esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Machine Learning Techniques to Predict a Patient's No-Show in the Healthcare Sector
## 6 Conclusions  
This study aimed to build a no-show classification model for patients and investigate the meaningful features that signal that a patient will not attend the scheduled medical appointment. To achieve the results, we applied some ML techniques to identify the factors that may contribute to the absenteeism of the patients and, above all, to predict the likelihood of individual patients not attending the scheduled appointments.  
After analyse the performance metrics, the Random Forest model seemed to be a good choice to be the final model for the available dataset. It revealed the best recall rate (0.91) and achieved an overall false-negative rate equal to 0.08% of the total observations. The proposed predictor and analysis results demonstrate that the attributes that can influence the patient's attendance in a medical consultation are the lower weather temperature and the appointment time. However, many other factors may implicitly influence the patient's no-show that could not be inferred from the analyzed dataset.  
The results obtained from the data analysis represent a starting point in the development of efficient patient no-show classifiers in Brazil. In particular, this work can help the improvement of solutions to the public health system with the help of machine learning techniques. For future studies, we are in the process of collecting a larger dataset that includes records from every medical appointment made by the public health system in southern Brazil. The availability of additional information on patients and medical appointments may help to improve the ability of the model to learn new behaviours.  
Conceptualization, L.H.A.S., W.D.P. and A.M.d.R.F.; Funding acquisition, V.R.Q.L. and S.D.C.; Methodology, W.D.P., A.M.d.R.F. and J.L.V.B.; Writing--original draft, L.H.A.S., W.D.P. and A.M.d.R.F.; Writing--review & editing, V.R.Q.L., A.M.d.R.F., J.L.V.B. and S.D.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior, Brasil (CAPES)--Finance Code 001 and Fundacao de Amparo a Pesquisa do Estado de Santa Catarina (FAPESC)--EDITAL DE CHAMADA PUBLICA FAPESC No. 06/2017. It was also funded by ILIND--Instituto Lusofono de Investigacao e Desenvolvimento, under project COFAC/ILIND/COPELABS/1/2020 and COFAC/ILIND/COPELABS/3/2020, Fundacao para a Ciencia e a Tecnologia under Projects UIDB/04111/2020 (COPELABS-Cognitive and People-centric Computing) and UIDB/05064/2020 (VALORIZA-Research Centre for Endogenous Resource Vaporization).  
Not Applicable, the study does not report any data.  
The authors declare no conflict of interest.
# Numerical Simulation Research on Plane Alignment Parameters of Desert Highway
## 3 Results and Analysis
### Analysis of the Results of the Flat-Curve Embankment Model
#### 3.2.2 Characteristics of Horizontal Wind Speed Distribution  
For the section at z = 1.5 m on the windward side, the wind-sand velocity at positions 3 m, 5 m, 10 m, 20 m (s = 3 m, 5 m, 10 m, 20 m) was extracted from the midpoint of the slope foot of the line. The results are shown in Figure 10. It can be seen that the wind speed change of the sand-carrying wind is distributed in a \"V\" shape, the speed at the midpoint of the line is the smallest, and the speed on both sides is the largest, and the speed change range gradually decreases with the increase in the curve radius, indicating that the convex  
Figure 8: Flow-field distribution at the cross-section of embankment Y = 150 m under different surface windward forms.  
Figure 9: Flow-field distribution of embankment cross-section at different line positions on convex windward curve.  
curved embankment has a lateral transport effect on wind-sand flow. Additionally, the smaller the radius is, the stronger the lateral transport ability is.  
The horizontal wind speed of the concave windward curve is also extracted. The results are shown in Figure 11. It can be seen that the wind speed of the sand-carrying wind is distributed in an inverted \"U\" shape. As the value of the curve radius increases, the difference in wind-sand flow velocity decreases gradually, indicating that the concave curve embankment had a gathering effect on the wind-sand flow, and the smaller the radius, the stronger the gathering ability is.  
Figure 10: Wind-speed variation on the windward side of the \(Z=1.5\) m convex curve.
## 2 Materials and Methods
### Statistical Analysis  
An Excel database (Microsoft Corporation, Microsoft Excel 2010) was established, and data were entered using a double data entry method. The statistical analysis was performed using the R software version 4.3.3 (R Core Team 2024, Vienna, Austria). A sample size calculation was performed prior to the study initialization. For a 1% difference in %BF between the two methods, for a two-tailed test with a 0.8 power, a minimum of 34 subjects was necessary.  
