esa-sceva/satcom-chunk-collection huggingface dataset:
# Partial Discharge Pattern-Recognition Method Based on Embedded Artificial Intelligence
## 5 System Testing
### Comparison of Different Feature Extraction Methods  
To emphasize that this paper enhanced the distinction of PD types through the stacking of PRPD maps, we conducted tests on the PRPD maps in various scenarios: without stacking, after stacking 10 times, 15 times, and 20 times. Subsequently, we converted these PRPD maps into grayscale images using the same method as the input for the CNN model. Each set of data was then processed by the three models under identical conditions. The results obtained are shown in Table 7.  
\begin{table}
\begin{tabular}{c c} \hline
**PD Type** & **Monolithic Accuracy** \\ \hline Plate Discharge & 99\% \\ Ball-Plate Discharge & 99\% \\ Tip Discharge & 97\% \\ Average value & 98.33\% \\ \hline \end{tabular}
\end{table}
Table 6: Monolithic test results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Insolution** & **Plate Discharge** & **Ball-Plate Discharge** & **Tip Discharge** & **Average** \\
**Thickness** & **Mode** & **Model** & **Model** & **Accuracy Rate** \\ \hline
3.5 mm & 99\% & 99\% & 93\% & 97\% \\
4.5 mm & 99\% & 99\% & 97\% & 98.3\% \\
5.5 mm & 97\% & 93\% & 85\% & 91.7\% \\
6.5 mm & 92\% & 88\% & 82\% & 87.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Different insulation media thickness test results.  
\begin{table}
\begin{tabular}{c c} \hline
**PD Type** & **Monolithic Accuracy** \\ \hline Plate Discharge & 99\% \\ Ball-Plate Discharge & 99\% \\ Tip Discharge & 97\% \\ Average value & 98.33\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Recognition results for the three models with varying degrees of superposition.  
\begin{table}
\begin{tabular}{c c} \hline
**PD Type** & **Accuracy** \\ \hline Plate Discharge & 99.65\% \\ Ball-Plate Discharge & 99.25\% \\ Tip Discharge & 99\% \\ Average value & 99.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: PC-side model test results.  
From the above table, it can be concluded that the PRPD plots generated by overlaying the data were more useful for the recognition of discharge patterns, especially for the tip discharge model. Among the three discharge models, the plate discharge and the ball-plate discharge exhibited similar waveforms, and, even after 10 superimpositions, the recognition accuracy remained low. However, after 15 superimpositions, the average accuracy of model recognition could reach 98.17%, with no significant improvement observed after 20 superimpositions. Additionally, it is important to note that, as the number of superimpositions increased, the program consumed more processing resources and time, thereby significantly increasing the workload on the microprocessor. Consequently, we made the decision to construct the data features using 15 superimpositions.
## 2 Theoretical Background and Problem Statement  
The n-dimensional discretized model of a n-link robotic arm is shown in Figure 1. In the given framework, \(q=[q_{1},q_{2},\ldots,q_{n}]\) signifies the angular vector, and \(\tau=[\tau_{1},\tau_{2},...,\tau_{n}]^{T}\in R^{n}\) denotes the output torque of n-DOF robotic arm. \(M(q)\in R^{n}\) denotes the mass matrix dependent on the robotic arm. \(C\big{(}q,\ \dot{q}\big{)}\in R^{n}\) denotes the Coriolis and centrifugal force matrix, which depends on the robot's velocities and accelerations. \(G(q)\in R^{n}\) encapsulates the combined influence of gravity's forces. Incorporating input saturation nonlinearity is imperative in order to tackle control intricacies and bolster stability within an n-degree-of-freedom robotic arm framework. The asymmetric input saturation can be defined as follows:  
\[\tau_{i}=\left\{\begin{array}{ll}\tau_{i\ max},&u_{i\ max}<u_{i}\\ g_{ri}(u_{i}),&0\leq u_{i}\leq u_{i\ max}\\ g_{li}(u_{i}),&u_{i\ min}\geq u_{i}\geq 0\\ -\tau_{i\ max},&-u_{i\ max}>u_{i}\end{array}\right. \tag{1}\]  
The dashed lines represent the initial positions of the robotic arm, with \(q_{1},q_{2},...q_{n}\) indicating the joint rotation angles of the robotic arm.  
