esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
_Astragalus__monglicus_, an important medicinal plant species, exhibits low tolerance to high-salt environments, which restricts its growth in saline-alkaline areas. Understanding its salt-tolerance mechanisms is crucial for overcoming the technical challenges of industrialized cultivation in these regions. However, studies on the salt-tolerance mechanisms of _Astragalus__monglicus_ are limited. This study examines two _Astragalus__monglicus_ germplasms with distinct differences in salt tolerance (LQ: salt-tolerant, DT: salt-sensitive), and investigates their physiological adaptations and molecular mechanisms under salt stress (200 mmol/L NaCl) using an integrated analysis of morphology, physiology, metabolomics, and transcriptomics. Specifically, LQ showed smaller reductions in plant height, root length, root thickness, and fresh weight (29.0%, 5.0%, 2.8%, and 22.3%, respectively), compared to DT, which exhibited larger reductions (42.9%, 44.9%, 46.3%, and 41.4%, respectively). The results indicated that the salt-tolerant germplasm (LQ) enhanced antioxidant enzyme activities in response to salt stress, including SOD, POD, and CAT, and accumulating osmoregulatory substances. In LQ, the activities of SOD, POD, and CAT increased by 22.8%, 10.9%, and 8.8%, respectively, significantly higher than those of DT, which showed increases of 2.9%, 8.5%, and 1.4% in SOD, POD, and CAT activities, respectively. The contents of soluble sugar and protein in LQ increased by 2-fold and 16.9%, respectively, compared to 67.0% and 18.8% increases in DT. Additionally, the levels of MDA, H\({}_{2}\)O\({}_{2}\), and OFR in LQ showed smaller increases (14.7%, 41.0%, and 13.6%, respectively), compared to the larger increases observed in DT (58.0%, 51.2%, and 18.6%), indicating a reduced level of oxidative damage in LQ and enhanced tolerance to salt stress. Combined transcriptomic and metabolomic analyses revealed that 3510 differentially expressed genes (DEGs) and 882 differentially expressed metabolites (DAMs) were identified in the leaves of salt-tolerant germplasm LQ under salt stress, whereas the sensitive germplasm DT had 1632 DEGs and 797 DAMs, respectively. Differential genes and metabolites were involved in metabolic pathways such as flavonoid biosynthesis, isoquinoline alkaloid synthesis, and phenylalanine metabolism. In particular, LQ alleviated salt stress damage and enhanced salt tolerance by increasing oxidase activities in its flavonoid and phenylalanine metabolic pathways and regulating the expression of key genes and enzymes. This study provides valuable insights and empirical data to support the selection of appropriate _Astragalus__monglicus_ germplasms for saline regions and the development of improved cultivars.  
_Astragalus__monglicus_; salt stress; salt tolerance; metabolome; transcriptome This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 2 Experiments Details
### Optimization of the Slot Waveguide  
The electric field intensity in the vertical medium interface at the sidewall will be modified by the factor of \(\frac{n_{Si_{3}N_{4}}^{2}}{n_{CSe-PMMA}^{2}}\) because of the electric displacement continuity boundary condition (\(D=\varepsilon E\)) for the horizontal dielectric. Then, the light can be confined within the nanoscale low-refractive index slot, contributing to the high light intensity. A schematic image of the slot waveguide is shown in Figure 5a. Taking advantage of the high electric field intensity, the interaction between the signal light, pump light, and gain medium will be promoted. The stronger the ability of the slot waveguide to limit the light field, the stronger the interaction with the gain medium, and greater gain can be achieved. Meanwhile, a smaller effective cross-sectional area of the slot waveguide can facilitate a reduction in the threshold power of EDWA. Therefore, we optimized the size of the slot waveguide in terms of both the ability to confine the light and the effective cross-sectional area. We filled the NaYF\({}_{4}\)/\(\beta\)-NaLuF\({}_{4}\): Er, Yb NCs-PMMA into the slot as gain medium and measured its refractive index with an ellipsometer, as shown in Figure 5b. The measured values of the core layer were 1.512 at 1530 nm and 1.52 at 980 nm, respectively.  
The finite element method (FEM) was used to analyze the modal profiles of the Si\({}_{3}\)N\({}_{4}\) slot waveguide with a height (H\({}_{\rm r}\)) of 450 nm, width (W\({}_{\rm r}\)) of 450 nm, and slot width (W\({}_{\rm s}\)) of 200 nm. The optical field distributions under TE and TM polarization at 1530 nm are shown in Figure 6a,b, respectively. The optical field distributions under TE and TM polarization at 980 nm are shown in Figure 6c,d respectively.  
