esa-sceva/satcom-chunk-collection huggingface dataset:
# Wettability of Carbonate Reservoir Rocks:
## 5 Conclusions  
In this study, a set of capillary pressure and relative permeability curves measured on 19 carbonate rock samples was used to analyze and compare different wettability indices, such as Amott, Amott-Harvey, and USBM* as well as the relative permeability-based wettability measurement methods of [PERSON]'s rules of thumb, modified [PERSON]'s second rule, normalized water fractional flow, Lak, and modified Lak. The following conclusions can be drawn from this study based on 19 core plugs from a single well in a carbonate reservoir:  
* Generally speaking, good agreement was obtained between the relative permeability-based methods and Amott, Amott-Harvey, and USBM* techniques in identifying the wetness of carbonate samples, despite such methods being based on different concepts and physics. Thus, it is convenient to use relative permeability-based methods when the required data for the Amott-Harvey and USBM* methods are not available.
* There was a good correlation between the USBM* and the Amott-Harvey methods as well as between the Lak and the modified Lak approaches. Intermediate correlations were observed among the other methods.
* Comparing the wettability indices with rock quality indicators showed that as _FZI_, _RQI_, and Winland \(R_{35}\) increased (or rock quality improved), samples became more oil-wet.
* Rock quality indices _RQI_ and Winland \(R_{35}\) that are mainly a function of pore throats and their sizes were correlated with wettability indices higher than the _FZI_.
* The Lak and modified Lak indices, as two quantitative relative permeability-based wettability techniques, provided higher correlations with rock quality indicators than the Amott-Harvey and USBM* indices. Those wettability indices strongly affected by wetting characteristics of larger pore throats were higher correlated to rock quality indicators than indices taking into account the effect of both small and large pore throats.  
Conceptualization, M.F.-P. and A.M.-P.; methodology, M.F.-P. and A.M.-P.; software, M.F.-P.; validation, M.F.-P., A.M.-P. and B.G.; formal analysis, M.F.-P., A.M.-P. and B.G.; investigation, M.F.-P., A.M.-P., S.A.G. and B.G.; resources, M.F.-P.; data curation, M.F.-P., A.M.-P. and B.G.; writing--original draft preparation, M.F.-P., A.M.-P., S.A.G. and B.G.; writing--review and editing, M.F.-P., A.M.-P., S.A.G. and B.G.; visualization, M.F.-P., A.M.-P., S.A.G. and B.G.; supervision, A.M.-P. and B.G.; project administration, M.F.-P., A.M.-P. and B.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability StatementData available on request from A.M.-P.  
[PERSON] is grateful to Kansas State University for supports through faculty start-up funds.  
The authors declare no conflict of interest.
## 3 Literature Review
### Census Data Distribution  
Another vital aspect to explore concerns the mechanisms employed by census bureaus for sharing data with relevant or requested authorities. [PERSON] [6] delves into the reports from the 2010 world program on population and housing censuses to comprehend the primary methods utilized for disseminating census data. It was found that approximately 63 countries predominantly employ paper publications for data distribution. In contrast, 34 countries rely on static web pages, and a mere 17 countries have adopted interactive online databases. Predominantly, developing nations utilize paper-based methods, alongside CD-ROMs and DVDs, for data distribution. Notably, almost all countries, including the United States, lack a distributed and decentralized system capable of meeting the requirements of all stakeholders in a timely manner.
## 3 Spectrum Management Issues
### Cognitive Radio
Spectrum management determines and manages various spectrum issues for primary and secondary users [105; 106; 107]. Intelligence management is built on an efficient cognitive engine that uses a diverse range of synthetic intelligence methods (e.g., genetic algorithm and rule-based systems) and neural networks to manage the network resources [108; 109]. Interference management focuses on the implementation aspect of the CR, which involves various characteristics of the radio channels (such as resource allocation for the users), in addition to link quality and channel awareness, which highly depend on the precise selection of transmission power [110]. Small cells have low transmission power; thus, the coverage areas under such small cells can apply CR for efficient resource optimization and high data rate services [111]. To utilize the CR paradigm in the most efficient way for higher throughput, spectrum sensing is a fundamental element in it. Moreover, its methodology is close to the spectrum opportunity definition [112]. Spectrum sensing can be presented in three different scenarios, namely, (a) local spectrum sensing by the small cell BS; (b) centralized cooperating spectrum sensing, where users are activated by the small cell BS, apply spectrum sensing, and deliver the outcomes to the small cell BS that serves, performs as a central unit; (c) distributed cooperating spectrum sensing, where users are activated by the small cell BS, apply spectrum sensing, and report the consequences to the closest neighbors, as shown in Figure 3. A considerable portion of radio spectrum resources is largely underutilized in most cases and has necessitated the adoption of CR to maximize resource usage [113]. In [114], the authors have suggested that the concept of spectrum allocation through SS is used mainly for optimal spectrum usage, especially for small cells. Given that spectrum allotment is under the governance and control of regional regulatory authorities and policies, the prospective newly licensed bands in each region probably lie in the range of 3-10 GHz, whereas the 95-150 GHz bands can be obtained through the SS mechanism facilitated by opportunistic utilization [115]. Several types of research have been conducted on spectrum management issues related to spectrum sensing, throughput enhancement, spectrum allocation, channel estimation and optimization, and cluster formation. A few of the latest studies are discussed in further subsections.
