esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Differential Accumulation of Terpenoids  
Using GC-MS, LC-MS (POS), and LC-MS (NEG), differential metabolites in the metabolic pathway were identified and screened. In YJ, VOCs accumulate from the flower bud stage, and their content progressively increases, peaking during the blooming period, followed by a gradual decline. The detected substances are primarily sesquiterpenes and monoterpenes. After screening, a visual analysis using bar charts revealed a significant accumulation of terpenoid VOCs in YJ, especially in YJ2 and YJ3, including terpineol, cis-muurola-4(15),5-diene, (-)-myrtenol, pinene, bicyclo[3.1.0]hexane, 4-methylene-1-(1-  
Figure 3: Volcano plots (**A**) YJ2 vs. YJ1; (**B**) YJ3 vs. YJ2; and (**C**) YJ4 vs. YJ3 and a Venn plot (**D**) showing differentially accumulated metabolites in YJ petals, specifically at different developmental stages, detected using GC–MS.  
methylethyl)-, myrtenyl acetate, trans-linal oxide (furanoid), 2,3-dehydro-1,8-cineole, and linalool. Among these, the contents of (-)-myrtenol, linalool, pinene, myrtenyl acetate, and terpineol are significantly higher than that of other terpenoids, indicating that they are key floral aroma components in YJ (Figure 4 and the chromatogram is shown in Supplementary Figure S5).  
In LC-MS (POS), a multitude of compounds were found to accumulate in NW, particularly during the NW1 and NW4 stages, including substances such as p-mentha-1,3,8-triene, ganodermic acid TQ, maslinic acid, piperitone, betulinic acid, and 3-trans-p-counaroyltoutamic acid. However, the expression levels of ganodermic acid TQ, juvenile 2, maslinic acid, squamolone, betulin, piperitone, and betulinic acid are low in NW2 and NW3. In YJ, high expression levels were observed for p-mentha-1,3,8-triene, juvenile 2, squamolone, and betulinic acid, while ent-16a-hydroxy-17-acetoxy-19-kauranal was specifically expressed in YJ4. Conversely, betulin exhibited specifically low expression levels in YJ2 and YJ3 (Supplementary Figure S6). During the flower bud stage, YJ and NW accumulate a high count of non-volatile substances.  
The LC-MS (NEG) and LC-MS (POS) patterns were consistent, indicating that a substantial quantity of metabolites accumulated predominantly in NW. For instance, 9,10-epoxyoctadecenoic acid, 6 beta-hydroxyasiatic acid, ursolic acid, oleanolic acid, resveratrol, and hypogeic acid accumulated significantly in either NW1 or NW4 samples. Additionally, 10E,12Z-octadecenoic acid and eicosapentaenoic acid accumulated in YJ2, YJ3, and YJ4 (Supplementary Figure S7). Among these metabolites, ursolic acid was particularly abundant in this detection mode. The accumulation levels of this substance are similar in the bud stages of YJ and NW flowers, but the content of ursolic acid in the YJ flower bud stage rapidly decreases.
# Resilience Assessment for the Northern Sea Route Based on a Fuzzy Bayesian Network
## 3 Model Established for NSR Resilience Measurement
### Belief Propagation Analysis  
Bidirectional reasoning and probabilistic inference can be performed in the Bayesian network by updating evidence formation, which is called \"propagation analysis\" [76]. Generally, propagation analysis in Bayesian networks includes forward-propagation analysis and backward-propagation analysis. The former is basically a cause-to-effect analysis in which the parent nodes measure their impact on the child nodes, and the latter is widely applied to update the probability distribution of the parent nodes for certain specified states of the target nodes.  
