esa-sceva/satcom-chunk-collection huggingface dataset:
# Water Quality Evaluation and Analysis by Integrating Statistical and Machine Learning Approaches
###### Abstract  
Water quality assessment plays a vital role in environmental monitoring and resource management. This study aims to enhance the predictive modeling of the Water Quality Index (WQI) using a combination of statistical diagnostics and machine learning techniques. Data collected from six river locations in Malaysia are analyzed. The methodology involves collecting water quality data from six river locations in Malaysia, followed by a series of statistical analyses including assumption testing (shapiro-wilk and breusch-pagan tests), diagnostic evaluations, feature importance analysis, and principal component analysis (PCA). Decision tree regression (DTR) and autoregressive integrated moving average (ARIMA) are employed for regression, while random forest is used for classification. Learning curve analysis is conducted to evaluate model performance and generalization. The results indicate that dissolved oxygen (DO) and ammoniacal nitrogen (AN) are the most influential parameters, with normalized importance scores of 1.000 and 0.565, respectively. The breusch-pagan test identifies significant heteroscedasticity (\(p\)-value \(=(3.138e^{-115})\)), while the Shapiro-Wilk test confirms non-normality (\(p\)-value \(=0.0\)). PCA effectively reduces dimensionality while preserving 95% of dataset variance, optimizing computational efficiency. Among the regression models, ARIMA demonstrates better predictive accuracy than DTR. Meanwhile, random forest achieves high classification performance and shows strong generalization capability with increasing training data. Learning curve analysis reveals overfitting in the regression model, suggesting the need for hyperparameter tuning, while the classification model demonstrates improved generalization with additional training data. Strong correlations among key parameters indicate potential multicollinearity, emphasizing the need for careful feature selection. These findings highlight the synergy between statistical pre-processing and machine learning, offering a more accurate and efficient approach to water quality prediction for informed environmental policy and real-time monitoring systems.  
water quality prediction; machine learning; statistical analysis; principal component analysis 2025184944  
10.3390/a18080494
# Dynamic Responses Measured by Optical Fiber Sensor for Structural Health Monitoring
## 4 Shock and Vibration Tests
### Drop Test  
The free-fall drop table (model DP-1200-60, King Ton Inc., New Taipei City, Taiwan), as shown in Figure 14, was employed to perform the drop test. The impact impulse exerted on the test specimen by the drop table can be adjusted using different target materials. There are three different types of impact impulses, including half sine pulse, square pulse and trapezoid pulse, provided by the drop table. In this work, the half sine impulse was adopted through the use of four half-spheres of Teflon as the target material, as shown in Figure 15. The test specimen made of copper is fixed on the drop table through a steel fixture. The two ends of the beam are clamped on the fixture. Between the beam and the drop table, there is a 20 mm standoff to allow beam bending. The table was dropped from a height of 300 mm along the four guiding rods onto four half-spheres of Teflon. An accelerometer was attached on the drop table to measure the acceleration response of the table during the drop test. The acceleration of the drop table shown in Figure 16 indicates that the beam was subjected to a half sine impulse during the drop test. The transient response of the beam was measured by both the strain gauge and FBG sensor as shown in Figure 17. It can be observed that the strain measured by the FBG sensor is in a good agreement with the strain gauge. The average difference between the FBG and strain gauge is 4.2%. This demonstrates that the FBG sensor is capable of monitoring the transient response of a structure subjected to shock and impact loadings.  
Figure 14: Free-fall drop table.  
Figure 15: Four half-spheres served as the impact target.
# Real-Time Tracking and Position Control of an Elastic Cable-Driven Winch System
## 3 Controller Design  
In this study, motor speed control is implemented using the PID method. In contrast, PD and sliding mode control methods are used for load position control. Figure 2 illustrates the control block diagram of this winch system.  
The following electrical and mechanical equations describe the mathematical model of the DC motor used in this study:  
\[V_{s}=V_{Ra}+V_{La}+E_{emf} \tag{12}\]  
where \(V_{s}\) is the supply voltage, \(V_{Ra}=R_{a}I_{a}\), \(V_{La}=L_{a}\frac{dI_{a}}{dt}\), \(E_{emf}=K_{b}\dot{\theta_{m}}\)  
\[T_{s}=T_{e}-J_{m}\ddot{\theta}_{m}-T_{w}-T_{L} \tag{13}\]  
In Equation (11), \(T_{e}\) is the magnetic motor torque (\(T_{e}=K_{t}I_{a}\)), \(T_{w}\) is the frictional torque (\(T_{w}=B\dot{\theta}_{m}\)), \(T_{L}\) is the mechanical load torque, and \(T_{s}\) is the output torque. The parameters of the DC motor used are shown in Table 1.
