esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Photovoltaic Modules, in General  
Figure 1 shows the structure of a superstrate-type solar cell module, which is a typical solar cell module. The smallest unit in a solar battery is the cell. A crystalline Si solar cell comprises 210 mm, 182 mm, or 166 mm wafers and is half or 1/3 cut. The voltage of one solar cell is as low as around 0.7 V. Therefore, dozens of solar cells are connected in series in a solar cell module. In addition, because the PV module is installed outdoors, it has a structure in which multiple cells are covered with white tempered glass, fillers, a back sheet, and an aluminum frame. The internal cells are connected using an interconnector, the ends of which are pulled out from the rear surface and connected to a terminal output cable inside the terminal box to remove the generated power.  
A bypass diode was installed inside the solar cell module. Figure 2 shows a schematic diagram of the operation of the solar-cell module. When the solar cell that constitutes a solar cell module does not generate power in the shade, the resistance of the cell increases, hindering the flow of current and generating heat owing to resistance loss. To prevent this, a bypass diode is connected in parallel to the solar-cell module as a current path that bypasses the shaded portion. Bypass diodes are typically placed in each string of the solar modules, corresponding to 20 or fewer solar cells. Bypass diodes can reduce the impact (decrease in current) on the remaining strings when some cells stop generating power owing to shadows or failures.  
Figure 1: Typical structure of photovoltaic modules.
# An Improved Artificial Bee Colony for Feature Selection in QSAR
## 1 Introduction
[34] carried out a comparative study on different binary ABC algorithms on feature selection. BitABC [35] employs bitwise operators such as AND, OR, XOR to generate new candidate solutions, and the binary ABC algorithm uses different functions to convert continuous vector to binary vectors, such as rounding function [36], sigmoid function [37], and tangent function [38]. The experimental results showed that BitABC generated better feature subsets in shorter computational time. Moreover, many studies combine ABC with other optimization algorithms, such as DE [39], ACO [40], and PSO [41], and they achieve promising results as well. However, the ABC algorithm is seldom applied to regression and prediction problems and achieve promising results. To improve the accuracy and interpretability of QSAR that is a regression and prediction problem, we apply ABC algorithm to feature selection in QSAR. Major novelties and contributions of our study are described as follows:  
1. To save the process of converting continuous space into discrete space and reduce the consumption of computing resources, a two-point crossover operator and a two-way mutation operator are employed to generate food sources in employed bee phase and onlooker bee phase. 2. To achieve fast convergence, a novel greedy selection strategy is employed to greatly reduce the possibility of food sources being abandoned. 3. Furthermore, we investigate the influence of different threshold values that determine whether to implement the scout bee phase on the performance of QSAR and draw an interesting conclusion that the scout bee phase is redundant when dealing with the feature selection in low-dimensional and medium-dimensional regression problem. The rest of this paper is organized as follows: Section 2 reviews the related work of FS methods based on SI. Section 3 briefly describes QSAR modeling and the FS problem. Section 4 presents the basic ABC algorithm and proposes two improved ABC variants for FS in QSAR. Section 5 describes the experimental datasets and parameter settings. Section 6 presents the experimental results. Conclusions are given in Section 7.
###### Abstract  
As in any other industry, nuclear energy results in the accumulation of some waste, which needs to be managed safely and responsibly due to its radiotoxicity. In the case of highly radioactive waste, geological disposal in stable rock is considered a broadly accepted solution. For the evaluation of the long-term safety of a geological repository, the assessment of radionuclide transport needs to be carried out. Radionuclide transport through engineered and natural barriers of the repository will highly depend on the barriers' transport-related properties, which will be determined by coupled thermal, hydraulic, chemical, mechanical, biological, and radiation processes taking place in those barriers. In this study, the thermo-hydro-chemical (THC) state of bentonite was analysed considering CO\({}_{2}\) gas diffusion and temperature-dependent solubility in water. Reactive transport modelling of bentonite under non-isothermal conditions was performed with the COMSOL Multiphysics software (v6.0), coupled with the geochemical solver Phreeqc via the iCP interface. The modelling demonstrated that the consideration of chemical processes in bentonite had no significant influence on non-reactive Cl\({}^{-}\) transport; however, it would be important for other radionuclides whose sorption in porous media depends on the porewater pH. Based on the modelling results, changes in the bentonite mineralogical composition and, subsequently, porosity depend on the partial CO\({}_{2}\) pressure at the bentonite-granite boundary. In the case of low CO\({}_{2}\) partial pressure at the bentonite-granite interface, the calcite dissolution led to a slight porosity increase, while higher CO\({}_{2}\) partial pressure led to decreased porosity near the interface.  