Figure 5: (**a**) Schematic of slot waveguide; (**b**) The measured refractive index curve of core material.  
Figure 6: The modal profiles of Si\({}_{3}\)N\({}_{4}\) slot waveguide (**a**) TE polarization at 1530 nm; (**b**) TM polarization at 1530 nm; (**c**) TE polarization at 980 nm; (**d**) TM polarization at 980 nm.  
Under TE polarization, the slot can confine the light well, while more light fields leak into the upper and lower cladding under TM polarization. This was not conducive to the interaction between the light field and the gain polymer. Therefore, we only considered the TE polarization mode, and the related parameters under the TM polarization mode in this paper were only used for reference and comparison.
# Evolution of Hemodynamic Parameters Simulated by Means of Diffusion Models
## 2 Experimental Data  
The experimental data used in this study were sourced from the AMULET research project [14], under which a multicenter, prospective, randomized, open-label and controlled, parallel group trial was conducted (ClinicalTrials.gov Identifier: NCT03476590) at nine locations throughout Poland. In total, 605 patients with heart failure were recruited. To our study, we examined electrocardiogram (ECG--electrocardiogram) and impedance (ICG--impedance cardiogram) curves recorded with the use of an ICG device (Cardioscreen 2000, Medis, Illmenau, Germany). This non-invasive diagnostic method allows one to collect a set of specific hemodynamic parameters, such as: heart rate (HR--heart rate), diastolic and systolic blood pressure (DBP--diastolic blood pressure, SBP--systolic blood pressure), stroke volume (SV--stroke volume), and thoracic fluid content (TFC--thoraric fluid content). In our analysis, TFC has been used as an illustrative example. The value of TFC is the inverse of chest impedance with unit (1/Ohm).  
TFC values were measured during a clinical examination of 605 patients, performed in a relaxed, seated position [14]. The total number of measurements made is 2860, with the number of individual patient observations differing for each patient within the measurement set. The results registered are illustrated in Figure 1. At least half of the patients were investigated several times, with the observation period lasting for up to 12 months per patient.  
All intervals of the investigated TFC value have been divided arbitrarily into 15 discrete value slots, as shown in Figure 1. The assumed number of slots must be greater than five to allow statistical analysis (especially \(\chi^{2}\) tests) but not too high to avoid complexity of calculations. With computer calculation, we estimate that 15 slots allow an acceptable accuracy of calculations. The time series was registered as follows: due to the irregular flow of the measured data, we adopted a registration period that was 31 days long for each slot. The result of the measurement was placed inside a given slot if number of days between the sequenced examination of the patient remained within the k \(\pm\) 0.5 range, with k being the duration of the registration period between measurements. Each slot includes hits counted during measurements registered over a five-month period. Hits registered during each single measurement period are illustrated below.  
One can observe that, for the increasing \(\Delta\)t, the total number of TFC hits registered tends to have the same stationary distribution of TFC, as shown in Figure 1. We assumed that the period of 5 months during which the measurements were made (see Figure 2 and comments to Figure 1) is sufficient for proper estimation of stationary distribution.
# Improving Ship Maneuvering Safety with Augmented Virtuality Navigation Information Displays
## 2 Methodology
### Detailed Assumptions of Simulation Study (Simulation Scenarios)
#### 2.3.2 Scenario 2. General Cargo Ship  
The simulation test scenario assumed that experts would perform a series of simulation trials consisting of safe berthing at an angle to the shore of the general cargo ship Arklow River, using a spring line, to Chemikow Quay in the port of Swinoujscie (Figure 4).  
The detailed assumptions of Scenario 2 were as follows:  
* Objective to be achieved: The lowest resultant value of the speed at the moment of contact of the ship's hull with the fender while achieving the shortest possible maneuver time.
* Main ship parameters: \(\circ\)  LOA = 90 m, breadth = 14 m, draught = 5.7 m; \(\circ\)  Displacement = 5906 t;  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Displays** & **Series** & **Number of Trials** \\ \hline Visual display & 1\_Visual & 20 \\ ECDIS & 1\_ECDIS & 20 \\ Pilot\_precise & 1\_Pprecise & 20 \\ AV & 1\_AV & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Display types and number of trials for the container ship scenario.  