# Frequency Selective Surfaces: Design, Analysis, and Applications
## 4 Methods for Analysis of FSS  
Various techniques are employed for the numerical analysis of frequency selective surface (FSS) structures, particularly focusing on their planar and doubly periodic characteristics. The assumption that the FSS is infinite in extent allows for simplification in the analysis, where the problem can be reduced to a single unit cell. This reduction is facilitated by the application of boundary conditions that define the symmetry of the periodic, planar FSS [114]. Such simplifications are particularly effective when all elements within the unit cell are identical and the excitation is uniform across these cells, which is a common scenario in FSS analysis [115].  
The analysis of FSS is typically framed within an integral equation approach that describes the behavior of the unit cell. This integral equation is modified to account for the periodic arrangement of cells, often utilizing the Floquet theorem. The theorem enables the transformation of continuous convolution integrals into infinite summations, where each term represents a product of the spectral equivalent surface and the spectral Green's function [116]. Recent advancements in numerical methods, such as the method of moments (MoM) and finite element methods (FEM), have further enhanced the efficiency of these analyses, allowing for more complex configurations and materials to be modeled accurately [115].  
Moreover, the application of Floquet theory is critical in understanding the interactions within the periodic structures of FSSs. This theory provides a framework for analyzing systems with periodic coefficients, thus allowing for the exploration of various phenomena such as resonance and scattering in FSS configurations [117]. The integration of these theoretical approaches with numerical techniques has led to significant improvements in the design and optimization of FSSs for various applications, including telecommunications and radar systems [114, 115].
## 2 Background and Related Work  
### SDN-Based Backhaul Architectures  
The SDN paradigm with a control plane that is separate from the data plane and with a centralized SDN controller has spurred significant research interest in wireless networks [13; 14; 15]. An extensive set of studies have developed SDN-based architectures for the backhaul of wireless network traffic [16; 17; 18]. Given the complexity of the backhaul, most of this work has considered layered or tiered architectures [12; 19; 20; 21; 22; 23; 24], whereby intermediate gateway nodes perform various protocol related functions [25; 26; 27; 28]. For instance, there may be gateway nodes that interface with Internet of Things nodes [29; 30] or specific wireless network technologies, such as wireless local area networks [31]. Moreover, the efficient interconnection via metropolitan area networks to the Internet at large has received increasing attention [32; 33].  
This case study considers the LayBack architecture [12] which can encompass the layering structures of a wide range of other proposed architectures and wireless technologies while allowing for fine-grained SDN control, as elaborated in Section 3. The enabling idea is the generalization of the decomposition approach referred to as _network utility maximization_ (NUM) to a multi-tier system.
## 2 Materials and Methods
### Soil-Vegetation-Atmosphere Transfer Model
Table 3 shows the annual accumulated rainfall and its anomalies during the period from 2008 to 2018. The anomalies were calculated by subtracting each annual value from the 2008-2018 average. We can observe that in 2009 the anomalies were most intense in both Cabrobo and Ibmirim, but during 2014, the anomalies were lower than in 2012 or 2017. It is worth mentioning that the 2014 dataset presented fewer gaps than the other years, and an ongoing intense drought episode was established in 2012. Therefore, each simulation started at 00:00 UTC on 1 January of each respective year and ended at 23:00 UTC on 31 December of each selected year. The SITE model uses six input meteorological variables (hourly samples) as initial conditions: rainfall (mm), air temperature (\({}^{\circ}\)C), specific humidity (g kg\({}^{-1}\)), wind speed (m s\({}^{-1}\)), global radiation (W m\({}^{-2}\)), and longwave radiation emitted by the atmosphere (W m\({}^{-2}\)). Output variables simulated by the model are net radiation (R\({}_{n}\)) photosynthetically active radiation (PAR), soil heat flux (G), sensible heat flux (H), latent heat flux (LE), net CO\({}_{2}\) exchange (NEE), net primary production (NPP), gross primary production (GPP), the fraction of absorbed photosynthetically active radiation (FAPAR), and leaf area index (LAI). Rainfall, air temperature, atmospheric pressure, wind speed, and global radiation are directly measured at INMET stations. Specific humidity (\(q\)) and longwave radiation emitted by the atmosphere (\(L\downarrow\) were calculated based on classic methods that will be described in the following sections.
