Types Of Modules Research Articles

Proton-Induced Switching of Paramagnetism: Reversible Conversion between a Low and High Spin CoIII Center within a Heterobimetallic Core.

The development of molecular species with switchable magnetic properties has been a long-standing challenge in chemistry. One approach involves binding an analyte, such as protons, to a compound to trigger a change in magnetism. Transition metal complexes have been targeted for this type of magnetic modulation because they can undergo changes in their spin states. However, heterobimetallic complexes have had limited utility because of a lack of ligands that create differentiated structures around each metal center that are often necessary to regulate the electronic and magnetic properties. To circumvent this problem, we have used a tripodal ligand with phosphinic amido groups to prepare a complex with a discrete [CoIII(μ-OH)FeIII] core and an overall system spin of ST = 5/2. Deprotonation readily produces a species with a unique [CoIII(μ-O)FeIII] core and an ST = 1/2 system spin. X-ray diffraction studies, electron paramagnetic resonance spectroscopy, and Mössbauer spectroscopy pinpoint the hexacoordinate CoIII center as the cause of this spin change: the typical SCo = 0 spin state of the CoIII center in the [CoIII(μ-OH)FeIII] complex switches to a rare SCo = 2 spin state in the [CoIII(μ-O)FeIII] analogue; this change turns on antiferromagnetic coupling between the two metal centers. Computational studies link an increase in π bonding within the Co-oxido unit to the change in the CoIII spin state. The conversion is reversible and provides a blueprint for using oxido/hydroxido ligands within a heterobimetallic core to regulate the spin state of a metal site and thus modulate the paramagnetism of a system.

Recovery of Iodine in the Gaseous Phase Using the Silicone Hollow Fiber Membrane Module

Iodine, being an important resource, must be recovered and reused. Iodine is not only attracted to the hydrophobic silicone membrane but also easily vaporized. In this study, we explored the use of five types of silicone hollow fiber membrane modules (SFMMs) for separating iodine in the gaseous phase. In the SFMM, iodine gas and the recovery solution (sodium sulfite and sodium carbonate at a concentration of 10 mM each) were flowed outside and inside the silicone hollow fiber, respectively, in a co-current-flow manner. At an iodine gas flow rate of 0.2 L/min (8.4 × 10−3 mmol-I2/L), the capture efficiency of iodine into the SFMM was approximately 100% for all five SFMMs. With increasing feed gas flow rates, the capture efficiency of iodine decreased, reducing to approximately 50% at 0.8 L/min. However, the recovery efficiency of iodine in the recovery solution was 60–30% at 0.2–0.8 L/min. This decrease in capture efficiency with increasing flow rates was because iodine could not spread and diffuse successfully in the SFMM, resulting in a low recovery efficiency of iodine. Thus, we next improved the structure of the SFMM by placing a perforated pipe in the center of the module. The perforated pipe effectively directs the iodine feed gas from the holes in the pipe to the hollow fiber membrane bundle wrapped around the pipe. With the improved SFMM, the capture efficiency markedly increased to approximately 100% in the range of the flow rates tested in our experiments. The recovery efficiency also increased to ≥70%. These data illustrate the potential application of the improved SFMM for recovering iodine in the gaseous phase.

Small molecules that targeting p53 Y220C protein: mechanisms, structures, and clinical advances in anti-tumor therapy.

The p53 protein is regarded as the "Guardian of the Genome," but its mutation is tumor progression and present in more than half of malignant tumors. The pro-metastatic property of mutant p53 makes a strong argument for targeting mutant p53 with new therapeutic strategies. However, mutant p53 was considered as a challenging target for drug discovery due to the lack of small molecular binding pockets. Among them, mutant p53 Y220C creates a narrow crevice since the side chains dynamics on protein surface, which is suitable for designing small molecules to occupy the cavity and recovery the tumor suppressing function. Here, we describe the mechanism of p53 related signal pathway and how p53 Y220C regulate the tumorigenesis. We review the two types of p53 Y220C modulators including restoring the conformation of mutant p53 Y220C protein to wild-type p53 protein and recruiting histone acetyltransferase p300/CBP to acetylate p53 Y220C thus enables p53 Y220C dependent upregulation of apoptotic genes and downregulation of DNA damage response pathways. We also report clinical advances and challenges of these molecules in p53 Y220C medicated tumor therapy.

