Advanced Schemes Research Articles

Secure Cloud-Based Media Sharing: Scalable Access Control and Privacy-Preserving Deduplication

This research delves into the realm of secure deduplication schemes, strategically designed to enhance storage efficiency within cloud environments. An initial focus is placed on an advanced AES encryption scheme utilizing a message-derived key, resulting in a consistent mapping of identical plaintexts to ciphertexts. The proposed AES framework not only incorporates convergent encryption but also provides meticulous security definitions. While the landscape of cloud computing boasts numerous techniques for data security, existing methodologies fall short in addressing nuanced aspects related to ciphertext. In response to this gap, our study introduces a pioneering information management paradigm. This comprehensive framework encompasses data gathering, sharing, and restrictive distribution, with a particular emphasis on multi-owner privacy preservation within the cloud. Under this innovative framework, data owners gain the capability to securely disseminate private information to predefined groups of clients through the cloud infrastructure. The research presented herein serves as a significant contribution to the ongoing discourse on secure deduplication in cloud storage. By combining advanced encryption techniques with a forward-looking information sharing strategy, our approach seeks to elevate the effectiveness of data security protocols in cloud environments

An entropy-based scheme for protection of DC microgrids

The design of an appropriate scheme for protection of DC microgrids is still a significant challenge. This paper proposes a new method for protection of DC microgrids when subjected to various types of faults and modes of operation. The proposed method shares some features of the differential protection scheme and adopts the Shannon entropy to evaluate the "informational value" of current waveforms in the presence and absence of a fault. It uses the relative entropy of the current waveforms at the two sides of the faulty line to detect and characterize a fault. In the case of failure in the primary protection system, the entropy variations of the current waveforms are used to detect the faulty line. The main features of the proposed method include a) accuracy in a noisy environment with poor data synchronization (due to the delay in communication links) and varying network topology, b) fast operation regardless of the fault type and location, c) ability to detect high-impedance faults, and d) provision of backup protection. The efficiency of the proposed method is demonstrated by comparing the simulation results of several case studies in a typical ring DC network with those obtained using an advanced differential protection scheme.

Effect of Single and Double Moment Microphysics Schemes and Change in Cloud Condensation Nuclei, Latent Heating Rate Structure Associated with Severe Convective System over Korean Peninsula

To investigate the impact of advanced microphysics schemes using single and double moment (WSM6/WDM6) schemes, numerical simulations are conducted using Weather Research and Forecasting (WRF) model for a severe mesoscale convective system (MCS) formed over the Korean Peninsula. Spatial rainfall distribution and pattern correlation linked with the convective system are improved in the WDM6 simulation. During the developing stage of the system, the distribution of total hydrometeors is larger in WDM6 compared to WSM6. Along with the mixing ratio of hydrometeors (cloud, rain, graupel, snow, and ice), the number concentration of cloud and rainwater are also predictable in WDM6. To understand the differences in the vertical representation of cloud hydrometeors between the schemes, rain number concentration (Nr) from WSM6 is also computed using particle density to compare with the Nr readily available in WDM6. Varied vertical distribution and large differences in rain number concentration and rain particle mass is evident between the schemes. Inclusion of the number concentration of rain and cloud, CCN, along with the mixing ratio of different hydrometers has improved the storm morphology in WDM6. Furthermore, the latent heating (LH) profiles of six major phase transformation processes (condensation, evaporation, freezing, melting, deposition, and sublimation) are also computed from microphysical production terms to deeply study the storm vertical structure. The main differences in condensation and evaporation terms are evident between the simulations due to the varied treatment of warm rain processes and the inclusion of CCN activation in WDM6. To investigate cloud–aerosol interactions, numerical simulation is conducted by increasing the CCN (aerosol) concentration in WDM6, which simulated comparatively improved pattern correlation for rainfall simulation along with intense hydrometer distribution. It can be inferred that the change in aerosol increased the LH of evaporation and freezing and affected the warming and cooling processes, cloud vertical distribution, and subsequent rainfall. Relatively, the WDM6 simulated latent heating profile distribution is more consistent with the ERA5 computed moisture source and sink terms due to the improved formulation of warm rain processes.

