esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on the Application of Laser Cleaning on the Weld of the Gas Fuel Supply Pipe for DF Engine
## 3 Results and Discussions
### Application of Laser Cleaning to the Prototype  
Figure 10 shows the cleaning process method of a prototype using a portable laser cleaning equipment. The prototype was placed on the stage, and the operator laser cleaned welds of the prototype while moving the portable laser head. As for laser irradiation conditions, energy density and a laser beam overlap rate were fixed as 5 J/cm\({}^{2}\) and 50%, respectively. A defocused distance and laser head angle were controlled in the ranges of these conditions for facilitating laser cleaning, which were identified in the fundamental experiments. The experimental result indicated that the laser cleaning.  
using the RA-885 chemical at room temperature, and two to five hours were required for processing. Before cleaning, the weld surface indicated that it was rough and non-uniform due to the remaining products of welding fume on it, which were caused by evaporated particles of alloying elements and oxidation. However, after chemical cleaning, contaminants formed on the weldment surface of the prototype was completely eliminated. In addition, the chemical cleaning process made the surface of the weldment smooth. The roughness of the chemical cleaned surface is about 1.2 \(\upmu\)m. As for the laser cleaned prototype, contaminants on the surface of its weldment was completely eliminated as well. The enlarged image of the surface indicates traces of laser beams irradiated. At this time, as can be seen from Figure 4, it can be seen that the roughness of the laser cleaned surface is about 5 \(\upmu\)m. Moreover, a component analysis based on X-ray.  
Diffraction was conducted to identify a state of oxide removal formed on the weldment of the prototype. Figure 12 illustrates the XRD analysis results of prototype before and after cleaning. Before laser cleaning, iron oxides and main components of the base metal, Fe, were detected simultaneously. However, when chemical or laser cleaning was conducted, iron oxides were completely removed in the prototypes. Under such conditions, only the components of the base metal were detected.  
Figure 11: Surface images of prototypes before and after cleaning; (**a**) Before cleaning, (**b**) Chemical cleaning, (**c**) Laser cleaning.  
The result of applying laser cleaning to the prototype indicated that the laser cleaning technology cleaned the surface of the prototype at a satisfactory level, without resulting in a toxic work environment and environmental pollution issues, which can be caused by chemical solvents utilized for chemical cleaning. In addition, the roughness of approximately 5 \(\upmu\)m was formed on the surface of the weldment after laser cleaning. Based on this result, it is expected that a weldment, which was laser cleaned, can exhibit greater adhesive force of paint for anticorrosion of the product than a smooth weldment that was chemically cleaned [20; 21].
# Selective Removal of Diesel Oil Hydrocarbons in Aerobic Bioremediation
## 2 Materials and Methods
### Analysis of Diesel Oil  
The diesel oil concentration in the microcosm was estimated using the solvent extraction standardized by the Environmental Protection Agency (EPA) method 3546 (moisture 15-30% by mass) [18], based on microwave heating. In this method, 2 g of the microcosm soil was mixed with 30 mL of solvent (acetone and \(n\)-hexane with ratio 1:1 by volume) and 2 g of anhydrous sodium sulphate, added to dry the soil. The sample was heated at 110 \({}^{\circ}\)C and power 1100 W for 15 min through microwaves, was kept at a constant temperature of 110 \({}^{\circ}\)C and power 1100 W for 10 min, and then it was cooled for 20 min. After the extraction, the sample was filtered through a 0.45 \(\upmu\)m filter. This procedure was replicated, thus obtaining two extracts.  
The diesel oil concentration was quantified using gas chromatography (GC-FID), with the EPA method 8015 [19]. The gas chromatograph was equipped with a flame ionization detector and a DB-5 fused silica capillary column, operated with helium as the carrier and with injector and detector maintained at 220 \({}^{\circ}\)C and 250 \({}^{\circ}\)C, respectively. The following temperature program was used: initial temperature of 50 \({}^{\circ}\)C, ramp to 320 \({}^{\circ}\)C at 8 \({}^{\circ}\)C min\({}^{-1}\), and a constant temperature of 320 \({}^{\circ}\)C for 40 min.  
The chromatogram was analyzed evaluating the peaks between 6 and 33 min, since this time range contains the characteristic peaks of diesel oil.  
The residual diesel oil concentration was calculated using a calibration line achieved with the commercial diesel oil used in the test.  
