esa-sceva/satcom-chunk-collection huggingface dataset:
The concept behind their use is to mimic signaling caused by microbial infection, and in the case of priming to promote a faster and more vigorous activation of plant defense responses once microbial infection starts [23; 24; 25]. In cucurbits, several elicitors have been used thus far, including compounds such as acibenzolar-S-methyl, chitin and chitosan, subsets of plant extracts such as laminin and oligogalucuronides, and whole plant extracts like this of _Reynoutria sachalinensis_[26; 27; 28]. The latter has been effective against several PM fungi including _P. xanthii_ in cucurbits, _Levevillula taurica_ in tomatoes, and _Uncinula necator_ in grapes [29; 30]. Proteomics, either alone [31; 32; 33; 34] or in combination with other omics technologies [35; 36; 37; 38], has made a substantial contribution in plant research during the past fifteen years, particularly in host-microbe symbiosis [39; 40] and plant-pathogen interactions [33; 41; 42; 43; 44]. In _Cucurbitacae_, proteomic applications have been used to study plant-pathogen interactions with viruses [45; 46; 47; 48], bacteria [49], and fungi [50; 51; 52; 53; 54; 55]. A de novo assembly of the _C. pepo_ subsp. pepo (zucchini cultivar) genome was released a few years ago as a high-quality draft, annotating 34,240 genes, 27,870 of which were protein coding [2]. This followed the earlier release of the first _C. pepo_ subsp. pepo (zucchini cultivar \"True French\") genome. The predicted proteome consisted of 33,966 primary protein sequences from translated transcripts and represented the leaf transcriptome of a zucchini cultivar used in breeding programs for disease resistance [3]. Proteomics studies on _C. pepo_-fungal interactions are limited in number, with studies mainly focusing on transcriptomics and selected gene expression analyses of obligate parasites such as _P. xanthii_[12; 50; 56; 57]. The aim of this study was to investigate early proteomic changes in the phyllosphere of a _C. pepo_ cultivar susceptible to powdery mildew after artificial infection with _P. xanthii_ (PX) conidia and assess the impact of _R. sachalinensis_ (RS) plant extract treatment. We used a nano-LC-HRMS/MS, Q-Exactive-Orbitrap approach to generate proteomes from the phyllosphere of four different treatment groups. Differentially Expressed Proteins (DEPs) were identified in each treatment, with varying levels of up- and down-regulation. Functional pathway enrichment analyses revealed common biochemical pathways, and identified several pathways enriched in treated versus Control groups, suggesting distinct early plant response mechanisms modulated by PX and RS. To our knowledge this is, thus far, the first report on _C. pepo_ whole phyllosphere proteomic profiles under _P. xanthii_ infection and _R. sachalinensis_ elicitor treatment.
## 6 Results and Comparison
### Performance Comparison
#### 6.3.1 Virtex 4 Comparison  
The proposed cryptographic accelerator design has demonstrated superior performance in terms of throughput/area and resource efficiency, particularly in low-resource environments such as IoT devices on Virtex 4 [15; 25; 26; 32]. In comparison to the reconfigurable BEC design specified in [25], the proposed pipelined architecture offers significant advantages, utilizing 84.8% fewer hardware resources for one PA and PD computation, and operating at a 4.073 times higher frequency. Furthermore, the addition of point halving to PA and PD computations increases hardware resource utilization by 85.2%. Similarly, the proposed non-pipelined architecture offers significant advantages, utilizing 85.3% fewer hardware resources for one PA and PD computation and operating at a 1.85 times higher frequency. The addition of point halving to PA and PD computations further increases hardware resource utilization by 85.7%  
In [26], two solutions are proposed. The first solution employs the FF Gaussian-based multiplier, and the proposed non-pipelined architecture achieves 2.65 times higher throughput/area while utilizing 74.2% fewer FPGA slices. The proposed 2-stage pipelined architecture achieves 3.84 times higher throughput/area, using 73.3% fewer hardware resources than the architecture in [26]. The second solution in [26] uses three parallel-connected Gaussian FF multipliers. Our experimental results show that the proposed non-pipelined architecture achieves 2.73 times higher throughput/area, and the proposed 2-stage pipelined architecture achieves 3.96 times higher throughput/area while utilizing 88.7% fewer resources.  