The results of the measurements are reported as the mean \(\pm\) standard deviation (SD), followed by the range in brackets. The statistical significance was set at 0.05. The Shapiro-Wilk test was used to check data normality. Paired samples \(t\)-tests or Wilcoxon matched-pairs signed-ranks tests were performed in order to analyze the differences between the variables. The differences between the two methods were analyzed first for the entire study group. The analysis was then stratified according to the subjects' sex, BMI category, and by the presence of abdominal obesity. In the latter case, the classification criterion was the WHtR, with the 0.5 value as the cut-off. The results of the analysis with the subjects divided in subgroups according to the central obesity criteria provided by the WHO and the International Diabetes Federation can be found in the Supplementary Materials.  
The concordance between the %BF measured by the two techniques was assessed using the concordance correlation coefficient (CCC) and concordance plots. The following cut-off points were adopted for the CCC: negligible (CCC < 0.1), weak (CCC = 0.1-0.39), moderate (CCC = 0.4-0.69), strong (CCC = 0.7-0.89), and very strong (CCC = 0.9-1) [35]. Bland-Altman (BA) plots were performed in order to evaluate the agreement between the two measuring techniques. The differences \(d_{i}\) between the %BF pairs obtained with the two methods (\(i=1\), \(2\), \(\ldots n\), where \(i\) labels the subject and \(n\) represents the sample size) were plotted against their means. The bias was defined as the mean value of the differences, and the 95% limits of agreement were calculated as the mean \(\pm\) 1.96 SD, where SD is the standard deviation of the differences [36].
## 5 Agro-Climatic Mapping
### Estimation of Crop Potential Areas
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & **IGBP** & **UMD** & **LAI/FPAR** & **NPP** & **PFT** \\ \hline \(0\) & _Water_ & _Water_ & _Water_ & _Water_ & _Water_ \\ \hline \multirow{3}{*}{1} & _Evergreen_ & _Evergreen_ & _Evergreen_ & _Evergreen_ & _Needleleaf_ & _Needleleaf_ \\  & _Needleleaf_ & _forest_ & _forest_ & _crops_ & _vegetation_ & _trees_ \\ \hline \multirow{3}{*}{2} & _Evergreen_ & _Evergreen_ & & _Evergreen_ & _Evergreen_ \\  & _Broadleaf_ & _Broadleaf_ & _Strubs_ & _Broadleaf_ & _Evergreen_ \\  & _forest_ & _forest_ & & _vegetation_ & _Broadleaf trees_ \\ \hline \multirow{3}{*}{3} & _Deciduous_ & _Deciduous_ & & _Deciduous_ & _Deciduous_ \\  & _Needleleaf_ & _Needleleaf_ & _Broadleaf_ & _Needleleaf_ & _Needleleaf_ \\  & _forest_ & _forest_ & & _vegetation_ & _trees_ \\ \hline \multirow{3}{*}{4} & _Deciduous_ & _Deciduous_ & & _Deciduous_ & _Deciduous_ \\  & _Broadleaf_ & _Broadleaf_ & _Savanna_ & _Broadleaf_ & _Broadleaf_ \\  & _forest_ & _forest_ & & _vegetation_ & _trees_ \\ \hline \multirow{3}{*}{5} & _Mixed_ & _Mixed_ & _Evergreen_ & _Annual_ & \\  & _forest_ & _forest_ & _Broadleaf_ & _Broadleaf_ & _Shrub_ \\  & _forest_ & _forest_ & _vegetation_ & _Shrub_ \\ \hline \multirow{3}{*}{6} & _Closed_ & _Closed_ & _Deciduous_ & _Annual grass_ & \\  & _shrublands_ & _shrublands_ & _Broadleaf forest_ & _vegetation_ & _Grass_ \\ \hline \multirow{3}{*}{7} & _Open_ & _Open_ & _Evergreen_ & _Needleleaf_ & _Needleleaf_ & _Needleleaf_ \\  & _shrublands_ & _Shrublands_ & _Broadleaf_ & _Needleleaf_ & _Chruan_ & _Broadleaf_ \\ \hline \multirow{3}{*}{8} & _Woody_ & _Woody_ & _Needleleaf_ & _Needleleaf_ & _Chruan_ & _crops_ \\  & _swannas_ & _swannas_ & _forest_ & & _Chruan and_ \\ \hline \multirow{3}{*}{9} & _Savannas_ & _Savannas_ & _Non-vegetated_ & & _Chruan and_ \\  & _forest_ & _forest_ & _built-up_ & \\ \hline \multirow{3}{*}{10} & _Grasslands_ & _Grasslands_ & _Urban_ & & _Snow and ice_ \\  & _forest_ & & & & _Barren or_ \\  & _wetlands_ & & & & _sparse vegetation_ \\ \hline \multirow{3}{*}{12} & _Croplands_ & _Croplands_ & & & \\  & _urban and_ & _urban and_ & & & \\  & _built-up_ & _built-up_ & & & \\ \hline \multirow{3}{*}{14} & _Cropland/Natural_ & & & & \\  & _vegetation mosaic_ & & & & \\ \hline \multirow{3}{*}{15} & _Snow and ice_ & & & & \\ \cline{1-1}  & _Barren or_ & _Barren or_ & & & \\ \cline{1-1}  & _sparsely vegetated_ & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Land cover descriptions of MCD12Q1.
