esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The method has a two-level structure including the heterogeneous base learners at the base level and the meta-learner at the high level. It is worth mentioning that the advantages of the stacking method have been explored in different disciplines. [PERSON] et al. [46] stated that the stacking-based credit scoring model obtained an improved predictive performance over base learners with respect to the average accuracy and error. Another example is a study of flood frequency analysis where the ANN with stacking outperformed both the standalone ANN and the combination of ANNs using the averaging strategy [47]. In this work, four machine learning algorithms, namely, SVM, ANN, LR, and NB, were selected as candidate base learners of the stacking ensemble method to predict the spatial landslide susceptibility. Furthermore, a strategy of evaluating the importance level for stacking ensemble method components is presented. The strategy was achieved by using the resampling strategy and [PERSON]'s correlation analysis methods. We firstly constructed an initial ensemble model comprised of four machine learning algorithms. Then a series of estimation points of performance for each base learner was obtained via resampling strategy. The scatter of these point pairs reflected their correlations, which could be measured using the [PERSON]'s correlation coefficient. The strong correlated base learners were treated as inappropriate components and could be eliminated from the initial ensemble model. The performance of ensemble models including remaining base learning algorithms was compared with the initial ensemble model to verify this strategy. This strategy was used to construct a proper stacking ensemble machine learning model for a realistic case: Lushui area, southwest China, where landslides occur frequently. The structure of the paper is as follows. Section 2 gives a description of the study area. Section 3 presents the data and methodology used in this study. Sections 4 and 5 provide the results and discussion, respectively, and conclusions are given in the last section.
## 2 Overview of Network Automation and Enabling Technologies
### Overview of Network Management Automation  
Autonomic network management is proposed as one of the solutions for the management of large and complex networks. As indicated above, it comes from autonomic computing, a manifesto started with IBM in 2001 [1; 6; 13]. IBM introduced the concept of self-management and self-adaptation of networks. Self-management implies that a network can make decisions on its own, while self-adaptation means that the network is aware of its environment and can adapt the performance according to the changes in the environment. The autonomic systems described by IBM are equipped with four self-management prop erties [13], such as self-configuration, self-optimization, self-protection, and self-healing. These properties are discussed in detail below.
* Self-configuration offers the opportunity to discover new or evolving devices on a network, and to automatically establish routes and other configurations required to seamlessly connect the device.
* Self-optimization is the ability of the network to change its settings to match its actions better with the system's user-defined purpose.
* Self-protection requires a network to protect itself from a possible attack, such as denial-of-service (DoS) attacks or the modification of firewall rules based on alleged malicious traffic.
* Self-healing is the capacity to correct, by itself, any issues that may occur, whether or not from the attempted behavior of the device.
# Multimodel Phishing URL Detection Using LSTM, Bidirectional LSTM, and GRU Models
## 4 Experimental Work and Results
### Result and Discussion
#### 4.3.3 Comparative Analysis  
In the literature, several methods have been proposed for URL detection. A feed-forward neural network was employed to classify URL as legitimate or malicious. One study [38] proposed a model to detect URL using a feed-forward neural network. The malicious URL dataset used contained 48,006 legitimate website URLs. The trained model  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Accuracy (\%)** \\ \hline LSTM & 96.9 \\ Bi-LSTM & 99.0 \\ GRU & 97.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison table for the accuracy of the proposed LSTM, Bi-LSTM, and GRU models.  
Figure 8: (**a**) Training and validation loss for LSTM model. (**b**) Training and validation loss for Bi-LSTM model. (**c**) Training and validation loss for GRN-based RNN model.  
exhibited an accuracy of 97%. They performed feature extraction, which reduced 16 features to 2 features. Once the model was trained, to make it easy to test the new link, they deployed the web app using a Python framework named Flask. The user can place the URL, and the model can classify whether the URL is malicious or legitimate.  
