esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Secondary Metabolite Production and Elicitation Technique
Those abiotic stresses could act as physical or chemical elicitors. For instance, a chemical elicitation caused by high salinity is due to the increased levels of NaCl absorbed by the plant, which would lead to osmotic and ionic stresses. A physical abiotic elicitor such as temperature is usually accompanied by drought stress. NaCl, or salt, has been widely used as a type of chemical elicitor that has presented remarkable results considering increased production of secondary metabolites, as mentioned before. Additionally, plant stress-regulatory phytochromes such as MeJA, jasmonic acid and brassinosteroids are considered well-known endogenous elicitors for enhanced phytochemical production (Figures 2 and 3). However, studies have shown that the exogenous application of JA has a profound effect on the modulation of SM biosynthetic pathways, hence enhancing the production of secondary metabolites, as is exemplified in the following section. Studies have shown that secondary metabolite production is somehow dependent on abiotic factors. Scientists have observed that under exposure to abiotic stresses, plants tend to produce many secondary metabolites that contribute powerfully to defending the plant from the damaging effects that are caused by the abiotic stress. Therefore, in many references, scientists concluded that abiotic stresses are considered elicitors that cause the production of secondary metabolites by modulation the biosynthetic pathways at the biochemical or transcription level. Thus, an abiotic elicitor, whether it is a specific stress-inducible gene overexpression, stress-inducible transcription factor, growth hormone or any molecule that is activated during stress and causes secondary metabolite production, can be used to modulate and regulate the biosynthetic pathways of pharmacologically active phytochemicals in medicinal plants. Figure 2: (**A**) The vinblastine biosynthetic pathway. (**B**) Jasmonic acid elicitation of vinblastine in _Catharantulus rouses_. JA content is increased when the plant is exposed to salinity or drought stress or both together. Then, the AP2/ERF TF known as ORCA3 is activated and binds to the regulatory _cis_-element (JERE) that is found upstream of the _STR_ gene that encodes for a key regulatory enzyme in the vinblastine biosynthesis pathway.
# Performance Evaluation of China's First Ocean Dynamic Environment Satellite Constellation
## 2 Data and Methods  
### Data Products  
The Geophysical Data Records (GDR) of the HY-2B/C/D satellites, which were available from the National Satellite Ocean Application Service ([[https://osdds.nsoas.org.cn](https://osdds.nsoas.org.cn)]([https://osdds.nsoas.org.cn](https://osdds.nsoas.org.cn)), accessed on 23 September 2023), were examined for validation in the current study. Considering that the ocean dynamic environment satellite constellation was built after the launch of the HY-2D satellite, the data time frame was from 24 May 2021 to 27 May 2022. The HY-2B altimeter GDR data included Cycle 68 to Cycle 93. The HY-2C altimeter GDR data included Cycle 25 to Cycle 62, and the HY-2D altimeter GDR data included Cycle 01 to Cycle 37.  
The Jason-3 altimeter GDR data are used in this study as the comparison for Cycle 195 to Cycle 303, which correspond to the time range of 25 May 2021 to 28 May 2022. The Jason-3 data version was Version F.  
This study evaluated the global observation efficiency using data collected from Sentinel-3A, Sentinel-3B, Sentinel-6A, Cryosat-2, Saral, and HY-2B/C/D satellites from 1 January 2022 to 14 January 2022. The complete products of Sentinel-3A and Sentinel-3B contain three NetCDF files, namely, reduced measurement, standard measurement, and enhanced measurement data. The study used the standard measurement data of the SRAL product. In addition, the Cryosat-2 Level-2 GDR ([[https://science-pds.cryosat.esa.int](https://science-pds.cryosat.esa.int)]([https://science-pds.cryosat.esa.int](https://science-pds.cryosat.esa.int)), accessed on 23 September 2023) and Saral GDR ([[http://www.aviso.altimetry.fr](http://www.aviso.altimetry.fr)]([http://www.aviso.altimetry.fr](http://www.aviso.altimetry.fr)), accessed on 23 September 2023) satellite altimetry data were used to calculate the observation efficiency of multiple satellites. The data from Sentinel-3A, Sentinel-3B, and Sentinel-6A were shared by EUMETSAT through Copernicus Online Data Access ([[https://www.eumetsat.int](https://www.eumetsat.int)]([https://www.eumetsat.int](https://www.eumetsat.int)), accessed on 23 September 2023).  
