esa-sceva/satcom-chunk-collection huggingface dataset:
# Complex-Valued CNN-Based Defect Reconstruction of Carbon Steel from Eddy Current Signals
###### Abstract  
Eddy current testing (ECT) has become a widely adopted technique for non-destructive testing (NDT) due to its effectiveness in detecting surface and near-surface defects in conductive materials. However, traditional methods mainly focus on defect detection and face significant challenges in extracting geometric information such as defect size and shape, which is crucial for structural health monitoring (SHM) and remaining useful life (RUL) assessment. To address these challenges, this study proposes a defect reconstruction approach based on a complex-valued convolutional neural network (CV-CNN), which directly leverages both amplitude and phase information inherent in complex-valued impedance signals. The proposed framework employs convolution, pooling, and activation operations specifically designed within the complex-valued domain to facilitate the high-fidelity reconstruction of defect morphology as well as precise multi-class defect classification. Notably, this approach processes the complete complex-valued signal without relying on prior structural parameters or baseline data, thereby achieving substantial improvements in both defect visualization and classification performance. Moreover, when compared to a complex-valued fully convolutional neural network (CV-FCNN), CV-CNN demonstrates a superior average classification accuracy of 85%, significantly outperforming the CV-FCNN model. Experimental results on carbon steel specimens with standard electrical discharge machining (EDM) notches under multi-frequency excitation confirm these advantages. This contribution provides a promising solution in the field of NDT for intelligent and precise defect detection.  
c 2025 15 6599  
School of Mechatronics and Vehicle Engineering, Chongqing Jiaotong University, Chongqing 400074, China; [EMAIL_ADDRESS]  
26 April 2025  
31 May 2025  
11 June 2025  
**Citation:** [PERSON]; [PERSON] Complex-Valued CNN-Based Defect  
Reconstitution of Carbon Steel from Eddy Current Signals. _Appl. Sci._**2025**, 15, 6699. [[https://doi.org/10.3390/app15126599](https://doi.org/10.3390/app15126599)]([https://doi.org/10.3390/app15126599](https://doi.org/10.3390/app15126599))  
**Copyright:** 62025 by the authors.  
Lousence MDPI, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license  
([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# A Method of Discriminating Between Power Swings and Faults Based on Principal Component Analysis
###### Abstract  
Distance protection is widely applied in AC transmission systems. It may operate incorrectly under power swings, so a power swing blocking unit (PSBU) is needed to work with the distance protection relay. Such a unit should not only block the protection relay in time when a power swing occurs, but also deblock the protection relay after detecting a fault during the power swing. In this paper, a method that satisfies these requirements is proposed. To discriminate between power swings and faults, the characteristics of three-phase voltage under a power swing and fault situation are used. Principal Component Analysis (PCA) is applied to extract and quantify the characteristics. To detect faults during power swings, an index is proposed, and the change rate of the index is used to form the criterion. Simulations for different kinds of power swing and fault situations are conducted based on a two-end system and a nine-bus system in PSCAD/EMTDC. The simulation test results indicate that the proposed method can block the protection relay reliably under a power swing and deblock the relay quickly after detecting a fault during the power swing. Moreover, the proposed method is compared with other methods. The comparison results show that the proposed method has an advantage in terms of response speed and is less affected by measurement noise.  
power swing; three-phase shorting fault; power swing blocking and deblocking; principal component analysis 2020 IEEEexample: accepted: 2025
## 4 Artificial Intelligence (AI)-Enabled 6G Networks
### Botnet Detection  
The ever-growing IoT network in smart cities suffers from botnet attacks where a large number of IoT devices are infected by malware to execute repetitive and malicious activities and launch cyber-attacks such as Denial of Service (DoS), distributed DoS (DdoS), or data theft against critical smart city infrastructure [71; 72]. For efficient and reliable botnet detection, ML and DL approaches have been restored as potential solutions in the literature (Figure 13). [PERSON] et al. [73] proposed a combined Bayesian Optimization Gaussian Process (BO-GP) algorithm and Decision Tree (DT) classifier for detecting botnet attacks on the IoT devices. [PERSON] et al. [74] proposed a DL-based botnet detection system for the resource-constrained IoT devices. The dimensionality of the large volume of network traffic data is reduced using the LSTM autoencoder (LAE) having a Relu activation function and a learning rate of 0.001. A deep Bi-LSTM model, with six input neurons, four dense hidden layers, and an output layer, is then used for botnet detection on the low-dimensional feature set. A Relu activation function is employed in the input and hidden layers, whereas sigmoid and softmax activation functions are used at the output layer for binary and multiclass classification, respectively.  
