esa-sceva/satcom-chunk-collection huggingface dataset:
# Pipe Crack Recognition Based on Eddy Current NDT and 2D Impedance Characteristics
## 3 Feature Extraction of the ECT Signal Based on the Impedance Diagram  
### Embedded Eddy Current Testing System  
Figure 2 shows the experimental system. An external absolute eddy current probe is used for ECT. First, the probe illustrated in Figure 2c is driven by a 100 kHz sine wave, which is generated by the DDS (Direct Digital Synthesizer) technology and controlled by the host computer. Figure 3 depicts the related electronic circuits. We use the orthogonal lock-in amplifier (LIA) technology to decompose the original signal [18], and a two-dimensional impedance diagram is synthesized by the decomposed signals to reflect the variations of the coil impedance. The characteristic components of the impedance diagram are extracted through an image processing algorithm, and the defects in the girth weld are identified by utilizing the difference in the impedance variations caused by the weld and the defects. All the test specimens in this paper are pipe sections of the API SPEC 5L X70 type, whose features are described in Table 1. The physical photograph is shown in Figure 4.  
Figure 1: Signals obtained in the presence and in the absence of girth weld defects. (**a**) Eddy current testing (ECT) signal in the presence of the girth weld defects. (**b**) Original signal of the girth weld.  
Figure 2: _Cont._Figure 4: Physical photographs of the test specimens. (**a**) Photograph of the girth weld. (**b**) Photograph of the girth weld with crack. (**c**) Size of the crack.  
Figure 3: Electronic circuits of the experimental setup. (**a**) Drive circuit of the probe. (**b**) High-speed data acquisition circuit. MCU: Microcontroller Unit; DC-DC: Direct Current converter.  
Figure 2: Experimental setup. (**a**) Block diagram of the experimental setup. (**b**) Physical layout of experimental setup. (**c**) Photograph of the employed external absolute eddy current probe. PC: Personal Computer; UART: Universal Asynchronous Receiver/Transmitter; DDS: Direct Digital Synthesizer; USB: Universal Serial Bus; FPGA: Field-Programmable Gate Array.
## 2 Models and Methods
### Mathematical Model
#### 2.2.3 Boundary Conditions  
1. [label=(0)]
2.1. Inlet boundary  
The cold air outlets of two refrigerating units were set as velocity-inlet boundaries. A standard temperature of frozen food storage of 255.15 K was used in this study, so the temperature of cold air outlets was set at the same temperature. The air velocity was set as 13 m/s [23].
2. Outlet boundary  
The return air outlets of two refrigerating units were set as outflow boundaries.
3. Wall boundary  
For the Robin boundary condition, walls of the refrigerated room were characterized by their thermal conductivity, which was 0.022 W/(m\(\cdot\)K) for polyurethane [35]. The temperature from the outer side of the wall, outside the refrigerated room, was 293.15 K [23].
4. Packages of aquatic products  
Aquatic products and their packages form product package units whose physical and thermophysical parameters are shown in Table 1[36; 37; 38]. The packages of aquatic products were considered as porous media, with a porosity of 0.3 [16]. The effective diameter of porous medium was determined by the lengths of fresh fish, which was 0.18 m on average [39]. The initial temperature of aquatic products was 261.15 K.
## 4 Results
### Evaluating Numerical Efficiency
#### 4.2.3 Cohesive Phase-Field Fracture Model  
As mentioned in previous section, one of the main advantages of the CPFM approach is its insensitivity to the length scale parameter, which makes it physically appropriate for small-scale fracture simulations. As shown in Figure 12a, the reaction force-displacement curve from the CPFM approach closely matches that of the hybrid model. Further analysis in Figure 12b, comparing the reaction force-displacement curves for different values of \(l_{c}\), shows that the CPFM model consistently converges with the hybrid model. This convergence is beneficial as it allows the simpler and less computationally intensive CPFM model to be used in small-scale simulations without reducing accuracy. However, it is important to note that the observed convergence depends on the thickness of the interface and its material properties. In cases beyond the reference model in this study, a convergence study may be required to adjust the CPFM parameters, including the interface thickness, accordingly.  
As can be observed in Figure 13, the CPFM approach is more computationally efficient than other fracture models. Overall, the results indicate that the CPFM fracture method is the preferred technique for simulating fracture in multiphase materials, especially at smaller scales, provided that the thickness of the interface is relatively large compared to the other phases. In scenarios where the interface thickness is very small, such as the ITZ in concrete materials, the thickness can be artificially increased and the material properties scaled to make CPFM a fracture approach that is insensitive to interface width, as discussed in [36].
