esa-sceva/satcom-chunk-collection huggingface dataset:
# Passion and Health: How Winter Swimming Influences Blood Morphology and Rheology
## 2 Materials and Methods
### Research Methods  
Blood samples were collected from the subjects from the antecubital vein in the amount of 5 ml into Vacuette EDTA K2 tubes at the beginning and at the end of the winter swimming season by a qualified nurse. Morphological and rheological indicators were determined in the blood tested at the Blood Physiology Laboratory of the Central Research and Development Laboratory, University of Physical Education in Krakow. This study was approved by the Ethics Committee of the Regional Medical Chamber in Krakow (approval No.: 182/KBL/OIL/2023) and followed the tenets of the Declaration of Helsinki.  
Morphological and Rheological Assessments  
The blood morphological analyses included red blood cell count (RBC) [10\({}^{6}\)/mm\({}^{3}\)], hemoglobin (HGB) [g/dL], hematocrit (HCT) [%], mean corpuscular hemoglobin (MCH) [pg], mean corpuscular hemoglobin concentration (MCHC) [g/dL], mean corpuscular volume (MCV) [um\({}^{3}\)], red blood cell distribution width (RDW) [%], white blood cell count  
Figure 1: CONSORT flow diagram of study participants.  
(WBC) [\(10^{3}\)/mm\({}^{3}\)], platelet count (PLT) [\(10^{3}\)/mm\({}^{3}\)], mean platelet volume (MPV) [\(\mu\)m\({}^{3}\)], and platelet distribution width (PDW) [%]. The indicator markings were performed with an ABX Micros 60 analyser (Horiba ABX SAS, Montpellier, France).  
Blood rheology parameters, such as erythrocyte aggregation index (AI) [%], amplitude and total extent of aggregation (AMP) [arbitrary units], and half-time of total aggregation (T1/2) [s], as well as the deformability parameter of elongation index (EI), were measured with a Laser-Assisted Optical Rotational Red Cell Analyser (Lorrca) (MaxSis Lorrca(r), RR Mechatronics, the Netherlands) by using the method described by [PERSON]. Mean EI values were determined at shear stress values of 0.30 Pa, 0.58 Pa, 1.13 Pa, 2.19 Pa, 4.24 Pa, 8.23 Pa, 15.95 Pa, 30.94 Pa, and 60.00 Pa with the automatic analysis function of the Lorrca system, which allows for the assessment of erythrocyte deformability as a function of shear stress and erythrocyte aggregation.  
Fibrinogen determinations [g/L] were performed by using a CHROM-7 coagulometer (Poland).  
Blood plasma viscosity (BPV) [mPas] was measured with a DVNext Brookfield Ametek Cone/Plate Rheometer (Brookfield, Middleborough, MA, USA). The torque measuring system of the device consists of a calibrated beryllium copper spring connecting the drive mechanism to a rotating cone and sensing the resistance to rotation caused by the presence of a plasma sample between the cone and a stationary flat plate. The resistance to the rotation of the cone produces a torque that is proportional to the shear stress in the plasma sample. The measurements were conducted at 37 \({}^{\circ}\)C with shear rates of 450/s and employed a CP-40Z spindle.
## 4 Data Acquisition and Processing  
The research uses a map-based questionnaire to obtain public perception data. It involves participatory mapping, questionnaires and semi-structured interviews.  
The participatory mapping (PM) method is the main method of data acquisition in the participatory geographic information system (PPGIS) [29]. Mapping data obtained by the PM method focuses on the active expression of the community peoples' perception of community land use, development planning, landscape change and ecological protection through mapping, emphasizing the formation of understanding of local knowledge, and social or cultural phenomena from bottom to top [14]. The semi-structured interview is a typical qualitative data collection method. it conducts flexible interviews around certain topics, leaving sufficient room for interviewees to express their ideas in their own language [30], which helps to further understand the interviewee's views thereby improving the richness of the interview data.  
The core of this method requires the interviewee to mark the place or area that he thinks has some value with numbered marking points on the survey map prepared in advance, and to state the reason.  
The survey uses maps with detailed marks as the basic layer to analyze, classify and code points or areas with different values in the site, and attach corresponding value type options. At the same time, the survey questionnaire is used to record the demographic characteristics of the interviewees. In addition, the interest demands of stakeholders are recorded by semi-structured interviews.