Figure 9 shows a comparison of the accuracy of the different algorithms, and Table 3 shows extended information about precision, recall, and F1 score. In this figure, we have compared the performance of other existing algorithms such as logistic regression (LR) [32], XGBoost (XGB) [34], multinomial naive Bayes (MNB) [35; 36], and k-nearest neighbor (KNN) [37; 38]. It can be observed from Table 3, that the accuracies obtained using LSTM, Bi-LSTM, GRU, LR, XGBoost, MNB, and KNN were 97%, 99%, 98%, 96%, 85%, 95.7%, and 92.4%, respectively.
# Recent Development of High-Energy Short-Pulse Lasers with Cryogenically Cooled Yb:YAG
## 4 Cryogenically Cooled Yb:YAG Lasers
### High-Energy Picosecond Laser
The central area of each crystal is a square Yb:YAG crystal with a side length of 30 mm, a thickness of 2 mm, and an atomic doping concentration of 3%, surrounded by 10 mm wide Cr4+:YAG crystal cladding to suppress the ASE effect and parasitic oscillation, and the front surface is bonded to a 3 mm thick undoped YAG crystal to suppress the ASE effect and provide structural support. The front and rear surfaces are, respectively, coated with high transmission/high reflection coatings with wavelengths of 940-1030 nm. Each crystal is pumped by a laser diode array with a pump power of 6000 W, a pump wavelength of 940 nm, a pump pulse width of 380 us, and a pump spot diameter of 16 mm. The pump light is reflected by the spherical mirror and passes through the gain area several times so that the total absorption rate of the pump light is greater than 90%, which can reduce the doping concentration and prevent the thermal conductivity of the Yb:YAG crystal from being too small at low temperature, which affects the thermal effect management [11]. The system is cooled by liquid nitrogen, and the crystal temperature is kept below 130 K under the maximum pump power. The difference in crystal temperature with and without laser extraction was compared, and the crystal temperature increased by about 8 K without laser extraction. The optical path design makes the laser pass through the gain medium four times, and the laser is amplified eight times through the two crystals. When the pump injection energy is 3.5 J, the 70 mJ seed injected by the preamplifier is amplified to a maximum of 1.26 J, and the optical efficiency is about 36%. In 25,000 shots, the average pulse energy is 1.2 J, and the beam quality M2 in the two directions is 1.32 and 1.39, respectively. Due to the narrowing of the gain, the full width at half maximum (FWHM) of the output spectrum of the main amplifier is 0.35 nm. Due to the limitation of the transformation limit, the maximum pulse width after compression is 3.9 ps. After amplification, the beam is expanded, collimated, and injected into the compressor. In order to prevent damage to the grating, the diameter of the light spot is about 40 mm, and 1740 lines/mm grating is used to compress the pulse width to 4.48 ps. The mirrors are all coated with a high reflection coating of 1030 nm, which makes the overall compression efficiency greater than 90%, and the pulse energy after compression is 1.1 J. The resulting 1.1 J, 1 kHz, 4.5 ps laser output has been used to generate high-energy femtosecond laser pulses at a kHz repetition rate.
## 3 Results and Discussion
### Interlaminar Fracture Toughness  
To characterize the initiation and propagation of the mode I delamination, G is represented by a function of the R curve (resistance curve) with respect to the delamination length. The test was conducted on each of the three unidirectional (UD) and woven (WN) specimens.  
Figure 5 shows the average R curve of UD and WN with and without buckypaper interleaving. In both cases, the fracture toughness improved due to buckypaper interleaving. As depicted in Figure 5, the buckypaper-interleaved specimens exhibited a higher mode I interlaminar fracture toughness than those of the specimens without interleaving. The difference was noticeable in the woven carbon fiber prepreg specimens. 'Rising' R curve behavior was observed in both the unidirectional and woven carbon fiber prepreg specimens. In other words, the fracture energy increased steadily with an increase in crack length. Additionally, the difference in fracture toughness increased as the delamination progressed.  
Figure 4: In-plane shear strength (black) and shear modulus (blue).  
Figure 5: R curve of mode I delamination: (**a**) UD and (**b**) WN.  