In this study, NDBC buoy data from 1 January 2021 to 31 May 2022 were provided by the National Data Buoy Center ([[https://www.ndbc.noaa.gov](https://www.ndbc.noaa.gov)]([https://www.ndbc.noaa.gov](https://www.ndbc.noaa.gov)), accessed on 23 September 2023). The NDBC dataset has been extensively utilized for model validations and satellite system calibrations [11]. In addition, to avoid the influence effects of land (as well as islands), only data from buoys located more than 50 km offshore and at water depths greater than 1000 m were selected in the study conducted.
## 4 Results and Comparison
### Results
In addition, we have reported our results on Virtex-7 FPGA at a maximum 161 MHz frequency, where our processor takes 4.45 ms latency and consumes 77 mW for the execution of one PM operation of ECC; this higher power consumption is due to a maximum circuit frequency.
# The Cell Membrane of _Sulfolobus_ spp.--Homeoviscous Adaption and Biotechnological Applications
## 2 Cell Membrane and Lipids of _Sulfolobus_ Spp.  
The thermoacidophilic genus _Sulfolobus_ belongs to the phylum Crenarchaeota and is a promising player for biotechnology [15], since it harbors a couple of valuable products, such as extremozymes [16], trehalose [17], archaeocins [18] and lipids for producing archaeosomes [13]. Genetic tools for this genus have rapidly advanced in recent years [19], generating new possibilities in basic science and for biotechnological applications alike. The cultivation conditions are preferably at around 80 \({}^{\circ}\)C and pH 3 [20]. _Sulfolobus_ species are able to grow aerobically and can be readily cultivated on a laboratory scale. These organisms became a model organism for Crenarchaeota and for adaption processes to extreme environments [21, 22, 23, 24, 25, 26]. _Sulfolobus_ species were found in solfataric fields all over the world [27].  
A major drawback of cultivating this organism was the lack of a defined cultivation medium. Until 2019, the \"Brock Medium\" served as the prevalent used cultivation medium. However, recently, a defined cultivation medium, the so-called \"Vienna Defined Medium\" or \"VD Medium\", was developed. By using this, typical shortcomings of complex media like batch-to-batch variability, as well as the occurrence of inhibiting compounds, can be eliminated, while average specific growth rate and final cell density of the model Archaeon _Sulfolobus acidocaldarius_ match the Brock medium [28].
## 3 Results and Discussion
### Reaction Kinetics Mechanism
#### 3.3.2 Proposed MB Decay Reaction Mechanism onto SA/PVP/TiO\({}_{2}\)  
The MB degradation mechanism begins with the adsorption of the dye on the surface of the nanocomposite by electrostatic interactions [36], followed by its photodegradation. At pH values of 3-7, the beads have a negative surface charge. In addition, TiO\({}_{2}\) contains terminal oxygen atoms that consequently increase the interaction between the beads' surface and nitrogen atoms in the MB molecules [1]. Under the irradiation of light, electron-hole pairs are formed in TiO\({}_{2}\) and the generated OH\({}^{\bullet}\) and O\({}_{2}\)\({}^{\bullet-}\) radicals are concentrated on the surface [34]. The MB dye is then degraded into smaller molecular fragments, such as CO\({}_{2}\), H\({}_{2}\)O, and H\({}^{+}\), by these hydroxyl radicals or superoxide ion radicals.  
Table 3 compares the produced nanocomposite beads to other TiO\({}_{2}\)-based nanocomposites that have previously been investigated for the elimination of various organic dyes in the water. When compared to previously reported nanocomposite beads, the removal effectiveness of the herein prepared SA/PVP/TiO\({}_{2}\)-3 nanocomposite beads was almost higher than that of the other TiO\({}_{2}\)-based composites, with the latter also presenting unfavorable synthesis methods and cost.
# Kernel Learning by Spectral Representation and Gaussian Mixtures
## 5 Conclusions  
Although GaBaSR's result are promising, there are still quite a lot of work to do. For example, sampling the \(\mathbf{\omega}_{j}\) is quite slow, and there is a need to update the matrix \(\Phi\) for only one sample. [PERSON] et al. [18] states that this can be done using low-rank updates. However, he does not present any procedure to perform such task, the low-rank updates are being consider for the next phase of GaBaSR. Thus, it is necessary to research how many samples \(M\) are required in order to obtain a low rank approximation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **MSE** & \(\mathbf{R^{2}}\) \\ \hline Mauna LOA CO\({}_{2}\) & 758.08513 & \(-1.60705\) \\ California houses & 2.00777 & \(-0.50784\) \\ Boston house-price & 47.66904 & 0.43533 \\ Diabetes & 8094.08752 & \(-0.36496\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results Linear Regression SVM.  