## 3 Numerical Model  
### Establishment of the Three-Dimensional Numerical Model  
The software Abaqus was used to establish the large section tunnel model for simulation. The influence range of the excavation stress was 3-5 times the tunnel diameter, and the boundary dimensions of the model were set as 200 m long in the X direction, 45 m long in the Z direction, and 160 m long in the Y direction. The buried depth of the tunnel was 100 m and the distance from the bottom boundary was 60 m (Figure 3a). The tunnel excavation section has a maximum excavation span of 30.52 m, a height of 17.84 m, and an excavation area of 421.73 m\({}^{2}\). The surrounding rock, primary lining, secondary lining, and temporary support were simulated using the eight-node reduced integration solid element (C3D8R). As shown in Figure 3b,c, the excavation section of the tunnel was made up of four circles cut into a horseshoe shape. The thickness of the first primary lining solid unit was 28 cm, the thickness of the second primary lining solid unit was 20 cm, and the total thickness of the primary lining was 48 cm. The temporary support was mainly carried by I-steel, and the thickness of the solid unit was 20 cm. The thickness of the second lining solid unit was 75 cm. The model was divided by the hexahedral grid. The important positions, such as the tunnel excavation section and surrounding rock, were closely divided. The number of elements generated by the model was 26,514. Normal displacement was constrained on the front, back, sides, and bottom surfaces of the model, and no constraint was set on the top surface.
# Aggregatable Subvector Commitment with Efficient Updates
## 2 Preliminaries
### Newton Interpolation  
Given \(n\) interpolation points \((x_{0},v_{0}),(x_{1},v_{1}),\ldots,(x_{n-1},v_{n-1})\), where no two \(x_{i}\) are the same, the Newton interpolation polynomial is a linear combination of Newton basis polynomial. We can express the Newton interpolation function as follows:  
\[N(x)=\sum_{i\in[0,n)}a_{i}\cdot w_{i}(x). \tag{1}\]  
We denote the \(i\)-th basis polynomial of the Newton interpolating polynomials [21] as  
\[w_{i}(x)=\prod_{j=0}^{i-1}(x-x_{j}), \tag{2}\]  
where \(w_{0}(x)=1\). The coefficients are defined as  
\[a_{i}=[v_{0},v_{1},v_{2},\ldots,v_{i}], \tag{3}\]  
where \([v_{0},v_{1},v_{2},\ldots,v_{i}]\) is the notation for the difference quotient, and the specific calculation process is as follows:  
\[\begin{split} a_{0}&=[v_{0}]=v_{0}\\ a_{1}&=[v_{0},v_{1}]=\frac{v_{1}-v_{0}}{x_{1}-x_{0 }}\\ a_{2}&=[v_{0},v_{1},v_{2}]=\frac{[v_{1},v_{2}]-[v_{0 },v_{1}]}{x_{2}-x_{0}}\\ a_{3}&=[v_{0},v_{1},v_{2},v_{3}]=\frac{[v_{1},v_{2 },v_{3}]-[v_{0},v_{1},v_{2}]}{x_{3}-x_{0}}\\ a_{4}&=[v_{0},v_{1},v_{2},v_{3},v_{4}]=\frac{[v_{1 },v_{2},v_{3},v_{4}]-[v_{0},v_{1},v_{2},v_{3}]}{x_{4}-x_{0}}\\ \ldots\\ a_{n-1}&=[v_{0},v_{1},v_{2},\ldots,v_{n-1}]=\frac{ [v_{1},v_{2},\ldots,v_{n-1}]-[v_{0},v_{1},\ldots,v_{n-2}]}{x_{n-1}-x_{0}}\\ \end{split} \tag{4}\]  
Therefore, the Newtonian polynomials are expanded in the following form:  
\[N(x)=[v_{0}]+[v_{0},v_{1}](x-x_{0})+\cdots+[v_{0},v_{1},\ldots,v_{n-1}](x-x_{ 0})(x-x_{1})\cdots(x-x_{n-2}). \tag{5}\]  
We can see that \(\forall i\in[0,n),N(x_{i})=v_{i}\).  
