esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background Work
### Data Orchestration
#### 2.1.1 Current Technological Trends in the Steps of Data Orchestration  
Any data orchestration tool or platform has a goal to highlight data overlooked from previous procedures applied to the provided set. Thus, each data point can act in harmony with all other available data while it is also examined as an individual unit and a part of the more extensive data set. Due to the broad spectrum of applications and fields utilizing these techniques, data orchestration solutions present various workflows, procedures, and milestones. There is no universal system. However, a general association of procedures and workflows that can define data orchestration can be identified. This workflow is comprised of the following steps [21].  
Organize  
Data are shaped and structured in this step according to the system before admission to ensure integrity and formatting. This step includes data validation to ensure that the incoming data are correct and not malicious, put labels or combine new data with existing information. Usually, APIs are provided by companies for this task to facilitate the process.  
Transform  
After the collection and validation of the data, data orchestration tools transform the data into one standard format. Making all the data have the same format makes the data analysis quicker and helps avoid possible errors, thus minimizing the risk of feeding the system with false information that will lead to mistaken conclusions.  
Activate  
Activation is the process of sending the available data to the tools requesting them. Such tools can be analytics platforms, management solutions, business intelligence, and more.
## 3 Results and Discussion
### Structural and Morphological Consequences of Processing
#### 3.1.1. _Molar Mass_
As reported before by other authors for other nanofillers, a better dispersion of nanoparticles may be achieved after successive processing stages, with the possibility of having established crosslinking interactions with the matrix and contributing to buffering the degradation rate and stabilizing the molar mass to a greater extent [47; 48; 49; 50]. Figure 2: Molar mass distributions of PLA, plasticized PLA-PEG, and PLA-PEG-NFC bionanocomposites after the subsequent stages of compounding, dog-bone, and film specimen preparation. Overall, the contribution of the compounding stage was significant in terms of \(M_{w}\) variation, given the preferential degradation of long PLA chains. Then, the injection molding caused global thermo-mechanical degradation due to applying shear forces and temperature that significantly reduced \(M_{w}\) and \(M_{n}\) and involved the most aggressive processing conditions. Afterward, the consequences of the degradation due to the thermo-compression were primarily perceivable in terms of \(M_{w}\) due to heat that caused the generation of a low molar mass population visible in the obtained distributions. In terms of composition, the molar mass of pure PLA was reduced by 30% and 34% for \(M_{n}\) and \(M_{w}\), respectively, in line with what has been reported in the literature for raw PLA with three reprocessing cycles [34]. In general, the use of PEG enhanced the thermochemical degradation of PLA [45,46]. Although PEG-400 promoted more degradation during the compounding stage for plasticized PLA, the differences after injection and compression molding were non-relevant for the PEG with dissimilar molar mass. At the end of the processing sequence, the \(M_{n}\) and \(M_{w}\) were globally reduced by 53% and 54% for PLA-400 and by 58% and 55% for PLA-1500, respectively. Comparable molar mass reductions were reported in multi-reprocessed plasticized PLA with acrylate PEG [45]. Finally, even though the presence of NFC was non-critical, it seemed to augment degradation in combination with PLA-400, while it contributed to retaining the molar mass when reinforcing PLA-1500 compositions, especially at the highest concentrations (5 wt%).
_Appl. Sci._**2015**, \(5\), 1603-1615; doi:10.3390/app5041603  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
Application of Linear Quadratic Gaussian and Coefficient Diagram Techniques to Distributed Load Frequency Control of Power Systems  
**[PERSON] 1,\({}^{\dagger}\), [PERSON] 2,\({}^{\dagger}\) and [PERSON] 1,\({}^{\star}\)**  
1. Department of Electrical Engineering, Faculty of Energy Engineering, Aswan University, Sahary, 81528 Aswan, Egypt; E-Mail: [EMAIL_ADDRESS]
2. Department of Electrical Engineering, Faculty of Engineering, Minia University, Main road, 61111 Minia, Egypt; E-Mail: [EMAIL_ADDRESS]
3. These authors contributed equally to this work.
4. Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +20-115-378-949; Fax: +20-97-3481-234.  
