esa-sceva/satcom-chunk-collection huggingface dataset:
# Plant Volatile Organic Compounds Evolution: Transcriptional Regulation, Epigenetics and Polylploidy
## 3 The Temporal Pattern of Emissions and Its Epigenetic Regulation
Together with H3 acetylation, methylation (di- and trimethylation) of the histone H3K4 (H3K4 me3 and H3K4 me2) constitutes the main histone marks in clock gene regulation, altering the expression period of _CCA1_ and _TOC1_. In _Arabidopsis_, the H3K4 methyltransferase SET DOMAIN GROUP 2/ARABIDOPSIS TRITHORAX RELATED 3 (SDG2/ATXR3) contributes to H3K4 me3 accumulation and the expression of oscillatory genes [121; 123]. Furthermore, another histone mark regulating clock gene expression is H3K36 me2, of which the accumulation at _TOC1_, _CCA1_ and _LHY_ promoters is associated with their transcriptional repression [127]. At the same time, LYSIN-SPECIFIC DEMETHYLASE 1 (LSD1)-LIKE histone demethylases LDL1 and LDL2 interact with _CCA1_ and _LHY_ to repress _TOC1_expression. LDL1 and LDL2 interact with histone deacetylase HDA6, and the LDL1-HDA6 complex binds to the promoter of _TOC1_ and represses its expression by deacetylation and demethylation [128]. In plants, the circadian clock core loop is self-regulated by the interaction of its clock genes. Epigenetic modifications are part of this complicated regulatory system, in which morning components repress the evening ones and vice versa, controlling the different clock gene expressions by means of histone modifications and chromatin reshaping, and thus fine-tuning the changes between day and night phases. The proposed epigenetic model and its precise regulation of the circadian clock would impact VOC emission at multiple levels in the short- and long-term (i.e., during the plant's lifespan and subsequent generations), influencing plant fitness and natural selection [129]. Figure 2: Circadian clock regulation of VOC emission and its epigenetic regulation. **(A)** The main oscillator of the circadian clock is formed of the morning components _CIRCADIAN CLOCK ASSOCIATED 1 (CCA1)_ and _LATE ELONGATED HYPOCOTYL (LHY)_ and the evening components _TIMING OF CAB EXPRESSION 1 (TOC1)_ and _ZEITLUPE (ZTL)_. The morning and evening components regulate each other in a negative feedback loop. Light regulates _ZTL_ expression, which ubiquitinates TOC1 for degradation. These clock genes control the daily expression of terpene synthases and phenylpropanol/benzenoid pathway genes. **(B)** Methylation and acetylation of the H3 histone of the main clock genes causes chromatin remodeling and subsequent changes in these genes’ expression, affecting the biosynthesis and emission of VOCs. Histone deacetylases (HDACs), induced by TOC1 and LHY, cause H3 deacetylation in the _TOC1_ promoter, repressing it. Meanwhile, the REVEILLE 8/LHY-CCA1-LIKE 5 (RVE8/LCL5) TF has an opposite role, promoting hyperacetylation of H3 in the _TOC1_ gene. Di- and trimethylation of H3 histone also alters the expression of _CCA1_, _TOC1_ and _LHY_. In _Arabidopsis_ SET DOMAIN GROUP 2/ARABIDOPSIS TRITHORAX RELATED 3 (SDG2/ATXR3) and LDL1-HDA6 alter methylation and acetylation patterns of these circadian genes.
## 2 Materials and Methods
### AuNP Synthesis and Their Biofunctionalization with VEGF-FAM Peptides  
Gold nanoparticles were synthesized by a modified Turkevich's method of chemical reduction of the chloroauric acid by the reducing agent trisodium citrate [32;33]. AuNPs were synthesized by chemical reduction of HAuCl\({}_{4}\) (1 mM) with TSC (1 wt %). The plasmonic band centered at 518 nm and the full width at half-maximum of about 50 nm confirmed the formation of monodisperse spherical gold colloids with a diameter of 12 nm. The actual concentration of the gold nanoparticles synthesized was estimated of 18 nM, with \(\varepsilon\) = 1.9 \(\times\) 10\({}^{8}\) M\({}^{-1}\) cm\({}^{-1}\), calculated according to the equation: In \(\varepsilon\) = \(k\) ln \(D\) + \(a\), where \(\varepsilon\) is the extinction coefficient, in M\({}^{-1}\)cm\({}^{-1}\), D is the core diameter of the nanoparticles, and k = 3.32111, a = 10.80505 [34].  