Development of an interactive graphical interface for the simulation and analysis of different types of amplitude modulation (AM)

The development of interactive tools allows users to explore and visualize various conditions in signal behavior. This work presents a graphical interface focused on Amplitude Modulation (AM), where the types of modulation, undermodulated, modulated, and overmodulated are visualized. Using object-oriented programming, the interface integrates text and numeric input fields, interactive buttons, and graph visualizers. These elements allow users to easily adjust parameters such as frequency, amplitude, and timing of the information, carrier, and modulating signals. The tool offers a clear and dynamic visualization of different modulations, facilitating the understanding of theoretical concepts related to AM modulation. Additionally, it provides an intuitive theoretical-practical learning experience that reinforces both teaching and research in this field, offering support in various areas of study and experimentation.

Nilai Religius dalam Novel Assalamu’alaikum Baitullah Karya Asma Nadia

This research is motivated by shifts in increasingly complex religious values. The widespread news regarding the phenomenon of excessive selfies during the Umrah pilgrimage illustrates a shift in religious values. This research aims to describe the religious values ​​contained in the novel Assalamu'alaikum Baitullah by Asma Nadia. The method used is qualitative with descriptive data analysis techniques. This research uses two data collection techniques, namely observation techniques and literature study techniques. Primary data was obtained from the novel Assalamu'alaikum Baitullah by Asma Nadia and secondary data was obtained from various relevant sources. The theory used is the Atmosuwito religious value theory. The results of the research show that there are six aspects of religious values ​​in the novel Assalamu'alaikum Baitullah by Asma Nadia, namely; 1) surrender, submission, and obedience, 2) a life full of glory, 3) inner feelings of connection with God, 4) feelings of sin, 5) feelings of fear, and 6) recognizing the greatness of God. Novels that contain religious values, if studied, can influence someone to apply religious values ​​in everyday life. This research will be recommended as module type printed teaching material for second semester high school students in class XII.

Performance Analysis of Novel Lightweight Photovoltaic Curtain Wall Modules Under Different Climatic Conditions

Due to limited roof area, photovoltaic (PV) has gradually been installed on other facades of buildings. This research investigates the practical application of a lightweight PV curtain wall. We use EnergyPlus to build a base office building model of fit with a lightweight PV curtain wall. The performance of two typical lightweight PV curtain wall modules is evaluated in five sample Chinese cities of different climates. Simulations were carried out to determine the power generation of faux architectural material PV curtain wall modules (FAM PVCWMs) for the best cavity distance per facade in various cities. We discovered that, in Harbin, Beijing, and Shanghai, the capacity of PV curtain wall modules installed on the south facade is the best, while in Chengdu and Guangzhou, it is the west facade. We also analyzed the power generation and the impact on the indoor environment when installing semi-transparent PV curtain wall modules (ST PVCWMs). Compared with glass, the ST PVCWM’s power generation increased by at least 50%, while the glare index setpoint exceeded time reduced by at least 30.19%. Furthermore, when installed on the north facade of Chengdu and similar cities, it can ensure more than 50% of daylight indexed time and create a more favorable indoor environment.

Improving the accuracy of a platformless inertial navigation system for an aircraft during rapid roll rotation

The article addresses the problem of enhancing the accuracy of an autonomous strapdown inertial navigation system (SINS) for aircraft with rapid rotation about their longitudinal axis. This rapid rotation stabilizes the aircraft's position along the longitudinal axis while imposing strict requirements on the roll gyroscope's scale factor (SF) error in the SINS. This is essential to ensure that the roll angle error remains on par with the pitch and yaw angle errors. A novel method for periodic correction of the roll gyroscope's SF within an autonomous SINS is proposed. The method utilizes signals from a single-axis indicator gyrostabilizer based on a microelectromechanical system (MEMS) gyroscope. The correction involves determining the numerical value of the roll gyroscope's SF error during flight and subsequently compensating for its influence on the roll angular velocity measurements. The functional scheme of the method is presented, along with formulas for calculating the correction coefficient. The efficiency of the proposed approach in improving the accuracy of the SINS is evaluated using a typical inertial module and an indicator gyrostabilizer with a MEMS gyroscope as an example. The method significantly enhances the accuracy of roll angular velocity and coordinate measurements in an autonomous SINS without requiring expensive precision gyroscopes.