The compounds that characterize the diesel oil were identified by standard kits, namely  
* Polyclic Aromatic Hydrocarbons (acenaphithylene, anthracene, benzo(\(\alpha\))anthracene, fluorene, phenanthrene, pyrene), 500 ug/mL each component in acetone.  
Comparing the retention times of the analyzed peaks to the standard ones, the \(n\)-alkanes from C8 to C24 and six PAHs (namely, acenaphithylene, fluorene, phenanthrene, anthracene, pyrene, and benzo(\(\alpha\))anthracene) were determined.  
Two gas chromatographic analyses were performed for each extract.  
Figure 1 reports the chromatogram of the tested commercial diesel oil.
# A Framework to Model Bursty Electronic Data Interchange Messages for Queueing Systems +
## 4 Discussion
### Framework
#### 4.1.1 Feature: Identification-Selection  
When modelling EDI messages, we found that different XML schemas were dependent on the \"Mode\" attribute of the message. We identified 78 elements and selected 23 for analysis. From those 23 elements, we further reduced the elements down to 8.  
Further to this, we identified six keywords that we used as part of our modelling decisions based on the structured text of the log files.  
Combining both the structured text and the XML documents, our final selection was 14 elements out of 29. We believe we captured the important elements for modelling service times and inter-arrival times of EDI messages. These elements may be useful to other researchers and DevOps when modelling their EDI messages.  
We now explore the results of the next component of our framework via Section 4.1.2, feature classification.
## 6 Discussion
### Utilizing an RTM  
Lastly, case (4) (Section 4.4) follows the typical atmospheric correction scheme applied to correct airborne data and its posterior coupling with the SFM. This way, there are no mathematical inconsistencies and the convolution is performed following Equation (12). Consequently, compared to case (3), more accurate SIF estimations resulted especially outside the dip of the O\({}_{2}\)-A band. Following this strategy, SIF can be estimated for all the sensor heights evaluated (3-20 m) with a relative error lower than 10% across the O\({}_{2}\)-A absorption region when using high spectral resolution sensors (\(\leq\)0.4 nm). For lower spectral resolution sensors, only SIF estimated at those wavelengths close to the bottom of the O\({}_{2}\)-A absorption band were within 10% of accuracy. Without a doubt, the lower spectral resolution leads to lower sensitivity for quantifying spectral differences between modelled vs. measured signals as part of the SFM. However, apart from this fact, the explanation about why in this section poorer results are achieved for lower resolution sensors can be explained by the application of the same SFM without changing any of the following stopping criteria that halt the iterative process: the threshold in the \(\overrightarrow{x_{\rho}}\), \(\overrightarrow{x_{F}}\) increasing steps, the threshold in the \(\pi\sigma_{\overrightarrow{x_{F}},\overrightarrow{x_{\rho}}}\), or the maximum number of iterations allowed; which can be insufficient when decreasing the SR. In addition, other considerations must be highlighted in case (4) regarding the use of a RTM in order to simulate the atmospheric transfer functions involved in Equation (12). The use of an RTM implies that the final accuracy of the estimate SIF is highly dependent on the accuracy achieved on characterizing the atmospheric state.  
In this paper, an ideal atmospheric characterization is assumed, showing therefore the maximum accuracy expected on the estimated SIF following this technique. As pointed out in Section 3.3, a wide number of parameters can generally be varied in atmospheric RTMs to characterize the atmospheric state. In this respect, when using the downward acquired solar irradiance at sensor level (\(E\)) to model the atmospheric state, this can be easily performed by minimizing the spectral error between the measured versus the modelled solar irradiance spectrum. In this respect, the separate measurement of solar irradiance terms for the global (\(E\)) and diffuse (\(E_{dif}\)) contributions can improve the atmospheric characterization process since the diffuse component better accounts for aerosol scattering.
