esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Process Recommendation Method That Integrates Disjoint Paths and Sequential Patterns
## 4 Experimental Results and Analysis
### Time Efficiency Test  
From the user's perspective, the offline processing phase does not affect their experience, and thus, only the online recommendation phase was tested for time efficiency. In the real dataset, 50, 100, 150, 200, 250, and 300 process models were randomly selected as new process library models. In the simulated dataset, 15, 30, 45, 60, and 75 process models were randomly selected as new process library models to observe the changes in data.  
Figure 9: Accuracy comparison of five methods: (**a**) real dataset; (**b**) simulation dataset.  
From Figure 10, it can be observed that the response time of the five methods was positively correlated with the number of models in the process library. The proposed method exhibited a higher time efficiency compared with the TPS, MCS, and MDS, as the MCS is calculated based on graph structure and has a complexity of O(n), where \(n\) is the number of subgraphs; TPS and MDS have a maximum complexity of O(n\({}^{3}\)), where \(n\) is the number of process library models, and are calculated based on a matrix. On the other hand, the proposed method has a complexity of O(m*n), where \(m\) is the number of edge-disjoint paths of the reference model and \(n\) is the number of CPSPs of the process library model, and is based on an array, making it more advantageous in terms of time efficiency.  
Furthermore, the time efficiency of this method is only slightly lower than that of the VPS, which has a complexity of O(m*n), where \(m\) is the number of vertex-disjoint paths of the reference model and \(n\) is the number of vertex-disjoint paths of the process library model because the number of vertex-disjoint paths extracted using the VPS is smaller than the number of CPSPs mined using this method, resulting in fewer traversals. However, the experimental results show that the difference between the time spent using this method and the VPS was only a few milliseconds, which had no significant impact on the user experience. Therefore, considering both the accuracy and time efficiency, this method outperformed the other methods.
## 2 Related Work
### Analysis of Traditional Mapping Approaches  
Chaos is a seemingly random motion that determines the dynamic system, and it is ubiquitous in nonlinear systems. Chaotic motion has three remarkable characteristics: (1) it is highly sensitive to initial values; (2) ergodicity of motion trajectory; (3) randomness. It can traverse every orbit according to its own rules within a certain range, not repeating itself nor crossing itself. Because of the randomness as well as the ergodicity and regularity of chaotic variables, they have been applied to the optimization search problem by many scholars, which can not only effectively maintain the diversity of the population, but also help the algorithm to jump out of the local optimum and improve the global searching ability.  
Logistic mapping and tent mapping are the most commonly used at present. Most studies use logistic mapping to carry out chaotic operation. [PERSON] et al. used Logistic mapping in the initial stage to make individual search more accurate and avoid falling into local optimal. Meanwhile, chaotic processing was only performed on some currently superior individuals to speed up the convergence rate [22].  
The mathematical expression of Logistic mapping is expressed as:  
\[x(t+1)=\mu x(t)(1-x(t)) \tag{9}\]  
where \(t\) is the iteration time steps, \(x(t)\in[0,1]\), \(\mu\) is a tunable parameter to ensure that the mapped \(x(t)\) is always within \([0,1]\), \(\mu\in[0,4]\). The results show that when the value of \(\mu\) meets the condition of 3.5699456 \(<\mu\leq 4\), especially when it is close to 4, the value generated by iteration is in a random distribution state. However, the value probability of Logistic mapping is high at both ends of the interval, and thus the optimization speed of the algorithm will be affected by the unevenness of Logistic traversal.  
Tent mapping has better traversal uniformity and convergence speed than logistic mapping and can be used to optimize the search ability of the algorithm. In view of the decrease of population diversity, it is easy to fall into the problem of local optimal solution. However, most of the current literature studies are focused on PSO algorithm. There are few studies on PIO algorithm. In the existing literature, most of the studies use tent mapping to initialize the population, which effectively improves the diversity of the population. By chaotic search, the adaptive tent mapping optimization is used to help our algorithm to jump out of the local optimum and enhance the global search ability. Among them, the advantage of tent mapping can make up for the disadvantage of PIO algorithm, and therefore chaotic mapping can be considered to apply to the optimization of PIO algorithm.
