esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Classified Elastic Waves for AE Source Localization Based on Self-Organizing Map
## 2 AE Source Localization
### Use of Ray Tracing for the Source Localization  
In the source localization using ray tracing, the source is localized on the basis of first travel times of approximated ray paths. Although ray tracing performs the approximation of ray paths considering the diffraction or the refraction, ray tracing requires an analytical model to approximate the heterogeneity of the materials. If the classified waves are applied to the source localization using ray tracing, the localization does not require approximating the heterogeneity of the materials to compute the first travel times of approximated ray paths since classified waves consist of straight propagation.  
In order to detail the source localization, the illustration of the localization is shown in Figure 2. Moreover, the possible pulse-origination time \(P_{ij}\) is defined by subtracting the first travel time from the arrival time, obtained as  
\[P_{ij}=A_{i}-Tr_{ij}, \tag{3}\]  
where \(Tr_{ij}\) is travel time of the wave, wherein the start is the sensor \(i\) and the end is the candidate \(j\). The AE source is selected from the candidates, and the index of the selected candidate is the minimum variance of the origination time \(\sigma_{j}^{2}\), obtained as  
\[\sigma_{j}^{2}=\frac{1}{N_{s}}\sum_{i=1}^{N_{s}}\left(\overset{-}{P}_{j}-P_{ij }\right)^{2}, \tag{4}\]  
where \(N_{s}\) is the number of used sensors, and \(\overset{-}{P}_{j}\) is the average of \(P_{ij}\). It is noted that the computation of \(\sigma_{j}^{2}\) is not required of all located sensors in the model, and it implies that the used sensors in the source localization can be selected by the result of classifications. Moreover, although the particular location of the sensors contributes to the determinant of 0 in computation of the TDOA method, the application of ray tracing has potential to localize all of the sources since the computation of matrix equations is not required. However, the interval of candidates contributes to the accuracy of the localization because the candidate is selected as the AE source. Thus, it is possible that the accuracy of the source localization based on ray tracing is lower in comparison with the TDOA method if the interval of the candidates is not sufficient in the model. Furthermore, decreasing the interval of candidates contributes to increasing computational time because the numbers of the candidates are increased in the models.  
Figure 2: Illustration of the localization using ray tracing.
## 3 Results
### 3.3. _Comparison Between the _Trabecular Bone Density Measurement Method_  
_Using the _Hounsfield_  
_Unit and _Trabecular Microstructure Parameters_  
Spearman correlation was used to find the correlation between two measurement methods (Figure 3). Correlation coefficients were obtained from trabecular microstructure parameters and Hounsfield unit values in the first cervical vertebra, which are known to have a similar accuracy to the general bone density. The Tb.th, Tb.n, and BV/TV of the trabecular pattern showed a significant positive correlation with the HU value, and the Tb.sp showed a significant negative correlation with the HU value.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Mx** & **Mn** & **Co** & **Cv** & **\(p\)-Value** \\ \cline{2-5}  & **Median(IQR)** & **Median(IQR)** & **Median(IQR)** & **Median(IQR)** & \\ \hline Tb.th & 3.84(0.85) \({}^{\text{a}}\) & 3.49(0.53) \({}^{\text{b}}\) & 3.21(0.36) \({}^{\text{c}}\) & 3.25(0.50) \({}^{\text{bc}}\) & 0.025 \\ Tb.n & 0.14(0.034) \({}^{\text{a}}\) & 0.14(0.05) \({}^{\text{ab}}\) & 0.13(0.03) \({}^{\text{b}}\) & 0.14(0.05) \({}^{\text{ab}}\) & 0.032 \\ Tb.sp & 3.51(0.73) \({}^{\text{c}}\) & 3.74(1.05) \({}^{\text{bc}}\) & 4.51(3.21) \({}^{\text{a}}\) & 4.27(2.79) \({}^{\text{ab}}\) & 0.015 \\ BV/TV & 53.42(9.78) \({}^{\text{a}}\) & 50.29(12.35) \({}^{\text{b}}\) & 47.50(16.72) \({}^{\text{bc}}\) & 46.58(24.06) \({}^{\text{c}}\) & 0.008 \\ HU & 1458.69(227.17) \({}^{\text{a}}\) & 1414.19(265.78) \({}^{\text{b}}\) & 697.14(264.81) \({}^{\text{c}}\) & 540.51(226.40) \({}^{\text{d}}\) & \textless{}0.001 \\ \hline \hline \end{tabular}  
* IQR: Interquartile range; Tb.th: Trabecular thickness; Tb.n: Trabecular number; Tb.sp: Trabecular separation; BV/TV: Bone volume fraction; HU: Hounsfield unit; Mx: Arterial base of the maxilla; Mn: Mandibular body; Co: Mandibular condyle; Cv: First cervical vertebra. \({}^{\text{ab},\text{c},\text{d}}\) The small caps indicates the statistically relation between sites. The same letters indicate non-significant difference between sites (p > 0.05)  
\end{table}
Table 5: Comparison of the bone mineral density parameters among regions.