# Development of Fog Detection Algorithm Using GK2A/AMI and Ground Data
###### Abstract  
Fog affects transportation due to low visibility and also aggravates air pollutants. Thus, accurate detection and forecasting of fog are important for the safety of transportation. In this study, we developed a decision tree type fog detection algorithm (hereinafter GK2A_FDA) using the GK2A/AMI and auxiliary data. Because of the responses of the various channels depending on the time of day and the underlying surface characteristics, several versions of the algorithm were created to account for these differences according to the solar zenith angle (day/dawn/night) and location (land/sea/coast). Numerical model data were used to distinguish the fog from low clouds. To test the detection skill of GK2A_FDA, we selected 23 fog cases that occurred in South Korea and used them to determine the threshold values (12 cases) and validate GK2A_FDA (11 cases). Fog detection results were validated using the visibility data from 280 stations in South Korea. For quantitative validation, statistical indices, such as the probability of detection (POD), false alarm ratio (FAR), bias ratio (Bias), and equitable threat score (ETS), were used. The total average POD, FAR, Bias, and ETS for training cases (validation cases) were 0.80 (0.82), 0.37 (0.29), 1.28 (1.16), and 0.52 (0.59), respectively. In general, validation results showed that GK2A_FDA effectively detected the fog irrespective of time and geographic location, in terms of accuracy and stability. However, its detection skill and stability were slightly dependent on geographic location and time. In general, the detection skill and stability of GK2A_FDA were found to be better on land than on coast at all times, and at night than day at any location.  
f 10 August 2020; 26 September 2020; Published: 28 September 2020
## 3 Results and Discussion
### The Synthetic and Experimental Data Comparison  
In order to evaluate the correctness of the simulated signals, the real life experiment and corresponding simulations were generated, in which two concrete bricks with dimensions 620 \(\times\) 620 \(\times\) 77 mm and 620 \(\times\) 620 \(\times\) 95 mm at distances 500 and 700 mm were radiated in a pseudo-monostatic radar configuration, and reflected signals were registered for comparison; then, the registered signals were correlated.  
The correlation ranged from 38% to 79%; the sample results are shown in Figure 7. Comparison shows that simulated signals contain more high-frequency components than the real measured signals. This can be explained by the fact that  
1. In order to make calculations faster, the simulation was generated using simplified antenna model possessing * A frequency-independent antenna and substrate dielectric losses equal to 0.026; * An infinitely thin perfect electric conductor of antenna surfaces;
2. The radar signal was measured on a 50 \(\Omega\) load instead of actual antenna impedance (Figure 8), due the lack of the active high-impedance RF probe for frequencies 2 to 10 GHz.  
The comparison shows that the simulation is as accurate as the simulation model: e.g., the high-frequency components in the simulation reduced in amplitude if the concrete was simulated to experience losses (up to \(\tan\delta=0.2\) in Figure 7). In order to improve the conformance of the simulated signals to the real life signals, the simulation has to be seriously redesigned to eliminate the counted simplifications; this was not conducted in the present study due to project implementation time constraints.
# Prophet-CEEMDAN-ARBiLSTM-Based Model for Short-Term Load Forecasting
## 2 Method
### [PERSON] forecasting method is an advanced time series method based on traditional decomposition technology, consisting of the trend term, seasonality term, and holiday term. The approximation is expressed below.  
\[y(t)=g(t)+s(t)+h(t)+\epsilon_{t}, \tag{1}\]  
where \(g(t)\) is the trend term, \(s(t)\) is the seasonality term, and \(h(t)\) is the impact of holidays on data behavior. \(\epsilon_{t}\) represents the error that the [PERSON] does not consider.  
The Prophet method comprises two types of trend methods: the nonlinear saturating growth method and the piecewise linear method. The linear growth method is adopted, and it is expressed as:  
\[g(t)=\Big{(}k+a\Big{(}t)^{T}\delta\Big{)}t\Big{(}n+a\Big{(}t)^{T}\gamma\Big{)}, \tag{2}\]  
where \(k\) denotes the growth rate, \(n\) denotes the offset variable, \(\delta\) denotes the growth rate correction vector, \(\gamma\) denotes the changepoint correction vector, and \(a(t)\) denotes the correction parameter vector.  
The electricity load series usually comprise different types of seasonal features. Choosing Fourier series to approximate periodicity, the seasonal model can be mathematically expressed. The [PERSON] uses the Fourier series to approximate the periodicity of the load series. The calculation formula of the seasonal model is as follows:  
\[s(t)=\sum_{n=1}^{N}\Big{(}a_{n}\text{cos}\Big{(}\frac{2\pi nt}{P}\Big{)}+b_{n} \text{sin}\Big{(}\frac{2\pi nt}{P}\Big{)}\Big{)}, \tag{3}\]  
where \(P\) represents the seasonal period, the value of yearly seasonality \(P\) is 365, and the value of weekly seasonality \(P\) is 7.  
Figure 1: The proposed forecast method, including Prophet, CEEMDAN, and ARBiLSTM block.
