esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
ML algorithms like SVM or tree-based methods have emerged as one of the most flexible, intuitive, and powerful approaches for analyzing complex data. Machine-learning algorithms proved to be very useful in disciplines like logistics for predictive analysis for certain operations [19] and also in healthcare applications. These methods naturally lend themselves to creating patient subgroups for risk classification, making them highly suitable for diagnostic purposes. These algorithms can provide a more reliable virtual genetic test for familial hypercholesterolemia diagnosis, outperforming the clinical Dutch Lipid Score [20]. Using the ML methods for classifying the FH is often limited by the dataset size, especially when it comes to patients who had either confirmed or ruled out the FH variants through genetic testing. Therefore, machine-learning algorithms like decision trees (DT), logistic regression (LR), naive Bayes (NB), random forest (RF) [21], and extreme gradient boosting (XGB), explored as alternative classification methods for FH diagnosis, are often combined with techniques for handling imbalanced or small datasets [22]. While machine learning holds promise in enhancing the genetic diagnosis of FH, it is not intended to replace the Dutch Lipid Score on a population level but rather to complement it in specialized clinical settings [20]. In line with these advancements, this study aims to evaluate machine-learning algorithms like SVM, RF, and XGBoost, but also employ the CatBoost algorithm, which is currently a state-of-the-art gradient boosting technique on decision trees, to enhance the diagnostic accuracy of familial hypercholesterolemia classification. Moreover, the ensemble methods are evaluated to increase the accuracy of FH classification. In this paper, the preliminary results of using machine-learning (ML) algorithms applied to an extensive and balanced dataset of patients enrolled in one of the Polish lipid clinics are presented, to enhance the accuracy and objectivity of FH classification, providing a more reliable alternative to traditional scoring methods. The conducted research helps address the challenge of insufficient validation studies on ML models for detecting FH. It provides valuable insights into the potential of these models in clinical applications. To the best of my knowledge, it is the first time that the accuracy of machine-learning algorithms in the classification of FH was tested on such a large and diverse dataset.
# A Secure Communication in IoT Enabled Underwater and Wireless Sensor Network for Smart Cities
## 3 System Model
### Two Nodes Reliable Model (2n-Rm)
#### 3.2.4 Reliable Paths Establishing  
By using 2N-RM, all of the selected nodes become source nodes and start discovering other reliable nodes. Consequently, multiple reliable receivers are being found and it continues until the sink nodes reached. Reliable nodes discovery from a single source to multiple sinks are depicted in Figure 4.  
After discovering the reliable nodes, multiple paths are established from source to sink nodes. We converted our reliable nodes into tree topology. In this topology, the initial source node becomes a root node and all sinks behave as a leaf node. Multiple intermediate nodes to make a path from root to leaf nodes are considered as child nodes. Tree topology of reliable paths is shown in Figure 5. At each step, it can be seen that each child has only one parent, there is neither any loop, nor any circuit. In case of multiple request receiving, a node selects parent on the basis of first come first serve. Due to acoustic transmission, nodes that are closer to each other have efficient energy consumption, delay, throughput, and reliable data transmission.  
Figure 4: Reliable Nodes Discovery.
## 2 Materials and Methods
### Methodology
#### 2.3.1 Atmospheric Correction Procedure over Sargassum Dominated Waters
POLYMER\({}_{\text{ext}}\) is thus used here to derive the sea surface reflectance. Briefly, POLYMER\({}_{\text{ext}}\) consists in first determining the effect of the Rayleigh scattering. Second, the aerosol and sunlight reflectances are estimated over _Sargassum_-free pixels based on the NIR bands. Third, the estimation of aerosol/sunglint reflectances is performed over _Sargassum_ dominated pixels by extrapolating the atmospheric reflectances derived through the 1 st and 2 nd steps over the _Sargassum_-free pixels that are located in the vicinity of the _Sargassum_ contaminated pixel. Finally, the satellite reflectance measured over the _Sargassum_ contaminated pixel is corrected for the atmosphere to provide the sea surface reflectance. The OLCI surface derived reflectance image after atmospheric correction is obtained for 12 spectral bands between 400 nm and 865 nm.
