esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Federated Learning for Early Health Prediction
### Federated Learning for Disease and Mortality Prediction
#### 6.2.6 COVID-19 Mortality  
[PERSON] et al. in [91] introduced a pioneering application of federated learning to enhance mortality prediction in hospitalized COVID-19 patients. Their research employed machine learning techniques in a federated framework, enabling collaborative model training on electronic health records (EHRs) from various healthcare facilities while maintaining data privacy. By aggregating insights from different sources, this approach seeks to improve the accuracy of mortality prediction for COVID-19 patients, a critical aspect of healthcare management during the pandemic. The paper underscores the significance of federated learning in addressing urgent healthcare challenges, such as COVID-19, by leveraging distributed data to enhance patient outcomes without compromising data security, exemplifying the potential of federated learning in public health emergencies.  
In another work, [PERSON] et al. in [92] presented a significant advancement in healthcare analytics by applying federated learning to enhance mortality prediction for COVID-19 patients. Their research leverages electronic health records (EHRs) from multiple healthcare facilities to collaboratively train predictive models while preserving patient data privacy. By aggregating insights from diverse sources, the study aims to improve the accuracy of mortality prediction, a crucial aspect of managing patients during the COVID-19 pandemic. The paper underscores the potential of federated learning in addressing pressing healthcare challenges, demonstrating how it can harness distributed data to enhance patient outcomes while ensuring data security and privacy in the context of a global health crisis.
## 4 Experiments and Analyses
### Damage Evaluation Using Hilbert-Huang Transform  
HHT, as a combination of EMD and HT, can be used for non-stationary signals to obtain the instantaneous frequency. The Hilbert marginal spectrum statistically represents the cumulative amplitude distribution of each frequency point. If there is energy of a certain frequency in the signal, it means that there must be a vibration wave of that frequency. In other words, the Hilbert marginal spectrum can reflect the actual frequency component of the signal more accurately. After the wavelet packet transform, HHT was applied to preprocessed signal. The IMFs of the EMD are shown in Figure 9. The signal was decomposed into six IMFs and residues. Then, HT was performed for each IMF and residue. The Hilbert marginal spectrum of different delamination damages was obtained by integrating Hilbert spectrum and is shown in Figure 10.  
In Figure 10, the red, magenta and green lines represent the component of the fundamental frequency (80 kHz), the second-order harmonic (160 kHz) and the third-order harmonic (240 kHz). From Figure 10, the fundamental frequency is the main component of the signals. The amplitude of the fundamental frequency is the highest, and the second-order harmonic is obvious. The amplitude of the third-order harmonic is very smaller than fundamental frequency and second-order harmonic. The amplitude of the fundamental frequency decreased slightly with the increase of delamination damage. The change of the second-order harmonic amplitude is not obvious with the increase of delamination damage. The amplitude of the third-order increased slightly with the increase of  
Figure 8: Wavelet packet energy of the undamaged pipe.  
delamination damage. In fact, with the increase of delamination damage, the nonlinear effect in the pipe increases. The energy released from the fundamental frequency to the higher-harmonics will increase. According to the obtained Hilbert marginal spectrum, the nonlinear coefficient was calculated. The changing curves of second-order and third-order nonlinear coefficients with the increase of delamination damage are shown in Figures 11 and 12.  
The variation trend of the second-order and third-order nonlinear coefficient is similar. When there is no delamination length, the second-order and third-order nonlinear coefficient were lowest. The nonlinear coefficient is increased greatly when the damage occurs, especially the second-order nonlinear coefficient. Then, with the increase of delamination damage, the second-order and third-order nonlinear coefficient are increased. The nonlinear coefficient increased with the delamination damage, which indicated that a nonlinear coefficient could describe the change of damage.  
Figure 10: Hilbert marginal spectrum processed by WPT+HHT of different delamination damages.  
(**a**) 0 mm; (**b**) 4 mm; (**c**) 8 mm; (**d**) 12 mm; (**e**) 16 mm; (**f**) 20 mm.  
Figure 9: Intrinsic Mode Functions (IMF) of the preprocessed signal.