## 3 Results and Discussion
### Crystallographic Texture Analysis  
Figure 8 shows the inverse pole figures plotted for the rolling (RD) and normal (ND) directions with intensity scales calculated from the EBSD images in Figure 4. In all alloys after 0.3 + 600 (30), \(\beta\)-phase grains do not have any preferential crystallographic orientation in any of the presented directions (Figure 8a-c). This lack of orientation may be due to the formation of the twinned structure and the corresponding texture being blurred. Increasing the PDA temperature of the 22-6 alloy to 800 \({}^{\circ}\)C promotes the formation of a weak [111]\({}_{\beta}\)<110\(>_{\beta}\) recrystallization texture, which is confirmed by the maximum intensities in <110\(>_{\beta}\) | RD and <111\(>_{\beta}\) | ND (Figure 8d) [30; 42]. In other alloys, no such features are observed after 0.3 + 800 (30), and the distribution of crystallographic orientations appears random (Figure 8e,f). This difference in crystallographic texture formation may be associated with the transition from low- to high-Zr alloys, and may be associated with increased Nb content (22-6 alloy), which aligns with the findings reported in [42]. Changes in crystallographic texture resulting from 1.5 + 600 (30) support this hypothesis. A strong [111]\({}_{\beta}\)<110\(>_{\beta}\) crystallographic texture is formed in the 22-6 alloy after 1.5 + 600 (30) (Figure 8g). In the 18-15 alloy, this same texture is weaker (Figure 8h). However, in the 41-12 alloy, this type of texture is not fully formed (Figure 8i), which is probably due to the partial development of recrystallization. This is confirmed by the fact that after 1.5 + 800 (30), a strong [111]\({}_{\beta}\)<110\(>_{\beta}\) recrystallization texture is formed in the high-Zr alloy (Figure 8l). For the 22-6 and 18-15 alloys, increasing the PDA temperature from 600 to 800 \({}^{\circ}\)C after intermediate CR does not lead to noticeable changes in the crystallographic texture (Figure 8j,k). After severe CR and PDA at 600 \({}^{\circ}\)C in all alloys, a strong [111]\({}_{\beta}\)<110\(>_{\beta}\) recrystallization texture is formed (Figure 8m-o), which is consistent with studies conducted on Ti-Nb- and Ti-Zr-based SMAs presented in [7; 32; 43; 44; 45]. It is important to note that when transitioning from high-Zr alloys to low-Zr alloys, along with a combined increase in Nb content from 12 to 22 at.%, the intensity of the maxima on the inverse pole figures increases (Figure 8m-o). This confirms the enhancement in the crystallographic texture. In summary, the low-Zr alloys are more susceptible to the formation of a favorable [111]\({}_{\beta}\)<110\(>_{\beta}\) recrystallization texture during TMT. This formation requires a lower degree of deformation by CR, and its  
intensity increases more significantly with an increasing degree of deformation compared to alloys with a higher Zr content.
## 3 Results
### Drought Evolution and GPP Response to Drought in Yunnan
#### 3.3.2 Gppsif Is More Sensitive to Drought Response  
To compare the response of the three GPP datasets to drought in Yunnan Province, eight regions evenly distributed throughout the province and with different vegetation types were selected for analysis in this study (Figure 10): Shangri-La (alpine meadow), Dali (coniferous forest), Zhaotong (grassland), Luxi (broadleaf forest), Yuxi (farmland), Jinghong (broadleaf forest), Wenshan (scrub), and Qujing (coniferous forest). Figures 11 and 12a-h show the monthly mean changes of GPPSIF, GPPGLASS, GPPLUE, and drought index sc_PDSI in Yunnan Province and eight regional drought years (2009-2011).  
Compared to the other two products, the GPPSIF in Yunnan Province responded more rapidly to drought (Figure 11). The GPPSIF increased normally from March to June (pre-drought period), from 128.72 gC\(\cdot\)m\({}^{-2}\cdot\)month\({}^{-1}\) to 222.522 gC\(\cdot\)m\({}^{-2}\cdot\)month\({}^{-1}\), and in July (beginning of the drought) the GPPSIF increased slowly, only 8.29 gC\(\cdot\)m\({}^{-2}\cdot\)month\({}^{-1}\) more than in June. The GPPSIF started to decrease in August, and fell to 206.07 gC\(\cdot\)m\({}^{-2}\) in September, while the other two GPPs maintained an upward trend from April to August until September when they started to decline.  