# Towards the Internet of Smart Trains:
## 6 IoT-Enabled Services: From More Efficient Operations to New Business Models
### Train Control Systems
#### 6.4.2 Safety Assurance and Signaling Systems
The results presented by the researchers show that, in a forest scenario, the GNSS-based system does not fulfill the requirements, and that it is required a sensor fusion structure composed by on-board positioning sensors. Therefore, a positioning system composed by a Doppler radar sensor and a GNSS receiver will meet the requirements. A WSN architecture focused on secure railways is described in [133]. Such a system measures acceleration and makes use of ultrasounds to identify spoilage on railroads. Another system for detecting objects on the tracks is presented in [134], where the researchers apply image processing and electromagnets for the detection. Additionally, [PERSON] et al. [135] use a WSN for early earthquake detection in high-speed railways that is able to send fast warnings to the control center.
## 3 Results and Discussion
### Pharmacokinetic Study in Dogs  
Plasma samples from a total of 24 dogs were analysed using the validated UPLC-MS/MS detection method to investigate the pharmacokinetic changes of Hu7691 in dogs. The main pharmacokinetic parameters, including the area under the curve (AUC0-t), peak  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Hu7691 Concentration**} & \multicolumn{3}{c}{**Intraday**} & \multicolumn{3}{c}{**Interday**} \\ \cline{2-7}  & **Mean \(\pm\) SD** & **Precision (\%)** & **Accuracy (\%)** & **Mean \(\pm\) SD** & **Precision (\%)** & **Accuracy (\%)** \\ \hline
5 & 4.9 \(\pm\) 0.3 & 6.9 & 97.7 & 5.2 \(\pm\) 0.1 & 1.8 & 104.0 \\
15 & 14.7 \(\pm\) 0.4 & 2.5 & 98.0 & 15.1 \(\pm\) 0.9 & 6.0 & 100.4 \\
400 & 394.7 \(\pm\) 4.1 & 1.1 & 98.7 & 423.6 \(\pm\) 23.2 & 5.5 & 105.9 \\
800 & 1478.9\(\pm\) 11.9 & 1.4 & 106.1 & 834.1 \(\pm\) 33.9 & 4.1 & 104.3 \\ \hline \hline \end{tabular} SD, standard deviation.  
\end{table}
Table 2: Inter- and intra-assay precision and accuracy of Hu7691 in beagles (n = 5).  
Figure 3: Linear fitting equation of the Hu7691 signal versus concentration.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Spiked**} & \multicolumn{2}{c}{**4 h (Ice)**} & \multicolumn{2}{c}{**12 h (Autosampler)**} & \multicolumn{2}{c}{**15 Days (\(-\)16 to \(-\)24 \({}^{\circ}\)C)**} & \multicolumn{2}{c}{**Freeze-Thaw Cycles (\(\times\)3)**} \\ \cline{2-7}  & \multirow{2}{*}{**Mean \(\pm\) SD**} & **Accuracy (\%)** & **Mean \(\pm\) SD** & **Accuracy (\%)** & **Mean \(\pm\) SD** & **Accuracy (\%)** & **Mean \(\pm\) SD** & **Accuracy (\%)** \\ \hline
15 & \(16.2\pm 1.3\) & 107.6 & \(16.3\pm 0.6\) & 108.8 & \(13.9\pm 0.2\) & 93.1 & \(15.8\pm 1.7\) & 105.3 \\
800 & \(804.5\pm 17.4\) & 100.6 & 865.3 \(\pm\) 10.9 & 108.2 & \(735.9\pm 23.7\) & 91.9 & \(726.3\pm 4.6\) & 90.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Stability of Hu7691 (n = 3).  
plasma concentration (Cmax), and elimination half-life (T\({}_{1/2}\)), are displayed in Table 4. As the concentration vs. time curves show (Figure 4), the absolute bioavailability of Hu7691 in dogs treated orally with the low, medium, and high doses of Hu7691 were 86.7%, 50.8%, and 50.5%, respectively. The \(AUC\) and \(C_{\max}\) of Hu7691 in the dog plasma increased with the increasing dose. For oral administration, the in vivo bioavailability was considerably higher at the low dose than at the medium and high doses.
