method, such as Lagrange interpolation, provided that \(|S^{\prime}|\geq k\).  
There are some secret recovery methods that eliminate the need for remembering the secret shares via making it possible to recover each share by asking the owning participant a number of personal questions [88]. Moreover, entropy was used to model and design more generalized secret sharing systems wherein more than one secret is shared, and each secret can be reconstructed only by a qualified set of participants [233].
## 1 Introduction
### Emphasis on Challenges and Technological Solutions  
As the maritime industry remains the backbone of global trade, it inevitably grapples with a plethora of challenges that amplify in magnitude and complexity with each passing year. Among the most pressing issues is environmental pollution, a consequence  
Figure 1: Comprehensive Overview of Maritime IoT Applications and Interconnections.  
of decades-long practices and processes. Emissions from ships, oil spills, ballast water discharge, and waste disposal have taken a toll on our marine ecosystems, threatening marine life and, subsequently, human health. The carbon footprint of ships alone accounts for a significant portion of global greenhouse gas emissions, propelling the urgency for sustainable interventions [8].  
Another formidable challenge is maritime security. In an age where piracy has evolved from rogue seafarers to organized crime syndicates with advanced tools, ensuring the safety of both cargo and crew has never been more critical. Threats of terrorism, smuggling, and illegal fishing add further layers to this intricate challenge [9; 10].  
Yet, in the midst of these challenges lies the potential for innovation. Technological solutions, particularly IoT, emerge as beacons of hope. For environmental concerns, IoT-enabled sensors can monitor emissions in real-time, ensuring compliance with international standards and pinpointing areas for improvement. Intelligent systems can predict and prevent potential oil spills, while also monitoring the discharge of ballast water, ensuring it aligns with environmental standards [11].  
In terms of security, IoT has the potential to revolutionize maritime safety protocols. Advanced tracking systems can keep vessels safe from piracy by identifying and alerting ships about potential threats. Additionally, integrated communication tools ensure timely responses during emergencies, while surveillance systems using interconnected cameras and sensors can monitor every nook and cranny of a ship, deterring illicit activities [12].  
As we sail further into this article, we will explore how these technological innovations, anchored by IoT, have the potential not just to address but to redefine the very challenges that have beset the maritime industry.
## 3 Sound Source Localization Fusion Algorithm of a Three-Plane Five-Element Microphone Array
### Performance Analysis of Direction-Finding via the Fusion Algorithm
#### 4.2.1 Analysis of the Elevation Angle Measurement Precision of the Sound Source  
The measurement precisions of the elevation angle for the X0Y, X0Z, and Y0Z planes and the three-plane array were simulated, compared, and analyzed. The results are shown in Figure 2 for an array element spacing \(a\) of 1 m, a sound velocity \(c\) of 340 m/s, a time-delay estimation error value \(\sigma_{\tau}\) of 1 \(\upmu\)s, and an arbitrary planar elevation angle ranging from 0\({}^{\circ}\) to 90\({}^{\circ}\).  
As shown in Figure 2, the elevation angle estimation error \(\sigma_{\theta}\) is greatly influenced by itself, where the error measured in the X0Y plane decreases with increasing elevation angle \(\theta\), and the error measured in the X0Z and Y0Z planes increases with increasing elevation angle \(\theta\). Through the three-plane fusion algorithm, the error is weakly influenced by itself, with an error of less than 0.001\({}^{\circ}\). In conclusion, the fusion algorithm has advantages over a single-plane microphone array in elevation angle measurement precision, and its stability is obviously better than that of the X0Y, X0Z, and Y0Z plane approach.
