esa-sceva/satcom-chunk-collection huggingface dataset:
# EV Charging Behavior Analysis Using Hybrid Intelligence for 5G Smart Grid
## 1 Introduction
In natural language processing, image recognition has made remarkable achievements. Therefore, a learning-based approach driven by real data is ideal for solving this problem. For example, some papers have proposed a real-time EV charging strategy based on reinforcement learning [16]. When EVs participate in demand response plans with real-time price signals, making full use of these price signals can greatly reduce charging costs. Another paper proposed a ML-based demand response strategy to control electric vehicle charging in response to real-time pricing, thereby minimizing the overall energy cost of electric vehicles. However, these algorithms all arrange users' charging strategies based on electricity prices to minimize user charging costs. In order to maximize the profit of the electricity supplier, it is necessary to predict the actual charging behavior of the user so as to formulate an optimal electricity price to maximize profit. Based on the above reasons, we propose a 5G smart grid electric vehicle charging behavior prediction method based on hybrid intelligence. This method consists of two parts, one is a smart grid network architecture based on cloud computing and fog computing, and the other is a user behavior analysis method based on human in the loop (HITL)-based K-Means clustering and k-nearest neighbors (k-NN) algorithm running on this structure. The smart grid network architecture based on cloud computing and fog computing is the hardware foundation for implementing intelligent algorithms and consists of three layers. The user layer is used to collect user information, the service layer is responsible for data analysis, request response, and provides cloud computing, fog computing, data driven charge and discharge scheduling determined by artificial intelligence technology, and finally multiple communication technologies are integrated at the network layer to ensure the interaction between the user layer and service layer. This structure can give full play to the big data computing capability of cloud computing and the real-time response capability of fog computing, ensuring the efficient real-time operation of the proposed algorithm. The HITL-based K-Means clustering and k-NN algorithm is the core of software predicting the charging behavior of EV. It marks the data set through an automatic process, making it unnecessary for new users to join the charging network clustering. This method can dispatch electric vehicles in the smart grid more efficiently. Cross-validation experiments and performance evaluation measures show that this technology can classify the charging behavior of users and has good prediction ability. The classification results can be used as an important basis for scheduling charge and discharge. In this article, we first present the three-tier architecture of a smart grid. Then we introduce the application of hybrid artificial intelligence in V2G systems. Finally, we conclude the article.
## 3 Decomposition Method for Countermeasures against Excessive Fault Currents
### Overall Procedure of the Proposed Method  
The overall procedure of the proposed method is as follows, with Figure 3:  
**Step 1**: An initial OPF is done on the network base case.
**Step 2**: Bus fault currents are calculated.
**Step 3**: Fault currents are compared to the given upper limits of FLCs to identify the critical locations with excessive fault currents. The algorithm proceeds to the next step when there is a critical FLC that violates the CB limit; otherwise, the algorithm stops.
**Step 4**: The subproblem is solved by running the FFLC-OPF on the network.
**Step 5**: If the FFLC-OPF finds a feasible solution, the \(z\) value or the least among the FLC membership  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(X\) **Variable** & **Discrete Value for CLR [pu]** & **Discrete Value for Bus Splitting [pu]** \\ \hline \(X_{min}\) & 0.00001 & 0.00001 \\ \(X_{a}\) & 0.10000 & - \\ \(X_{b}\) & 0.20000 & - \\ \(X_{max}\) & 0.30000 & 10.0000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter setting example for discretization. CLR: current limiting reactors.  
function values is checked if within the following stopping criterion:  
\[1-z<\varepsilon_{z} \tag{11}\]  
where \(\varepsilon_{z}\) is the tolerance of convergence equal to 0.01. If the stopping criterion is not satisfied, the algorithm proceeds to Step 6; otherwise, it goes to Step 8. On the other hand, if the FFLC-OPF cannot find a feasible solution, the previous \(X\) variable limit that yielded a feasible solution is set as the maximum limit before the algorithm proceeds to Step 6.
**Step 6**: The sensitivities w.r.t. \(\Delta X\)s using Equation (9) are evaluated.
**Step 7**: The main problem is solved to determine the main decision variables (\(X\) variables) that will remove (or reduce) the FLC violations. Subsequently, this network with new \(X\) variables is passed on to the next step.