geological disposal; coupled thermo-hydro-chemical processes; modelling; COMSOL Multiphysics; iCP; Phreeqc; reactive transport +
Footnote †: journal: Applied Sciences
## 4 Discussion
is equivalent to that of CE-CT and liver MRI to identify primary liver tumors and metastatic disease. The sensitivity of CE-CT and liver MRI for the detection of primary liver tumors is 96% and 100%, respectively, while PET-CT imaging with \({}^{18}\)F-FDG and \({}^{68}\)Ga-FAPI-04 has a sensitivity of 65% and 96%, respectively. Moreover, Ga-FAPI-04 detected more metastatic lesions than \({}^{18}\)F-FDG, especially peritoneal carcinomas and bone metastasis, thus improving liver cancer evaluation and subsequent therapeutic management [38]. Given the superiority of MRI over CT in primary hepatic tumors and the emergent implementation of hybrid imaging techniques, PET/MRI with FAP-targeted radiopharmaceuticals may have a future central role in the detection and characterization of both HCC and CCA, thus further improving diagnostic performance. Similar comparisons were also performed between \({}^{68}\)Ga-FAPI-46 PET-CT, \({}^{18}\)F-FDG PET-CT, and conventional CT, confirming superior detection efficacy and tumor-to-background uptake of PET-CT with \({}^{68}\)Ga-FAPI-46 for the detection of primary tumor, lymph nodes, and distant metastases, especially regarding grade 3 CCA [28]. Treatment options for CCA are limited and dependent on the stage and location of the tumor. Surgical resection is curative in the early stages. However, CCA is often diagnosed in advanced stages, and chemotherapy treatment is very often required. Radiotherapy can be used as a palliative treatment or in case of an incomplete resection [6]. In the last decade, the so-called radiotherapanostics, combining PET imaging with targeted radioligand therapy (RLT), has continuously expanded [58]. In this context, FAP-targeting agents can be bound to therapeutic isotopes such as the \(\beta-\) emitters 90-Yttrium (90Y) and Lutetium-177 (177 Lu) for therapeutic purposes. Given the encouraging results of RLT with 90Y-FAPI and 177 Lu-FAPI for other tumor entities such as sarcoma, pancreatic adenocarcinoma, and breast cancer [59; 60; 61; 62; 63], the study by [PERSON] et al. indicated promising therapeutic use in CCA, worthy of further research [28].
## 1 Introduction
The aim of this paper is to present a method to impose a CTSA for the purpose of making a path tracking controller better under low friction conditions. From the definition of tire slip angle, CTSA is derived. To consider CTSA in controller design procedure, LQR and MPC are adopted as a controller design methodology. \(\delta_{f}\) and \(\delta_{r}\) and \(\Delta M_{z}\) are selected as a control input, which make three input configurations. When imposing CTSA in LQR, the control inputs, \(\delta_{f}\) and \(\delta_{r}\), are clipped by the constraint. In MPC, the constraint on steering angle is simply added into the optimization procedure. To distribute \(\Delta M_{z}\) into tire forces generated by various actuator combinations, a control allocation is applied. Through the control allocation, the effects of various actuator combinations composed from FWS, 4 WS, 4 WID and 4 WIB, on path tracking performance is analyzed. A simulation is done on a vehicle simulation software, CarSim 8.02, to verify path tracking performance with CTSAs. From simulation results, it is concluded that a path tracking controller with CTSA shows  
Figure 1: \(F_{y}\) with respect to \(\alpha\) and \(\mu\). quite a better performance in terms of path tracking than one without CTSA on low-friction road surface. The contributions of this paper can be summarized as follows:  
1. This paper presents a method to impose a CTSA on steering angles in order to generate the maximum lateral force on a low-friction road surface. The constraint is simply derived from the definition of tire slip angle and easily applied into steering angles, i.e., control inputs of LQR and MPC. From simulation results, it is verified that the CTSA can greatly improve the path tracking performance on low-friction surface. 2. The proposed CTSA makes the most of the lateral tire force. This means that there are little tire forces left to improve the control performance with extra actuators. Under that condition, the effects of various combinations composed from the actuators such as RWS, 4 WID, and 4 WIB on control performance are discussed from simulation results. 3. From the discussion on simulation results, it is recommended that LQR is better than MPC in terms of computation time and simplicity of controller structure, and that the actuator combination, FWS + RWS, shows the best performance under CTSA. This paper consists of five sections. In Section 2, design procedures for LQR and MPC with various actuator combinations are presented. Moreover, the derivation procedure for CTSAs is also presented. In Section 3, performance measures on path tracking are presented for the purpose of tuning LQR and MPC. In Section 4, simulation is conducted and simulation results are discussed in view of the performance measures. The conclusion of this paper is drawn in Section 5.
