esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Security of Online Interfaces: Adversarial Handwritten Arabic CAPTCHAs Generation
###### Abstract  
With the increasing online activity of Arabic speakers, the development of effective CAPTCHAs (Completely Automated Public Turing Tests to Tell Computers and Humans Apart) tailored for Arabic users has become crucial. Traditional CAPTCHAs, however, are increasingly vulnerable to machine learning-based attacks. To address this challenge, we introduce a method for generating adversarial handwritten Arabic CAPTCHAs that remain user-friendly yet difficult for machines to solve. Our approach involves synthesizing handwritten Arabic words using a simulation technique, followed by the application of five adversarial perturbation techniques: Expectation Over Transformation (EOT), Scaled Gaussian Translation with Channel Shifts (SGTCS), Jacobian-based Saliency Map Attack (JSMA), Immutable Adversarial Noise (IAN), and Connectionist Temporal Classification (CTC). Evaluation results demonstrate that JSMA provides the highest level of security, with 30% of meaningless word CAPTCHAs remaining completely unrecognized by automated systems falling to 6.66% for meaningful words. From a usability perspective, JSMA also achieves the highest accuracy rates, with 75.6% for meaningless words and 90.6% for meaningful words. Our work presents an effective strategy for enhancing the security of Arabic websites and online interfaces against bot attacks, contributing to the advancement of CAPTCHA systems.  
c 2025 15 2972
# Efficient and Robust Arabic Automotive Speech Command Recognition System
## 3 Methodology
### Classifier Selection
#### 3.3.1 Machine-Learning Models  
For machine-learning models, we evaluated four algorithms that do multi-class classification, all of which displayed promising results in the field of speech recognition. These algorithms are K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR). In the process of model tuning and hyperparameter selection, we manually identified potential parameters that we believed would benefit our models. Subsequently, we employed grid-search techniques to systematically choose the optimal ones. The resulting parameter values are as follows: For KNN, we utilized 3 neighbors; for RF, we employed 150 estimators with a maximum depth of 250; in SVM, we set the regularization parameter C to 1 and utilized the linear kernel function; and, finally, for the LR model, the regularization parameter C was set to 3, with a maximum number of iterations of 100. Additionally, we utilized L2 (Ridge) regularization and employed the lbfgs solver, as it is more suitable for larger datasets.
# Search and Rescue in a Maze-like Environment with Ant and Dijkstra Algorithms
## 3 Search and Rescue in Maze-like Environments
### Search, and Rescue: A Problem of Two Sequential Phases
#### 3.2.2 Phase II (Rescue): Signal Routing and Victim Extraction  
Once the agents find the victim, they should stop the exploration and the agent at the target should find a relaying route leading back to the source node in order to pass the target's information. This is done by finding and setting up the shortest possible link between the agents that are injected in the area. Among the possible methods used to find the most optimal path, we employ a simple variant of the Dijkstra Algorithm that uses the distance as the cost. As a route is calculated backwards to the source, the selected next-hop agents are kept fixed in position to form a relay network to assist the rescue operation.
## 3 Low-Power Electronic Converters
### Single-Phase Rectifiers
#### 3.1.1 Half-Wave Rectifier
In this circuit, two Schottky diodes are used, and for the positive polarity of the input voltage \(v_{i}\), capacitor \(C\) is charged through a diode \(D_{1}\) and resistor \(R\). For negative polarity of the input voltage, the diode \(D_{1}\) is turned off, and the capacitor through the diode \(D_{2}\) provides the output voltage over the load \(R_{L}\):  
\[v_{o}=v_{C}-V_{D2}. \tag{8}\]  
The maximum value of the voltage to which the capacitor can be charged for the positive half-cycle of the input voltage is  
\[V_{C,\max}=V_{pm,0}-V_{D1}-v_{R1}, \tag{9}\]  
Figure 10: Circuit diagram of a half-wave rectifier and basic waveforms of the current and voltage. where the \(V_{D1}\) is the voltage between the anode and cathode over the diode \(D_{1}\), and \(v_{R1}\) is the voltage over the \(R_{1}\). In view of the practical usability of the circuit, the resistance value of the \(R_{1}\) can be determined by the following formula [48]:  
\[R_{1}=\frac{V_{pm,0}-V_{D1}}{I_{D1,\text{max}}}, \tag{10}\]  
where \(I_{D1,\text{max}}\) is the maximum initial value of the input current for the present small piezoelectric thin-film harvester.