Academic Editors: [PERSON] and [PERSON]  
_Received: 11 September 2015 / Accepted: 30 November 2015 / Published: 4 December 2015_  
**Abstract:** This paper presented both the linear quadratic Gaussian technique (LQG) and the coefficient diagram method (CDM) as load frequency controllers in a multi-area power system to deal with the problem of variations in system parameters and load demand change. The full states of the system including the area frequency deviation have been estimated using the Kalman filter technique. The efficiency of the proposed control method has been checked using a digital simulation. Simulation results indicated that, with the proposed CDM + LQG technique, the system is robust in the face of parameter uncertainties and load disturbances. A comparison between the proposed technique and other schemes is carried out, confirming the superiority of the proposed CDM + LQG technique.  
**Keywords:** load frequency control (LFC); coefficient diagram method (CDM); linear quadratic Gaussian (LQG); integral control (I); kalman filter
## 2 Materials and Methods
### Data Preprocessing  
To establish the PM\({}_{2.5}\)-AOT relationship model (parameterization), the ground-based PM\({}_{2.5}\) measurements and satellite-derived MAIAC AOT values must be collected at the closest spatial location and time. For each monitoring site (PM\({}_{2.5}\)), the 1 km pixel of MAIAC AOT where the monitoring site locates was selected and its AOT value was extracted to form a PM\({}_{2.5}\)-AOT data pair. PM\({}_{2.5}\)-AOT data pairs with either PM\({}_{2.5}\) values less than 3.0 \(\upmu\)g/m\({}^{3}\) or missing PM\({}_{2.5}\)/AOT measurements were omitted. In addition, the days with less than two PM\({}_{2.5}\)-AOT data pairs available were excluded as a regression slope cannot be estimated from only a single data pair. MAIAC AOT pixels of water areas were removed through flagging because the high moisture content around water areas tends to be misidentified as PM\({}_{2.5}\) by estimation models.
## 3 Results and Discussion
### Antioxidant Activity and Compounds

Table 2: Effect of salinity and biostimulant on hydrophilic antioxidant activity (HAA), ABTS antioxidant activity, total phenols, and total ascorbic acid (TAA) content in _Diplotaxis tenuifolia_ leaves, regardless of harvesting time. Figure 2: Effect of biostimulant and harvesting time on total phenol content in _Diplotaxis tenuifolia_ leaves, regardless of salinity level. Different letters indicate significant differences according to [PERSON]’s honestly significant difference (HSD) test at \(p\) = 0.05; Roman numbers (I–VI) indicate the successive harvests; BC: no biostimulants added; BA: plants treated with Auxym®; and BT: plants treated with Trainer®. All data are expressed as mean \(\pm\) SE (standard error), n = 3.  