**Step 8**: The new set of \(X\) variables is used as additional set of constraints such as in Equation (8c).
**Step 9**: The \(X\) variables are discretized by simply rounding the intermediate values to the nearest reactance values that represents bus splitting and additional CLRs. This is the final output of the algorithm.
**Step 10**: The process stops.  
Figure 3: Flowchart of the overall procedure. OPF: optimal power flow; FLCs: fault level constraints; FFLC-OPF: fuzzy fault level constrained optimal power flow.  
In this paper, FFLC-OPF for the subproblem was implemented on the OPF module in MATPOWER [26], and it was chosen because of its extensible structure for a specified purpose with OPF. In addition, KNITRO solver was used to solve the AC OPF portion of the algorithm. Basically the proposed decomposition method is to the nonlinear optimization problem including binary and discrete decision variables, and obviously those variables have nonlinear effects on equality and inequality constraints. That is, there might be several local optima other than the solution of the proposed method, but it can provide practical solutions with the least deviation from the original network.
# Near-Surface Material Phases and Microstructure of Scandate Cathodes
## 3 Results  
### Surface Morphology of Scandate Cathodes  
Images of the surfaces of two scandate cathodes are presented in Figure 1. It is seen in Figure 1a,c that the W particle size is \(\sim\)1 \(\upmu\)m and the pores are open, i.e., not obstructed by impregnate material. Two types of particles, each with a size on the order of 100 nm, are distributed across the surfaces of W grains: Sc\({}_{2}\)O\({}_{3}\) and BaAl\({}_{2}\)O\({}_{4}\), as determined from EDS point analysis of composition. Complementary and more detailed analysis is also discussed in a related article [37]. Additionally, a critical observation, apparent in Figure 1b,d, is the highly faceted surface morphology of W grains in both cathodes. Based on Wulff analysis and as discussed in a separate publication [37], these facets correspond to the [100], [110] and [112] crystallographic planes of W.  
The nanoscale particles with diameter on the order of 10 nm, which decorate W facets in a fine dispersion, are assumed to be either Ba or BaO, based on results reported in the literature [37; 38]. It appears, however, that numerous Ba/BaO particles were distributed over the W surfaces for cathode #1, while relatively fewer particles were observed on the surface of cathode #2, as shown in Figure 1b,d. As discussed in a previous paper [37], these Ba/BaO particles may result from transformation of a metallic Ba monolayer that exists at high temperature but is unstable during cooling after testing; this transformation may occur during cathode cooling after operation, creating the nanoscale particles observed here, and these particles would likely oxidize upon exposure to oxygen. Considering the emission test times, it may be reasonable to observe relatively lower areal density of Ba/BaO particles on the W facets of cathode #2, which had undergone emission testing for 9,840 h. This is more than three times the duration of testing for cathode #1, which could have resulted in lower (sub-monolayer) Ba coverage on the W surfaces of cathode #2 due to Ba loss during operation, leading to lower density of the Ba/BaO particles.  
Figure 1: Surface morphology of two scandate cathodes. (**a**,**b**) are low- and high-magnification SEM images of the surface of cathode #1. (**c**,**d**) are low- and high- magnification SEM micrographs of cathode #2. All micrographs were obtained in secondary electron imaging mode.
## 2 Algorithm Principle
### Standard INS+LEO-Dual Satellite (INS+LEO 2-Satellite) Integrated Navigation Algorithm  
The working principle of the standard INS+LEO-dual satellite (INS+LEO2-satellite) integrated navigation algorithm is as follows. Firstly, the satellite ephemeris data provided by the LEO satellite, and the position and velocity information provided by the INS, are used to calculate the pseudorange and pseudorange rate corresponding to the INS position and velocity. In this study, the INS we use and refer to is a strapdown INS. Secondly, the measurement value of integrated navigation is taken as the difference between the pseudorange and pseudorange rate measured by the LEO satellite, and the pseudorange and pseudorange rate calculated previously. The optimal estimation of error between the LEO satellite and INS is obtained using an unscented Kalman filter (UKF). Finally, the two systems are calibrated separately. This two-way assistance method enables the standard INS+LEO2-satellite integrated navigation mode to work stably for a period, and meet the basic navigation and positioning requirements for the carrier flight. The process is depicted in a schematic diagram in Figure 1, and the corresponding principles and procedures are as follows.  
