esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
To solve most of the methods that are difficult to effectively extract fault features and unstable model training under unbalanced data, this paper proposes a new rolling bearings fault diagnosis method based on MTF and MARN, draws the following conclusions:  
1. The vibration signals are converted into two-dimensional images using MTF to avoid the loss of original signal information, while retaining the temporal correlation.
2. Using the residual structure as the body of the network avoids the degradation problem of the model; meanwhile, the introduction of the mixed attention mechanism can enhance the feature extraction ability.
3. The superiority of MTF as a data preprocessing method was confirmed on the CWRU bearing dataset, which further confirmed that the mixed attention mechanism can extract more features.
4. The model performs stably on the unbalanced dataset, obtaining average recognition accuracies of 99.5% and 99.2% on the divided balanced and unbalanced data, respectively.  
Although the proposed method has been improved at both the data level and the model level, diagnostic unbalanced data also have excellent fault diagnosis results; the MARN is trained from the beginning and needs a longer training period. In further research, the use of transfer learning methods can be considered to decrease training time. Moreover, the MTF-MARN method should also be used for datasets with complex operating conditions, such as strong noise and variable operating fault datasets.  
Figure 17: Comparison of different methods for t-SNE (from **(a–e)** for FcNet/SE-ResNet/ResNet50/ NCNN/MobileNet-v3, and from **(1–4)** for D\({}_{\text{A}}\)–D\({}_{\text{D}}\), respectively).  
**Author Contributions:** Conceptualization, L.X. and A.T.; methodology, A.T. and J.Z.; software, A.T.; validation, A.T., D.W. and L.X.; resources, D.W.; writing--original draft preparation, A.T.; writing--review and editing, A.T., J.Z. and D.W.; visualization, A.T.; supervision, L.X. and J.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Science and Technology Major Project of China (J2019-IV-0002-0069).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Case Western Reserve University Bearing Data. [[https://engineering.case.edu/bearingdatacenter](https://engineering.case.edu/bearingdatacenter)]([https://engineering.case.edu/bearingdatacenter](https://engineering.case.edu/bearingdatacenter)) (accessed on 25 December 2023).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Damping of Liquid Sloshing Using Hydrophobic Walls in the Off-Impulse Regime
## 5 Discussion  
The higher damping in the case of hydrophobic walls is not intuitive, as the consequent nonslip condition should lead to lower dissipation in the boundary limit near the wall container. However, this result is in agreement with the results of [13], who measured the pressure load on the lateral walls for both uncoated containers and containers with a hydrophobic coating. They found that hydrophobicity caused a reduction in impact pressure, and consequently a decrease in sloshing effects. We found the same phenomenon here. In our work, quantitative measurements of the damping coefficient \(\tau\) allow us to specify the reduction in sloshing due to the hydrophobic walls. Thus, our study complements the results of [13].  
An important characteristic is that even though the waves are bidimensional, the damping coefficient depends on the width \(W\) through the aspect ratio \(\lambda=W/L\), i.e., no dependence on the width direction was observed, as detailed in Section 4.2. However, it is well known that \(\tau\) may depend on the width of the container \(W\), as explained by [12; 19], which is the case when a boundary layer develops on the lateral wall and causes dissipation  
Figure 10: Pressure–time history for two cycles with a given external forcing. The contact time with the wall for the green wave is pulse-like for the first two peaks, but not for the third peak.  
due to the viscosity of the fluid. In the present case, the pressure load is exerted on the whole lateral wall.  
Finally, although coating the walls of a container with Vaseline is not a convenient technique for reducing sloshing in real containers, as the Vaseline could mix with the transported liquid, use of hydrophobic adhesives [20, 21] as a solution for sloshing reduction could be explored further. Moreover, our results on the influence of the aspect ratio \(\lambda\) could be studied more deeply through experiments performed with containers of the same length but with different widths, as in [22].
## 4 Model Predictive Control for Power Optimization and DDPG-Based Optimization Scheduling
### Model Predictive Control for Power Optimization
#### 4.1.2 System Model Prediction  
Based on the current flight state vector and control input vector, the changes in the flight state over a short future time horizon are predicted. Using the predicted flight states and their corresponding control inputs, the electrical power consumption at future time instances is forecasted.
