esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Comparison of Different Augmentation Methods  
All LCNNs were trained on Datasets B1 (IGD-Augmentation) and B2 (DCGAN-Augmentation) simultaneously and evaluated the results, which are shown in Figure 11. Combining Table 4 and Figure 11, we can see that both augmentation methods improved the accuracy of all LCNNs. We can see that only the ShuffleNetV2 series models achieved higher accuracy after training on Dataset B2; therefore, we could not conclude that one enhancement method gave universally better results. However, we can confirm that data augmentation using DCGAN is as effective as IGD method.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Transfer** & **Parameters** & **Complexity** & **Weight** & **Accuracy (\%)** & **TP (\%)** & **TN (\%)** \\  & **Learning** & **(Millions)** & **(GFLOPs)** & **(MB)** & **Accuracy (\%)** & **TP (\%)** & **TN (\%)** \\ \hline MobileNetV1 & Yes & 4.25 & 1.16 & 12.50 & 92.79 & 92.22 & 93.37 \\ MobileNetV2 & Yes & 2.26 & 0.59 & 7.47 & 91.83 & 90.2 & 93.59 \\ MobileNetV3-Small & Yes & 1.53 & 0.12 & 6.32 & 92.31 & 90.74 & 94.23 \\ MobileNetV3-Large & Yes & 4.23 & 0.45 & 16.70 & 93.08 & 92.42 & 93.85 \\ ShuffleNetV2-0.5 & Yes & 0.35 & 0.04 & 1.77 & 91.73 & 90.64 & 92.89 \\ ShuffleNetV2-1.0 & Yes & 1.27 & 0.15 & 5.29 & 93.27 & 92.94 & 93.6 \\  & No & & & & 91.06 & 90.51 & 91.62 \\ ShuffleNetV2-1.0-SE & Yes & 1.45 & 0.17 & 6.15 & 94.23 & 93.73 & 94.75 \\  & No & & & & 92.12 & 91.32 & 92.94 \\ ShuffleNetV2-1.5 & Yes & 2.5 & 0.30 & 10.00 & 93.85 & 93.35 & 94.36 \\ ShuffleNetV2-2.0 & Yes & 5.38 & 0.59 & 20.90 & 93.17 & 93.09 & 93.26 \\ EfficieNet-b0 & Yes & 4.05 & 0.80 & 15.90 & 92.31 & 90.89 & 94.02 \\ EfficieNet-b1 & Yes & 6.58 & 1.29 & 25.70 & 93.27 & 91.82 & 94.82 \\ EfficieNet-b2 & Yes & 7.77 & 1.79 & 30.30 & 93.37 & 92.63 & 94.13 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance validation results of different LCNNs trained on Dataset A.
# A General Calibration Method for Dual-PTZ Cameras Based on Feedback Parameters
## 4 The Estimation of Intrinsic Parameters
### Radial Distortion Fitting Method  
The camera's radial distortion is caused by the manufacturing process used to produce the camera lens, which reflects the influence of the camera lens on imaging. The tangential distortion of the camera is caused by an installation error in the lens assembly. In order to simplify the model, this study only considers the variations in the first radial distortion parameter caused by zooming. Inspired by Ref. [10], we directly establish the function between the zoom value and the first parameter, \(k\), of the radial distortion; the function can be modeled with an \(n\)-order polynomial:  
\[k(z)=c_{n}z^{n}+c_{n-1}z^{n-1}+\cdots+c_{1}z+c_{0} \tag{7}\]  
where \(c_{n}\) is the coefficient of the polynomial. A polynomial of the \(n\)-order (Expression (7)) has \(n+1\) unknown coefficients; therefore, if we take more than m sets (\(m\geq n+1\)) of calibration results, our model fit can obtain simultaneous expressions:  
\[\left\{\begin{array}{l}k_{1}=c_{n}z_{1}^{n}+c_{n-1}z_{1}^{n-1}+\cdots+c_{1} z_{1}+c_{0}\\ k_{2}=c_{n}z_{2}^{n}+c_{n-1}z_{2}^{n-1}+\cdots+c_{1}z_{2}+c_{0}\\ \cdots\\ k_{n+1}=c_{n}z_{n+1}^{n}+c_{n-1}z_{n+1}^{n-1}+\cdots+c_{1}z_{n+1}+c_{0}\\ \cdots\\ k_{m}=c_{n}z_{m}^{n}+c_{n-1}z_{m}^{n-1}+\cdots+c_{1}z_{m}+c_{0}\\ \end{array}\right. \tag{8}\]  
Expression set (8) can be written as a matrix (Expression (9)):  
\[\left[\begin{array}{cccc}z_{1}^{n}&z_{1}^{n-1}&z_{1}&1\\ z_{2}^{n}&z_{2}^{n-1}&z_{2}&1\\ \cdots&\cdots&\cdots\\ z_{m}^{n}&z_{m}^{n-1}&z_{m}&1\end{array}\right]_{m\times(n+1)}\cdot\left[ \begin{array}{c}c_{n}\\ c_{n-1}\\ \vdots\\ c_{1}\\ c_{0}\end{array}\right]_{(n+1)\times 1}=\left[\begin{array}{c}k_{1}\\ k_{2}\\ \vdots\\ k_{m}\end{array}\right]_{m\times 1} \tag{9}\]Expression (9) can also be written as follows:  
\[ZC=K \tag{10}\]  
The polynomial coefficient matrix obtained by the least square method is:  
\[C=\left(Z^{\mathrm{T}}Z\right)^{-1}Z^{\mathrm{T}}K \tag{11}\]  
Based on the results, we can obtain the specific function of Expression (7). Due to the different fitting effects of the polynomial with different orders, we need to select a polynomial with a better fit as the distortion parameter estimation expression in the process of application, which can be seen in Section 6.3.2.  