It is more obvious from the data of small regions, such as Region 2 (Figure 12b): that GPPSIF increased normally from 124.75 gC\(\cdot\)m\({}^{-2}\cdot\)month\({}^{-1}\) to 206.54 gC\(\cdot\)m\({}^{-2}\cdot\)month\({}^{-1}\) in March-May 2009, but GPPSIF decreased rapidly after the occurrence of drought in June, almost two months earlier than the normal decline period. Similarly, in several other regions, the GPP decreased rapidly in the same month or within the second month after the strong decrease in sc\(\_\)PDSI index. Although the reduction in GPP due to the normal decline in vegetation could not be excluded, most areas saw a decline that was 1-2 months earlier than normal. In contrast, GPPGLASS and GPPLUE began to decline only in July and  
Figure 12: Changes in the three GPPs and sc\(\_\)PDSI in typical regions of Yunnan Province, 2009–2011. (**a–h**) correspond to the data changes in areas (1)–(8) in Figure 10 respectively.  
August. In addition, the sc_PDSI index changed around June 2011, GPPSIF showed the same trend of change, indicating that GPPSIF responded better to drought.  
The response of GPPSIF to the drought recovery also showed a clear superiority. For example, the drought in Region 3 (Figure 12c) went through the following stages: a slight drought in January 2009, a slowdown in April 2010, and an increase in drought in May 2011. Correspondingly, the GPPSIF increased rapidly to 270.798 gC\(\cdot\)m\({}^{-2}\cdot\)month\({}^{-1}\) in June 2010, which was less than the 283.4552 gC\(\cdot\)m\({}^{-2}\cdot\)month\({}^{-1}\) in the same period of a normal year; the drought started to worsen in May 2011, and the GPPSIF decreased rapidly in June, dropping by nearly 70 gC\(\cdot\)m\({}^{-2}\) in September.
# A New COVID-19 Detection Method Based on CSK/QAM Visible Light Communication and Machine Learning
## 2 State-of-the-Art Techniques
### Work Related to the VLC Channel
#### 2.1.4 Model of a Channel Based on CSK/QAM Mapping  
In the context of visible light communication, the use of the colour shift keying modulation scheme has allowed the application of different techniques in relation to the optimisation of the colour space which, according to the IEEE 802.15.7 standard, presents nine valid schemes for the combination of colours with 4, 8 and 16 Color Shift Keying (CSK) constellations. However, performance should be improved by concatenating other modulation or coding methodologies. To improve spectrally efficient transmissions in VLC systems using CSK communication, coding and mapping techniques, such as bit-interleaved coded modulation with iterative demapping and decoding [54], are applied. These work best for high-speed VLC applications. Machine learning is also applied to find the most optimal combination of coding and modulation technologies. Its performance is based on the mapping of symbol permutations through points in an optimized CSK constellation, This offers benefits in terms of diversity, resistance to channel degradation monochrome, and increased security. This type of method can be combined with MIMO technologies [54] to evaluate systems through Monte Carlo simulations. For the generation of a multiuser channel, the optimisation of channel resources and the technical type of spatial division used are essential. Separation by means of CSK modulation techniques to maximise the minimum Euclidean distance between different points of a constellation or multiuser joint constellation [55] is used for this purpose. Techniques such as multiplexing of the symbols used in wireless channels where each 7-bit 128 QAM symbol is multiplexed by a complex value signal to form a 32 QAM with an additional gain of 40% is done to compensate for problems related to chromatic dispersion and non-Kerr linearity [56]. The use of constellation probability shaping is a high-order modulation format optimisation technology that optimises the probability distribution of each signal constellation point to improve the generalised mutual information and increase the transmission capacity of QAM modulation [57].
## 5 Experiments and Results
### Sweep Imaging Mode  
The sweep imaging mode can enlarge the cross-flight angle of view considerably and has higher aerial survey productivity. During flight tests, the roll gimbal stepped two times, and the iXU-RS1000 camera took three images in one sweeping cycle. The target pitch angle was 0\({}^{\circ}\) for each image, and \(X_{S}\) paralleled the aircraft velocity direction. The cross-flight overlap rate was 5% for two cross-flight adjacent images, and the target roll angles for the successive three images in one sweeping cycle were \(-\)50.58\({}^{\circ}\), 0\({}^{\circ}\), and +50.58\({}^{\circ}\), respectively. The total cross-flight angle of view for one sweeping cycle was enlarged to 157.36\({}^{\circ}\). The along-flight overlap rate was 5% between two successive sweeping cycles. The maximum exposure duration was 4.7 ms when the target roll angle was 0\({}^{\circ}\), which was the same as the one in the vertical imaging mode. The maximum magnitude of \(v_{A_{\rm F}}^{5}\) was 1.02 mm/s according to Equation (20), while the target roll angle was \(-\)50.58\({}^{\circ}\) or +50.58\({}^{\circ}\). Therefore,  
Figure 14: Mosaic image series of six successive images.  