CARNet: An Efficient Cascaded and Attention‐Based RNN Architecture for Modulation Classification in Cognitive Radio Network Using Improved Kookaburra Optimization Strategy

ABSTRACTIn cognitive radio (CR) networks, the automatic modulation classification (AMC) is considered as the significant role in smart wireless communications. Due to the high growth of deep learning in the modern days, neural network‐aided automated modulation categorization tasks have become highly demanded. Nevertheless, an enormous amount of attributes and the neural network's complexity make them complex to adopt in various scenarios. Moreover, the receiver systems are limited by the latency and less storage resources. Additionally, the detection system of the signal modulation is mostly hampered by overfitting issues and insufficient information. Therefore, a robust AMC model based on adaptive deep learning is implemented to determine the type of modulation used at the transmitter by observing the received signal. Initially, the necessary raw data for the suggested model is garnered from benchmark dataset. Also, the optimal features from the raw data are selected with the help of the fitness‐revised position updating in Kookaburra optimization (FPUKO) for minimizing the computation time and enhancing the accuracy rates in the classification process. Moreover, this optimal feature selection makes the modulation selection process quick and efficient. Finally, the optimal features are fed to a cascaded and attention‐based recurrent neural network (CA‐RNN) in the modulation classification, which is designed to classify the type of modulation used on the transmitter side. Various experiments are conducted for the designed framework by comparing it with the existing models to view its efficiency.

Method for Increasing the Linearity of The Phase in The Approximation Phase On 5G Antennas Using Modified Functions of The Subtractors

The possibility of reducing the phase distortion of the signal spectra in the input paths at the stage of approximation of frequency characteristics is shown. Noticeable progress in the technology of satellite and mobile telecommunications systems, as well as in radar systems, is largely associated with the use of broadband and ultra-broadband signals. Thus, in 2012-2013, the development of Wideband High Frequency (WBHF) radio technology (hereinafter referred to as broadband HF (HF) radio communication) took place in the direction of increasing data transmission rates over HF (HF) radio channels to meet the needs of subscribers of the armed forces of NATO countries. The development of broadband HF (HF) radio communication has led to the revision of the following NATO standards and their release in a new edition: - MIL-STD-188-110C «Interoperability and operational requirements for data modems», 03.01.2012; - MIL-STD-188-141C «Functional compatibility and operational requirements for medium and high frequency radio frequency systems», 12/27/2011. The work carried out experiments to compare frequency characteristics. The changes are also related to the introduction of broadband HF (HF) radio communication technology, which defines a family of broadband modulation types for a radio channel with the necessary bandwidth of the end-to-end amplitude-frequency response (AFC) of the transceiver path from 3 kHz to 24 kHz in 3 kHz increments.

ANALYSIS OF PROCESSES IN CONVERTER WITH TWENTY-FIVE-ZONE VOLTAGE REGULATION AND ACTIVE-INDUCTIVE LOAD

Analysis of the electromagnetic processes is organized beside this article in electric circuit with semiconductor commutator. Mathematical model is created for analysis electro-magnetic processes in semiconductor converter with width pulsed regulation of the output voltage. The broughted graphs, which reflect the electromagnetic processes in electric circuit. Method much parametric functions was used when performing calculation. The mathematical model of the converter is created for fifteen zoned regulations of the output voltage. Article is devoted to the development of a method of multi-parametric modulating functions by means of working out of new mathematical models and definition of functions and the algorithmic equations for the analysis on sub-system components of electromagnetic processes in electric circuits of variable structure with sinusoidal, direct and pulsing voltage. Introduction of functions with discrete parameters in the algorithmic equations for analysis of processes in circuits with semiconductor commutators simplifies modeling on subsystem components. The mathematical model of steady-state processes and transients in electric circuits of semiconductor converters of modulation type with multi-channel zonal use of phase and line voltages of a three-phase network of power supplies is developed. The mathematical model of electric circuits of thyristor shapers of electro-discharge pulses for the analysis and the matching of capacitors charging modes with decrease several times of electric resistance of technological load is also created. The obtained results have a great value for development theoretical electrical engineering in a direction of simplification of calculations of electromagnetic processes in electric circuits with semi-conductor converters of the electric power. The Electromagnetic processes in electric circuit under width-pulse regulation possible to analyse with use the algorithmic equations multivariable function, which argument are a system parameters semiconductor commutator, signal of control, phases to network of the power supply and time. Introduction multivariable function with discrete parameter in algorithmic equations of the analysis formed and connecting processes in electric circuit of the variable structure allows to reflect change of this structure under system components, simplifying modeling and analysis of such processes to account of the generalization of the got equations. Except specified correlations and diagrams designed model allows to analyse forms of the output voltages and current of the separate power modules.