As shown in Figure 6, the mode II delamination had only two large load drops and significant crack propagation in contrast to the continuous delamination and load drop of the mode I delamination for a 200 mm long specimen. Figure 6a,b shows the typical load-displacement curves of WN and UD from the mode I and mode II tests of our research, respectively. This non-consecutive delamination process led to an error in the average value of the R curve. Therefore, Figure 7 shows the interlaminar fracture toughness at the critical load point \(G_{IIc}\) for the mode II delamination. The \(G_{IIc}\) values of the buckypaper-interleaved UD and WN specimens increased by 45.9% and 16.6%, respectively. A test was conducted for each of the five UD and WN specimens. The \(G_{IIc}\) values of the WN laminates were lower than the \(G_{IIc}\) values of the UD laminates because of their lower moduli and strengths. Thus, the results at a 90\({}^{\circ}\) or 45\({}^{\circ}\) orientation rather than at a 0\({}^{\circ}\) orientation of UD would be different.
## 4 Results and Discussion  
### Multi-Task 2D-CNN Training  
A total of 2400 sample images were obtained through the experiment in Section 3. The network was trained by the stochastic optimization method of adaptive momentum. The training algorithm was the Adam algorithm. The number of epochs was 80, the learning rate was 0.0001, and the batch size was 30. As shown in Figures 9 and 10, the loss of the network decreased with the epochs and converged rapidly in 40 cycles. The correct rate of the two classifiers increased with the epochs and converged rapidly in 10 and 40 cycles. In addition, there was a slight fluctuation in the whole training curve, which indicated that the whole training process was very stable.  
T-SNE is a nonlinear dimension reduction algorithm that overcomes the shortcomings of data congestion and the poor visualization effect after dimensionality reduction by other methods [34]. It effectively realizes data dimensionality reduction and data visualization by mapping a low-dimensional manifold structure from high-dimensional data. Figure 11a,b shows the dimension-reduced visualization results of impact position before and after training in the training set. Figure 11c,d shows the dimension-reduced visualization results of impact energy before the training and after the training by the training set. Each color in the graph represents a class. The sample numbers in different categories are random, and the total is equal to the sample numbers by the training set. The distribution of samples was chaotic before the training, and after the training, the distribution of samples became categorized. The visualization results of feature vectors based on the t-SNE algorithm show that the multi-task 2D-CNN proposed in this paper can effectively extract hidden features about impact location and impact energy. The extracted feature vectors have good in-class compactness and between-class separateness, thus realizing sample classification and recognition.  
Figure 10: Accuracy curve of two classification tasks in the training set and validation set. **(a)** Accuracy curve of location; **(b)** accuracy curve of energy.  
Figure 9: Loss curve of the multi-task 2D-CNN.
## 4 Optimization Results  
### Simulation Conditions  
PROLITH X6.0 software is used to evaluate the lithography imaging performance. A water immersion lithography system with a reduction ratio \(R=4\) is used. The wavelength is set to 193.368 nm. The illumination is Y-polarized for the line-space mask pattern and TE-polarized for the contact hole mask pattern. The mask absorber of the line-space mask is composed of molybdenum silicide layer (refractive index = 2.343, extinction coefficient = \(-\)0.586), and the mask absorber of the contact hole mask is chrome (refractive index = 0.844, extinction coefficient = \(-\)1.654). The rigorous coupled wave analysis (RCWA) method is used to calculate the mask diffraction spectrum. The well-calibrated JSR ARX2895 JN photoresist is used. Dual bottom antireflective coatings (BARCs) are applied to reduce the substrate reflectivity. The thicknesses of BARCs are 33 nm and 43 nm for the line-space pattern and 24 nm and 43 nm for the contact hole pattern.  
A full physical photoresist model and 3-stage post exposure bake (PEB) model are used to simulate the process conditions accurately. The full physical photoresist model is capable of simulating the reaction and diffusion of a chemically amplified photoresist accurately. The 3-stage PEB model describes the temperature variation when the wafer is placed on a hotplate or chill plate and the wafer transition time between the two plates. Four process parameters are selected for optimization, including hotplate duration, transition duration, chill plate duration, and photoresist development time. The NA of the imaging system is chosen for optimization. To ensure the values of these parameters are realizable in lithography, these parameters are set in reasonable ranges. The minimum value and the maximum value are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Min.** & **Max.** \\ \hline Hotplate duration (s) & 10 & 100 \\ Transition duration (s) & 2 & 20 \\ Chill plate duration (s) & 10 & 60 \\ Development time (s) & 5 & 60 \\ NA & 1 & 1.35 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The ranges of the NA and process parameters.  