# Improved Trimming Ant Colony Optimization Algorithm for Mobile Robot Path Planning
## 2 Traditional Ant Colony Optimization (ACO) Algorithm
### Pheromone Update  
Ants leave pheromones on the paths they take, and the pheromones volatilize over time, changing the probability that a path will be chosen. Therefore, when the ant finds a reachable path, it needs to update the pheromone value of each edge contained in the path based on the total length of the path. After the \(t\)-th iteration of ants complete the search, the pheromone updating strategy shown in Equation (3) is used to adjust the pheromone concentration on the paths that the ants travel.  
\[\tau_{ij}(t)=(1-\rho)\cdot\tau_{ij}(t-1)+\rho\Delta\tau_{ij}(t) \tag{3}\]  
In Equation (3), \(\rho\) is the pheromone evaporation coefficient, generally between 0 and 1, and \(\Delta\tau_{ij}(t)\) represents the additional pheromone deposited on the path after the current iteration. The formula for \(\Delta\tau_{ij}(t)\) is given by Equation (4).  
\[\Delta\tau_{ij}(t)=\begin{cases}\begin{array}{ll}\frac{Q}{L_{k}},&\text{, if ant $k$ travels through path $(i,j)$}\\ 0&,\text{otherwise}\end{array}\end{cases} \tag{4}\]In Equation (4), \(Q\) represents the pheromone intensity coefficient, and \(L_{k}\) denotes the total path length traversed by the \(k\)-th ant to reach the destination. As indicated by Equation (4), the shorter the path length \(L_{k}\) that an ant travels during path planning, the more pheromone is deposited on that path. This increased pheromone concentration makes the path more attractive to other ants, thereby creating a positive feedback loop.
## 3 The Proposed Method
### Pose Estimation
#### 3.2.1 Human Detection for Pose Estimation  
To balance the accuracy and speed of detection, we used the You Only Look Once version 3 (YOLOv3) [35] model for human detection. By extracting each frame of the input video and resizing it to the appropriate size, the human in the video is being detected by the YOLOv3 model and marked with a rectangular box, called the bounding box. For a single person in the video, the model's output contains five predicted values: \(x\), \(y\), \(w\), \(h\), and \(confidence\). \(x\) and \(y\) are the center coordinates of the bounding box, \(w\) and \(h\) represent the width and height of the bounding box, and confidence represents the confidence that an object in the bounding box is human. The detection of the human body can be achieved by filtering out the bounding boxes whose confidence is below a certain threshold.
## 4 Simulations
### Simulation Results
#### 4.2.2 Encapsulated MAPbI3 Samples  
The effect of the encapsulation on the devices' performance has also been evaluated after 350 h of exposure to solar irradiation (Figure 12). During the encapsulation process, a slight reduction of 0.87% in the PCE was observed, remaining constant from that moment and until complete 350 h, corresponding to a total irradiance of 6051 kW/m\({}^{2}\).  
Figure 11: IV curve of the non-encapsulated MAPbI3 samples before and after the degradation process with UV lamp and full solar irradiation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ID** & **Voc (V)** & **Jsc (mA/cm\({}^{2}\))** & **FF (\%)** & **PCE (\%)** \\ \hline MAPbI3 & 1.09 & 21.66 & 68.3 & 16.18 \\ UV Lamp (47,820 W/m\({}^{2}\)) & 1.08 & 19.07 & 65.0 & 13.47 \\ Full solar irradiation (40,432 W/m\({}^{2}\)) & 1.08 & 18.28 & 64.7 & 12.83 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Non-encapsulated MAPbI3 samples results before and after the irradiation process.  