Design of Grid-Connected Solar PV Power Plant in Riyadh Using PVsyst

Solar energy is a quick-producing source of energy in Saudi Arabia. Solar photovoltaic (PV) energy accounts for 0.5% of electricity output, with a total installed capacity of 9.425 GW and 9353 solar power plants of various types globally. Many solar power stations will be established on different sites in the coming years. The capacity of these stations reaches hundreds of megawatts. The primary aim of this study is to facilitate the strategic and systematic assessment of the solar energy resource potential that impacts both large and small-scale solar power projects in Saudi Arabia. This study describes in detail the analysis, simulation, and sizing of a 400 MW grid-connected solar project for the Riyadh, Saudi Arabia site using the PVSyst 8 software program. The software-generated trajectories primarily represent the performance of a PV system at a certain location. It provides data for the geographical position used by maps for component sizing, projecting the installation under extremely realistic conditions. The report further examines the system’s behavior with various tilt and orientation settings of the PV panel, which yields superior simulation results at equivalent latitudes for any practical sizing. Three types of PV modules with different sizes are used to design the solar plant. The main project was designed using 580 WP and was compared with 330 WP and 255 WP power modules. This study confirmed that high-power PV modules are more efficient than small modules.

Radar Signal Sorting Method with Mimetic Image Mapping Based on Antenna Scan Pattern via SOLOv2 Network

Aiming at the problems, in which the traditional radar signal sorting method has high requirements for manual experience and poor adaptability, and considering the differences in received power caused by radar beam scanning under long-term observation, an end-to-end signal sorting method based on the instance segmentation network SOLOv2 and using an antenna scan pattern (ASP) is proposed in this letter. Firstly, the interleaved pulse sequences of multiple radar signals with various inter-pulse modulation types, scan patterns, and gain patterns are simulated, mimetic image mapping is constructed to visualize the interleaved pulse sequences as mimetic point graphs, and the index relationship between pulses and pixel points is recorded. Subsequently, the SOLOv2 instance segmentation network is used to segment the mimetic point graph at the pixel level, thereby clustering the discrete pixel points in the image. Finally, based on the index relationship recorded during the construction of the mimetic image mapping, the clustering results of points in the image are traced back to the clustering of pulses, achieving end-to-end intelligent radar signal sorting. Through simulation experiments, it was verified that, compared with YOLOv8-based, U-Net-based, and traditional signal sorting methods, the sorting accuracy of the proposed method increased by 9.26%, 11.17%, and 24.55% in the scenario of five signals with 30% missing pulse ratio (MPR), and increased by 13.33%, 18.88%, and 23.94% in the scenario of five signals with 30% spurious pulse ratio (SPR), respectively. The results show that by introducing the stable parameter, namely ASP, the proposed method can achieve signal sorting with highly overlapping parameters and adapt to non-ideal conditions with measurement errors, missing pulses, and spurious pulses.

Phenomenological study of acoustic emissions generated by gear meshing in planetary gearboxes

Abstract This study explores the frequency content of the envelope spectrum of acoustic emission (AE) signals from planetary gearboxes. Since AE signals contain continuous AE and burst-type AE, it is important to determine how different types of amplitude modulation affect these components. Phenomenological simulations of the AE signals were conducted to analyze the influence of the modulations. This approach quantitatively determines how the different harmonics in the envelope spectrum change depending on the level and type of modulation. The modulations under analysis are sinusoidal and single square wave. The simulations indicate that sidebands at the planet gear pass frequency around the gear mesh components are mainly attributed to the modulation of AE bursts, while harmonics at the planet gear pass frequency are primarily attributed to the modulation of the continuous AE. An application case to a wind turbine gearbox is also presented. The study highlights the influence of normal modulations or fault-related modulations in the AE envelope spectra from planetary gearboxes. It provides meaningful insights for establishing AE technology as a consistent condition monitoring technique for planetary gearboxes. The main practical value of the research is providing guidelines for the correct interpretation of AE envelope spectra in planetary gearboxes, which is crucial for avoiding gear failures.