Let \(i=1,2,\ldots,n\), where \(u_{i\ max}>0\) and \(-u_{i\ min}<0\) specify the maximum and minimum limits of the torque, respectively. The control laws \(u_{i}\) are designed with the known parameters \(u_{i\ max}>0\) and \(u_{i\ min}>0\); the functions \(g_{ri}(u_{i})\) and \(g_{li}(u_{i})\) are nonlinear and unknown with respect to \(u_{i}\).  
\[\ddot{q}=M^{-1}(q)\big{[}\tau(t)-C\big{(}q,\dot{q}\big{)}-G(q)\big{]} \tag{2}\]  
The inertia matrix \(M(q)\) possesses the properties of symmetry and positive definiteness. The matrix \(\Big{(}\dot{M}(q)-2C\big{(}q,\ \dot{q}\big{)}\Big{)}\) demonstrates skew-symmetry.
## 2 Materials and Methods
### Data Preprocessing
#### 2.3.2 Elimination of Slope Component  
The inclusion of a slope component in the measurement signal such as the signal of helix deviation of this study can pose a challenge when attempting to extract its periodicity through correlation function analysis. Therefore, it is crucial to eliminate the gradient component to derive the exact periodicity of the signal. In this paper, the helix deviation curve after the removal of DC and slope component for the \(i^{th}\) tooth number, \(f_{H\beta i}(x)\), is calculated with the following Equation (2).  
\[f_{H\beta i}(x)=f_{\beta i}(x)-y_{H\beta i}(x) \tag{2}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value (Gear Measuring Machine)** \\ \hline Normal Module (mm) & 0.5–12 \\ Number of Teeth (\(-\)) & 10–500 \\ Gear Outer Diameter (mm) & Max \(\varphi\)350 \\ Basic Circle Diameter (mm) & 0\(\rightarrow\)\(\varphi\)300 \\ Tooth Width (mm) & Max 400 \\ Tangent Length for Profile Measurement (mm) & \(\pm\)120 \\ Helis Angle (deg.) & 0\(\rightarrow\)\(\pm\)60 \\ Gear Shatt Length (mm) & 50–400 \\ Gear Weight (kg) & Max 50 \\ Resolution (V) & 0.0001 \\ Power Supply (V) & AC 100 \\ Capacitance (KVA) & 2 \\ Machine Weight (kg) & 1500 \\ Dimension (W \(\times\) D \(\times\) H) (mm) & 1133 \(\times\) 1071 \(\times\) 1995 \\ \hline \end{tabular}
\end{table}
Table 2: Specification of Gear Measuring Machine.  
Figure 2: Injection-molded plastic gear undergoing inspection process.  
\(f_{gi}(x)\) is regarded as the helix deviation curve after the DC component for \(i^{th}\) tooth number is eliminated, and \(y_{Hgi}(x)\) is regarded as the slope component of \(f_{gi}(x)\) calculated by utilizing the least square method.
## 1 Introduction
To prevent redundant searches, these individuals are memorized in a _mutation list_\((\mathbf{MU}_{list})\), ensuring that previously explored spaces are excluded from future searches. Integrating the local search operation to the GA retains the original advantage of GA, which that is its global explorative power, and simultaneously achieves an enhanced search performance thanks to the intensive local search ability. On top of the proposed HGA, we further apply a layered detection method to enhance BER performance and reduce computational complexity. The layered structure is inspired by a previously proposed approach based on successive cancellation systems [14]. The convergence of layered structure and search algorithm-based MIMO detection has been studied in [20; 27]. In [20], the authors sequentially connected QR decomposition-based detection and basic GA-based detection, but the improvement in BER performance was marginal. The authors of [27] tried to combine a layered structure with TS-based MIMO detection and achieved better BER performance and reduced computational complexity by updating the solution vector based on local search per the specific layer. Therefore, we consider the layered structure of the method to update the solution by layer as in [27] with the proposed HGA. The main contributions can be summarized as follows:First, an HGA scheme embedding tabu search is proposed for high-order quadrature amplitude modulation (QAM) in MIMO detection. The hybrid techniques of GA and TS enable efficient MIMO detection by balancing global and local searches. * A layered detection technique is applied to the proposed HGA scheme, which not only dramatically improves BER performance but also reduces computational complexity. * The advantages of the proposed HGA scheme over the conventional scheme are verified through various simulations considering up to 1024-QAM. Notably, the proposed HGA scheme shows enhanced performance compared to conventional detection schemes. * A complexity comparison between the proposed HGA and conventional detection schemes was performed. The HGA scheme leads to a substantial reduction in the complexity of MIMO detection for high-order QAM. The remainder of this paper is organized as follows. In Section 2, the MIMO system model is presented, and the basic GA and the GA-based MIMO detection scheme are briefly reviewed. In Section 3, the proposed HGA scheme using a layered process for MIMO detection is introduced. The performance of the proposed HGA scheme is evaluated in Section 4. Finally, Section 5 summarizes the results of this study.