Predicting the solubility of hydrogen in hydrocarbon fractions: Advanced data-driven machine learning approach and equation of state

BackgroundHydrogen is a free carbon source for energy that attracts massive interest to be utilized during energy transition period in the near future. Molecular hydrogen soon will be a key component in fuels productions. It can be produced through catalytic steam and natural gas. For such processes the accurate estimation of solubility of hydrogen in many alkanes’ solution can have a major impact on the design and optimization of such petrochemical processes. MethodsIn this study, advanced data-driven scheme consisting in a committee machine intelligent system (CMIS) model was proposed for predicting the solubility of hydrogen in many hydrocarbon liquids raging from light n-alkanes C1 to heavy n-alkanes of C46. The CMIS was gained by combining multilayer perceptron (MLP) and cascaded forward neural network (CFNN) beneath a sole model using gene expression programming (GEP). A wide range database of hydrogen solubility in a binary system of hydrogen and various n-alkanes types was considered for developing the intelligent model. Besides, the reliability of the physics-based models, namely the Peng–Robinson equations of state (PR EOS) was checked for predicting the solubility of H2 in n-alkanes. Significant findingsThe main results demonstrated that the developed CMIS scheme can estimate the solubility of hydrogen in n-alkanes with a quite low computational cost and excellent performance, where the yielded overall RMSE and R2 values were 0.0033 and 0.9972, respectively. Besides, the established machine learning based model is capable of providing a good match against a wide range of experiment data. Furthermore, it is found that the implemented CMIS paradigm is superior to the existing intelligent and physics-based models in terms of accuracy. Lastly, the result of this work is vital for increasing the accuracy of hydrogen solubility in various hydrocarbon solvents required for process optimization of the hydrogen production units.

Towards Flawless Designs: Recent Progresses in Non-Orthogonal Multiple Access Technology

High effectiveness and high reliability are two fundamental concerns in data transmission. Non-orthogonal multiple-access (NOMA) technology presents a promising solution for high-speed data transmission, which has long been pursued by academia and industry. However, there is still a significant road ahead for it to effectively support a wide range of applications. This paper provides a comprehensive study, comparison, and classification of the current advanced NOMA schemes from the perspectives of single-carrier (SC) systems, multicarrier (MC) systems, reconfigurable-intelligent-surface (RIS)-assisted systems, and deep-learning (DL)-assisted systems. Specifically, system implementation issues such as the transition from SC-NOMA to MC-NOMA, the relaxation of distinct channel gains, the consideration of imperfect channel knowledge, and the mitigation of error propagation/intra-group interference are involved. To begin with, we present an overview of the state-of-the-art developments related to the advanced design of SC-NOMA. Subsequently, a generalized MC-NOMA framework that provides the diversity–multiplexing gain by enhancing users’ signal-to-interference-plus-noise ratio (SINR) is proposed for better system performance. Moreover, we delve into discussions on RIS-assisted NOMA systems, where the receiver’s SINR can be enhanced by intelligently reconfiguring the reflected signal propagations. Finally, we analyze designs that combine NOMA/RIS-NOMA with DL to achieve highly efficient data transmission. We also identify key trends and future directions in deep-learning-based NOMA frameworks, providing valuable insights for researchers in this field.