## 2 Principe of Proposed End-to-End Optical Fiber System with the Adaboost Algorithm Receiver Design
### The Design of the Adaboost Algorithm Receiver
Similarly, all methods can be called from the Scikit-learn library [31]. The Adaboost algorithm is an iterative algorithm in which its core idea is to train different classifiers (weak classifiers) for the same training set, and then group these weak classifiers to form a stronger final classifier. The advantage of the Adaboost algorithm is that its classification accuracy [13] is often better than that of complex neural network, while reducing the amount of computation on the classification. The advantage of this method is that it does not need to analyze the huge data iteratively, but through a simple weak classifier instead of ANN, more accurately for one or several cases, so as to improve the degree of signal identification and reduce the bit error rate.
## 4 Design of Multimedia Independent Multipath Routing Algorithms
### Path Generation  
Some studies have applied B-spline algorithm to wireless networks. B-spline curves can be generated by parameters of any control point (\(\geq 2\)), and the path length of B-spline can be calculated by function integral. It has low computational complexity and good controllability.  
As shown in Figure 3, A B-spline curve can be divided into three parts: spreading, parallel, and converging. \(n_{s}\) and \(n_{t}\) represent the source node and sink node, respectively. Arc \(sp\) is controlled by points \(n_{s}^{\prime}\), \(n_{s}\), \(n_{cs}\), \(n_{ct}\). Arc \(pl\) is controlled by points \(n_{s}\), \(n_{cs}\), \(n_{ct}\), \(n_{t}\). Arc \(lt\) is controlled by points \(n_{cs}\), \(n_{ct}\), \(n_{t}\), \(n_{t}\), \(n_{t}\), \(n_{t}\).  
\[\left\{\begin{array}{l}S_{sp}(t)=\frac{1}{6}n_{s}^{\prime}(1-t)^{3}+\frac{1} {6}n_{s}(3t^{3}-6t^{2}+4)+\frac{1}{6}n_{cs}(-3t^{3}+3t^{2}+3t+1)+\frac{1}{6}n_{ ct}t^{3}\\ S_{pl}(t)=\frac{1}{6}n_{s}(1-t)^{3}+\frac{1}{6}n_{cs}(3t^{2}-6t^{2}+4)+\frac{1}{6 }n_{ct}(-3t^{3}+3t^{2}+3t+1)+\frac{1}{6}n_{t}t^{3}\\ S_{lt}(t)=\frac{1}{6}n_{cs}(1-t)^{3}+\frac{1}{6}n_{ct}(3t^{3}-6t^{2}+4)+\frac{1}{6 }n_{t}(-3t^{3}+3t^{2}+3t+1)+\frac{1}{6}n_{t}^{t}t^{3}\end{array}\right. \tag{19}\]  
Figure 3: Routing based on B-spline curve.  
Figure 2: B-spline multipath schematic diagram.  
Therefore, routing can be established by three parameters, i.e., \(K\), \(\Delta\theta\), \(\Delta\lambda\cdot\Delta\theta\) adjusts the density between paths to specify control points. By specifying the maximum expansion angle \(\theta_{\text{max}}\), then \(\Delta\theta\) can be calculated by the following formula:  
\[\Delta\theta=\frac{2\theta_{\text{max}}}{K-1} \tag{24}\]
# Passive UHF RFID Tag for Multispectral Assessment
###### Abstract  
This work presents the design, fabrication, and characterization of a passive printed radiofrequency identification tag in the ultra-high-frequency band with multiple optical sensing capabilities. This tag includes five photodiodes to cover a wide spectral range from near-infrared to visible and ultraviolet spectral regions. The tag antenna and circuit connections have been screen-printed on a flexible polymeric substrate. An ultra-low-power microcontroller-based switch has been included to measure the five magnitudes issuing from the optical sensors, providing a spectral fingerprint of the incident electromagnetic radiation from ultraviolet to infrared, without requiring energy from a battery. The normalization procedure has been designed applying illuminants, and the entire system was tested by measuring cards from a colour chart and sensing fruit ripening.  