As with forward-propagation analysis, three disruptive scenarios are defined to observe the corresponding impacts on system resilience and the decomposed capacities. The scenario description and BN inference results are presented in Table 14. In Scenario 1, it is observed that there is a lack of skilled seafarer teams (the state of (R1-2) is _false_), which reduces the absorptive capacity of system resilience from 93.4% to 89.2%; eventually, the resilience of the system descends to 78.1% from 78.5% in the base model. Scenario 2 simulates the case of two contributing factors in which the skilled seafarer team and the information prediction service are unavailable (the states of (R1-2) and (R2-2) are set as _false_). The results show that both the adaptive capacity and system resilience are reduced, the former from 98% to 94% and the latter from 78.5% to 77.8%, respectively. In Scenario 3, another contributing factor is considered on the basis of Scenario 2, that is, the state of a new factor, namely, the rescue and anti-pollution facility (R3-1) is further defined as false.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**State of the Post-Disruption Capacity** & **True** & **False** \\ \hline Expression & LC \(\times\) 0.80 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Conditional calculation of the recovery capacity.  
Figure 9: The base model of BN used to measure the resilience of the NSR.  
After simulating the developed BN model, the results indicate that the resilience of the NSR suffers from the most notable effect, decreasing to 75.9%, while the restorative capacity is lowered to 65.7% from 88.2%. A general insight obtained from the forward-propagation analysis indicates that all individual capacities are critical for the development of the resilience of the NSR.
# Machine Learning Powered Microalgae Classification by Use of Polarized Light Scattering Data
###### Abstract  
Microalgae are widely distributed in the ocean, which greatly affects the ocean environment. In this work, a dataset is presented, including the polarized light scattering data of 35 categories of marine microalgae. To analyze the dataset, several machine learning algorithms are applied and compared, such as linear discrimination analysis (LDA) and two types of support vector machine (SVM). Results show that non-linear SVM performs the best among these algorithms. Then, two data preparation approaches for non-linear SVM are compared. Subsequently, more than 10 categories of microalgae out of the dataset can be identified with an accuracy greater than 0.80. The  
states of microalgae [13; 14], cancerous tissues [15], atmospheric microparticles [16], and microplastics [17].  
[PERSON] et al. [18] demonstrated the potential of using the polarized signal to analyze biogenic and highly refractive particles in coastal waters. [PERSON] et al. [19] used polarized light scattering measurements to characterize particle size and composition of natural assemblages of marine particles. Based on the pre-trained model, the states and categories of particles can be recognized from the mixture. [PERSON] et al. [20] quantitatively studied the flocculation process with polarized light scattering. [PERSON] et al. [21] applied polarization parameters to recognize different states of _Microcytsis aeruginosa_, and gave an early warning strategy. In short, these works show the significance and value of a polarized light scattering dataset. However, there is still no such dataset of the diverse microalgae, which limits the optical polarization tools' applications in monitoring the microalgae in water.  
In this work, a dataset by polarized light scattering measurement is presented, including the information of polarization parameters of 35 categories of marine microalgae. For each category, 10 states of polarization (SOP) of incident light are applied to respectively illuminate the samples, and for each SOP, there are more than 1000 records of the particles. To analyze the dataset, several machine learning algorithms are applied and compared to build the classifier which is used to identify different categories. This work compares linear discrimination analysis (LDA) and different types of support vector machine (SVM). Results showcase that non-linear SVM performs the best among these algorithms. Then, two data preparation approaches for non-linear SVM are compared. Subsequently, we show that more than 10 categories of microalgae out of the dataset can be identified with an accuracy greater than 0.80. With this proposed technique and the dataset, these microalgae can be well differentiated by polarized light scattering.
## 4 Self-Healing Techniques  
### Expansive Agents and Mineral Admixtures  
At the micro-scale, the self-healing concrete can repair the formed cracks, while restoring the mechanical properties of the cement-based materials. The generation of self-healing concrete, in line with the sustainable development, has involved multiple studies into the natural healing process via bacteria, alongside chemical and autogenous self-healing [31; 32]. Furthermore, self-healing concrete can be improved through the integration of the microorganisms, hollow fibers, microencapsulation, polymers, mineral admixtures, and other expansive materials [31].  
Additional hydration of the unreacted cement component, expansion of the hydrated cementitious network matrix, obstruction of the cracks with the impurities in water, and formation of CaCO\({}_{3}\) are the mechanisms that can lead to the organic occurrence of the autonomous healing [33]. Moreover, the self-healing properties in concrete can be generated via the utilization of various geo-materials, polymers, and chemical admixtures [31]. Other form of self-healing properties in concrete can be achieved through the calcium carbonate precipitating microorganisms [34; 35]. The ability of concrete to autonomously repair itself can be enhanced by the amalgamation of mineral admixtures and cementitious materials with expansive properties. Nevertheless, direct contact of cement with water must be prevented from prematurely expanding and thereafter degradation of concrete.  