# Sizing Methodology of a Fast Charger for Public Service Electric Vehicles Based on Supercapacitors
## 2 Materials and Methods
### Electrical Analysis of an SC Working in a Fast-Charging System
#### 2.1.3 Power and Energy Dissipated during the Charging Process  
The total power dissipated in all components, \(p_{d}(t)\), can be calculated from (8), where  
\[p_{d}(t)=R_{T}\cdot\left(\frac{\Delta U}{2\cdot L\cdot\beta}\right)^{2}\cdot \left(e^{2(-\alpha+\beta)\cdot t}+e^{2(-\alpha-\beta)\cdot t}-2\cdot e^{2(- \alpha)\cdot t}\right). \tag{18}\]  
Its integration will result in the instantaneous energy dissipated, \(w_{d}(t)\), where  
\[w_{d}(t)=R_{T}\cdot\left(\frac{\Delta U}{2\cdot L\cdot\beta}\right)^{2}\cdot \left(\frac{e^{2(-\alpha+\beta)\cdot t}}{2\cdot(-\alpha+\beta)}+\frac{e^{2(- \alpha-\beta)\cdot t}}{2\cdot(-\alpha-\beta)}+\frac{e^{-2\cdot\alpha\cdot t} }{\alpha}-\frac{1}{2\cdot(-\alpha+\beta)}-\frac{1}{2\cdot(-\alpha-\beta)}- \frac{1}{\alpha}\right). \tag{19}\]Once the charging process is completed, the exponential functions are negligible, and the total dissipated energy, Wd_total, can be expressed as follows:  
\[\text{Wd}_{\text{\_total}}=\text{R}_{\text{T}}\cdot\left(\frac{\Delta\text{U}}{ 2\cdot\text{L}\cdot\beta}\right)^{2}\cdot\left(\frac{1}{2\cdot(\alpha-\beta)}+ \frac{1}{2\cdot(\alpha+\beta)}-\frac{1}{\alpha}\right) \tag{20}\]  
Replacing in (20) \(\alpha\) and \(\beta\), calculated in (5) and (7), Wd_total can be simplified as  
\[\text{Wd}_{\text{\_total}}=\frac{1}{2}\cdot\text{C}_{\text{eq}}\cdot(\text{U} _{01}-\text{U}_{02})^{2}. \tag{21}\]  
An interesting conclusion is thus reached; the energy dissipated only depends on the equivalent capacitance and initial voltages but not on the resistance of the circuit.
# Hybrid Harmony Search for Stochastic Scheduling of Chemotherapy Outpatient Appointments
###### Abstract  
This research deals with the same-day chemotherapy outpatient scheduling problem that is recognized as a leading strategy to pursue the objective of reducing patient waiting time. Inspired by a real-world context and different from the other studies, we modeled a multi-stage chemotherapy ward in which the pharmacy is located away from the treatment area and drugs are delivered in batches. Processes in oncology wards are characterized by several sources of uncertainty that increase the complexity of the problem; thus, a stochastic approach was preferred to study the outpatient scheduling problem. To generate effective appointment schedules, we moved in two directions. First, we adopted a late-start scheduling strategy to reduce the idle times within and among the different stages, namely medical consultation, drug preparation and infusion. Then, since the problem is NP-hard in the strong sense, we developed a hybrid harmony search metaheuristic whose effectiveness was proved through an extended numerical analysis involving another optimization technique from the relevant literature. The outcomes from the numerical experiments confirmed the efficacy of the proposed scheduling model and the hybrid metaheuristic algorithm as well.  
