esa-sceva/satcom-chunk-collection huggingface dataset:
# Molecular and Hormonal Mechanisms Regulating Fleshy Fruit Ripening
## 3 Ripening Is Influenced by Multiple Hormones and Light
### Auxin Delays Ripening and Antagonises the Effects of Ethylene
#### 3.3.3 Brassinosteroids  
The brassinosteroids (BRs) are steroid hormones that play key roles in plant development and defence, and they also have a strong influence on aspects of fruit ripening in several fruits, including tomato, strawberry and persimmon. Application of brassinolide (BL, the most active brassinosteroid) to tomato fruit enhanced the accumulation of transcripts encoding the ethylene biosynthesis genes _ACS2_, _ACS4_, _ACO1_, _ACO4_ and the key carotenoid biosynthesis gene _PSY1_, and there was a concomitant acceleration of tomato fruit ripening [174]. Application of the BR biosynthesis inhibitor brassinazole (BZ) to persimmon fruit delayed aspects of ripening, and when the brassinosteroid 24-epit Brassinolide (EBR) was applied externally, transcripts of _DkACO2_, _DkAC51_ and _DkACS2_, plus several genes encoding cell wall-modifying enzymes (_DkPG1_, _DkPL1_, _DkPE2_, _DkEGase1_), were upregulated. Furthermore, application of BR to large green strawberries promoted ripening, whereas the inhibitor BZ inhibited ripening. Furthermore, downregulation of brassinosteroid receptor _FaBRI1_ expression by VIGS also retarded the development of red colour in green strawberry fruit [175].
## 3 Results and Discussion
### Qualitative and Quantitative Analysis of the VOCs by HS-GC-IMS
Conversely, formic acid, hexyl ester-M, formic acid, hexyl ester-D, and styrene-D concentrations were elevated in cultivar FD following drought stress but decreased in WNZ (Figure 2A,B). These variations in VOC accumulation between cultivars are likely linked to differences in their metabolic adaptations to drought stress. Moreover, an intriguing finding from this study was the increase in methylated volatile compounds under drought stress, which contributed to the enhanced sweet and fruity characteristics of fresh tea leaves. However, due to the absence of molecular and epigenetic data, the specific mechanisms by which drought conditions regulate the biosynthesis of methylated VOCs in tea remain unclear. Previous studies have suggested that environmental stresses, such as cold and drought, as well as postharvest processing, can induce changes in 5-methylcytosine (5 mC) DNA methylation [17; 41; 42]. These epigenetic modifications are known to affect the expression of key genes involved in flavor biosynthesis, subsequently influencing the accumulation of compounds such as theanine, catechins, terpenoid [41], and indoles [42]. Investigating DNA methylation as a potential regulator of increased methylated VOCs in response to drought stress in tea could serve as a promising avenue for future research. It is noteworthy that the alteration in linalool oxide levels observed under drought stress may be indicative of its role as a signaling molecule in the tea plant's stress response. Figure 2: Comparisons of the identified VOCs in 4 groups by HS-GC-IMS. (**A**) The VOC fingerprint comparisons of FD-CK and FD-D; (**B**) the VOC fingerprint comparisons of WNZ-CK and WNZ-D; (**C**) heatmap clustering of VOCs in FD-CK, FD-D, WNZ-CK, and WNZ-D. Previous research has demonstrated that linalool can be converted to linalool oxides via 6,7-epoxylinalool, and that infestations by green leafhoppers activate the metabolic flux from linalool to linalool oxides and their glycosides [43]. These findings may shed light on the regulatory mechanisms underlying linalool oxide accumulation in response to stress in tea plants. In conclusion, the present study identifies several VOCs as potential metabolic markers for breeding drought-resistant tea cultivars with enhanced aroma profiles. These insights may also provide a basis for improving tea quality through targeted manipulation of metabolic pathways involved in aroma compound biosynthesis.
# Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings
## 1 Introduction
[PERSON] found that the Kernel Principal Component Analysis (KPCA) was a common nonlinear principal component analysis method when researching support vector classification algorithms [31]. It maps the input vector to a high-dimensional space through some preselected non-linear mapping, so that the input vector has better separability, and then performs linear principal component analysis on the mapped data in the high-dimensional space to obtain the non-linear principal components. However, the vibration signals are time series data arranged according to the time axis, and the system model will change gradually when the bearing is running. Normal parameters often deviate from the statistical boundary of the model, causing the original model to lose its existing effect and lead to a false alarm. Aiming at the problems brought by the weak incipient fault characteristics, this paper proposes a nonlinear dynamic incipient fault detection method for rolling bearings. This method first establishes a dynamic kernel matrix by Dynamic Kernel Principal Component Analysis (DKPCA) method. Then the moving window algorithm is used to update the latest samples to the dynamic kernel matrix and discard the oldest samples without losing the time dynamics of the data during the model building process. Finally, the updated matrix is decomposed into multiple subspaces for detection, and fault information is fully mined. Based on this, the model update process is improved through the autocorrelation matrix, which simplifies the recursive formula and improves the calculation speed. The rest of the paper is arranged as follows. The basic theory is introduced in the second part. The nonlinear dynamic incipient fault detection method is proposed in the third part. The feasibility of the proposed method is proved by simulation experiments in the fourth part. The conclusion of proposed method is made in the fifth part.
# Solving the Manufacturing Cell Design Problem through an Autonomous Water Cycle Algorithm
## 3 The Manufacturing Cell Design Problem
### Problem Statement  
To find the optimal design of the production plant, where the inter-cell part exchange is minimized, the problem is schematized into the part-machine matrix, where each coordinate shows which machine processes a particular part. Through the reorganization of the rows and columns of the matrix different configurations can be tested. From this initial matrix, two others are derived: the machine-cell and the part-cell matrices, representing the cell that currently allocates the machines and parts, respectively.  
The idea in this optimization problem is to minimize the so-called exceptional elements, which are parts that move from one cell to another to satisfy the production workflow [11]. The mathematical model representing this problem is described as follows:  
* Modeling parameters:
* _M_: the number of machines.
* _P_: the number of parts.
\(C\): the number of cells. * \(M_{max}\): the maximum number of machines in a cell. * \(x_{ij}\): the \((i,j)\) element in the machine-part incidence matrix, meaning \[x_{ij}=\left\{\begin{array}{ll}1&\text{if the $i$-th machine processes the $j$-th part,}\\ 0&\text{otherwise,}\end{array}\right.\] (1) where \(i\in\{1,\ldots,M\}\) is the machine number and \(j\in\{1,\ldots,P\}\) is the part number.
* Decision variables:
* \(y_{ik}\): the \((i,k)\) element in the machine-cell incidence matrix, meaning \[y_{ik}=\left\{\begin{array}{ll}1&\text{if the $i$-th machine is located in the $k$-th cell,}\\ 0&\text{otherwise,}\end{array}\right.\] (2) where \(k\in\{1,\ldots,C\}\) is the cell number.
* \(z_{jk}\): the \((j,k)\) element in the part-cell incidence matrix, meaning \[z_{jk}=\left\{\begin{array}{ll}1&\text{if the $j$-th part is located in the $k$-th cell,}\\ 0&\text{otherwise.}\end{array}\right.\] (3) The goal of the optimization problem is given by \[\texttt{minimize}\ \sum_{k=1}^{C}\sum_{i=1}^{M}\sum_{j=1}^{P}x_{ij}z_{jk}(1-y_{ ik}),\] (4) subject to \[\sum_{k=1}^{C}y_{ik}=1,\ \forall\ i,\] (5) \[\sum_{k=1}^{C}z_{jk}=1,\ \forall\ j,\] (6) \[\sum_{i=1}^{M}y_{ik}\leq M_{max},\ \forall\ k.\] (7)
# Covert Anti-Jamming Communication Based on Gaussian Coded Modulation
## 7 Conclusions  
In this paper, a novel covert anti-jamming communication system was proposed. The proposed system exploited the Gaussian-coded time-frequency modulation method to obtain covertness. As the signal samples of the proposed system followed Gaussian distributions, the signal was less detectable than the traditional binary modulated signals. We proposed the SSR-algorithm-based SJE algorithms to estimate and remove the effects of hostile jamming. In contrast to previous studies, the proposed system used the Gaussian codebook method to achieve a coding gain when the finite-field messages were encoded. We analyzed the undetectability of the proposed system as a measurement of the covertness of communications, and compared the undetectability with that of the conventional binary modulated system. We also demonstrated its BER performance through numerical simulations. The proposed system can be applied in eavesdropping- and jamming-critical applications, such as military communications in electronic warfare scenarios.  