screen printing; printed electronics; passive RFID tag; optical sensor; microcontroller; spectral fingerprint +
Footnote †: journal: Sensors
## 4 Conclusions  
This paper presents a blood-flow-monitoring circuit system based on a single-element transducer. The blood-flow-monitoring circuit system consists of a transducer, an analog interface circuit, and an FPGA. To acquire Doppler sample volumes without reconstruction of ultrasound images, this work proposes an automatic range-gate-positioning scheme which can blindly track the focal-depth value by using an accumulation of difference values of adjacent ensembles and a hysteresis-slicing function. The automatic range-gate-positioning scheme, along with the hysteresis slicing function, has been implemented in the FPGA. Most of other single-element-based pulsed-wave Doppler devices adopt a manual adjustment of the range gate [13; 14; 15]. In addition, a recent work which includes a vessel-wall-detection algorithm presents an automatic tracking of the vessel wall [16]. However, it relies on a specific A-mode echo pattern, and correspondingly, it essentially needs to use a relatively higher center frequency. On the other hand, the proposed automatic range-gate-positioning scheme does not rely on a specific A-mode echo pattern, and it utilizes the Doppler amplitude deviation to find the vessel region. Thus, the proposed scheme can reduce the center frequency of the ultrasound, and correspondingly, it can reduce the overall operating frequency of the system. In addition, the proposed scheme can be implemented in FPGA with fewer resources since it uses addition-only computation (except filter parts). The FPGA utilizes 5.6% of slice look-up-tables (LUTs) for implementation of the range-gate-positioning scheme. This implies that the proposed scheme can be compactly implemented in the FPGA. In measurements, the circuit system was utilized to interrogate the customized flow phantom model, which includes two vessel-mimicking channels. The circuit system successfully acquired Doppler sample volumes by positioning a range gate at a fluid channel. In addition, the estimated Doppler shift frequency shows a good agreement with the theoretical value.  
Conceptualization, H.-T.P. and J.-Y.U.; methodology, H.-T.P.; investigation, H.-T.P.; validation, H.-T.P. and J.-Y.U.; writing--original draft preparation, H.-T.P. and J.-Y.U.; project administration, J.-Y.U.; funding acquisition, J.-Y.U. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Basic Science Research Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Education under Grant 2019 RIIIA3A01060591.  
Not applicable.  
The data presented in this study are included within the article.  
Electronic Design Automation (EDA) tools were supported by the Integrated Circuit Design Education Center (IDEC), South Korea.  
The authors declare no conflict of interest.
# Supervised Machine Learning Techniques to the Prediction of Tunnel Boring Machine Penetration Rate
## 3 Materials and Methods
### Model's Assessment  
In this study, four performance indices, namely coefficient of determination (\(R^{2}\)), mean absolute error (\(MAE\)), root mean square error (\(RMSE\)), and variance account for (\(VAF\)), were used to measure the  
prediction performance and accuracy of the developed models. These indices are widely used to assess the accuracy of the developed models in the related studies as well [72; 73; 74; 75; 76; 77; 78; 79]. Furthermore, the recently proposed _a\({}^{20}\)-index_ have been used for the evaluation of models. These indices are computed as follows:  
\[VAF=\left[1-\frac{\textit{var}\left(y-y^{\prime}\right)}{\textit{var}\left(y \right)}\right]\times 100 \tag{1}\]  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(y-y^{\prime})^{2}} \tag{2}\]  
\[R^{2}=1-\frac{\sum_{i}(y_{i}-f_{i})^{2}}{\sum_{i}(y_{i}-\overline{y})^{2}} \tag{3}\]  
\[MAE=\frac{1}{N}\sum_{j=1}^{N}\left|y_{i}-y_{j}\right| \tag{4}\]  
where y denotes the measured values, \(\overline{y}\) and \(y^{\prime}\) indicate mean and predicted the values of y, respectively, \(N\) denotes the total number of data. Note that, a predictive model with R\({}^{2}\) of 1, _VAF_ of 100%, and _RMSE_ and _MAE_ of zero is defined as a perfect model. The dataset should be divided into two phases (i.e., training and testing) to develop the model and then evaluate the developed model. Thus, in this study, according to suggestions of several studies [21; 80; 81; 82], 80% of the collected data (or 167 datasets) are randomly selected to train the models and the remaining 20% of the data (or 42 datasets) are used to test and validate the models. The simple ranking technique proposed by [PERSON] et al. [83] was used in this research. According to this technique, the total rate of each model is calculated for training and testing dataset, separately. The highest rate value for each performance index is five because we have five models, and the best performance index is assigned the highest rating (5). The total performance rating of each model is then calculated by summing up its total rank of training and total rank of the testing dataset. More information regarding the simple ranking technique can be found in the original study, [PERSON] et al. [83].  
Furthermore, the following, recently proposed engineering index, the _a\({}^{20}\)-index_, is proposed for the reliability assessment of the developed AI models:  
\[a^{20}-index=\frac{m^{20}}{M} \tag{5}\]  
where \(M\) is the number of dataset samples and \(m^{20}\) is the number of samples with a value of rate experimental value/predicted value between 0.80 and 1.20. Note that for a perfect predictive model, the values of _a\({}^{20}\)_-index values are expected to be unity. The proposed _a\({}^{20}\)-index_ has the advantage that their value has a physical engineering meaning. It declares the amount of the samples that satisfies predicted values with a deviation of \(\pm\) 20% compared to experimental values.
