esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Identification of Trend Components in Singular Spectrum Analysis
## 1 Introduction  
Singular spectrum analysis (SSA) [1; 2; 3; 4] is a non-parametric, adaptive technique used for time series analysis. It can effectively solve various problems, such as decomposing a time series into identifiable components (e.g., trends, periodicities, and noise). Trend extraction is especially useful in its own right, as well as a prerequisite for forecasting. The benefit of SSA over other methods is that it does not require that the time series and its components assume any specific parametric form. Nevertheless, automation of SSA has yet to be achieved, limiting its application. This paper presents a step in the direction of automating SSA for trend extraction.  
SSA consists of two stages: Decomposition and Reconstruction. After the Decomposition step, it is necessary to group the obtained elementary components in order to reconstruct time series components, such as the trend. However, automating the grouping step in SSA is challenging due to the nonparametric nature of SSA. The success of grouping depends on the separability of the component of interest, meaning that there should be no mixture of elementary components corresponding to the trend with elementary components corresponding to the residual (periodicity or noise).  
**Novelty.** While there are some known algorithms for automated grouping [5; 6; 7; 8], they only work if signal components such as trend and periodic ones are separable with SSA. However, if trend and periodic components are mixed, automated grouping is not able to separate them and, as a consequence, cannot extract the trend. At the same time, thereare algorithms that can improve separability [9]. This paper proposes a novel approach to automated trend extraction that combines automated trend identification and separability improvement with automation of the methods of separability improvement called IOSSA, FOSSA and EOSSA (OSSA means Oblique SSA). The latter algorithm is new and was not justified before; therefore, we include a separate section devoted to EOSSA.  
To connect the paper's structure with the proposed method, we present a flowchart in Figure 1.  
**Paper's structure.** Let us outline the structure of this paper. Section 2 (the green block in Figure 1) provides a brief description of the SSA algorithm and the basic concepts. Section 3 (the blue block in Figure 1) introduces the notion of separability and briefly describes the methods for improving separability. In Section 4 (the yellow blocks in Figure 1), the method for automated identification of trends is described, followed by the proposal of combined methods that can both improve the separability of the trend components and automatically extract them. Section 5 (the violet block in Figure 1) is dedicated to justifying the EOSSA method, which was introduced in Section 3. The proposed methods are compared through numerical experiments, and their results are demonstrated for a real-life time series in Section 6. Section 7 concludes the paper.
# The Gyrotrons as Promising Radiation Sources for THz Sensing and Imaging
## 2 Advantages of the Gyrotrons as Powerful Radiation Sources for Sensing and Imaging
### Remote Atmosphere Sensing Using Gyrotrons  
The gyrotrons have demonstrated their potential as radiation sources for the remote sensing of clouds in the atmospheric window at 94 GHz (where the Rayleigh scattering from the droplets in the cloud have a cross-section that is proportional to \(\lambda^{-4}\)) a long time ago [78]. Ground-based radiometry in this frequency range has been extremely useful in detecting upper-atmosphere trace elements [78]. Nowadays, the Gyro-TWA (Gyrotron Travelling Wave Amplifiers) are considered as even more appropriate, as they offer a 10-fold increase in the available bandwidth and a fivefold increase in the peak power over the amplifiers used in the current cloud profiling radars. It is expected that this will lead to a significant increase of the radar sensitivity, enabling the detection of smaller particulates, with higher resolution, at both longer ranges and shorter timescales. The technology also has the potential to be applied to the ground-based mapping of space debris, which is a major consideration for all orbiting systems, including environmental monitoring satellites [79]. A novel W-band Gyro-TWA for cloud radar applications developed at the University of Strathclyde is based on a helically corrugated resonant structure and utilizes an axis-encircling (aka uniaxial) electron beam formed in an electron-optical system with a cusp electron gun. This tube can provide a maximum power of 5 kW at its center frequency of approximately 94 GHz with an instantaneous frequency bandwidth of 10 GHz operating at a high pulse repetition frequency of 2 kHz [80].