ДЕРЕКТЕРДІ КӨРІНЕТІН ЖАРЫҚ АРҚЫЛЫ БЕРУГЕ БОЛАТЫН МОДУЛЯЦИЯ ӘДІСТЕРІН ЗЕРТТЕУ

The emergence of new directions based on the optical range in the field of communications is explained by existing problems in organizing modern wireless infrastructure. Currently, the radio frequency band widely used for data transmission is overloaded and cannot meet the rapidly growing traffic and demand for wireless data services. Additional implementation of wireless systems based on the use of optical range can help solve existing problems. Visible light communication, as an integral part of optical wireless communication, can realize the requirements for data transmission speed in the network, and reduce the load on the bandwidth of existing wireless networks. The article discusses modulation methods in a wireless optical communication network. Modulation characteristics that are important for system performance are studied. For types of pulse modulation, the power spectral density is calculated and studied to obtain time waveforms with a given signal-to-noise ratio. A model of an ideal transmission and reception system for OOC-NRZ in the presence of additive white Gaussian noise is constructed, which is suitable for indoor visible light data transmission systems. Signal shapes and the influence of instrumental and external noise were studied. Calculated values of the bit error probability based on experimental data are given. The proposed simulation model can be used in the design of real transmitting and receiving devices for a visible light communication system.

Filtering-free complex-valued DSB-16QAM generation for a 100 G direct detection optical interconnection.

In this Letter, a complex-valued double-sideband 16QAM (CV-DSB-16QAM) signaling scheme is proposed and experimentally demonstrated in a 100-Gb/s intensity modulation/direct detection (IM/DD) interconnection system. Unlike the conventional real-valued double-sideband (DSB) quadrature amplitude modulation (QAM) of relatively lower spectral efficiency (SE) and single-sideband (SSB) QAM relying on sharp-edged optical filtering, the CV-DSB-16QAM signal is generated by combining two independent sideband modulated QPSK signals using a single intensity modulator with an optical filtering-free profile, which also saves one photodiode and one analog-to-digital-converter compared with the twin-SSB scheme. Compared to typical pulse amplitude modulation or SSB schemes, the proposed approach offers a compelling alternative for complex-valued DD systems' evolution, particularly in scenarios with high SE demands and controllable chromatic dispersion. The experimental demonstration evaluates a 25-GBaud CV-DSB-16QAM signal over a 2-km standard single-mode fiber (SSMF), achieving a net bit rate of 100 Gb/s and occupying only a 25.1-GHz electrical bandwidth with a 4% guard band.

Spectrum‐sensing algorithm based on graph feature fusion

AbstractGraph‐based spectrum sensing in noisy environments has major implications for civilian and military signal processing applications. However, existing algorithms suffer from high computational complexity and performance deterioration at low signal‐to‐noise ratios (SNRs). Therefore, a spectrum‐sensing algorithm based on graph feature fusion using a quadratic form derived from self‐loop weights and the graph Laplacian matrix is proposed in this study. The sum of the first and second block maxima of the power spectrum of the observed signal is selected as the input to the graph converter. Self‐loop weights are combined with the Laplacian matrix to construct the graph quadratic form, which serves as the test statistic for decision‐making. By applying majorisation and the extreme value theory, it is demonstrated that the proposed algorithm outperforms existing methods. The simulation results confirm the robust spectrum‐sensing performance across various signal modulation types and pulse shapes. Thus, compared to existing algorithms, except block range‐ and energy‐detection‐based methods, the proposed algorithm demonstrates the best spectrum‐sensing performance under low SNRs and channel‐fading conditions, while achieving the lowest computational complexity. The proposed approach enables more efficient and accurate spectrum sensing, fostering advancements in communication technologies and defence applications.

An Automatic Modulation Recognition Algorithm Based on Time–Frequency Features and Deep Learning with Fading Channels

Automatic modulation recognition (AMR) stands as a crucial core technology within the realm of signal processing and perception, playing a significant part in harsh electromagnetic environments. The time–frequency image (TFI) of communication signals can manifest modulation characteristics and serve as a foundation for signal modulation recognition and classification. However, under the influence of the electromagnetic environment, communication signals are exposed to varying degrees of interference, which poses a challenge to the recognition of modulation types. Taking into account the effects of interference and channel fading, this paper introduces a communication signal modulation recognition algorithm based on deep learning (DL) and time–frequency analysis. This approach employs short-time Fourier transform (STFT) to generate time–frequency diagrams from time-domain signals. Subsequently, it binarizes the image and feeds it as input data to the neural network. Our research presents a composite deep convolutional neural network (CNN) architecture known as the composite dense-residual neural network (CDRNN). This architecture focuses on enhancing the feature extraction and identification, aiming to achieve accurate recognition of modulation types in harsh electromagnetic environments. Finally, simulation results validate that the proposed deep learning algorithm holds remarkable advantages in boosting the accuracy of modulation type recognition with better adaptability. The algorithm shows better performance even in harsh electromagnetic environments. When the signal-to-noise ratio (SNR) is 18 dB, the recognition accuracy can reach 92.1%.