Cracks formed at the micro-scale can be filled via the calcium and magnesium silicates that dissolve in water, thus, making the pore solution of concrete [36]. However, these methods do not repair cracks in their entirety; rather, the plugging effect diminishes the size of the cracks. Through the tensile preloading, high-strength concrete is damaged; these simulate the crack formations, subsequently demonstrating the self-healing properties when exposed to water and moist environments [37]. The cementitious composites can be incorporated with the synthetic fibers to achieve complete healing of the simulated cracks [38]. The crack formations at the micro-scale have autonomously been repaired more efficiently via the use of supplementary cementitious materials (SCMs) [33]. Improved self-healing of the cracks in concrete and a reduction in the cement consumption are the primary benefits of these SCMs [39]. Additionally, high-performance fibers can be used to reinforce such cementitious composites [40].
# Brain Extraction Methods in Neonatal Brain MRI and Their Effects on Intracranial Volumes
## 2 Materials and Methods
### Manual Segmentation for Brain Extraction  
To create a reference brain mask for each image volume, a strictly defined, fully manual procedure was followed using the 3D Slicer (version 5.2.2) software [38]. The mask was drawn slice by slice in the axial plane, considering the cerebral contour [39], excluding non-brain structures, such as the neck, ears, eyes, scalp, and skull, with reference to brain atlases [40; 41], using the original images without preprocessing as input. Each brain mask was also divided into stack of slices, creating three masks for each BE method, from the bottom to the top of the brain, to assess whether the methods produced different results depending on the anatomical region. The _bottom mask_--slices 1 to 11--covered the base of the skull, and part of brain stem and cerebellum (inferior limit: first slice, level of the spinal cord; superior limit: level of the pons, when the ocular globes and the lenses are visible). The _middle mask_--slices 12 to 22--included subcortical structures such as the basal ganglia (superior limit: below the level of the _centrum semiovale_). The _top mask_--slices 23 to 33--covered the _centrum semiovale_ and the vertex (superior limit: top of the skull).  
Rater 1 (T.F.V.) performed all segmentations, which were validated by another two experienced raters (rater 2--N.C.M. and rater 3--H.A.F.). To evaluate intra-rater reliability, rater 1 re-segmented six randomly chosen subjects from previously segmented data after one month. Inter-rater accuracy was assessed including segmentations from raters 2 and 3, which manually outlined six randomly selected subjects from the dataset.
## 3 Data and Methods  
### Data  
The RapidEye constellation-based optical remote sensing satellite system possesses unique capabilities, consisting of five satellites with identical parameter configurations. These satellites operate in a sun-synchronous orbit, enabling the imaging of any area on Earth within a single day, with a revisit period as short as one day. Each satellite is equipped with a five-band multi-spectral optical imager featuring a swath width of 80 km and a spatial resolution of up to 5 m. The RapidEye satellites accumulated a substantial archive of images over nearly 11 years of operation. Satellite parameters are detailed in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Orbital Altitude & 630 km \\ Number of Satellites & 5 \\ Orbit Inclination & 97.9\({}^{\circ}\) \\ Revisit Period & \textless{}1 day \\ Swath Width & 77 km \\ Spatial Resolution & 5 m \\ Band 1 & Blue (440-510 nm) \\ Band 2 & Green (520-590 nm) \\ Band 3 & Red (630-685 nm) \\ Band 4 & Red edge (690-730 nm) \\ Band 5 & NIR (near infrared 760-850 nm) \\ Imager & Multi-spectral push-broom imager \\ Time on Orbit & \textgreater{}7 years \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of RapidEye.  
Figure 1: Study area and corresponding RapidEye images. (**a**) Study area and image coverage. The blue rectangles show the coverage of the RapidEye images. The stars are the epicenters of the 2019 Ridgecrest earthquake from the USGS. The magenta rectangles are Sentinel-1 coverage. The red lines indicate the active faults. (**b**) True-color RapidEye images over the study area.