Figure 4: The situational-navigational plan of Scenario 2 for general cargo ship berthing. Red—initial position, black—destination.  
\begin{tabular}{c c} \(\circ\) & Block coefficient = 0.853; \\ \(\circ\) & Main engine: 1 \(\times\) diesel, total power = 1499 kW; \\ \(\circ\) & Max speed = 12 kn; \\ \(\circ\) & Type of propulsion: 1 \(\times\)cpp, counter-clockwise; \\ \(\circ\) & Number of rudders: 1; \\ \(\circ\) & Max rudder angle = 45.0 deg.; \\ \(\circ\) & Number of thrusters: 1; \\ \(\circ\) & Bow thruster power = 250 kW. \\ \(\bullet\) & Navigational-maneuvering conditions: \\ \(\circ\) & the initial position of the ship: 30\({}^{\circ}\) angle, distance of one length and two breadths of the ship from Chemikow Quay; \\ \(\circ\) & the ship's final position: Chemikow Quay; \\ \(\circ\) & initial SOG: longitudinal forward v\({}_{\rm w}\) = 0.5 kn, transverse v\({}_{\rm p}\) = 0.0 kn; \\ \(\circ\) & heading: HDG = 159\({}^{\circ}\). \\ \(\bullet\) & The criteria used for assessment: \\ \(\circ\) & Primary: the value of the resultant speed of the ship determined at the moment of contact with a fender; \\ \(\circ\) & Secondary: efficiency of maneuvering—time length of maneuvers. \\ \(\bullet\) & The moment of measuring significant parameters: contact of the hull of the ship with a fender. \\ \end{tabular}  
The types of displays used, the name of the series and the number of trials in a given series of the Scenario 2 are presented in the Table 3.
# A Review of Explainable Deep Learning Cancer Detection Models in Medical Imaging
## 2 Background
### Taxonomy
#### 2.1.4 Model Agnostic vs. Model Specific  
Model agnostic refers to explanation methods that are able to explain any model and are not restrained to a certain type (e.g., SHAP). A common approach for model-agnostic methods is to change the inputs and measure the corresponding change in output. Then to use this to determine what change in inputs produces the greatest change in outputs. Model specific explanation methods only work with a specific model. For example, Grad-CAM [84] produces feature visualizations for convolutional neural networks, but does not work with LSTMs. These methods often use certain aspects of model architecture, for example feature maps produced from graph convolutions.
### Support Vector Machine (SVM) Algorithm  
Vapnik invented the SVM in 1995 [55], and it is a popular and successful learning algorithm for the classification of linear and nonlinear regression problems. The SVM algorithm delivers reliable prediction outcomes and is practicable for high-dimensional feature spaces, is robust and has good noise resistance [56; 57]. In many disciplines, many effective SVM implementations with classification and regression issues have been documented [58; 59; 60]. The following is a summary of SVM's basic theory.  
As illustrated in Figure 5, a training set {(u\({}_{k}\), v\({}_{k}\)), \(k\) = 1,2,......, \(n\)} is chosen for an SVM model, where u\({}_{k}\) = [u1\({}_{k}\), u2\({}_{k}\),......, u\({}_{nk}\)] \(\in R^{n_{k}}\) is the input data, v\({}_{k}\)\(\in R^{n_{m}}\) is the output data corresponding to u\({}_{k}\), and n is the number of training samples. The goal of the SVM is to identify an optimal hyperplane function _f(x)_ (defined by the weight vector w and the offset \(b\)), that passes through all data items with the insensitive loss coefficient \(\varepsilon\) (based on two supporting hyperplanes, w.u - \(b=\varepsilon\) and w.u - \(b=-\varepsilon\)).  
Figure 4: Random Forest structure.  
