esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions
The residual signal-to-noise ratio (SNR) shows pronounced oscillations for the station (without Eccosorb). The time-varying multipath amplitude of the residual SNR for the station equipped with microwave-absorbing material shows consistently much more reduced magnitude, confirming a limited multipath footprint in the carrier-phase observations. While individual satellite SNR values show a diminished power in the station with Eccosorb, the error involving carrier phase residuals also show a reduced noise level--a confirmation from processed GNSS observables in the form of post-fit residuals. The multipath frequency content obtained by performing Morlet wavelet analysis on the residual SNR shows the complexity of the frequency content in the environment from our continuous GNSS stations. While the multipath footprint is prevalent, particularly at lower elevation angles, the use of Eccosorb material reduces the effect but does not wholly remove it. Therefore, the next phase of our research will employ the multipath stacking maps using the raw post-fit carrier phase residuals to further mitigate the multipath error that is more evident at lower elevation angles. Conceptualizations, A.H. and F.N.T. ; methodology, A.H.; formal analysis A.H.; software, A.H.; data curation, A.H. and F.N.T., writing--original draft preparation, A.H.; writing--review and editing, A.H. and F.N.T. ; visualization, A.H.; supervision, F.N.T. ; project administration, F.N.T. ; funding acquisition, F.N.T. and A.H. All authors have read and agreed to the published version of the manuscript. Addisu Hunegan is funded by the Luxembourg National Research Fund/Fonds National de la Recherche (FNR) under project code O18/12909050/VAPOUR/. Not applicable. The RINEX data is available from the authors upon reasonable request. The authors like to express their heartfelt appreciation to the editor and anonymous reviewers for their insightful remarks and recommendations that helped improve the manuscript. The authors are grateful to [PERSON] and [PERSON] for their assistance in erecting the GNSS stations on the JFK building's roof, Kirchberg campus. The authors declare no conflict of interest.
## 1 Introduction
[36] used modeling methods combined with Monte Carlo simulation and multiple regression to analyze the impact of duct leaks. They found that the operating point of the fan is related to the location and size of the rupture. [PERSON] et al. [37] analyzed the air leakage between the joints of the ventilation ducts in tunnel ventilation systems. They believe that by using duct systems with lower Atkinson friction coefficients and selecting duct systems with leak-free couplings, the cost of ventilation systems can be reduced. In summary, a large number of research results have shown that an important factor leading to construction delays due to insufficient effective air volume in the working face is air leakage in ducts. The prediction of duct leakage can effectively determine the fan capacity and accurately meet the ventilation requirements of the working face. The derivation of ventilation leakage rates for specific engineering projects through theoretical calculations and numerical simulations has been widely confirmed. However, the small amount of data on existing achievements has led to the need to test their generalizability. In order to optimize energy efficiency, reduce ventilation costs, and fully grasp the migration and diffusion laws of pollutants in ventilation systems considering duct leakage, this paper conducts research on tunnel ventilation systems. The law of duct leakage has been explored based on the data organization of hundreds of duct models. The leakage data under different conditions were compared with existing empirical equations, and an estimation equation for the effective airflow rate of tunnels considering duct leakage was obtained. And the mechanism of the impact of duct leakage on the spatiotemporal distribution of pollutants was discussed. This project supplements and improves previous research results, and establishes a set of tunnel construction ventilation technology systems. The calculation of air leakage provides support for the in-depth study of ventilation theory and technology in underground engineering.
# Integrating Environmental Data for Mental Health Monitoring: A Data-Driven IoT-Based Approach
## 2 Related Work
### Fuzzy Logic in Environmental Data Analysis  
Fuzzy logic, a mathematical approach based on degrees of truth rather than binary logic, has emerged as a powerful tool for analysing complex and uncertain environmental data. Environmental systems often involve intricate interdependencies and imprecise information, making traditional analytical methods less effective [20]. Fuzzy logic provides a framework for dealing with such uncertainties, enabling researchers to model complex environmental phenomena more accurately. Applications of fuzzy logic in environmental data analysis include air quality monitoring, climate change modelling, and disaster risk assessment. For instance, fuzzy logic systems can evaluate air quality indices by integrating multiple parameters, such as pollutant concentrations, humidity, and temperature, to produce comprehensive assessments [21].  
The adaptability of fuzzy logic allows for the incorporation of expert knowledge and subjective judgments, making it particularly useful for decision-making in environmental management. Despite its advantages, the implementation of fuzzy logic requires careful design and validation to ensure reliability and accuracy. As environmental challenges become increasingly complex, the role of fuzzy logic in providing actionable insights is expected to grow significantly.
