esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges
## 4 Discussion and Conclusions  
We surveyed that researchers use multiple approaches and datasets to get better recognition rates. The literature review suggests that a proper process needs to be followed, which includes preprocessing, segmentation (text-area detection and line, word, and character segmentation), recognition, and postprocessing. We discussed the pros and cons of each technique discussed in this survey. For example, segmentation-based approaches give better results than segmentation-free approaches, and vertical/horizontal projection produces good results for word and character segmentation. However, OCR results depend upon a good dataset as well. Some datasets are available for the Arabic OCR, but only a few are publicly available. Postprocessing and dataset availability require more attention from researchers. For postprocessing, if Google spelling checker-like algorithms are implemented and improved, then this stage can perform very well, enhancing the overall result of the OCR system. We need a publicly available dataset with an extensive vocabulary of printed and handwritten text (characters and words) for the dataset. In conclusion, we presented a survey of the state-of-the-art Arabic OCR, which has come a long way in recent years, with several approaches and techniques developed to improve its accuracy and performance. However, many challenges still need to be addressed, including dealing with the variability and complexity of the Arabic script and the large number of dialects and variations in the language. Despite these challenges, the potential benefits of Arabic OCR are clear, and researchers and developers are working hard to continue to improve and refine the technology. We will likely see even more accurate and reliable Arabic OCR systems with continued research and development. Conceptualization, S.F., M.S.A. and S.H. ; methodology, S.H., M.S.A. and M.A.K. ; investigation, M.S.A. and S.H. ; resources, M.S.A. and S.H. ; writing--original draft preparation, M.S.A. and S.H. ; writing--review and editing, S.F., M.S.A., S.H. and M.A.K. ; visualization, M.S.A., S.H. and M.A.K. ; supervision, S.F. and M.A.K. ; project administration, S.F. and M.A.K. ; funding acquisition, S.F. All authors have read and agreed to the published version of the manuscript. This work is funded by the Deputyship of Research & Innovation, Ministry of Education, Saudi Arabia, through project number 964. In addition, the authors would like to express their appreciation for the support provided by the Islamic University of Madinah. Not applicable.
# AI-Based Resource Allocation in E2E Network Slicing with Both Public and Non-Public Slices
## 3 Algorithm Design
### Independent Proximal Policy Optimization (IPPO)  
The MDP modeled above for this problem can be solved using various reinforcement learning methods. We tried several popular reinforcement learning methods and found that the IPPO algorithm is the most effective. Therefore, we propose to use the IPPO algorithm to solve the RB resource allocation problem of (30).  
The Proximal Policy Optimization (PPO) algorithm is an On-policy algorithm that can be used for both continuous action space tasks and discrete action space tasks. PPO is based on the idea of the Trust Region Policy Optimization (TRPO) algorithm, but its algorithm implementation is simpler and more effective.  
The IPPO algorithm is the multi-agent version of the PPO. Each agent is trained independently with the PPO algorithm. PPO is based on the Actor-Critic architecture, so two neural networks, the policy network and the value network, are required in the training process. The policy network is mainly responsible for choosing actions based on the current state and policy, interacting with the environment to receive feedback, and continuously updating the policy. The value network is mainly responsible for learning the state value function, which is used to help the policy network to update the strategy. In this paper, each slice corresponds to an agent, so the number of agents is \(S\). Therefore, the number of neural networks required for the IPPO algorithm is \(2S\).
# Demystifying DFT-Based Harmonic Phase Estimation, Transformation, and Synthesis
## 1 Introduction
### Paper Structure  
This paper is divided into two major parts. The first part, corresponding to Section 2, is devoted to the estimation of the phases of individual sinusoids using light but robust DFT-based analysis procedures. Two commonly used DFT filter banks and three window functions are considered. Robust phase estimators are found, taking into consideration the specificities of the filter banks and window functions, notably their frequency responses. It has been shown that the relative performance of the six-phase estimation alternatives mainly depends on the main lobe width of the magnitude of those frequency responses and the associated near-end and far-end leakage properties. In particular, using the lower  
Figure 1: In many harmonic signal processing applications, individual harmonics are processed separately. Signal modification converts the original fundamental frequency (\(\omega_{0}\)), the original harmonic magnitudes (\(A_{\ell}\)), and phases (\(\phi_{\ell}\)), into new values (\(\omega_{0}^{S}\), \(A_{\ell}^{S}\), \(\phi_{\ell}^{S}\)) in the synthesis process.  