## 2 Materials and Methods
### Transition Transport Conditions  
#### 3.2.1 Straight-Line Acceleration Scenarios (High Adhesion Pavement)  
The adhesion coefficient of the road surface while the sprayer was performing linear acceleration on the pavement was set at 0.8, and its speed was increased from 5 km/h to the designated desired vehicle speed of 20 km/h. In Simulink, the simulation time was set to 10 s with a simulation step size of 0.001 s. The simulation results are shown in Figure 12a-d, and representative parameters of the simulation results are summarized in Table 5.  
The analysis results indicate that under high-adhesion road conditions, the control system effectively determines wheel slip rates during straight-line acceleration. As shown in Figure 12a, the sprayer's speed increases smoothly and steadily without overshooting after the introduction of the anti-slip control module. Figure 12b shows that without control, the sprayer's trajectory deviation is significant, approaching 2.5 m at the end of the 10 s simulation. With control, the deviation is nearly eliminated, ensuring effective straight-line driving. The analysis of Figure 12c,d and Table 5 reveal that without control, the slip  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Evaluation Indicators** & **No Control** & **PID + Average Distribution** & **SMC + Optimal Distribution** \\ \hline Steady-state longitudinal & 2.56\% & 1.17\% & 1.19\% \\ velocity error rate/- & 43.61 & 17.60 & 16.62 \\ Front-wheel peak slip rate/\% & 40.63 & 17.06 & 17.35 \\ Trajectory deviation/m & \(-\)2.38 & 0.00 & 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of parameter indexes under the condition of transfer field transportation–straight line acceleration.  
Figure 12: State parameters of sprayer under the condition of transfer field transportation–straight line acceleration: (**a**) longitudinal speed; (**b**) driving path; (**c**) front-wheel slip rate; (**d**) rear-wheel slip rate.  
rates of both the front and rear wheels exceed 0.4, indicating severe wheel slip. Using the \"SMC + optimal distribution\" method reduces the peak slip rate of the front wheels to 0.1662, while the \"PID + average distribution\" control method is slightly better for the rear wheels, controlling the peak slip rate at 0.1706.
# Distribution of Operating Costs Along the Value Chain of an Open-Pit Copper Mine
## 2 Materials and Methods
### Estimation of Operating Costs
#### 2.4.4 Hauling  
The hauling fleet comprised 40 trucks, with three payload capacities of 40, 60, and 100 tons, a fill factor of 0.9, and an availability rate of 85%. The truck cycle time for both pit-to-waste dumping and pit-to-stockpiles/Run-of-Mine (ROM) pad routes was considered fixed at approximately 46 min based on empirical observations. Each route spans about 3.4 km, which is assumed to be constant given the almost unchanging location of the pit loading point. Annually, an average of 48,000,000 tons of material was hauled, i.e., 10,500,000 tons of ore and 37,500,000 tons of waste. This represents approximately a 9% reduction compared to the annual volume of material loaded due to compaction and minor spillage losses during hauling.  
The hauling operating cost was calculated using the following equations:  
\[K_{Ht}=K_{Hmr}+K_{Hlab}+K_{Hf}+K_{Hold}+K_{Hc}+K_{HO} \tag{7}\]  
\[OPEX_{H}=\frac{K_{Htc}}{M_{H}} \tag{8}\]  
where \(K_{Ht}\) is the total cost of hauling (USD/year); \(K_{Hmr}\) is the cost of maintenance and repairs (USD/year); \(K_{Hlab}\) is the labor cost inclusive of salaries and wages for hauling operators and technicians (USD/year); \(K_{Hf}\) is the cost of fuel associated with hauling (USD/year); \(K_{Hcd}\) is the cost of equipment depreciation (USD/year); \(K_{Hc}\) is the cost of consumables (i.e., lubricants, spare parts, etc.) associated with hauling (USD/year); \(K_{HO}\) is the other costs (USD/year); \(OPEX_{H}\) is the loading operating cost (USD/ton); \(K_{Htc}\) is the total cost inclusive of the 10% contingency (USD/year); and \(M_{H}\) is the annual volume of material hauled (tons/year).
