esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### The 12-Parameter Transformation
#### 4.6.1 Transformation Parameters  
To define a 12-parameter transformation, rotation matrix, scale matrix, and skew matrix are often combined in one matrix by mutual multiplication. Result of the multiplication is the matrix \(\mathbf{U}\), which can be written as [9]  
\[\mathbf{U}=\mathbf{H}\cdot\mathbf{S}\cdot\mathbf{R}=\begin{bmatrix}u_{11}&u_{1 2}&u_{13}\\ u_{21}&u_{22}&u_{23}\\ u_{31}&u_{32}&u_{33}\end{bmatrix} \tag{74}\]  
where parameters \(u_{ij}\) along with the parameters \(t_{x}\), \(t_{y}\) and \(t_{z}\) represent 12 transformation parameters for the 12-parameter transformation.  
Based on Equations (72) and (74), observation equations can be written [9]:  
\[v_{x}^{i} =t_{x}+u_{11}\cdot X_{i}^{i}+u_{12}\cdot Y_{i}^{i}+u_{13}\cdot Z_ {i}^{i}-X_{o}^{i}\] \[v_{y}^{i} =t_{y}+u_{21}\cdot X_{i}^{i}+u_{22}\cdot Y_{i}^{i}+u_{23}\cdot Z_ {i}^{i}-Y_{o}^{i} \tag{75}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Standard Deviation** \\ \hline \(t_{x}\) (m) & 380.278 & 1.183 \\ \(t_{y}\) (m) & 155.903 & 0.264 \\ \(t_{z}\) (m) & 653.169 & 1.224 \\ \(\mu_{x}\) (ppm) & 13.597 & 0.135 \\ \(\mu_{y}\) (ppm) & \(-\)3.149 & 0.040 \\ \(\mu_{z}\) (ppm) & \(-\)26.094 & 0.143 \\ \(r_{x}\) (\({}^{\prime\prime}\)) & \(-\)5.212 & 0.008 \\ \(r_{y}\) (\({}^{\prime\prime}\)) & \(-\)5.991 & 0.028 \\ \(r_{z}\) (\({}^{\prime\prime}\)) & 12.003 & 0.008 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Parameters of 9-parameter transformation from HDKS to HTRS96.  
\[v_{z}^{i}=t_{z}+u_{31}\cdot X_{i}^{i}+u_{32}\cdot Y_{i}^{i}+u_{33}\cdot Z_{i}^{i}-Z _{o}^{i}\]  
Following Equation (75), design matrix \(\mathbf{A}\) and observation vector \(\mathbf{I}\) can be obtained as  
\[\mathbf{A}=\begin{bmatrix}1&0&0&X_{i}^{1}&Y_{i}^{1}&Z_{i}^{1}&0&0&0&0&0\\ 0&1&0&0&0&0&X_{i}^{1}&Y_{i}^{1}&Z_{i}^{1}&0&0&0\\ 0&0&1&0&0&0&0&0&X_{i}^{1}&Y_{i}^{1}&Z_{i}^{1}\\ &&&&\vdots\\ 1&0&0&X_{i}^{n}&Y_{i}^{n}&Z_{i}^{n}&0&0&0&0&0\\ 0&1&0&0&0&0&X_{i}^{n}&Y_{i}^{n}&Z_{i}^{n}&0&0\\ 0&0&1&0&0&0&0&0&X_{i}^{n}&Y_{i}^{n}&Z_{i}^{n}\end{bmatrix}\quad 1=\begin{bmatrix}X_{o}^{1} \\ Y_{o}^{1}\\ Z_{o}^{1}\\ \vdots\\ X_{o}^{n}\\ Y_{o}^{n}\\ Z_{o}^{n}\end{bmatrix} \tag{76}\]
# End-to-End Service Availability in Heterogeneous Multi-Tier Cloud-Fog-Edge Networks
## 1 Introduction  
The internet, in its present form, is an intricate mosaic of interconnected networks, devices, and protocols. As we journey towards a more connected world, the imminent influx of the Internet of Things (IoT), coupled with advancements in edge and fog computing, promises to add layers of complexity to this landscape. This evolution, coupled with the impending amalgamation of space-air-ground-ocean networks, foreshadows an even more sophisticated internet architecture on the horizon.