Figure 12: Deep Learning (DL)-based network traffic anomaly detection.
# Graphene-Based Flexible Electrode for Electrocardiogram Signal Monitoring
## 2 Research on Graphene-Based Flexible ECG Electrodes
### Biodegradable Graphene ECG Electrodes  
As people's demand for daily wearable health monitoring increases [54], consumption of health monitoring products such as ECG electrodes will also increase greatly. However, due to cost, durability, and other factors, wearable devices such as the widely used commercial gel electrodes are mostly made of non-degradable materials. To meet the environmental protection needs, this section will introduce examples of high-performance, biodegradable graphene ECG electrodes.
## 3 A Beam-Hopping-Based Global Beam Scheduling and User Resource Allocation Algorithm
### Algorithm A: Beam-Footprint Partitioning Algorithm
#### 3.1.1 Initial Beam Candidate Generation  
In the initial beam candidate generation phase, a density-weighted clustering strategy is applied to account for the spatial distribution of users. For each user, the number of neighboring users within a radius \(R\) is computed as a local density indicator. Users in high-density regions are selected and replicated to form a virtual user set, thereby guiding the K-means algorithm to place more cluster centers in areas with concentrated user demand. Clustering this density-enhanced user set yields a preliminary set of beam center coordinates, denoted as \(\{\mu_{1},\mu_{2},\ldots,\mu_{MN}\}\), by minimizing the sum of squared distances between users and their assigned cluster centers:  
\[\min\sum_{j=1}^{MN}\sum_{x_{i}\in C_{j}}||x_{i}-\mu_{j}||^{2} \tag{8}\]  
where \(x_{i}\) denotes the coordinate of \(i\)-th user, and \(C_{j}\) represents the set of user coordinates covered by the \(j\)-th beam.
# Prediction of Phytochemical Constituents in Cayenne Pepper Using MIR and NIR Spectroscopy
## 2 Materials and Methods  
### Chilli Samples  
One hundred samples (individual chiliies) of green cayenne pepper (\"Blade\" variety) were provided by Austchili (Bundaberg, Queensland), the largest supplier of fresh chili in Australia. These were hand-selected from plants across two field sites to ensure that a wide range of environmental variability was incorporated into the samples chosen. The majority of samples selected were at the mature stage, and all were a uniform dark green color. Across all of the samples, the average length was 117 \(\pm\) 7 mm (range 92-133 mm), while the average fresh weight was 12.4 \(\pm\) 1.9 g (range 7.5-16.2 g). The dry matter ranged from 8.4-12.6\(\%\)\(w/w\) (mean of 10.5 \(\pm\) 0.9\(\%\)\(w/w\)).  
The samples were shipped to the laboratory overnight on ice before the NIR spectra were collected and the samples subsequently dried. The MIR spectra were collected after the drying process.