# Smooth Fractional Order Sliding Mode Controller for Spherical Robots with Input Saturation
## 5 Simulation Study
### Tracking Performance Analysis  
The results in Figure 2a indicate that all four controllers can track the desired angular velocity. The NFO-PID SMC has a shorter adjustment time and does not have overshoot. The adjustment time  
Figure 5: Curves of second-layer sliding mode surfaces \(S\).  
Figure 6: Curves of first-layer sliding mode surfaces \(s_{1}\).  
Figure 7: Curves of first-layer sliding mode surfaces \(s_{2}\).  
is shortened from 6.1 s to 3.4 s. In Figure 2b, it can be seen that the controller without a filter causes wave-like behavior and has a larger opposite speed at the start stage. However, the proposed method yields a smoother dynamic response, showing that the filter is effective for the smooth control of spherical robots. Though the filter is used in the IO SMC, the wave-like response still occurs in the control process, but this phenomenon is hardly observed in the NFO-PID SMC. Figure 3 proves that the NFO-PID SMC and IO SMC can both track the output of the filter \(\overline{x}_{2d}\); furthermore, the NFO-PID SMC controller forces error \(e_{2}\) to begin to converge earlier than the IO SMC does. This case also shows that the NFO-PID SMC has better tracking performance than IO SMC.  
Figure 4 shows that the NFO-PID SMC controller can provide the spherical robot with smooth input. The output of the two controllers reaches the maximum limit in a very short time, but the output of IO-SMC stays in the maximum value for a longer time than the NFO-PID SMC. This explains why the wave motion still occurs in the IO SMC. Figure 5 highlights the better dynamic response of the second-layer sliding surfaces of the NFO-PID SMC controller compared to the IO-SMC controller. Figures 6 and 7 show that the shell and pendulum subsystems are asymptotically stable, and the fractional sliding surface proposed in Equations (24) and (25) converges to zero faster than the integral sliding surface. This indicates that the fractional sliding mode surface proposed in this paper is effective. In summary, the NFO-PID SMC controller successfully tracks the desired velocity, and the goal of better control performance is achieved.
# Physicochemical Properties and Volatile Profile of Chito: A Traditional Dry-Cured Goat Meat Product
## 2 Materials and Methods
### Analysis of the Meat Product
#### 2.2.6 Analysis of Volatile Compounds  
Volatiles were extracted from chito samples using headspace solid phase microextraction (HS-SPME GC-MS) with a DVB/CAR/PDMS-50/30 \(\upmu\)m fibre according to [PERSON] et al. [9] and [PERSON] et al. [17], with some modifications. In a 20 mL headspace vial (Agilent Technologies, CA, USA), 2.5 g of chito sample (previously ground) was mixed with 5 mL of 25% NaCl solution (KEM, Naucalpan, Mexico) for 1 min. Then, 3 \(\upmu\)L of 3-octanol solution (Fluka Chemika, Buchs, Switzerland) (0.3 \(\upmu\)L/mL methanol) (Tedia Company, Fairfield, OH, USA) was added as an internal standard, and the vial was sealed using screw caps with silicone/PTFE septa (Agilent Technologies, USA) along with a mini-nert valve.  
Prior to the injection, the vial containing the sample was equilibrated at 90 \({}^{\circ}\)C for 30 min in agitation. Then, the SPME fibre was exposed to the sample for another 30 min under the same conditions with continuous agitation to maximize the adsorption of analytes.  
Immediately, the fibre was retracted and inserted into the inlet injection port of a GC 7890B gas chromatograph (Agilent Technologies, CA, USA) for desorption for 7 min at 260 \({}^{\circ}\)C.  
Afterwards, volatiles were analysed using an HP 7890B gas chromatograph (Agilent Technologies, CA, USA) equipped with an HP-5 MS column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\mu\)m; Agilent Technologies, USA) directly coupled to an HP 5977a mass-selective detector. Helium served as the carrier gas at a constant flow rate of 1 mL/min in splitless mode. The needle temperature was 250 \({}^{\circ}\)C. The column's initial temperature was set at 40 \({}^{\circ}\)C for 1 min with a ramp of 15 \({}^{\circ}\)C/min until it reached 250 \({}^{\circ}\)C; the temperature was maintained for 1 min. The mass spectrometer was operated in electron impact mode (70 eV) with a scanning range of 30-550 \(m/z\). Volatiles were identified by matching their retention times and mass spectra with those from authentic compounds available from computerized spectral databases (NIST/EPA/NIH 75 K), analyzing the spectrum, and ensuring a match equal to or greater than 80%. All concentrations of identified volatile compounds were reported as \(\mu\)g/kg.