## 4 Materials and Methods
### Gene Cloning and Sequence Analysis  
Candidate genes were selected from DEGs analysis on the terpene metabolic pathway of _P. suffruticosa_ transcriptome database [42]. The cDNAs were synthesized from the total RNA of the petals in Stage 3 of _P. suffruticosa_ 'Huangguan' as the templates, and PCR reactions were referenced to _ApexHF_ HS DNA polymerase FS Master Mix (Accurate Biotechnology, AG12206, China). According to tree poney transcriptome data, _PsHMGR1_ and _PsTPS1_ genes were cloned using the RACE-PCR approach. The PCR product was cloned into the pClone007 vector (Beijing Tsingke Biotech Co., Ltd., TSV-007 VS, Beijing, China) and transformed into _E.coil_ DH5\(\alpha\) for sequencing confirmation. Primers were listed in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Primer Name** & **Primer Sequence (5’-3’)** & **Utilization** \\ \hline _PsHMGR_-F & ATGGACGTCCGACGACCA & \\ _PsHMGR_-R & TTAAGAAGCAACTTTGAGATACG & Gene cloning \\ _PsTPS1_-F & ATGTCTACTGAAGCTTCC & \\ _PsTPS1_-R & TCATATTGCAATATGATGACAC & \\ _qPSHMGR_-F & CGTCAAATGAGGGAGAGAATG & \\ _qPSHMGR_-R & AAATGAGGGTGTGTGT & \\ _qPSTPS1_-F & TGGGCCTCTGTTTTCTA & \\ _qPSPS_-1R & TICGCTTTTAGGACTTCGGT & \\ _18S_-F & CGCCTCTGATACATTAGCATGG & qRT-PCR \\ _18S_-R & CGTTGTGGATGAAGAACCCCCA & \\ _Actin_-F & CGGGTCTCGGATTGGAGGTTCA & \\ _Actin_-R & TTCGCTTTTAGGACTTTGGG & \\ _pCAMBIA1301-PsHMGR_-F & TATGACCATGATGATGACAC & \\ _pCAMBIA1301-PsHMGR_-R & GTCCGAGCTGCAGAC & Vector \\  & CAACTTTGGATACGTCTTTG & construction \\ _pCAMBIA1301-PsTPS1_-F & GGAATTCCATGTCACTGAAGCTTCC & \\ _pCAMBIA1301-PsTPS1_-R & CGGGATCCCGTCATATTGCAATATGAC & \\ hyg(501)-F & GAGCATATACCCCCGGAGTC & Positive PCR \\ hyg(501)-R & CAAGACCTGCCTGAAACCGA & testing \\ \hline \hline \end{tabular}
\end{table}
Table 1: Primers used in the experiments.  
The physicochemical properties, secondary structure characterization, 3D structure model building, transmembrane domains, hydrophilic or hydrophobicity analysis, protein signal peptide sequences, and protein conserved domains of PsHMGR1 and PsTPS1 proteins were performed using the Expasy ProtParam tool, SOPMA, SWISS-MODEL server, TMHMM2.0, Expasy ProtScale, SignalP 6.0, and NCBI Conserved Domains V3.21 software, respectively.
# Steel Corrosion Behavior in Light Weight Fly-Ash Based Alkali Activated Mortars
## 1 Introduction
For comparison seek, normal weight fly ash-based mortars (M) with silica sand as aggregate were also tested in the same conditions to highlight the effect of the mortar porosity related to the presence of the aggressive agents (i.e., chlorides) in the steel corrosion behavior. In a previous study, it was found that the effect of the increased and more interconnected porosity of LVM increased the tendency of the steel corrosion after few days of exposure in 0.6 M sodium chloride aqueous solution (simulating the salt concentration in seawater) [33]. These findings demonstrated that LVM layer is not able to provide suitable protection to steel structures in this harsh environment. However, fireproof coatings can also be applied on steel structures not located in coastal regions, in which the presence of chloride ions is limited to smaller concentrations and/or to seldom events (e.g., distribution of deicing salts in winter season). Therefore, in this study we discussed the durability of the coated steel by immersion in less aggressive environments, i.e., tap water and 0.2 M NaCl aqueous solution, to highlight the durability of the coated systems. Results about the pore size distribution and the microstructure of the two mortars have been also reported to correlate the physical and the durability properties of the developed mortars. Lastly, to investigate the effect of the alkali activated binder on the steel corrosion mechanism, electrochemical testing was carried out in different electrolytes simulating the alkaline environment of the pore solutions (i.e., leachate pore solution and sodium hydroxide (NaOH) aqueous solutions at different pH) with the presence of chlorides.