Figure 2: A convenient paradigm in harmonic signal processing: a holistic harmonic magnitude and phase structure represented by a shift-invariant and fundamental frequency-independent magnitude model (MM), and phase model (PM), respectively. Signal transformation just involves modifications to these models.  
bound for the error variance of any unbiased estimator, which is set by the Cramer-Rao lower bound (CRLB) [17], it has been demonstrated that the performance of one of the studied windows is not only closer to the CRLB when conditions are favorable but also exhibits a lower performance deviation when signal conditions are adverse.  
In the second part of the paper, corresponding to Section 3, we address the computation of a time-shift invariant harmonic phase model (HPM) that implements the holistic PM represented in Figure 2 and validates the harmonic signal processing paradigm that the block diagram of Figure 2 substantiates. Using the concepts of vertical phase coherence, as well as detailed and interpretable test cases, both ground truth and estimated PM are illustrated and compared under various test conditions. This section concludes with an illustration of the Harmonic Phase Model (HPM) of actual natural voice signals and an explanation of its usefulness in demystifying the phasegram of a representative periodic signal. Finally, Section 4 concludes this paper with a summary of the most significant concepts and results presented, in addition to a perspective on further research and application scenarios.
# A Thorough Evaluation of GaN HEMT Degradation under Realistic Power Amplifier Operation
## 4 Measurement Description
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Phase** & **Description** & **Details** \\ \hline \multirow{3}{*}{Preliminary/Final Control Measurements} & Full DC I/V Characteristics & \(V_{G}=-4\)-2 \(V\), step 0.5 V, \(V_{D}=0\)-30 V, step 2 V \\  & DC I\({}_{G}\)—\(V_{D}\) Transcharacteristic & \(V_{G}=-4\) V, \(V_{D}=0\)-30 V, step 0.5 V \\  & DC I\({}_{D}\)—\(V_{G}\) Transcharacteristic & \(V_{G}=-4\)-2 V, step 0.1 V, \(V_{D}=4\) V \\ \hline \multirow{3}{*}{Stress Sessions} & Low-Frequency Tuned-Load Class AB & 60 h total stress time divided into 8 sessions \\  & Low-Frequency Class-E & (1 h-1 h-4 h-6 h-12 h-12 h-12 h-12 h) \\ \hline \multirow{3}{*}{Intermediate Control Measurements} & DC I\({}_{G}\)—\(V_{D}\) Transcharacteristic & \(V_{G}=-4\) V, \(V_{D}=0\)-30 V, step 0.5 V \\  & DC I\({}_{D}\)—\(V_{G}\) Transcharacteristic & \(V_{G}=-4\)-2 V, step 0.1 V, \(V_{D}=4\) V \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the stress measurement sequence. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Symbol** & **Tuned-Load Class AB** & **Class-E** \\ \hline Quiescent Gate Voltage & \(V_{GQ}\) & \(-1.8\) V & \(-1.9\) V \\ Quiescent Drain Voltage & \(V_{DQ}\)* & 28 V & 28 V \\ Quiescent Drain Current & \(I_{DQ}\) & 80 mA/mm & 60 mA/mm \\ Fundamental Frequency & \(f_{0}\) & 2 MHz & 2 MHz \\  & \(Z_{L}\) & \(60.5+\mathrm{j}4.7\ \Omega\cdot\mathrm{mm}\) @\(f_{0}\) & \(85.3+112\ \Omega\cdot\mathrm{mm}\) @\(f_{0}\) \\ Load & & \(4.6-\mathrm{j}0.2\ \Omega\cdot\mathrm{mm}\) @\(2f_{0}\) & \(35.8-144\ \Omega\cdot\mathrm{mm}\) @\(2f_{0}\) \\  & & \(3.7+\mathrm{j}0.4\ \Omega\cdot\mathrm{mm}\) @\(3f_{0}\) & \(6.40-\mathrm{j}64.0\cdot\mathrm{mm}\) @\(3f_{0}\) \\ Average Gate Current & \(I_{G0}\) & \(0.63\) mA/mm & \(\approx\)0 mA/mm \\ Average Drain Current & \(I_{D0}\) & 285 mA/mm & 185 mA/mm \\ Input Power Available & \(P_{av}\) & 16.3 dBm & 14.9 dBm \\ Output Power & \(P_{out}\) & 5.1 W/mm & 3.5 W/mm \\ Dissipated Power & \(P_{diss}\) & 2.7 W/mm & 1.4 W/mm \\ Drain Efficiency & \(\eta_{D}\) & 65\% & 67.8\% \\ Maximum Drain Voltage & \(\upsilon_{Dmax}\) & 53.2 V & 86.7 V \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Stress conditions applied at 30 \({}^{\circ}\)C.  