To prepare the peptide-coated nanoparticles, 173 nM VEGFCysFAM or 520 nM VEGFGly FAM were added to 9 nM Au colloidal dispersion under stirring; then, the unbound peptide molecules were removed by centrifugation step (8000 r.p.m., 15 min) and the collected pellets were redissolved in MilliQ water.
## 5 Results and Discussion
### Engineering Application Effect Analysis  
Based on the above reasonable spacing determination method, the spacing of the pre-excavated double retracement channels for the 22-116 working face at the Lijahao Mine is determined. Through the results from the numerical simulation analysis, when the protective coal pillar is wider than 20 m, the ARC is in the decreasing deviatoric stress environment, the plastic zone shape is elliptical, and the plastic zone size effect on the surrounding rock stability is considered. The spacing between the two retracement channels, set to 25 m, is determined during the application of the actual project.  
To obtain the surrounding rock deformation in the ARC at the end of the mining period, 17 surface displacement monitoring stations were uniformly arranged in the ARC. The displacement between the floor and roof and between the two sides are shown in Figure 12. With a 25 m protective coal pillar set, the surface displacement of the ARC is small overall: the average is 22.9 mm between the roof and floor and 17.1 mm between the two sides. The deformation and damage in the surrounding rock in the ARC are not significant, with a small amount of sinking in the roof and an insignificant coal wall spalling phenomenon, which does not affect the normal use of the ARC.  
Figure 12: Surface displacement changes at 17 monitoring points in the auxiliary retracement channel.
# The Porcupine Measure for Comparing the Performance of Multi-Objective Optimization Algorithms
## 6 \(M\)-Dimensional Attainment Surfaces
### Stability Analysis
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline \multirow{2}{*}{**Problem**} & \multirow{2}{*}{**Algorithm**} & \multicolumn{4}{c}{**Naive**} & \multicolumn{4}{c}{**Optimized**} \\  & & **Mean** & \(\sigma\) & **Min** & **Max** & **Mean** & \(\sigma\) & **Min** & **Max** \\ \hline \multirow{6}{*}{WFG1} & OMOPSO & 3.054 & 2.178 & 0.2596 & 9.4 & 3.092 & 2.245 & 0.5106 & 9.728 \\  & SMPSO & 32.34 & 6.054 & 17.29 & 43.92 & 32.38 & 6.132 & 16.57 & 42.92 \\ \cline{2-10}  & OMOPSO & 1.308 & 1.11 & 0.2418 & 4.886 & 1.32 & 1.153 & 0.2445 & 5.071 \\  & VEPSO & 43.55 & 4.06 & 30.43 & 52.71 & 43.16 & 4.031 & 32.27 & 52.73 \\ \cline{2-10}  & VEPSO & 19.62 & 6.619 & 8.857 & 47.63 & 18.73 & 4.149 & 8.679 & 28.34 \\  & SMPSO & 6.631 & 2.949 & 0.8017 & 17.61 & 6.701 & 2.782 & 3.491 & 17.05 \\ \hline \multirow{6}{*}{WFG2} & OMOPSO & 49.88 & 6.116 & 32.83 & 60.57 & 49.29 & 6.517 & 30.85 & 60.54 \\  & SMPSO & 0.01869 & 0.06869 & 0.0 & 0.3711 & 0.01736 & 0.06466 & 0.0 & 0.3442 \\ \cline{2-10}  & OMOPSO & 59.43 & 4.403 & 47.63 & 67.35 & 59.64 & 4.397 & 47.61 & 66.87 \\  & VEPSO & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\  & SMPSO & 59.76 & 4.612 & 46.3 & 66.91 & 59.83 & 4.716 & 46.26 & 67.61 \\ \hline \multirow{6}{*}{WFG3} & OMOPSO & 26.98 & 4.088 & 18.44 & 37.32 & 28.99 & 4.524 & 20.43 & 43.72 \\  & SMPSO & 3.099 & 2.262 & 0.1569 & 8.927 & 3.078 & 2.097 & 0.2126 & 7.497 \\ \cline{2-10}  & OMOPSO & **62.55** & **3.341** & **55.16** & **68.42** & **66.07** & **3.722** & **58.36** & **73.14** \\  & VEPSO & 0.5199 & 0.1456 & 0.2931 & 0.8233 & 0.5875 & 0.1695 & 0.2527 & 0.8897 \\ \cline{2-10}  & VEPSO & 1.177 & 0.48 & 0.2131 & 2.315 & 1.243 & 0.5278 & 0.2095 & 2.5 \\  & SMPSO & **59.17** & **3.402** & **52.04** & **64.47** & **62.57** & **3.668** & **55.66** & **69.12** \\ \hline \multirow{6}{*}{WFG4} & OMOPSO & 26.78 & 2.433 & 22.7 & 30.65 & 26.37 & 2.919 & 20.3 & 31.05 \\  & SMPSO & 9.348 & 1.915 & 5.002 & 13.57 & 9.306 & 1.989 & 5.122 & 13.62 \\ \cline{2-10}  & OMOPSO & 63.0 & 4.539 & 55.21 & 75.74 & 63.82 & 4.798 & 55.75 & 77.11 \\  & VEPSO & 0.02337 & 0.03589 & 0.0 & 0.1569 & 0.02468 & 0.04244 & 0.0 & 0.1929 \\ \cline{2-10}  & VEPSO & 0.02615 & 0.09773 & 0.0 & 0.533 & 0.02406 & 0.08814 & 0.0 & 0.4763 \\  & SMPSO & 71.11 & 3.919 & 62.78 & 80.38 & 71.92 & 4.072 & 63.2 & 82.07 \\ \hline \multirow{6}{*}{WFG5} & OMOPSO & 25.67 & 4.393 & 17.47 & 34.0 & 25.79 & 4.472 & 17.55 & 34.4 \\  & SMPSO & 17.61 & 3.64 & 11.16 & 27.63 & 17.65 & 3.824 & 10.68 & 27.83 \\ \cline{2-10}  & OMOPSO & 26.21 & 2.314 & 22.2 & 30.72 & 26.31 & 2.432 & 21.48 & 31.12 \\ \cline{2-10}  & VEPSO & 17.94 & 2.65 & 10.6 & 22.42 & 17.54 & 2.691 & 10.56 & 21.72 \\ \cline{2-10}  & VEPSO & 25.13 & 4.301 & 14.71 & 33.27 & 25.12 & 4.463 & 13.9 & 33.76 \\ \cline{2-10}  & SMPSO & 5.143 & 1.277 & 2.736 & 8.069 & 5.187 & 1.338 & 2.539 & 8.049 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Naive vs. optimized porcupine measure (3-objective WFG problem set).
## 4 Experiments
### The Effectiveness of DDM  
In the next section of this paper, this article explores the overall effectiveness of using both internal and external residual layers and a dual multi-head attention mechanism in our model. Our ablation experiments showed that the use of internal and external residual layers and two attention mechanisms is highly effective. This is because modeling the interaction between words and selecting important words can better help the model learn informative news representations, and internal and external residual layers and two attention mechanisms can better fit such interactions and select important words. Furthermore, news-level attention is also useful because modeling the interaction between words and selecting important words can aid in learning informative news representations [11]. However, we also experimented with using residual and multi-head attention mechanisms at the news level and found that this negatively impacted the results. This suggests that it is important to modify the model in appropriate places, as too many residual structures may disrupt previously extracted news-level features, and too many instances of the multi-head attention mechanism may result in overfitting.  
Figure 3: Comparison of the results of various optimization mechanisms.