Conceptualization, S.P. and H.-N.L.; formal analysis, H.C.; methodology, H.C.; supervision, H.-N.L.; validation, H.C. and S.P; writing-original draft, H.C., S.P. and H.-N.L.; Writing-review and editing, H.C., S.P. and H.-N.L. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge the support from the Electronic Warfare Research Center at the Gwangju Institute of Science and Technology (GIST), originally funded by the Defense Acquisition Program Administration (DAPA) and the Agency for Defense Development (ADD).  
The authors declare no conflict of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.  
Figure 11: BER performance of the proposed system over a frequency-selective channel at JNR = 0 dB for the codebook method.
## 5 Discussion and Comparison to SCPT Results
### Shear Crack Geometry  
Since the SCPT allows to determine the shear crack development, a comparison of this calculated failure shear crack geometry with the experimentally recorded shear crack patterns resulting from the manual documentation, as well as automatized DIC measurements is conducted (Figure 8). Slight differences in the two experimentally derived shear crack patterns for subtest B1-1 are detected due to the measuring at opposite sides of the beam. Overall, the calculated shear crack geometry by using the SCPT shows an excellent agreement with the actual failure shear crack. While both shear cracks are congruent until approx. the mid-depth of the beam, a marginal deviation in the inclination of both cracks is detected for loads close to shear failure (Figure 8).  
The comparison in Figure 8 clearly demonstrates that the SCPT is capable of calculating a well-matching shear crack development purely based on the implemented constitutive laws and kinematic relationships, without an empirical addition. In any state of crack propagation before reaching the maximum failure load and, thus, at each load level, the information about the geometry of the theoretical shear crack is available.
## 5 Validation
### Validation of Drag Models  
For the simulations, the Schiller-Naumann and Brucato drag models were considered. Both drag models are acceptable for general use in gas-liquid bioreactors [17; 18; 19]. As shown in Figure 7, the highest gas hold-up was observed near the impellers in both drag models. This is because of the gas accumulation in the recirculating flow region formed near the impellers. The lowest gas hold-up was observed in the bottom of the tank under the lower impeller since the rise of the bubbles by the buoyancy forces overcomes the recirculation flow of the fluid. These trends were comparable to the experimental data [38], except in the upper part close to the free surface of the reactor. The difference in gas-holdup can be attributed to the difference in scale of the actual reactors used in the experiment and the simulation. Although the gassing rates per unit volume were similar, the volume of the reactor used in the experiment was about hundred times larger than the volume of the reactor used in the simulation. Intuitively, as the scale of the reactor increases, the gas-holdup may increase due to the increase in the travel distance and residence time of the bubbles. This can be seen in the results of a study [39] which compared the gas hold-up distribution in stirred tank reactors with the same configurations but different scales. Additionally, a mismatch of gas hold-up in the upper part of the reactor has been reported previously [17]. In this study, we used the Schiller-Naumann drag model because of its simplicity and as it is less time-consuming than the Brucato drag model, while maintaining the equivalent gas distribution trends [40].
## 2 Network Attacks in Distributed Photovoltaic Microgrids  
### Distributed Photovoltaic Microgrid Model  
The photovoltaic microgrid model is a small-scale electrical system integrated with photovoltaic generation and energy storage systems, as shown in Figure 1. It can operate independently or be interconnected with the main grid to meet local power demand. This model is based on complex photovoltaic generation systems that utilize photovoltaic cells to capture sunlight and convert it into direct current (DC) electricity. Photovoltaic cells employ different technologies such as monocrystalline silicon and polycrystalline silicon, each with its own efficiency and durability characteristics. To ensure efficient power generation, the photovoltaic microgrid model employs maximum power point tracking (MPPT) control algorithms such as perturb and observe (P&O) and incremental conductance (IncCond), which adjust the operating parameters of the photovoltaic array to maximize power output as sunlight conditions change. DC-DC converters, especially those equipped with insulated gate bipolar transistors (IGBTs), are responsible for seamless energy transfer between the photovoltaic array and the energy storage system. Grid-tied inverters convert DC electricity into alternating current (AC) electricity injectable into the grid and integrate safety mechanisms such as anti-islanding protection. The operation of photovoltaic microgrids heavily relies on real-time monitoring by sensor networks and information transmission through communication channels. Data aggregation equipment (DAE) transmits data packets to the dispatch center of the main grid. Due to the low inertia and fast response time of power electronic devices, even momentary communication delays or denial-of-service (DoS) attacks on sensors can lead to equipment failures and system blackouts. If interconnected with the main grid, these issues may have more severe consequences. The following sections will discuss network attacks and their models in the field of photovoltaic microgrids
# Methodology for Studying the Reliability of Interlocking Devices in Bulgarian Railways
## 3 Results  
According to the described methodology, a statistical assessment of the failures of the signalling equipment was performed and the reliability indicators of the interlocking devices were determined. The analysis was carried out separately for the devices belonging to the different divisions--STD in Bulgaria--Sofia, Plovdiv, and Gorna Oryahovitsa.