designed to minimize the impact of the operating condition (e.g., dissipated power) on their value. Also, external factors, such as the ambient temperature, were controlled to limit their influence on the circuits implementing the load. Indeed, the load condition, which can be monitored directly from the acquired data, demonstrated an excellent stability, and its variations were limited to less than 1 \(\Omega\) for both the selected stress conditions at both the fundamental and harmonics.
## 4 Results
### Microleakage Evaluation with Score  
Figure 4 and Table 3 present the microleakage scores of all experimental groups. Three-way ANOVA revealed that the factors 'pH', 'location of margin', and'material' significantly affected the microleakage score (\(p\) = 0.028). Although the interactions between these three factors were statistically significant, the data were interpreted using the main effect model. With regards to the materials, groups R and A exhibited a significantly lower microleakage score than group G (\(p\) = 0.001). In group G, statistically significant differences were noted between the occlusal and gingival margin scores (\(p\) = 0.001); however, no significant difference was detected between the different pH conditions (\(p\) > 0.05). The microleakage score in the gingival margin was higher than that in the occlusal margin (\(p\) = 0.001). In group R, there were significant differences between the different pH conditions and between the locations of the margin. In that group, the microleakage scores in  
Figure 3: Microleakage analysis of the experimental groups. Representative images of confocal laser scanning microscopic photograph showing the rhodamine B dye penetration. (**A–C**) Representative images of pH 4, pH 7 and pH 10-stored giomer restorations, respectively; (**D–F**) pH 4-, pH 7- and pH 10-stored RMGI restorations, respectively; (**G–**J**) pH 4-, pH 7- and pH 10-stored alkasite restorations, respectively.  
the occlusal margin were significantly higher than those in the gingival margin (\(p\) = 0.001). In the gingival margin of group R, the microleakage score of the pH 4-stored specimen was significantly lower than that of the pH 7-stored specimens (\(p\) = 0.028). In the gingival margin of group A, the pH 4-stored specimens showed lower microleakage scores than the pH 7-stored specimens (\(p\) = 0.028). The enamel margins showed a higher microleakage than the gingival margins (\(p\) = 0.001).
# Defect Tailoring in HfO2/Si Films upon Post-Deposition Annealing and Ultraviolet Irradiation
## 3 Results
### Electrical Characterization  
Figure 6 presents the electrical characterization of the films. The film resistivity was calculated from the four-point measurement both in forward and reverse bias at \(\pm\)5 V, as shown in Figure 6a. The film resistivities in forward bias range from 6 \(\times\) 10\({}^{5}\)-1.4 \(\times\) 10\({}^{6}\)\(\Omega\).cm in the S1, S2, and S4 samples, while sample S3 has a maximum forward resistivity of 6 \(\times\) 10\({}^{9}\)\(\Omega\).cm. On the other hand, sample S1, treated at 200 \({}^{\circ}\)C, has the highest resistivity difference (h in Figure 6a) between the maxima at forward and reverse bias, while this difference reduces upon post-deposition treatment, as illustrated by the grey arrows in Figure 6a. The lowest resistivity and lowest h-difference occurred in the S4 sample, treated with UV irradiation and annealed at 350 \({}^{\circ}\)C, which suggests a nearly conductive film due to the high concentration of oxygen vacancies [15]. In contrast, the sample with the highest resistivity, S3, which was only irradiated with UV, may have more electronic traps that would avoid forward conduction and, correspondingly, reduce the barrier in reverse bias, as shown in Figure 6a. Figure 6b presents the Schottky plots for ln (l) vs. 1/E\({}^{2}\) of the films. Samples S1 and S2 have good linear behavior, indicating that these are the predominant conduction mechanisms, while samples S3 and S4 have a negative slope region at low 1/E\({}^{2}\) values followed by a linear region, indicating more than one conduction mechanism, which can be explained by the formation of defects during UV irradiation [16].  