_Article_  
**Induction Thermography for Surface Crack Detection and Depth Determination**  
[PERSON]\({}^{\copyright}\)  
Institute for Automation, University of Leoben, Leoben A-8700, Austria; [EMAIL_ADDRESS];  
Tel.: +43-3842-402-5317  
Received: 15 December 2017; Accepted: 6 February 2018; Published: 9 February 2018  
**Abstract:** In the last few years, induction thermography has been established as a non-destructive testing method for localizing surface cracks in metals. The sample to be inspected is heated with a short induced electrical current pulse, and the infrared camera records--during and after the heating pulse--the temperature distribution at the surface. Transforming the temporal temperature development for each pixel to phase information makes not only highly reliable detection of the cracks possible but also allows an estimation of its depth. Finite element simulations were carried out to investigate how the phase contrast depends on parameters such as excitation frequency, pulse duration, material parameters, crack depth, and inclination angle of the crack. From these results, generalized functions for the dependency of the phase difference on all these parameters were derived. These functions can be used as an excellent guideline as to how measurement parameters should be optimized for a given material to be able to detect cracks and estimate their depth. Several experiments on different samples were also carried out, and the results compared with the simulations showed very good agreement.  
**Keywords:** thermography; induction heating; crack detection; crack depth
# An Improved V-Net Model for Thyroid Nodule Segmentation
###### Abstract  
Early diagnosis of increasingly common thyroid nodules is crucial for effectively and accurately managing the disease's monitoring and treatment process. In practice, manual segmentation methods based on ultrasound images are widely used; however, owing to the limitations arising from the imaging sources and differences based on radiologist opinions, their standalone use may not be sufficient for thyroid nodule segmentation. Therefore, there is a growing focus on developing automatic diagnostic approaches to assist radiologists in nodule diagnosis. Although current approaches have yielded successful results, more research is needed for nodule detection because of the complexity of the thyroid region, irregular tissues, and blurred boundaries. This study proposes an improved V-Net segmentation model based on fully convolutional neural networks (V-Net) and squeeze-and-excitation (SE) mechanisms for detecting thyroid nodules in two-dimensional image data. In addition to the strengths of the V-Net approach in the proposed model, a squeeze-and-excitation (SE) mechanism was used to emphasize important features and suppress irrelevant features by assigning weights to the significant features of the model. Experimental studies utilized the Digital Database Thyroid Image (DDTI) and Thyroid Nodule 3493 (TN3K) datasets, and the improved V-Net-based model was validated using the V-Net, fusion V-Net, and SEV-Net methods. The results obtained from the experimental studies demonstrate that the proposed model outperforms the V-Net, fusion V-Net, and SEV-Net models, with a Dice score of 84.51% and an IoU score of 76.27% for the DDTI dataset. Similarly, on the TN3K dataset, it achieved superior performance compared to all benchmarked models, with Dice and IoU scores of 83.88% and 75.50%, respectively. When considering the results in the context of the literature, the proposed model demonstrated the best performance among all models, achieving an average score of 80.39% for the DDTI dataset and 79.69% for the TN3K dataset, according to both Dice and IoU metrics. The model, with a Dice score of 84.51%, competes at a competitive level with Ska-Net, which exhibits the best performance in this metric with a score of 84.98% on the DDTI dataset, whereas it achieved the best performance among existing models with an IoU score of 75.5% on the TN3K dataset. The achievement of the proposed model may make it an effective tool that radiologists can use for thyroid nodule detection.  