In [32], a digit serial pipelined multiplier architecture is proposed for the computation of PM. In comparison, we find that the proposed non-pipelined architecture uses 89.9% fewer hardware resources and provides 2.76 times higher throughput than [32]. However, the two-stage pipelined architecture uses 89.5% fewer hardware resources and achieves 4.012 times higher throughput/area.  
For less constrained applications, ref. [15] used a digit parallel multiplier, which consumes more hardware resources and provides less throughput/area than the proposed non-pipelined and two-stage pipelined architecture. These results illustrate the potential of the proposed designs to enhance the security and efficiency of cryptographic computations in various applications.
# Research on Slope Stability Prediction Based on MC-BKA-MLP Mixed Model
## 4 (M-C)-BKA-MLP Prediction Model  
### Stability Prediction Process  
1. Output variable calculation  
The index system based on the M-C strength criterion and the BKA-MLP model were used to predict the slope stability index-safety factor. The slope safety factor F, which was obtained by the solution of 458 groups of characteristic parameters based on the Bishop method, was taken as the output. The process of improving the MLP model using the BKA algorithm is shown in Figure 4.  
Figure 4: BKA-MLP model construction process.  
Figure 3: Kendall correlation coefficient matrix.  
Sample Input  
A total of 458 groups of slope samples were used as datasets to train and test the prediction model. Columns one to six corresponded to a total of six input variables from X\({}_{1}\) to X\({}_{6}\), and the last column corresponded to the output variables of the model.  
Sample division  
Based on the size of the dataset, it is divided into the training set and the test set according to 7:3; that is, the model learns the potential law of input mapping to output on 320 datasets and makes predictions on 138 sets of previously unseen data to evaluate the model performance.  
Cross verification  
In order to make full use of data resources and fully reflect the generalization ability of the model, the model is evaluated with the fivefold cross-validation method. During each iteration, the training set is randomly divided into five parts to obtain five subsets, four of which are used for model training and the other for testing the model, and the absolute error of the BKA-MLP model on the test subset is obtained. Repeat the above process, each time selecting a different subset of the test, and finally obtain the average absolute error, which is used as the error function in the optimization process.  
Parameter setting and optimization  
The number of kites is set to 20, the maximum number of iterations of the BKA algorithm is set to 50, and the learning rate is 0.5. The number of neurons in the hidden layer and the number of iterations are optimized by the BKA algorithm. The lower bound of the optimized number of neurons is 10, the upper bound is 100, the lower bound of the iteration number is 100, and the upper bound is 1000.  
Calculated output  
Four indexes, namely the mean absolute error (MAE), mean bias error (MBE), mean square error (RMSE), and determination coefficient (R\({}^{2}\)), were used to evaluate the predictive performance of the BKA-MLP model. MAE directly reflects the absolute difference between the predicted and true values, MBE reflects systematic bias, RMSE better reflects the model's performance in extreme cases, and R\({}^{2}\) represents the proportion of variability explained by the model; the greater the R\({}^{2}\), the more interpretable the model.
# From Climate Conditions to the Numerical Slope Stability Analysis of Surface Coal Mines
## 2 The Methodology and the Case Example of Greek Lignite Mines
In this work, Greek lignite mines are employed for the soil properties that represent fine-grained materials. [PERSON] et al. [31] recently discussed an overview of the geotechnical characterization of the soil materials found in Greek lignite mines. Nevertheless, these properties are not specific to a site but characterize this type of surface mine slope. Greek mines have been systematically exploited and were made extremely large and deep to increase lignite excavation production and efficiency. Exploitable lignite deposits are principally located in Western Macedonia (northern Greece) and secondarily in the Peloponnese (southern Greece) (Figure 2). In the first case, the central basin is Ptolemais-Amyntaio, while in the Peloponnese, the lignite basin is at the Megalopoli area. Greek lignite mines present a ground profile with an overburden zone of sterile materials (soil-rock deposits of several tens of meters) and a lower zone of exploitable lignite deposits. The sterile material primarily consists of marls and stiff or even hard clays, while weaker cohesive soils and sands are also observed, e.g., [32; 33]. As a result, soil mechanics (rather than rock mechanics) principles are central to stability issues. Overall, several researchers have analyzed the geologic formations, e.g., [33], and ground profiles of Greek lignite mines from a geotechnical perspective, e.g., [32; 34]. Due to the immense excavations, large and deep slopes are created during open-pit mining. Thus, slope stability is critical for safe and efficient lignite mining and the valorization of lignite mining areas after closure and abandonment. Moreover, the geotechnical characterization of the soils found on the slopes is vital for slope stability analysis. Several past failures demonstrate that slope stability has not always been rigorously evaluated, resulting in problems in the mines' safety and management [34], often related to rainfall events. Figure 2: Geographical distribution of lignite mining areas and Koppen–Geiger classification for Greece [35].