Figure 4: Mauna LOA CO\({}_{2}\) from 1958 to 2001 and the prediction.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **M** & **Swaps** & **MSE** & \(\mathbf{R^{2}}\) \\ \hline Mauna LOA & 250 & 5 & 0.60522 & 0.99789 \\ CO\({}_{2}\) & 250 & 5 & 2.91313 & \(-1.16892\) \\ California houses & 250 & 5 & 3.10931 & 0.97060 \\ Boston & 250 & 5 & 5918263.63330 & \(-869.13210\) \\ Diabetes & 250 & 5 & 5918263.63330 & \(-869.13210\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results GaBaSR Regression.  
In the experiments, it is possible to observe that GaBaSR is more accurate when performing a classification task rather than a regression task. This is an opportunity to improve the regression model. It means that the regression model needs more research to improve its performance or perhaps that a different model to learn the regression task is needed.  
Conceptualization, L.R.P.-L. and A.M.-V.; Methodology, L.R.P.-L. and R.O.G.-M.; software L.R.P.-L. and A.G.; validation, R.O.G.-M.; formal analysis, L.R.P.-L. and A.M.-V.; resources, A.M.-V.; data curation, A.G.; writing--original draft preparation, L.R.P.-L. and R.O.G.-M.; writing--review and editing, A.M.-V. and A.G.; supervision, A.M.-V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
All the data was cited and can be downloaded with their respective citation.  
The authors wish to thank the The National Council for Science and Technology (CONACyT) in Mexico and Escuela Militar de Mantenimiento y Abastecimiento, Fuerza Aerea Mexicana Zapopan.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
AUC Area Under the Curve  
MSE Mean Squared Error  
SVM Support Vector Machine  
\(R^{2}\) Coefficient of determination  
MKL Multiple Kernel Learning  
SDP Semi-Definite Programming  
SMO Sequential Minimal Optimization  
BaNK Bayesian Nonparametric Kernel  
GaBaSR Gaussian Mixture Bayesian Nonparametric Kernel Learning using Spectral Representation  
GMM Gaussian Mixture Model  
MCMC Markov Chain Monte Carlo  
UCI University of California, Irvine
_Int. J. Mol. Sci._**2012**, _13_, 17077-17103; doi:10.3390/ijms131217077  
International Journal of  
**Molecular Sciences**  
**ISSN 1422-0067**  
www.mdpi.com/journal/ijms  
_Review_  
**Plant Gandular Trichomes as Targets for Breeding or**  
**Engineering of Resistance to Herbivores**  
**[PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 2, [PERSON] 3 and [PERSON] 1,***  
1 Department of Population Biology, Institute for Biodiversity and Ecosystem Dynamics,  
1098 XH Science Park 904, Amsterdam, The Netherlands; E-Mails: [EMAIL_ADDRESS] (J.J.G.);  
[EMAIL_ADDRESS] (B.C.J.S.); [EMAIL_ADDRESS] (J.M.A.)  
2 Department of Plant Breeding, Subtropical and Mediterranean Horticulture Institute \"La Mayora\"  
(IHSM), Spanish Council for Scientific Research (CSIC), Experimental Station \"La Mayora\",  
E-29750, Algarrobo-Costa, Malaga, Spain; E-Mail: [EMAIL_ADDRESS]  
3 Department of Plant Physiology, Swammerdam Institute of Life Sciences, 1098 XH,  
Science Park 904, Amsterdam, The Netherlands; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +31-20-5257-793; Fax: +31-20-5257-754.  