## 2 Materials and Methods
### Statistical Analysis  
Descriptive characteristics at baseline were compared according to monitoring sites and daily cough extent. Student's _t_-test was used to examine the comparison of the means of continuous variables, and the chi-square test was used to examine the comparison of proportions of categorical variables. The Pearson correlation analysis was used to examine the correlation between daily concentrations of air pollutants, including PAH and TSP. The generalized estimating equations (GEEs) were used to examine the relationship between cough occurrence and PAH exposure [6]. GEE models for same-day (lag 0 defined as 24 h period before the health response) through 5 day lag were constructed with adjustments for potential confounders; monitoring sites, gender, age, body mass ranking, and the presence or absence of asthma. Daily concentrations of SO\({}_{2}\), NO\({}_{2}\), and PM\({}_{2.5}\) were further adjusted to discriminate specific effects of PAHs from other air pollutants, which have been shown to be associated with asthma exacerbation in children and adults in addition to these site- and subject-specific variables [18]. Model regression parameters were presented as the regression coefficients with 95% confidence intervals. SPSS Version 26 (IBM Corp., Tokyo, Japan) was used for all analyses. \(p\) < 0.05 was considered statistically significant.
## 2 Materials and Methods
### Study Design and Brain Samples
#### 2.2.2 Transmission Electron Microscopy (TEM) and Energy-Dispersive X-Ray Spectrometry (EDX)  
Three mm block brain samples--cut with ceramic knives and handled with plastic forceps, free from metal contamination--were used for TEM work.
###### Abstract
Especially through time series models, these predictions provide an effective method for forecasting future values based on past datasets. By thoroughly analyzing patterns and changes in previous periods, these models offer valuable insights into future trends. Despite the widespread use of time-dependent predictions in various fields, there is a literature gap concerning a standardized or comprehensive assessment that measures the applicability of time series models in predicting Ecological Footprint. In this study, the forecasting of Ecological Footprint (EF) was performed using various time series models, and the performances of these models were compared. In this context, the deep learning model of Long Short-Term Memory (LSTM), classical time series methods such as Auto-Regressive Integrated Moving Average (ARIMA), and Holt-Winters, and a developed hybrid model combining ARIMA with Support Vector Regression (ARIMA-SVR) were used. The study findings demonstrated that the ARIMA model, known as a popular and successful method in the literature, outperformed other methods in EF forecasting. The contributions of this paper can be summarized as follows: \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Carbon** & **Crop Land** & \begin{tabular}{c} **Grazing** \\ **Land** \\ \end{tabular} & \begin{tabular}{c} **Forest** \\ **Products** \\ \end{tabular} & \begin{tabular}{c} **Fishing** \\ **Grounds** \\ \end{tabular} & \
\begin{tabular}{c} **Built-Up** \\ **Land** \\ \end{tabular} &
\begin{tabular}{c} **Total** \\ \end{tabular} \\ \hline Ecological Footprint & 1.562 & 0.485 & 0.125 & 0.264 & 0.083 & 0.063 & **2.582** \\ \hline Biocapacity & 0.485 & 0.184 & 0.643 & 0.136 & 0.063 & **1.510** \\ \hline
**Ecological** & & & & & & **\(-\)1.072** \\
**Deficit** & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: World total Ecological Footprint and biocapacity 2022 in gha/cap. Evaluating the forecasting capabilities of LSTMs, ARIMAs, Holt-Winters, and hybrid ARIMA-SVR models for the worldwide Ecological Footprint (gha). * The ARIMA (1,1,0) model is recommended for Ecological Footprint forecasting. * Comparing the forecasting performances of models under unforeseen decrease conditions due to COVID-19 lockdowns. * Statistically validating the performance of the proposed ARIMA (1,1,0) model.