# Drought Propagation in Brazilian Biomes Revealed by Remote Sensing
## 2 Materials and Methods
### Study Areas and Drought Events
#### 2.1.2 Cerrado: The 2014 Drought  
The Cerrado (usually called the \"Brazilian savanna\") biome covers a large part of southeastern Brazil and its vegetation encompasses savanna formations as well other types, ranging from grasslands to forests [55]. The Cerrado is a seasonal ecosystem characterized by distinct dry and wet seasons [56], with a rainy season during the austral summer (December-February) [57]. The region was affected by an intense South Atlantic Convergence Zone (SACZ) episode during mid-to-late December 2013 [57]. There was a rainfall deficit until the end of the rainy season, in March 2014, and this scenario was repeated in 2015, which resulted in a scarcity of soil moisture [58]. Water supplies, agriculture, and energy generation were severely affected by this event, since the region is highly populated and contains the main hydropower plants in the country, numerous industrial centers, and irrigation fields [6]. The selected study area was located in a region roughly corresponding to the Upper Parana River basin. This area is predominantly covered by farming (77%), with some parts covered by forest vegetation (13%).
## 4 The Concept of GORBA for Fast Rescheduling
### Evolutionary Optimization  
Evolutionary Algorithms are inspired by the fundamental mechanisms of information processing of natural evolution especially _heredity_ and _selection of the fittest_. As with nature the scope of application is very wide. Information is coded on some sort of strings comparable to the chromosomes. The algorithmic counterpart of evolution works with a population of solutions called _individuals_, represented by one chromosome in most cases. Heredity is performed by the selection of two individuals acting as parents. They produce offspring by exchanging information of their chromosomes (crossover) and stochastic changes (mutation). Selection takes place when parents are selected or when the offspring have to compete for survival in the next generation. The latter can be performed among offspring only or among offspring and parents, resulting in an elitist algorithm where the best individual never dies. This is a wanted deviation from the paradigm, as we are interested in optimization rather than permanent adaptation.  
The main area of application of EAs covers tasks for which no exact or specialized solutions exist mainly because of their complexity. The most important precondition for EA application is the existence of an evaluation function serving as a fitness measure. It must allow the quantification of quality differences. Another precondition is the amount of time required to assess an individual, as EAs need to process some ten thousands of individuals or more depending on the application on hand. As the start population can be seeded with some already existing solutions for the same or a similar problem, the amount of generations and, hence, time required to come up with a good or at least feasible solution can be shortened. In our case, the solutions of the heuristics described in the previous section can serve as starting points for the evolutionary search. Nevertheless, EAs are known to improve solutions fast in the beginning, but to converge slowly in the end. This issue is discussed in the next section. For more information about EAs and their applications, the interested reader is referred to the following books [29; 30; 31].
## 3 Results and Discussion
### Correlation Between E-Nose and GC-MS-O  
To explore the correlations between E-nose sensor responses and the aroma-active compounds identified by GC-MS-O, a correlation heatmap was constructed using the steady values from each of the ten E-nose sensors as the X variables, and the average hedonic scores of aroma-compounds as the Y variables. The resulting heatmap provides a clustering overview of sensor responses in relation to key odorants, facilitating the identification of compound-sensor patterns specific to each ham type. As shown in Figure 10, the 10 sensors can be grouped into different clusters based on their responses. For instance, a cluster of three sensors (W1C, W3C, and W5C) exhibited negative correlations with certain aromactive compounds, including Hexanal (B1), Benzaldehyde (B4), Unknown compound 9 (UC9), and Unknown compound 10 (UC10). In contrast, the other seven sensors (W6S, W3S, W2S, W2W, W5S, W1W, and W1S) showed positive correlations with these compounds. According to the GC-MS-O results, these compounds were exclusively found in FZWH, indicating that the E-nose could effectively distinguish FZWH from other ham samples based on its specific responses to these aroma-active compounds. The findings align with the PCA results from the E-nose analysis. Additionally, the same sensor cluster (W1C, W3C, and W5C) exhibited negative correlations with 2-Noncal, (E)- (B8), Pentadecane (C4), Unknown compound 2(UC2), 3-Octanone (D3), and Unknown compound 1 (UC1), while the other seven sensors correlated positively with these compounds. The GC-MS-O results indicated that these compounds were unique to BHCH, demonstrating that the E-nose could similarly differentiate BHCH from other ham samples.  