# Challenges Associated with Implementing 5G in Manufacturing
## 1 Introduction  
5G promises to deliver faster download speeds, lower latency and higher capacity, as shown in Table 1; Table 2 below. The goal of Industry 4.0 is to maximize efficiency by utilizing these features across all processes and assets at all times; to provide a better understanding of the manufacturing processes across their manufacturing sites in near real-time. With the expected increase in data requirements ranging from mission-critical to massive machine connectivity, the deployment of 5G has raised expectations that it will open new opportunities for manufacturing business models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network Type** & **Average Download Speeds** & **Peak Download Speeds** & **Theoretical Download Speeds** \\ \hline
3G & 8 Mbps & -20 Mbps & 42 Mbps \\ \hline
4G & 32.5 Mbps & 90+ Mbps & 300 Mbps \\ \hline
5G & 130 Mbps-240 Mbps & 599 Mbps+ & 10–50 Gbps \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data Speeds for 3G, 4G and 5G.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Network Type** & **Milliseconds (ms)** \\ \hline
3G Network & 60 ms (Typical) \\ \hline
4G Network & 50 ms (Typical) \\ \hline
5G Network & 1 ms (theoretical) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of 3G/4G and 5G latency times [1].  
According to [PERSON] et al. [2], \"The Fourth Industrial Revolution or simply 'Industry 4.0' is how manufacturing industry expects to maximise the innovations of 5G wireless communications by automating industrial technologies and utilising other enabling technologies such as artificial intelligence (AI) and machine learning. Industry expects this to lead to more accurate decision making such as automation of physical tasks based on historical information and knowledge, or improved outcomes for a wide range of vertical marketplaces not just in manufacturing but verticals such as agriculture, supply chain logistics, healthcare, energy management and an ever-increasing number of industries becoming more aware of the potentials of 5G.\" It is expected to achieve this in three ways through the use of network slicing, Figure 1.  
* eMBB (Enhanced Mobile Broadband)
* URLLC (Ultra-Reliable Low Latency Communications)
* mMTC (Massive Machine-Type Communications)
## 3 Background
### ADAM Optimization Algorithm  
The ADAM optimization algorithm represents a significant advancement in the realm of stochastic optimization for training neural networks. Standing for Adaptive Moment Estimation, ADAM is renowned for its efficiency and adaptability, making it a popular choice in the field of deep learning. The algorithm incorporates adaptive learning rates, dynamically adjusting the rate for each parameter based on past gradients. It utilizes two moving averages, capturing the trend and variance of the gradients, and includes bias correction mechanisms to ensure accurate parameter updates, particularly during the initial stages of optimization. Mathematically, ADAM computes moments and updates parameters, demonstrating its adaptive nature. The algorithm's advantages include its adaptability to various tasks, efficient convergence, and robustness to noisy gradients. However, effective hyperparameter tuning is crucial for optimal performance. Despite its additional memory requirements, ADAM's widespread adoption underscores its effectiveness in optimizing neural network parameters and its pivotal role in contemporary machine learning applications.
# Recent Trends and Progress in Corrosion Inhibitors and Electrochemical Evaluation
## 3 Types of Corrosion Inhibitors
### Composite-Based Corrosion Inhibitors
#### 3.4.6 Layered Double Hydrooxides  
Layered double hydroxides (LDHs), also known as anionic clays, are a family of two-dimensional nanomaterials that serve as a typical example of the anion exchange mechanism. Their general formula is [M\({}^{2+}\)\({}_{1-x}\)M\({}^{3+}\)\({}_{x}\)(OH)\({}_{2}\)]\({}^{x+}\) (A\({}^{n-}\)\({}_{x/n}\))\(\cdot\)mH\({}_{2}\)O, which resembles the layered structure of brucite, and they have recently taken on a significant role not only because of their distinctive two-dimensional structure, but also because of their anion exchange capabilities. LDH's crystallinity, morphology, and conductivity (depending on the LDH composition) can improve the electrochemical performance of materials used as a electrodes for batteries [99], fuel cells [100], supercapacitors [101], and other energy conversion and storage devices. Due to its distinctive structure, it has the capacity for controlled release and selective absorption, which is advantageous and important in the field of anticorrosion [102; 103; 104].  