# Predictive Path Following and Collision Avoidance of Autonomous Connected Vehicles
## 2 Problem Formulation
### Map Representation Using Bezier Curves  
Two of the necessary requirements of any autonomous driving controller is the ability to handle the road boundaries generated from digital maps and have a path representation that could be followed by the motion controller.  
In [19], an automatic procedure for generating driving boundaries from OpenStreetMap is presented, which expands its original representation, replacing polylines by polynomial-based roads, whose sections are defined using Bezier curves. This method has been validated successfully on a peri-urban environment without any human intervention. Therefore, we assume that the road is represented by polynomials whose sections are defined using Bezier curves. The road corridor is represented by two Bezier curves, one for the left boundary and the other for the right one. The path that should be follow by the vehicle, which is assumed to be the at the center of the lane is represented also by a Bezier curve.  
Bezier curves [20] have recently attracted many researchers for digital map representation for autonomous driving [19]. They are defined as  
\[\mathcal{B}(\theta,n)=\sum_{i=0}^{n}b_{i,n}(\theta)\mathbf{P}_{i},\ \ 0\leq\theta\leq 1, \tag{7}\]  
where \(n\) is the order of the polynomial, \(\mathbf{P}_{n}\in\mathbb{R}^{2}\) are the control points defining the curvature of the curve (see Figure 2), and \(b_{i,n}\) the Bernstein coefficients given by  
\[b_{i,n}(\theta)={}^{n}C_{i}\theta^{i}(1-\theta)^{n-i},\ \ \ i=0,\cdots,n, \tag{8}\]  
with \({}^{n}C_{i}=\frac{n!}{i!(n-i)!}\).  
Depending on the Bezier polynomial degree, it would be possible to define any arbitrary road as one Bezier curve. However, higher degrees of the polynomial leads to high computation time and difficulties in calculating the control points. Moreover, for the purpose of autonomous driving, defining the whole road in just one huge polynomial is not required and makes the control problem intractable. Therefore, the common approach in computer graphics is to define complex paths by concatenating quadratic and cubic curves, taking into consideration the continuity constraints [19]. The overall control problem is to utilize the kinematic bicycle Model (5) to design a controller that follows the planned reference path defined by a Bezier Curve (7) and avoid collision with nearby objects, that are assumed to be connected with our own vehicle, by doing necessary maneuvers. The controller should be able to respect the road boundaries or corridor which are also assumed to be represented by a left and right Bezier curves.
# Current Sensorless MPPT Control for PV Systems Based on Robust Observer
## 4 Design of Luenberger Robust Observer Based on AEM  
In this section, the details related to the design of the robust Luenberger observer are presented to obtain a current estimation that allows the PV system to operate in a sensorless way.  
Consider the boost based PV system (11) with output \(y\)  
\[\dot{x} =Ax+g(x)u+D\phi \tag{15}\] \[y =Cx+Ew\]where \(C\) is the output matrix, \(D\) and \(E\) are matrix that maps the system uncertainties \(\phi\) and output disturbances \(w\).  