The function _f(u)_ in nonlinear regression is determined as follows:  
\[f(u)=\sum(_{i=1}^{n}(\alpha_{i}-\alpha_{i}^{*})K(u_{i},u_{j})+b) \tag{6}\]  
with  
\[\sum_{i=1}^{n}(\alpha_{i}-\alpha_{i}^{*})=0,\ \ C\geq\alpha_{i},\ \alpha_{i}^{*} \geq 0,\forall i \tag{7}\]  
The penalty constant C is used to manage the penalty error, \(\alpha_{i},\ \alpha_{i}^{*}\) are the Lagrange multipliers, and \(K\left(u_{i},\ u_{j}\right)\) is the kernel function defined as follows:  
\[K\big{(}u_{i},u_{j}\big{)}=<\Phi(u_{i}),\Phi\big{(}u_{j}\big{)}\ > \tag{8}\]  
The mapping function \(F\) is a nonlinear mapping function. The most often used kernel functions are linear, polynomial, sigmoid, and Gaussian functions:  
Linear kernel function:  
\[K\big{(}u_{i},u_{j}\big{)}=u_{i}\cdot u_{j} \tag{9}\]  
Polynomial kernel function:  
\[K\big{(}u_{i},u_{j}\big{)}=\big{(}\gamma u_{i}\cdot u_{j}+c\big{)}^{d} \tag{10}\]  
Sigmoid kernel function:  
\[K\big{(}u_{i},u_{j}\big{)}=tanh\ \big{(}\gamma u_{i}\cdot u_{j}+c\big{)}^{d} \tag{11}\]  
Gaussian kernel function:  
\[K\big{(}u_{i},u_{j}\big{)}=exp\Big{(}-\gamma\big{(}u_{i}-u_{j}\big{)}^{2} \Big{)}^{d} \tag{12}\]
# A Survey of the Effects of Vehicle Configuration on Urban Air Mobility +
## 2 Aerial Urban Vehicle Configurations and Concepts
### Horizontal Take-Off and Landing (HTOL)  
Although due to the lack of sufficient space in urban areas for runways, the most suitable configuration for urban applications seems to be VTOL vehicles; some HTOL aircraft are being used for this application. One major benefit of using HTOLs instead of VTOLs would be their higher passenger capacities. Using the currently available spaces in small airports, HTOL urban aircraft can be used for short to mid-range transportation.
## 3 Results
### Annual and Seasonal Evaluation  
As a resource employed for turbid plume identification and characterization, the spectral signature of the three different water bodies on a cloud-free day is presented in Figure 4. The reflectance peak for Aveiro lagoon's runoff (red line) is clearly different from the other two. It lies between the green and yellow bands, which characterize waters dominated by suspended sediments. The other two water bodies (sea outfall outflow and ocean) have distinguishable reflectance peaks, although they present an analogous behavior. For the marine outfall's turbid plume (the black line), it is possible to see the peak on the same wavelength for the Aveiro lagoon, although it presents a much less intense signal. For the coastal ocean water, the reflectance peak happens between the blue and green bands, one of the peaks for chlorophyll absorption.  
From 1 January 2016 to 31 December 2019, there were 463 Sentinel-2 products available for the ROI. From the number of scenes observed for the four years of study, there were 230 cloud-free images. Figure 5 shows a compilation of the number of images available per year, classified according to the cloud coverage over the outfall and the plume visibility.  
The most favorable year for the plume's detection was 2017, as on only 1 day out of 65, the patch was not visible. For the other years, the wastewater plume was detected in: 50% of the cases in 2016, 78.5% in 2018, and 86.5% in 2019. In addition, 2018 was identified as the year with higher data availability. These results represent a good sign for the turbid  
Figure 3: São Jacinto sea outfall turbid plume identification and study flowchart.  
plume's monitoring through Sentinel-2 imagery, considering the morning fog events in Aveiro that might align with the time of the satellite's passage over the region--between 11:20 and 11:40 a.m.  
For the seasonal analysis, the evaluation of Figure 6 indicates that the ratio between cloud coverage and clear sky days is balanced overall. In winter and spring, fewer images were available considering the total image number per season, 111 and 105, respectively. Furthermore, clouds were less present in summer and autumn. Additionally, the number of cloud-free images for winter and summer exceeds the number with cloud coverage. Winter and autumn are the seasons with the best turbid plume view, meaning 56 out of 59 cloud-free images and 58 out of 60, respectively; summer and spring follow. Nevertheless, in summer, the number of days where the plume was not visible is greater than in the other seasons, even under clear sky conditions.  
Figure 4: True color image and example of a spectral signature for ocean water (blue pin), Aveiro lagoon run-off (red pin), and São Jacinto sea outfall plume (black pin) on a cloud-free day (21 March 2019).  
Figure 5: Annual São Jacinto sea outfall plume visibility.