## Data and Methods
### Summary of Differences between CLARA-A2 and ISCCP-H
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Satellite**} & \multirow{2}{*}{**Start**} & \multirow{2}{*}{**End**} & \multicolumn{2}{c}{**Equatorial**} & \multirow{2}{*}{**In CLARA-A2**} & \multirow{2}{*}{**In ISCCP-H**} \\  & & & **Overpass Time** & & \\ \hline
**NOAA-7** & 24 Aug 1981 & 7 Jun 1986 & 0230 PM & yes & yes \\
**NOAA-8** & 3 May 1983 & 31 Oct 1985 & 0730 AM & no & yes \\
**NOAA-9** & 25 Feb 1985 & 11 May 1994 & 0230 AM & yes & yes \\
**NOAA-10** & 17 Nov 1986 & 17 Sep 1991 & 0730 AM & no & yes \\
**NOAA-11** & 8 Nov 1988 & 13 Sep 1994 & 0200 AM & yes & yes \\
**NOAA-12** & 14 May 1991 & 15 Dec 1994 & 0730 AM & yes & yes \\
**NOAA-14** & 30 Dec 1994 & 23 May 2007 & 0130 PM & yes & yes \\
**NOAA-15** & 13 May 1998 & 31 Dec 2012 & 0730 AM & yes & yes \\
**NOAA-16** & 21 Sep 2000 & 9 Jun 2014 & 0200 PM & yes & yes \\
**NOAA-17** & 24 Jun 2002 & 31 Dec 2012 & 1000 AM & yes & yes \\
**NOAA-18** & 30 Aug 2005 & 31 Dec 2012 & 0200 PM & yes & yes \\
**NOAA-19** & 2 Jun 2009 & 31 Dec 2012 & 0200 PM & yes & yes \\
**MetOp-A** & 20 Jun 2007 & 31 Dec 2012 & 0930 AM & yes & yes \\
**MET-2** & Jul 1983 & Jul 1988 & & no & yes \\
**MET-3** & Aug 1988 & Apr 1990 & & no & yes \\
**MET-4** & May 1990 & Jan 1994 & no & yes \\
**MET-5** & Feb 1994 & Dec 2006 & no & yes \\
**MET-6** & Mar 1997 & May 1998 & no & yes \\
**MET-7** & Jun 1998 & Dec 2012 & no & yes \\
**MSG-1** & Jul 2006 & Apr 2007 & no & yes \\
**MSG-2** & May 2007 & Dec 2012 & no & yes \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Satellites and their temporal coverage in CLARA-A2 and ISCCP-H. Only the satellites covering the European study domain and being operational before the end of the study period (31 Dec 2012) are listed.
## 3 Development of PT Signal Processing  
### Pulsed Phase Thermography (PPT)  
Pulsed Phase Thermography (PPT), which simultaneously combines the advantages of PT and LIT, was initially developed in 1996 by [PERSON] and [PERSON] [6]. Pulsed Phase Thermography (PPT) is the phase analysis in the frequency domain of PT and is a signal processing method that solves the problem of heating inhomogeneity as it is less sensitive to the non-uniformity of heating, surface irregularities, and environmental reflections [30]. In Pulsed Phase Thermography (PPT), data are transformed from the time domain to the frequency domain using the one-dimensional discrete Fourier transform [31]. FFT can be used in any waveform to extract amplitude and phase angle from the transient signals. As a quick algorithm of DFT, FFT can play an influential role in analyzing the amplitude and phase of every harmonic frequency and is widely used in PPT. It can also image large components on airframes relatively fast. Since PPT is widely used in the aerospace industry to inspect metallic and composite components, it can be considered a valid alternative to time-domain PT. The procedure to extract the phase from the thermal data \(T(k)\) is based on the Fourier transform, which is computed according to the well-known formula [32; 33]:  
\[F_{n}=\Delta t\sum\
olimits_{k=0}^{N-1}T(k\Delta t)exp^{\frac{\beta\text{ }m\text{t}}{N}}=Re_{n}+Im_{n} \tag{6}\]  
where \(R_{e}\) and \(I_{m}\) are the real and imaginary parts of the transform sequence, \(F_{n}\), respectively, \(N\) is the number of thermal images, \(j^{2}=-1\) is the imaginary number, and \(\Delta t\) is the sampling time interval. Real and imaginary parts of the complex transform are then applied to evaluate the amplitude and phase angle and can be expressed by Equations (7) and (8), respectively:  
\[A_{n}=\sqrt{Re_{n}^{2}+Im_{n}^{2}} \tag{7}\]  
\[\varnothing_{n}=tan^{-1}\bigg{(}\frac{Im_{n}}{Re_{n}}\bigg{)} \tag{8}\]  
presents enough phase contrast to be detected on the phase spectrum. The \"blind frequency\" and the defect depth are typically correlated by the following formula [34]:  
\[d=C_{1}\sqrt{\frac{\alpha}{\pi f_{b}}} \tag{9}\]  
where \(C_{1}\) is called a \"correlation constant\" and ranges between 1.5 and 2 [35].