content was not affected by salinity levels and harvesting time, while for TAA content a decreasing trend with harvesting time was recorded, with the highest values being measured at the first harvesting time for all of the tested salinity levels. The lack of effect of harvesting time on the total phenol content (data not shown) of perennial wall rocket plants has been previously suggested by [PERSON] et al. (2018), while [PERSON] et al. (2018) did not record a significant effect of salinity on the antioxidant activity and total phenol content of lettuce plants. These contradictory results indicate that growing conditions are pivotal for the responses of _D. temuifolia_ plants to salinity stress and biostimulant application. This could be due to the fact that there is a combinatory effect on the plant protective mechanisms that induces the biosynthesis of antioxidant compounds as well as their activity. As reported in the literature and suggested by the results of the present study, when several factors are tested at the same time (e.g., biostimulant applications, harvesting time, salinity stress) the response of plants to studied parameters may vary depending on the experimental conditions. This response may also differ compared to the response to single factors. For example, in our study total phenol content was affected by salinity and harvesting time or salinity and biostimulant application, but no effect was recorded when salinity and harvesting time were considered. In the study by [PERSON] et al. (2018), a significant increase in total phenol content was observed for rocket plants grown in a floating system and subjected to EC levels of 3.5 dS m\({}^{-1}\). [PERSON] and [PERSON] (2018) reported a variable effect of salinity levels (up to 9.6 dS m\({}^{-1}\)) on rocket plants (_Eruca sativa_ and _Dipidotaxis temuifolia_) depending on the growing period (March to April and May to June). Similarly, [PERSON] et al. (2018)
# Color Texture Image Complexity--EEG-Sensed Human Brain Perception vs. Computed Measures
## 7 Experimental Results
### EEG Experiment Analysis
#### 7.3.1 Natural Images
Participants Scoring  
First, we note participants' complexity levels distributions, where, for:  
- participant P1: 44.3% images are considered as low complexity, 40.2% as medium: and 15.5% as high (resulting in unbalanced classes),  
- participant P2: low: 18.6%; medium: 67%; high: 14.4% (unbalanced),  
- participant P3: low: 0%; medium: 37.1%; high: 62.9% (unbalanced),  
- participant P4: low: 20.6%; medium: 13.4%; high: 66% (unbalanced),  
- participant P5: low: 27.8%; medium: 43.3%; high: 28.9% (unbalanced),  
- participant P6: low: 2%; medium: 25.8%; high: 72.2% (unbalanced),  
- participant P7: low: 1%; medium: 22.7%; high: 76.3% (unbalanced),  
- participant P8: low: 6.2%; medium: 68%; high: 25.8% (unbalanced). When grouping the ERPs based on participants levels (Figure 8), the effect of complexity is visible on grand average, with a higher amplitude for P300-400 and a higher parietal activity, and, for the second P3b, we observe a higher activity for the P4 channel for a more complex level, and a more negative activity for a lower level. However, since ERP grand averages can be influenced by unbalanced classes that are given by the subjective responses, we look further into specific correlations. Analysing for each participant, the effect is anti-correlated for participant P5 at the P3 channel (N300: \(\rho=-0.26\); P300: \(\rho=-0.22\); 2 nd P3b: \(\rho=-0.24\)), and, for participant P7, also at P3 channel (2 nd P3b: \(\rho=-0.21\)), and correlated for participant P8 at P4 channel, for the P300 and the 2 nd P3b peaks with \(\rho=0.23\) and \(\rho=0.22\), respectively. Figure 7: GA ERP for _Nat_ images on three complexity levels according to CFD, at channel P4. Spectral Activity vs. CE  
Now, looking over the GA spectral activity shown in Figure 9 at the P4 channel, we observe a gradual increase in power after 22 Hz, once with an increased level of processing. The opposite effect was observed for the P3 channel, being significantly anti-correlated for participant P8 for the \(\alpha\) and \(\beta\) bands (\(\rho=-0.23\), \(\rho=-0.24\)). No important difference is observed when considering scalp activity. Figure 8: GA ERP for _Nat_ images on three complexity levels according to participants subjective evaluation, at channel P4. Figure 9: GA Spectrum for _Nat_ images on three complexity levels according to CE, at the T4 channel. Spectral Activity vs. CFD  
When comparing the power spectrum with the CFD values, correlations and anti-correlations are observed for half of the participants as shown below:  
- participant P2: Ch P3, \(\alpha\) band, \(\rho=-0.24\)  
- participant P3: Ch T4, \(\beta\) band, \(\rho=0.21\)  
- participant P6: Ch T4, \(\alpha\) band, \(\rho=-0.23\); \(\beta\) band, \(\rho=-0.25\)  
- participant P7: Ch P4, \(\beta\) band, \(\rho=-0.2\); Ch T4, \(\alpha\) band, \(\rho=-0.21\)  
The correlation effect in the power spectrum as with CFD levels can also be observed on grand average, in Figure 10, for channel T4.