Table 3 shows that the observed reduction was mainly due to the decrease in the open circuit current, going from 21.66 mA/cm\({}^{2}\) (non-encapsulate pristine sample) to 20.61 mA/cm\({}^{2}\) (encapsulated pristine sample) and to 20.58 mA/cm\({}^{2}\) (encapsulate sample exposed to full solar irradiation). However, the open circuit voltage remained constant (around 1.09 V).
## 3 Preliminaries
### Setup Phase
#### 4.1.3 Routing Metric Calculation
The choice between the equations depends on the simulation context, and all required metrics are collected locally during runtime.
## 2 Materials and Methods
### SIF Simulation by SCOPE Model  
#### 2.3.1 SCOPE Model Description  
We used the SCOPE model to retrieve vegetation parameters from the measured canopy reflectance (Ref) and then to simulate the canopy SIF (SIF\({}_{\rm SCOPE}\)). SCOPE is an integrated energy balance and radiative transfer model [20; 39], which integrates the energy balance process with the radiative transfer of solar radiation (RTMo), thermal radiation emitted by the vegetation (RTMt) and re-emission as fluorescence (RTMf). In the energy balance process, a biochemical module processes the emission efficiency of fluorescence according to the other two energy de-excitation pathways: photochemical quenching via photochemical and non-photochemical quenching via heat dissipation. SCOPE can complete the calculations of canopy reflected radiation, thermal radiation and SIF in company with energy, carbon and water flux. In our study, we applied SCOPE version 2.0 to simulate SIF\({}_{\rm SCOPE}\). To perform the simulation, the model needs input data associated with leaf optical and biochemical properties, canopy structure, meteorology and sun-view geometry (Table 2) [21]. The major input variables were derived from field measurements and model inversion. Other required variables of the SCOPE model were assumed as their default values. Our analysis concentrated on the SIF estimation results at 687 nm and 760 nm (SIF\({}_{687}\) and SIF\({}_{760}\)), standing for the red and far-red solar-induced fluorescence, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Date** & 21 April & 22 May & 1 July & 22 July & 31 August & 24 & 25 & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\  & & & & & & September & & November \\ \hline Cab (\(\mu\)g cm\({}^{-2}\)) & 43.53 \(\pm\) 3.51 & 43.83 \(\pm\) 3.14 & 45.42 \(\pm\) 3.25 & 41.76 \(\pm\) 3.82 & 40.87 \(\pm\) 6.36 & 43.48 \(\pm\) 5.38 & 46.23 \(\pm\) 5.59 & 42.88 \(\pm\) 3.26 \\ LAI (m\({}^{2}\) m\({}^{-2}\)) & 4.31 \(\pm\) 0.58 & 4.37 \(\pm\) 1.07 & 4.56 \(\pm\) 0.49 & 4.85 \(\pm\) 1.07 & 4.63 \(\pm\) 0.62 & 4.41 \(\pm\) 0.68 & 4.37 \(\pm\) 0.40 & 4.24 \(\pm\) 0.48 \\ \hline \end{tabular}
\end{table}
Table 1: Leaf chlorophyll content (Cab) and canopy structure variable (LAI) used in this study.