## 1 Introduction
The demonstrated PDLS-based filter consists of a polarizing beam splitter (PBS), two HBF segments of equal length, an ordered WRC of a QWR and an HWR before the first HBF segment, and an ordered WRC of an HWR and a QWR ahead of the second HBF segment. The second HBF segment is butt-coupled to one port of the PBS so that its slow axis should be oriented at 22.5\({}^{\circ}\) with respect to the horizontal axis of the PBS, which enables the maximum spectral visibility without the use of an additional HWR. As described in Section 2, considering the filter transmittance obtained by Jones calculus, we found the AOA sets of the four wave retarders, which could bring extra phase shifts (\(\psi\)'s) from 0\({}^{\circ}\) to 360\({}^{\circ}\) to the narrowband transmittance function. From filter transmission spectra calculated according to the AOA sets found above, it is confirmed that the narrowband comb spectrum can be continuously frequency-tuned by properly adjusting the AOA's, clearly demonstrating the continuous wavelength tunability of the proposed comb filter adopting composite WRC's. As can be found in Section 3, this theoretical prediction is verified by experimental demonstration showing eight measured narrowband transmission spectra spaced 0.1 nm apart in wavelength. Finally, a brief summary and conclusion on our continuously wavelength-tunable narrowband comb filter are given in Section 4.
# An Analysis of Satellite Multichannel Differential Code Bias for BeiDou SPP and PPP
## 7 Conclusions
The precise products obtained from the CNES ([[http://www.ppp-wizard.net/products/REALTIME/](http://www.ppp-wizard.net/products/REALTIME/)]([http://www.ppp-wizard.net/products/REALTIME/](http://www.ppp-wizard.net/products/REALTIME/)) (accessed on 7 June 2021)) include the satellite orbit and clock offset. The DCB and OSB products from the CAS were obtained from the following website: [[ftp://ftp.gipp.org.cn/product/dcb/mgex/2021/](ftp://ftp.gipp.org.cn/product/dcb/mgex/2021/)]([ftp://ftp.gipp.org.cn/product/dcb/mgex/2021/](ftp://ftp.gipp.org.cn/product/dcb/mgex/2021/)) (accessed on 1 July 2020). The authors gratefully acknowledge the IGS MGEX for providing the GNSS data. We are also grateful for the BDS broadcast ephemerides provided by the Test and Assessment Research Center of the China Satellite Navigation System Administration Office. We also acknowledge the CAS for providing DCB and OSB products. The authors declare no conflict of interest.
## 6 ML-Driven RIS-NTN Systems: Future Perspectives and Challenges
ML methods must adapt to efficiently support 3D virtual reality, space and underwater connectivity, autonomous devices, backscatter communication, and energy harvesting, further adding to the intricacy of the problem. **Discrete Phase Shift Optimization:** The emergence of discrete phase shift models due to hardware limitations poses a significant challenge. Current techniques for solving discrete phase shift problems, such as quantization or brute-force searching, have performance degradation or complexity limitations. The need for advancements in optimization algorithms or adapting DRL algorithms to provide more efficient solutions is pressing, as more than the current solutions are needed to meet the demands of the problem. While RL shows promise for attaining optimal control strategies in wireless communications, there is a need to create precise RL techniques that are both computationally efficient and energy-efficient, particularly for aerial systems. These methods, once perfected, can accurately simulate real-world multi-agent uncertain situations and seamlessly interface with 3D virtual reality to fulfill requirements for SHD, EHD, high-throughput, and ultra-reliable low-latency communications, offering a bright future for this field. The concurrent optimization of active and passive beamforming poses significant challenges, particularly when dynamically sensing channel state information in NTNs with RIS assistance. New RL architectures such as soft-deep deterministic policy gradient (DDPG) algorithms have been proposed to address these challenges, but further investigation is needed to improve network performance in real-time and adapt to the evolving variations in RIS reflective element amplitudes [140; 141]. Developing simplistic methods that can deliver results comparable to exhaustive search techniques continues to present a significant challenge. While traditional optimization strategies such as the greedy algorithm hold potential, it is crucial to enhance the efficiency of RL algorithms in wireless applications in order to address these challenges effectively.