medical image segmentation; thyroid nodules; ultrasound; deep learning; improved V-Net
# GLClick: Interactive Segmentation Combining Global and Local Features
## 3 Methods
### MLP Segmentation Head  
In this work, we optimize the segmentation head architecture by integrating outputs from multiple global-local feature fusion modules into the baseline model. Each feature map undergoes transformation through independent multi-layer perceptron (MLP) layers, which are designed to enhance expressiveness and capture complex interactions among features. The MLPs are configured with several hidden layers using ReLU activation functions, promoting non-linearity and enabling the model to learn intricate feature representations. To ensure compatibility across varying feature map dimensions, we employ bilinear interpolation for resizing. This method not only maintains the smoothness of the features but also effectively preserves spatial relationships, which are critical for segmentation tasks. Following this, a \(1\times 1\) convolution is applied to standardize the dimensions of all feature maps, facilitating seamless fusion in subsequent operations. This step is crucial, as it mitigates potential dimensional mismatches that could disrupt the integration process. After unifying the dimensions, we upsample all feature maps to a consistent resolution and concatenate them. This concatenation achieves deep integration across features, allowing for a rich combination of information while preserving the distinct characteristics inherent in each feature map. The fusion process enhances the complementarity among different features, resulting in a more robust representation for segmentation tasks. To restore the original image resolution, we perform an upsampling operation, ensuring that the segmentation results accurately correspond to each input pixel. An additional MLP layer then fuses the concatenated features to generate a single-channel feature map that condenses the segmentation information. This MLP layer is pivotal in synthesizing the rich information from various sources into a unified representation.  
Finally, we apply a sigmoid function to convert the feature map into a probability map, where each pixel value represents the likelihood of belonging to the target category. The choice of the sigmoid function allows us to obtain a continuous range of probabilities, reflecting the model's confidence in its predictions. By applying a predefined threshold of 0.5, we convert the probability map into a binary segmentation map. This thresholding process effectively delineates the target object, enabling efficient interactive segmentation and providing a clear, actionable output for further processing or user interaction. The integration of MLPs for feature enhancement, alongside effective resizing and fusion strategies, culminates in a robust segmentation architecture that leverages the strengths of both global and local feature representations, ultimately improving segmentation accuracy and efficiency.
# Solder Joint Defect Detection in the Connectors Using Improved Faster-RCNN Algorithm
## 2 Materials and Methods
### Important Improvements
#### 2.2.1 Data Augmentation  
In this paper, 625 solder joint sample images were collected, including five types of qualified solder joints, multi-tin solder joints, less-tin solder joints, connected welding solder joints, and thin tip solder joints. Among them, 100 images were randomly selected as test images, and the remaining 525 images were used as training data, including 336 images of training set, 84 images of verification set and 105 images of test set. The deep neural network contains a large number of hidden layers and weights, and the data amount is too small to adjust the weights, which leads to the under-fitting of the model after training, thus affecting the accuracy of the model, and the actual detection effect will be poor. However, the limited welding capacity makes it difficult to continue expanding the data volume beyond the existing solder spot samples, and this is leading to the use of data augmentation to realize the expansion of the data set.  
Data augmentation is a means of applying a set of basic forms of image processing to generate new images and, at the same time, corresponding label files for deep network training, usually involving rotation, mirror flipping, random shearing, brightness switching, contrast switching, etc. They can increase the data set. At the same time, differenttransformation eliminates the effect of target's position, brightness, color and other properties in the image, so that the model can better recognize the target and improve the accuracy of the model. The data augmentation technology employed in this paper includes left-right flipping, up-down flipping, diagonal flipping, random brightness, and random contrast, and the results of this change are shown in Figure 6. In order to improve the robustness of the model, all transformations are random.  
After data augmentation, the training data in this paper were expanded from 525 images to 1654 images, including 1059 images in the training set, 265 images in the validation set and 330 images in the test set.
## 2 Theoretical Background of Proposed Method
### Iemd  
The occurrence of mode mixing destroys the physical meaning of the subsequent IMF. In addition, the issue of the judgement of real IMFs is also worth studying. Therefore, in this study, in order to improve the mode mixing problem, frequency band filtering was used to improve the classical EMD, so as to effectively suppress the occurrence of mode mixing in the process of classical EMD. However, many false modes are generated at the same time. For this reason, cluster analysis was used to obtain the real IMFs in this paper. Hence, combining a band-pass filter and cluster analysis, an IEMD (as shown in Figure 1) is proposed.  