Figure 5: (a) Height distribution graph and (b) roughness root mean square (RRMS) of HfO\({}_{2}\) thin films.
# Electrical Energy Forecasting and Optimal Allocation of ESS in a Hybrid Wind-Diesel Power System
## 1 Introduction
A hybrid multi-objective particle swarm optimization algorithm, which consists of a three-point estimation method, MOPSO, and a probabilistic power flow method, is developed to optimize the placements and sizes of the ESSs. Furthermore, power-voltage sensitivity analysis is proposed in this paper to select candidate buses for the installation of the ESS with the purpose of minimizing iteration times. The novelty of this work, distinguishing it from previous studies, is as follows: (1) the candidate buses for the ESSs' installation were selected by the power-voltage (\(P\)-\(V\)) sensitivity analysis, which reduced the computational burden; (2) the capacity of the total ESS was determined by a forecasting method; (3) instead of running the Monte Carlo method, a three-point estimation method was employed to discretize the wind distribution; and (4) both the cost and greenhouse gas emissions were reduced by hybrid multi-objective particle swarm optimization. The rest of this paper is organized as follows: Section 2 formulates the problem. Section 3 presents the method for solving it. Section 4 describes several case studies to demonstrate the proposed algorithm, and Section 5 draws conclusions.
# QoS-Aware Scheduling Algorithm Enabling Video Services in LTE Networks
## 6 Conclusions  
A new improved scheduler that supports the LTE network downlinked direction is proposed in this study. It considers one of the crucial services over LTE networks, that is, video streaming, which is affected by different parameters including jitter, limited bandwidth, and delay, as considered in this study. To efficiently control the RBs allocation process and fulfil the QoS requirements, this study carried out a performance assessment of prevalent resource allocation techniques in LTE networks and proposes an improved scheduling technique focused on video streaming using the LTE-Sim simulator. The jitter index and delay threshold were regarded as the primary parameters to improve the video streaming service by taking the channel state and HoL delay into account. The proposed methods were validated by comparing the simulation results with well-known LTE DL scheduling algorithms, including PF, M-LWDF, EXP-PF, and EXP-rule schedulers. From the simulation outcomes, we can conclude that the proposed techniques performed better than the other algorithms. The efficiency of the proposed PrOEXPRULE technique improves video streaming performance with respect to fairness, delay, packet loss ratio, and packet throughput, even when there is an increased number of users. The results produced by the proposed algorithm demonstrated its ability to reduce delay and packet loss ratio, in addition to increasing packet fairness and throughput comparing to proposed LTE downlink algorithms.
# Practical Work of Three-Phased Induction Motor Modeling in a Classical Spreadsheet Software
## 3 Equivalent Model of the Induction Motor
### Electro-Mechanical Characteristics Calculation
#### 3.3.2. Functioning with \(V/f\) = Constant  
When an induction motor is supplied such that \(V/f\) = Constant, it becomes possible to place the operating point of the motor/load couple at any point of the (torque vs. speed) plane. In this case, the induction motor characteristics are similar to those of a DC motor supplied with a constant flux and an adjustable armature voltage. These very interesting operating conditions are programmed in the majority of induction motors' industrial electronic variable speed devices.  
The electromagnetic torque of the motor \(T_{em}\) can be given, as a function of the slip \(s\), by the following formula:  
\[T_{em}=\frac{3p}{l}\cdot\left(\frac{V}{\omega}\right)^{2}\cdot\frac{1}{\frac{ R}{sl\omega}+\frac{sl\omega}{R}}=2T_{max}\cdot\frac{1}{\left(\frac{s_{max}}{s} \right)+\left(\frac{s}{s_{max}}\right)}. \tag{23}\]  
When an induction motor is supplied so that the ratio \(V/f\) is constant, the electromagnetic torque only depends on the speed difference \(\Omega_{5}-\Omega\). The torque/speed characteristics are similar to each other by simple translation along the \(\Omega\) axis (Figure 8).  