# Software Defect Prediction Using Heterogeneous Ensemble Classification Based on Segmented Patterns
## 7 Experiments and Results
### KME vs. Ensemble Classifiers
Although the purposed approaches have been compared with the best traditional machine learning algorithms, we also compare the proposed KME with the results of one of the most recent sophisticated approaches called defect prediction based on deep forest (DPDF) [38]. Specifically, we used the common datasets used in their published results and in our approach, 13 datasets, to compare our approach against in terms of precision and recall as shown in Table 8. The results show that KME outperforms the DPDF in all datasets except for pc2 and tomcat-6.0. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multicolumn{2}{c}{**Pre**} & \multicolumn{2}{c}{**Rec**} \\ \cline{2-5}  & **DPDF** & **KME** & **DPDF** & **KME** \\ \hline mc1 & 0.290 & **0.866** & 0.020 & **0.628** \\ mw1 & 0.630 & **0.729** & 0.430 & **0.519** \\ pc1 & 0.250 & **0.828** & 0.130 & **0.659** \\ pc2 & **0.980** & 0.901 & **0.720** & 0.417 \\ pc3 & 0.260 & **0.776** & 0.070 & **0.546** \\ pc4 & 0.770 & **0.881** & 0.210 & **0.727** \\ pc5 & 0.610 & **0.832** & 0.370 & **0.730** \\ ant-1.7 & 0.640 & **0.801** & 0.480 & **0.635** \\ camel-1.6 & 0.460 & **0.841** & 0.120 & **0.621** \\ lucene-2.4 & 0.690 & **0.862** & 0.820 & **0.853** \\ poi-3.0 & 0.840 & **0.929** & 0.820 & **0.925** \\ tomcat-6.0 & **0.840** & 0.788 & 0.120 & **0.567** \\ xalan-2.6 & 0.760 & **0.878** & 0.680 & **0.820** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison between defect prediction based on deep forest (DPDF) [38] and KME. Figure 10: G-mean for the ensemble classifiers and KME.
## 3 Results
### Synthesis of Results
#### 3.2.2 Games, Types, and Purposes  
Table 7 shows the distributions for Subject, Mnemonic Type, and Type of Game. In particular, most (12/32, 37.5%) mnemonics serious games do not focus on any subject. Images are the most common (18/32, 56.3%) mnemonic device. Most (14/32, 43.8%) such games are quizzes, but cognitive games are not far behind (11/32, 34.4%). Such games are either educational (18/32, 56.3%) or they are neither educational nor professional (14/32, 43.8%).
# Urban Plants Classification Using Deep-Learning Methodology: A Case Study on a New Dataset
## 1 Introduction  
Often, plant classification requires the eye of an expert in botanics. Subtle differences in leaves or petal forms might differentiate between different species. On the contrary, there may be high intra-class variability, where species belonging to the same class exhibit very different visual characteristics. Therefore, an accurate automatic plant classification might be of great assistance to a non-expert person who studies agriculture, travels, or grows plants at home.  
Plants classification from their images is just an application of a more general task of image classification. In order to train supervised models for this task, one needs a large volume of high-quality training data. However, not many datasets with plant images categorized by species are publicly available for research, and those which are publicly available are far from covering all plant species over the world.  
This paper introduces a new dataset, Urban Planter, for plant species classification with 1500 images categorized into 15 categories. The motivation behind the Urban Planter dataset was to collect datasets of plant species growing in our district, which are barely covered by existing datasets. This research may have a practical application in the form of a tool that helps people who cultivate plants at home recognize new species and provide appropriate care recommendations. Because urban species are hardly covered by benchmark datasets, state-of-the-art pre-trained classification methods cannot reliably recognize them. Furthermore, we hope that, by expanding our dataset to include edible plants in the future and supporting people in growing food at home, our research will contribute to the UN's 2030 Agenda for Sustainable Development. The dataset contains 15 house and garden plant species that can be grown mostly in a desert climate and are barely covered by existing datasets. The dataset was collected to develop a mobile application assisting urban planners in identifying plants and discovering best growing methods. However, this paper does not describe the application but focuses on the analysis of Urban Planter. We report the results of experiments performed on the new dataset. The experiments aimed at testing the quality of Urban Planter. We explored whether accurate classification models can be trained on an Urban Planter. In addition, we explored transfer learning with two other datasets: ImageNet ([[http://www.image-net.org/](http://www.image-net.org/)]([http://www.image-net.org/](http://www.image-net.org/)) (accessed on 24 April 2022)), which is a classical choice for pre-training, and Oxford102--a much smaller dataset but more relevant to plants classification. We also experimented with two-steps transfer learning, where models, pre-trained first on ImageNet and then on Oxford102, were trained and applied on Urban Planter. The results show that, although Oxford102 is more related to plants classification, the size and the rich diversity of ImageNet are advantageous for accurate classification.