The specific steps are as follows:  
1. Use fast Fourier transform (FFT) to obtain the spectrum of the acceleration response signal, seeking each peak frequency as the center frequency of the band-pass filter, and then let the signal go through to the specific frequency-band filter. Namely, using FFT to analyze the acceleration signal \(\ddot{x}_{p}(t)\), utilizing the frequency spectrum of FFT to choose the peak frequency, and then roughly estimate each frequency range, for example, \(\mu_{jL}<\mu_{j}<\mu_{jH}\), then let the signal \(\ddot{x}_{p}(t)\) pass the band-pass filter with specific bandwidth \(\mu^{\prime}_{jL}<\mu_{j}<\mu^{\prime}_{jH}\), (here \(\mu_{jL}<\mu^{\prime}_{jL}\), \(\mu^{\prime}_{jH}<\mu_{jH}\)) eventually, the signal \(\ddot{x}_{p}(t)\) will be differentiated into limited sub-band acceleration signals.  
Figure 1: The proposed IEMD method.  
2. After filtering, each sub-band signal is processed by EMD, and IMFs component of each sub-signal are obtained successively.
3. After the signal is processed according to the two steps mentioned above, multiple IMF groups are obtained. The key problem is how to identify the real IMFs from multiple IMF groups. In view of this, cluster analysis [26] using the multivariate data analysis method is introduced in this paper to solve the problem of the determination of the real IMF. Through a two-time cluster analysis, the screened IMF can be guaranteed to be the real IMF, and there is no mode mixing among the IMFs.
# Analysis of the Accessibility of the Swinoujscie-Szczecin Fairway for Deep-Draught Ships
## 5 Summary
As in the Swinoujscie-Szczecin fairway, permissible draught varies depending on hydrological conditions, and under-keel clearance must be reassessed regularly. Similar modernization efforts, including dredging and navigation system updates, have been undertaken to improve access [40]. Another example is the Ems estuary (Germany-Netherlands), where navigational constraints are mainly driven by tidal variability and sediment accumulation [41]. In this system, extensive sediment management strategies, including tidal pumping and dredging, have been implemented to maintain access for large vessels. Despite differing in scale and hydrodynamics, all these estuarine systems face common challenges in ensuring reliable navigation for deep-draught vessels. The comparison confirms that managing UKC, forecasting water level variations, and optimizing vessel traffic remain crucial for safety and efficiency in inland-coastal shipping corridors. This research received no external funding. Not applicable. The source of the data is the Institute of Meteorology and Water Management--National Research Institute (IMGW-PIB). The author declares no conflicts of interest.
## 4 Conclusions  
Treatment of an industrial wastewater containing high concentrations of refractory pollutants has always been a challenging problem in the field of wastewater treatment. In this study, an AOP-combined BWTP scheme was investigated for the treatment of the reclaimer wastewater containing a high concentration of MDEA and other organic pollutants such as formate, phenol, and thiocyanate. Although there were several choices of AOP applicable to the scheme, we concluded that the peroxone pretreatment using ozone and H\({}_{2}\)O\({}_{2}\) would be the best, because it could selectively decompose MDEA into biodegradable compounds with a significantly reduced ozone requirement. When adopting AOP as a pretreatment step for the subsequent BWTP, its effect on the microorganisms in the BWTP should also be carefully examined. Some criteria such as microbial toxicity, biodegradability, and pollutant biodegradation kinetics could be imperative, and the respirometer test was very effective to evaluate these criteria. Although the detailed operating conditions should be further optimized in the future, we believe that the given AOP-combined BWTP scheme could be the feasible solution for the successful treatment of the reclaimer wastewater.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app12083960/s1](https://www.mdpi.com/article/10.3390/app12083960/s1)]([https://www.mdpi.com/article/10.3390/app12083960/s1](https://www.mdpi.com/article/10.3390/app12083960/s1)), Figure S1: Chromatograms of AOP-pretreated wastewater: (a) Fenton-pretreated. (b) ozone-pretreated, and (c) peroxone-pretreated wastewater; Figure S2: The toxicity of raw reclaimer wastewater and AOP-pretreated wastewaters; Figure S3: _OURexo_ profiles of Fenton-, ozone-, and peroxone-pretreated wastewater. Note that Fenton-pretreated wastewater was indeed diluted five times with 1 N NaOH solution during the sample preparation procedure (see, Section 2.2).  