## 1 Introduction
### Classification of TA Systems  
Depending on the nature and mode of action of antitaxins (proteins or RNAs), different types of TA systems have been described [17; 18; 19; 20; 10]. Note that toxins are always proteins.  
Type I and III systems rely on RNA antitaxins. Type I antitaxins are anti-sense RNAs that bind toxin mRNAs. This leads to translation initiation inhibition and degradation of the RNA duplex (for a recent review, see [20]). Type III antitaxins are composed of repeat motifs that are recognized and bound by the toxins [17; 21], leading to their sequestration.  
Type II, IV and V antitaxins are proteins that either sequester, act as antagonists or inhibit the translation of their cognate toxins.  
Type II antitaxins are small unstable proteins composed of two domains: an amino-terminal DNA-binding domain (DBD) and a carboxy-terminal region involved in toxin binding [22; 23; 24]. Note that in some type II antitaxins (e.g., MqSA), the DBD is located in the C-terminal region and the toxin binding domain in the N-terminal part of the protein [25]. Formation of the antitoxin-toxin complex results in toxin sequestration and inactivation. These complexes are also often responsible for negative autoregulation of the TA operon [26; 27]. In the case of type IV and V antitaxins, only one example has been described. The type IV CbeA antitoxin acts as the antagonist of its cognate toxin and promotes polymerization of FtsZ and MreB, the toxin targets [19; 28]. In type V, the GhoS antitoxin is described as an endoribonuclease that degrades its cognate toxin-encoding mRNA [18].
# River Water Quality Monitoring Using LoRa-Based IoT
## 4 System Design
### IoT Node  
For the IoT Node, an Arduino shield, or a Hardware Attached on Top (HAT), is designed, and its electrical schematic is shown in Figure 19. As the Arduino works at 5 V and the LoRa module works at 3.3 V, to ensure a good adaptation of logic levels, a voltage-level shifter (TXB0108 [30]) is inserted between these two devices.  
The Printed Circuit Board (PCB) of this Arduino shield is shown in Figure 20. This board is also compatible with Arduino Uno.  
The final prototype of the IoT Node subsystem, with the components assembled in a box and prepared for installation close to a river, is shown in Figure 21.  
In IoT Node, a program is developed for the Arduino board in C/C++, whose main function is to take the sensor readings and create a payload with these values to be sent to the LoRa network. Figure 22 shows the program flowchart.  
Figure 17: Relationship between turbidity and voltage (adapted from [29]).  
Figure 18: SX1276, LoRa module characteristics (adapted from [20]).  
Figure 19: Electrical schematic of IoT Node subsystem.  
Figure 20: PCB developed for IoT Node subsystem (Arduino shield).
## 3 Results and Discussion
### Chemical Characterization of Biota and Partition Studies
#### 3.4.1 Copepods  
Regarding _crustaceans_ copepods, results are summarized in Figure 3A (herbivorous) and Figure 3B (carnivorous).  
Data showed that PAHs were detected in both taxa at higher concentrations than PCBs, with a total concentration from 270 \(\mu\)g/Kg (winter) to 1.3 mg/Kg (summer) for PAHs with respect to 43 \(\mu\)g/Kg for PCBs (detected only in summer campaign) for herbivorous copepods. For carnivorous copepods, PAHs concentrations from 230 \(\mu\)g/Kg (winter) to 4.3 mg/Kg (summer) were measured, with no detection of PCBs. The measured concentrations are in the same range of those obtained by [PERSON] and colleagues in estuarine water (in which seasonal variability was also assessed) [50] or by [PERSON] and colleagues in Marseille Bay (Mediterranean Sea) [51].  