###### Abstract
Thus, it is fundamental to chemically characterize the treated effluents throughout the process to understand which products and in what concentrations they are formed and, in parallel, to understand the ecotoxicological effects of ozonation, to consistently appraise if this process is efficient in reducing the environmental hazardous potential of treated wastewater [18; 19; 20; 21]. Alternatively, to reduce the by-products formation, separation processes can be considered, such as adsorption or membrane processes. While such a holistic approach considering both the assessment of effects of ozone-treated effluents to the aquatic biota and chemical characterization has been addressed [18; 22], their hazardous potential to human health has been largely neglected, especially if one considers the actual assessment of toxicological effects of treated water in relevant biological models. However, this is a critical aspect, especially when considering water reclamation, where ozonation has been considered a promising treatment [15]. Within this overall context, the present work aimed to understand whether ozonation applied to degrade PPCPs in wastewater is efficient in reducing their human health hazardous potential. For this purpose, synthetic effluent solutions with PPCPs that are commonly found in treated wastewaters, namely methyl- and propylparaben, paracetamol, sulfamethoxazole, and carbamazepine [18], were prepared (using PPCPs in mixture, with different combinations or individually) and then treated with ozonation. The aim of the work was tackled by comparing the toxicity of untreated and treated solutions to human skin keratinocytes (HaCaT cell line), providing evidence of the human health safety of reclaimed water use. The concentrations of the tested PPCPs on WWTP effluents differ widely. For instance, concentrations of paracetamol, sulfamethoxazole, and carbamazepine between 10 and 100 \(\upmu\)g/L have been reported [23]. In the present work, the maximum tested concentration of each contaminant was set at 1 mg/L, and dilutions up to 12.5% were considered, aiming both to comply with the analytical methods and to allow measurable effects on the cytotoxicity to the tested cell line, while following other studies (e.g., [24]), accounting for a worst-case scenario.
## 2 Materials and Methods
### Datasets
#### 2.1.3 Contourlet Transform  
The traditional Discrete Wavelet Transform (DWT) domain has limited directional information as only along with horizontal, vertical, and diagonal dimensions. On the other hand, the Contourlet transform has a variety of arbitrary shapes and contours that are not limited to three dimensions. The Contourlet transform is executed on the normalized B-mode images which decouple the multiscale and the directional decompositions using a filter bank [7].  
The conceptual theme of a Contourlet transform is the decoupling operation that comprises a multiscale decomposition executed as pyramidal decomposition by a Laplacian pyramid and a following directional decomposition by engaging a directional filter bank. Fundamentally, the Contourlet transform is constructed by the grouping of nearby wavelet coefficients, since they are locally correlated to ensure the smoothness of the contours. Therefore, a sparse expansion is obtained for natural images by first applying a multi-scale transform, followed by a local directional transform to gather the nearby basis functions at the same scale into linear structures. Thus, it establishes a wavelet-like transform for edge detection and then a local directional transform for contour segment detection. The overall result is similar to an image expansion using basic elements that are more likely contour segments, and thus the name Contourlets. Performance comparison of DWT and Contourlet transform in terms of a better descriptor of contour segments are  
Figure 2: Example of binarization and ROI segmentation where the first row is the benign case, and the second row is the malignant case. (**A,D**) Normalized B-mode US images; (**B,E**) binary images, and (**C,F**) lesion boundary outlined automatically.  
shown in Figure 3. It is observed that, for DWT, the contour detection is performed with limited three dimensions, and the detection becomes fader with the increase of decomposition levels. On the other hand, for Contourlet transform, contour detection with a wide range of 32 dimensions and detection become smoother with the increase of pyramidal decomposition levels. In DWT coefficient images, the tumor shadowing effect is not visualized whereas in Contourlet transformed coefficient images the shadowing effect is visualized. Moreover, from the literature [7], the Contourlet transform can provide a better description, arbitrary shapes, contours, and more directional information. In addition, the directional decomposition levels contain a variety of directions which is not fixed, and the directional sub-bands increase along with the increase of the pyramidal decomposition levels.  
Figure 3: Discrete Wavelet Transform (DWT) versus Contourlet scheme: illustrating the successive refinement of these two different multi-resolution transform domains near a smooth contour.