# Effects of Viewing Displays from Different Distances on Human Visual System
## 3 Results  
### Subjective Visual Discomfort Rating  
After the test, overall results showed that only the display type had a significant effect on the subjective visual discomfort as depicted in Table 2. The results revealed a significant difference in the display type pre- and post-test in term of the total subjective visual discomfort score: _Wilks' lambda_ = _0.864, F(2, 75) = 5.911, p = 0.004, partial eta squared = 0.136_. Given the significance of the overall tests, the univariate main effects were examined. The display type was significant for conducting pre- and post-tests. As presented in Table 2, after the participants watched videos on 2D and 3D displays, individuals under the 3D condition had higher scores [total subjective visual discomfort score: _F (1, 76)_ = 11.752, \(p\) = 0.001, partial eta squared = 0.134. The scores before the participants watched any videos were not significantly different for those watching 2D and 3D displays.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Mean (SD)**} & \multicolumn{3}{c}{**Statistics**} \\ \hline
**Display** & **2D** & **3D** & \(F\) a  & \(p\) b  & _\(\eta^{2}\)_ & _\(\eta^{2}\)_ \\ Pre & 1.18 (0.958) & 1.23 (1.03) & 0.051 & 0.822 & 5.911 & 0.004 & 0.136 & 0.001 \\ Post & 8.30 (7.94) & 16.20 (12.02) & 11.752 & 0.001 & 5.911 & 0.004 & 0.136 & 0.134 \\ \hline
**Distance** & **3H** & **6H** & \(F\) a  & \(p\) a  & \(p\) b  & _\(\eta^{2}\)_ & _\(\eta^{2}\)_ \\ Pre & 1.35 (1.00) & 1.05 (0.994) & 1.826 & 0.181 & & & & 0.023 \\ Post & 12.8 (11.3) & 11.70 (10.5) & 0.228 & 0.635 & 0.923 & 0.402 & _0.024_ & _0.03_ \\ \hline \hline \end{tabular}
\end{table}
Table 2: Score means (standard deviation) for total subjective visual discomfort score.
# UV-Curable Silicone-Modified Polyurethane Acrylates for Food Freshness Monitoring
## 4 Conclusions  
UV-curable, pH-sensitive, intelligent silicone-modified materials for monitoring the freshness of high-protein food such as shrimp and pork were prepared. The materials have good pH-sensitive properties with changes in the color visible to the naked eye and obviously expanded pH values in the range of 2-12. When the pH values were lower than 5, the color was mainly deep red. When the pH values increased from 6 to 12, the color changed from pink to yellowish brown. The materials also exhibited long-term color stability because their color remained even when they were immersed for over 5 h. The distinctive color variation in the UV coatings in different aqueous solutions of different solvents can be observed by the naked eye, which suggests the UV coatings can be used as potential sensors for monitoring wastewater with different solvents. The investigation monitoring the freshness of shrimp and pork revealed that these pH-sensitive intelligent materials can be applied to monitor the freshness of high-protein food. These UV-curable, pH-sensitive, intelligent silicone-modified materials exhibited quite good adhesive properties of grade 0 to glass slides and fairly good thermal stability with \(T_{\text{d5}}\) in the range of 202-245 \({}^{\circ}\)C. The UV-curable, silicone-modified, pH-sensitive intelligent materials have potential applications in monitoring the freshness of high-protein food such as shrimp and pork.  
The following are available online at [[https://www.mdpi.com/article/10.3390/coatings14060728/s1](https://www.mdpi.com/article/10.3390/coatings14060728/s1)]([https://www.mdpi.com/article/10.3390/coatings14060728/s1](https://www.mdpi.com/article/10.3390/coatings14060728/s1)), Table S1: Feed ratios for the preparation of PUAs. Table S2: Feed ratios for the preparation of NR-PUAs.  
Conceptualization, X.Y.; formal analysis N.H., H.Z., N.S. and S.S.; investigation, N.H., S.S. and L.X.; data curation, N.H. and J.M.; writing--original draft preparation, N.H.; writing--review and editing, X.Y.; project administration, G.L. and X.Y.; funding acquisition, M.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the \"Pioneer\" and \"Leading Goose\" R&D Program of Zhejiang, grant number 2023C01188.  
Data Availability Statement: Data is contained within the article.  