Unusual energy–structure–property relation in a metallic glass coupled with temperature-dependent relaxation memories

Elucidating the individual effects of high- and low-temperature relaxations on the internal structure and material properties is a fascinating theme in the field of metallic glasses because it can lay the foundation for an advanced glass-property tuning scheme through the control of different types of relaxations. In this study, several glass models were constructed using melt-quenching (MQ) processes, in which high- and low-temperature relaxations were controlled by their cooling rates. We found unusual energy–structure–property relationships; even when the glass alloys had equivalent potential energies (or free volumes), they exhibited different local-order development, elastic stiffness, and plastic deformation properties, which depended on the thermal histories in the glass-forming MQ process. The local-order development was primarily affected by the high-temperature relaxation occurring at approximately the glass transition temperature. Meanwhile, the plastic deformation behavior of the metallic glasses was significantly affected by both low- and high-temperature relaxations. We also found an interesting relaxation memory effect in the glass alloys constructed by the present MQ processes; the glass alloys exhibited an atomic-scale dynamic that was affected by the temperature-dependent relaxation memories (i.e., high and-low-temperature relaxation memories) of previous MQ processes. These results provide new insights into the relaxation physics of glass-forming liquids and imply the possibility of an advanced tuning scheme for metallic glasses.

Development of a Fully-Implicit Second-Order system Thermal-Hydraulics analysis code for reactor modeling and simulations

There has been continuous advancement in numerical techniques and software engineering in the field of system thermal-hydraulics over the past decade. The current state-of-the-art involves the use of fully-implicit high-resolution schemes in conjunction with the Jacobian-Free Newton-Krylov (JFNK) method for both single-phase and two-phase flows. However, the application of these advanced schemes in system thermal-hydraulics codes is not a common practice. Given the opportunity and the need to develop a new modern system thermal–hydraulic code from scratch, there is no doubt that these recent advancements should be leveraged. In this work, we introduce a new modern object-oriented system thermal-hydraulics code named RETA. RETA is characterized by several key features, including: second-order temporal and spatial discretization schemes for both incompressible and compressible flows, as well as single-phase and two-phase flows; fully-implicit solution schemes utilizing both the Newton and PJFNK methods; and an object-oriented design of major fluid components and physical models, which allows for easy extension. A series of verification and demonstration studies are conducted to verify the implementation, demonstrate the accuracy improvement, and evaluate the performance of the new code. It is concluded that results from the second-order scheme are generally more reliable. Without the concern of a code stability issue, the second-order scheme should be the preferred and default choice for a code user or an analyst.

Gradient Statistics-Based Multi-Objective Optimization in Physics-Informed Neural Networks.

Modeling and simulation of complex non-linear systems are essential in physics, engineering, and signal processing. Neural networks are widely regarded for such tasks due to their ability to learn complex representations from data. Training deep neural networks traditionally requires large amounts of data, which may not always be readily available for such systems. Contrarily, there is a large amount of domain knowledge in the form of mathematical models for the physics/behavior of such systems. A new class of neural networks called Physics-Informed Neural Networks (PINNs) has gained much attention recently as a paradigm for combining physics into neural networks. They have become a powerful tool for solving forward and inverse problems involving differential equations. A general framework of a PINN consists of a multi-layer perceptron that learns the solution of the partial differential equation (PDE) along with its boundary/initial conditions by minimizing a multi-objective loss function. This is formed by the sum of individual loss terms that penalize the output at different collocation points based on the differential equation and initial and boundary conditions. However, multiple loss terms arising from PDE residual and boundary conditions in PINNs pose a challenge in optimizing the overall loss function. This often leads to training failures and inaccurate results. We propose advanced gradient statistics-based weighting schemes for PINNs to address this challenge. These schemes utilize backpropagated gradient statistics of individual loss terms to appropriately scale and assign weights to each term, ensuring balanced training and meaningful solutions. In addition to the existing gradient statistics-based weighting schemes, we introduce kurtosis-standard deviation-based and combined mean and standard deviation-based schemes for approximating solutions of PDEs using PINNs. We provide a qualitative and quantitative comparison of these weighting schemes on 2D Poisson's and Klein-Gordon's equations, highlighting their effectiveness in improving PINN performance.