## 2 Materials and Methods
### Main Research
#### 3.2.7 Summary of Research Data  
Table 14 presents the summary results of the studies, on the basis of which it is possible to compare and determine the optimal technological solution for the polyfractional composition of the binder in the SCC. According to the summary table, all evaluation criteria demonstrate a similar tendency to improve evaluation parameters, depending on the packing density of the polyfractional binder. Meanwhile, samples using a three-component binder composition exhibited more effective transformation processes of the physical and mechanical characteristics of concrete, achieving more stable results (according to the assessment of variation coefficients) compared to samples using a two-component composition. Effects of two-component compositions relative to the reference sample are as follows:  
* An increase in compressive strength of 15-20%;
* An increase in bending strength of 5-7%;
* An increase in freeze-thaw resistance of 43-75%;
* A decrease in water absorption of 3-4%;
* An increase in water permeability of 33%;
* An increase in chemical resistance of 1.6-16.8% (depending on the environment). Effects of three-component compositions relative to the reference sample are as follows:  
* An increase in compressive strength of 22-26%;
* An increase in bending strength of 7-11%;
* An increase in freeze-thaw resistance of 71-100%;
* A decrease in water absorption of 8-10%;
* An increase in water permeability of 67%;
* An increase in chemical resistance of 5-30% (depending on the environment). Regarding the optimal solution for polyfractional composition, the choice leans towards Type 6, with a fraction ratio of 15% of 1500 cm\({}^{2}\)/g, 75% of 3000 cm\({}^{2}\)/g, and 10% of 4500 cm\({}^{2}\)/g. This decision is justified by the fact that changes in the physical and mechanical parameters of Type 6 samples compared to Type 7 samples are insignificant, while the production cost of Type 7 composition relative to Type 6 is considerable. The changes in the physical and mechanical characteristics of Type 7 concrete compositions in comparison to Type 6 are as follows: an increase in compressive strength by 1.8%; an increase in bending strength by 1.4%; unchanged freeze-thaw resistance; a decrease in water absorption of less than 1%; unchanged water permeability; and an increase in chemical resistance of less than 1%. The validity of the influence of the Type 6 composition is confirmed by significant transformations in the physical and mechanical characteristics of the concrete compared to the reference sample, which are a 26% increase in compressive strength; a 10% increase in bending strength; a 100% increase in freeze-thaw resistance; a 100% decrease in water absorption; a 67% increase in water permeability; and a 29% increase in chemical resistance (NaCl), 5% (HCl), and 7% (Na\({}_{2}\)SO\({}_{4}\)).
## 1 Introduction
The ITU-R stated that in regions where there are no localized parameters, the recommended rain heights obtainable from the global mean zero-degree isotherm height (ZDIH) map should be adopted [11]. However, several studies have revealed that the ITU-R ZDIH map provides poor estimation when compared with measurements from both ground-based and satellite-borne radars [12; 13; 14; 15; 16]. The spatio-temporal variation of rain-induced attenuation informed the decision to conduct this study to obtain accurate knowledge of the spatial and seasonal distribution of rain height. Precise estimates of rain height are therefore crucial to help maximize the efficiency of communication links, especially in systems that suffer from high rain attenuation such as high-frequency radio links. Telecommunications systems can also benefit from the inclusion of more elaborate geographic data, as the studies by [PERSON] et al. [17] have pointed out. Moreover, the application of artificial intelligence with machine learning to rainfall estimation models offers a potential approach to improve the estimation of rain height. Kernel density estimation (KDE) is one of the best non-parametric approaches in meteorological research that can contribute to the improvement in the estimation of the spatio-temporal distribution of meteorological information [18]. By employing KDE, researchers can estimate rain height distribution accurately and identify the influence of extreme variations on rain-induced attenuation, which is vital for mitigating their impacts on communication networks [19]. Incorporating KDE with the help of machine learning improves the accuracy of prediction, as well as identifying additional interactions during analysis which could not be spotted by using common statistical approaches [20].
# Fuzzy Rough C-Mean Based Unsupervised CNN Clustering for Large-Scale Image Data
## 3 Experiments
### Performance Measure  
We adopted four widely utilized clustering performance measures to evaluate the performance of the proposed method, Normalized Mutual Information (NMI) [80], Clustering Accuracy (ACC) [81], Mean of F-Measure (MFM), and Mean of Area Under the Curve (MAUC) [82].  
NMI is defined as  
\[\mathrm{NMI}(\mathrm{C},\mathrm{Y})=\frac{\mathrm{I}(\mathrm{C},\mathrm{Y})}{ \sqrt{\mathrm{H}(\mathrm{C})\mathrm{H}(\mathrm{Y})}} \tag{17}\]  
where C is the class label, Y is the cluster label, H(.) stands for the Entropy and \(\mathrm{I}(\mathrm{CY})=\mathrm{H}(\mathrm{C})-\mathrm{H}(\mathrm{C}/ \mathrm{Y})\) denotes the mutual information between C and Y. A higher NMI is more consistent for clustering results.  