Since copepods tend to spend their entire lives mostly in the water column [52], comparison with PAH and PCB contamination in this matrix seems to be fully justified. Concerning PAHs, the same congeners (NaPh, AcPY, Flu, Phe, Ant, FITH and BkFI) detected in seawater were detected in both herbivorous and carnivorous copepods (Figure 1 vs. Figure 3A,B). Furthermore, the same seasonal trend observed for water (i.e., higher number of congeners and concentrations in summer sampling, rather than in winter) was observed for copepods. This behavior suggests that a possible transfer of micropollutants from water to copepods had occurred. To support this hypothesis, it is important to note that Pheand Ant, which were found to be almost the most abundant PAH compounds in water, confirmed this trend also in herbivorous copepods.  
In contrast, Pyr, BaA and Chr, which were detected in the herbivorous copepods, were not present in carnivorous copepods and in seawater. The presence of these congeners could be explained taking into account feeding habits, since it was recently demonstrated that  
Figure 3: Concentration of PAHs and PCBs detected in herbivorous (**A**) and carnivorous (**B**) copepods. Both summer (orange) and winter (green) sampling are reported. Analytical conditions are detailed in the Experimental section.  
herbivorous copepods could be deceived in their feeding by microplastics and nanoplastics, upon which algae and phytoplankton grow [53]. Such micropolymers have been shown to adsorb organic and inorganic pollutants (such as PAHs and PCBs) [54], thus suggesting that they could be an additional source of PAH congeners once ingested by organisms [55].  
Moreover, the same hypothesis could be formulated to explain the detection of PCB11 and 101 in herbivocous copepods (Figure 3A), which were not previously detected in the water column and that were below detection limits in carnivorous organisms. The observed PCB contamination does not exceed limits imposed by European and Italian regulation, since both congeners are not dioxin-like and limits on biota are referred only to dioxin-like compounds.
## 7 Discussion
### Technology Maturity and Deployment Difficulty  
A further qualitative analysis was conducted to evaluate the eight key technologies based on the deployment maturity and technical difficulty. The maturity level was determined by assessing the depth, breadth, and frequency with which a given technology was discussed in the reviewed literature. High maturity technologies were implemented in multiple studies across diverse regions. In contrast, low maturity technologies were only presented with conceptual or experimental engagement. In regard to deployment difficulty, integration complexity, technical skill requirements, and costs and infrastructure needs are considered as the key indicators. A matrix of the outcome has been developed in Table 3. An operational approach to rating the maturity level and scoring the deployment difficulty is detailed in Appendix B.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Industry 4 Design Principles** & **Industry 4 Technologies** & **Intentions** \\ \hline \multirow{6}{*}{Interconnection} & \multirow{3}{*}{CPPS} & Establishing the integrations and interoperability by interconnecting hardware and software components. \\  & & Capturing raw production data in real time from smart sensors across the interconnected networks. \\ \hline \multirow{6}{*}{Information transparency} & \multirow{3}{*}{DT} & Visualising physical assets in a digitised context. \\  & & Simulating production workflows and \\  & & equipment behaviour. \\  & & Visualising physical assets in a virtualised context. \\  & MR & Providing valuable information in manufacturing processes for human operators. \\ \hline \multirow{2}{*}{Decentralised decisions} & \multirow{2}{*}{Big data analytics} & Providing optimal decisions in \\  & & production processes. \\ \hline \multirow{6}{*}{Technical assistance} & \multirow{3}{*}{Advanced robotics} & Advanced manipulators to replace \\  & & human operators. \\  & & Fulfilling autonomous intralogistics. \\  & & Fabricating products with an extremely complex \\  & & structure in one go. \\  & & Achieving a prompt prototype in the product \\  & & development process. \\  & MV & Monitoring production processes in real time. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Industry 4 design principles and the related technologies.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Technology** & **Maturity Level** & **Deployment Difficulty** & **Remarks** \\ \hline Cyber–physical systems (CPS) & High & High & Critical integration demands; software–hardware complexity. \\  & & & Wide use, but requires sensor \\ Industrial IoT (IIoT) & High & Medium & interfacing and \\  & & & network configuration. \\ Digital twin (DT) & Medium & Medium-High & Bi-directional integration \\  & & & remains immature. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Industry 4 technology maturity and deployment difficulty.