## 2 Main Factors Affecting Spectral Reflection Characteristics of Leaves
### Moisture--Mid-Infrared  
The characteristic absorption bands of water molecules all appear in the infrared spectrum. The vibration bands of water are located at a base frequency of 2.66, 2.73, and 6.27 (strong absorption); doubling frequency of 1.4 and 1.9 (sharp band and strong absorption); and combined frequency of 0.96 and 1.1 (weak absorption) [31]. Since leaves contain water, five absorption bands of water appear on the plant reflection spectrum at 970, 1190, 1440, 1920, and 2660 nm in the near-infrared region. Among these bands, the reflectivity of leaves is the lowest. The absorption of water in leaves plays an important role in the optical properties of leaves. The increase or decrease in water content directly affects the enhancement or weakening of water absorption intensity. The reflectance of leaves changes with the water content of leaves.
## 4 Discussion  
The application of low-temperature plasma technology in potato storage has significant practical and theoretical significance [37]. With the prominence of the sprouting problem during potato storage, the use of low-temperature plasma technology can not only effectively inhibit sprouting, but also improve the quality of potatoes and extend its storage period. In practical application, low-temperature plasma technology by adjusting the processing parameters, such as voltage, airflow rate, and processing time, the application of this technology not only reduces the germ infection and nutrient loss caused by sprouting, but also effectively prolongs the freshness period of potato and ensures its quality [38]. In addition, low-temperature plasma has a positive effect on the antioxidant capacity of potatoes. By increasing the activity of antioxidant enzymes (e.g., SOD and CAT), plasma treatment significantly enhanced the antioxidant capacity of potato [39]. This enhanced antioxidant capacity helps to inhibit quality degradation, such as browning and nutrient loss, triggered by oxidative reactions during storage [11]. The low-temperature plasma technology also improved the texture of the potato. This effect suggests that low-temperature plasma can effectively delay the degradation of potato texture and maintain its good taste and appearance. In addition, the plasma treatment reduced the accumulation of sugar and the consumption of dry matter, which in turn maintained the nutrient content of the potato and further improved its storage quality [40].  
In terms of theoretical significance, the low-temperature plasma technology provides new ideas for in-depth study of the effects of plasma on plant physiology and metabolic processes [41]. This technology provides important theoretical support for the investigation of the inhibition mechanism of potato germination and the storage and preservation of other agricultural products. With the continuous development of technology, low-temperature plasma technology is expected to be widely used in agricultural storage, food processing, and other fields to promote the progress of green and environmentally friendly storage technology [42].  
Although low-temperature plasma technology has shown excellent results in potato storage, it still faces the problems of high equipment cost and technical standardization. The future needs to further optimize the treatment process and study its long-term effect, in order to promote the application of this technology on a larger scale [43]. Overall, low-temperature plasma technology provides an effective solution for potato storage and brings an innovative breakthrough in preservation technology for the food industry [44].
## 3 Hybrid Method
### Modified Syndrome Generator  
The conventional syndrome generator as discussed in Section 2.2 can be split into two steps: First, the residual polynomial \(\mathit{res}_{i}(x)\), of the received code word \(r(x)\), is generated by the equation \(\mathit{res}_{i}(x)=r(x)\:\mathit{mod}\:\phi_{i}(x)\), where the minimal polynomial \(\phi_{i}(x)=g_{i,m}x^{m}+...+g_{i,0}\) and residual polynomial \(\mathit{res}_{i}(x)=\mathit{res}_{i,m-1}x^{m-1}+...+\mathit{res}_{i,0}\). Second, the syndrome can be calculated by substituting the primitive element \(\alpha^{i}\) in the residual polynomial and is expressed as:  
\[S_{i}=\sum_{k=0}^{deg(\phi_{i}(x))-1}\mathit{res}_{i,k}.(\alpha^{i})^{k} \tag{6}\]  
It is clear that by splitting the syndrome generation, \(\mathit{res}_{i}(x)\) does not have any dependency on the field extensions. In fact, the polynomial division used in \(\mathit{res}_{i}(x)\) is identical to a Linear Feedback Shift Register (LFSR) with its coefficient from \(\phi_{i}(x)\). We introduce the idea to have the coefficients of the LFSR as programmable. Figure 4 represents a hardware realization of the SRU array in a serial fashion with programmable feedback coefficients. In most cases, depending on the data interface width of the flash memory interface, we can unfold the serial interpretation of the SRU to process more bits in parallel. Because of the relationship between the conjugacy class and \(\phi_{i}(x)\)[7], it is sufficient to generate \(t\) SRU units. These SRUs can compute \(\mathit{res}_{i}(x)\) of different minimal polynomials in tandem.  