In the Excel file given alongside this paper, the attentive reader will notice that the curves network presented in this paper is constructed with the original \(T_{em}=f(s)\) curve, given that \(T_{X\%}(s)=f(s-(1-X))\), where \(X\) is the percentage of the nominal frequency for which the torque vs. speed plane is drawn. Figure 8 shows the curves network that was obtained with the respective frequency percentages of 20%, 40%, 60%, 80%, and 100%.  
Currently, the most common inverters are PWM (pulse width modulation) inverters, whose control mode keeps the \(U_{1}/f\) ratio constant and obtains almost sinusoidal currents, where \(U_{1}\) is the RMS value of the \(V\) fundamental.  
Figure 8: Electromagnetic torque translation along the \(\Omega\) axis.
## 2 Materials and Methods
### Classification Algorithms for Big Data Analysis Using ML
#### 2.2.3 GBM (Gradient Boosting Algorithm)  
[PERSON] (2001) proposed a method that uses a gradient descent algorithm as a boosting method to encompass classification and regression. In other words, for a given loss function and weak prediction model, a model that can minimize the loss function is found, and the resulting variable is initialized with an equation that can best predict the result. Thereafter, the slope or residual is calculated, and a model suitable for the residual that can minimize the loss function is constructed. The obtained model is added to the previous model, and this process is repeated. Like this, gradient boosting is a generalization of a boosting algorithm, and it is a method of learning a model focusing on errors [26].  
Gradient boosting (GBM), a method proposed by [PERSON] (2001), is one of the ensemble techniques that improves the accuracy of classification by synthesizing the predictions of multiple classifiers compared to a decision tree that uses a single classifier. Boosting is an iterative procedure used to change the distribution of data so that the basic classifier focuses on hard-to-classify data. Among them, gradient boosting (GBM) forms a final classifier by repeating the process of giving a high weight for the incorrectly classified observation value, but a low weight for the correctly classified observation value to the next observation by starting with the same weight for the observed data [27]. It approximates the function \(f\), which is as shown in Equation (4), based on a linear combination of weak learners \(h\). It is optimized in a greedy manner by selecting parameter \(\theta_{j}\) and weight \(\alpha_{j}\) of a weak learner iteratively [28].  
\[f(x)=\sum_{j=1}^{M}\alpha_{j}h_{j}\big{(}x;\theta_{j}\big{)} \tag{4}\]  
\(f:\mathbb{R}^{n}\rightarrow\mathbb{R}\); \(h:\mathbb{R}^{n}\rightarrow\mathbb{R}\); \(x\in\mathbb{R}^{n}\): input vector; \(\alpha_{j}\in\mathbb{R}\): real-valued weight; \(M\): the number of weak learners.  
Gradient boosting is a widely used method, and it has excellent predictive power in binary data or continuous data. However, it has its own disadvantages, given that the parameters need to be adjusted well and the training time is long. In addition, due to the nature of the tree-based model, the predictive power tends to be poor in high-dimensional data retrieval [19].
## 2 Overview of the System and Its Modeling  
### Topology of the Small-Scale Power System  
Though there are a lot of topologies in small-scale power systems, an isolated small-scale power system model, which represents an island with residential areas, is used in this paper (shown in Figure 1). DG1 and DG2 are thermal power plants driven by diesel engines. DG1 is a fixed speed synchronous generator (FS-SG) equipped with a speed governor and an automatic voltage regulator. DG2 is assumed to include two types of generators in this study. One is the same type as DG1 with the role of conventional FS-SG, and the other is an adjustable speed generator (ASG) that employs a doubly-fed induction generator (DFIG), which is the new strategy proposed in this paper for a small-scale power system. The difference between the performances of the two types of generators for DG2 is evaluated from the point of view of stabilization of the power system frequency fluctuation. The wind power station is modeled as a single permanent magnet synchronous generator equipped with a power converter and aerodynamic variable speed wind turbine. Loads A and B represent a consumer and are modeled using constant impedance components. Rated voltage at each node and impedance of each transmission line and transformer are shown in Figure 1, but the power rating of generators is not indicated because several combinations of generator power rating are considered in this study. However, the total capacity of DG1 and DG2 is fixed to 10 MVA. The impedance of each transformer is based on the self-capacity base. The parameters of the power plants and loads are shown in Table 1.