The weighting coefficients \(\alpha_{1}\) and \(\alpha_{2}\) in the cost function Equation (6) are set to 0.5 and 0.5, respectively.
## 5 Conclusions  
Feed is susceptible to spoilage during processing, transportation and storage; this results in an unpleasant harsh taste, which is mainly caused by the oxidative rancidity of lipids in the feed. In this study, QG, a natural antioxidant with the highest content and strongest free radical scavenging ability, was screened and identified from forage marigold via offline DPPH-HPLC-Q-TOF-MS/MS. In addition, it was used as a natural antioxidant added to feed soybean oil and its effect in delaying the oxidative rancidity of the soybean oil was evaluated with different detection methods. These results showed that QG effectively reduced the peroxide value and acid value, decreased the formation of secondary oxidation products such as aldehydes, ketones and acids, etc., and inhibited the oxidation of unsaturated fatty acids such as oleic acid and linoleic acid. It also showed comparable or better effects than the positive control. Therefore, QG derived from the forage plant marigold can be used as a safe and effective natural antioxidant to stabilize feed soybean oil and extend the shelf life of feed by delaying the oxidative rancidity of soybean oil. This study provided valuable information for the development of natural antioxidants present in marigolds, and future studies should shed light on their antioxidant mechanisms.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/molecules27092865/s1](https://www.mdpi.com/article/10.3390/molecules27092865/s1)]([https://www.mdpi.com/article/10.3390/molecules27092865/s1](https://www.mdpi.com/article/10.3390/molecules27092865/s1)), Figure S1: Mass spectra of marigold flower extract peak 1, peak 3 and peak 4; Figure S2: HPLC chromatograms of a standard solution containing the three reference compounds (a) and the methanolic extract of marigold (b). Table S1: Calibration curve parameters of three antioxidants identified in the marigold; Table S2: Relative content of volatile compounds identified in soybean oil treated with different antioxidants under accelerated oxidation.  
**Author Contributions:** Conceptualization, X.H., Z.Q. and J.Z.; methodology, X.H., Z.Q. and J.Z.; software, X.H.; formal analysis, X.H., Z.Q. and J.Z.; investigation, W.G. and X.Y.; experiments, X.H. and X.Y.; writing--original draft preparation, X.H.; writing--review and editing, X.H., Z.Q. and J.Z.; supervision, J.Z.; project administration, Z.Q. and J.Z.; funding acquisition, Z.Q. and J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Hong Kong Scholars Program (2021-164), the Hunan Provincial Natural Science Foundation of China (2021J40245), the Hunan Provincial Key Research and Development Project (2020 NK2031), and Special Funds for Development of Local Science and Technology from Central Government (2019 XF5067).  
Not applicable.  
The authors thank Hebei Chenguang Biotechnology Co., Ltd., for this assistance in collecting the marigold materials.  
The authors declare no conflict of interest.  
Samples of the compounds are not available from the authors.
## Materials and Methods
### Data and Study Areas
#### 2.1.4 HYDE Database  
While the HISDAC-US data are only available for the US, we also used gridded surfaces from the HYDE 3.2 database [6], containing a global model of the fraction of urban area per 5\({}^{\prime}\) grid cell, over very long time periods from 10,000 BC to 2010 (Figure 4c,d). The land use fractions in HYDE are based on \"historical population, cropland and pasture statistics, combined with satellite information and specific allocation algorithms\" [6]. Despite the long temporal coverage, HYDE urban area fractions do not provide much spatial detail (Figure 4c,d), and thus are not suitable for morphological analysis of historical urban areas or similar spatially explicit analyses over long time periods. Employing historical maps for such purposes may potentially fill these gaps.  