Conflicts of Interest: [PERSON], [PERSON] and [PERSON] are employed by Yuyao Yuandong Chemical Co., Ltd. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Research on Stagger Coupling Mode of Pulse Duration and Tool Vibration in Electrochemical Machining
###### Abstract  
Tuning the coupling of pulse duration and tool vibration in electrochemical machining (PVECM) is an effective method to improve machining accuracy and surface quality. In general, the pulse is set at the same frequency as the tool vibration, and a symmetrical distribution is attained at the minimum inter-electrode gap. To analyse the characteristics of the electrolyte fluid flow and of the electrolysis products in the oscillating inter-electrode gap, a dynamic simulation of the PVECM process was carried out. The simulation results indicated that the electrolyte pressure and gas void fraction when the pulse arrived as the inter-electrode gap was narrowing clearly differed from those when the inter-electrode gap was expanding. Therefore, in addition to the traditional symmetry coupling mode, two other coupling modes called the pre-position and the post-position coupling modes are proposed which use a pulse either just before or just after the minimum inter-electrode gap. Comparative experiments involving the feed rate and machining localization were carried out to evaluate the influence of the three coupling modes. In addition, current waveforms were recorded to analyse the differences between the three coupling modes. The results revealed that the highest feed rate and the best machining localization were achieved by using the pre-position coupling mode.  
stagger coupling; electrochemical machining; pulse duration; vibration +
Footnote †: journal: Applied Sciences
## 5 Interoperability Problem  
With the advent of computer-aided design and quality engineering tools, the speed of software development has changed drastically. The integration of operations, data, models, and results from many disciplines has become the key to effective teamwork in industry. As a result of the high pace of development, the requirements for the information to be transferred and the methods for transferring knowledge, information, and data between designers and engineers have increased. Transfer issues have posed serious obstacles that have only been defined recently [31; 67].  
In collaborative design and engineering projects, most of the transferred data are reprocessed in CAD and other industrial systems. The increasing amount of information transferred between the CAx family systems is a kind of adoption of data exchange standards, and these practices are successfully supported using the PDM methodology. A significant fraction of transactions between systems fail due to the loss of semantics and/or the poor interoperability of software tools [61].  
Figure 4: Data sent from system A to B using the STEP standard [14; 34; 35; 66; 76; 80].  
Figure 3: Using IGES data exchange standard.  
When formulating the problem of interoperability and operations between users X and Y, we can distinguish three concepts defined for each of the users, languages (\(\Sigma^{X}\) and \(\Sigma^{Y}\)) containing the possibility of coding objects, areas of representation (Q\({}^{X}\) and Q\({}^{Y}\)) containing representations of q\({}^{X}\) and q\({}^{Y}\) objects, and spaces of reflection (U\({}^{X}\) and U\({}^{Y}\)) defining the semantics of the objects. A reflection space is a collection of all entities that are relevant to a specific application or context.  
Usually, the set of all possible representations of Q is a subset of U. In general, there is a very significant relationship between Q and U. The relation between the concepts is shown in Figure 5. The dashed lines show the process of mapping Q to the subset U. The relationship \(a\in\Sigma\) causes the transition from the state q\({}^{1}\) to q\({}^{2}\), the output of y, and the transition from u\({}^{1}\) to u\({}^{2}\)[52; 72; 85].  
In order to treat X and Y users separately, the definition of a reflection space (U) should be known to each side. Let us suppose that a member of team X, while working on a joint project, sends data to team member Y. The transfer initiated by [PERSON] issuing the command t \(\in\Sigma^{X}\) such as the string y\({}^{X}\) = \(\lambda^{X}\)(t, q\({}^{X}\)) will be the serial code of the object q\({}^{X}\). Then, the string y\({}^{X}\) will serve as an input string for the Y system, which will then be transferred to the q\({}^{Y}\) state (Figure 6). The transfer can be considered successful if u\({}^{X}\) = u\({}^{Y}\).  
Figure 5: Language specificity \(\Sigma^{*}\) for each user (X, Y, etc.).