## 7 Conclusions and Future Work  
Data growth and demand for real-time processing in edge computing environments require efficient management of resources while ensuring high-quality service delivery to users. Existing resource allocation methods have the major challenge of adapting to dynamic network conditions and user needs, which results in sub-optimal performance and user dissatisfaction.  
We introduced the Ubunye framework, which utilizes machine learning techniques to optimize resource allocation in edge computing. By implementing a reverse auction mechanism and intelligent service classification, Ubunye effectively matches resources to user demands while considering essential QoS and QoE metrics.  
Our evaluation results indicate that the Ubunye classifier achieved an accuracy of over 90% in service classification. The allocator showed better bandwidth utilization of over 22% than the other methods. The Ubunye framework offers a promising solution to tackle resource allocation challenges in edge computing, effectively addressing QoS and QoE requirements. The insights from this research contribute to the ongoing efforts to optimize edge computing systems and open avenues for future advancements in this field.  
There are some promising directions for future work. Integrating more advanced artificial intelligence techniques, such as deep neural networks, can be further explored to improve service classification and demand forecasting. Expanding Ubunye to include resource management in multi-cloud and federated environments could increase its applicability and robustness. Another possible future work is to analyze the impact of different network topologies and mobility conditions on service performance to evaluate a more dynamic adaptation to user needs. We can also consider using other data sets in the classifier to evaluate different network traffic scenarios.  
Figure 21: Summary: the average of each performance metric was calculated, and the highest metric was used as a benchmark.  
**Author Contributions:** Conceptualization, K.A.M., A.R.R. and F.C.D.; methodology, K.A.M., A.R.R. and F.C.D.; software, K.A.M., D.M.L. and G.A.d.A.; validation, A.R.R. and F.C.D.; investigation, K.A.M. and G.A.d.A.; writing--original draft preparation, K.A.M.; writing--review and editing, all authors; visualization, K.A.M, D.M.L. and G.A.d.A.; supervision, F.C.D. and A.R.R.; project administration, K.A.M. and A.R.R.; funding acquisition, A.R.R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** FUNCAP funded this research--Fundacao Cearense de Apoio ao Desenvolvimento Cientifico e Tecnologico (MLC-0191-00164.01.00/22-SPU No: 06219586/2022).  
**Data Availability Statement:** The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# The Cut-Off Frequency of High-Pass Filtering of Strong-Motion Records Based on Transfer Learning
###### Abstract  
A high-pass cut-off frequency in filtering is critical to processing strong-motion records. The various processing procedures available nowadays are based on their own needs and are not universal. Regardless of the methods, a visual inspection of the filtered acceleration integration to displacement is required to determine if the selected filter passband is appropriate. A better method is to use a traversal search combined with visual inspection to determine the cut-off frequency, which is the traditional method. However, this method is inefficient and unsuitable for processing massive strong-motion records. In this study, convolutional neural networks (CNNs) were used to replace visual inspection to achieve the automatic judgment of the reasonableness of the filtered displacement time series. This paper chose the pre-trained deep neural network (DNN) models VGG19, ResNet50, InceptionV3, and InceptionResNetV2 for transfer learning, which are only trained in the fully connected layer or in all network layers. The effect of adding probability constraints on the results when predicting categories was analyzed as well. The results obtained through the VGG19 model, in which all network layers are trained and probability constraints are added to the prediction, have the lowest errors compared to the other models. The coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are 0.82, 0.038, 0.026, and 2.99%, respectively.  
