esa-sceva/satcom-chunk-collection huggingface dataset:
# A Scalogram-Based CNN Approach for Audio Classification in Construction Sites
## 5 Experimental Setup
### Preprocessing  
After the audio signals have been split into chunks of 30 ms, they have been resampled to 22,050 Hz for memory-saving purposes. This resampling procedure does not affect the quality of the classification, since the energy of audio signals related to construction sites is vanishing at frequencies higher than 10 kHz.  
For each resampled chunk, the CWT has been extracted (we used the Python ssqueezpy package, available at: [[https://github.com/OverLordGoldDragon/ssqueezpy](https://github.com/OverLordGoldDragon/ssqueezpy)]([https://github.com/OverLordGoldDragon/ssqueezpy](https://github.com/OverLordGoldDragon/ssqueezpy)), accessed on 10 November 2023). The Morlet wavelet [23] has been used in this work. The obtained matrix has been then resized to \(227\times 227\) in order to be compliant with the input layer of the used AlexNet (see Section 5.3). For simplicity and memory-saving purposes, the obtained resized matrix has been rescaled to the interval \([0,\,255]\), converted to integer numbers, and saved as images.  
Some random images related to the extracted CWT from Classes 1, 3, and 4, respectively, are shown in Figure 3. These scalograms clearly capture salient localized events in sound frames, as shown by the horizontal lines or cloud-like points in Figure 3. Spectograms of the same signals are generally unable to capture salient time/scale characteristics.
# On the Detection and Removal of Oxides from Laser-Treated Surfaces
)4 UJP Praha, Nad Kaminkou 1345, 15600 Praha, Czech Republic; [EMAIL_ADDRESS] Correspondence: [EMAIL_ADDRESS]  
[PERSON]  
2 New Technologies Research Centre, University of West Bohemia, Univerzitni 8, 30100 Pizen, Czech Republic; [EMAIL_ADDRESS] (M.J.); [EMAIL_ADDRESS] (D.M. ).3  
[PERSON]  
3 Laserinstitutt Hochschine Mitwieda, University of Applied Sciences Mittweida, Technikumplatz 17, 09648 Mittweida, Germany; [EMAIL_ADDRESS] (O.P. ); [EMAIL_ADDRESS] (S.K. ); [EMAIL_ADDRESS] (U.L. ); [EMAIL_ADDRESS] (J.S. )4 UJP Praha, Nad Kaminkou 1345, 15600 Praha, Czech Republic; hajicek@uip.cz4 Correspondence: [EMAIL_ADDRESS]  
[PERSON] M.  
of metallurgical bonding between the metallic coating and the metallic substrate [14; 15], leading to reduced adhesion. On the other hand, the adhesion of an oxide ceramic coating may be enhanced by suitable substrate oxidation [16]. In other applications, the laser-induced oxidation of the final metallic surface is carried out on purpose, e.g., to improve the corrosion resistance [17; 18] or to provide localized electrical insulation in biomedical applications [19]. Oxidation may also play a role in the formation of so-called laser-induced periodic surface structures [20]. Therefore, there is a need for oxide content control on laser-treated surfaces. This, in turn, entails its detection (and qualitative or quantitative evaluation) and--where undesirable--removal. In this work, the applicability of two commonly available analytical techniques--EDS and XRD--for the comparative/semi-quantitative determination of oxide content on representative laser-treated materials is investigated. A simplified practical method that circumvents the complications associated with non-ideal samples and allows for a semi-quantitative comparison of the oxide content after different variations of the laser treatment is proposed. Furthermore, three techniques for reducing the oxides, namely heat treatment in a reducing atmosphere, moderate laser ablation, and acid etching, are applied. The efficiency of these methods is evaluated.
# Unraveling the Scale Dependency of SIF-Based Phenology: Amplified Trends and Climate Responses
## 2 Data and Methods  
### Study Area  
The deciduous forest region of the Northern Hemisphere, located at latitudes greater than 30 \({}^{\circ}\)N and encompassing parts of Europe, Asia, and North America, is the focus of this study. Main climate types include temperate continental, temperate oceanic, temperate monsoon, and Mediterranean climates.  