## 4 Conclusions  
A single CDWL-based approach for windshear altering of multiple runways was demonstrated at Beijing Daxing International Airport during the field experiment. With an angle pointing accuracy of 0.01\({}^{\circ}\) of the scanning lidar, orthogonal wind components on each runway were retrieved according to continuity airflow, which was consistent with anemometer observations. Due to the high spatial and temporal resolutions of the CDWL, it captured a fine structure of the complex flow, such as the microburst and vortex. The intensity of the microburst is much stronger than that of the general windshear, and the maximum windshear ramps of the microburst exceed 25 m/s. Gusts and vortexes are also noteworthy for their wide range of influence. By traversing and counting all severe shear ramps filtered by the windshear severity factor, the windshear was well evaluated and identified. In the windshear verification experiment, 10 crew reports were accumulated for 66 consecutive days, and the PoTA reached 0.81%, and the PoD reached 100%. This shows that the proposed approach can effectively reduce PoTA. Since pilot reports for verification are limited in the initial detection, more windshear events are needed to find a more optimized threshold by taking into account both PoD and PoTA.  
Flights are expected to encounter less windshear in the future with the early alerting of the ground-based lidar. Such verification experiments in the BDIA are helpful for the application of the approach in other airports, which suffer frequent windshear. In the future, we will further extend the capability of new approaches for turbulence and tailwind alerting based on the lidar system.  
Conceptualization, H.X.; Methodology, J.Y. Software, J.Y. and L.S.; Formal Analysis, H.X. and J.Y.; Investigation, J.Y.; Resources, J.Y., G.Z., Y.L., M.Z. and J.L.; Data Curation, J.Y.; Writing--Original Draft Preparation, H.X. and J.Y.; Writing--Review and Editing, H.X. and J.Y.; Visualization, J.Y.; Supervision, H.X. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data Availability Statement: Data underlying the results presented in this paper can be obtained from the authors upon reasonable request. We are grateful to BDIA for providing anemometer data and flight crew reports. We thank [PERSON], [PERSON] and [PERSON] for their contribution to the field experiment. The authors declare no conflict of interest.
# Early Risk Prediction in Acute Aortic Syndrome on Clinical Data Using Machine Learning
## 4 Clinical Dataset  
Clinical datasets in Acute Aortic Syndrome (AAS) are indispensable reservoirs of patient information that significantly contribute to advancing our comprehension of this intricate cardiovascular ailment. These datasets comprise a wide array of clinical histories, imaging results, laboratory tests, and treatment outcomes [14], offering a comprehensive overview of AAS presentations. Leveraging the richness of these datasets allows for discerning patterns, identifying risk factors, and tailoring diagnostic and therapeutic approaches to individual patients [1, 15]. Meticulous data integration, cleaning, and analysis enable clinicians to derive valuable insights into various facets of AAS, facilitating early detection, risk prediction, and personalized patient care [15, 16].  
The dynamic utilization of clinical datasets in AAS research involves continuous refinement and analysis to uncover hidden patterns and enhance diagnostic and treatment strategies. By employing advanced computational techniques, including machine learning, to extract meaningful information from these datasets, clinicians are empowered to make evidence-based decisions and formulate effective treatment plans [1, 15]. This iterative process of data analysis and refinement not only contributes to the scientific understanding of AAS but also drives ongoing progress in heart medicine, enabling clinicians to better  
Figure 2: Project process and roadmap.  
predict, diagnose, and manage this life-threatening cardiovascular condition in emergency department settings.