## 4 Experiment and Analysis
### Performance of the Models
#### 4.2.2 Model Performance for Each Site  
To further analyze the spatial performance of the models, the R and RMSE values between the observed and estimated NMRI using these models over the 329 sites was calculated, and the results are presented in Figure 8. MLR has a significantly poor performance, with the R values of most sites lower than 0.7 and RMSE values higher than 0.03. The R values of 261 out of 329 sites for the RF model are greater than 0.7, and only 226 (203) out of 329 sites for the GRNN (BPNN) model are greater than 0.7. Meanwhile, 95% of the total sites report an RMSE of less than 0.03 for the RF model, and only 67% (55%) report an RMSE of less than 0.03 for BPNN (GRNN). This shows that, in terms of both R and RMSE, the RF results are superior to those of BPNN and GRNN in most sites. The randomness of RF, which is manifested in choosing observations at random and choosing features at random, makes the estimated results more robust. Comparing the two machine learning methods with relatively poor results, the sites where R values for BPNN are worse than those of GRNN are mainly concentrated in the eastern area with sparse site distribution, while the sites where RMSE values for BPNN are better than those of GRNN are mainly concentrated in the western coastal area, with dense site distribution. This is mainly due to the fact that BPNN has a disadvantage of easily converging to local minima, whereas GRNN improves the local approximation ability and has the advantage of global convergence. Therefore, GRNN is more stable and less sensitive to the density of site distribution than the BPNN.  
Based on the comparison and analysis of the overall accuracy and the performance for each site with these models, we can summarize that the machine learning methods show an obvious superiority over the traditional linear fitting methods, and among the three machine learning methods, RF shows the best performance. BPNN and GRNN have slightly poor performance, and their overall performance is comparable.  
Figure 7: Scatter plots of the comparison model results: (**a**) BPNN-model fitting; (**b**) GRNN-model fitting; (**c**) RF-model fitting; (**d**) MLR-model fitting; (**e**) BPNN-cross-validation; (**f**) GRNN-cross-validation; (**g**) RF-cross-validation; (**h**) MLR-cross-validation. The dashed line is the 1:1 line. Footnote: BPNN (Back-Propagation Neural Network), GRNN (Generalized Regression Neural Network), RF (Random Forest), MLR (Multiple Linear Regression).  
Figure 8: Spatial distribution of R and RMSE between the observed and estimated NMRI over the PBO H2O sites for the four models: (**a**) BPNN-R; (**b**) BPNN-RMSE; (**c**) GRNN-R; (**d**) GRNN-RMSE; (**e**) RF-R; (**f**) RF-RMSE; (**g**) MLR-R; (**h**) MLR-RMSE.
## 3 On-Body Communication and Connectivity
### Co-Channel Interference and Coexistence Techniques  
With the widespread deployment of wireless networks in our daily living environment, BAN solutions are subject to strong co-channel interference, especially on the unlicensed industrial, scientific and medical (ISM) radio bands which are presently populated by various wireless technologies. The resulting interference can severely impact the connectivity and thus the communication performances.  
In this regard, the IEEE 802.15.6 standard has proposed specific coexistence strategies, including beacon shifting, channel hopping, and active super-frame interleaving. In the first approach, each BAN coordinator adopts a different beacon shifting pseudo-random sequence to reduce the interference with neighboring BANs.  