The study used MODIS land cover data MCD12Q1, which presents yearly distribution maps of global land cover types at a spatial resolution of 500 m. Pixels identified as deciduous forest (including both broadleaf (DBF) and needleleaf (DNF) types) were extracted for the period 2008-2017, according to the International Geosphere-Biosphere Programme (IGBP) classification. To match SIF products at three different scales, the deciduous forest data were resampled to 1-km, 5-km, and 50-km resolutions. During resampling, internal consistency was maintained. If over 50% of the pixels within a given window were identified as deciduous forest, the land cover classification for the central pixel was determined to be deciduous forest. The study area is shown in Figure 1.
## 3 Optimization Algorithms
### Whale Optimization Algorithm
#### 3.1.2 Exploitation Phase  
The position update equation of the shrinking encircling mechanism is shown in Equation (17). By decreasing \(\overline{ap}\) from 2 to 0 and substituting its value into Equation (18), And when the pace coefficient \(\bar{A}\) is in the range of [-1, 1], the search process of the whale algorithm enters the development stage. The current best position \(\overline{Q}^{*}(t)\) is the prey location or the nearest position to the prey. The whale \(\overline{Q}(t)\) then approaches to the best position in order to find the best solution.  
Spiral updating position  
When humpback whales hunt, they use their air vents to generate air bubbles. Since they swim rapidly in a nine-shaped or circular path underneath the water level, this spiral-shape air-bubble net traps the prey. Therefore, this algorithm generates a spiral to update the calculated distance between the whale and the current optimal position as follows:  
\[\overline{Q}(t+1)=\overline{D}_{ws}\cdot e^{bl}\cdot cos(2\pi l)+\overline{Q}^ {*}(t) \tag{21}\]  
\[\overline{D}_{ws}=\left|\overline{Q}^{*}(t)-\overline{Q}(t)\right| \tag{22}\]  
\(\overline{D}_{ws}\) denotes the distance between the whale location and the current best position, \(b\) is a constant to designate the logarithmic spiral shape, and \(l\) is a random number in the range of [-1, 1]  
When humpback whales hunt, they swim along the spiral path generated by their air vent bubbles. In order to simulate the behavior of both modes simultaneously, it is assumed that there is a 50% chance that each choosing between the shrinking encircling and the spiral updating mechanisms. The spiral update position during the process is shown below:  
\[\overline{Q}(t+1)=\left\{\begin{matrix}\overline{Q}^{*}(t)-\bar{A}\cdot \overline{D}_{we}\ \ \text{if}\ p<0.5\\ \overline{D}_{ws}\cdot e^{bl}\cdot cos(2\pi l)+\overline{Q}^{*}(t)\ \ \text{if}\ p\geq 0.5\end{matrix}\right. \tag{23}\]  
where \(p\) is a random number in the range of [0, 1].
###### Abstract  
The power system on the offshore platform is of great importance since it is the power source for oil and gas exploitation, procession and transportation. Transformers constitute key equipment in the power system, and partial discharge (PD) is its most common fault that should be monitored and identified in a timely and accurate manner. However, the existing PD classifiers cannot meet the demand for real-time online monitoring due to their disadvantages of high memory consumption and poor timeliness. Therefore, a new MobileNets convolutional neural network (MCNN) model is proposed to identify the PD pattern of transformers based on the phase resolved partial discharge (PRPD) spectrum. The model has the advantages of low computational complexity, fast reasoning speed and excellent classification performance. Firstly, we make four typical defect models of PD and conduct a test in a laboratory to collect the PRPD spectra as the data sample. In order to further improve the feature expression ability and recognition accuracy of the model, the lightweight attention mechanism Squeeze-and-Excitation (SE) module and the nonlinear function hard-swish (h-swish) are added after constructing the MCNN model to eliminate the potential accuracy loss in PD pattern recognition. The MCNN model is trained and tested with the pre-processed PRPD spectrum, and a variety of methods are used to visualize the model to verify the effectiveness of the model. Finally, the performance of MCNN is compared with many existing PD pattern recognition models based on convolutional neural network (CNN), the results show that the proposed MCNN can further reduce the number of parameters of the model and improve the calculation speed to achieve the best performance on the premise of good recognition accuracy.  