## 3 Results and Discussion  
### Single Pile Loading Test  
Figure 5 shows axial load-settlement results for five single test piles. The ultimate bearing capacity (\(Q_{ult}\)) of a pile was considered to be the load required to displace its head by 10% of its diameter [41]. A safety factor of 3 was applied to calculate the allowable bearing capacity (\(Q_{all}\)) of the piles. The estimated bearing capacities of the test piles are listed in Table 2, which shows that WMP1 had the largest bearing capacity followed in order by CMP1, WMP2, PCP, and CMP2. The axial stiffness of each pile, Kslope was evaluated from the initial slope (the linear part) of the load settlement curve as shown in Figure 3 and Table 2[19; 42]. The results show that WMP with shear keys was stiffer than CMP. [PERSON] and [PERSON] proposed the following analytical solution for calculating a pile's axial stiffness under vertical loading [43]:  
\[K_{eq}=G\tau_{0}\frac{\frac{4\eta}{(1-\
u)\zeta}+\left(\frac{2\pi\rho}{\zeta} \right)\left[\frac{\tanh(\mu L)}{\mu L}\right]\left(\frac{L}{r_{0}}\right)}{1+ \left\{\left[\frac{4\eta}{\pi\lambda}(1-\
u)\right]\left[\frac{\tanh(\mu L)}{ \mu L}\right]\left(\frac{L}{r_{0}}\right)\right\}} \tag{3}\]  
where \(r_{0}\) is the pile radius; \(\zeta=\ln(r_{ml}/r_{0})\); \(r_{m}=2.5\rho(1-\
u)L\); \(\zeta=E_{sl}/E_{sb}\) (\(\xi=1\) in this study); \(\rho=E_{sm}/E_{sl}\) (\(\rho=0.5\) in this study); \(\mu L=[2/(\zeta\lambda)]0.5(L/r_{0})\); \(\lambda=E_{p}/G\); \(\eta=r_{b}/r_{0}\) (\(\eta=1\) for PCP and CMP; \(\eta=1.67\) for WMP); \(r_{b}\) is the radius at the pile's base; \(L\) is pile length; \(E_{sl}\) is the [PERSON]'s modulus of soil at the pile toe level; \(E_{sb}\) is the [PERSON]'s modulus of soil below the pile tip; \(E_{saw}\) is the average soil Young's modulus along the pile shaft; \(\
u\) is the [PERSON]'s ratio of the soil; \(G\) is shear modulus of the soil at the pile toe level; and \(E_{p}\) is the [PERSON]'s modulus of the pile material. Note that the calculation of the waveform micropile's stiffness had a shaft diameter of 16.7 mm [44].  
The stiffness of each pile was calculated by using Equation (2), \(K_{eq}\), which is also listed in Table 2. The calculated values are similar to the estimated \(K_{slope}\) values from the load settlement curves.  
Figure 4: Experimental set-up for underpinned foundation loading test. (**a**) Preparation of loose sand layer; (**b**) installation of pile foundation and preparation of dense sand layer; (**c**) installation of linear variable differential transformers (LVDTS); (**d**) initial loading \(Q_{i}\) to existing foundation; (**e**) connection of micropile to existing foundation; and (**f**) additional loading \(Q_{a}\) to underpinned foundation.
## 3 Results
### Results from the Human Subjects Testing
#### 3.2.5 Stance Phase
The same procedure was followed for the calculation of the late stance, knowing for each cycle the end of the stance phase. The durations of the subphases of the stance phase are given in Table 9. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Participant 1**} & \multicolumn{2}{c}{**Participant 2**} & \multicolumn{2}{c}{**Participant 3**} \\ \hline \multicolumn{6}{c}{Toe Strike} \\ \hline Leg and prosthesis used & mean & std & mean & std & mean & std \\ \hline Healthy leg, own prosthesis & 10.3\% & 0.6\% & 9.3\% & 1.9\% & 8.5\% & 0.6\% \\ Healthy leg, MyFlex-\(\delta\) & 8.6\% & 0.5\% & 8.0\% & 1.2\% & 8.0\% & 0.5\% \\ Prosthetic leg, own prosthesis & 8.4\% & 0.8\% & 8.4\% & 0.9\% & 11.2\% & 0.7\% \\ Prosthetic leg, MyFlex-\(\delta\) & 9.3\% & 0.5\% & 8.6\% & 1.5\% & 9.6\% & 0.9\% \\ \hline \multicolumn{6}{c}{Heel-Off} \\ \hline Leg and prosthesis used & mean & std & mean & std & mean & std \\ \hline Healthy leg, own prosthesis & 48.5\% & 1.8\% & 