The heatmap also shows that the 10 sensors have weak correlations with certain aromactive compounds, such as 2-Nonanone (D2), 2(3H)-Furanone, dihydro-5-propyl- (D9), 5-Butyldihydro-2(3H)-furanone (D8), 2,6-Dimethylpyrazine (H1), and Dihydro-5-pentyl-2(3H)-furanone (D6). This weak correlation may account for the discrepancies between the GC-MS-O and E-Nose results. Among the 10 sensors, the W5 sensor displayed a relatively high correlation with the aroma-active compounds identified by GC-MS-O. However, this correlation was not sufficient for comprehensive analysis. Ultimately, the E-Nose is a chemical measurement system that assesses the chemical properties of sample gases rather  
Figure 10: Correlation between aroma-active compounds and E-nose signals.  
than their odor characteristics [23]. A significant limitation of current E-Nose designs is the lack of specific odor sensors. To enhance the E-Nose's performance and mimic biological olfactory systems more accurately, incorporating additional sensors that respond specifically to aroma-active compounds is essential.
# Effect of Rock Abrasiveness on Wear of Shield Tunnelling in Bukit Timah Granite
###### Abstract  
The abrasiveness of rocks significantly influences the wear endured by excavation tools in tunnel boring machines (TBMs). In slurry shield TBMs, wear occurs not only in excavation tools but also in discharge pipes and pumps of slurry transport systems. To explore this phenomenon, this study investigated the effect of rock abrasiveness on the wear of both disc cutters and slurry discharge pipes in slurry shield TBMs. The radial wear of disc cutters and the thickness of slurry discharge pipes were measured and monitored at a TBM site in Singapore. The relationship between the weathering grade of Bukit Timah granite and the wear coefficient of disc cutters was analysed, and a correlation between the Cerchar Abrasivity Index values and the wear coefficient of 19 inch disc cutters was suggested. Additionally, the average wear rates of slurry discharge pipes used for Bukit Timah granite were determined based on the pipe thickness measurements. The wear rates of weathering grades G(I) to G(IV) of Bukit Timah granite were observed to be highly similar to each other, whereas the wear rates of G(V) grade and mixed ground were 1.55 times higher than those of the G(I) to G(IV) grades. The correlation between the slurry discharge velocity and the wear rate in slurry discharge pipes was thus derived. The accurate assessment of the effect of rock abrasiveness on both the wear of disc cutters and the wear rate in slurry discharge pipes, investigated in this paper, will highlight the need for efficient maintenance of the excavation tools in TBMs and encourage the development of various equipment wear prevention measures in order to minimize overall construction costs and time.  