According to Figure 1, assuming that the position of satellite #1 in Earth-centered Earth-fixed (ECEF) coordinates is \((x_{1},y_{1},z_{1})\), the position of satellite #2 in ECEF coordinates is \((x_{2},y_{2},z_{2})\), the true position of the carrier in the ECEF coordinate system is \((x_{u},y_{u},z_{u})\), and the position calculated by INS is a known quantity \((x_{I},y_{I},z_{I})\), \(\rho_{1}\) and \(\rho_{2}\) represent the true ranging values of satellites #1 and #2, respectively. \(\rho_{I1}\) and \(\rho_{I2}\) represent the ranging values of satellite #1 and satellite #2 obtained by the INS extrapolation solution, respectively. We derive the following equation:  
\[\left\{\begin{array}{l}\rho_{1}=\sqrt{\left(x_{1}-x_{u}\right)^{2}+\left(y_{ 1}-y_{u}\right)^{2}+\left(z_{1}-z_{u}\right)^{2}}\\ \rho_{2}=\sqrt{\left(x_{2}-x_{u}\right)^{2}+\left(y_{2}-y_{u}\right)^{2}+ \left(z_{2}-z_{u}\right)^{2}}\end{array}\right. \tag{1}\]  
and the position calculated by INS is:  
\[\left\{\begin{array}{l}\rho_{I1}=\sqrt{\left(x_{1}-x_{I}\right)^{2}+\left(y _{1}-y_{I}\right)^{2}+\left(z_{1}-z_{I}\right)^{2}}\\ \rho_{I2}=\sqrt{\left(x_{2}-x_{I}\right)^{2}+\left(y_{2}-y_{I}\right)^{2}+ \left(z_{2}-z_{I}\right)^{2}}\end{array}\right. \tag{2}\]  
According to Equations (1) and (2), we use the difference between \(\rho_{1}\) and \(\rho_{I1}\) and the difference between \(\rho_{2}\) and \(\rho_{I2}\) as the filtered observations, combined with the least squares (LS) algorithm or Kalman filter algorithm, to obtain the three-dimensional position solution.
# Arabic Cursive Text Recognition from Natural Scene Images
## 6 Conclusions  
This paper aims to summarize recent research that has been performed in cursive scene text recognition specifically in Arabic text in natural images. The most significant and recently-proposed approaches have been explained. Initially, the paper presented details of various cursive scripts; later, the discussion narrowed down with respect to Arabic scene text recognition. Furthermore, this survey examines various text localization algorithms, feature extraction techniques and state-of-the-art classifiers. Most researchers prefer to localize scene text manually in order to get high precision. Another reason for manual extraction of Arabic text is its appearance with various sizes, orientations and font styles. Thus, scale-invariant features are also used because they play an important role in feature extraction. Some authors proposed hybrid feature extraction techniques, which are based on an adapted scale-invariant feature extraction with the combination of their own proposed method. As learned from the presented work, numerous authors proposed unsupervised learning classifiers for Arabic text recognition in natural images. For the purpose of exposing the challenges associated with cursive script, the research under discussion may be exploited by supervised learning methods, so as to obtain a benchmark performance on Arabic scene text recognition research. The discussion of available Arabic scene text datasets including the proposed EASTR dataset was presented. As observed from recently-proposed work, the deep learning architecture is used to address the problems of scene text detection and recognition. Therefore, the research based on deep learning architecture has significant importance in cursive scene text analysis. The future directions were explored by keeping in view the complexities associated with Arabic and Arabic-like scene text recognition after highlighting the current status of cursive scene text detection and recognition research.  
**Author Contributions:** [PERSON] devised a main conceptual idea and worked out almost all of the technical details and contributed in writing of the manuscript with the help of [PERSON] but under the supervision of [PERSON] and [PERSON]. The compiled work is verified by [PERSON] who also contributed in some parts of presented contents. All authors provided critical feedback, and helped shape the research, analysis and manuscript.  