The Acc gives the same weight for each class. The final result is obtained by the average value of the accuracy rate of each class independently. Acc is defined as.  
\[\mathrm{Acc}=\frac{\sum_{i=1}^{n}\mathrm{Acc}_{i}}{m} \tag{18}\]  
where \(m\) is the number of classes and \(\mathrm{Acc}_{i}\) stands for the accuracy rate for the \(i\)th class.  
Binary class problems are shown in Table 2 and donate categorized results of True and False. Here, the minority class is considered positive and the majority class is considered negative. Several measures can be deduced from the confusion matrix for the binary class problem.  
\[\mathrm{Recall}=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FN}} \tag{19}\]  
\[\mathrm{Precision}=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}} \tag{20}\]  
\[\mathrm{F}-\mathrm{Measure}=\frac{2\times\mathrm{recall}\times\mathrm{Precision}}{ \mathrm{recall}+\mathrm{Precision}} \tag{21}\]  
The mean F-Measure can be defined for multi-class problems as follows:  
\[\mathrm{MFM}=\frac{\sum_{i=1}^{m}\mathrm{(FM)}_{i}}{m} \tag{22}\]  
where \(m\) is the total number of classes and \(i\) is the index for positive class.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **No of Sample** & **No. of Classes** & **Image Size** \\ \hline ILSVRC12 & 1,250,000 & 1000 & 256 \(\times\) 256 \\ MNIST & 70,000 & 10 & 28 \(\times\) 28 \\ YTF & 10,000 & 41 & 55 \(\times\) 55 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of Datasets.  
Mean AUC is the average of the pairwise AUC values of all pairs of classes which is defined as  
\[\begin{split}\text{MAUC}=\frac{2}{m(m-1)}\sum_{i<j}\bigl{(}\text{ AUC}\ \bigl{(}C_{i},C_{j}\bigr{)}\bigr{)}\\ =\frac{2}{m(m-1)}\sum_{i<j}\bigl{[}A\bigl{(}C_{i},C_{j}\bigr{)}+A \bigl{(}C_{j},C_{i}\bigr{)}\bigr{]}\end{split} \tag{23}\]  
For two classes \(C_{i}\) & \(C_{j}\), the value of AUC (\(C_{i}\), \(C_{j}\)) represents the probability of being assigned to the \(i\)th class by the classifier. A randomly selected sample from the first class (\(i\)th class) has a higher probability to assign compared to a randomly selected sample from the second class (\(j\)th class) and vice versa.
## 2 Materials and Methods
### Field Trials  
Field trials were established in late spring 2017 and were cultivated in Site 3 (polluted) and in Site 5 (unpolluted). In each site, three industrial hemp monoecious cultivars (Uso 31, Felina 32, Futura 75) were cultivated (each plot about 500 m\({}^{2}\)).  
After a primary tillage with ripper and rolling narrow to obtain a good preparation of seedbed, industrial hemp was sown by Agris staff to guarantee a proper distribution of varieties following the layout of the experimental design. The experimental design applied was the randomized complete block design (RCBD) with three replications and the same randomization over the years. To guarantee the exact positioning every year, each plot trial was georeferenced using a global navigation satellite system (GNNS) receiver, the Geomax Zenith 35 Pro TAG with RTK correction for centimetre-scale accuracy.  
To evaluate the potential phytoextraction of industrial hemp, samples of hemp were collected at seed maturity stages. The same day or the day after irrigation, samples of whole plants (including roots) were collected. Two samples of 25-250 whole plants for each plot were harvested depending on the dry matters (DMs) quantities required for the analyses. The whole plants sampled were separated into different parts: roots, stems, leaves, seeds, only the roots were rinsed with tap water and deionized water. Fresh weight and dry weight (after the samples dried at 65 \({}^{\circ}\)C for 72 h) were determined and finally, Zn, Pb and Cd tissue contents were analysed. For plant sample nitric acid and hydrogen peroxide digestion was applied [39], in the oven MARS 5, CEM and the matrix was analysed using an Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES) OPTIMA 7300 DV, PerkinElmer.