cut-off frequency of high-pass filtering of strong-motion records; transfer learning; convolutional neural networks (CNNs)
# A Numerical Investigation of Laser Beam Welding of Stainless Steel Sheets with a Gap
## 2 Simulation Model
### Description of the Model
An isometric view of the entire computational domain is shown in Figure 1. The domain measured 30 mm \(\times\) 8 mm \(\times\) 5 mm, with the latter measurement being the height and varying slightly depending on the gap width. It was ensured that boundary conditions were not influencing the solution by performing test simulations on larger geometric domains. The initial grid shown in Figure 1 consists of 76,800 elements. The number of elements changed constantly during the course of each simulation due to the dynamic mesh refinement, reaching values of 250,000, depending on the specific simulation. Simulation S5, presented in Section 4, featured, due to the additional level of mesh refinement, approximately 50,000 additional elements. The refined mesh can be seen in Figure 2. The rise in computational times, however, mostly stems from the thus required smaller time step, as laid out in detail in Section 2.3. Figure 1: Spatial domain of the CFD model with initial (unrefined) computational grid; measurements in (m).
## 4 Thermographic Techniques for Breast Cancer Detection
### Dynamic Infrared Thermography  
To enhance the thermal contrast due to smaller and deeper breast tumors, dynamic imaging after or during the application of a cold stimulus was performed. In addition to canonical dynamic breast thermography, an alternative procedure known as rotational thermography was suggested to allow the whole breast surface to be imaged.
## 6 Performance modelling  
Determining effective models for use in solution comparison is integral to the frameworks success. Models are built from the solution data database. The modelling process is discussed in detail in Section 5.1. The modelling approach favoured in the presented framework involves determining the performance of a metric using linear regression over the range of features.  
The framework is built so that any facet of the system may be replaced to achieve better performance. One simple example would be to update the performance modelling module should further research dictate another modelling method to be more effective.
###### Abstract  
In this paper, we investigate the physical layer security in a two-tier heterogeneous wireless sensor network (HWSN) depending on simultaneous wireless information and power transfer (SWIPT) approach for multiuser multiple-input multiple-output wiretap channels with artificial noise (AN) transmission, where a more general system framework of HWSN only includes a macrocell and a femtocell. For the sake of implementing security enhancement and green communications, the joint optimization problem of the secure beamforming vector at the macrocell and femtocell, the AN vector, and the power splitting ratio is modeled to maximize the minimal secrecy capacity of the wiretapped macrocell sensor nodes (M-SNs) while considering the fairness among multiple M-SNs. To reduce the performance loss of the rank relaxation from the SDR technique while solving the non-convex max-min program, we apply successive convex approximation (SCA) technique, first-order Taylor series expansion and sequential parametric convex approximation (SPCA) approach to transform the max-min program to a second order cone programming (SOCP) problem to iterate to a near-optimal solution. In addition, we propose a novel SCA-SPCA-based iterative algorithm while its convergence property is proved. The simulation shows that our SCA-SPCA-based method outperforms the conventional methods.  
heterogeneous wireless sensor networks; SWIPT; physical-layer secrecy; secure beamforming; successive convex approximation +
Footnote †: journal: Electronics Electronics
# Meander Structure Analysis Techniques Using Artificial Neural Networks
## 3 Results
### Results Obtained with Feedforward Neural Network
The \(S_{11}\) parameter represents the impedance matching in a meander structure, which affects reflections from the meander structure's ports and periodical inhomogeneities. The FFNN structure for the prediction of \(S_{11}\) was the same as that for \(S_{21}\) and was equal to (6-9-16-1). For clarity in the results, in order to avoid a huge overlap for the curves, the \(S_{11}\) results for MS1 and MS2 (Figure 6c) and for MS3 and MS4 (Figure 6d) are presented in separate figures. The average MAE of \(S_{11}\) for all 74 MSs did not exceed \(2.47\pm 1.55\) dB (Table 2). The largest MAE was obtained in the passband due to the greater variation in \(S_{11}\), influenced by the reflection of the signal from the output of the meander structure. The maximum peak error value of MS1 was equal to 14.55 dB at 4.03 GHz (Figure 6c).
# Prediction of Flight Delays at _Beijing_ Capital International Airport Based on Ensemble Methods
## 3 Methodology  
### Ensemble Methods  
Ensemble methods use multiple models to obtain better performance. Conventional ensemble methods include bagging, boosting, and stacking methods [24].