stochastic scheduling; health care; harmony search; flow time Article
# Optimizing BiLSTM Network Attack Prediction Based on Improved Gray Wolf Algorithm
###### Abstract  
Aiming at the problems of low accuracy of network attack prediction and long response time of attack detection, bidirectional long short-term memory (BiLSTM) was used to predict network attacks. However, BiLSTM has the problems of difficulty in parameter setting and low accuracy of the prediction model. This paper first proposes the Improved Grey Wolf algorithm (IGWO) to optimize the BiLSTM (GGWO-BiLSTM). First, IGWO uses Dimension Learning Hunting (DLH) strategy to construct the wolf neighborhood. In the established wolf neighborhood, the BiLSTM parameters are iteratively optimized to obtain a prediction model with fast convergence speed and small reconstruction error. Secondly, the dataset is preprocessed, and the IP packet statistical signature (IPDCF) is defined according to the characteristics of denial of service (DOS) and distributed denial of service (DDOS) attacks. IPDCF was used to establish the time series model and network traffic time series data were input into IGWO-BiLSTM to get the prediction results. Finally, the DOS and DDOS network packets were input into the trained prediction model to obtain the prediction results of attack data. By comparing the predicted values of IGWO-BiLSTM normal network packets and attack packets, a reasonable threshold is set to provide the basis for the subsequent attack prediction. Experiments show that the IGWO-BiLSTM can reach 99.05% of the fitting degree and accurately distinguish network attacks from normal network demand increases.  
intrusion detection; deep learning; time series model; cyber-attacks +
Footnote †: journal: Applied Sciences
## 4 System Architecture
### The Universal Software Radio Peripheral (USRP)  
The USRP consists of three basic functional blocks: the baseband section, the IF section, and the RF section, as shown in Figure 17. The RF section contains the RF circuit (USRP's Daughterboard) responsible for transmitting and receiving the RF signal at the operating frequency. The IF section (the USRP motherboard) contains the processing FPGA that performs the signal processing, such as filtering, modulation, and demodulation. Additionally, it contains the analog-to-digital converter (ADC) and the digital-to-analog converter (DAC).  
Figure 16: The block diagram for the implemented NB-IoT system.  
In this work, NI USRP B200 is used, which has the following features:  
* It is the first fully integrated USRP that covers RF range from 70 MHz to 6 GHz.
* It uses SuperSpeed USB 3.0.
* It covers up to 56 MHz of instantaneous bandwidth.
* It is a full duplex device and SISO (1 TX and 1 RX).
# Efficient Wireless Monitoring and Control of a Grid-Connected Photovoltaic System
## 3 Description and Design of the Photovoltaic System  
### Wireless Smart Photovoltaic System (WSPS)  
#### 3.1.1 PV Modules  
In this research, each PV module provides a maximum power of 20.1 W\({}_{\text{p}}\) for the standard conditions, with a maximum power voltage of 17.5 V and a maximum power current of 1.15 A. The open-circuit voltage is 21.6 V and the short-circuit current is 1.28 A. The commercial PV modules used are the MS-20P (Sumsol, Madrid, Spain). In this case, the experiments have been conducted for three PV modules series-connected.  
In order to know the values of the MPP for different environmental conditions, the current-voltage (I-V) and power-voltage (P-V) characteristic curves of the PV module are obtained in simulation and in experimental tests, as Figure 2 depicts. For example, for 910 W/m\({}^{2}\) and 42 \({}^{\circ}\)C, the MPP is 19.8 W for a voltage of 17 V.  
Figure 1: Architecture of the two-stage wireless grid-connected photovoltaic (PV) system.
###### Abstract
Only the additive white Gaussian noise signal model, which includes several time-frequency modifications, is useful when using feature fusion to enhance performance [34]. Despite the fact that each of these methods modifies AMC for well-researched picture-identification problems in some way, they all require an intricate image-processing stage. The literature demonstrates that the AMC issue is solved using a broad range of feature extraction and selection techniques, as well as machine learning models. Without any prior knowledge of feature engineering, DL outperforms ML in recognition performance [35; 36]. However, most of the current AMC methods perform poorly when several standard convolutional layer structures are added to a heavily deep network architecture. This occurs because extracted feature maps are represented ineffectively and trainable parameters are expensive. These techniques also need a lot of training time. AMC based on scalogram images and a deep convolutional neural network is explicitly presented in this paper. The CNN architecture automatically identified the modulation types after receiving the scalogram pictures created by the CWT approach, which is used to extract 2D data from modulated signals. The described methodology effectively utilizes deep convolutional networks to address AMC difficulties (ConvNet). For robust modulation classification against channel impairments, a well-performing and cost-efficient AMC system based on CWT transform is presented. As an innovation in this work, the combination of feature visualization with the help of CWT transformation with deep convolutional neural networks has been used. This approach was chosen because, by visualizing the features with the help of scalogramtransformation and sending these features to the deep convolutional network, the ability to recognize patterns related to each modulation in the convolutional network increases. The contributions of the presented study are as follows:  
* 2D-scalogram images are used in order to automatically detect modulation type. * CWT-based scalogram images are used for the visualization of modulation features in order to increase the performance of the proposed method. * The CNN architecture is proposed to automatically classify scalogram images. * Simulation results indicate that the presented model showed better accuracy compared to other methods. This article is divided into five parts. Section 2 addresses both the materials and the process. The model under discussion is covered in further depth in Section 3. The research results are presented in Section 4. Section 5 summarizes the results and makes recommendations for further study directions.