# A CFD Study of the Hydrodynamic Characteristics of an Autonomous Underwater Helicopter
## 2 Methodology and Numerical Approach
### Numerical Approach  
To investigate the hydrodynamics of the AUH well, the finite volume method (FVM) is applied to mesh the model. The mesh is divided into two types: structured mesh and unstructured mesh to handle complex geometry [34]. The density of the mesh will be concentrated around the profile of the disk-shaped underwater vehicle, while other areas will have gradually larger mesh sizes. To simulate with the least error compared with the experimental results, the size of the mesh elements is critical. The size of the mesh has a direct impact on the accuracy of CFD results, so we relied on several evaluation criteria such as skewness, orthogonal quality, and \(\Delta y^{+}\). We estimated the appropriate parameters for this simulation the boundary layer thickness.  
\[\delta=0.035L\text{Re}^{-\frac{1}{7}} \tag{12}\]  
where \(L\) is the diameter of AUH, Re is the Reynolds number defined by \(\rho lV^{\frac{1}{3}}/\mu\), and \(\
abla\) is the volume of AUH.  
To correctly model the first layer, the thickness can be expressed as follows:  
\[\Delta y=L\Delta y^{+}\sqrt{80}\text{Re}^{\frac{-13}{14}} \tag{13}\]  
In this case, the dimensionless wall distance value usually satisfies the simulation of the AUH for \(20<\Delta y^{+}<200\)[14], so the first layer thickness \(\Delta y=0.86\) mm. The total thickness of the boundary layer around the AUH hull was calculated with Equation (12) to be approximately 5.44 mm and the growth rate was 1.2. Figure 4 demonstrates the tetrahedral grid used in this study. The overlapping mesh is employed in the simulation. The computational zone is segmented into a background zone and a dynamic zone.  
The dependency of the number of elements can lead to errors in the CFD calculation process; thus, the round-off and interval were ignored together with the discretization uncertainty [35]. Likely, the number of inflation layers, mesh size, and assumptions made  
Figure 4: The (**a**) tetrahedral structured computational domain grid, (**b**) initial mesh, (**c**) boundary layer after refining the mesh boundary layer mesh, and (**d**) boundary layer mesh.  
in the numerical modeling may not fully capture the complex fluid dynamics around the AUH and similar aspects. Besides CFD factors, surface roughness, small protrusions, temperature changes over time, and other time-varying factors also have an impact on error generation. The number of elements for the dynamic zone is 0.4 million. The grid independence is checked by calculating the drag force along the \(x\)-axis (F\({}_{\text{x}}\)), with the total number of elements, as shown in Table 2. The elements are increased by refining the surface meshes on the AUH. In the numerical domain with 2.8 \(\times\) 10\({}^{6}\) elements, the drag force tends to be stable. Therefore, meshes of the same sizes are created. If a higher number of elements are generated, it will lead to longer calculation times without a significant improvement in accuracy.
# Mobility Prediction of Mobile Wireless Nodes
## 2 Mobility Models  
The number of existing mobility models has been growing quickly to meet the needs of research. Mobility models are necessary to evaluate the performance of a new network technique before it is given credence and implemented in a real network [4]. Many mobility models (e.g., random, temporal, and spatial-dependency models) have been created to be used with different network statuses. However, incoming and advanced mobility models are being designed to fit multiple applications. The RSSGM model is among these new mobility models, and is suitable for applications predicting patterns of recurrent visits to specific stations with semi-similar routes [1].
###### Abstract  
This study comprehensively characterized and compared the aroma differences between four different grades of Fenjiu (FJ, the most representative light-flavor Baijiu). Aroma-active compounds were analyzed by liquid-liquid extraction (LLE) coupled with gas chromatography-olfactometry-mass spectrometry (GC-O-MS). A total of 88 aroma-active compounds were identified, and 70 of them were quantified. The results showed that a majority of aroma compounds in high-grade FJ had higher aroma intensities and concentrations. Among these compounds, there were 28 compounds with odor activity values (OAVs) greater than one in all four wines, which indicated that they might contribute to the characteristic aroma of FJ. Temporal dominance of sensation (TDS) and quantitative descriptive analysis (QDA) were used to characterize the sensory differences. The results suggested that high-grade FJ had a rich, pleasant and lasting retronasal aroma perception and exhibited pleasant orthonasal aroma of floral, fruity, sweet and grassy. Partial least squares regression (PLSR) analysis effectively distinguished four kinds of FJ and revealed associations between the orthonasal aroma attributes and the aroma compounds with OAVs >1. There were 15 compounds with variable importance in projection (VIP) values >1, and they were considered potential aroma markers for quality prediction.  
light-flavor Baijiu (LFB); grades; GC-O-MS; odor activity values (OAVs); sensory evalution