###### Abstract  
This paper developed an artificial intelligence home environment monitoring system by using the Android Studio development platform. A database was constructed within a server to store sensor data. The proposed system comprises multiple sensors, a message queueing telemetry transport (MQTT) communication protocol, cloud data storage and computation, and end device control. A mobile application was developed using MongoDB software, which is a file-oriented NoSQL database management system developed using C++. This system represents a new database for processing big sensor data. The k-nearest neighbor (KNN) algorithm was used to impute missing data. Node-RED development software was used within the server as a data-receiving, storage, and computing environment that is convenient to manage and maintain. Data on indoor temperature, humidity, and carbon dioxide concentrations are transmitted to a mobile phone application through the MQTT communication protocol for real-time display and monitoring. The system can control a fan or warning light through the mobile application to maintain ambient temperature inside the house and to warn users of emergencies. A long short-term memory (LSTM) model and a convolutional neural network (CNN) model were used to predict indoor temperature, humidity, and carbon dioxide concentrations. Average relative errors in the predicted values of humidity and carbon dioxide concentration were approximately 0.0415% and 0.134%, respectively, for data storage using the KNN algorithm. For indoor temperature prediction, the LSTM model had a mean absolute percentage error of 0.180% and a root-mean-squared error of 0.042 \({}^{\circ}\)C. The CNN-LSTM model had a mean absolute percentage error of 1.370% and a root-mean-squared error of 0.117 \({}^{\circ}\)C.  
A Note: KNN; LSTM; CNN; application system; MQTT protocol; Node-RED software; machine learning; home monitoring system
## 2 Rf Model: Simulation Results vs. Experimental Measurements
### Impedance Matching Procedure  
The impedance matching in the real machine operation is done in three steps: first, the sliding shorts are placed corresponding to the desired resonant frequency; after, the coupler is moved to find the best coupling (minimum of \(|S_{11}|\)-parameter). Finally, since the insertion of the coupler changes slightly the resonant frequency, the trimmer is moved to finely tune the resonant frequency at desired value. A comparison between the experimental and simulated optimum coupler position was carried out (see Figure 8).  
For frequencies lower than 25 MHz, the coupling is constrained to the 50 mm stroke limitation (corresponding to \(d=96\) mm). While the simulations allow a global optimization able to find the absolute best working point into the three-dimensional space parameters (shorts, coupler, and trimmer position), in the real machine operation also slight differences on the shorts position can result in few millimeters of dispersion and in \(|S_{11}|\)-parameter local minima values where, however, the matching still remains very good (\(<\)\(-15\) dB). The maximum discrepancy between the average of experimental data and the simulated value  
Figure 8: Impedance matching condition for various resonant frequencies: absolute best coupler position found with the model (green dots), and coupler position used during twenty-five years of machine operation (blue dots). The dashed black line highlights the maximum limit of the coupler stroke.  
Figure 7: \(|S_{11}|\)-parameter curves, for the cavity operating at \(f_{0}\simeq 33.74\) MHz, obtained by varying the distance of the coupler capacitor from the median plane. The red curve shows the best matching found when \(d=123.5\) mm. From right to left the movable coupler tip has been inserted into the cavity, with steps of 2.5 mm, starting at \(d=146\) mm and reaching \(d=96\) mm.  
is only about 8.5 mm at 33.7 MHz. Figure 7 shows that when moving the coupler 7.5 mm from the best position, the matching is still very good since \(|S_{11}|\) goes from \(-35\) dB to about \(-20\) dB.
# Higher-Order-Mode Triple Band Circularly Polarized Rectangular Dielectric Resonator Antenna
## 3 Measurement Results and Discussion  
To verify the simulation, the optimized antenna design is fabricated and measured. The fabrication of the proposed CP DRA is shown in Figure 12. Reasonable agreement is achieved between simulation and measurement for resonance frequency, AR bandwidth, and the antenna's gain. As can be seen in Figure 13, triple bands are obtained in the simulation. In contrast, in the measurement, a dual band is obtained with larger impedance bandwidth. A closed agreement between simulated and measured bandwidths of lower resonant frequency is achieved, of 46% (2.66-4.26 GHz) and 48% (2.43-4.35 GHz), respectively. In the upper band, a measured bandwidth of 13.3% (4.82-5.61 GHz) with 52.5% improvement from the simulated of the two upper bands. Both simulated and measured the reflection coefficient results showed a similar trend. However, some mismatch can be seen due to inaccuracy during the fabrication of the antenna. This also might be caused by  
Figure 11: Beamwidth of the proposed DRA at 3 GHz. \(\theta\)  
the air gap between the DRA and the ground plane in the fabricated antenna compared to the one in simulation.  