**Funding:** This research is funded by Malaysia-Japan International Institute of Technology (MJJIIT) under the grant number R.K430000.7743.4J010.  
**Acknowledgments:** The authors would like to thank the Center for Artificial Intelligence and RObotics (CAIRO-ikhoza) lab under MJIIT, Universiti Teknologi Malaysia for arranging funds to conduct this research.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Optimization of DC Energy Storage in Tokamak Poloidal Coils
## 2 Operations of PF Coils in a Tokamak  
### Tokamak Magnetic Configuration  
A tokamak is a complex architecture designed to confine and control high-temperature plasma using magnetic fields, with the ultimate goal of sustaining nuclear fusion processes [4]. The magnet system of DTT is schematized in Figure 1 (see also [26] for EU DEMO). Among the different types of coils, PFs are essential for shaping and controlling the plasma within the tokamak, necessitating precise and responsive PS systems [15].  
The tokamak core is a toroidal (doughnut-shaped) chamber where the fusion reactions occur. This chamber is surrounded by magnetic coils that work together to create the stable magnetic field configuration necessary for plasma confinement. The TF coils produce a magnetic field around the torus in a continuous loop, providing the primary confinement mechanism. However, the stability and precise shape of the plasma are governed by the PF coils. These coils are arranged in different loops that encircle the torus vertically and horizontally, controlling the plasma's cross-sectional shape and position.  
PF coils are critical to the operation of a tokamak, as they dynamically adjust the plasma shape and stability. To fulfill this role, PF coils require highly variable current profiles with peaks reaching tens of kiloamperes. Additionally, these coils must handle voltage profiles that span several kilovolts. The ability to rapidly and accurately control these high current and voltage levels is essential for maintaining the desired plasma configuration and ensuring the overall stability of the fusion process [4; 23].
## 3 Results and Discussion
### Prediction of IENU  
The initial dataset contains 341 entries with four variables: NC, T, D, and PY. It was transformed into four distinct versions to facilitate comparative analysis: Data 1 (302 entries), Data 2 (256 entries), Data 3 (341 entries), and Data 4 (286 entries). Histograms demonstrated the distribution quality within these datasets (Figure S2). Notably, Data 2 and Data 4 were identified as the most suitable for predictive modeling due to the close alignment between their training and testing distributions. The removal of outliers rendered the data more effective for predicting IENU.
# Battery Management System Hardware Concepts: An Overview
## 4 Measurement
### Voltage Measurement
#### 4.2.2 Pack Voltage Measurement  
The pack voltage acquisition is done by a separate measurement unit, typically consisting of a voltage divider, an impedance converter, a filter and an Analog Digital Converter (ADC). The voltage divider is necessary for scaling down the pack voltage to an adequate measuring voltage, which is in the range of the ADC. Depending on the pack voltage and on the electric strength of the resistors, it may be necessary to use several resistors for a safe voltage divider. In addition, a Zener diode should be placed in parallel to the measuring resistor for protecting the following measurement circuits against overvoltage. Since the voltage divider is designed to be highly resistive in order to keep the losses low, the voltage tap of the measuring resistor should be very high-resistance in order not to load the voltage divider. For this reason, an impedance converter that is followed by the filter circuit and the ADC should be used to tap the voltage.