After the specific Expressions (3)-(6) in Section 4.1 and Expression (7) in Section 4.2 have been obtained, they are all functional relations with respect to \(z\); when we estimate intrinsic parameters, we merely need to substitute the feedback zoom value into the function.
# Analyzing Multiscale Spatial Relationships between the House Price and Visual Environment Factors
## 3 Experiment and Results
### Results of the Global Regression Models
Specifically, the GVI and SVI are positively correlated with house prices; that is, the larger the GVI and SVI are, the higher house prices are. According to the literature review, street greening not only plays a role in the decoration of the city but also has a certain impact on the physical and mental health of urban residents. The more green vegetation visible on the street, the greater the proportion of visible sky, and the better walking experience for pedestrians. Therefore, the reasonable street design will improve the house prices of the community to a certain extent. The NAI has a positive correlation with the housing price of the two cities. According to the definition above, the NAI is the proportion of natural elements and artificial elements in a panorama. The more natural elements in the panorama, the higher the NAI. With the improvement of people's quality of life, residents are more willing to spend more time with the natural environment, which not only adds vitality to the city but also brings visual comfort to the surrounding residents. Therefore, the natural environment of the community is also a factor affecting house prices. In summary, in the covariates, the distance between transportation facilities, such as subway stations and bus stations, has a significant negative correlation with house prices in both cities, indicating that the closer to the subway station and bus station, the higher the house price. The distance between educational facilities, such as primary and secondary schools, has a significant negative correlation with urban house prices, indicating that the closer to the school, the higher the house prices. These two conclusions are consistent with the previous literature and our practice. Because the closer it is to the subway station and bus station, the more convenient it is for residents to travel, and the closer it is to the school, the better the educational resources. Among the structural attributes, the area is still the most critical factor affecting house prices. The larger the area, the higher the house prices. Among the visual elements, it is found that there is a significant negative correlation between the architectural visual index and house price in the two cities. It means that the larger the architectural visual index, the reverse promotion of house price growth. The possible reason is that the larger the architectural visual index, the denser and towering buildings, so it accounts for a large proportion of the street view. According to previous studies, dense buildings may bring people a greater sense of depression, which will directly affect residents' walking experience and mental health.
# A Lightweight Graph Neural Network Algorithm for Action Recognition Based on Self-Distillation
## 3 Algorithm
### Input Features  
The most direct way for featuring the nodes of a skeleton graph at a specific frame \(t\) are 3D coordinates, represented as \(x_{t}^{c}=\{x_{t,1}^{c},x_{t,2}^{c},x_{t,3}^{c}\}\), where the numbers \(1,2,3\) are three different coordinate axes, and \(c\) represents coordinates. These features are denoted as _pos_ (position). To emphasize the importance of joint variations between frames, the movement features are defined as one frame displacement, \(x_{t}^{m}=\{x_{t+1,1}^{c}-x_{t,1}^{c},x_{t+1,2}^{c}-x_{t,2}^{c},x_{t+1,3}^{c}- x_{t,3}^{c}\}\), where \(t\) is the frame index. The movement features are denoted as _mov_.