Reconstruction of Angstrom resolution exit-waves by the application of drift-corrected phase-shifting off-axis electron holography

Phase-shifting electron holography is an excellent method to reveal electron wave phase information with very high phase sensitivity over a large range of spatial frequencies. It circumvents the limiting trade-off between fringe spacing and visibility of standard off-axis holography. Previous implementations have been limited by the independent drift of biprism and sample. We demonstrate here an advanced drift correction scheme for the hologram series that exploits the presence of an interface of the TEM specimen to the vacuum area in the hologram. It allows to obtain reliable phase information up to 2π/452 at the 1 Å information limit of the Titan 80–300 kV environmental transmission electron microscope used, by applying a moderate voltage of 250 V to a single biprism for a fringe spacing of 1 Å. The obtained phase and amplitude information is validated at a thin Pt sample by use of multislice image simulation with the frozen lattice approximation and shows excellent agreement. The presented method is applicable in any TEM equipped with at least one electron biprism and thus enables achieving high resolution off-axis holography in various instruments including those for in-situ applications. A software implementation for the acquisition, calibration and reconstruction is provided.

Soft sensor model predictive control for azeotropic distillation of the separation of DIPE/IPA/water mixture

BackgroundA novel advanced control scheme for azeotropic distillation (AD) shows superiority in improving product quality and economic benefit. MethodsA soft sensor model predictive control (SS-MPC) strategy was developed for the separation of the diisopropyl ether (DIPE)/ isopropyl alcohol (IPA)/water mixture via AD. The soft sensor model was built based on time-delayed neural networks (TDNN), while the weight of the SS-MPC controller was optimized by multi-objective genetic algorithm. Significant findingsProduct purity of two distillation columns was controlled simultaneously by the proposed soft sensor model predictive controller, meanwhile, avoiding the measurement of composition in practice. The dynamic performance was compared with the conventional PID scheme and MPC scheme in the face of ±10 % feed disturbances. The results show that SS-MPC can better control the product purity than PID without using the composition detection instruments.

Region and cloud regime dependence of parametric sensitivity in E3SM atmosphere model

The Department of Energy (DOE)’s Energy Exascale Earth System Model (E3SM), including its atmosphere model (EAM), has many relatively new features. In a previous study we conducted a systematic parametric sensitivity analysis for EAM based on short, perturbed parameter ensemble (PPE) simulations, mainly focusing on global mean climate features and metrics. While parameter values in global climate models are generally invariant in space and time, model response to parameters perturbation may vary by regions and climate regimes, which motivates the need to better understand the EAM model behaviors and physics at regional scale and process level. In this study, using the same set of PPE simulations and a similar sensitivity analysis framework, we identify parameters that cause largest sensitivities over different regions and compare model responses in fast atmospheric processes to the parameters across different cloud regimes for several important cloud-related fidelity metrics. We find that cloud forcing has opposite response to some parameters over mid-latitude vs. tropical land. We also analyze how the parametric sensitivity varies as stratocumulus transitions to shallow convection and to deep convection over ocean. Low cloud forcing and shortwave cloud forcing in the subtropical eastern Pacific are most sensitive to the parameters controlling the width of the probability density function (PDF) of the subgrid vertical velocity (w’) (gamma) and the damping of the w’ skewness (c8) near the coast but become more sensitive to the parameter affecting the damping of the w’ variance (c1) further offshore. Detailed interpretation of the spatial dependence of parametric sensitivity is provided. We also investigate how the parametric sensitivity evolves with prediction duration. This study improves our process-level understanding of cloud physics and parameterization and provides insights for developing more advanced regime-aware parameterization schemes in global climate model.