Axial ratio plots of the simulated and measured results are presented in Figure 14. It is found that the given axial ratio shows a good agreement between the simulations and measurements. The simulations resulted in a percentage bandwidth of 6.87%, 2.8%, and 2.9%, and the measured results are 3%, 4.5%, and 3.18%, respectively. Figure 14b showed that the beamwidth was compatible between simulated and measured results, although the measured beamwidth is 17% smaller than the simulated. The simulated and measured gains are plotted in Figure 15; both results show a good resemblance. The simulated gains of 5.38 dBic, 8.5 dBic, and 9.3 dBic is obtained at 3 GHz, 4.9 GHz, and 5.4 GHz. Simultaneously, the measured gain is 6.25 dBic, 9.44 dBic, and 9.3 dBic at the respective frequencies.  
Figure 12: The fabricate of the proposed CP DRA.  
Figure 13: Simulation and measurement reflection coefficient results of the proposed DRA.  
Radiation patterns of the proposed DRAs are studied by both simulation and measurements. Figure 16 shows radiation patterns of phi 0\({}^{\circ}\) dan 90\({}^{\circ}\) at 3, 4.9, and 5.4 GHz, respectively. It is found that the antenna is left-handed circularly polarized (LHCP), with the co-polar and cross-polar ratio of at least 20 dB. A comparative study on different approaches to generate CP using a DRA structure is presented in Table 2. The proposed DRA exhibits triple-band CP operation with the most considerable impedance bandwidth. Besides, there is a significant improvement in the obtained gain compared to the other literature. Additionally, the wide measured beamwidth of 112\({}^{\text{o}}\) promotes a better receiving signal over a wide range.  
Figure 14: The axial ratio of simulated and measured of the proposed DRA. (**a**) AR versus frequency and (**b**) AR versus degree (\(\theta\)).  
Figure 15: Simulated and measured gain of the proposed DRA.
# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## 7 Future Research Directions  
Despite the potential of combining UAVs with 5G methods, research into UAV-assisted wireless networks is still in its infancy. Several unanswered questions must be further investigated. This section highlights the most explored topics for future directions in the field.
## 2 Methodology
### Manufacture of LWAs from Wastes
#### 2.1.1 Raw Materials  
For the preparation of the LWAs, mixtures of three different clays with four types of industrial organic waste were used. The three different clays were a Spanish yellow clay (SY) supplied by the company Comercial Ceramicas de Bailen, S.A. (Bailen, Jaen, Spain) and two Portuguese clays, a red clay (PR) and a yellow clay (PY), supplied by Corbario Minerais Industriais S.A. (Colmenas, Portugal). Four different types of waste were used: almond and hazelnut shells (AHS) supplied by Unio Corporacio Alimentaria de Reus (Catalonia, northern Spain), wastewater sewage sludge from the manufacture of paper money (PS) supplied by CELSUR (Andalusia, southern Spain), cork dust (CP) supplied by EUROTPON NUNEZ, S.L. (Extremadura, western Spain) and lastly, coffee grounds (CG) supplied by the Portuguese company DELTA Cafes.
# Feasibility of a Patient-Specific Bolus Using the Life-Casting Method for Radiation Therapy
## 2 Materials and Methods
### Air-Gap Quantification  
Figure 2 shows the patient-specific bolus produced using the LC method and the commercially available SuperFlab bolus (Radiation Products Design Inc., Albertville, MN, USA) attached to the in-house phantom. To assess the conformity of the patient-specific bolus to the patient's skin surface and the presence of an air gap between the bolus and skin, CT scans were conducted with both the SuperFlab bolus and the patient-specific bolus attached to the in-house phantoms. For the SuperFlab bolus and patient-specific bolus trials, radiation therapists optimized the air gaps to the best of their abilities by following the standard protocol at our institute, where the testing was conducted. CT was performed using a Discovery CT590 RT simulator (GE Healthcare, Milwaukee, WI, USA). All the reconstructed CT images had a slice thickness of 2.5 mm. the air-gap volumes were analyzed using the CT images.  
Figure 1: Fabrication procedure for a patient-specific bolus using the life-casting (LC) method.