# Efficient Node Deployment of Large-Scale Heterogeneous Wireless Sensor Networks
## 7 Conclusions  
This work introduced an efficient routing algorithm for WSNs for better information fusion. The proposed algorithm considered the environmental conditions and other important parameters such as link load balancing, node energy, and reliability. The algorithms provided a model for the environment conditions where the routing protocol could avoid highly languorous areas. The algorithm is compared to two of the recently published algorithms correcting some of the used equations and adding more realistic parameters to be considered during the routing process. One of the proposed algorithm drawbacks is the time complexity where it takes more time than the other two algorithms that is compared with. However, it is a tradeoff between the performance and time complexity. Another drawback that could be worth mentioning is the uncertainty in the input data due to the inefficiency of the sensor nodes themselves. The future of this research involves the practical implementation of the proposed protocol on real networks. It is planned to implement the proposed algorithm on Altera Cyclone IV FPGA Development Board is one of the best choices for implementation. Other future work includes adding more realistic problems, such as obstacles in the monitoring field. In addition, sensors data is usually unreliable, and it might be noisy or include many redundancies. A fuzzy-based technique might help cleaning the data before it reaches the sink node. Moreover, comparing between a synchronous and asynchronous techniques with different types of networks and sizes could be another extension to the work in this paper.  
Conceptualization, F.H.E. and R.A.R.; methodology, F.H.E., R.A.R. and A.Y.K.; software, F.H.E.; validation, F.H.E., R.A.R.; formal analysis, F.H.E., R.A.R., A.Y.K. and A.T.A.; investigation, F.H.E., R.A.R., A.Y.K., A.T.A., K.Y. and M.A.A.; resources: F.H.E., R.A.R. and M.A.A.; data curation: F.H.E., R.A.R., A.Y.K., A.T.A. and M.A.A.; writing--original draft preparation: F.H.E. and R.A.R.; writing--review and editing: F.H.E. and R.A.R.; visualization, F.H.E.; supervision: R.A.R.; project administration, R.A.R.; funding acquisition: F.H.E., R.A.R., A.Y.K., A.T.A., K.Y., M.A.A. All authors have read and agreed to the published version of the manuscript.  
This research has been funded by Scientific Research Deanship at University of Ha'il--Saudi Arabia through project number RG-20 147.  
Figure 13: Influence of initial deployment strategy of mobile nodes on coverage ratio.
## 2 Materials and Methods
### Characterization Structure of Collagen
#### 3.3.2 Fourier Transform Infrared Spectroscopy (ATR-FTIR)  
Figure 2 shows the FTIR spectra absorption rate measurements for ASC in the range 4000-800 cm\({}^{-1}\), which are comparable. All collagens contain a primary absorption band in the amide band region, however the FTIR spectra for AAC, LAC, and CAC differ slightly, suggesting that their secondary structures varied. Five major absorption peaks have been identified, amide A, amide B, amide I, amide II, and amide III [15].  
Previous studies have found that amide A peaks are typically recorded between 3400 and 3440 cm\({}^{-1}\) and are frequently associated with N-H stretching vibrations. Nonetheless, in this study the amide A peak was the most prominent peak at (3289.46 cm\({}^{-1}\)) for all collagen extractions, which were lower than previously reported. This is because the N-H stretching functional group of amide A interacts with amines of several proteins and complex molecules, shifting the amide A band to a lower frequency range than usual. It also suggests that hydrogen bonding exist in every collagen [39]. When the CH\({}_{2}\) group is stretched asymmetrically  
readings for AAC were 2939.08 cm\({}^{-1}\), those for CAC were 2920.44 cm\({}^{-1}\), and the reading for LAC was marginally lower than those for AAC and CAC recorded at 2918.58 cm\({}^{-1}\). Amide I peaks are typically found between 1600 and 1700 cm\({}^{-1}\) due to their strong absorption, which is principally caused by C=O stretching vibrations along the polypeptide spine [54]. Lower frequency number peaks are associated with a decrease in molecular organisation, making it a sensitive predictor of the secondary structure of peptides [55].  