## 2 Data
### Explanatory Variables  
As a public dataset, Sina Weibo posts are used as an explanatory variable in this study. Specifically, during the registration process, the new users of Sina Weibo (also called Weibo) are informed that they can control the openness of their posts, and only public posts can be viewed and downloaded. Therefore, in this research, all of the Weibo posts we use are publicly visible.  
The processing of the dependent variables is based on the data crawling procedure programmed by the Scrapy structure of Python, in which the key word \"inferiority\" was searched on Weibo between 2012 and 2017. There were 1,627,991 pieces of crawling data, which contained the content of the posts, the launch data, and information about the place, and the geographical location coordinates. Based on previous research, some posts that were initially downloaded were not relevant to this study, and the data cleaning had to be performed before we could proceed to the next step of the analysis. The specific workflow was as follows.  
Firstly, this was a binary supervised classification problem; therefore, a training dataset was needed. However, to our knowledge, there is no such available dataset. Therefore, five researchers with a background in psychology marked random samples of 20,000 posts from 1,627,991 posts. Four of them were divided into two groups to read the posts and assess their relevance to inferiority. In cases of differing opinions, a fifth researcher participated in the discussion, and the posts were marked as per the principle of the minority obeying the majority.  
Secondly, according to the data partitioning convention of machine learning, the labeled completed posts were divided into a training set, a validation set, and a test set consisting of 12,000, 4000, and 4000 posts, respectively. Since the support vector machine (SVM) is accurate for the Weibo classification [7; 17; 20], we chose the SVM to classify the data in this study. Additionally, the SVM was tested to identify the above dataset with an accuracy of 84.21%, which meets the classification accuracy requirement criteria.  
Finally, 1,627,991 posts were classified using the SVM to identify the target posts. A total of 1,370,931 posts were identified as reflecting inferiority; among them, 48,725 posts with geographic coordinates (405 posts in 2012, 1932 posts in 2013, 2963 posts in 2014, 4613 posts in 2015, 14,954 posts in 2016, and 23,858 posts in 2017) were used for a further analysis. The overlay analysis and the statistical analysis of the extracted coordinate information and urban district vector graph of China indicated that the posts related to inferiority were posted in 288 cities. Taking the uneven population distribution into account, it was essential to correct the population distribution to prevent a deviation of the results. Considering the characteristics of the social media data, this paper used the ratio of the number of Weibo posts related to inferiority to the population of each region as the explanatory variable [20; 24; 28].
# Options for Performing DNN-Based Causal Speech Denoising Using the U-Net Architecture +
## 4 Experiment and Performance Evaluation
### Datasets for Model Training  
In our experiments, the speech samples were sourced from the Centre for Speech Technology Voice Cloning ToolKit (CSTR VCTK) Corpus [32], which includes utterances from 56 individuals (28 males and 28 females). We utilized recordings from 54 of these individuals, each contributing approximately 400 sentences, as training material. The recordings of the remaining two (one male and one female) were set aside for testing. Originally sampled at 48 kHz, these files were down-sampled to 8 kHz for our tests. Noise data were incorporated from the Diverse Environments Multi-channel Acoustic Noise Database (DEMAND) [33], featuring six categories of ambient noise, each with three distinct recordings. During training, noise was randomly mixed with speech at signal-to-noise ratios (SNR) of \(-5,5,10,\) and 15 dB. For real-time speech denoising, we employed the OLA method with \(L_{f}\)= 256 and \(L_{s}=64\) at a frame updating rate of 125 times per second, ensuring that processing for each frame was completed within 8 milliseconds.  
During the training phase, two percent of our data served as the validation set. We selected the Adam optimizer [34] and processed mini-batches of 2048 observations. The training process was empirically set at a maximum of 60 epochs. The best model was identified based on the lowest validation loss. We conducted the above model training in MATLAB(r) R2024a, utilizing an NVIDIA(r) 3090 GPU (NVIDIA, Santa Clara, CA, USA) to accelerate processing speed.
## 2 Data and Methods
### Methods
#### 2.3.3 Trend Analysis and Test Methods  
In this paper, the combination of the Theil-Sen median trend analysis and the Mann-Kendall trend test (M-K) [38] was used to analyze the trend change characteristics of ETa in the Yiluo River Basin.  