Figure 3: Flow chart for the hybrid system.  
Once the residuals are computed, the values of the \(res_{i}(x)\) are compared for non-zero values. An error is triggered if any of the \(res_{i}(x)\) has non-zero coefficients in them, and the GPU kernel routines for the other stages of the BCH decoder are executed.
## 4 Proposed Approach
### Motivation
#### 4.1.1 Why Decision Tree?  
When creating an ML model, the model's accuracy is not the only thing of paramount importance. The model needs to be both accurate and interpretable [59], especially when an understanding of the dataset is required. The KNN model is considered to be one of the simplest ML models. However, KNN requires some kind of encoding for categorical variables, as it depends upon the number line for creating classification criterion. This makes KNN unsuitable for data that have features existing on different scales, and in [PERSON] et al. [1], KNN does not give the best accuracy.  
The logistic regression model also gives bad results even though it is interpretable. SVC and ANNs are black box models [60]. RF is an interpretable model, but [PERSON] et al. [1] showed that it did not give the best accuracy. In addition, RF depends on a large number of decision trees. The best model was the one that used SVC in conjunction with RFs, but as discussed above, SVC is a black box, and using such models can be dangerous [61]. Thus, it was concluded that models that perform feature selection and are interpretable should be selected. DTs have both these advantages [62].
## Materials and Methods
### Hyp HPLC Quantification  
Hyp content was quantified according to previous protocols [22; 72]. Air-dried seedlings were ground into powder, and a 0.1-g sample was used to extract Hyp. Ethanol extract (10 \(\mu\)L) was analyzed by HPLC (symmetry C18, 250 mm \(\times\) 4.6 mm, 5 \(\upmu\)m and column temperature 60 \({}^{\circ}\)C; Waters ACQUITY Arc, Waters Corporation, Milford, MA, USA). Hyp content was evaluated with a peak area comparison with a reference standard (hypericin, 56690; Sigma Chemical Co., St. Louis, MO, USA).
# Ten Years of Experience with Scientific TerraSAR-X Data Utilization
## 2 TerraSAR-X Proposals, Scientific Orders and Literature Review
### Scientific TerraSAR-X Acquisitions  
By the end of June 2017, more than 235,000 individual data takes were acquired and stored in the TerraSAR-X mission archive. A data take is a continuously acquired strip of SAR raw data which can consist of several individual scenes. 100,711 of them were initiated for scientific and mission control purposes. Figure 2 shows their yearly distribution between 19 June 2007 and 30 June 2017. General ordering was opened up on 1 January 2008. Hence, all acquisitions in 2007 were exclusively related to commissioning activities. Therefore, they are not considered in the following analyses.  
Figure 1: Countries of the principle investigator’s (PI) institutions: 955 PIs submitted proposals. All countries with more than 20 PIs were considered.  
For the purpose of this review, all data takes acquired on request of scientific users after 1 January 2008 are examined to examine the role of different modes and polarizations, to identify the preferred settings and geographic areas of interest.
## 2 Project Overview
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Observation** & **Mining** & \multicolumn{2}{c}{**Maximum Principal Stress \(\sigma\)1**} & \multicolumn{2}{c}{**Intermediate Principal Stress \(\sigma\)2**} & \multicolumn{2}{c}{**Minimum Principal Stress \(\sigma\)3**} \\
**Point** & **Depth (m)** & **Numerical** & **Orientations** & **Tilt** & **Numerical** & **Orientations** & **Tilt (\({}^{\circ}\))** & **Numerical** & **Orientations** & **Tilt (\({}^{\circ}\))** \\  & **Value (MPa)** & **C** & **C** & **Value (MPa)** & **C** & **Value (MPa)** & **C** & **Value (MPa)** & **C** \\ \hline
160 m level & 510 & 17.46 & 211.5 & 30.8 & 16.53 & \(-\)56.8 & 2.7 & 13.49 & 217.7 & \(-\)59 \\
115 m level & 555 & 24.59 & 250.4 & 0.7 & 20.57 & \(-\)19.2 & 18.7 & 15.27 & 158.2 & 71.2 \\
90 m level & 580 & 30.22 & 229.3 & 1.2 & 16.55 & \(-\)29.2 & 83.8 & 14.00 & 139.1 & 6.0 \\
18 m level & 652 & 31.33 & 224.9 & 7.0 & 21.86 & \(-\)41.2 & 27.8 & 18.04 & 121.9 & 61.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Raw rock stress test results. Figure 1: Vertical stress variation from 160 m to \(-\)18 m.  