## 2 Materials and Methods
### Experimental Setup for RF Heating System  
In this research, we utilized a RF heating system with a power output of 9 kW and a frequency of 40.68 MHz. The system consists of two electrode plates, one on the top and one on the bottom, as depicted in Figure 2a. Both the top and bottom electrode plates have dimensions of 520 mm \(\times\) 520 mm. The bottom electrode plate is fixed in position to secure the samples in place, while the top electrode can be adjusted to generate a range of electric field intensities, including 450 kV/m, 225 kV/m, 150 kV/m, and 112.5 kV/m. In order to achieve the most efficient heating rate with a uniform temperature distribution, as described in [21; 22], the central position of the bottom electrode was chosen as the heating position for our experiment. As shown in Figure 2b, the contaminated soil samples were placed at this central position on the bottom electrode.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Soil (g) & 100 g \\ Particle size (mm) & 0.10–0.25 mm \\ Porosity & 0.67 \\ Volume of soil jars & 117.75 cm\({}^{3}\) \\ Bulk density & 0.85 g/cm\({}^{3}\) \\ Diesel fuel & 20, 80 g \\ \hline \hline \end{tabular}
\end{table}
Table 1: The properties of soil samples.  
Figure 1: Contaminated soil sample where (**a**) is the saturated soil sample with the commercial grade diesel B7, (**b**) the unsaturated soil sample with the commercial grade diesel B7, and (**c**) is the processed soil sample without the commercial grade diesel B7.  
The power conversion in a material using RF heating depends on the operating frequency, dielectric loss factor, and electric field density inside the material. The power absorbed per unit volume in a material [23] can be calculated using the following formula:  
\[P=\omega\varepsilon_{0}\varepsilon^{\omega}|E|^{2} \tag{1}\]  
where \(P\) presents the power dissipated per unit volume (W/m\({}^{3}\)), \(\omega\) denotes the angular frequency (\(\omega=2\pi f,f\) Radio frequency), \(\varepsilon_{0}\) denotes the permittivity of free space (\(8.85\times 10^{-12}\) F/m), \(\varepsilon^{\omega}\) presents dielectric loss factor of the material and it represents the ability of a material to convert the RF energy into heat and \(E\) is the electric field intensity (V/m).  
In this paper, the radio frequency heating system was operated at a frequency of 40.68 MHz, which is within the optimized Industrial, Scientific, and Medical (ISM) band as suggested in [24; 25]. The electric field intensity was calculated using the following formula:  
\[E=\frac{V}{\Delta d} \tag{2}\]  
where \(E\) denotes the electric field intensity (V/m), \(V\) is the voltage during the heating process (V), and \(\Delta d\) presents the distance between electrodes (m).
## 4 Fluid Dynamic Performance
### Factors
#### 4.1.1 Size and Shape of Microbots  
The size and shape of the microbot prototypes varies according to their functionality. Most of the existing studies have based their prototypes in real bacterias or spermatozoa in order to simulate their movement to find the best way for the propulsion mechanism. However, in some cases, mimicking the morphology of some living organisms to simulate the propulsion mechanism does not mean an efficient movement in artificial microbots.  
Figure 1: Computational fluid dynamics simulations for a Newtonian fluid around objects with different morphology at \(Re=22.3\). The flow direction is from right to left. Adapted from [63].  
As is well-known, the drag force varies with the geometry (size and shape) of the body immersed in the fluid. Rounded or curved shapes travel through the fluid more easily. Figure 1 shows the differences in the velocity field of a Newtonian fluid flow around different body shapes. The upper half of each figure corresponds to the contour plots of the velocity field. Meanwhile, the bottom half of the figure shows the stream lines of the flow. It is obvious that there is an improvement in the flow disturbance from the bulkiest body to the most slender bodies. In the latter cases, the drag is minimized, and, therefore, the wake behind the object is also smaller than for the bulkier models.
## 2 Background and Related Works
### Sparse Autoencoder (SAE)  
An autoencoder consists of two parts: an encoder and a decoder. In the encoder step, the autoencoder transforms the original feature into a latent representation. It is model-interpretable information. In the decoder step, the autoencoder reconstructs the latent representation into new meaningful features.  