abrasiveness; _CAI_; wear; disc cutter; slurry pipe; wear coefficient; Bukit Timah granite
## 2 Materials and Methods
U-Pb isotopic analysis was performed via laser ablation inductively coupled with plasma mass spectrometry (LA-ICP-MS) targeting oscillatory-zoned grains free of fractures and inclusions. Analytical conditions included the following: Laser system LSX-213 G2+ (beam diameter: 30 \(\mu\)m) (Teledyne CETAC Technologies: Bozeman, MT, USA), ICP-MS (Agilent 7800) (Agilent Technologies: Santa Clara, CA, USA). Standardization: Plesovice zircon (U-Pb ratios), 91500 zircon, and NIST 610-614 glasses (elemental concentrations). To minimize common Pb contamination, only concordant ages (90-100% concordance) were retained, whereas discordant data (<90% concordance) were excluded. Age calibration employed 207 Pb/206 Pb correction [37; 38; 39]. Data reduction utilized ICP-MS-Data-Cal for isotopic/elemental calculations and Isoplot 3.0 for concordia diagrams, probability density plots, and weighted mean ages. This protocol complies with international microbeam geochronology standards, ensuring data accuracy and cross-laboratory comparability. Detrital zircon LA-ICP-MS U-Pb dating was performed on 16 samples from 4 representative wells across structural domains (Northern Steep Slope: B8-2-a, B8-2s-b; Central Uplift: C3-1-a; and Southern Gentle Slope: KL1-1-a). Of the 614 analytical spots, 443 concordant points (concordance > 90%) yielded valid age constraints, representing a reliability rate of 72.1% (Table 2). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Well**} & \multirow{2}{*}{**Depth Range/m**} & \multirow{2}{*}{**Sampling Depth/m**} & \multicolumn{2}{c}{**Number of Concordant**} & \multicolumn{2}{c}{**Valid Data**} & \multirow{2}{*}{**Percentage**} \\  & & & **Points** & & **(Conordance \textgreater{} 90\%)** & \\ \hline \multirow{2}{*}{C3-1-a} & 3450 & 3450 & 93 & 61 & 65.6\% \\  & 3515 & 3515 & 109 & 80 & 73.4\% \\ \multirow{2}{*}{KL1-1-a} & 2670 & 2670 & 92 & 70 & 76.1\% \\  & 2775 & 2775 & 105 & 64 & 61.0\% \\ \multirow{2}{*}{B8-2-a} & 3164–3167 & 3165 & 92 & 67 & 72.8\% \\ \multirow{2}{*}{B8-2s-b} & 3190–3195 & 3190 & 123 & 101 & 82.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical Information of Detrital zircon LA-ICP-MS U-Pb dating Samples.
## 2 Background Concept and Problem Modeling
### Collision Detection in Workspace Using the Oriented Bounding Box (OBB)  
The definition of workspace \(\mathbf{W}\) is a space where an actual robot operates. In many cases including the robot manipulator, \(\mathbf{W}\) space is in three-dimensional Euclidean space \(\mathbb{R}^{3}\). To confirm that \(\bar{q}_{t}\in\mathbf{Q}_{\text{\tiny{int}}}\), collision detection methods have to be applied in workspace \(\mathbf{W}\)[21; 23]. To this end, the oriented bounding boxes (OBB) [24] is employed for collision detection. In OBB, both the robot arm and obstacles are modeled as a box. Because the modeling is implemented in OBB, the collision checking between 2 boxes only has 15 cases: three faces of the first box, three faces of the second box, and nine edge combinations between the first box and second box [25]. Based on two box checking, the higher level checking between one robot and one obstacle can be implemented as a repeated two OBB checking [26].
## 3 Results and Discussion
### Differences in Volatile Compounds of Large Yellow Croaker Under Different Storage Conditions
#### 3.5.3 Amino Acid Degradation Pathway  
The amino acid degradation pathway during storage of the large yellow croaker is shown in Figure 4. While changes in meat odor are often attributed to lipid oxidation, amino acid degradation also plays a significant role in the deterioration of fish during storage [31]. The analysis of the three treatment groups revealed several sulfur- and nitrogen-containing compounds with relatively small peak areas, such as 2-phenylethyl ester, pyridine, and 3,\(\mathcal{A}\)-dimethylbenzene. These results suggest that the applied storage treatments--cold storage (CS), slurry ice (SI), and crushed ice (CI)--may have effectively suppressed amino acid degradation in large yellow croaker. According to Table 2, the volatile compounds also did not produce substances with an unpleasant odor. Typically, odors resulting from amino acid degradation stem from protein degradation, leading to the production of sulfur- and nitrogen-based compounds. This process involves enzymes breaking down proteins into various amino acids (Figure 4), which are then further decomposed through deamination, desulfurization, and decarboxylation reactions to yield organic acids, sulfur compounds, ammonia, amines, and hydrocarbons. Based on the changes in the relative content of volatiles observed (Table 2), it was evident that the compounds derived from amino acid degradation were present in minimal amounts across the three treatments during the storage of large yellow croaker. This suggests that lipid oxidation was the primary contributor to the characteristic volatile profile of large yellow croaker during storage, while the contribution of amino acid degradation was relatively minor.  
Certain compounds have been widely reported to play key roles in the odor deterioration of large yellow croaker fillets. As illustrated in Figure 4, the potential formation pathways of the characteristic volatile compounds identified in this study are summarized based on the relevant literature and the exploration of volatile substances in this experiment. These pathways primarily include amino acid degradation, lipid oxidation, and thermal reactions, which are commonly associated with spoilage and processing-induced changes in fish products.