LDHs loaded with corrosion-inhibiting anions (such as nitrite, nitrate, and 2-benzothizollythio-succunic acid) have been examined as effective additives for steel corrosion prevention because of their unique anionic exchangeability [105]. The combination of the organic inhibitor and the LDHs nanocontainer shows significant promise for the prevention of corrosion. The layered hydrotalcite structures were divided into nanosheets with a single layer, and then these exfoliated nanosheets were restructured, using an organic inhibitor to synthesize the MgAl-LDHs loaded with 5-aminoidazole (AIA). In the 3.5 wt.% NaCl solution, the AIA LDHs as-synthesized showed controlled release behavior and effective anticorrosion performance on the copper [106]. Using MgAl-NO\({}_{2}\) LDHs, [PERSON] et al. [86] created CIs to prevent corrosion of the steel reinforcing in concrete. In contrast to chloride-contaminated simulated carbonated concrete pore solution, the results show that chloride-free simulated carbonated concrete pore solution has a noticeably greater MgAl-NO\({}_{2}\) LDH inhibition efficacy. In simulated carbonated concrete pore solution without chloride, MgAl-NO\({}_{2}\) LDH, IE is greater than 92%, but in the presence of chloride, the value is only less than 75%. LDH-C6(m\({}_{2}\)im)2-I and IL@LDH-C6(m\({}_{2}\)im)2-I are two coatings of MgAl-LDHs that have been modified using imidazolium-based diatomic ionic liquids. The two MgAl-LDH coatings were investigated for their surface morphologies and anticorrosion properties when submerged in 3.5 wt.% NaCl [107]. According to a survey of the literature, we can conclude that loading an inhibitor into LDHs can enhance the corrosion IE; however, the next stage could involve adding more inhibitors to LDHs.  
Figure 9: Schematic diagram of the micro-arc oxidation coating process [95].
# Man-in-the-Middle Attacks in Mobile Ad Hoc Networks (MANETs): Analysis and Evaluation
## 3 Man-in-the-Middle Attacks  
The phrase \"Man-in-the-Middle\" was coined in a basketball game, where a player in the middle attempts to intercept the ball as the two other players attempt to pass it [27; 28]. A similar idea is used in MANET, where MITM attackers put legal nodes' communications at risk by altering their messages. Such assaults have serious effects on the communication, particularly if the message's content includes information about safety. In MANET, the attacker should meet the following two requirements in order to execute an MITM assault: (1) The attacker node must receive a message containing significant information; (2) The assailant should be capable of interpreting the message's content. Figure 2 shows two ways in which MITM assaults can be initiated in the MANET.  
* Passive Mode: The communication line between legal nodes can be passively eavesdropped on by an attacker.
* Active Mode: Attackers have the active capability to delay or drop the content of data that is received in a communication.  
Figure 3 illustrates the explanation for both the active and passive modes of MITM assaults in MANET. We can observe that passively, the MITM assailant can eavesdrop on the channel involving valid nodes, and actively, the MITM assailant can drop or delay the legal data that are broadcast over the system.  
* MITM as Message Delayed: The successful delivery of messages to each valid node is essential to the operation of MANET. In this assault architecture, the harmful nodes purposefully pass the messages on to the neighboring nodes with a \"delay\" factor. Since MANET communications are extremely sensitive, delaying such signals might have disastrous effects on the network.