## 3 Results and Discussion
### Performance Test of SBS Modified Asphalt with Chinese Evaluation Methods
#### 3.1.2 Softening Point  
Softening point is one of the indicators used to characterize the high temperature performance of asphalt binder. It is the critical temperature which the physical state of asphalt shifts from viscid-plastic to viscous flow. The higher the softening point, the better the high temperature performance of asphalt binder [10]. The softening point of the asphalt binder with different SBS modifier dosages is as shown in Figure 2.  
From Figure 2, for SK70# and SK90# matrix asphalt, the addition of SBS modifier increased the softening point of the asphalt binder. The softening point of SBS modified asphalt with the modifier dosage of 4.5%, 5%, 5.5% and 6% increased 5.7%, 12.8%, 22.5% and 26.4% respectively compared to the SK70# matrix asphalt. The increase was 21.2%, 26.3%, 33.6% and 46.6% respectively, compared to the SK90# matrix asphalt [11]. The single factor variance analysis was conducted with different asphalt binder types and different SBS modifier dosages for softening point of the asphalt binder. The results are as shown in Table 4.  
From Table 4, the SBS modifier had significant influence on the softening point in both SK70# and SK90# matrix asphalt, which meant the addition of SBS modifier could improve the temperature  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Source**}} & \multirow{2}{*}{**df**} & \multirow{2}{*}{**\(F\)-Value**} & \multirow{2}{*}{**\(p\)-Value**} \\ \cline{3-3} \cline{5-5} \multicolumn{1}{c}{} & & & \\ \hline \multirow{2}{*}{SBS modifier dosage} & 70\# matrix asphalt & 4 & 7.65 & 0.064 \\  & 90\# matrix asphalt & 4 & 5.89 & 0.076 \\ \hline \multirow{2}{*}{Temperature} & 70\# matrix asphalt & 2 & 37.64 & 0.001 \\  & 90\# matrix asphalt & 2 & 16.88 & 0.007 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Variance analysis of penetration for SBS modified asphalt.  
Figure 2: Softening point of matrix asphalt and SBS modified asphalt.  
performance of asphalt binder [12]. The F-value of two kinds of asphalt binder in the variance analysis was compared and the results showed that the F-value of SK90# matrix asphalt was bigger than that of SK70# matrix asphalt. Therefore, the improving effects of SBS modifier on the softening point of SK90# matrix asphalt were significantly more than that of SK70# matrix asphalt, which meant the SBS modifier had the better improving effects on fluxed bitumen.
## 1 Introduction
[PERSON] (2013) [17] constructed the objective function and geometric constraint conditions of the shortest track length, highest target accuracy, lowest drilling cost, and minimum friction resistance of the string for the horizontal well with a two-dimensional double-increase profile, and used the artificial fish swarm optimization method for the numerical solution. [PERSON] et al. (2014) [18] instituted objective functions of the wellbore length, friction resistance, and torque, as well as simple geometric constraints for the modified catenary wellbore trajectory of a 3D triple-increment profile; normalized multiple objective functions into single objective functions through linear weighting; and optimized solutions by using the tolerance method. [PERSON] et al. (2014) [19] established the objective function of the measured depth and actual vertical depth of a borehole orbit for a seven-section three-dimensional orbit and used the new particle swarm optimization algorithm for the numerical solution. [PERSON] et al. (2015) [20] set up the dual objective function and geometric constraint conditions for the minimum depth of well track measurement and the minimum drilling torque for the seven-section three-dimensional trajectory, and used the multi-objective genetic algorithm method for the numerical solution. [PERSON] et al. (2019) [21] used the vector algebra method and minimum curvature method to solve the trajectory design problem of the intersection of two wells. [PERSON] (2016) [22], [PERSON] (2016) [23], [PERSON] (2018) [24], [PERSON] (2018) [25], [PERSON] (2018) [26], [PERSON] (2019) [27], [PERSON] (2019) [28], and [PERSON] et al. (2020) [29] also carried out relevant studies on three-dimensional orbit. For the problem of well trajectory optimization, the existing methods often take a single target as the research object, and the research on the problem of well trajectory to target optimization is less. At the same time, most of them take the minimum friction and the shortest well length as the objective function without considering the target accuracy and other optimization objectives. Concurrently, some optimization algorithms are used to obtain local optimal solutions. In this paper, various optimization algorithms [30; 31] were selected in the investigation; and, combined with the existing conditions, the formation wellbore stability, formation strength, and work of the deflection tool are considered to carry out a combination of multiple factors to achieve the trajectory optimization research;moreover, the borehole trajectory optimization method is established with multiple objective functions, such as wellbore trajectory length, target error, friction torque, open-hole extension limit, etc. Compared with the previous studies, more comprehensive factors were considered and more objective functions were established in this paper.