## 5 Analysis of Numerical Simulation Results
### Analysis of Horizontal Lateral Displacement  
Figure 11 shows the horizontal lateral displacement curve after the construction of the lower pipe is completed. It can be seen that the numerical simulation results are basically consistent with the on-site monitoring data. The farther the inclinometer pipe is from the pipe jacking axis, the smaller the lateral deformation, and the position of the maximum lateral displacement gradually moves up. Figure 11 shows the horizontal lateral displacement curve after the construction of the pipe is completed. The numerical simulation results are basically consistent with the on-site monitoring data. It can be seen that: (1) The farther the inclinometer pipe is from the pipe jacking axis, the smaller the lateral deformation. This is because, in the process of pipe jacking construction, the surrounding soil is disturbed, and the closer to the pipe, the greater the disturbance is. (2) The maximum lateral deformation occurs near the pipeline, because most of the lower pipeline is located in the rock stratum, the rock stratum disturbance is small during construction, and the deformation on both sides of the pipeline is small. The soil mass near the upper part of the lower pipe is disturbed greatly, so the maximum lateral deformation occurs above the pipe during the construction of the lower pipe jacking. (3) The farther the inclinometer pipe is from the pipe jacking axis, the position of the maximum lateral displacement gradually moves up.
# Support Vector Machine-Based EMG Signal Classification Techniques: A Review
## 3 Myoelectric Signal Classification
### SVM-Based Myoelectric Signal Classification
This method was used onin four different databases, obtaining 84.2%, 85%, 86.4%, and 89.2%, respectively, in classification accuracy. In the case of hyperspectral image classification, in [73], a guided filter is incorporated into the SVM classifier. This originates from the fusion of spectral and spatial features with the help of the PCA method. The authors classified more than nine classes with the spatial features of the SVM and achieved an average 98.92% classification accuracy. Other images that also have been classified are digital mammograms, in search of microcalcifications for diseases prevention. For instance, [PERSON] et al. [74] used a database of 76 digital mammograms, and the obtained accuracy with a polynomial kernel in SVM was 94%. The authors of [39; 60] used GA combined with SVM to classify images obtained by fusing multifrequency RADARSAT-2 synthetic aperture radar and Thaichote multispectral images. The results provided high classification accuracy at over 95%. The Content-Based Image Retrieval (CBIR) technique, which is a developing trend in digital image processing, aims at recovering a queried image from a large database. In this field, [PERSON] et al. [75], in the search for an image, extracted color, form and texture from an image, and later they used SVM for the classification, obtaining 76.6% accuracy with the help of the standardized Euclidean metric. Moreover, in addition to extracting voltage signals from the brain, images can also be extracted. [PERSON] et al. [76] differentiated between healthy individuals and those who suffer from [PERSON]'s disease. They used structural Magnetic Resonance Imaging (sMRI) data, from which a features extraction was done with the aid of Voxel-Based Morphometry (VBM), and the features reduction with the PCA. Finally, the classification accuracy was 84.17%. [PERSON] and [PERSON] [77] used SVM to classify characters strings, i.e., text classification. The fact that such character strings did not have the proper format to be classified was solved with the help of the Stemmers-Stemming algorithm. For classification, they uses RBF kernel, with LibSVM to distinguish between related phrases with shopping and food, obtaining a correct classification of 64.86%. Other signals frequently used are those of digital modulation. For example, [PERSON] [78] used second-, fourth- and sixth-order cumulants as signal features, and also employed an RBF kernel combined with a method of cross-validation grid parameters selection to improve the SVM-based classification accuracy, reaching 92.2%.