Amide I was found at the peak of this study, and the absorption rates of AAC, LAC, and CAC were all within the same range (1640.07 cm\({}^{-1}\)). In addition, assessments of the amide II absorption rate for all extracted collagen remained within a similar range, 1541.30 cm\({}^{-1}\). The amide II peaks have wavelengths between 1500 and 1600 cm\({}^{-1}\) and are associated with N-H bending and C-N stretching vibrations, as well as the helical shape of three collagens. Since the NH group in the peptide chain is involved in hydrogen bonding, CAC had a low value. As a result, the amide group's position moves to a lower intensity. When the wavelength decreases, the number of hydrogen bonds in collagen increases [54]. The amide peak of the peptide III bond indicates complicated interactions between molecules. As shown in Figure 2, it arises due to the C-N stretching and N-H bending vibrations of the peptide group. Amide III is detected for normal collagen range between 1200 cm\({}^{-1}\) and 1350 cm\({}^{-1}\) for maximum absorption. According to the data, the absorption rate of amide III was within the normal range for AAC (1231.92 cm\({}^{-1}\)), CAC (1235.64 cm\({}^{-1}\)) and LAC (1231.92 cm\({}^{-1}\)). As amide III was in the standard range, a collagen triple helix structure could be observed [56].
## 2 Methods and Materials
### Statistical Machine Learning Models and SHAP Method
#### 2.4.2 Support Vector Machines  
Support Vector Machines (SVMs) are powerful supervised learning models. SVM constructs a hyperplane or a set of hyperplanes in a high-dimensional or even infinite-dimensional space, depending on the characteristics of the dataset. Let \(S=\{(\mathbf{x}_{1},\mathbf{y}_{1}),\ldots,\)\((\mathbf{x}_{n},y_{n})\}\) be a training dataset, where \(\mathbf{x}_{i}\in\mathbb{R}^{d}\) and \(y_{i}\in\{-1,1\}\). The training dataset is called linearly separable if there exists a hyperplane (hyperspace) \((\mathbf{w},b)\) such that for \(\mathbf{x}_{i}\) holds that \(y=\text{sign}(\langle\mathbf{w},\mathbf{x}_{i}\rangle+b)\); alternatively, the latter can be written in the form of inequalities as  
\[y_{i}\cdot(\langle\mathbf{w},\mathbf{x}_{i}\rangle+b)>0\;\;\text{for all}\;\;\mathbf{x}_{i} \tag{6}\]  
The hyperplanes (hyperspaces) which have this property are infinite; therefore, to select the optimal solution, it is important to  
\[\arg\min_{(\mathbf{w},b)}\lvert\mathbf{w}\rvert^{2}\;\;\text{for all}\;\mathbf{x}_{i}\;: \;y_{i}\cdot(\langle\mathbf{w},\mathbf{x}_{i}\rangle+b)\geq 1 \tag{7}\]  
There are several formulations for problems similar to the previous one, which incorporate additional constraints such as the regularisation terms. In such cases, Equation (7) is transformed to the following:  
\[\min_{\mathbf{w},b,\delta}\Biggl{(}\lambda\lvert\mathbf{w}\rvert^{2}+\frac{1}{m}\sum _{i=1}^{m}\zeta_{i}\Biggr{)}\;\;\text{for all}\;\mathbf{x}_{i}\;:\;y_{i}\cdot( \langle\mathbf{w},\mathbf{x}_{i}\rangle+b)\geq 1-\zeta_{i}\;\text{and}\;\zeta_{i}\geq 0 \tag{8}\]  
The linearity is not always the situation which is faced, so a more sophisticated method is to embed our dataset into a higher feature space using the kernel functions. The kernel function \(K(\mathbf{x},\mathbf{x}^{\prime})=\langle\psi(\mathbf{x}),\psi(\mathbf{x}^{\prime})\rangle\), where the function \(\psi\) is some domain space on some Hilbert space. The more common kernels are  
* The Gaussian kernel which is defined as \(K(\mathbf{x},\mathbf{x}^{\prime})=e^{-\frac{\lvert\mathbf{x}-\mathbf{x}^{\prime} \rvert^{2}}{2\sigma}}=e^{-\gamma\lvert\mathbf{x}-\mathbf{x}^{\prime}\rvert^{2}}\).