The second technique, which is only applicable to narrow-band channels, consists of choosing polynomial-based channel hopping sequences to avoid having neighboring BANs use the same radio channel. The active super-frame interleaving technique enables BAN coordinators to cooperatively coordinate the schedule of their active super-frames. Additionally, the carrier sense multiple access with collision avoidance (CSMA/CA) medium access control protocol could also be adopted to reduce the interference by letting the BANs nodes sense the occupancy of the radio channel prior to any data transmission.  
The packet error rate distributions of co-channel interference among up-to five co-located BANs are presented in Figure 4. IEEE 802.15.6 proposed coexistence technique comparisons are highlighted. It is observed that both time shared and channel hopping approaches are well-suited to minimizing interference from neighboring BANs. However, for a dense deployment, new or enhanced schemes must be proposed.  
Figure 3: Average packet delivery ratio under walking, stand–sit and running mobility patterns at varied transmission power from \(0\) dBm, \(-10\) dBm and \(-20\) dBm.
## 1 Introduction
However, because the statistical data currently available is unable to meet the need for a large scale, it is not possible to establish an effective evaluation system for the comprehensive and quantitative assessment of the regional oxygen balance, nor is it possible to analyze the reasons for any reported imbalance. Urban agglomeration is the merging of different metropolitan areas that interconnect at a regional scale. Thus, urban agglomerations provide a suitable scale for an atmospheric study [31]. China is experiencing rapid urbanization, where natural surfaces and economic activities are undergoing significant change [32; 33; 34; 35], and metropolitan areas are sensitive to the atmospheric oxygen balance. After more than 30 years of reforming and opening-up, the Pearl River Delta (PRD) has become one of China's three major urban agglomerations [36; 37; 38]. The level of urbanization in the PRD is high and the urban system is well developed, with cities at all levels having a close connection. At the same time, land cover in the PRD has changed dramatically as large amounts of natural land have been developed [39]. The rapidly developing economy has resulted in significant economic and structural changes in the PRD [40]. However, the characteristics of the cities in the PRD vary substantially. Dongguan formed according to market demands, Guangzhou evolved from the old city, and Shenzhen was built to comply with modern urban planning theory. The oxygen balance in the PRD can be extended to the other rapidly urbanizing regions in China, because the PRD is experiencing an innovative Chinese urbanization. Therefore, this study focused on the oxygen balance of the PRD in 2011 and established an oxygen balance calculation model by applying remote sensing gross primary productivity (GPP) data via the C-FIX model.
# Application of Machine Learning Techniques to Predict a Patient's No-Show in the Healthcare Sector
## 3 Related Work
Three machine-learning algorithms (logistic regression, JRip, and Hoeffding tree) were compared and all models have precision and recall of around 90%. As a result, they found the most important features that impact the no-show rate in a paediatric clinic context: patient location, age group, nationality, appointment time, department, and clinic subspeciality. This work has some findings in its data explorations for underage patients in a paediatric clinic but lacks in exploring other than the traditional classifiers. In [37], a predictive model was developed to determine the risk-increasing and risk-mitigating factors associated with missing appointments. Then the model was used to assign a risk score to patients on an appointment basis across a range of medical specialities. Machine learning models were applied to different specialities and the performance varied by area: F1 score ranging from 0.4 to 0.61. Rheumatology is a speciality that there must be other factors that influence the no-show rates because only a 0.31 F1 score was reached. A key strength of this study is that it has included records from every medical speciality for an entire country, Wales. Nevertheless, only variables presented in the original dataset was considered and the influence of variables, such as weather or the impact of other events, were not taken into consideration for the predictive model. The work presented in [38] shows that the most important features in the prediction models developed were history of no-shows, appointment location and speciality. Information as age, day of the week, time of appointment, gender and nationality did not have a significant impact on the results. As a result, both JRip and Hoeffding Trees algorithms yielded a reasonable degree of accuracy. Then, this study shows that the higher the rate of a previous no-shows, the higher the chances that a patient will commit a no-show on subsequent visits. A comparison was obtained between the model developed in this study and the related works considered. In Table 1, we can see that the related works have distinct no-show rates, and the number of data from patients collected and data provenance vary significantly. These factors can influence the model's accuracy in many ways. Different from the studies mentioned above that used a dataset from Brazil, this work collected an anonymized dataset from a public health centre, ensures the correctness of the samples and approved and authorized the usage of the data by the university Ethics Committee, to follow all restrictions of the General Data Protection Law. Related to performance measures, we can verify that none of the described works has the accuracy achieved in the model performed in this study. Moreover, this work is relevant as it provides a new look at the no-show problem in Brazil and can help restructure our public policies.