Conceptualization, C.-K.A. and M.-W.L.; methodology, G.-T.O., C.-K.A. and M.-W.L.; software, G.-T.O. and M.-W.L.; validation, G.-T.O. and M.-W.L.; formal analysis, G.-T.O., C.-K.A. and M.-W.L.; investigation, G.-T.O., C.-K.A. and M.-W.L.; resources, G.-T.O., C.-K.A. and M.-W.L.; data curation, G.-T.O. and M.-W.L.; writing--original draft preparation, G.-T.O. and M.-W.L.; writing--review and editing, G.-T.O., C.-K.A. and M.-W.L.; visualization, G.-T.O., C.-K.A. and M.-W.L.; supervision, M.-W.L.; project administration, M.-W.L.; funding acquisition, M.-W.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry and Energy (MOTIE) of the Republic of Korea (No. 20214810100010).  
Not applicable.  
We appreciate the kind help of POSCO providing us with the wastewater and sludge samples used in this research.  
The authors declare no conflict of interest.
## 3 Error Characterization and Simulation Method
### Model and Retrieval Method
Degrading the vertical resolution can improve the retrieval precision and increase the retrieval speed. The retrieval precision of the four radiometers with different retrieval grids are shown in Figure 4. There is usually a trade-off between precision and resolution according to different applications. Figure 4: The retrieval precision of the four radiometers with different retrieval grids, and only random noises are added: (**a**) 118 GHz temperature retrieval; (**b**) 190 GHz H2O retrieval; (**c**) 240 GHz O3 retrieval; (**d**) 643 GHz HCl retrieval. The 1 km, 2.5 km, 5 km represent the different retrieval grids. The “Profile” represents the typical profile.
# Recent Advances in Spectroscopy Technology for Trace Analysis of Persistent Organic Pollutants
## 2 Applications of Spectroscopy Technology in Detection of POPs
### SPR Sensors
The porous dielectric film immobilized on the SPR sensor chip is an effective method to dramatically improve the sensitivity, due to it possessing a large internal surface area and extending the interaction depth to the entire thickness of the film [74; 75]. However, this makes the fabrication process of the SPR sensors complicated and exhaustive. To overcome this problem, [PERSON] et al. adopted nanoporous gold (NPG) film as an effective and alternative approach for the SPR sensor in the trace detection of BaP [35]. The NPG film could not only dramatically enhance the sensitivity for the great enrichment of BaP throughout the entire thickness but also support the surface plasmon wave with a simple structure of the SPR sensor (Figure 4b). The detection limit for BaP was 5 pmol-L\({}^{-1}\), which was increased by 20 times  
Figure 4: (**a**) Schematic of polychlorinated biphenyl (PCB)-sensitive optical detection system using surface plasmon resonance, (**b**) Illustration of surface plasmon resonance (SPR) sensors based on the nanoporous gold (NPG) film. Republished with permission from [35; 38] (Copyright © 2008, Elsevier). Figure 3: Common strategies for preparation of modified surface including: (**a**) Physical adsorption, (**b**) covalent immobilization (thiol-based and self-assembled monolayers (SAMs)-based), (**c**) capture and (**d**) polymer film deposition methods. compared to that of conventional SPR sensors (Figure 5). Therefore, this suggested that the technique in SPR sensors shows enormous potential for trace detection and monitoring of small-molecular environmental pollutants [35].