## 2 Materials and Methods
### Validation of the TRNSYS Model
As can be appreciated from the values of solar radiation and dry-bulb temperature, the rigor of the meteorological conditions in the summer season is alike in both cities. \begin{table}
\begin{tabular}{c c c} \hline
**Driving Temperature (\({}^{\circ}\)C)** & **Useful Fraction Registered** & **Useful Fraction TRNSYS** \\ \hline
75 & 0.348 & 0.4 \\
80 & 0.293 & 0.365 \\ \hline \end{tabular}
\end{table} 
Table 1: Registered and simulated fractions of the incident solar radiation used to drive the absorption chiller for driving temperatures of 75 and 80 \({}^{\circ}\)C. \begin{table}
\begin{tabular}{c c c} \hline
**Collectors Slope (\({}^{\circ}\))** & **Energy (kWh/m\({}^{2}\)-day) Elche** & **Energy (kWh/m\({}^{2}\)-day) Madrid** \\ \hline
10 & 7.19 & 6.90 \\
20 & 7.24 & 6.96 \\
30 & 7.10 & 6.86 \\
40 & 6.80 & 6.59 \\
60 & 5.72 & 5.60 \\ \hline \end{tabular}
\end{table} 
Table 2: Average daily radiation received per square meter of inclined collector surface. The thermal energy that is extracted from the hot water storage tank to feed the absorption machine can be translated into cooling capacity by using the EER of the chiller. There are different ways of modelling the performance of a single-effect absorption chiller using LiBr-H\({}_{2}\)O as working fluid in the environment of TRNSYS. A first way could be using the Type 107 of the TRNSYS library. However, the manufacturer does not provide operating data for cold water temperatures other than 7 \({}^{\circ}\)C for the Yazaki WFC-SC5 installed at the Miguel Hernandez University as well as for the Yazaki WFC-SC10 planned in this work, and therefore Type 107 cannot be used. A second way could be developing a model based on energy and mass balances and heat transfer equations [8]. This kind of models need the estimation of the heat transfer coefficients and heat exchange areas of the components that constitute the absorption chiller to simulate its behaviour. The approach implemented in this work was using the EER values obtained by parameterizing the performance curves provided by the manufacturer (Table 3). This approach considers the nominal value of 31 \({}^{\circ}\)C for the cooling water inlet temperature and a chilled water outlet temperature of 7 \({}^{\circ}\)C.  
Obviously, the cooling water inlet temperature will vary depending on the outdoor conditions and the cooling tower performance, while the chilled water outlet temperature will be constant if, as it is supposed, the absorption chiller participates in the base cooling demand of the hotel, and whenever it works, it will be at full load. Figure 4: Weather conditions on an hourly basis for the summer season (June–September) in Madrid (red dots) and Elche (green triangles). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Tg, in (\({}^{\circ}\)C)** & **Cap (kW)** & **Qgen (kW)** & **EER** \\ \hline
75 & 16.6 & 23.0 & 0.722 \\
80 & 24.3 & 32.5 & 0.748 \\
85 & 31.0 & 43.0 & 0.721 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: EER for driving temperatures of 75, 80 and 85 \({}^{\circ}\)C under nominal conditions.
# A Communication Framework for Image Transmission through LPWAN Technology
## 5 Discussion  
Wireless networks are used in a wide range of applications every day, LPWAN technologies are used in IoT scenarios, while their low data transfer capacity limits their use to low-payload applications. This article presents a framework to transport images through the use of LoRa modulation. Processing and classification, compressive sensing, transmission and receiving of LoRa symbols, postprocessing, and image reconstruction techniques were all employed for this goal.  
Previous research has revealed how to transfer images using LoRaWAN networks. See Table 1. Nevertheless, there is very little information available about these proposals, and to  
Figure 10: (**a**) original image, (**b**) reconstructed image with a percentage of 90% sparsity with a PSNR of 30.02 dB, and (**c**) reconstructed image with a percentage of 95% sparsity with a PSNR of 26.81 dB.  
Figure 9: Real component of a received LoRa frame signal.  
the best of our knowledge, there are no LoRa modulation implementations in transmitters and receivers with open platforms; thus, not only is a framework to transport images with a LoRa modulation into LPWAN networks shown, this work also shows how it can be implemented in SDR devices, allowing for future tests to improve the technology.
# Use of SAR and Optical Time Series for Tropical Forest Disturbance Mapping
## 2 Material and Methods
### Main Processing
#### Evaluation Concept for Disturbance Mapping
The confusion matrices are provided in Tables 13-15 for Peru and Tables 19-11 for Gabon. We estimate the products' user and producer accuracies from the error matrix using the estimated area proportions and Equations (6)-(8) from [79]. The benchmark non-forest mask is an essential a priori information, and errors in forest/non-forest masking strongly influence forest disturbance detection accuracies. We therefore derived a second set of confusion matrices, where all non-forest areas and misclassified sample points related to FNF mask errors are removed from the reference data set and where area weights of the strata are then recalculated accordingly, in order to evaluate the methods' accuracies independently from forest mask errors. The results are provided in Tables 16-18 for Peru and Tables 12-14 for Gabon.