## 3 Results and Discussion
### Ferroelectric Properties of PVDF-TrFE  
Next, the polarization of the ferroelectric PVDF-TrFE layer and its dipolar coupling effect on the surface potential were investigated. Figure 3a schematically shows the polarization process of the ferroelectric PVDF-TrFE layer. In this work, we denoted negative polarization when the PVDF layer was polarized to have negative dipole centers on the friction surface and positive polarization when it was polarized to have positive dipole centers instead. For the polarization of the PVDF-TrFE layer, a high electric field of 24 kV/cm was applied between the electrodes while maintained at 90 \({}^{\circ}\)C for 6 h. Afterward, the polarization vs electric field (P-E) curve of the differently polarized PVDF-TrFE layers was measured. As shown in Figure 3b, the remanent polarization (P\({}_{\mathrm{r}}\)) increased after the polarization process, regardless of the polarization direction. In general, the spin-coated PVDF-TrFE film exhibited both \(\alpha\)- and \(\beta\)-phases, of which the \(\beta\)-phase exhibited a ferroelectric property. Thus, a relatively higher \(\beta\)-phase formation was desired to achieve the electric dipole alignment effect. To further investigate the augmentation of the \(\beta\)-phase through polarization, XRD and FTIR analyses were performed, as shown in Figure 3c and Figure S3 (Supplementary Materials), where both spectra indicated the increase of the \(\beta\)-phase and the suppression of the \(\beta\)-phase. These results show that the ferroelectric properties of PVDF-TrFE could be effectively improved by applying an appropriate polarization process. In addition, to investigate the influence of the polarization direction, the surface potential of each layer was measured by non-contact mode KPFM over an area of 25 \(\upmu\)m\({}^{2}\). Figure 3d shows the surface potentials of the PVDF-TrFE layers after different polarizations: non-polarization, negative polarization and positive polarization. The results indicated that the polarization direction had an explicit influence on its surface potentials. The measured surface potentials of the non-, negative and positive polarizations were \(-\)330 mV, \(-\)410 mV and \(-\)260 mV, respectively. When the ferroelectric layer was electrically polarized in the negative or positive direction, the surface potential either increased or decreased compared with the non-polarized state, depending on the polarization direction.  
Figure 3: (**a**) Schematic diagrams of three different polarizations: negative polarization, non-polarization and positive polarization. (**b**) P-E curves of PVDF-TrFE after three different polarization processes. (**c**) X-ray diffraction (XRD) analysis of PVDF-TrFE, depending on the polarization direction. (**d**) Kelvin probe force microscopy (KPFM) images of the PVDF-TrFE surface for different polarizations scanned over an area of 25 \(\upmu\)m\({}^{2}\).
# BPMNE4 IoT: A Framework for Modeling, Executing and Monitoring IoT-Driven Processes +
## 7 End-User Study  
In the _Design Science Research Methodology_, the evaluation step (cf. Figure 2--Step 5) shall observe and measure how well the designed artifact (cf. Section 5) supports a solution for the addressed problem (cf. Section 1.1). For this purpose, we conducted a controlled study with 30 participants to evaluate the proposed BPMN extension (BPMNE4 IoT) with respect to user-friendliness, effectiveness, and workload (see RQ1). Study participants were asked to model the same IoT process and thereby compare IoT process modeling in BPMNE4 IoT with BPMN 2.0. Note that we have compared our approach with the process modeling standard BPMN 2.0, as there is no agreed upon standard or tool for modeling IoT-driven BPs. Furthermore, the BPMN 2.0 standard is widely used and the participants are largely familiar with it. Moreover, comparing our framework with related works is complicated due to the fact that these works are often purely conceptual and the availability of modeling tools as open source software artifacts is limited. Specifically, we validated the following two hypotheses:  
**Hypothesis 1 (H1).**: _When modeling IoT-driven BPs with BPMNE4 IoT, the workload decreases compared to the workload of BPMN 2.0 (cf. Objectives O3 and O7)._  
Figure 19: Process to control the temperature in a smart office.  