Both Newton interpolation and Lagrange interpolation are classic interpolation methods, but Newton interpolation has unique advantages. It uses the difference quotient form to gradually construct the interpolation polynomial, which has computational superiority; when a new interpolation point needs to be added, the previously calculated difference quotient result can be directly used, and only the newly added high-order difference quotient terms need to be calculated, thus avoiding the cost of complete recalculation. This progressive construction feature makes Newton interpolation particularly efficient when dealing with dynamically growing data sets. In contrast, Lagrange interpolation requires reconstructing all basis functions for each new point added.
# From Climate Conditions to the Numerical Slope Stability Analysis of Surface Coal Mines
## 5 Numerical Analysis of Slope Stability of Lignite Mines under Extreme Rainfall Events
### Effect of Rainfall on Slope Stability
This almost constant SF is due to the small total rainfall discharge that infiltrates into the relatively large-scale slope of 50 m height; the total quantity of rainwater is inadequate in inducing a significant dissipation of suction stresses and a decrease of soil's shear strength. As the intensity increases further, it affects the SF more clearly. For the highest value of the medium range--6.4 mm/h or 153.6 mm/day--and the two extreme intensities--9 mm/h and 17 mm/h or 216 mm/day and 408 mm/day--the SF decrease is almost the same, and the reduction rates are 8.3%, 8.9%, and 9.3%, respectively. Thus, an upper limit is practically identified for the effect of the rainfall intensity on SF, signifying that more extreme rainfall will not further affect the minimum SF reached. Figure 10: Slope safety factor variation with rainfall time duration, for various values of (**a**) g\({}_{\text{a}}\) and (**b**) H\({}_{\text{w}}\).
## 3 First Generation: Virtual Inertia Topologies
### Other Topologies
Virtual oscillator controller (VOC) is another approach where, instead of mimicking synchronous/induction generators, a non-linear oscillator is implemented within the controller to synchronize DG units without any form of communication [78; 79]. This approach is particularly beneficial for a grid largely dominated with DGs, as the controller is intrinsically able to maintain synchronism and share the total system load [80].
# Leveraging IGOOSE-XGBoost for the Early Detection of Subclinical Mastitis in Dairy Cows
## 5 Prediction Model for Subclinical Mastitis in Dairy Cows Based on IGOOSE-XGboost
### Building the IGOOSE-XGBoost Regression Model for Subclinical mastitis in Dairy Cows  
5.3.1. Correlation Analysis  
Correlation analysis was conducted for the attributes listed in Table 3, to ensure variable independence and minimize redundancy. Pearson's correlation coefficient was utilized to quantify the associations among variables. This coefficient measures the similarity between attributes and is suitable for analyzing correlations among continuous variables. A coefficient near 0 shows no connection or a very weak correlation (0 to 0.2), a coefficient between 0.2 and 0.4 indicates a weak correlation, a coefficient between 0.4 and 0.6 indicates a moderate correlation, a coefficient between 0.6 and 0.8 indicates a high correlation, and a coefficient between 0.8 and 1 indicates an extremely strong association [32]. The correlation heat map depicted in Figure 3 illustrates these relationships, based on the Pearson's correlation coefficients between variables.  
Figure 4 displays the Pearson correlation coefficients among the different variables, with darker colors indicating stronger correlations. This figure illustrates the relationship between various variables and SCS. In this study, features with correlation coefficients greater than or equal to 0.4 were selected. Ultimately, x7, x8, x13, and x21 (i.e., Milk Fat Percentage, Peak Milk, Urea Nitrogen, SCS) were identified and used to construct a regression model.  
Figure 4: Correlation heat map.
# Automated Docking System for LNG Loading Arm Based on Machine Vision and Multi-Sensor Fusion
###### Abstract  
With the growth of global liquefied natural gas (LNG) demand, automation technology has become a key trend to improve the efficiency and safety of LNG handling. In this study, a novel automatic docking system is proposed which adopts a staged docking strategy based on a monocular camera for positioning and combines ultrasonic sensors to achieve multi-stage optimization in the fine docking stage. In the coarse docking stage, the system acquires flange image data through the monocular camera, calculates 3D coordinates based on geometric feature extraction and coordinate transformation, and completes the preliminary target localization and fast approach; in the fine docking stage, the ultrasonic sensor is used to measure the multidirectional distance deviation, and the fusion of the monocular data is used to make dynamic adjustments to achieve high-precision alignment and localization. Simulation and experimental verification show that the system has good robustness in complex environments, such as wind and waves, and can achieve docking accuracy within 3 mm, which is better than the traditional manual docking method. This study provides a practical solution for automated docking of LNG loading arms, which can significantly improve the efficiency and safety of LNG loading and unloading operations.  