To improve safety and efficiency, the mine urgently needs systematic optimization of support technologies, including standardized selection criteria, process controls, and evaluation methods. Establishing a scientific approach to support design based on rock mass conditions will be key to enhancing shaft stability and long-term mining sustainability.
## 3 Results and Discussion
### Interpretation of FTIR Spectra
All spectra exhibited absorption bands at 1020-1110 cm\({}^{-1}\), which are assigned to glycoside C-O stretching vibration and/or to C-O-H and C-O-C bond deformation, pertaining to carbohydrates [41]. The band at 960-970 cm\({}^{-1}\) is related to the C-H out-of-plane deformations of trans vinyl groups of carotenoids [42]. The band at 840-890 cm\({}^{-1}\) is attributed to C-H out-of-plane bending vibrations of 1,3,5-tri-substituted aromatic derivatives, whereas the bands at 810-840, 760-810 and 730-680 cm\({}^{-1}\) are associated with C-H out-of-plane bending vibrations of para-, ortho- and meta-substituted aromatic derivatives, respectively [43]. According to the percentage relative intensities of the ATR-FTIR spectra bands of the flower samples that emerged after derivatization, smoothing and normalization (Table S1), all samples showed the characteristic band of skeletal vibrations of phenolic aromatic rings, at 1490-1610 cm\({}^{-1}\), with a relative intensity higher than 90%, in most cases. Furthermore, most of the extracts' FTIR spectra demonstrated the stretching vibrations of phenolic hydroxyl groups at 3200-3300 cm\({}^{-1}\) and of phenolic C-O groups at 1220-1270 cm\({}^{-1}\). Moreover, the bands of substituted phenolics, at 840-890, 810-840 and 760-810 cm\({}^{-1}\), were observed in all samples as having medium or elevated relative intensities. These observations indicated that the flower extracts are rich in phenolics, which can be correlated with the TPCs and antiradical and antioxidant activities demonstrated by the spectrophotometric assays. These phenolic compounds stemming from the flower extracts could emerge as good candidates for molecular docking studies, also clarifying the mechanism by which phenolic compounds could act as antioxidants _in vivo_.
## 3 Methodology and Algorithm Descriptions
### Dataset and Preprocessing  
The dataset used in this study originates from a real-world e-commerce platform and comprises transactional purchase records collected over a six-month period. Each transaction represents a single customer purchase session and includes a list of purchased items. The raw dataset initially contained approximately 27,000 transactions and over 3800 unique items.  
To ensure data quality and relevance for association rule mining, several preprocessing steps were applied. First, only transactional data were retained by removing non-essential attributes such as customer demographics, allowing the analysis to focus exclusively on item co-occurrence patterns. Next, items that appeared in fewer than 10 transactions--representing less than 0.04% support--were removed to reduce dimensionality and enhance computational efficiency, resulting in a refined set of 1215 unique items. Transactions containing fewer than two items were then excluded, as they could not yield meaningful association rules; the final dataset comprised 22,476 transactions with an average of 4.3 items each. The data were subsequently transformed into a binary transaction matrix, where each row represents a transaction and each column represents an item, with a value of 1 indicating the item's presence and 0 its absence. This binary format is essential for applying both the Apriori and FP-Growth algorithms in RapidMiner. Despite the preprocessing steps, the transaction matrix remained sparse, with an item density of approximately 0.35%, a typical feature of retail datasets where most transactions involve only a limited number of items.  
This refined dataset was then used uniformly in both algorithmic workflows (Apriori and FP-Growth) within RapidMiner. In RapidMiner, for the sorting order and tree structure, the default settings were used. By ensuring consistent preprocessing and parameter settings, the experimental design allowed for a fair, controlled comparison of algorithm performance across multiple dimensions.