* MITM as Message Dropped: This kind of assault also indicates a \"black hole\" assault in MANET, which is when the attacker willfully drops the valid message they have  
Figure 3: Illustration of both the active and passive modes of MITM assaults in MANET.  
Figure 2: MITM attacks in MANET.  
just received, hence preventing further valid message propagation. As a result, the assailant prevents the valid node from receiving any communication, which then prevents the messages from reaching their intended recipients. Dropping the messages can have a big effect on the network because they carry delicate data, such as in collision avoidance. Imagine an instance where valid nodes are supposed to announce the presence of black ice on the path.
# Light-Induced Advanced Oxidation Processes as PFAS Remediation Methods: A Review
## 6 Light Sources  
To date, the application of photocatalysis to PFAS compounds has mostly been performed by means of the use of artificial light sources of varying wavelengths of ultraviolet A (UVA 315-400 nm) using lamps with a light emission peak at 365 nm, and ultraviolet C (UVC 100-280 nm) using lamps with peaks at 254 and 185 nm [54]. The wavelength of 185 nm itself can degrade PFAS, since the absorption of PFOA has been confirmed for this wavelength. However, this range of wavelength at the industrial level, because of the high costs, represents a market block for AOP as a possible approach for PFAS-contaminated wastewater [133].  
The employment of UVA lamps with a peak at the wavelength of 365 nm has mostly been used in systems of power ranging from 125 to 300 W in TiO\({}_{2}\)-based photocatalysts with Pt, Pd, and Ag nanoparticles or in the presence of multi-wall carbon nanotubes (MWCNTs) [57; 66]. For UVC (254 nm), studies report emission powers from 16 to 500 W for pure TiO\({}_{2}\), TiO\({}_{2}\) doped with Cu, Fe, and Pb ions, and composites with molecularly imprinted polymers. Studies have also been reported using In\({}_{2}\)O\({}_{3}\) and Ga\({}_{2}\)O\({}_{3}\) in sheaf, needle, microsphere, nanotube, and nanoparticle forms, as well doping with nitrogen and with CeO\({}_{2}\)[78; 82]. Applications using ZnO have been also reported [143].  
Given the present situation, an advantage could be to move towards possible photocatalysts that can absorb in the near-UV range. Indeed, by using solar concentrators, the near-UV part of the solar spectrum is reflected and focused, thus permitting its utilization with photocatalysts. Anodized aluminum mirrors with special acrylic resin coatings are interesting examples for exploiting the near-UV part of the solar spectrum [144].
## 4 Classification of Energy Efficient MAC Protocols for UWSNs
### _Efficiency Reservation (ER-MAC)_  
Efficiency Reservation MAC (ER-MAC) exploits the property of propagation time delay between communicating nodes, in a centralized topology, to establish the relative position of sensor nodes. It consists of cluster groups, the sink/anchor nodes in a cluster group is responsible of its member nodes utilization and reliably control of data transmission based on their relative location and directions. ER-MAC also implements duty cycling, where it incorporates the sleep mode into the communicating members of the cluster group to conserve energy [84].
## 4 Simulation and Experimental Results and Discussion
### WRF Simulation Analysis
Lower-resolution numeric predictions (e.g., WRF9 or even GFS with 13 km resolution), on the other hand, can be easily obtained from open databases and do not require heavy computational resources for processing and simulations.
## 4 Experiments
### Results and Discussion
#### 4.4.1 Comparisons with Base Classifiers  
In this section, we compare the proposed method with four conventional machine learning algorithms. For simplicity, all the conventional algorithms use their default parameters. We adopt a class imbalance ratio of 10 as an example. The classification results are given in Table 4 in terms of TPR, FPR, precision, F1-score, G-mean, and Kappa on the test data.  