## 5 Discussion
### Advantages of EPDPN Model  
Our study offers a novel strategy for protecting WSN-IoT networks using clever, data-driven approaches. The EFDPN model, with its novel convergence of feature selection and classification strategies, heralds a promising frontier in the security of cyber-physical systems, promising a robust, effective, and intelligent infrastructure capable of fending off the constantly evolving cyberthreats and fostering a safer and more secure cyber-physical landscape. The following are the primary advantages of the proposed system:  
1. The model can precisely identify different types of incursions by combining F3S and DPN, reducing false positives;
2. The F3S method makes it easier to extract pertinent information, improving the model's capacity to pinpoint threats with greater accuracy while requiring less computational effort;
3. By including tunicate swarm optimization (TSO), the sigmoid transformation function can be adjusted, improving the model's ability to detect intrusions;
4. Thanks to better feature selection and decreased dimensionality, the EFDPN model efficiently decreases training time, boosting efficiency without compromising the detection rate;
5. The architecture of the EFDPN model allows for scalable deployment, making it adaptable to various network sizes and complexities;
6. The model is capable of identifying and mitigating a wide range of attack categories, including brute force, botnet, and web attacks, thereby providing a robust defense mechanism;
7. The model's compatibility with established benchmark datasets (UNSW-NB 15 and NSL-KDD) showcases its readiness for real-world applications and further testing;
8. Given its feature set and capabilities, the EFDPN model has substantial potential for implementation in real-time environments, offering a timely response to security breaches;
9. The model is designed to minimize the usage of resources, such as memory, through intelligent design choices in the classification and feature selection phases, which contribute to overall system efficiency.
## 4 Discussion  
Accuracy assessment of the classification results of remote sensing images is a key step before application for scientific investigation and policy decision. According to the sampling procedures  
Figure 8: Accuracy assessment by different sampling methods.  
Figure 7: Accuracy assessment by different sampling methods.  
for accuracy assessment by attributes, such as ISO 2859, ISO 19113, or ISO19114, there are three different classes of the accuracy inspection procedures, i.e., reduced inspection, standard inspection, and tightened inspection [27; 28; 29]. In this study, we designed an accuracy assessment method for the tightened inspection of accuracy assessment of remote sensing image classification results. The tightened inspection of accuracy assessment presented a lower error tolerance rate for the classification results of remote sensing images and was more suitable for higher accuracy requirements of remote sensing image applications.  
Our proposed method considered both spatial autocorrelation and spatial heterogeneity during the accuracy assessment. Compared with the existing sampling methods, the proposed method obtained more sample points in the studied region with higher land use fragmentation. The proposed method overcame the shortcomings of the random sampling method (SRS) to avoid sample points randomly allocated in the studied region. Compared with stratified sampling method (SS) and spatial sampling of the gray level co-occurrence matrix method (GLCM), the proposed method significantly reduced the redundant information between different sample points. Thus, our proposed method is more suitable for the accuracy assessment of classification results of remote sensing images.  
However, there are some limitations of our proposed method. Our method only considers how to distribute sample points and ignores how to determine the required sample size. In addition, our proposed method only selects a 3 pixel by 3 pixel window for the studied zone. However, we did consider the potential impact of window size on the accuracy assessment of the classification results of remote sensing images.
## 1 Introduction
[44] applied four machine learning approaches to retrieve red-tide cell abundance on the southern coast of Korea from airborne hyperspectral imagery, namely feed-forward neural network, support vector machine, ensemble bagged tree, and Gaussian process regression. The paired data of in situ spectra and red tide cell abundance were used to train and validate the machine learning models. These models produced accurate red tide cell abundance maps. [PERSON] et al. [45] presented a deep convolutional neural network (CNN) model, U-Net, for the automatic pixel-wise detection of red tides in Korean coastal areas from GOCI images. The red tide index maps predicted by the trained U-Net showed considerable matching spatial occurrence tendencies of the three red tide species to ground-truth information. These studies demonstrated the synergistic effect for red tide detection using state-of-the-art machine learning and deep learning approaches, and accumulated satellite images over the years. However, in order to detect red tide blooms in coastal areas, the use of high spatial resolution (<5 m) images is essential. To the best of our knowledge, research on red tide detection using deep learning methods and satellite images with a high spatial resolution has yet to be conducted. In this study, we developed a deep CNN model, U-Net, for _M. polykrikoides_ detection on the southern coast of Korea from PlanetScope imagery with a high spatial resolution of 3 m. We (1) performed visual inspection and spectral analysis of waters containing _M. polykrikoides_ using PlanetScope image, (2) determined the optimal threshold of the conventional red tide index (RTI), (3) trained and tested the U-Net model using pairs of PlanetScope images and red tide maps, and (4) evaluated the performance of RTI and the trained U-Net model for _M. polykrikoides_ red tide detection.