## 3 Numerical Simulation
### Test Validity of the Image Integrity Authentication  
After obtaining the signature and the key, the attacker can obtain the original text. Then the attacker can tamper with the original text and use the same encryption techniques to obtain the ciphertext and send it to the recipient. The integrity authentication algorithm can prevent the tampering attacks by checking whether the original message has been tampered with. The receiver performs the image integrity authentication algorithm to obtain \(N+1\) layers, and compares the calculated \((N+1)\)th layer with the \((N+1)\)th layer in the ciphertext to calculate the SSIM value. As shown in Table 3, this algorithm is very sensitive to tampering, even if one pixel value is changed, the SSIM value greatly changes. The SSIM value decreases to below 0.08 when nine elements are changed. It shows that the algorithm can effectively resist a tamper attack.  
Figure 14: (**a**) image signature of ‘[PERSON]; (**b**) image signature of ‘[PERSON]; (**c**) image signature of all zero; (**d**–**f**) the corresponding encrypted data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(x\) & \(y\) & \(h\) & _Pixels’Number–Changed_ & **SSIM–Authentication** \\ \hline
0 & 0 & 0 & 0 & 1 \\
75:75 & 75:75 & 99 & 1 & 0.5835 \\
75:76 & 75:76 & 99 & 4 & 0.5276 \\
75:77 & 75:77 & 99 & 9 & 0.0585 \\
100:100 & 100:100 & 99 & 1 & 0.5934 \\
100:101 & 100:101 & 99 & 4 & 0.1045 \\
100:102 & 100:102 & 99 & 9 & 0.0706 \\
75:75 & 75:75 & 189 & 1 & 0.5508 \\
75:76 & 75:76 & 189 & 4 & 0.0764 \\
75:77 & 75:77 & 189 & 9 & 0.0495 \\
100:100 & 100:100 & 189 & 1 & 0.5680 \\
100:101 & 100:101 & 189 & 4 & 0.0854 \\
100:102 & 100:102 & 189 & 9 & 0.0719 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results with different numbers of pixels changed.
## 3 Results
In the state of Quintana Roo, the use of sunscreen in sinkholes is only regulated in two of the eleven municipalities (Solidaridad and Tulum, Table 1), which still allow the use of biodegradable sunscreens. In the state of Yucatan, biodegradable sunscreens are permitted in sinkholes throughout the state, except in the state capital, Merida, where no sunscreen is permitted inside the sinkholes. Finally, in the state of Campeche, there is currently no legislation that regulates or protects the quality of the water in the sinkholes (Table 2). \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Category** & **Name** & \begin{tabular}{l} **Decree** \\ **Date** \\ \end{tabular} & \begin{tabular}{l} **Year Publication** \\ **of the** \\ **Management** \\ \end{tabular} & \begin{tabular}{l} **With** \\ **Management** \\ \end{tabular} & \begin{tabular}{l} **With** \\ **Regulation of** \\ **Sunscreens** \\ \end{tabular} & \
\begin{tabular}{l} **Allows** \\ **NONBIO** \\ \end{tabular} &
\begin{tabular}{l} **Allows** \\ **BIO** \\ \end{tabular} \\ \hline FFPA & Yum Balam & 1994 & 2018 & YES & YES & NO & YES \\ FFPA & Isla Cozummel & 2012 & 2016 & YES & YES & NO & YES \\ NP & Arrecife de Puerto Morelos & 1998 & 2000 & YES & NOT & & \\ NP & Arrecife de Cozumel & 1996 & 1998 & YES & YES & NO & YES \\ NP & Arrecife de Xcalia & 2000 & 2004 & YES & YES & NO & YES \\ NP & Costa Occ. de l Muijers, Pta & 1996 & 2016 & YES & YES & NO & YES \\ NP & Cancun y Pta Nizuc & 1998 & 2015 & YES & YES & NO & YES \\ NP & Isla Conroy & 1998 & 2015 & YES & YES & NO & YES \\ NP & Tulum & 1981 & - & NO & & & \\ BR & Arrecife de Sian Ka’an & 1986 & 2014 & YES & YES & NO & YES \\ BR & Banco Chinchoro & 1996 & 2000 & YES & YES & NO & YES \\ BR & Sian Ka an & 1986 & 2014 & YES & YES & NO & YES \\ S & Playa de la Isla Conroy & 1986 & - & NO & & & \\ BR & Tluuron Ballena & 2009 & 2015 & YES & YES & NO & YES \\ FFPA & Bala’an Ka’ax & 2005 & 2007 & YES & NO & & \\ FFPA & Uaymi & 1994 & 2014 & YES & YES & NO & YES \\ FFPA & Manglares de Nichupte & 2008 & 2014 & YES & NO & & \\  & Reserva de la Biosfera & 2016 & 2018 & YES & NO & & \\ BR & Caribe Mexicano & 2016 & 2018 & YES & NO & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Analysis of the regulation of the use of sunscreen in protected aquatic systems of the Yucatan Peninsula. FFPA: Flora and Fauna Protection Area; BR: Biosphere Reserve; NP: National Park; S: Sanctuary; BIO, biodegradable sunscreen; NONBIO, non-b biodegradable sunscreen.