An encoding function \(f\) maps the input data sample \(x\in\mathbb{R}^{m}\) to a low-dimensional latent feature space \(\mathbb{R}^{n}\). It is formulated as:  
\[h=f(x)=\phi\Big{(}W_{1}^{T}x+b_{1}\Big{)} \tag{1}\]  
where \(W_{1}^{T}\) is the transformed weight matrix (\(W_{1}\)) between the input layer and hidden layer, \(\phi\) is the activation function, \(b_{1}\) is a bias, and \(h\) is a latent vector of the hidden layer. A decoding function \(g\) maps \(h\) to reconstructed feature \(x^{\prime}\). It can be expressed as:  
\[x^{\prime}=g(x)=\phi\Big{(}W_{2}^{T}h+b_{2}\Big{)} \tag{2}\]  
where \(W_{2}^{T}\) is the weight matrix (\(W_{2}\)) between the hidden layer and output layer and \(b_{2}\) is a bias. The purpose of the autoencoder is to find a structure that minimizes the reconstruction error, and the objective function is defined as follows:  
\[\min_{\theta}J_{AE}(\theta)=\frac{1}{2n}\sum_{i=1}^{n}\parallel x_{i}-x_{i}^{ \prime}\parallel^{2} \tag{3}\]  
where \(J_{AE}\) is the reconstruction loss of the autoencoder and \(n\) is the number of input samples. SAE's structure is based on an autoencoder. However, in a basic autoencoder, when reconstructing the input, the distribution of reconstructed features has a lot of overlap due to problems such as overfitting [33], and simply copying the input may not represent key information well. To solve these problems, we add a sparsity constraint as a penalty term that can suppress the output of neurons when the number of neurons in the hidden layer is large and formulated as follows:  
\[\min_{\theta}J_{SAE}(\theta)=\frac{1}{n}\sum_{i=1}^{n}\parallel x_{i}-x_{i}^{ \prime}\parallel^{2}+\varepsilon\sum_{j=1}^{s}KL(\rho||\hat{\rho}_{j}) \tag{4}\]  
where \(J_{SAE}\) is the objective of the sparse autoencoder (SAE), s is the number of neurons in the hidden layer, \(\hat{\rho}_{j}\) is the activation probability, and \(\rho\) is the sparse parameter. KL divergence is briefly a measure of how differences between two probability distributions [34].
# Path Loss Prediction Based on Machine Learning: Principle, Method, and Data Expansion
## 4 Data Expansion: How to Get Enough Data?  
As mentioned, the performance of the machine-learning-based models heavily relies on the amount of data. Here, we propose two schemes to expand the training dataset.
## 2 Materials and Methods
sequence possessed a signal peptide, at its N-terminus, which directs the protein towards secretion, we used the consensus sequences obtained from the tools Signalp 6.0 [73] and Targetp2 [74]. In addition, the sequences were also filtered using DeepTMHMM [75] to remove transmembrane domain signals. The secreted genes were then annotated for carbohydrate-active enzymes (CAZymes) with run_dbCAN4 v4.1.4 ([[https://github.com/linnabrown/run_dbcan](https://github.com/linnabrown/run_dbcan)]([https://github.com/linnabrown/run_dbcan](https://github.com/linnabrown/run_dbcan)), accessed on 4 November 2023), a standalone tool from the dbCAN3 web server ([[https://bcb.unl.edu/dbCAN2/](https://bcb.unl.edu/dbCAN2/)]([https://bcb.unl.edu/dbCAN2/](https://bcb.unl.edu/dbCAN2/)), accessed on 4 November 2023), using the tools HMMER v3.3.2 [76], DIA-MOND v2.1.8 [77], and dbCAN_sub with the dbCAN3 [78] database v12. The analysis was performed against all 6 available classes of carbohydrate-active enzymes (CAZys): carbohydrate-binding modules (CBMs), glycoside hydrolases (GHs), polysaccharide lysates (PLs), auxiliary activities (AAs), carbohydrate esterases (CEs), and glycosyltransferases (GTs). Only sequences that were identified as CAZy enzymes with 2 or more tools (HMMER, DIAMOND, and dbCAN_sub) were considered for the results, as recommended by dbcan3. The secreted genes were also annotated for effector genes using EffectorP v3.0 [79], where proteins were predicted to interact or not interact with plants at the apoplast and cytoplasmic level; through the tool, we could observe the secreted effector proteins associated with plant infection by fungi, including the main locus of infection of the protein. To better understand the adaption of the freshwater hyphomycetes to the environment, proteins with psychrophilic and thermophilic characteristics were predicted using the machine learning tool ThermoProt [80], for the secretome, effector genes, and CAZy genes.