## 1 Introduction
estimated that the mass loss over the entire SETP was \(-\)5.99 \(\pm\) 2.78 Gt/yr using GRACE satellite gravity data from 2003 to 2009 [3]. The published results show that glaciers in the SETP have been in a state of strong ablation and retreat in recent decades. However, there are large differences between different satellite geodetic observations. [PERSON] et al., [PERSON] et al., and [PERSON] et al. all used ICESat satellite altimetry data, and their glacier mass balance estimates were significantly different [15, 16, 17]. Although [PERSON] et al. [5] and [PERSON] et al. [18] both used optical remote sensing stereo image pairs, their estimated glacier mass balance results also exhibit some discrepancies. The glacier mass balance obtained by [PERSON] et al. [20] using the 90 m resolution TanDEM-X DEM product is also different from the above results. Due to the limitations of the traditional glaciological method and the large spread of estimations obtained by satellite geodetic monitoring technology, the results of the existing glacier mass balance estimations are highly controversial and uncertain. Satellite InSAR (Interferometric Synthetic Aperture Radar) is one of the most effective earth observation technologies for large-scale topographic mapping, with high-resolution, all-day, and all-weather earth observation capabilities and large imaging area [21]. The German Aerospace Center (DLR) and Astrium GmbH jointly implemented the TanDEM-X science program, and on 21 June 2010, DLR successfully launched the TanDEM-X (TDX) satellite, which operates in a two-satellite constellation mode with the previous TerraSAR-X (TSX) satellite, constituting a bi-static interferometry system [22]. Because the bi-static observation model can effectively eliminate the effects of atmospheric disturbance and temporal incoherence, this technology has great advantages and broad application prospects in the field of high-resolution and high-precision DEM mapping on a global scale [23; 24]. The bi-static InSAR technique has successfully obtained glacier mass balance data in the Tien Shan, Nyainqentanglha Mountains, Karakoram, Himalayas, Puruogangri Icefield, western Qilian Mountains, Geladandong Glacier, Patagonia Icefield, and Alpine Mountain Range, demonstrating its effectiveness in monitoring valley glaciers [25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37]. In this paper, we investigate the mass balance of glaciers in the SETP, using bi-static InSAR detection and inversion methods. We also investigate non-local mean filter and the multi-source systematic error correction methods to enhance the accuracy of our observations. Using TanDEM-X bi-static InSAR data and historical topographic data, we obtained glacier mass balance results for the SETP. The spatial distribution of the glacier mass balance in relation to climatic data was also investigated to reveal the response of glaciers to climate change in this region.
# A Drone-Enabled Approach for Gas Leak Detection Using Optical Flow Analysis +
## 3 Methodology  
As discussed in Section 1, gas leaks in industrial sites can cause financial loss and human injuries, thus many companies invest in finding solutions assisting inspectors to more accurately locate and assess the possible gas leaks. These approaches involve enhancing the visualization of gas emanations and automating the detection and characterization of gas leaks. The optical methods especially thermal imaging systems are one of the most common approaches since the minimum setup is required and an inspector can carry the equipment while moving alongside the pipeline. However, the hazardous nature of leaked gases and the length of pipelines motivated companies to find alternative ways to conduct the inspections. The use of aerial platforms like a drone is one of the most convenient and effective ways to conduct such inspections. A drone can move alongside a specimen and obtain the required data in a shorter amount of time compared to a conventional approach. In addition, drones can minimize the risk of any human injuries as there is no need for close-contact.  