# Deducing Energy Consumer Behavior from Smart Meter Data +
## 4 Proposed Framework
### Gap Filling Service
#### 4.2.2 Wiener Filling Algorithm  
The Wiener filling algorithm is an extension of a Wiener predictor. The Wiener predictor assumes that there exist a linear relationship between the next sample and the previous samples. By trying to predict the missing samples from both sides of the gap, and combining the results, it estimates the missing samples [35]. For larger gaps, this method relies on earlier predictions to close the gaps. This result in errors being accumulated over the gaps. The method is fast and is therefore suited for large data with small gaps. The implementation of the algorithm used in this work can be found in [36].
## 4 Analysis of Old Hanger Test Detection
### Hanger Appearance Inspection  
The original hanger's anchor cup, anchor ring, and connecting cylinder were placed in a humid environment for a long time and had varied degrees of corrosion, with the outside thread of the anchor cup corroding the most. Anchor corrosion reduces the hanger's lifespan, cable force adjustment, and replacement. A visual examination found that the heat-shrink sleeve-sealing between the connecting cylinder and the cable body was damaged, and the PE sheath had several transverse and longitudinal scratches, water buildup, and no penetrating fracture. Figure 12 illustrates the old hanger's visual examination.  
Figure 11: Detection road map of the hanger test.  
As the hanger's PE sheath is directly exposed to the atmosphere for years, rain, wind, ultraviolet radiation, and the accidental impact of the joint role of the disease cause cracks, scratches, voids, etc. In this hanger, the jacket steel pipe protects the PE jacket from UV radiation and accidental impact. Rainwater pooled in the outer jacket steel pipe, submerging the lower end of the hanger PE sheath for a long time. During the construction of the hanger, the PE sheath is easy to cut with the steel pipe's inner wall, increasing the danger of damage.  
A study found that the following causes promote PE sheath surface damage:  
1. Factory faults. Due to the complexity of hanger manufacture, damage cracking, hanger installation construction, and neglect during construction directly damage the bridge hanger after installation.
2. Human factors. Transportation, storage, coil, unfolding, towing, lifting, traction, anchoring, tensioning, and adjustment are all complicated. The PE jacket hanger, made of flexible polymer, is easily damaged during transportation, hanging, tensioning, and other processes.
3. Live load affects. The function of automobiles, pedestrians, and other living loads causes hanger stress changes, hanger and beam vibration increases, and hanger elongation, which causes PE material fatigue, cracking, and protection system failure.
## 1 Introduction  
The production of wine generates a large amount of by-products, known as grape pomace, that are composed by a mix of grape skins and seeds [1]. Grape pomace constitutes a relevant environmental issue related to the production of wine [2] even though it represents a consistent source of functional compounds such as polyphenols and dietary fiber [3]. These compounds can exert a positive impact on human health, especially for the prevention of diseases associated with oxidative stress such as cancer, stroke and coronary heart disease [4]. Others main constituents of grape pomace are colorants, minerals and organic acids [5, 6]. Moreover, considering that grape pomace is classified as a Generally Recognized As Safe (GRAS) matrix by the U.S. Food and Drug Administration [7], this by-product possess a high potential to be used as alternative to synthetic antioxidants in food processing such as butylated hydroxytoluene and butylated hydroxyanisole, coinciding with consumers demand for healthy and functional foods with no chemical additives [8]. For this reason, academic and industrial research is focusing on the use of grape pomace powder (GPP) as a food additive or novel ingredient in different food productions [9].  
Dairy products contain a low concentration of phenolic compounds, antioxidants and fibers [10]. To this purpose, the fortification of cheeses with non-dairy ingredients represents an improved strategy to enhance the functional and bioactive properties of the final products [11]. So far, the fortification with GPP was proposed for various dairy products obtained with bovine milk such as fermented milk beverages [12], yogurt [13] and processed semi-hard and hard cheeses [14].  
Sicily is a region of southern Italy characterized by an intense breed of sheep that play an important role in the protection of the local cultural heritage related to the traditions of shepherds, uses and habits of the mountain populations and typical cuisine. In this region, sheep milk is almost totally processed into traditional cheese types, but several cheese producers pushed research institutes to develop dairy functional foods in order to enlarge the number of dairy products and, especially, to provide cheese with a positive image among consumers aware of the effects of bioactive compounds on the human body.  
The objective of the present work was to produce a novel fresh ewes' milk pressed cheese with the addition of GPP and selected _Lactococcus lactis_ strains resistant to the main grape polyphenols [15]. The final cheeses were subjected to the evaluation of the microbiological, physicochemical, sensory and functional aspects.