61.4\% & 2.1\% & 51.4\% & 1.8\% \\ Healthy leg, MyFlex-\(\delta\) & 51.0\% & 1.3\% & 66.1\% & 1.7\% & 54.2\% & 1.0\% \\ Prosthetic leg, own prosthesis & 46.9\% & 0.9\% & 48.3\% & 1.6\% & 45.7\% & 1.0\% \\ Prosthetic leg, MyFlex-\(\delta\) & 49.4\% & 1.0\% & 47.4\% & 1.6\% & 43.9\% & 0.9\% \\ \hline \multicolumn{6}{c}{Toe-Off} \\ \hline Leg and prosthesis used & mean & std & mean & std & mean & std \\ \hline Healthy leg, own prosthesis & 74.3\% & 7.4\% & 77.6\% & 1.8\% & 67.1\% & 1.6\% \\ Healthy leg, MyFlex-\(\delta\) & 73.6\% & 2.2\% & 79.1\% & 2.0\% & 70.6\% & 5.0\% \\ Prosthetic leg, own prosthesis & 63.3\% & 1.0\% & 69.2\% & 1.4\% & 61.0\% & 7.3\% \\ Prosthetic leg, MyFlex-\(\delta\) & 64.2\% & 1.3\% & 68.6\% & 1.7\% & 58.4\% & 1.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Stance phase events. Mean values (mean) and standard deviations (std) are calculated from 50 gait cycles. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Participant 1**} & \multicolumn{2}{c}{**Participant 2**} & \multicolumn{2}{c}{**Participant 3**} \\ \hline \multicolumn{6}{c}{Early Stance Duration} \\ \hline Leg and prosthesis used & mean & std & mean & std & mean & std \\ \hline Healthy leg, own prosthesis & 10.3\% & 0.6\% & 9.3\% & 1.9\% & 8.5\% & 0.6\% \\ Healthy leg, MyFlex-\(\delta\) & 8.6\% & 0.5\% & 8.0\% & 1.2\% & 8.0\% & 0.5\% \\ Prosthetic leg, own prosthesis & 8.4\% & 0.8\% & 8.4\% & 0.9\% & 11.2\% & 0.7\% \\ Prosthetic leg, MyFlex-\(\delta\) & 9.3\% & 0.5\% & 8.6\% & 1.5\% & 9.6\% & 0.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Stance phase subphases durations. Mean values (mean) and standard deviations (std) are calculated from 50 gait cycles.
## 1 Introduction
compared the model predictive control and optimal causal control for one degree of freedom having point absorber in a range of wave conditions in [25]. A WEC composed of a point absorber, a hinged arm, and a direct mechanical drive PTO system was addressed in [26]. Maximum power point tracking algorithms were applied to increase the capture width ratio. Model predictive control was applied to a Wavestar prototype to maximize energy extraction in [27]. All previous papers have addressed the significant contributions in the design, modeling, and control of the WEC. In order to create a new vision for increasing wave energy extraction, an adjusting mechanism for a WEC can be proposed, and experimental investigations are needed to evaluate the performance of the proposed WEC. In this paper, a novel point absorber wave energy converter (WEC) using an inertial adjusting mechanism is proposed to investigate the performance. A new conceptual design of an inertial adjusting mechanism is proposed to carry out experiments under different working conditions. Here, some added masses are employed to adjust the natural frequency of the power take-off (PTO) into the excitation frequencies of the incident waves. A coupled mechanical and hydrodynamic time domain simulation of the truncated floating buoy connected to the bidirectional gearbox is presented. The simulation is based primarily on MATLAB/Simulink. Due to working in different wave conditions, the determination of the hydrodynamic parameters is calculated and simulated in the frequency domain using the ANSYS AQWA wave interaction simulation software (version number 2021R1). Then, a prototype is fabricated and exploited in a wave tank test. Finally, the test results of the prototype under different wave conditions are presented. In each test, buoy elevation, speed, and induced torque were measured and recorded. Additionally, the mean incident wave power and generated power were determined to calculate the WEC's overall efficiency. The results from the wave tank tests are compared to evaluate the performance of the proposed WEC.