LNG loading arm; automatic docking; vision-based positioning; YOLOv8 target detection; multisensor fusion; precision alignment +
Footnote †: journal: Applied sciences  
Article
## 5 Conclusions  
1. The CO\({}_{2}\) flux in the karst underground river open-flow section of the Heilongtan River shows a pattern of underground river outlet > river sections without submerged plants > river sections with submerged plants (<0). The CO\({}_{2}\) flux at the underground river outlet is high during the day and low at night, while the CO\({}_{2}\) flux in the river sections is low during the day and high at night.  
2. The river sections with submerged plants exhibited a substantial correlation (\(p<0.05\)) with the CO\({}_{2}\) flux, although the underground river outlet and river sections without submerged plants showed weaker correlations (\(p>0.05\)). DO (\(p<0.001\)) is an important variable that may regulate variations in CO\({}_{2}\) flux.  
3. The end element mixed model calculation shows that at the underground river outlet, river sections without submerged plants, and river sections with submerged plants, the contribution ratio of endogenous organic carbon to TOC is 68.71%, 88.66%, and 96.40%, respectively. This is significantly higher than the ratios found in the Beijing and Xijiang Rivers of the Pearl River, and it close to the ratio of the Dongjiang River. Combining the distribution of submerged plants with the hydrological circumstances of the Heilongtan and Dongjiang Rivers, it is evident that the metabolism of submerged plants plays a significant regulatory function in the variation of CO\({}_{2}\) flux.  
4. The photosynthesis of submerged plants significantly inhibits the carbon flux at the river sections with submerged plants, with metabolism inhibiting carbon emissions of approximately 0.131 Tg C/yr from the river sections without submerged plants to the river sections with submerged plants. In addition, it shows that submerged plants can absorb high-intensity \(p\)CO\({}_{2}\), suggesting a mechanism that goes from release to absorption.  
Conceptualization, D.L. and L.Z.; data curation, D.L., X.P. and C.H.; validation, C.H. and L.Z.; formal analysis, D.L., C.L., H.L. and X.X.; investigation, D.L., L.Z. and G.C.; writing--original draft preparation, D.L.; writing--review and editing, L.Z. and C.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by (Geological Survey Project of China Geological Survey Bureau) grant number (DD20230111, DD20230512).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The dates are not publicly available due to the privacy of the data.  
The authors declare no conflicts of interest.
## 3 Results and Discussion  
### Antino Acid Composition Analysis of GCPs  
The amino acid composition analysis was conducted to identify the types and quantities of amino acids present in the peptide mixture, which is critical for understanding their antioxidant properties. In total, 17 amino acids were detected, including 6 essential amino acids and 7 hydrophobic amino acids. As shown in Table 3, the total amino acid content in GCPs was 69.092 g/100 g, with hydrophobic amino acids accounting for 20.97 g/100 g and essential amino acids for humans accounting for 24.084 g/100 g. Phenylalanine, the most abundant amino acid in GCPs, serves as a precursor for synthesizing other compounds with antioxidant activity, such as phenylacetic acid and phenyllactic acid [24], thus having an impact on the antioxidant activity of substances [25]. Other hydrophobic amino acids, such as Ala, Leu, and Ile, have also been found to play a role in antioxidant activity [26]. Additionally, studies have shown that arginine possesses antioxidant properties, and its content in GCPs was relatively high [27] at 5.270 g/100 g. Therefore, it was speculated that GCPs have potential antioxidant functions.