# NMR and LC-MS-Based Metabolomics to Study the Effect of Surfactin on the Metabolome of Flax
## 2 Materials and Methods  
### Plant Material and Culture Conditions  
Flasseeds of the Progress variety were supplied by a French company (LINEAR Semences de Lin, Martainneville, France). Plants were grown in a hydroponic system in Hoagland solution [23; 24] in a growth chamber at 21 \({}^{\circ}\)C, 70% relative humidity, and 16 h of photoperiod. After 30 days of culture, surfactin was added to the medium of half the cultivated plants. A total of 25 plants were used for this study, with 13 plants allocated to the control group and 12 plants allocated to the surfactin-treated group. Then, the control and treated plants were harvested at two different kinetic points: 56 h (56 h), with 6 control plants and 5 surfactin-treated plants, and 10 days (10 D), with 7 control plants and 7 surfactin-treated plants, after applying treatment (Figure 1). The aerial and root parts of each plant were separated, immersed in liquid nitrogen, stored at \(-80\)\({}^{\circ}\)C, and freeze-dried. The dry weight of each freeze-dried plant sample was precisely determined using a Sartorius ME235P balance, and the average dry weight of each organ in each condition of culture was determined to evaluate the growth of the plant (Section 3.1).  
Surfactin was obtained through the purification of an extract of _Bacillus velexensis_ (purity 90%) [25]. Briefly, 30 mg of surfactin was dissolved in 50 mL of MilliQ water, and 1 mL of this solution was added to 49 mL of the plant culture medium directly in Falcon(r) tubes for a final concentration of 12 \(\upmu\)M (CMC value \(\sim\)15 \(\upmu\)M). The treatment with surfactin was performed 30 days after the transfer of plants in a hydroponic system, and the control condition resulted in the addition of 1 mL of MilliQ water instead of surfactin stock solution.  
Figure 1: Flax plants cultivated in hydroponic devices. Photos taken before the 56 h harvest (**left**) and the 10 D harvest (**right**); for each kinetic point, the left rack shows the control plants, and the right rack shows the surfactin-treated plants. Wooden stick height: 30 cm.
# Adaptive Visual Servoing Control for Hoisting Positioning Under Disturbance Condition
## 7 Conclusions  
In this paper, we propose a visual servo scheme for hoisting positioning under disturbance condition. Through simulation and experimental verification, we can draw the following conclusions:  
(1) We define the visual error by the image error of the feedback signal based on dynamic equations containing disturbance. The relationship between visual error and disturbance is established, which lays a foundation for improving control stability;  
(2) In view of the problem that it is difficult to model disturbance terms, a nonlinear disturbance observer is introduced to obtain the equivalent disturbance. On this basis, an adaptive control algorithm with disturbance compensation is proposed to improve the robustness and convergence of the visual servoing scheme;  
(3) The results of experiment show that the algorithm in this paper can output stable control signals by adjusting the adaptive gain when the visual signal has a small vibration, and our method shows satisfactory positioning accuracy.  
Visual servoing control for hoisting positioning is still a problem worthy of further study, including research on robust uncalibrated visual servoing control and velocity estimation from finite visual features.  
Figure 11: Visual errors of experiment A.  
Figure 14: Linear velocities and angle velocity of experiment B.  
Figure 12: Linear velocities and angle velocity of experiment A.  
Figure 13: Visual errors of experiment B.  
**Author Contributions:** Conceptualization, S.T. and K.Z.; methodology, S.T.; software, J.Z.; validation, H.S., J.S.; resources, H.S.; writing--original draft preparation, S.T.; writing--review and editing, H.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by The National Key R & D Program of China, grant number 2017 YFC0703903, 2017 YFC0704003, and The National Natural Science Foundation of China, grant number 51705341, 51905357.  
**Acknowledgments:** The authors are grateful to the editors and the anonymous reviewers for providing us with insightful comments and suggestions throughout the revision process.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Methods
### Measures  
#### 2.2.1 Route Learning Test  
We created an experimental route learning test (RLT). First, a route was chosen of approximately 1.8 km in length, spanning 21 intersections. This route was walked during every session of the training, and the time to complete the route was tracked for every participant. Depending on the learning condition for their group, the participants were either guided by the experimenter or had to find the route themselves. The experimenter always corrected the participants whenever a wrong turn was taken. See Figure 1 for the route that the participants had to walk. Route learning performance (RLT) was assessed at three moments during the training: in the first training trial (only for the TEL group) (T0); halfway during the training (T1); and at the end of the training (T2). During the RLT, the participants of all three groups were instructed to complete the route they had learned in the foregoing weeks to the best of their knowledge. In that case, the PL and EL participants did not receive any guidance in making their route choices. An error was scored when a participant took a wrong turn at an intersection. A researcher corrected the participants immediately when they made an error, and errors were recorded on a scoring sheet. To avoid as much as possible that the PL and EL participants could make errors during the training, no RLT was administered for those two groups at T0. If we had started the first walking session with all participants having to find the way by themselves, they would have made multiple errors, which would have very damaging for any subsequent learning, even if that had been guided by the experimenter or by an errorless learning technique.