Intelligent encryption with improved zealous method to enhance the anonymization of public health records in cloud

<p>In order to keep the cost of installing advanced encryption scheme (AES) hardware to a minimum and to hide the protected key from hackers, existing networks’ tactics are employed in this study. This is the biggest drawback of the present methods since there is no security while keeping the concealed key of the AES encryption method. The user is unable to remember all the AES keys since it is necessary to connect with several people using various AES keys. The suggested approach successfully counters both facet and brute force assaults. The advanced encryption method is the safest algorithm to utilize for trustworthy encryption, according to the findings. But the issue is that the advanced encryption scheme makes brute force attack less effective. Honey encryption is thus used. The recommended method encrypts the dataset after dataset anonymization to ensure privacy. An enhanced zealous technique is used to do anonymization when a middle dataset is created using the supplied key. After the dataset has been sorted, the dataset with the higher rank is the one that gets encrypted first. The user receives these datasets together with a decryption key for the encrypted records, enabling them to swiftly obtain the data they need.</p>

PLL-based Enhanced Control of Boost PFC Converter for Smart Farming Lighting Application

Controlled environment farming has become an attractive solution to increase the food production with limited use of resources. LED lights are often used in this type of farming to increase plant yield. LED lights are dc load, whereas the grid provides ac power. This necessitates the use of power factor correction (PFC) converter as an interface between the grid and the lights. This paper proposes an innovative control method for the boost PFC converter, where an advanced phase-locked loop scheme is developed that can eliminate measurement dc offset and provide harmonic robust estimation of the grid voltage fundamental component. The applied nonlinear control uses the existing two-loop control architecture. Based on a baseline PI controller, a nonlinear function is added to make the controller react faster when it is far from the reference and vice-versa. Comprehensive simulation studies are conducted to assess the performance of the proposed method under various challenging test scenarios. Compared to the baseline method, the proposed technique achieved 42%∼65% reduction in total harmonic distortion depending on the test cases, which makes the technique a suitable candidate for improving the operational efficiency and, consequently, the running cost of a smart farming lighting system on an industrial scale. Results show the effectiveness of the proposed method over the conventional counterpart.

Effect of Bronchial Blood Flow on Respiratory Heat Exchange: A Mathematical Analysis for Infectious Diseases

Abstract The present study aims to mathematically analyze the role of bronchial blood flow on heat transfer in respiratory infections. In general, the exchange of heat transfer in various infectious diseases like COVID-19 caused by SARS-CoV-2 has adversely affected respiration by reducing the physiological efficiency of the human respiratory tract. The mechanism of heat exchange through airway walls with the bronchial blood circulation still needs to be thoroughly studied for infectious diseases. In this article, a three-dimensional (3D) spatio-temporal theoretical model is developed to estimate the possible role of bronchial blood on heat exchange during breathing. The local description of the model is presented in a comprehensive and consistent dimensionless framework to explicitly state the actual physiological background. The global description is framed by a multicompartment-based approach, and the algorithm is solved using an advanced numerical scheme to ensure computational tractability. The numerical study elucidates the role of inhalation air temperature, breathing cycles, blood perfusion rate, and mucosal hydration. The outcomes of the algorithm estimate the parameters of the isothermal saturation boundary (ISB), which is defined as the position in the respiratory tract where the temperature of inhaled air comes in equilibrium with the body core saturation temperature. The derived results help to understand the pathophysiological threshold limits and recommend the values to evaluate respiratory distress. With the variations of inspiratory flow conditions, it is observed that the ISB position shifts to the distal branches with the increment in inhalation temperature, breathing rate and virus infection, and decrement in blood perfusion rate. The two antiparallel effects are observed: inhalation of cold air transmits the viral infection, and inhalation of warm air produces thermal injury. However, both can be well controlled by suitable ventilation rates. The observed threshold values may be helpful in clinical trials to correlate the anatomic configuration with pathophysiology.