Keywords:transformer; partial discharge (PD); pattern recognition; MobileNets convolution neural network +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Influence of Nozzle Diameter on the Erosion Performance of HBS
To verify the conclusions above, the stress distribution of HBS at different nozzle diameters was obtained, as shown in Figure 13, with a standoff distance of 0.5 cm and a jet velocity of 100 m\(\cdot\)s\({}^{-1}\). On one hand, observing the shape of the erosion hole, it can be seen that the change in the side erosion of the crater from the use of a 2.4 mm nozzle diameter is the most significant, followed by that of a 1.8 mm nozzle diameter, and a 1.2 mm nozzle diameter shows the weakest change. On the other hand, the stress concentration zone of the erosion hole at a nozzle diameter of 2.4 mm is significantly larger than that of the two with smaller diameters. All the results indicate that the erosion of the side of the crater is significantly enhanced with the increase in nozzle diameter. Figure 12: The erosion depth and the erosion volume versus nozzle diameter. (**a**) Depth of erosion; (**b**) volume of erosion. In brief, for the erosion depth, the optimal nozzle diameter is 1.8 mm for the jet erosion of HBS. An increase in nozzle diameter corresponds to an increase in erosion volume, which is consistent with the conclusions in Section 3.1.1. Therefore, when using a high-pressure water jet to exploit marine NGH, it is suggested that the nozzle diameter be greater than 1.8 mm to obtain higher mining efficiency.
# Optimization Algorithm for Surface Defect Detection of Aircraft Engine Components Based on YOLOv5
## 5 Results
### Performance Comparison of Mainstream Detection Algorithms  
To further evaluate the detection performance of the proposed improved model in the current stage, experiments were conducted to compare and analyze it with other object detection algorithms under the same experimental conditions. We selected classic one-stage detection algorithms such as SSD (Single Shot MultiBox Detector), RetinaNet, and FCOS (Fully Convolutional One-Stage Object Detection), as well as representative algorithms of the same category like YOLOv3, YOLOv4, and YOLOv7.  
By conducting experiments on the same dataset and evaluation metrics, we were able to objectively assess the effectiveness and superiority of the proposed improved model. For each algorithm, we recorded its detection accuracy, detection speed, and model size, as shown in Table 3.  
Through the comparison of experimental results, we found that the proposed improved model has significant advantages in terms of detection performance compared to traditional algorithms and algorithms of the same category. It demonstrates improvements in overall detection accuracy, detection speed, and model size. It surpasses the new-generation YOLOv7 model in all aspects. Moreover, the targeted improvements in the model show better performance in terms of target localization accuracy, small target recognition, and difficult-to-detect target recognition. This makes it capable of meeting the requirements for high-accuracy, high-speed, and lightweight aviation engine component defect detection applications.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm Name** & **AP50/\%** & **FPS/s** & **Weight/MB** \\ \hline SSD & 24.9 & 45.3 & 98.0 \\ RetinaNet & 46.6 & 22.7 & 34.9 \\ FCOS & 48.5 & 17.9 & 85.2 \\ YOLOv3 & 49.7 & 38.4 & 27.5 \\ YOLOv4 & 50.0 & 43.1 & 35.4 \\ YOLOv5 & 52.3 & 131.6 & 13.8 \\ YOLOv7 & 54.3 & 81.6 & 70.2 \\ YOLOv5-Ours & 62.9 & 101.02 & 12.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of detection performance of mainstream algorithms in this dataset.
# De-Noising Process in Room Impulse Response with Generalized Spectral Subtraction
## 4 Results Analysis and Discussion  
This section presents the RIR de-noising performance of the GBSS and compares it with the reference RIRs and the compensation method. The estimated RTs and EDT are used to evaluate the feasibility of the algorithm. The advantage of the algorithm was the two factors in Equation (4), which provide flexibility for the GBSS in reducing noise levels to significantly improve the dynamic range of the EDCs. Here, \(\beta\) values from 0.1 to 0.001 (step 0.01) were applied depending on the estimated posterior SNR. The \(\alpha\) was set as Equation (5) with \(\alpha_{0}\) ranging from 1 to 6 (step 0.25) [32]. The illustration of the de-noising effects by using the two parameters within the range was analyzed separately. Furthermore, the inadequacy of de-noising RIRs through the use of \(\alpha_{0}\) smaller than 3 and \(\beta\) higher than 0.1 will also be discussed. Finally, the RIRs measured in the meeting room convoluted with the pink noise level at \(-55\) dB, filtered in the octave band at 1 kHz, were used to present the de-noising results.