# Development of Potato Mass Estimation System Based on Deep Learning
## 1 Introduction
Such RCNN series methods use a two-stage detector comprised of two networks: one that selects the candidate in the image in which an object is presumed to exist (region proposal) and the other that classifies objects through a detector. While this method has the advantage of a high accuracy, it has a disadvantage of a limited processing speed. Since it is necessary to detect potatoes at each location in real-time to fertilize by location and yield maps based on yields in the field, this method was not suitable for use in this study. On the other hand, in the case of the one-stage detector that has recently come to be widely used for object detection, localization and classification are achieved at the same time as the region proposal step is removed from the existing two-stage detector and objects are detected at once. Although this method is very fast, its accuracy tends to be a little lower than that of the two-stage detector, and a representative one is the YOLO algorithm [16; 17]. In general, the purpose of research on the yield monitoring of potatoes was to measure the number of potatoes harvested. However, since potatoes are not constant in size and weight for each individual, it is necessary to measure the real-time harvest weight for accurate yield monitoring. Therefore, the objective of the present study is to develop and evaluate a potato mass estimation system that can ultimately determine the yield of potatoes by sorting out and tracking in real time the potatoes being harvested using the camera in the harvester and deep learning technology and applying an estimation formula that can estimate the mass of potatoes to the result.
## 2 Materials and Methods
### Data Sources  
Sentinel-2 is a high-resolution multispectral imaging satellite constellation, including Sentinel-2A (launched in June 2015) and Sentinel-2B (launched in March 2017). One satellite has a 10-day revisit period, and together, they have a complementary 5-day revisit period. The Sentinel-2 satellites have a multispectral instrument (MSI) that can cover 13 spectral bands with spatial resolutions of 10, 20, or 60 m [30]. The Sentinel-2 satellite can provide high-resolution Earth observation data. The data have broad application prospects. Therefore, we used Sentinel-2 remote sensing images as the data source to construct a dataset of images showing smoke from straw burning.  
In this study, we used Sentinel-2 image data captured under clear weather conditions. Interference from clouds and fog was excluded in advance. Information about the bands in the Sentinel-2 images is shown in Table 1. Sentinel-2 provides data at two processing levels: level 1C (L1C) and level 2A (L2A). L1C has undergone radiometric calibration and geometric correction, and L2A is the product of atmospheric correction based on L1C [31].  
Figure 1: Maps of the study area.  
There are many repositories of Sentinel-2 data, such as the United States Geological Survey, the European Space Agency (ESA), etc. This paper used Sentinel-2 data freely available on the ESA website ([[https://scihub.copernicus.eu/dhus/#/home](https://scihub.copernicus.eu/dhus/#/home)]([https://scihub.copernicus.eu/dhus/#/home](https://scihub.copernicus.eu/dhus/#/home)), accessed on 27 April 2022). Since straw burning mainly occurs in April, May, October, and November, the imaging times were these months in 2021. The data level was L2A, and the latitude and longitude were from 121\(\lx@math@degree\)38\({}^{\prime}\) E to 131\(\lx@math@degree\)19\({}^{\prime}\) E and from 40\(\lx@math@degree\)50\({}^{\prime}\) N to 46\(\lx@math@degree\)19\({}^{\prime}\) N. We used four cloud-free and fog-free images with visible smoke to make datasets. The image information is shown in Table 2.