SPARTA: Solar parameterization for the radiative transfer of the cloudless atmosphere

The high-performance SPARTA broadband clear-sky solar irradiance model is presented. It evaluates global (GHI), diffuse (DIF) and direct normal (DNI) irradiances from atmospheric transmittance functions developed using an advanced 2-band hybrid spectral integration scheme, calculates aerosol extinction using a universal and highly accurate aerosol transmittance scheme, and incorporates a versatile parameterization of the aerosol circumsolar solar irradiance. The model's performance is assessed against 1-min quality-assured measurements at three research-class radiometric ground stations spanning 15 years of data combined, and it is benchmarked against three high-performance radiative transfer models. The performance assessment proved that SPARTA was superior to the reference models for GHI, DIF and DNI at the three observational sites combined. SPARTA produced unbiased estimates of the three solar irradiance components (remarkably, it was the only model producing unbiased estimates of DIF) and yielded the smallest standard deviations of the model−observation differences (≈2 %, ≈10 % and ≈2 % for GHI, DIF and DNI, respectively). SPARTA was the only model capable of improving the estimation of DIF when using observed inputs of the aerosol scattering optical properties instead of modelled values. The results of the performance assessment proved that, provided accurate inputs to the model, SPARTA produces predictions with similar uncertainty than ground observations, especially if the ground sensors are not optimaly maintained or they are not A or B ISO-9060 class.

Constrained Hybrid Monte Carlo Sampling Made Simple for Chemical Reaction Simulations.

Most electrochemical reactions should be studied under a grand canonical ensemble condition with a constant potential and/or a constant pH value. Free energy profiles provide key insights into understanding the reaction mechanisms. However, many molecular dynamics (MD)-based theoretical studies for electrochemical reactions did not employ an exact grand canonical ensemble sampling scheme for the free energy calculations, partially due to the issues of discontinuous trajectories induced by the particle-number variations during MD simulations. An alternative statistical sampling approach, the Monte Carlo (MC) method, is naturally appropriate for the open-system simulations if we focus on the thermodynamic properties. An advanced MC scheme, the hybrid Monte Carlo (HMC) method, which can efficiently sample the configurations of a system with large degrees of freedom, however, has limitations in the constrained-sampling applications. In this work, we propose an adjusted constrained HMC method to compute free energy profiles using the thermodynamic integration (TI) method. The key idea of the method for handling the constraint in TI is to integrate the reaction coordinate and sample the rest degrees of freedom by two types of MC schemes, the HMC scheme and the Metropolis algorithm with unbiased trials (M(RT)2-UB). We test the proposed method on three different systems involving two kinds of reaction coordinates, which are the distance between two particles and the difference of particles' distances, and compare the results to those generated by the constrained M(RT)2-UB method serving as benchmarks. We show that our proposed method has the advantages of high sampling efficiency and convenience of implementation, and the accuracy is justified as well. In addition, we show in the third test system that the proposed constrained HMC method can be combined with the path integral method to consider the nuclear quantum effects, indicating a broader application scenario of the sampling method reported in this work.

VerFHS: Verifiable Image Retrieval on Forward Privacy in Blockchain-Enabled IoT

In the scenario of the Internet of Things (IoT) co-located with the cloud, many applications such as face recognition, traffic monitoring and medical diagnosis usually outsource a large amount of generated image data to cloud servers to reduce the burden of local storage. Many secure encrypted image retrieval schemes have been proposed to protect data privacy. However, existing work incurs storage and communication burdens, lacks verifiability of query results and has potential forward security threats. To solve these issues, we propose the VerFHS framework in this paper, which can satisfy Verifiability, Feedback, High-Security. Specifically, we first present an extended secure k-NN algorithm to protect indexes, cleverly design ciphertext inner product for similarity comparison, and use the reward mechanism of blockchain to build a monitoring and feedback mechanism for cloud servers. Then we demonstrate an enhanced VerFHS scheme in the dynamic setting (VerFHSD) that uses a permutation matrix to process image encryption against adaptive attacks during dynamic updates. VerFHSD prevents cloud servers from making search queries over newly added images via previous tokens, thereby achieving forward security. The formal security analysis shows that our schemes protect the privacy of images, indexes & query tokens and forward security. And extensive experiments using the real-world dataset demonstrate that our scheme not only has the highest search accuracy all the time, but also achieves efficient queries at the millisecond level, when compared with other advanced image retrieval schemes.