Figure 15: Image attitude deviation in the vertical imaging mode over time.  
the maximum exposure duration was 2.3 ms. Figure 16 shows the mosaic image series of two adjacent sweeping cycles. Image 1, image 2, and image 3 are successive images of one sweeping cycle, and image 4, image 5, and image 6 are successive images of the next sweeping cycle. Table 2 presents the imaging attitude of each image in Figure 16. The imaging attitude was accurately controlled, and gap-free coverage of the survey area was maintained. The enlarged image in image 4 shows that the black and white line pairs for evaluation can be clearly distinguished, which verifies that the image motion was well compensated. The enlarged areas in image 1 and image 3 are distinct, which implies that the residual of the IMC was well constrained. Figure 17 shows the image attitude deviation over 180 s. Ten sweeping cycles were included, and thirty images were taken. The image's azimuth or roll deviation was less than 0.1\({}^{\circ}\), and the image's pitch deviation was less than 0.35\({}^{\circ}\). The image attitude in the sweep imaging mode was correctly controlled.
## 1 Introduction
The MENC problem is to simultaneously minimize the power consumption on each node and to maintain the global connectivity of the network, which belongs to the NP-Complete problem. Additionally, the optimal operation mode for each node that can be a cluster head or a regular sensor node with a high or low transmitting range of signals is determined by the MA, so that the energy consumption can be minimized [25]. The energy consumption for each operation mode is different. These two [24,25] do not consider sensing coverage requirements when using MAs to optimize sensor networks. In this paper, we utilize the MA to optimize the sensing coverage of a CWSN while minimizing the number of activated nodes (i.e., the SCP encountered in a given CWSN). This study presents a MA-based coverage-preserving algorithm to optimize the point coverage of a CWSN. We investigate how the MA can be applied to an optimization problem in CWSNs. Note that many sensor nodes may be redundant after all of the nodes are deployed. In general, redundant sensor nodes cover sensing areas that are overlapped with other neighboring sensor nodes. In order to reduce energy consumption, the CoCMA determines which sensor nodes should be switched to sleeping modes. The CoCMA can also awaken some of the sensor nodes when sensing coverage declines. This step is called the wake-up scheme, which is different from that in the MA. Using evolutionary operations, our CoCMA is time consuming, so we develop a wake-up scheme that is less complex to determine an optimal schedule for nodes, awakening one or more sensor nodes near a dying one. The lost coverage of POIs can therefore be retrieved. The CoCMA can allow a minimum number of active sensor nodes to achieve maximum coverage and prolong the network lifetime simultaneously. This is very different from the aforementioned algorithms; for example, the NSGA-II [17] only considers coverage optimization in the phase of the initial deployment without considering long-term operation. The CoCMA can be incorporated into different routing protocols. We have applied the proposed CoCMA to a CWSN [26] and compared their performances with the existing approaches in [26-29]. Simulation results show that the proposed CoCMA can not only prolong the network lifetime considerably but also maintain the higher sensing coverage. The remainder of this paper is organized as follows. Section 2 describes the preliminary models and assumptions. In Section 3, we formulate the SCP and present the energy-efficient coverage control that employs a MA to solve the SCP. Section 4 shows our experimental results in comparison to different approaches and then provides discussions on the comparisons. Finally, Section 5 gives our conclusions.