###### Abstract  
The aim of this study is to assess the capacity of the Surface Water Ocean Topography (SWOT) satellite to resolve fine scale oceanic surface features in the western Mediterranean. Using as input the Sea Surface Height (SSH) fields from a high-resolution Ocean General Circulation Model (OGCM), the SWOT Simulator for Ocean Science generates SWOT-like outputs along a swath and the nadir following the orbit ground tracks. Given the characteristic temporal and spatial scales of fine scale features in the region, we examine temporal and spatial resolution of the SWOT outputs by comparing them with the original model data which are interpolated onto the SWOT grid. To further assess the satellite's performance, we derive the absolute geostrophic velocity and relative vorticity. We find that instrument noise and geophysical error mask the whole signal of the pseudo-SWOT derived dynamical variables. We therefore address the impact of removal of satellite noise from the pseudo-SWOT data using a Laplacian diffusion filter, and then focus on the spatial scales that are resolved within a swath after this filtering. To investigate sensitivity to different filtering parameters, we calculate spatial spectra and root mean square errors. Our numerical experiments show that noise patterns dominate the spectral content of the pseudo-SWOT fields at wavelengths below 60 km. Application of the Laplacian diffusion filter allows recovery of the spectral signature within a swath down to the 40-60 km wavelength range. Consequently, with the help of this filter, we are able to improve the observation of fine scale oceanic features in pseudo-SWOT data, and in the estimation of associated derived variables such as velocity and vorticity.  
satellite altimetry; SWOT; western Mediterranean Sea; fine scale; SWOT simulator; ROMS model; filtering 2018 10 599 3390/rs10040599
## 3 Results
### AOPP Results  
Figure 2 shows the values of AOPP for healthy people and patients with MI. Patients with MI were divided into groups because of the concentration of uric acid. Blood samples from people after MI have higher levels of AOPP in comparison to healthy people (0.696 \(\pm\) 0.187 vs. 0.342 \(\pm\) 0.048 umol/g, \(p\) < 0.001), especially for samples with high concentrations of serum uric acid (0.822 \(\pm\) 0.223 umol/g).  
The values of AOPP in dependence on serum uric acid for healthy subjects and patients with MI are shown in Figure 3A,B, respectively. Considering the normal distribution of the data, [PERSON]'s correlation was employed for regression analysis to investigate these relationships further. A weak negative correlation between AOPP and uric acid was found in healthy people (\(r=-0.36\), \(p=0.05\)) and patients with MI with uric acid levels less than 8 mg/dL (\(r=-0.40\), \(p=0.083\)). For patients with MI with higher values of serum uric acid (\(>\)8 mg/dL), a positive correlation was observed (\(r=0.53\), \(p=0.012\)).  
\begin{table}
\begin{tabular}{c c c} \hline
**Characteristic** & **Mean \(\pm\) SD** & **Reference Values** \\ \hline Age & 69.36 \(\pm\) 12.175 & not applicable \\ Gender (male) & 60\% & not applicable \\ Troponin I & 5.04 \(\pm\) 7.505 & \textless{}0.04 ng/mL \\ CK-MB & 28.23 \(\pm\) 20.917 & \textless{}25 U/L \\ Total protein & 6.49 \(\pm\) 0.702 & 6–8 g/dL \\ CRP & 30.85 \(\pm\) 44.303 & \textless{}5.00 mg/L \\ Cholesterol & 204.09 \(\pm\) 62.687 & \textless{}190 mg/dL \\ LDL cholesterol & 125.58 \(\pm\) 48.677 & \textless{}115 mg/dL \\ HDL cholesterol & 45.12 \(\pm\) 12.538 & \textgreater{}40 mg/dL \\ Triglycerides & 127.30 \(\pm\) 108.171 & \textless{}150 mg/dL \\ \hline \end{tabular} Data are presented as mean \(\pm\) standard deviation. SD: standard deviation; CK-MB: creatine phosphokinase-MB; CRP: C-reactive protein; LDL cholesterol: low-density lipoprotein; HDL cholesterol: high-density cholesterol.  
\end{table}
Table 2: The baseline characteristics of the study group included patients with post-myocardial infarction.  
Figure 2: Levels of advanced oxidation protein products for healthy people and patients after myocardial infarction; MI: myocardial infarction; AOPP: advanced oxidation protein products; SUA: serum uric acid.  
Figure 3 also indicates that the relationship between AOPP and uric acid shows a \"J-shaped curve\" in all uric acid ranges instead of a weak negative correlation for smaller uric acid values and a strong positive correlation for higher values. The values of AOPP in patients after MI were significantly higher than in healthy subjects and were associated with the concentration of uric acid. A J-curve showing the relationship between uric acid and AOPP obtained for people after an MI was observed. This observation may have important clinical implications and will allow us to determine the valid value of uric acid.