In addition, their effects on the antioxidant system are also significant since they can maintain and re-establish homeostasis [5; 11; 23; 24]. It has been reported that by-products from lesser-consumed tropical fruits have several bioactive compounds, such as acai seed, which can prevent weight gain, adiposity, and dyslipidemia, according to in vivo data (male mice, C57 BL/6) [25]. Lychee peel has considerably higher amounts of phenolic compounds and scavenging capacity than other parts of the fruit. At the same time, its seeds prevent the growth of cancerous cells, in addition to antihyperglycemic, antihyperlipidemic, antiplatelet, and antiviral activities [26; 27]. Jackfruit peel extracts have shown a high total flavonoid content, which correlates with its high antioxidant activity (AOXA) against the 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical [28]. Using different by-products from lesser-known tropical fruits as food ingredients is an area of interest for further research. In this sense, passion fruit peel (PFP) supplementation is considered an important source of dietary fiber. Studies in humans demonstrated that a diet supplemented with passion fruit peel (PFP) flour could improve metabolic parameters, such as reducing fasting glycemia and glycated hemoglobin in type 2 diabetic individuals, as well as reducing fasting glycemia and triglyceride levels in hypercholesterol women [29]. It was recently found that many is a potential source of pro-vitamin A carotenoids (pulp) and dietary fiber (peel).Still, their use as a food ingredient is distant from being commercially available, since there are no reports of actual research because of their small cultivation area [30; 31]. In this sense, some commercially available products have been developed from different by-products, but they are not widely accessible worldwide, such as the case of PFP flour developed in Brazil, jackfruit peel and pulp flour in India, lychee seed oil in China, mamey seed oil in several local markets in Mexico, and newly developed acai seed powder in the United States. Therefore, the present review analyses the nutritional composition and content of bioactive compounds in lesser-consumed tropical fruits, their by-products (peel and seed), and their antioxidant and anti-inflammatory potentials. The information discussed herein will be useful to understand their possible health benefits. It will serve to increase their consumption, as well as to promote the use of their by-products, which are currently underutilized.
# Space-Based Displacement Monitoring of Coastal Urban Areas: The Case of Limassol's Coastal Front
## 2 Materials and Methods
### Case Study
Taking into consideration the geology of the area, the seismicity of Cyprus in the last five years [37], the sea level rise [38] due to the thermal expansion of seawater due to ocean warming and water input from land ice melt, the significant vulnerability to climate change, and the rapid construction of skyscrapers and tall buildings in the area, land subsidence monitoring is an increasingly eminent component of risk planning and decision-making regarding infrastructure. To date, no cases of land subsidence have been identified in the study area and quantified through various InSAR techniques such as PSI.
## 4 Experimental Research
### Experimental Results and Analysis  
#### 4.3.1 Comparative Experiments of Multi-Scale Luminance Enhancement Modules  
The multi-scale luminance-enhancement module was tested with the more common image-enhancement network models Retinexnet [23], RRDnet [24], and EnlightenGAN on a dataset consisting of 500 low-illumination coal-rock images, with PSNR and SSIM selected as the evaluation metrics for quantitative analysis of the model quality. Figure 11 shows from left to right: the low-illumination image, the high-illumination image, the Retinexnet enhancement effect, the RRDnet enhancement effect, EnlightenGAN enhancement effect, the model's infrastructure enhancement effect, and the model's enhancement effect. The objective analysis is shown in Table 2. The darker parts of the figure represent coal seams and the lighter parts represent rock seams.  