# A Complete Pipeline for Heart Rate Extraction from Infant ECGs
## 1 Introduction
When detected, the times between consecutive R-peaks (\(\Delta_{\text{R-R}}\)) can be used to calculate the instantaneous HR in beats per minute (see Equation (1)). This provides a precise beat-by-beat HR measurement derived from the ECG. \[\text{HR}=60/(\Delta_{\text{R-R}}) \tag{1}\]  
There are many pre-established open source methods designed to preprocess ECG data and then extract the R-peaks [28, 29, 30, 31, 32, 33, 34, 35, 36]. While some comparative analyses have been previously carried out [23, 28], no analysis exists evaluating the effectiveness of these  
Figure 1: A visualisation of the different complexes with an ECG. \(\Delta_{\text{R-R}}\) represents the time between R-peaks, as depicted in Equation (1). methodologies on the ECGS of infants and young children. Many of these methods also investigate other ECG complexes, such as T-waves and P-waves, as well as the QRS complex as a whole (Figure 1). In the present paper, only the R-peak detection is analysed. For the ECG preprocessing step, usually a decision must be made as to whether the signal is of sufficient quality for further processing and analysis. Noise can be reduced by frequency-filtering approaches, with low-pass filters (LPFs) for removing aspects such as white noise, high-pass filtering (HPF) used for aspects such as baseline drift, bandpass filtering (BPF) to remove low-frequency and high-frequency noises, and notch filtering for mains interference. Alternatively, other approaches, such as wavelets and empirical mode decomposition, have also been used [37; 38; 39]. Typical ECG pipelines only encompass the preprocessing of the ECG signal and then the R-peak detection. Given the inherent noise level in infant and young children ECGs, and particularly ECG recordings in the natural environment, this paper aims to encapsulate the ECG to HR process as a complete open access pipeline, also accounting for any HR cleaning and HR signal quality measurements (Figure 2). First, pre-existing open access ECG pipelines from the literature were evaluated. The best-performing method was then chosen as the basis to develop our new open source pipeline for the infant ECGs and subsequent infant HR signals. A range of additional preprocessing options were evaluated, along with some ECG post-processing steps. The data and code that will be made available upon request is listed in Appendix A. In brief, the key innovations in the proposed pipeline are as follows. The ECG preprocessing is improved by specifically tailoring the frequency filtering to infant ECGs. A local peak correction is added to allow for heavy filtering without affecting the underlying heart rate. A mathematically outlined approach is described for cleaning the HR signal in instances of minor mislabelling. A new HR signal quality index (SQI) is developed to help automatically reject areas of unrecoverable signals, which will help to account for the high levels of noise in infant ECG. Finally, a focus on computational efficiency was followed throughout to allow for the application to large amounts of data.
## 2 Results and Discussion
\begin{table}
\begin{tabular}{l l l l l l} \hline
3-(Methylthio)-1- & 500\({}^{\mathrm{a}}\) & Boiled potato, & 1990.2 & 2006.8 & ND \\ propanol & & rubber & & & \\
1-Decanol & 400\({}^{\mathrm{a}}\) & Orange flowery, & 10.7 & 9.4 & 7.7 \\  & & special fatty & & & \\ Benzyl alcohol & 200,000\({}^{\mathrm{f}}\) & Citrusy, sweet & 150.4 & 137.8 & ND \\
2-Phenylethanol & 14,000\({}^{\mathrm{a}}\) & Flowery, pollen, & 14504.8 & 18164.6 & 9165.8 \\  & & perfumed & & & \\
1-Dodecanol & 1000\({}^{\mathrm{i}}\) & Unpleasant at high concentration, & 897.0 & 1770.7 & ND \\  & & flowery at low concentration & & & \\ \hline Subtotal (\(\mathrm{\SIUnitSymbolMicro g/L}\)) & & 256309.2 & 194081.4 & 130900.2 \\ Subtotal (\%) & & & 64.7 & 59.2 & 46.6 \\ \hline