**Hypothesis 2** (**H2**).: _The time required to model a given process description with IoT characteristics into an IoT-driven BP model with BPMNE4 IoT is less, compared to the time required with BPMN 2.0 (cf. Objectives O3 and O7)._
# Research on Digital Meter Reading Method of Inspection Robot Based on Deep Learning
###### Abstract  
Aiming at solving the issue of blurred images and difficult recognition of digital meters encountered by inspection robots in the inspection process, this paper proposes a deep-learning-based method for blurred image restoration and LED digital identification. Firstly, fast Fourier transform (FFT) is used to perform blur detection on the acquired images. Then, the blurred images are recovered using spatial-attention-improved adversarial neural networks. Finally, the digital meter region is extracted using the polygon-YOLOv5 model and corrected via perspective transformation. The digits in the image are extracted using the YOLOv5s model, and then recognized by the CRNN for digit recognition. It is experimentally verified that the improved adversarial neural network in this paper achieves 26.562 in the PSNR metric and 0.861 in the SSIM metric. The missing rate of the digital meter reading method proposed in the paper is only 1% and the accuracy rate is 98%. The method proposed in this paper effectively overcomes the image blurring problem caused by the detection robot during the detection process. This method solves the problems of inaccurate positioning and low digital recognition accuracy of LED digital meters in complex and changeable environments, and provides a new method for reading digital meters.  
YOLOv5s; DeblurGAN; blurred reduction; digital meter identification +
Footnote †: journal: Applied Sciences
# Secured Big Data Analytics for Decision-Oriented Medical System Using Internet of Things
## 3 The Proposed IoMT-Enabled Model
\[ln_{t}=\ (R_{\alpha}\ -\ D_{\alpha}\ /\ T)\cdot\beta\ +\ P_{d} \tag{4}\]  
where \(D_{\alpha}\) denotes the transmitting time of hello packets towards the neighbors, \(R_{\alpha}\) is receiving time of the hello packets, \(T\) is fixed time interval, \(\beta\) is available bandwidth for a wireless channel among connected nodes and \(P_{d}\) indicates the delay rate for the processing of data packet. After the computing of the cost function, the SBD-EC model performs the greedy method to accomplish a reliable and optimal forwarding scheme, as discussed below. 1. The source node from the graph \(G(V,N)\) computes the cost function for each neighbor connected with different vertexes \(V\). 2. It selects the vertex \(V\) with the minimum cost value. However, it stops on a stage if reaches a local minimum, which implies that the neighbors do not have the nearer vertex to the source node than the vertex itself. Afterward, the SBD-EC model presents a secured edge-cloud algorithm for data collection and storage. The edge nodes are mobile and are connected to the sink node and cloud layer. The edge nodes collect the data from the sink node and are further forwarded towards the cloud level. The edge nodes are rotated at the rate \(R\) which can be computed as given in Equation (5). \[R=\ d_{n}\pm d_{ini}/t \tag{5}\]  
where \(d_{n}\) denotes the covered distance of the mobile edge node from sink node at a time \(t\), and \(d_{ini}\) denotes its initial distance. Moreover, the SBD-EC model also secures edge cloud communication by using the Schmidt-Samoa cryptosystem [32]. In the SBD-EC model, the sink node, mobile edges, and cloud systems utilize pairs of public \(X\) and private \(Y\) keys for data encryption and decryption. The generated pair of keys ensures data security by using lightweight encryption and decryption mathematical functions. Firstly, the two large distinct prime numbers \(p\) and \(q\) are chosen, and security keys are generated as given in Equations (6) and (7). \[X\ =\ p^{2}\cdot q \tag{6}\]  
\[Y=\ X^{-1}mod\ lcm(p-1,q-1) \tag{7}\]  
where \(X\) and \(Y\) denote public and private keys. The data encryption \(C\) from the mobile edge towards a cloud system is computed using Equation (8). \[C\ =\ D^{X}\cdot mod\ X\ +\ S_{e}(Y,D) \tag{8}\]  
where \(D\) represents the medical data and \(S_{e}\) is a signature of the mobile edge node using the private key \(Y\). On the other hand, the encrypted data \(C\) is converted to its authentic form by the cloud servers using Equation (9). \[D\ =\ c^{Y}\ mod\ p\cdot q\ +\ V_{c}(X,D) \tag{9}\]  
where \(V_{c}\) is verification function performed by cloud system using the public key of mobile edge node \(X\). The flow chart of the SBD-EC model is described in Figure 1:0
## 4 Computational Experiments
### Hardware Used in the Computational Experiments  
The hardware setup for the sequential algorithm comprised the AMD Ryzen 9 5950X desktop CPU, featuring 16 cores, 32 threads, and 16 GB of DDR4 RAM. Despite the availability of the AMD Epyc 7643 server CPU, the AMD Ryzen 9 5950X was selected for sequential tests due to its superior performance in our analysis. Although both CPUs are implementations of the same Zen 3 microarchitecture, the Ryzen 9 5950X, with its higher clock speeds (3.4 GHz base and 4.9 GHz boost compared to the 2.3 GHz base and 3.6 GHz boost of the Epyc 7643 CPU), was able to achieve around 13.2% faster mean computational times. Higher clock speeds can have a marked impact on CPU performance [42], resulting in faster execution of instructions and improved throughput.  