## 2 Materials and Methods
### Model Evaluation Methods  
The input layer in all three networks to predict the features studied in this study included inlet air temperature, infrared power, drying time and the output layer also included \(MR\) (Table 1).  
Figure 6: Architecture of the SVR used for modeling the wheat moisture content in this study.  
Table 1 presents the inputs and output factors. Therefore, three neurons including inlet air temperature, IR power, and drying time were employed in the input layer of ANN and ANFIS networks and one neuron was allocated for the output layer (_MR_). The Neural Network Toolbox of MATLAB software (v4.01) was also employed to design and evaluate ANN, ANFIS, and SVR to predict the _MR_ of parboiled hulls.  
It is recommended to normalize the data along a correct spectrum to stop any numerical overflow during the training of ANN, ANFIS, and SVR. [PERSON] et al. [23] noted that normalization can help improve the network. Input and output values are standardized between 0.00 and 1.00 using Equation (16). [23].  
\[X_{n}=\frac{X_{b}-X_{\min}}{X_{\max}-X_{\min}} \tag{16}\]  
where \(X_{\text{n}}\) is the normalized value of the parameter, \(X_{\text{b}}\) is the real value of each parameter, \(X_{\max}\) is the maximum value of the desired parameter and \(X_{\min}\) is the minimum value of the desired parameter.  
Three indicators (R-square, mean squared error (MSE), and mean absolute error (MAE)) were applied for the evaluation of the ANN, ANFIS, and SVR. These indices are shown in Equations (17)-(19), respectively [52].  
\[R^{2}=\frac{\left[\sum\limits_{i=1}^{N}\left(X_{i}-X_{mean}\right)^{2}\right] -\left[\sum\limits_{i=1}^{N}\left(X_{i}-X_{p}\right)\right]}{\left[\sum \limits_{i=1}^{N}\left(X_{i}-X_{mean}\right)^{2}\right]} \tag{17}\]  
\[MSE=\frac{\sum\limits_{i=1}^{N}\left(X_{i}-X_{p}\right)^{2}}{N} \tag{18}\]  
\[MAE=\frac{1}{N}\sum\limits_{i=1}^{N}\left|X_{i}-X_{p}\right| \tag{19}\]  
where \(X_{\text{i}}\) and \(X_{\text{p}}\) are the measured (actual) and predicted values of output, \(X_{\text{mean}}\) is the average predicted values and N is total data
# Efficient Resource Allocation for Beam-Hopping-Based Multi-Satellite Communication Systems
## 1 Introduction  
### Motivation  
Multi-beam low earth orbit (LEO) satellite communication systems generate multiple isolated point beams within their coverage by using multi-beam antenna technology and then provides broadband access services to areas with weak infrastructure [1; 2]. However, as a result of the small size and light weight of LEO satellites, their on-board resources face severe limitations [3; 4].  
In order to solve the problem of limited payload, power and spectrum resources on LEO satellites, multibeam antenna technology has become one of the functions that must be included in the design of satellite communication systems. At first, the use of multibeam antenna technology was mainly based on the fixed allocation of on-board resources. However, the distribution of traffic generated by practical applications is always uneven, which leads to low resource utilization efficiency and reduced system capacity. Therefore, the beam-hopping (BH) technology is adopted in multibeam systems [5; 6; 7]. In the BH satellite system, temporal resources are divided into several timeslots. In each timeslot, the satellite selects a portion of beams to allocate frequency bands and power resources based on the current traffic requirements and channel conditions of each cell. In the next timeslot, the satellite \"hops\" the beam to other cells based on changes in demand and channel conditions. This beam-hopping resource allocation mechanism can have better flexibility and higher resource utilization efficiency, and can adapt well to the uneven distribution of ground users and dynamic changes in communication services [8; 9].