_Article_  
Experimental Investigation on Semi-Active Control of Base Isolation System Using Magnetroheological  
Dampers for Concrete Frame Structure  
[PERSON] 1,2,*, [PERSON] 1,3,*, [PERSON] 1 and [PERSON] 1(c)  
School of Civil Engineering, Qingdao University of Technology, Qingdao 266033, China; [EMAIL_ADDRESS] (M.L.); [EMAIL_ADDRESS] (C.D.)  
Co-operative Innovation Center of Engineering Construction and Safety in Shandong Peninsula Blue Economic Zone, Qingdao University of Technology, Qingdao 266033, China  
Center for Infrastructure Engineering, Western Sydney University, Penrith, NSW 2751, Australia  
Correspondence: [EMAIL_ADDRESS] (W.F.); [EMAIL_ADDRESS] (C.Z.); Tel.: +86-532-8507-1692 (W.F.); +86-532-8507-1693 (C.Z.)  
Received: 22 August 2019; Accepted: 11 September 2019; Published: 14 September 2019  
**Abstract:** The traditional passive base isolation is the most widely used method in the engineering practice for structural control, however, it has the shortcoming that the optimal control frequency band is significantly limited and narrow. For the seismic isolation system designed specifically for large earthquakes, the structural acceleration response may be enlarged under small earthquakes. If the design requirements under small earthquakes are satisfied, the deformation in the isolation layer may become too large to be accepted. Occasionally, it may be destroyed under large earthquakes. In the isolation control system combined with rubber bearing and magnetroheological (MR) damper, the MR damper can provide instantaneous variable damping force to effectively control the structural response at different input magnitudes. In this paper, the control effect of semi-active control and quasi-passive control for the isolation control system is verified by the shaking table test. In regard to semi-active control, the linear quadratic regulator (LQR) classical linear optimal control algorithm by continuous control and switch control strategies are used to control the structural vibration response. Numerical simulation analysis and shaking table test results indicate that isolation control system can effectively overcome the shortcoming due to narrow optimum control band of the passive isolation system, and thus to provide optimal control for different seismic excitations in a wider frequency range. It shows that, even under super large earthquakes, the structure still exhibits the ability to maintain overall stability performance.  