The Luenberger observer structure has the following form  
\[\dot{\hat{x}}=A\hat{x}+g(x)u+L(y-C\hat{x}) \tag{16}\]  
The estimation error is defined as  
\[e=x-\hat{x} \tag{17}\]  
and its derivative is  
\[\dot{e} =\hat{x}-\hat{x} \tag{18}\] \[=(A-LC)e+D\phi-LEw\]  
A new extended system \(z\) is introduced, which is formed by the estimated state and the estimation error, and its dynamics are as follows  
\[\dot{z}=\bar{A}z+F\bar{w} \tag{19}\]  
where  
\[\bar{A}=\begin{bmatrix}A&LC\\ 0&A-LC\end{bmatrix}\,\ \ F=\begin{bmatrix}0&LE\\ D&-LE\end{bmatrix}\]  
Introducing a quadratic storage function for the extended system as  
\[V(z)=z^{T}Pz\,\ \ P=\begin{bmatrix}P_{1}^{-1}&0\\ 0&P_{2}\end{bmatrix} \tag{20}\]  
To analyze the stability of system it is required to compute the derivative of (20) which has the form  
\[\dot{V}(z)= z^{T}(\bar{A}^{T}P^{-1}+P^{-1}\bar{A})z+\bar{w}^{T}F^{T}P^{-1}z \tag{21}\] \[+z^{T}P^{-1}F\bar{w}\]  
and rewriting (21) in a matrix format  
\[\dot{V}(z)=\begin{bmatrix}z\\ \bar{w}\end{bmatrix}^{T}\begin{bmatrix}\bar{A}^{T}P^{-1}+P^{-1}\bar{A}&P^{-1}F \\ F^{T}P^{-1}&0\end{bmatrix}\begin{bmatrix}z\\ \bar{w}\end{bmatrix} \tag{22}\]  
To include the terms related to system uncertainties, these are added and subtracted in the following way  
\[\dot{V}(z)= \begin{bmatrix}z\\ \phi\\ w\end{bmatrix}^{T}\overbrace{\begin{bmatrix}M_{11}&P^{-1}F\\ F^{T}P^{-1}&\begin{bmatrix}-\tau_{1}&0\\ 0&-\tau_{2}\end{bmatrix}\end{bmatrix}}^{M}\begin{bmatrix}z\\ \phi\\ w\end{bmatrix} \tag{23}\] \[-\alpha V(z)+\tau_{1}\|\phi\|^{2}+\tau_{2}\|w\|^{2}\]  
where  
\[M_{11}=\bar{A}^{T}P^{-1}+P^{-1}\bar{A}+\alpha P^{-1}\]Substituting (13) and (14)  
\[\dot{V}(z)\leq\begin{bmatrix}z\\ \phi\\ w\end{bmatrix}^{T}M\begin{bmatrix}z\\ \phi\\ w\end{bmatrix}-\alpha V(z)+\tau_{1}\eta_{0}+\tau_{2}d_{0} \tag{24}\]  
Expanding matrix \(M\) a new representation in terms of matrix \(\bar{M}\) is obtained as  
\[\dot{V}(z)\leq\begin{bmatrix}\hat{x}\\ e\\ \phi\\ w\end{bmatrix}^{T}\begin{bmatrix}\bar{M}_{11}&\bar{M}_{12}&0_{n\times m}& \bar{M}_{14}\\ \bar{M}_{12  
Observe that (27) is a BMI due to multiplication of design variables (this is, \(\alpha\)P). To solve optimization problems subject to bilinear constraints arising in the AEM based design, an iterative procedure based on LMI has been applied in [25, 26]. Using this iterative procedure, the corresponding optimization problem (30) can be solved using an LMI solver such as SEDUMI without the need of specialized BMI solvers.
# Incremental Formula-Based Fix Localization
## 4 Evaluation Setup
### Research Questions and Evaluation Metrics  
We applied each of these four implemented techniques to several buggy programs to empirically investigate the following research questions:  
_RQ1: How effective is AgxFaults in finding angelic fix candidates, compared to program-formula and single-path formula approaches?_  
A technique is said more effective in finding angelic fix candidate if it can find more feasible fix candidates. A technique is said more precise in finding angelic fix candidate if it always produces feasible MFCs where the ratio of the number of its found feasible MFCs to the total number of its reported MFCs is high. Thus, to answer RQ1, we identify two metrics:  
(1) average number of feasible MFCs that the technique found for each run (i.e., _number of feasible MFCs found_), and  
(2) the number of feasible MFCs in the total number of MFCs that the technique reported for each run (_precision_).  
_RQ2: How effective is AgxFaults in localizing fault location, compared to program-formula and single-path formula approaches?_  
To evaluate the fault localization effectiveness of a technique, we identify three metrics:  
(1) the number of runs the technique were able to report the correct fault locations in the total of its runs (i.e., _successful fault localization rate_),  
(2) the number of runs the technique were able to report a correct fix candidate in total number of its runs (i.e., _successful fix localization rate_), and  
(3) the percent of program code lines that the developer need to examine before identifying the first faulty statement (i.e., _EXAM score_).  
_RQ3: How efficient and scalable are AgxFaults, compared to program-formula and single-path formula approaches?_  
We use the CPU time as a metric to measure the efficiency and scalability of a technique. Specifically, for each technique, we measure:  
(1) the CPU time spent by the solver to solve formulas (i.e., _Formula solving time_), and  
(2) the total running time the technique for each run (i.e., _Running time_).  
_RQ4: Can AgxFaults be applied to real bugs in large software projects?_