## 4 Experimental Results
### Experimental Results
#### 4.2.4 Effect on Object-Detection Performance
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **Rainy Input** & **HINet** & **MPRNet** & **MSPFN** & **PreNet** & **Proposed** \\ \hline \multicolumn{7}{c}{Deraining; Dataset COCO350/BDD350} \\ \hline PSNR & 14.79/14.13 & 18.068/15.61 & 18.01/16.65 & 18.23/17.85 & 17.53/16.9 & 18.879/17.98 \\ SSIM & 0.648/0.470 & 0.786/0.567 & 0.786/0.779 & 0.782/0.761 & 0.765/0.652 & 0.801/0.823 \\ \hline \multicolumn{7}{c}{Object Detection; Algorithm: YOLOv3; Dataset: COCO350/BDD350} \\ \hline Precision (\%) & 23.03/36.86 & 32.87/41.52 & 31.45/40.05 & 32.56/41.04 & 31.31/38.66 & 33.54/41.97 \\ Recall (\%) & 29.6/42.8 & 39.54/50.21 & 38.21/49.32 & 39.31/50.40 & 37.92/48.59 & 40.12/50.67 \\ IoU (\%) & 55.5/59.85 & 60.57/62.53 & 62.54/62.21 & 62.54/62.21 & 60.75/61.08 & 62.34/61.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison results of joint image deraining and object detection on samples of COCO and BDD datasets. Figure 10: Examples of joint deraining and object detection. The first row denotes samples of the deraining results on COCO350 dataset; the second row denotes samples of the deraining results on BDD350 dataset. The third and fourth rows shows the results of object detection performed on the first and second rows sequentially. YOLOv3 has been used for object detection.
# A Novel Monocular Vision Technique for the Detection of Electric Transmission Tower Tilting Trend
## 3 Establishment of Tower Tilt Calculation Model
### Model Building
#### 3.1.3 Tower Overlooking Imaging Model  
In order to measure the horizontal distance (PM) between the camera and the pole tower, a pole-tower overlooking model is established to observe the simulated scene. The simulated scene and lens imaging relationship are shown in Figure 8:  
Top view of the tree-line scene and the imaging relationship  
\[\Delta\text{OPQ}\cong\Delta\text{OUV},\ \ \Delta\text{ONE}\cong\Delta\text{OIG} \tag{12}\]  
Based on the scale of the three sides of a similar triangle in Formulas (12) and (13):  
\[\frac{\text{PQ}}{\text{UV}}=\frac{\text{OM}}{\text{OI}}\rightarrow\frac{\text {PQ}}{\text{UV}}=\frac{\text{Pz}}{\text{OI}} \tag{13}\]  
where, PQ and UV represent the known horizontal component on the tower and its horizontal pixel distance.
# RIS-Assisted High-Speed Communications with Time-Varying Distance-Dependent Rician Channels
## 2 System Model with Time-Varying Distance-Dependent Rician Channels
### Distance-Dependent Rician Factors  
The Rician factors \(\kappa_{d}^{\text{LoS}}\), \(\kappa_{d}^{\text{NLoS}}\), \(\rho_{mn}^{\text{LoS}}\), \(\rho_{mn}^{\text{NLoS}}\), \(\kappa_{mn}^{\text{LoS}}\) and \(\kappa_{mn}^{\text{NLoS}}\) in (6), (13) and (15) are all time-invariant, which is not practical for HSC systems, especially when the BS and the RIS are surrounded by obstacles. Therefore, we use the distance-dependent Rician factor \(\kappa(t)\)[20] in the proposed channel model, given as  