Performance analysis of GPU-based parallel algorithms was conducted on different GPUs with distinct hardware configurations. This approach was adopted to gain insights into the real-world applicability of the employed parallelization strategy, evaluating its performance and scalability across different architectures and available computing resources. The following GPU hardware was selected for the GPU-based computational experiments:  
* NVIDIA GeForce RTX 3090 GPU (Ampere architecture) with 10 496 CUDA cores and 24 GB of GDDR6X VRAM;
* NVIDIA Tesla T4 GPU (Turing architecture) with 2560 CUDA cores and 16 GB of GDDR6 VRAM;
* NVIDIA Tesla V100S PCIE GPU (Volta architecture) with 5120 CUDA cores and 32 GB of HBM2 VRAM;
* NVIDIA A100 PCIE GPU (Ampere architecture) with 6912 CUDA cores and 80 GB of HBM2e VRAM.  
Performance across the GPU devices will serve as benchmarks for evaluating algorithm efficiency and identifying potential bottlenecks. Initially, detailed performance analysis will primarily focus on the RTX 3090 and the A100 GPUs, both from the same Ampere architecture. While the former is a consumer-grade GPU, the latter is a professional-grade device. One of the main differences between these GPU classes is that the RTX 3090 was designed to have a high number of CUDA cores, whereas the A100 was designed for high memory bandwidth. This analysis aims to thoroughly evaluate the behavior of parallel algorithms in terms of performance and scalability across a spectrum of hardware configurations, assessing their adaptability to different available hardware resources.
## 5 Results
### Option Price for EGARCH Price Model
#### 5.4.4 Final Option Values  
The two methods, RL and LSM, were used to find the decision boundary (policy) shown in Figure 13. The decision boundaries obtained from both methods were then tested on 10,000 paths to estimate the option value. Each method was repeated ten times to account for the stochastic nature of underlying price uncertainty, modeled by EGARCH(1,1,1). The average option value, with \(S_{0}=USD70\), strike price = USD 80 was USD 15.01 for the RL method and USD 14.91 for the LSM method.
## 1 Introduction
The SMF removal efficiencies in STPs varied from 50% to 90% [25, 27, 28, 29]. [PERSON] et al. [30] also reported that HHCB was detected in the concentration range of 1.78-12.7 \(\mu\)g/L in the influent from an urban STP located in New York State, while in a sewage influent in rural Kentucky it was present in the range of 0.043-7.032 \(\mu\)g/L. Surface water monitoring results of SMFs have been reported in other parts of Korea [8, 25, 31], but no survey has been conducted in the Han River, which passes through Seoul metropolitan area. Therefore, the aims of this study are (a) to measure the SMF levels in STP influents, effluents, the mainstream of the Han River, and its tributaries; (b) to compare the SMF levels in the Han River samples with the levels found in PCPs; and (c) to assess the possible sources and discuss the major removal mechanisms of SMFs in the Han River.