In this study, a process pipeline is proposed involving three main steps as shown in Figure 2: (a) a fixed-size frame buffering, (b) a stabilization technique to remove unwanted motions of hovering drone, and (c) an approach for detection and visually-enhance gas emanations using optical flow analysis of thermal image streams. The system collects frames and keeps them in a buffer. When the buffer reaches a predefined size, the frames are batched and passed to the stabilization technique minimizing the effect of the drone's motions on the gas leak detection algorithm. Finally, the proposed algorithm is employed to process the frames and to provide expected results: (a) probabilistic map, (b) hotspot, and (c) the area with the highest flow intensity.
# A Novel Battery-Supplied AFE EEG Circuit Capable of Muscle Movement Artifact Suppression
## 4 Simulation and Results: Comprehensive Analysis
### Simulation of Novel EEG-B3V3S/C1 Circuit Design  
As it is known by the circuit design section, our circuit includes the reference electrode for calculating the voltage reference of the collected raw EEG signal as well as the channel 1 electrode for collecting the raw EEG signal. According to their functionality, the first is placed behind the ear, and the other is placed on the Fp1 position (10-20 positioning system) [25]. The main purpose of this test is to simulate the characteristics of the circuit that were deemed important when deciding the requirements of the circuit presented in this study, namely CMRR, bandwidth in the 0.5 Hz to 3 kHz region, and, in particular, in the 4 Hz to 30 Hz alpha, beta and theta wavebands range. Another crucial element that must be examined in this simulation is the notch depth of the notch filter when it is functioning at approximately either 50 Hz or 60 Hz frequencies. Finally, a visual representation of both time and frequency responses will be presented further down to showcase the effectiveness of the circuit. In particular, it examines the circuit's ability to denoise the artifacts of the raw EEG signal acquisition process in the presence of strong compound muscle movements while preserving the signal integrity of the alpha, beta and theta wavebands.
# An Empirical Approach for Extreme Behavior Identification through Tweets Using Machine Learning
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Source** & **Extremists Type** & **Datasets** & **Source** & **Methodology** \\ \hline
[10] & ISIS & own & Twitter & machine learning \\
[14] & extremist groups & Own & web forum & lexicon-based \\
[15] & extremist groups & Own & web forum & lexicon based \\
[16] & extremist groups & Own & web forum & machine learning \\
[17] & ISIS & Own & web forum & machine learning \\
[18] & Hate-speeches & Available & Twitter & bag-of-words \\
[19] & ISIS & Own & Twitter & Clustering \\
[20] & ISIS & Own & Twitter & Network \\
[21] & ISIS & Own & Twitter & machine learning \\
[22] & radicalized users & Own & YouTube & lexicon-based \\
[23] & ISIS & Available & Twitter & machine learning \\
[24] & radicalized users & Own & Twitter & lexicon-based \\
[proposed] & Taliban & Own & Twitter & machine learning \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Previous work on radicalization detection. * Exploratory Data Analysis (EDA): An EDA is a technique used for in-depth statistical analysis in order to make a better understanding of the dataset and this is usually achieved through data visualization techniques. Since a tweet-based content is in an unstructured form that requires converting into a structured form before performing a statistical analysis. The resulting structured form of textual data often has high dimensions that are not practically viable to visualize using conventional visualization approaches. In order to resolve this issue, an unsupervised approach called Principal Component Analysis (PCA) [26] is adopted to project a high dimensional data onto a low dimensional space to get an insight by visualizing it onto a 2-D or 3-D plot. The 2-D or 3-D visualization help users to know how each tweet is related to every other tweet in terms of neighborhood and in terms of assigned group labels. * Classification Model Building for Sentiment Analysis: Supervised machine learning algorithms are used for classifying tweets having extreme contents. Several classification methods including very commonly known ones like naive Bayes', decision tree, random forest, K Nearest Neighbors (KNN), Support Vector Machine (SVM) and lesser-known ones like ensemble classification methods with boosting and bagging approaches were applied for predictive analytics purpose. Furthermore, to boost the accuracy of the classification models, feature sets such as \(n\)-grams and TF-IDF are empirically evaluated. The remainder of the paper is presented as follows: In Section 2, detailed reviews of detecting radicalized groups from the web using sentiment analysis are discussed; In Section 3, the proposed methodology for detecting the extreme content is presented. The experimental setup for evaluating the proposed framework, and the results are demonstrated in Section 4; Section 5 concludes the proposed framework and its effectiveness for detecting extreme contents in Twitter data in the context of Afghanistan.