\[\kappa(t)=10^{0.1(\varrho-\iota d(t))}, \tag{17}\]  
where \(\varrho>0\) and \(0<\iota<1\) are environmental parameters and \(d(t)\) denotes the distance between the transmitter and receiver in meters. Thus, we have \(\kappa_{d}^{\text{LoS}}(t)=\varrho-\iota\cdot d_{0}(t)\), and other Rician factors can be obtained in the same way. Consequently, (6), (13) and (15) can be rewritten as  
\[h_{d}(t)=A_{0}(t)\bigg{(}\sqrt{\frac{\kappa_{d}(t)}{\kappa_{d}(t)+1}}h_{d}^{ \text{LoS}}(t)+\sqrt{\frac{1}{\kappa_{d}(t)+1}}h_{d}^{\text{NLoS}}(t)\bigg{)}, \tag{18}\]  
\[h_{mn}(t)=A_{mn}^{\text{MR}}(t)\bigg{(}\sqrt{\frac{\kappa_{\text{RIS}}(t)}{ \kappa_{\text{RIS}}(t)+1}}h_{mn}^{\text{LoS}}(t)+\sqrt{\frac{1}{\kappa_{\text {RIS}}(t)+1}}h_{mn}^{\text{NLoS}}(t)\bigg{)}, \tag{19}\]  
and  
\[g_{mn}(t)=A_{\text{BS}}^{mn}\bigg{(}\sqrt{\frac{\rho_{\text{RIS}}}{\rho_{ \text{RIS}}+1}}g_{mn}^{\text{LoS}}+\sqrt{\frac{1}{\rho_{\text{RIS}}+1}}g_{mn }^{\text{NLoS}}(t)\bigg{)}, \tag{20}\]  
where \(\kappa_{d}(t)=\kappa_{d}^{\text{LoS}}(t)\), \(\kappa_{\text{RIS}}(t)=\frac{1}{M}\sum_{m=1,n=1}^{\sqrt{M},\sqrt{M}}\kappa_{ nm}^{\text{LoS}}(t)\), and \(\rho_{\text{RIS}}=\frac{1}{M}\sum_{m=1,n=1}^{\sqrt{M},\sqrt{M}}\rho_{mn}^{ \text{LoS}}\). Based on (18)-(20), the total channel \(h(t)\) can be written as  
\[h(t) =h_{d}(t)+h_{\text{RIS}}(t) \tag{21}\] \[=h_{d}(t)+\sum_{m=1,n=1}^{\sqrt{M}_{\text{L}}\sqrt{M}}h_{mn}(t) \exp\big{(}-\jmath\phi_{mn}(t)\big{)}g_{mn}(t).\]  
Note that the time delay of the \(mn\)-th element of the RIS itself is \(\phi_{mn}(t)/(2\pi f_{c})\). Therefore, the received signal in the \(mn\)-th cascaded link, \(s(t)\), is given as \(s(t-\tau_{mn}(t)-\frac{\phi_{mn}(t)}{2\pi f_{c}})\). Consequently, the received signal \(y(t)\) can be written as  
\[y(t)= \sqrt{P_{t}}\bigg{(}h_{d}(t)s\big{(}t-\tau_{0}(t)\big{)} \tag{22}\] \[+\sum_{m=1,n=1}^{\sqrt{M}_{\text{L}}\sqrt{M}}h_{mn}(t)\exp\big{(} -\jmath\phi_{mn}(t)\big{)}g_{mn}(t)s\big{(}t-\tau_{mn}(t)-\frac{\phi_{mn}(t)}{2 \pi f_{c}}\big{)}\bigg{)}+w(t),\]  
where \(P_{t}\) is transmitted power and \(w(t)\) is an additive white Gaussian noise with power \(\sigma^{2}\) in a time slot \(t\).
# Big Data Collection in Large-Scale Wireless Sensor Networks
## 2 Overview of WSNs
### WSN Application Areas
#### 2.8.1 Environmental Applications  
Nowadays, environmental problems, such as floods, fires, and pollution, are the most recurrent. The use of WSNs in environmental applications is becoming increasingly important and common, thus improving environmental awareness and the effectiveness of control methods [22; 47; 49]. Therefore, WSNs can contribute to the development of risk-response systems, natural-disaster detection, energy management, and other systems. Environmental applications can be broken down into five classes, as shown in Figure 10: weather monitoring, geological monitoring, habitat monitoring, pollution monitoring, and energy monitoring.