The proposed model achieved scores of 21.288 and 0.783 in PSNR and SSIM, respectively, which is better than its infrastructure and the more common image-enhancement models. The model also enhanced the brightness, with the uniform lighting effect close to the natural light and no distortion. Therefore, the model effectively enhanced the  
Figure 11: Comparison of experimental results of coal and rock enhancement images. Subfigures (**a–f**) show representative samples selected from the dataset, reflecting typical coal and rock interface scenarios.  
low illumination coal-rock image caused by the complex and harsh environment of the coal mine.  
The integration of the multi-scale luminance-enhancement module provides significant improvements both quantitatively and qualitatively. As demonstrated by the comparative results in Table 2 and Figure 11, this module yields the highest PSNR (21.288) and SSIM (0.783) scores among all compared methods, indicating its effectiveness in enhancing low-illumination coal-rock images while preserving structural fidelity. More importantly, by fusing features across different perceptual fields via hybrid dilated convolution, the module successfully eliminates localized overexposure and underexposure, leading to a more uniform illumination distribution across the image. This reduces false activations and segmentation errors in subsequent modules. From an engineering perspective, the improved brightness uniformity and contrast restoration significantly enhance the model's robustness in non-ideal imaging conditions, such as variable lighting, reflective surfaces, or partial occlusions: scenarios frequently encountered in real underground environments.
# Reliability-Based Optimization of Impeller Structure Using Exponential Function Approximation
###### Abstract  
This study proposes a decoupling approach based on first-order exponential function approximation for reliability-based optimization. It establishes an explicit relationship between the failure probability and design parameters within only one implementation of reliability analysis, without the repeated reliability analysis involved in traditional approaches. Then, the obtained explicit exponential function approximation is used to decouple the reliability-based optimization problem and transform it into an equivalent deterministic optimization problem, where a general optimization algorithm can be adopted to solve steadily with a neglectable computational cost. The proposed approach is applied to conduct the reliability-based optimization of a centrifugal compressor impeller design. The results indicate that the proposed method can provide significantly improved computational efficiency while maintaining optimization accuracy comparable to that obtained by traditional double-loop optimization methods. Meanwhile, sensitivity analysis reveals the relative importance of design parameters to structural reliability.  
m pacs: 27
# Transfer Learning with Deep Recurrent Neural Networks for Remaining Useful Life Estimation
## 5 Conclusions  
This paper presents a transfer learning algorithm with bi-directional LSTM neural networks for RUL prediction of a turbofan engine. Our algorithm addresses one of the major challenges in RUL prediction: the difficulty of obtaining a sufficient number of samples in data-driven prognostics. Our transfer prognostic model works by exploiting data samples from different but approximately related tasks for remaining useful life estimation. Our method was validated on the C-MAPSS Datasets with extensive experiments by comparing its performance with those models without transfer learning. The experimental results showed that transfer learning is effective in most cases except when transferring from a dataset of multiple operating conditions to a dataset of a single operating condition, which led to negative transfer learning. How to prevent negative transfer is still an open problem. In future work, we will develop more advanced transfer learning methods or data normalization schemes to improve the multi-condition RUL prediction performance.  
Figure 11: Training loss history for experiments in which the training data set is generated by randomly selecting 50 trajectories.  
**Author Contributions:** Conceptualization, A.Z., J.H., S.L.; methodology, A.Z., J.H., Z.L., Y.C.; software, A.Z., G.Y.; investigation, A.Z., J.H.; resources, S.L.; writing--original draft preparation, A.Z., J.H.; writing--review and editing, A.Z., J.H., Z.L. and Y.C.; supervision, S.L. and J.H.; project administration, H.W., J.H. and S.L. funding acquisition, H.W. and S.L.  
**Funding:** This research was funded by The National Natural Science Foundation of China under Grant Nos. 91746116 and 51741101, Project of Ministry of Industry and Information under Grant No. [2016]213, Science and Technology Project of Guizhou Province under Grant Nos. Talents [2015]4011 and [2016]5013, Collaborative Innovation [2015]02, National Natural Science Foundation of China: 61863005 and Project of Guizhou University's Technology Crowdfunding for Intelligent Equipment under Grant No. JSZC[2016]001.  
**Acknowledgments:** J.H. gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X Pascal GPU used for this research and the support of National Institute of Measurement and Testing Technology with providing the semi-anechoic laboratory.  
**Conflicts of Interest:** The authors declare no conflict of interest.