## 4 Experimental Results and Discussion  
### Verification of Mathematical Model  
According to the parameters of the E-CSS experiments, the oil and water production, the temperature of the produced liquid and the experimental temperature monitoring results, the experimental verification of the analytical formula of the horizontal well E-CSS deduced before can be carried out. The comprehensive thermal conductivity of the reservoir is 2.28 W/(m\(\cdot\)K), and the comprehensive thermal diffusivity is 0.62 \(\times\) 10\({}^{-6}\) m\({}^{2}\)/s. Figure 3 shows the comparison of the experimental and calculated temperatures for the E-CSS experiment. From Figure 3, the theoretical calculation value of the temperature at each point in the model fitted well with the experimental monitoring temperature. However, at the end of the production of E-CSS, due to the liquid production speed being about 0, the power of the electric heater over-temperature protection decreased, so the experimental temperature of measuring point one and measuring point two dropped significantly.  
Figure 3: Comparison of experimental and calculated temperature for E-CSS.
# Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review
## 3 Research Directions
### Alzheimer's Disease  
Similarly to the classification of studies on PD made in the previous subsection, papers focusing on handwriting in AD can be grouped in accordance with two main research questions:  
* _Disease insight_: a group of papers examined changes in handwriting of AD and MCI patients to identify patterns of sensorimotor dysfunction associated with the disease;
* _Disease diagnosis_: another group of works applied dynamic handwriting analysis for the purpose to develop a computer-aided diagnosis system. A schematic overview is provided in Table 4.  
It is worth noting that, in contrast to studies focusing on PD, less research effort has been made towards the investigation of AD; moreover, the literature still lacks studies involving the application of dynamic handwriting analysis to support monitoring of disease progression. This is largely due to the absence of effective cures that slow down disease symptoms. However, as MCI patients are at high risk to develop in AD, handwriting changes found in this condition may be used not only for the early disease diagnosis but also to monitor disease progression. Disease Insight  
[PERSON] et al. [34] were among the first to assess handwriting dynamics in patients with dementia of Alzheimer's type by making use of a digitizing tablet. Irrespective of medication or disease severity, patients wrote strokes of significantly less consistent length than controls and were disproportionately impaired by a reduction of visual feedback. Moreover, patients' strokes had a significantly less consistent duration and a significantly less consistent peak velocity than controls. The authors suggested that the more variable performance of patients indicates a degradation of the base motor program and resembles that of [PERSON]'s disease rather than PD. It may indeed reflect frontal rather than basal ganglia dysfunction; thus, it seems that relative movement duration may be useful to differentiate between subcortical dementias (like PD) and cortical dementias (like AD). In Reference [28], [PERSON] et al. adopted dynamic handwriting analysis to quantify differences in fine hand motor function in patients with probable AD and MCI compared to depressed patients and controls. All participants were instructed to perform two tasks. The first one consisted in drawing
# Comparative Analysis of Classifiers for Classification of Emergency Braking of Road Motor Vehicles
Apparently, just before the driver applies the emergency brake, they abruptly lift the accelerator, and the latter quickly returns to its original position [16; 11], as illustrated in detail in Figure 3.  
In principle, it might be possible to predict the emergency braking situation solely by considering the dynamics of the accelerator pedal. By saying \"to predict\" we actually mean to classify the current driver's intention: whether they are going to apply emergency brakes or just normally decelerate the car. A straightforward approach for classification of driver's intention could be based on a comparison of the rate of change of the position of accelerator with a predefined threshold. Exceeding the threshold would be associated with an urgency of the driving situation, and, consequently, with the intention of the driver to apply emergency braking soon. This assumption is based on the fact that the driver lifts their foot faster than the speed at which the pedal returns to its neutral position. In such a situation, the latter will, indeed, return to its initial position without being hindered by the moving foot of the driver. The motion pattern of the unhindered accelerator pedal could be established analytically from  
The main _objective_ of our research is to resolve the problem of emergency braking classification (EBC) solely from the motion pattern of the accelerator pedal by building an intelligent classifier. We also intend to verify the robustness and generality of the proposed solution to the EBC problem on the data of drivers who have not participated in the process of training the classifiers. In addition, we shall integrate and test the classifier as a brake assisting aid in the full-scale driving simulator that we initially used to collect the training data. The key motivation of our research is in the opportunity of reducing the time lag between two instants: when the driver moves their foot from the accelerator pedal (Figure 3a) and the moment when they press the brake pedal to its maximum position (Figure 3c). In properly classified cases, the proposed brake assisting system would be able to activate the brakes automatically and, as a consequence, to reduce the above-mentioned time lag.