## 1 Introduction
A CNN and TCN hybrid model combined with VMD-based data processing was developed to predict power load forecasting in [40]. A hybrid prediction model based on VMD, TCN and an error correction strategy for electricity load forecasting was presented in [41]. A hybrid model based on TCN and VMD for short-term wind power forecasting was proposed in [42]. A hybrid model based on GRU and TCN combined with VMD decomposition was developed to predict load forecasting in [43]. Although these studies mentioned above verified that deep learning methods based on VMD techniques can improve the prediction accuracy of load demand, some fields still require further study and improvement. For example, many studies often adopted deep learning models with shallow networks to improve the training efficiency, and they rarely considered using the models with deep networks to learn full features from the decomposed sub-series. Moreover, most studies scarcely focused on the correlations of sub-series and external factors, such as temperature, seasons, holidays, etc. Deep learning models with multiple hidden layers can indeed improve their feature extraction capabilities, but it is difficult to train these models with some sub-series and external factors. Therefore, it is necessary to construct a deep learning model with shallow networks to extract in-depth features from several sub-series and external factors. As a result, a novel STLF model based on VMD and a deep TCN-based hybrid method with SAM is proposed to fully capture the in-depth features of multiple sub-series and external factors. The main advantages of this research are as follows:  
1. The raw load data are decomposed into multiple stable sub-series using VMD. Furthermore, the external factors are also considered as input variables and reconstructed as a feature matrix, along with the sub-series for the training model. 2. A three-layer 1D-CNN network is constructed as a deep network to eliminate overfitting of the training model due to fewer hyperparameters, and reshapes the extracted features into time series for the TCN module. The TCN extracts the long-term temporal dependencies of the input matrix. Moreover, LSTM is used to further enhance the extraction of temporal features. SAM can amplify important information, and then weaken irrelevant information of the feature matrix. Thus, SAM dynamically adjusts the correlation weight of complex features to obtain important formation from the input data. 4. Compared with traditional models or other benchmarking models, the novel hybrid model performs more effectively and achieves greater prediction accuracies. The rest of this study is organized as follows. Section 2 introduces the basic methodologies of VMD, 1D-CNN, TCN, LSTM, SAM, as well as the framework of the proposed hybrid model. Section 3 describes the data preprocessing, evaluation criteria and experimental analysis. Finally, conclusion and future research are drawn in Section 4.
# The Role of Fungi in Food Production and Processing
## 2 Understanding the Research Prospects
### Mycotoxins and Food Safety
#### 2.5.7 Mycotoxins Toxic Levels  
Aflatoxin is very toxic at low levels [137], while patulin, although less potent, can still pose health risks at higher concentrations, particularly affecting the gastrointestinal system [138]. Ochratoxin A is toxic even at low levels, primarily targeting the kidneys and exhibiting carcinogenic properties. Fumonisins are toxic at moderate levels and are linked to esophageal cancer and neural tube defects [139]. Zearalenone is toxic at low to moderate levels, acting as an estrogen mimic and disrupting reproductive systems in humans and animals [134].