InvPT++: Inverted Pyramid Multi-Task Transformer for Visual Scene Understanding.

Multi-task scene understanding aims to design models that can simultaneously predict several scene understanding tasks with one versatile model. Previous studies typically process multi-task features in a more local way, and thus cannot effectively learn spatially global and cross-task interactions, which hampers the models' ability to fully leverage the consistency of various tasks in multi-task learning. To tackle this problem, we propose an Inverted Pyramid multi-task Transformer, capable of modeling cross-task interaction among spatial features of different tasks in a global context. Specifically, we first utilize a transformer encoder to capture task-generic features for all tasks. And then, we design a transformer decoder to establish spatial and cross-task interaction globally, and a novel UP-Transformer block is devised to increase the resolutions of multi-task features gradually and establish cross-task interaction at different scales. Furthermore, two types of Cross-Scale Self-Attention modules, i.e., Fusion Attention and Selective Attention, are proposed to efficiently facilitate cross-task interaction across different feature scales. An Encoder Feature Aggregation strategy is further introduced to better model multi-scale information in the decoder. Comprehensive experiments on several 2D/3D multi-task benchmarks clearly demonstrate our proposal's effectiveness, establishing significant state-of-the-art performances.

Curved Geometric Networks for Visual Anomaly Recognition.

Learning a latent embedding to understand the underlying nature of data distribution is often formulated in Euclidean spaces with zero curvature. However, the success of the geometry constraints, posed in the embedding space, indicates that curved spaces might encode more structural information, leading to better discriminative power and hence richer representations. In this work, we investigate the benefits of the curved space for analyzing anomalous, open-set, or out-of-distribution (OOD) objects in data. This is achieved by considering embeddings via three geometry constraints, namely, spherical geometry (with positive curvature), hyperbolic geometry (with negative curvature), or mixed geometry (with both positive and negative curvatures). Three geometric constraints can be chosen interchangeably in a unified design, given the task at hand. Tailored for the embeddings in the curved space, we also formulate functions to compute the anomaly score. Two types of geometric modules (i.e., geometric-in-one (GiO) and geometric-in-two (GiT) models) are proposed to plug in the original Euclidean classifier, and anomaly scores are computed from the curved embeddings. We evaluate the resulting designs under a diverse set of visual recognition scenarios, including image detection (multiclass OOD detection and one-class anomaly detection) and segmentation (multiclass anomaly segmentation and one-class anomaly segmentation). The empirical results show the effectiveness of our proposal through consistent improvement over various scenarios. The code is made available at https://github.com/JHome1/GiO-GiT.

Low-voltage DC-DC off-grid PV system: various irradiance studies

The off-grid photovoltaic (OGPV) system, also referred to as a stand-alone power system, generates electricity from solar energy independently of the main electricity grid. However, photovoltaic (PV) modules face limitations in consistently providing the necessary voltage. Thus, a DC-DC converter is employed to adjust the voltage to match the requirements of the load. This paper proposes a detailed analysis of irradiance patterns resembling in-lab and real-world conditions to address the challenge of selecting the optimal DC-DC OGPV system capable of functioning effectively under standard test condition (STC) and dynamic test condition (DTC), respectively. Three proposed irradiance analyses, tailored to different environmental scenarios, aim to identify output performances for DC-DC OGPV system which are aligning with standards set by the International Electrotechnical Commission (IEC) 61727. The DC-DC OGPV system is simulated using MATLAB Simulink. Furthermore, comparative studies involving different PV module types and several switching techniques (pulse generator and maximum power point tracking or MPPT implementations), where findings are instrumental in advancing the development and projection planning for medium to high-voltage OGPV systems, contributing to the global initiative for accessible and sustainable energy solutions aligned with sustainable development goal (SDG 7).