# Modeling of Cotton Yield Estimation Based on Canopy Sun-Induced Chlorophyll Fluorescence
## 3 Results  
Diurnal Variation in Canopy SIF and Biometric Parameter Responses of Cotton for Different Growth Periods  
The analysis showed that the fluctuation of diurnal variation in cotton canopy SIF showed a single-peak state of first rising and then decreasing on sunny days (Figure 2a), and reached the maximum value around 14:00-15:00. The diurnal variation trends of photosynthetically active radiation (PAR) and SIF were consistent, indicating that PAR affected the change in SIF, and the peak value appeared around 14:00-15:00 (Figure 2b). The diurnal variation in SIFy showed a horizontal linear fluctuation (Figure 2c). Cotton yield showed a trend of first increasing and then decreasing under different nitrogen application treatments, reaching the maximum value under N3 treatment, and the differences among different treatments reached significant levels (Figure 2d).  
Figure 2: Diurnal variation in canopy SIF and significant analysis of cotton yield under different treatments. (**a**–**c**) represent the diurnal variation in cotton canopy SIF, PAR, and SIFy, respectively; (**d**) represents the significant analysis of cotton yield under different treatments. (**d**) (a–e) indicates that at the significant level of 0.05, different letters represent significant differences, and the same letters represent significant differences.  
The variation trends of cotton AGB and LAI during the growing period were basically the same, increasing from the budding period, reaching the maximum value in the flowering and boll period, and decreasing during the boll period (Figure 3). At each growth period, cotton AGB and LAI showed a trend of first increasing and then decreasing with the increase in nitrogen application, and reached the maximum value under the N3 treatment. Cotton canopy SIF increased from the budding period, reached the maximum value at the flowering and boll period, and began to decrease at the boll period (Figure 3). With the increase in nitrogen application, cotton canopy SIF showed a trend of first increasing and then decreasing, and reached the maximum value under the N3 treatment. The change trend of SIF was basically consistent with that of AGB and LAI. These results indicated that AGB and LAI influenced the changes of SIF during the growth period of cotton.
###### Abstract
In NEB, whose spatial distribution of precipitation is mostly inserted in a semiarid zone, flanked by the rainy coastal region and borders with the Amazon and the Cerrado biomes [6], TRMM products have been used in various hydrometeorological applications. For example, [5] used TRMM data to estimate return periods for extreme precipitation events. In contrast [23] used TRMM data to monitor drought in the semiarid region. Most recently, the Integrated Multi-SatellitE Retrievals for GPM (IMERG) product from the GPM satellite constellation has been evaluated in different regions of the world against rain gauge such as in China [24; 25], Pakistan [26], different climatic zones of Brazil [27], the Ebro River basin in Spain [28], Chile [29], and Canada [30]. It was also evaluated under specific conditions such as orographic rains [28], different microphysical rain regimes [29], and heavy rain conditions such as during hurricane days [31] and typhoons [32]. Overall IMERG performance is satisfactory. However, has been reported [28; 29] that it strongly depends on altitude and precipitation regime. Concerning the Brazilian rivers basin, the performance of IMERG product was still poorly analyzed. In the specific context of the Sao Francisco River basin (SFB), which is the largest in extent and volume of NEB, the most recent studies with TRMM data have focused on the analysis of drought variability [33] or analysis of the precipitation trend [34]. However, these studies focused on the upper reach of the Sao Francisco River, which corresponds to the southern portion of the SFB. Thus, in this study we evaluated the IMERG product of the GPM mission, was developed as a continuation and improvement of the TRMM mission, to estimate precipitation over the entire length of the Sao Francisco River basin, comparing it with rainfall data measured on the surface.
## 3 Materials and Methods
### Antioxidant Activity  
Antioxidant activities of the polyphenols in grape pomace extract and the polyphenols recovered from mPEG-5000-grafted silica particles were tested using a 2, 2-diphenyl-1-picrylhydrazyl (DPPPH) free radical assay [63; 64]. Briefly, the activity of each sample was determined by mixing 20 \(\mu\)L sample with 980 \(\mu\)L \(6.1\times 10^{-5}\) M DPPH\(\bullet\) methanol solution in 2 mL cuvettes. The cuvettes were covered and incubated in the dark for 16 min. The absorbance at 517 nm was measured against a blank by using ultraviolet-visible spectrophotometer (Shimadzu, Japan). Since the total polyphenol contents of each sample vary, the results were normalized to the total polyphenonic content of each sample and expressed as normalized antioxidant activity (inhibition percentage (%)/(mg GAE/L)) in Equation (3).  
\[\textit{Normalized Antioxidant Activity}=\frac{\left(\frac{Absorbance_{t-0 \text{ min}}-Absorbance_{t-16\text{ min}}}{Absorbance_{t-0\text{ min}}}\right)}{Total\textit{ polyphenol content}\left(mg\ GAE/L\right)}\times 100 \tag{3}\]