Due to the model-based nature and the coarse spatial resolution, urban areas derived from HYDE are only of limited spatial compatibility when compared to the urban areas extracted at a resolution of 100 m \(\times\) 100 m; however, they represent the only data source consistently available for all six study areas. Herein, we cross-compare urban area fractions from HYDE to the urban areas extracted from historical maps in all six study areas (see Section 3.4). By employing both HISDAC-US and HYDE data for cross-comparison, we expect to gain insight into the relationships of our extracted historical urban areas to historical building densities (HISDAC-US), as well as to urban land in general (HYDE).
# Behaviour Investigation of SMA-Equipped Bar Hysteretic Dampers Using Machine Learning Techniques
## 1 Introduction
Utilising shape memory alloys (SMAs) as a smart material with recentting properties and less residual displacement with respect to steel bars could be a proper alternative. As the prices of SMAs are more than steel materials, their usage should be conducted in an optimum manner. To address these gaps, this study proposed a novel developed bar hysteretic damper as an added damper to an isolation system, in which the steel bars are substituted optimally by SMA bars with different mechanical properties and geometrical configurations. A comprehensive study was conducted to evaluate the role of different geometrical such as the length, number, and cross section of the SMA bars on the cyclic behaviour of SMA-equipped bar hysteretic dampers (SMA-BHDs). After evaluating existing analytical models for identifying the mechanical characteristics of SMA-BHDs, new alternative models with higher accuracy and lower error values were proposed by utilising different machine learning methods.
# Influence of Hydrothermal Carbonization on Catalytic Fast Pyrolysis of Agricultural Biomass
## 1 Introduction
A decrease in the number of hydroxyl groups is considered one of the key aspects in making hydrothermally carbonized biomass more hydrophobic, resulting in lowering its equilibrium moisture content [46], making physical dewatering easier [47; 48; 49; 50]. HTC increases the higher heating value (HHV) of the product (hydrochar) in comparison to the feedstock [51; 52; 53]. Moreover, the process of hydrothermal carbonization also removes a part of the inorganic fraction of biomass [54; 55]. Hydrothermal carbonization also has an influence on the morphology of the hydrocarbons [56; 57], and, as a consequence, makes the latter a good precursor for the production of activated carbon [58; 59] and also for direct application to soil or compost [60; 61]. Furthermore, positive influence of the HTC process on biomass grindability should not be overlooked, as particle size is an important aspect of pyrolysis [62; 63; 64]. Several studies on pyrolysis of HTC-treated materials could be found in the literature, including studies on kinetics [45], as well as studies focusing on the composition of pyrolysis oil/vapors [44; 65] or studying both aspects [66; 67]. However, looking at the heating rates applied within the scope of these studies, obtained values were not relevant for fast pyrolysis. The goal of this work is to determine the effect that HTC treatment has on fuel properties of raw biomass, relevant from the point of view of fast pyrolysis, as well as to determine the influence of the treatment on fast pyrolysis kinetics, using a bespoke cyclonic TGA, and comparing it with the influence of an in-situ addition of a catalyst (CaO). Determination of kinetics at process conditions relevant to fast pyrolysis, as well as the influence of HTC treatment and use of CaO on the kinetics of fast pyrolysis, is an important novelty of this work.
###### Abstract  
Ship detection under cloudy and foggy conditions is a significant challenge in remote sensing satellite applications, as cloud cover often reduces contrast between targets and backgrounds. Additionally, ships are small and affected by noise, making them difficult to detect. This paper proposes a Cloud Removal and Target Detection (CRTD) network to detect small ships in images with thin cloud cover. The process begins with a Thin Cloud Removal (TCR) module for image preprocessing. The preprocessed data are then fed into a Small Target Detection (STD) module. To improve target-background contrast, we introduce a Target Enhancement module. The TCR and STD modules are integrated through a dual-stage supervision network, which hierarchically processes the detection task to enhance data quality, minimizing the impact of thin clouds. Experiments on the GaoFen-4 satellite dataset show that the proposed method outperforms existing detectors, achieving an average precision (AP) of 88.9%.  
ship detection; cloud removal; double-layer supervised network; object detection; optical satellite images +
Footnote †: journal: applied sciences