**Esters** & & & & \\ Ethyl acetate & 7500\({}^{\mathrm{a}}\) & Fruity, sweet & 52205.7 & 69820.2 & 32920.7 \\ Isobutyl acetate & 1600\({}^{\mathrm{a}}\) & Solvent & ND & ND & 339.4 \\ Isoamyl acetate & 30\({}^{\mathrm{a}}\) & Banana & 14733.8 & 13999.9 & 24849.9 \\ Ethyl hexanoate & 5\({}^{\mathrm{a}}\) & Fruity, anise & 4107.2 & 2985.1 & 5535.6 \\ Hexyl acetate & 670\({}^{\mathrm{d}}\) & Pleasant fruity, pear & 142.4 & 144.9 & 562.6 \\ Ethyl lactate & 154,636\({}^{\mathrm{c}}\) & Lactic, raspberry & 11724.2 & 8119.4 & 2223.6 \\ Heplyl acetate & 1400\({}^{\mathrm{i}}\) & Almond, pear & 14143.9 & ND & 5733.0 \\ Methyl octanoate & 200\({}^{\mathrm{k}}\) & Intense citrus & 8.5 & 8.5 & 5.4 \\ Ethyl octanoate & 2\({}^{\mathrm{a}}\) & Pineapple, pear, & 5107.9 & 4284.0 & 13767.8 \\  & & floral & & & \\ Ethyl decanoate & 200\({}^{\mathrm{a}}\) & Fruity, fatty, & 1451.2 & 1522.5 & 7962.0 \\  & & pleasant & & & \\ Diethyl succinate & 500,000\({}^{\mathrm{c}}\) & Light fruity & 182.9 & 266.8 & 46.3 \\ Phenethyl acetate & 250\({}^{\mathrm{a}}\) & Pleasant, floral & ND & 37.2 & 310.4 \\ Ethyl dodecanoate & 1500\({}^{\mathrm{i}}\) & Flowery, fruity & 222.7 & 264.5 & 2028.6 \\ \hline Subtotal (\(\mathrm{\SIUnitSymbolMicro g/L}\)) & & & 104030.4 & 101453.0 & 96285.3 \\ Subtotal (\%) & & & 26.3 & 31.0 & 34.2 \\ \hline
**Acids** & & & & \\ Acetic acid & 200,000\({}^{\mathrm{a}}\) & Acid, fatty & 18901.2 & 11538.3 & 23016.1 \\ Propanoic acid & 8100\({}^{\mathrm{f}}\) & Vinegarish & 213.3 & 907.2 & ND \\ Isobutyric acid & 200,000\({}^{\mathrm{a}}\) & Fatty & 2916.4 & 2335.3 & 1798.4 \\ Isovaleric acid & 33.4\({}^{\mathrm{b}}\) & Fatty, rancid & ND & 1961.2 & 898.0 \\ Hexanoic acid & 3000\({}^{\mathrm{a}}\) & Cheese, rancid, fatty & 1709.8 & 1155.1 & 1714.3 \\ Heptaonic acid & 3000\({}^{\mathrm{a}}\) & Fatty, dry & trace & ND & ND \\ Octanoic acid & 500\({}^{\mathrm{a}}\) & Rancid, harsh, & 5205.9 & 3848.5 & 11208.7 \\  & & cheese, fatty acid & & & \\ Decanoic acid & 15,000\({}^{\mathrm{a}}\) & Fatty, unpleasant & 2877.7 & 2477.4 & 12089.7 \\ \hline Subtotal (\(\mathrm{\SIUnitSymbolMicro g/L}\)) & & & 31824.3 & 24223.0 & 50725.2 \\ Subtotal (\%) & & & 8.0 & 7.4 & 18.0 \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._
## 4 Proposed System
### Feature Extraction and Classification Techniques  
Deep learning is a part of the broader area of the machine learning approach based on an artificial neural network with four or more layers. Generally, the Deep learning technique has been used to extract features from images and classify those images depending on those extracted features. In the study, a deep learning-based robust CNN architecture namely BenSignNet is designed for BSL recognition.
###### Abstract  
This study evaluated the applicability of existing sediment yield and transport estimation models developed using data mining classification and prediction techniques and validated them. Field surveys were conducted by using an acoustic Doppler current profiler and laser in situ scattering and transmission at measuring points in the main stream of the Nakdong River located where the tributaries of the Geumho, Hwang, and Nam Rivers join. Surveys yielded estimations of water velocity, discharge, and suspended sediment concentrations were measured. In contrast with models based on the general watershed characteristics factors, some models based on hydraulic explanatory flow variables demonstrated an excellent predictability. This is because the selected submodels for validation, which provided excellent prediction results, were based on a large number of calibration data. It indicates that a sufficient number of reliable data is required in developing a sediment yield estimation model using data mining. For practical applications of data mining to extant sediment yield estimation models, comprehensive considerations are required, including the purpose and background of model development, and data range. Furthermore, the existing models should be periodically updated with the consideration of temporal and spatial lumping problems.  
data mining; model tree; sediment yield; sediment transport; specific degradation Article  
Evaluation and Validation of Estimated Sediment Yield and Transport Model Developed with Model Tree Technique. _Appl. Sci._**2022**, _12_, 1119. [[https://doi.org/10.3390/app1203119](https://doi.org/10.3390/app1203119)]([https://doi.org/10.3390/app1203119](https://doi.org/10.3390/app1203119))  
Academic Editor: [PERSON]  
Received: 2 December 2021  
Accepted: 14 January 2022  
Published: 21 January 2022  
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