# Prediction and Analysis of the Surface Roughness in CNC End Milling Using Neural Networks
###### Abstract  
In the metal cutting process of machine tools, the quality of the surface roughness of the product is very important to improve the friction performance, corrosion resistance, and aesthetics of the product. Therefore, low surface roughness is ideal for mechanical cutting. If the surface roughness of the product can be predicted, not only the quality of the product can be improved but also the processing cost can be reduced. In this study a back propagation neural network (BPNN) was proposed to predict the surface roughness of the processed workpiece. ANOVA was used to analyze the influence of milling parameters, such as spindle speed, feed rate, cutting depth, and milling distance. The experimental results show that the root mean square error (RMSE) obtained by using the back propagation neural network is 0.008, which is much smaller than the 0.021 obtained by the traditional linear regression method.  
surface roughness prediction; back propagation neural network; machine tool; milling; linear regression; ANOVA Article
## 3 Results and Discussion  
### Influence of Fe Application on the Amount of Fixed Nitrogen in Soil  
The cumulative amount of incorporated \({}^{15}\)N in soil collected from the Con plot on Days 2, 4, and 6 of incubation was 0.14 \(\pm\) 0.01, 0.22 \(\pm\) 0.01, and 0.53 \(\pm\) 0.01 \(\upmu\)g/g-dry soil, while that in soil collected from the Fe plot was 0.28 \(\pm\) 0.01, 0.81 \(\pm\) 0.10, and 1.32 \(\pm\) 0.07, respectively (Figure 1). The amount of fixed nitrogen in paddy soils with 50% and 75% water content was reported to be 0.00714 and 0.171 (\(\upmu\)g/g-dry soil) per day, respectively [34]. Given that the water content of the soils in this study was approximately 65%, the average amount of incorporated \({}^{15}\)N in the Con plot soil (0.08 \(\upmu\)g/g-dry soil/day) seems reasonable. On all days examined, the amount of incorporated \({}^{15}\)N in the Fe plot soil was significantly higher (2-3.7 times) than that in the Con plot soil. Since biological nitrogen fixation is the only way atmospheric nitrogen can be incorporated into soil, these results indicate the occurrence of biological nitrogen fixation in native (Con plot) soil and increased biological nitrogen fixation in iron-applied (Fe plot) soil. In our previous study on the same experimental paddy field, enhanced nitrogen-fixing activity, examined by acetylene reduction assay, of Fe plot soil compared with Con plot soil was observed [25]. This study, supplementing the results of the previous study, clearly demonstrates an increased amount of fixed nitrogen in Fe plot soil compared with Con plot soil upon iron application.
## 4 Experimental Signal Analysis
The results are shown in Figure 14. We can make a conclusion that the recovered signals in frequency are both uncorrelated with fault frequency; therefore, the EMD-ICA is difficult to inspect the multiple faults characteristics. Then, the conventional TSSA based on CP-ALS is employed to the measured fault signal analysis. The result is plotted in Figure 15. It is indicated that the multi-fault such as inner ring and rolling element faulty still cannot be separately identified by the conventional method. Figure 14: The result provided by empirical mode decomposition and independent component analysis (EMD-ICA): (**a**) recovered No. 1 fault signal; and (**b**) recovered No. 2 fault signal. Figure 13: The response of measured vibrational signal: (**a**) collected composite original single channel signal in time-domain; and (**b**) collected composite original single channel signal in frequency-domain. Figure 15: The result provided by canonical decomposition and parallel factors by alternating least squares method (CP-ALS): (**a**) recovered No. 1 fault signal; and (**b**) recovered No. 2 fault signal. The results of the proposed TSSA method based on CP-WOPT in this paper are shown in Figure 16, where Figure 16a,b, respectively, represents the recovered No. 1 fault signal and the recovered No. 2 fault signal, all in frequency domain. Fortunately, we can find the rotational frequency \(f_{r}\), and the fault frequency of the rolling element \(f_{b}\) in Figure 16a. In Figure 16b, the rotational frequency and its frequency multiplication, the bearing fault frequency of inner ring \(f_{i}\), and the twice faulty frequency of inner ring \(2f_{i}\) can be identified. Thus, we can determine that there are two faults in the bearing: the inner ring fault and the rolling element fault. The result is consistent with the theoretical calculation [38]. Therefore, the effectiveness of proposed method for blind source separation is demonstrated, and it has obvious advantages in extracting weak multi-fault features under the strong background noise in a single-channel signal.