# Evaluation of Glottal Inverse Filtering Techniques on
## 2 Analysis Methodology and GIF Methods
### QCP-Based Approaches  
#### 2.3.1 QCP  
QCP was proposed by [PERSON] et al. [27] as a new glottal inverse filtering technique that improves on the results from previous closed-phase analysis algorithms [26] using the weighted linear prediction (WLP) technique proposed by [PERSON] et al. [33], thus allowing for more accurate LPC-based models of speech. This was made possible through the definition of an attenuated main excitation (AME) weighting function that emphasizes the closed-phase time regions. Within the closed-phase region of the glottis, the voiced speech signal is solely influenced by VT, which allows fair estimations to be obtained. QCP takes advantage of this fact while enabling the processing of unconstrained length speech signals of sustained vocalizations. In constrast to IAIF-based techniques, in the QCP approach, only one high-order VT estimation is performed, and the glottal flow signal estimation is directly obtained while inverse filtering the original speech signal after cancelling the lip radiation effect.  
In contrast to IAIF-based methods, the AME weighting function of the QCP technique needs the computation of GCIs, which, together with the following three parameters, define its specific shape: the position quotient (PQ), which represents the relative starting position of the non-attenuated section; the duration quotient (DQ), which represents the relative length of the non-attenuated section; and the ramp quotient (RQ), which defines the relative duration of the transition ramp that connects attenuated and non-attenuated sections of each voiced speech period. Then, QCP can be tuned to a specific speech signal with these three control parameters, the vocal tract all-pole filter order \(N_{v}\), and the lip radiation coefficient \(d\).
# A Dataset of Pulsed Thermography for Automated Defect Depth Estimation
## 5 Experiments with Deep Models for Defect Detection and Depth Estimation  
### Network Architecture  
YOLOv5, introduced in [27], is a single-stage object-detection network known for its high efficiency, making it particularly suitable for industrial applications [28; 29; 30]. Figure 8 illustrates the architecture of YOLOv5. It is composed of three main components: the backbone, neck, and head.  
The backbone of the YOLOv5 model, CSPDarknet [31], efficiently extracts features from the input image. The neck of the YOLOv5 model is designed to generate feature pyramids, enhancing the model's robustness in detecting objects of varying scales. The specific network employed here was the Path Aggregation Network (PANet) [32]. Finally, the head of the model is responsible for generating the final detections, employing the features from the previous layers to produce outputs indicating class probabilities, object scores, and bounding box coordinates. The activation functions used in the model included Leaky ReLU [33] in the hidden layers and Sigmoid in the output layer.  
It is worth noting that YOLOv5 offers several variants, including nano, small, middle, large, and XL. The small variant was chosen for its optimal balance between model size and performance.  
\begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|} \hline \hline
**Deleg Method** & **10 mm** & **8 mm** & **7 mm** & **6 mm** & **5 mm** & **4 mm** & **3 mm** & **2 mm** \\ \hline MAE with manual labeling & 1.420 & 1.195 & 0.950 & 0.709 & 0.795 & 0.519 & 0.470 & 0.402 \\ \hline MAE with CAD-based labeling & 0.126 & 0.115 & 0.086 & 0.088 & 0.075 & 0.068 & 0.065 & 0.055 \\ \hline MSE with manual labeling & 3.6105 & 3.1602 & 1.9718 & 1.1343 & 1.5564 & 0.4883 & 0.5482 & 0.2947 \\ \hline MSE with CAD-based labeling & 0.0252 & 0.0182 & 0.0099 & 0.0090 & 0.0072 & 0.0059 & 0.0053 & 0.0037 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Objective labeling quality evaluation.  
Figure 8: Network architecture of YOLOv5 model from [34], which is licensed under CC BY 4.0.