_Received: 6 November 2012; in revised form: 28 November 2012 / Accepted: 5 December 2012 / Published: 12 December 2012_  
**Abstract:**  
**Glandular trichomes are specialized hairs found on the surface of about 30% of all vascular plants and are responsible for a significant portion of a plant's secondary chemistry. Glandular trichomes are an important source of essential oils, _i.e._, natural fragrances or products that can be used by the pharmaceutical industry, although many of these substances have evolved to provide the plant with protection against herbivores and pathogens. The storage compartment of glandular trichomes usually is located on the tip of the hair and is part of the glandular cell, or cells, which are metabolically active. Trichomes and their exudates can be harvested relatively easily, and this has permitted a detailed study of their metabolites, as well as the genes and proteins responsible for them. This knowledge now assists classical breeding programs, as well as targeted genetic engineering, aimed to optimize trichome density and physiology to facilitate customization of essential oil production or to tune biocide activity to enhance crop protection. We will provide an overview of the metabolic diversity found within plant glandular trichomes, with the emphasis on those of the Solanaceae, and of the tools available to manipulate their activities for enhancing the plant's resistance to pests.**  
**Keywords:** glandular trichome; plant-herbivore interactions; pathogen; Solanaceae; pest resistance; plant defense; leaf hair; tomato; plant breeding; genetic engineering
# A Review of Non-Destructive Damage Detection Methods for Steel Wire Ropes
## 3 Non-Destructive Detection Method for Steel Wire Ropes
### Electromagnetic Detection Method
#### 3.1.3 Detection Sensor
Two versions of this tailor-made circular TMR-based sensor array were put forward to inspect WRs with the diameters of 14 mm and 40 mm, respectively.
## 5 Parameter Optimization Method Based on DE Algorithm  
There are totally 12 parameters in STMCF as shown in Table 1.  
The values of these parameters will directly affect the performance of STMCF. Inspired by [21], a parameter optimization method based on DE algorithm is designed to automatically determine the value of each parameter. The flowchart of the method is illustrated in Figure 3. Each step is detailed in the following sections.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Name** & **Meaning** \\ \hline
1 & r & Local window radius of the guided filter. \\
2 & eps & Regularization parameter of the guided filter. \\
3 & W\_AD & The weight value of AD. \\
4 & W\_Census & The weight value of Census transformation. \\
5 & W\_grad\_x & The weight value of horizontal image gradient. \\
6 & W\_grad\_y & The weight value of vertical image gradient. \\
7 & cen\_win\_h & Window height of Census transformation. \\
8 & cen\_win\_w & Window width of Census transformation. \\
9 & T\_ad & The truncation threshold value of AD. \\
10 & T\_census & The truncation threshold value of Census transformation. \\
11 & T\_grad\_x & The truncation threshold value of horizontal image gradient. \\
12 & T\_grad\_y & The truncation threshold value of vertical image gradient. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Name and meaning of parameters in STMCF.
# Traffic Congestion Detection System through Connected Vehicles and Big Data
###### Abstract  
This article discusses the simulation and evaluation of a traffic congestion detection system which combines inter-vehicular communications, fixed roadside infrastructure and infrastructure-to-infrastructure connectivity and big data. The system discussed in this article permits drivers to identify traffic congestion and change their routes accordingly, thus reducing the total emissions of CO\({}_{2}\) and decreasing travel time. This system monitors, processes and stores large amounts of data, which can detect traffic congestion in a precise way by means of a series of algorithms that reduces localized vehicular emission by rerouting vehicles. To simulate and evaluate the proposed system, a big data cluster was developed based on Cassandra, which was used in tandem with the OMNeT++ discreet event network simulator, coupled with the SUMO (Simulation of Urban MObility) traffic simulator and the Veins vehicular network framework. The results validate the efficiency of the traffic detection system and its positive impact in detecting, reporting and rerouting traffic when traffic events occur.  
connected vehicles; big data; traffic congestion detection system; IoT +
Footnote †: journal: Sensors
###### Abstract
Therefore, it is better to develop models that reflect the complexity of real systems and combine different modeling approaches. This type of combination is called a hybrid modeling approach [15]. By combining different modeling approaches, a hybrid model could provide a more comprehensive and holistic view of the system and a useful approach to understanding complexity. Moreover, according to the authors of [16] a good model is the one that is not only solved with a relevant method (and has an internal coherence and robustness proven), but also the one that represents the reality it aims to relate to in a satisfactory way with respect to the stakeholders that will use it and the decisions it involves. Given that, hybrid methods allow firstly for involving different capabilities, as they result of mixing different methods (so taking the advantages of each involved method). Second, hybrid methods can highlight synergies between the involved methods, as well as complements, aiming at a better representation of a reality [17]. This paper deals with the case of the city of Dortmund, which is located in the federal state North Rhine-Westphalia, Germany. With a population of about \(600,000\) people, it is the seventh largest city in Germany and the 34 th largest city in the European Union. Based on the case study, we propose an SO approach as a hybrid model that integrates a system dynamics simulation model (SDSM) with a multi-period capacitated facility location problem (CFLP). We propose this integrated model as a decision support tool for future APL implementations as a last-mile distribution scheme. The paper is structured as follows. First, we use an SDSM to understand the behavior of the components of APL systems with respect to the specific customers and characteristics of the city of Dortmund. A planning horizon of three years (divided into months) is considered. Second, the problem is modeled as a multi-period CFLP. Taking into account the needs to be satisfied by the users, the goal is to find the minimum number of APLs to be installed per month within the time horizon. Several scenarios from the SDSM are considered and solved, taking into account different estimates for the requirements. Third, the performance of the associated solutions in a stochastic environment is evaluated using a Monte Carlo simulation. Finally, the conclusions present possible future work and applications.