* The polynomial kernels which are defined as \(K(\mathbf{x},\mathbf{x}^{\prime})=(1+\gamma<\mathbf{x},\mathbf{x}^{\prime}>)^{k}\).
* The sigmoid kernel which is defined as \(K(\mathbf{x},\mathbf{x}^{\prime})=\tanh(-\gamma<\mathbf{x},\mathbf{x}^{\prime}>+r)\).
## 2 Related Works
Indeed, in practical sidelink scenarios with a limited frequency spectrum, the radar sensing accuracy can impact communication reliability due to the conflict in bandwidth requirements [28]. Due to the lack of base station coordination, a traditional SPS scheme may lead to serious consecutive packet collisions. To our knowledge, there is currently a lack of extensive research on joint optimization for sensing and communication in sidelink JCS systems. Therefore, this paper leverages pertinent research and proposes the CCM-SPS scheme to address the issue of consecutive collisions in sidelink JCS systems. This scheme utilizes full-duplex collision detection and Q-learning reinforcement learning methods, optimizing both the sensing range (CRLB) and ultra-reliable low latency communications (URLLCs) quality in vehicular JCS systems.
## 4 Numerical Modal Analysis
### Modal Analysis Results for the Base Model
The accelerometer measurements revealed a peak amplitude of 138.08 m/s\({}^{2}\) at the secondary mode of 22.9 Hz and the second highest amplitude of 123.32 m/s\({}^{2}\) at the primary mode of 15.6 Hz. Based on these findings, the 22.9 Hz and 15.6 Hz frequency bands had the most pronounced effect on bolt loosening phenomena. Consequently, future analysis will examine these frequency bands to explore the variations in sunroof glass resonance frequency induced by design factors during the optimal design. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Mode**} & \multicolumn{4}{c}{**Natural Frequency [Hz]**} \\ \cline{2-5}  & **ODS** & **Modal Analysis** & **Difference** & **Percentage (\%)** \\ \hline \#1 & 15.600 & 15.851 & 0.251 & 1.609 \\ \#2 & 22.900 & 23.298 & 0.398 & 1.738 \\ \#3 & 28.900 & 31.864 & 2.964 & 10.256 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Natural frequency comparison of the ODS and modal analysis results.
# Automatic Blob Detection for Dental Caries
## 3 Results and Discussion  
Experimental results from the proposed approach are based on the analysis of the caries candidate detection process. Experiments were carried out on 11,114 bitewing X-ray images of the augmented dental dataset. The dataset was split into 10,090 training set images and 1024 test images. In order to process the data set, several individual stages were used to analyse the dataset, as have been discussed in the previous sections of this work. Figure 1 shows an overview of the processing stages that include: preprocessing, segmentation and feature extraction.  
Additionally, performance evaluation of the proposed approach was based on its ability to detect and locate dental caries on test data images. A Gaussian filter was used to reduce noise on the images before introducing a convexity threshold value to eliminate false positives regions from actual detected caries candidates. Figure 4 shows the caries detection algorithm's identification of caries locations on Gaussian filtered images before false positives are eliminated. Figure 5, shows the identification of dental caries locations remaining after introducing a convexity threshold value of 1.0 that eliminates false positives.  
The results comparison was performed against different caries detection diagnostic methods to determine if the results fell within acceptable limits or not. The caries detection diagnostic methods compared against include textural classification as discussed by [21], dental classification for periapical radiographs by [22] and c  
Figure 4: Gaussian filtered images of all caries detected regions, including false positives.  
Figure 5: Images of dental caries detected after eliminating false positives through the use of the convexity threshold.  
supervised learning model proposed by [23] for panoramic images. The comparison of our proposed method with other detection techniques is shown in Table 1.  