Streamer-induced kinetics of excited states in pure N2: I. Propagation velocity, and vibrational distributions of N2(C) and N(B) states

The emission spectra of a streamer discharge in pure nitrogen provide an important tool for investigating the fundamental kinetics of excited electronic states of N2 and benchmark data for validating advanced kinetic schemes for numerical models. In this work, we characterize a streamer monofilament developed in a dielectric barrier discharge configuration, including electrical characteristics, time-resolved images and N2/N emission spectra, all acquired with nanosecond temporal resolution. Time-resolved images and emission characteristics provide clear evidence of the formation of a cathode-directed streamer and allow determining the streamer propagation velocity and the typical values using the intensity ratio of nitrogen spectral bands in the center of the discharge gap. We also measure the vibrational distributions of the N2(C, 0–4) and N(B, 0–2) states. The population of N2(C, 0–4) state, initially formed by energetic electrons in the streamer head, changes later significantly due to the decrease in the mean energy and concentration of the streamer channel electrons. After a few tens of nanoseconds, the electron-impact excitation rate of N2(C) becomes negligible compared to its population by the N2() + N2() pooling. The experimental findings are supported and consistent with the 0D state-to-state kinetic model results and reveal the participation of high vibrational levels of N2() in the pooling reactions.

Advanced adaptive algorithm controlled single‐phase DSTATCOM operation during weak grid conditions

SummaryThis paper presents the performance of a single‐phase utility‐tied distribution static compensator (DSTATCOM) with nonlinear load operating under weak grid conditions. The DSTATCOM is controlled by a complex kernel risk‐sensitive p‐power (CKRSP) algorithm. Ever‐increasing nonlinearity of the load, rapidly increasing renewable energy system installed capacity, and rising heating loads, especially during the winter season, lead to various power quality issues that worsen during weak grid conditions. An advanced adaptive control scheme, that is, CKRSP, enforces the system to perform various multifaceted operations, including harmonics suppression, handling rapid load variations, voltage sag and swell, and active and reactive power control at the point of common coupling (PCC) during weak grid conditions. The proposed CKRSP algorithm outperforms conventional control algorithms regarding steady‐state error, convergence speed, and accuracy. During weak grid conditions, the proposed system ensures stable DC bus voltage with a reduced second harmonic component. This further assists the CKRSP control in better extracting the fundamental load current component using a smoother loss component of current . The proposed system can handle various power quality issues during weak grid conditions while ensuring the overall system's stability. The laboratory prototype results show that the presented system performs satisfactorily under weak grid conditions per IEEE 519‐2014 standards.

SIMULASI SKEMA MODULASI 1024-QAM DAN 4096-QAM PADA SISTEM KOMUNIKASI DIGITAL

Quadrature amplitude modulation (QAM) is a modulation scheme used by digital communication systems to obtain high data rates in bandwidth-limited channels. M-ary QAM schemes with M values greater than 256 are currently used by wireless network standards, such as WiFi 6 and WiFi 7 (final standard will be released in 2024), which use the 1024-QAM and 4096-QAM modulation schemes, respectively. With the continuous development of technology for digital communication systems, both wireline and wireless, it is essential to research regarding advanced modulation schemes. This study discusses the simulation of the 1024-QAM and 4096-QAM modulation schemes using MATLAB software to compare the performance of the two modulation schemes in data transmission through the AWGN channel without using an error control code. The simulation results show that 4096-QAM is superior to 1024-QAM but requires higher Eb/N0 and SNR values. The Eb/N0 value required by the 4096-QAM to achieve a bit error ratio (BER) of 10-4 is 31 dB, while the 1024-QAM requires an Eb/N0 value of 26 dB.