# Binary PSO with Classification Trees Algorithm for Enhancing Power Efficiency in 5G Networks
## 5 Numerical Results
The outage probability for various SINR thresholds in the case of 900 UEs is depicted in Figure 10. The outage probability is defined as the percentage of UEs that are unable to attain a certain SINR threshold. The proposed algorithm has the minimum outage probability because of the enhanced SINR of the users, which resulted from minimizing the interference. The \"BPSO only\" algorithm has larger outage probability than the proposed algorithm, since it has larger interference levels. Both algorithms consider the QoS of the users, as all the users have SINR larger than \(SINR_{thr}\) in both algorithms. The \"random 10%\" algorithm has larger outage probability than the \"BPSO only\" because of the deteriorated system performance. The \"always on\" algorithm has the largest outage probability because of the huge interference levels, as the SFR principle is not utilized and no on/off switching techniques are used, diminishing the total performance of the system. Figure 9: Power efficiency for various numbers of UEs.
## 11 Special Issue: \"Research on Biomedical Signal Processing\"
###### Abstract
[8] reported on the use of some of the features extracted from physiological signals (including heart rate, heart rate variability, respiratory rate, galvanic skin response) to recognize stress in response to moderate cognitive activation in daily-life settings. The relevance of the various features was investigated using two approaches: sequential forward feature selection (SFFS) and auto-encoder (AE) neural networks. The self-organizing map (SOM) method was used to provide a flexible representation of an individual's status. [PERSON] et al. [9] addressed the problem of predicting anxiety levels from physiological signals collected without any emotional elicitation. Electrocardiogram (ECG), electrodermal activity (EDA), and electromyogram (EMG) signals were considered. Features extracted from the ECG were found to be the most relevant for anxiety classification. [PERSON] et al. [10] combined biomechanical features and ML approaches to identify fencers' levels. In order to determine the best classifier for the novice or elite athlete class, four supervised models (extreme gradient boosting; multilayer perceptron (MLP); random forest; support vector machine) were trained and tested on biomechanical data. The MLP results identified it as the best model. This Special Issue also includes two reviews. The first ([PERSON] and [PERSON] [11]) concerns the application of ML in the field of intraoperative neurophysiological monitoring (IONM) as a diagnostic tool for the protection of patients from neural injury that may occur during surgery, improving patient safety and minimizing neurological damage. Although the application of ML to IONM remains limited, such an approach enables clinicians to perform objective and reliable IONM. The second review ([PERSON] and [PERSON] [12]) provides a comprehensive evaluation of compressive sensing (CS) research in biosignal compression, particularly in one-dimensional ECG. In recent years, home patient monitoring devices have become more and more common, something that has also required the acquisition and transmission of larger amounts of biomedical data and signals to the care center server for remote analysis. This has resulted in huge data flows, making it pivotal to reduce such flows while maintaining high accuracy of information. Conceptualization M.V. ; methodology: M.V., A.T. and L.B. ; data curation, A.T and L.B; writing--original draft preparation, M.V. ; writing--review and editing; A.T. and L.B. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest.
# Symmetry Criterion and Far-Field Control of Photonic-Crystal Surface-Emitting Lasers
## 3 Results and Discussion  
In this section, we will discuss the impact of in-plane PhC structure, vertical epi-structure, and period number on the FFP and divergence angle.  
Figure 2: The simulation results of the square-lattice PCSEL with circular air holes. (**a**) Magnetic field distribution inside the PhC layer. (**b**) Near-field pattern (NFP) and (**c**) far-field pattern (FFP) of the PCSEL. (**d**) The intensity profile at the center of (**c**) along x-direction.