Our proposed method achieved better accuracy than the others with the introduction of blob detection on dental radiographs. Despite our results falling within the acceptable range of a novel approach, [23] enjoyed marginally higher results, and this is attributed to various reasons. Some reasons include differences in the database analysed, where our approach used bitewing radiographs while [23] used panoramic radiographs. Another reason is attributed to the errors experienced when analysing bitewing radiographs, namely exposure errors and image augmentation anomalies.
# Thermal and Thermomechanical Analysis of Amorphous Metals: A Compact Review
## 1 Introduction
### Amorphous Metals: Preparation Techniques  
Despite their exceptional properties, the production of amorphous metals presents a significant challenge. To achieve the desired amorphous structure, rapid cooling from the molten state is required, typically at rates exceeding 10\({}^{6}\) K s\({}^{-1}\)[2]. This rapid solidification prevents the formation of crystalline phases and allows the retention of the disordered atomic structure [17]. Various techniques, such as melt spinning [18], vapor deposition, and thermal spraying [19], are employed to produce amorphous metal ribbons, coatings, and thin films [20]. The development of bulk metallic glasses (BMGs) has further expanded the potential applications of amorphous metals [21] due to overcoming the limitations of conventional amorphous metal ribbons and thin films. This was mostly achieved by tuning the composition and improving the glass-forming ability of some systems and reducing the critical cooling rate to numbers as low as 10 K s\({}^{-1}\). This achievement has opened up new opportunities for structural applications [22], biomedical implants [23], and advanced engineering components [24]. The numerous advantages and potential applications of bulk metallic glasses and amorphous metals have placed them in the forefront of research and development [25], with ongoing efforts to optimize their compositions [26], glass-forming ability [27], and production processes [28], and explore their diverse applications across various industries.
## 3 Proposed TH-CPSK-CEMC System
### Signal Model  
Utilizing \(M\)-ary CPSK for \(N\) users, the transmitted signal \(Q(t)\) for a given user \(k\) can be expressed as [26]  
\[Q^{k}(t)=\frac{Q_{s}}{N_{p}}\sum_{i=0}^{N_{p}-1}p\left(t-iT_{f}-c_{i}^{k}T_{c}- d_{\left\lfloor\frac{i}{N_{p}}\right\rfloor}^{k}T_{s}\right), \tag{1}\]  
where \(Q_{s}\) represents the molecular concentration per \(T_{\text{tx}}\), \(p(t)\) is a normalized concentration shaping function, \(T_{f}\) denotes the frame period, \(T_{s}\) specifies the timing of molecule release, and \(T_{c}\) corresponds to the duration of one period. As shown in Figure 4, the total transmission time, \(T_{\text{tx}}\), is structured by segmenting the total available time into \(N_{p}\) frames. Each frame accommodates \(N_{h}\) possible hopping positions for a molecule, calculated as \(N_{h}=T_{f}/T_{c}\). Consequently, the total transmission time is given by \(T_{\text{tx}}=N_{p}N_{h}MT_{s}\). The TH code \(\{c_{i}^{k}\}\) is modeled as an independent random variable, taking values such that \(0\leq c_{i}\leq N_{h},\forall\,k\) with equal probability. The TH code provides an additional shift of \(T_{c}\) in order to reduce the collision effect in multiple access settings. Moreover, the frame time is assumed to be an integer multiple of the chip time, that is, \(T_{f}=N_{h}T_{c}\). The term \(d_{\left\lfloor\frac{i}{N_{p}}\right\rfloor}^{k}\) indicates the symbol transmitted by the \(k\)th user (with \(1\leq d_{\left\lfloor\frac{i}{N_{p}}\right\rfloor}^{k}\leq M\)). This introduces an additional shift (\(T_{s}\)), used to distinguish the concentrations carrying bit 0 and bit 1. The  
Figure 3: Block diagram of TH-CPSK-CEMC system with \(M=2\).  
above equation captures the essence of TH-CPSK in a multiple access setting, highlighting how each user's signal is composed and transmitted within the MC network [26].