Sen trend analysis calculates the median slope of (n(n\(-\)1))/2 data combinations with the following formula:  
\[S=\mathrm{Meadian}\bigg{(}\frac{X_{i}-X_{j}}{j-i}\bigg{)},\ 1<i<j<n \tag{4}\]  
where \(X_{i}\) and \(X_{j}\) are the values at times i and j, respectively, and n is the length of the time series. When S > 0, the time series is in an upward trend, and when S < 0, the time series is in a downward trend.  
The Mann-Kendall trend test was used to evaluate the significance of the trend of ETa changes from 2001 to 2020. The principle is: the original hypothesis H\({}_{0}\) is n (x1, x2,..., xn) independent time series samples with the same distribution; the alternative hypothesis H\({}_{1}\) is a bilateral test. The formula is as follows:  
\[V(s)=\frac{n(n-1)(2n+5)}{18} \tag{5}\]  
\[sgn(X_{j}-X_{i})=\left\{\begin{array}{l}-1,X_{j}-X_{i}<0\\ 0,\ X_{j}-X_{i}=0\\ 1,\ X_{j}-X_{i}>0\end{array}\right. \tag{6}\]  
\[S=\sum_{x=1}^{n-1}\sum_{y=x+1}^{n}sgn(X_{j}-X_{i}) \tag{7}\]  
Define statistic Z:  
\[Z=\left\{\begin{array}{l}\frac{S+1}{\sqrt{V(s)}},\ S<\ 0\\ 0,\ S=0\\ \frac{S-1}{\sqrt{V(s)}},\ S>\ 0\end{array}\right. \tag{8}\]  
where S is the test statistic, Z is the standardized test statistic, and the value range is (\(-\infty\), \(+\infty\)); \(X_{i}\) and \(X_{j}\) denote the values of year i and year j, respectively, n is the time series constant, and sgn is a symbolic function. If \(|Z|\)\(>\)\(Z_{1-\alpha/2}\), the H\({}_{0}\) hypothesis is rejected at a given confidence level \(\alpha\); namely, there is a significant trend in the time series data. When \(\alpha\) is equal to 0.05, \(Z_{1-\alpha/2}\) is equal to 1.96. In this paper, the significance of each time series trend is judged based on the 0.05 confidence level. The Z value is used to assess the trendof the statistical data; a positive Z value indicates an upward trend and a negative Z value indicates a downward trend.  
Superimposing the classification results of the Theil-Sen trend analysis and Mann-Kendall trend test, the statistical results were divided into 5 types: S greater than or equal to 0.001 and Z greater than 1.96 as significantly increased; S greater than or equal to 0.001 and Z at \(-\)1.96 to 1.96 as slightly increased; S between \(-\)0.001 and 0.001 and Z at \(-\)1.96 to 1.96 as stable; S less than \(-\)0.001 and Z at \(-\)1.96 to 1.96 as slightly decreased; S less than \(-\)0.001 and Z less than \(-\)1.96 as significantly decreased.
## 3 Results and Discussion
### Structural Elucidation
#### 3.2.3 Structural Elucidation of DBP9  
The NMR data regarding the compound DBP9 show the presence of the A, C, and D rings; the protons H-11 (at 5.25 and 46.84 ppm); and carbons identified as C-9 and C-7 at 187.48 and 158.02 ppm, respectively. In the HMBC spectrum, both carbonyls were correlated with protons H-11, but only the first of which was correlated with protons H-3 and H-5, and only the second of which was correlated with the protons at 2.67 ppm, identified as proton H-30. The 1D and 2D NMR data show the \(n\)-butyl side chain but with a quaternary carbon at position C-29. In fact, the \({}^{13}\)C NMR spectrum showed the presence of a carbon at 83.09 ppm, which was identified as an oxygenated quaternary carbon engaged in the closure of a six-term cycle with the amino function at carbon C-4. The compound DBP9 was, therefore, very similar to the compound DBP2 reported in a previous article on the degradation products of ribesartan [16], for which the formation of a new bond between the positions N-28 and C-14 did not occur.