From Table 4, we can see that the class imbalance problem strongly affects the performances of the conventional machine learning algorithms. For example, the TPR value of the SVM algorithm is only 0.10, which indicates that a large number of spam samples (the minority class) are misclassified as non-spam (the majority class). This also causes the G-mean value of SVM to be only 0.31 and the Kappa value of SVM to be 0.16. Although the GNB method achieves a performance of 0.91, its false positive rate reaches 0.81. Therefore, its precision, F1-score and Kappa values, are the lowest. These results show that a large number of non-spam samples are misclassified as spam samples.  
Figure 4 presents the classification performance of each method in terms of TPR, FPR, precision, F1-score, G-mean, and Kappa. The histogram chart illustrates that only the CART method and our method have stable metrics; the other methods have large fluctuations. For example, the precision of SVM is 0.77, but its TPR is 0.10, F1-score is 0.18, and its Kappa is 0.16, which means that the method is highly influenced by the majority class, and the classification results tend toward non-spam. The TPR, precision, F1-score, and Kappa of CART method show stable performance within the range 0.50-0.57, but these values are lower than those of our approach.  
In comparison to the conventional machine learning algorithms on the same dataset, Table 4 and Figure 4 show that our approach performs better than other approaches. In particular, the F1-score value (70%) and Kappa value (67%) of our approach are much higher than those of the other methods.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **TPR** & **FPR** & **Precision** & **F1-Score** & **G-Mean** & **Kappa** \\ \hline SVM & 0.10 & 0.01 & 0.77 & 0.18 & 0.31 & 0.16 \\ CART & 0.57 & 0.05 & 0.53 & 0.55 & 0.74 & 0.50 \\ KNN & 0.47 & 0.02 & 0.65 & 0.55 & 0.68 & 0.51 \\ GNB & 0.91 & 0.81 & 0.1 & 0.18 & 0.41 & 0.02 \\ Our Approach & 0.70 & 0.03 & 0.70 & 0.70 & 0.82 & 0.67 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification results of different machine learning methods.
# Numerical Study of Pressure Attenuation Effect on Tunnel Structures Subjected to Blast Loads
## 4 Results
### Pressure Reduction Module Effect Analysis
#### 4.1.3 Linear Tunnel with Double-Pressure Relief Orifice Plate Explosion
It can be concluded that the agreement of the trend of the pressure attenuation rate is good, and the model gives reasonable predictions for different tunnel blast attenuation designs. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Weight of C-4** & **100 g** & **150 g** & **200 g** & **250 g** & **350 g** \\ \hline \multirow{4}{*}{Expansion chamber} & P1 & 11\% & 5.4\% & 17\% & 5.9\% & 20.6\% \\ \cline{2-7}  & P2 & 20\% & 24\% & 46\% & 26.2\% & 2.21\% \\ \cline{2-7}  & P3 & 59\% & 33\% & 49\% & 49.6\% & 22.8\% \\ \cline{2-7}  & P4 & 29\% & 28\% & 6.8\% & 1.9\% & 4.2\% \\ \hline \multirow{4}{*}{Single-orifice plate} & P1 & 1\% & 13.5\% & 8.6\% & 2.3\% & 10.9\% \\ \cline{2-7}  & P2 & 27.7\% & 14.9\% & 13.5\% & 5.7\% & 25\% \\ \cline{2-7}  & P3 & 89.3\% & 67.9\% & 44.1\% & 45.5\% & 12\% \\ \cline{2-7}  & P4 & 4.8\% & 15.4\% & 31.2\% & 33.3\% & 39.1\% \\ \hline \multirow{4}{*}{Double-orifice plate} & P1 & 11.4\% & 29.3\% & 2.9\% & 8.4\% & 3.9\% \\ \cline{2-7}  & P2 & 53.9\% & 62.7\% & 25.8\% & 22.7\% & 14.1\% \\ \cline{2-7}  & P3 & 138\% & 154\% & 64.3\% & 44.4\% & 19\% \\ \cline{2-7}  & P4 & 71.2\% & 93.3\% & 62.6\% & 18.3\% & 9.12\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Percentage error of different pressure attenuation models. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Weight of C-4 (g)** & & **100** & **150** & **200** & **250** & **350** \\ \hline \multirow{4}{*}{Position} & P1 & Experiment & 711.49 & 779.04 & 1568.8 & 1950.37 & 2294.61 \\ \cline{2-7}  & Simulation & & 792.96 & 1006.73 & 1523.11 & 1786.34 & 2204.64 \\ \cline{2-7}  & Experiment & 379.33 & 454.14 & 686.29 & 782.28 & 986.88 \\ \cline{2-7}  & Simulation & & 584.15 & 739.17 & 863.49 & 960.17 & 1126.15 \\ \cline{2-7}  & P3 & Experiment & 272.79 & 306.57 & 450.01 & 544.88 & 703.04 \\ \cline{2-7}  & Simulation & & 651.38 & 780.52 & 739.47 & 787.03 & 843.57 \\ \cline{2-7}  & Experiment & 38.18 & 52.33 & 71.64 & 108.96 & 166.3 \\ \cline{2-7}  & Experiment & 0.14 & 0.17 & 0.16 & 0.20 & 0.24 \\ \cline{2-7}  & P4 & & & & Average: 0.18 & \\ \cline{2-7}  & (L/D = 5.67) & & & & & Average: 0.18 & \\ \cline{2-7}  & Simulation & & 65.40 & 101.19 & 116.50 & 128.89 & 151.12 \\ \cline{2-7}  & Simulation & & 0.10 & 0.13 & 0.16 & 0.16 & 0.18 \\ \cline{2-7}  & & & & & Average: 0.15 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of experiment and numerical results of steel tunnel with double-pressure relief orifice plate.
## 5 Conclusions  
In this work, we proposed the Macro SOStream, an evolving algorithm for dynamic data stream clustering with online learning. Its performance for clustering tasks was compared to SOStream and DenStream algorithms over well-known synthetic datasets. The results obtained with Macro SOStream were promising because it managed to group the data stream properly, even in data drift and data evolution scenarios.  
Macro SOStream is based on SOStream evolving algorithm. However, it adopts the concept of macrocluster that allows the creation of clusters with arbitrary shapes. Due to this modification, the Macro SOStream can obtain a better performance than SOStream. The macrocluster-merging functionality proved to be very relevant for the Macro SOStream algorithm's good performance. That explains why the Macro SOStream achieved \(ARI=1.0\) on the four synthetic bases, while SOStream obtained values around \(ARI\approx 0.38\) for the Horseshoe, Moon, and Three Spirals datasets, and \(ARI=0.0\) for the T dataset.  
The hyperparameter-sensitive analysis results indicated that Macro SOStream has a similar performance to DenStream when both use their best hyperparameter configuration.  
The DenStream has a more significant hyperparameter range than Macro SOStream but presents a difference its learning mode. It has two moments of offline learning during clustering tasks, which influences the total execution time, making it a more costly algorithm than Macro SOStream. In contrast, Macro SOStream has fully online learning throughout its execution and a time execution better than DenStream.  
Furthermore, as a limitation of the work, we have the use of synthetic datasets to validate the SOStream Macro. For future work, we plan to apply the Macro SOStream algorithm to real-world datasets to assess their quality and performance in these cases. Another point is the execution time of the proposed algorithm, which is longer than SOStream, as there is one more step than this algorithm, and DenStream, which has offline and online execution time.  
Conceptualization, A.S.O.; methodology, A.S.O.; software, A.S.O. and R.S.d.A.; validation, A.S.O.; investigation, A.S.O.; writing--original draft preparation, A.S.O.; writing--review and editing, R.S.d.A. and L.A.G.; supervision, L.A.G. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES) [Finance Code 001].  
The data used can be found at [[https://github.com/[PERSON]/datastream/tree/v1.0](https://github.com/[PERSON]/datastream/tree/v1.0)]([https://github.com/[PERSON]/datastream/tree/v1.0](https://github.com/[PERSON]/datastream/tree/v1.0)), accessed on 18 June 2022.  
The authors declare no conflict of interest.