# Visualization Assisted Approach to Anomaly and Attack Detection in Water Treatment Systems
## 5 Experiments and Discussion
### Machine Learning Based Detection
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name of Method** & **Parameters** & **0\_1 csv** & **0\_2 csv** & **0\_3.csv** \\ \hline \multirow{3}{*}{AdaBoost Classifier} & algorithm & SAMME.R & SAMME.R & SAMME \\  & learning\_rate & 0.5 & 1 & 1 \\  & n\_estimators & 50 & 50 & 50 \\ \hline \multirow{3}{*}{RF} & criterion & entropy & entropy & entropy \\  & max\_depth & None & None & None \\  & min\_samples\_leaf & 1 & 1 & 1 \\  & n\_estimators & 50 & 50 & 50 \\ \hline \multirow{3}{*}{LR} & multinomialNB & alpha & 0 & 0 \\  & max\_iter & 1000 & 1000 & 1000 \\  & solver & liblinear & liblinear \\ \hline \multirow{3}{*}{SVM} & alpha & 0.00001 & 0.0001 & 0.0001 \\  & loss & shape & hinge & squared\_hinge \\  & penalty & l2 & elasticnet & l2 \\ \hline \multirow{3}{*}{DecisionTree Classifier} & criterion & gini & gini & gini \\  & min\_samples\_leaf & 1 & 1 & 1 \\  & splitter & best & random & random \\ \hline \multirow{3}{*}{Ridge Classifier} & alpha & 1.0 & 1.0 & 2.0 \\  & solver & auto & lsqr & lsqr \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Appropriate parameters of machine learning methods depending on the input dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name of Method** & **Parameters** & **0\_4 csv** & **0\_5 csv** & **0\_1\_2\_3\_4\_5 csv** \\ \hline \multirow{3}{*}{AdaBoost Classifier} & algorithm & SAMME.R & SAMME.R & SAMME.R \\  & learning\_rate & 1 & 0.7 & 1 \\  & n\_estimators & 50 & 100 & 50 \\ \hline \multirow{3}{*}{RF} & criterion & entropy & entropy & gini \\  & max\_depth & None & None & None \\  & min\_samples\_leaf & 1 & 1 & 1 \\  & n\_estimators & 50 & 240 & 240 \\ \hline \multirow{3}{*}{MultimonialNB} & alpha & 0 & 1 & 1 \\ \hline \multirow{3}{*}{LR} & max\_iter & 1000 & 1000 & 1000 \\  & solver & liblinear & liblinear & liblinear \\ \hline \multirow{3}{*}{SVM} & alpha & 0.00001 & 0.0001 & 0.0001 \\  & loss & squared\_hinge & squared\_hinge \\  & penalty & l2 & elasticnet & elasticnet \\ \hline \multirow{3}{*}{DecisionTree Classifier} & criterion & gini & entropy & entropy \\  & min\_samples\_leaf & 1 & 1 & 1 \\  & splitter & best & random & random \\ \hline \multirow{3}{*}{Ridge Classifier} & alpha & 1.0 & 1.0 & 2.0 \\  & solver & auto & lsqr & lsqr \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Appropriate parameters of machine learning methods depending on the input dataset. The results of the experiments are shown in Figure 6. It reflects the quality of attack classification by machine learning methods for each input dataset using the f1-score metric.
# Optimizing CNN-LSTM for the Localization of False Data Injection Attacks in Power Systems
## 3 Methodology
### Genetic Algorithm  
The GA is a probabilistic optimization strategy inspired by natural selection and evolution [25]. Essentially, it is an efficient, parallel, and global search method. It is more suitable for optimizing networks with high parameter dimensions than random search algorithms.  
When using the GA to solve problems, it is first necessary to define parameters such as population size, number of genes, crossover and mutation probability, and termination rules. Then, starting from any initial population, a group of individuals more adapted to the environment is produced through random selection, crossover, and mutation operations, causing the population to continuously evolve to a better area in the search space. After multiple rounds of reproduction and evolution, it eventually converges to the group of individuals most adapted to the environment, thereby obtaining a high-quality solution to the problem.  
The flowchart of the GA is shown in Figure 3. The steps of the algorithm are as follows:  
1. Population initialization: a random initial population is generated, and individuals within the population are encoded.
2. Fitness evaluation: the fitness of each individual is calculated, and individuals with low fitness are eliminated.
3. Selection: based on fitness assessment, the individuals with high fitness are selected to participate in the reproduction of the next generation.
4. Crossover: the selected individuals are paired according to a set crossover rate, and offspring are produced through crossover operations.
5. Mutation: random variations are introduced in the genotype, simulating the process of biological mutation.
6. Iteration and termination: the genetic operations are repeated until the termination condition is met, and the individual with the highest fitness is selected as the optimal solution to the problem.
# Power System Voltage Correction Scheme Based on Adaptive Horizon Model Predictive Control
## 2 Voltage Control Model Based on MPC
### Voltage Prediction
The voltage prediction based on trajectory sensitivity analysis is graphically illustrated in Figure 2. According to the proportion and superposition characteristics of linear systems, the predicted voltage at a sampling point \(k\) is composed of two parts: the voltage without control \(\hat{\mathbf{V}}_{k}^{*}\) during the prediction horizon and the voltage variation \(\Delta\hat{\mathbf{V}}_{k}\) cause by the control actions applied at each sampling points. At the initial control time, \(\hat{\mathbf{V}}_{k}^{*}\) is solved based on trapezoidal numerical integration of (1) and (2). \(\Delta\hat{\mathbf{V}}_{k}\) is solved based on the Jacobian obtained during the numerical integration process. The vector consist of all the predicted voltages at each sampling point can be expressed as:  
\[\hat{\mathbf{V}}_{k}=\hat{\mathbf{V}}_{k}^{*}+\Delta\hat{\mathbf{V}}_{k} \tag{5}\]