**Keywords:** LQR algorithm; base isolation; magnetorheological damper; shaking table test; semi-active control; quasi-passive control
# Metabolic Syndrome and Its Components among Taxi Drivers in the City of Tshwane, South Africa
## 3 Results
### Factors Associated with MetS  
Table 4 shows the hierarchical model results for taxi drivers. In Model 1, it was observed that there was no significant association between lifestyle factors and MetS besides for the duration in the driving industry and MetS. In model 2, family health history was added to Model 1. Family health history having diabetes and the duration in the driving industry were significantly associated with MetS (AOR = 2.075 (1.035; 4.160); \(p\) = 0.040 and AOR = 2.305 (0.261; 1.161); \(p\) = 0.010) shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variables** & \multicolumn{3}{c}{**Duration in the Taxi Industry**} \\ \hline  & **\textless{}10 Years** & \(\geq\)**10 Years** & \(p\)**-Value** \\ \hline Age (years) & & & 0.001 \({}^{*}\) \\ \textless{}50 & 153 (88) & 118 (63) & \\ \textgreater{}50 & 21(12) & 70 (37) & \\ \hline Hypertension & & & \\ No & 129 (74) & 102 (54) & 0.001 \({}^{*}\) \\ Yes & 45 (26) & 86 (46) & \\ \hline Diabetes & & & \\ No & 163 (94) & 169 (90) & 0.192 \\ Yes & 11 (6) & 19 (10) & \\ \hline BMI (kg/m\({}^{2}\)) & & & \\ Normal & 56 (32) & 48 (26) & 0.216 \\ Overweight & 65 (37) & 64 (34) & \\ Obesity & 49 (28) & 72 (38) & \\ Underweight & 4 (3) & 4 (2) & \\ \hline Exercise regularly & & & \\ No & 122 (70) & 135 (72) & 0.723 \\ Yes & 52 (30) & 53 (28) & \\ \hline Marital status & & & \\ Cohabiting & 19 (11) & 11 (6) & 0.001 \({}^{*}\) \\ Single & 91(52) & 53 (28) & \\ Ever married & 64 (37) & 124 (66) & \\ \hline Education status & & & \\ No formal schooling & 2 (1) & 4 (3) & 0.001 \({}^{*}\) \\ Primary school & 6 (4) & 25 (13) & \\ High school & 141 (81) & 146 (78) & \\ Post high school & 25 (14) & 13 (7) & \\ \hline Sleeping hours at & & & \\ most/days & & & \\ \textless{}5 h & 22 (13) & 23 (12) & 0.906 \\ \(\geq\)5 h & 152 (87) & 165 (88) & \\ \hline Alcohol use & & & \\ No & 66 (38) & 84 (45) & 0.193 \\ Yes & 108 (62) & 104 (55) & \\ \hline Smoking & & & \\ No & 124 (71) & 128 (68) & 0.511 \\ Yes & 50 (29) & 60 (32) & \\ \hline \hline \end{tabular} \({}^{*}\)Significant at 5% level.  
\end{table}
Table 3: Comparison of duration in the taxi industry and selected variables among taxi drivers.
## 4 HSI in Fresh-Cut Product Quality Assessment
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \multicolumn{2}{p{56.9 pt}}{**Qualitative Features**} & \multicolumn{2}{c}{**Description**} \\ \hline \hline \multirow{6}{*}{**Fresh-cut**} & \multirow{3}{*}{Color} & First element of quality monitoring and conveys consumer choice. It is an indirect indicator of features like freshness, desirability and variety, maturity, and safety, which are related to the physical, chemical, or microbial changes that occur in ripeness and the postharvest processing and handling stages [65]. \\ \cline{2-2}  & & \multirow{3}{*}{Surface defects} & Mainly due to mechanical injuries, insects, diseases, and over- or under-ripeness. Damaged products should be selected and removed, during postharvest processing, to reduce losses by avoiding secondary contaminations [66]. \\ \cline{2-2}  & & Physiological disorders & Appear during postharvest after internal or external stresses, like nutritional deficiency, senescence, suppressed respiration, and extreme temperature [2]. \\ \cline{2-2}  & & \multirow{3}{*}{Chilling injury} & Caused mainly by postharvest storing at low temperatures which produces internal browning, deteriorated texture, juiciness deficiency, and unpleasantness [64]. \\ \hline \multirow{6}{*}{**Fresh-cut**} & \multirow{3}{*}{Texture} & Capacity to withstand deformation actions like biting, chewing, and grinding, with an impact on food acceptability and consumer preferences [64]. Firmness is an indicator of the maturity stage and shelf life. Firmness loss in fruits is primarily caused by the enzymatic degradation of pectin present in the intercellular space and cell wall [62]. \\ \cline{2-2}  & \multirow{3}{*}{Nutritional value} & Represented by the caloric intake or elements that are important from a nutritional point of view. \\ \cline{2-2}  & & \multirow{3}{*}{Absence of antinutritional substances} & e.g., nitrates and nitrites, pesticide residues, insect or pest infestation, fecal contamination, naturally occurring undesirable compounds, and plant growth regulators [2,67]. \\ \cline{2-2}  & & \multirow{3}{*}{Absence of pathogenic microorganisms} & e.g., _Escherichia coli_, _Salmonella_ spp., and _Listeria monocytogenes_ whose presence is caused by soil or manure contamination, irrigation water, inappropriate packaging, and inadequate storage temperatures [68]. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Description of quality features for fresh-cut products.