Error Ratio Research Articles

Dragon fly algorithm based approach for escalating the security among the nodes in wireless sensor network based system

A new technology that is gaining popularity today is the Wireless Sensor Network. Smart sensors are being used in a variety of wireless network applications, including intruder detection, transportation, the Internet of Things, smart cities, the military, industrial, agricultural, and health monitoring, as a result of their rapid expansion. Sensor network technologies improve social advancement and life quality while having little to no negative impact on the environment or natural resources of the planet are examined in sensor networks for sustainable development. Real-world applications face challenges ensuring Quality of Service (QoS) due to dynamic network topology changes, resource constraints, and heterogeneous traffic flow. By enhancing its properties, such as maintainability, packet error ratio, reliability, scalability, availability, latency, jitter, throughput, priority, periodicity, deadline, security, and packet loss ratio, the optimized QoS may be attained. Real-world high performance is difficult to attain since sensors are spread out in a hostile environment. The performance parameters are divided into four categories: network-specific, deployment phase, layered WSN architecture, and measurability. Integrity, secrecy, safety, and security are among the privacy and security levels. This article leads emphasis on the trustworthiness of the routes as well as the nodes involved in those routes from where the data has to pass from source to destination. First of all, the nodes are deployed and cluster head selection is done by considering the total number of nodes and the distance from the base station. The proposed work uses AODV architecture for computing QoS parameters that are throughput, PDR and delay. K-means clustering algorithm is used to divide the aggregated data into three possible segments viz. good, moderate and bad as this process does not involve the labelling of aggregated data due to its supervised behavior. The proposed trust model works in two phases. In first phase, data is divided into 3 segments and labelling is done. In second phase, uses generated class objects are to be applied viz. the route records to publicize the rank of the routes followed by the rank of nodes. The proposed technique employed the statistical machine learning and swarm intelligence strategy with dragon fly algorithm in order to address the issues related effective rank generation of nodes and improving the network lifetime. Deep learning concepts can be combined with fuzzy logics approach for resolving issues like secure data transmission, trustworthiness of ranking nodes and efficient route discovery.

A Hybrid Millimeter-Wave and Free-Space-Optics Communication Architecture with Adaptive Diversity Combining and HARQ Techniques

We propose and demonstrate a hybrid communication architecture that combines millimeter-wave (MMW) in the radio frequency (RF) domain and free-space-optics (FSO) technologies using adaptive combining and hybrid automatic repeat request (HARQ) techniques. At the receiving end, we employed joint signal processing with an adaptive diversity combining technique (ADCT) based on a maximum ratio combining (MRC) algorithm. We derived closed-form expressions for the outage probability and throughput of the hybrid RF and FSO (RF/FSO) system, considering various characteristics of atmospheric turbulence in the FSO link. Experimental testing with 10-Gbaud quadrature phase shift keying (QPSK) data was conducted under different simulated atmospheric turbulence intensities, FSO and MMW speed-ratios, and forward error correction (FEC) overheads. Additionally, we validated improvements in terms of bit error ratio (BER), outage probability, and throughput performance.

I-CPA: An Improved Carnivorous Plant Algorithm for Solar Photovoltaic Parameter Identification Problem.

The carnivorous plant algorithm (CPA), which was recently proposed for solving optimization problems, is a population-based optimization algorithm inspired by plants. In this study, the exploitation phase of the CPA was improved with the teaching factor strategy in order to achieve a balance between the exploration and exploitation capabilities of CPA, minimize getting stuck in local minima, and produce more stable results. The improved CPA is called the I-CPA. To test the performance of the proposed I-CPA, it was applied to CEC2017 functions. In addition, the proposed I-CPA was applied to the problem of identifying the optimum parameter values of various solar photovoltaic modules, which is one of the real-world optimization problems. According to the experimental results, the best value of the root mean square error (RMSE) ratio between the standard data and simulation data was obtained with the I-CPA method. The Friedman mean rank statistical analyses were also performed for both problems. As a result of the analyses, it was observed that the I-CPA produced statistically significant results compared to some classical and modern metaheuristics. Thus, it can be said that the proposed I-CPA achieves successful and competitive results in identifying the parameters of solar photovoltaic modules.

Exploring the impact of spontaneous emission in two quantum game-based key-distribution protocols

In this work, we investigate the influence that spontaneous emission has on the performance and security of both a qubit and a qutrit quantum key distribution protocols, if these were to be implemented using two- and three-level atoms, respectively. These protocols were developed based on the mechanics of a quantum version of the Monty-Hall game. In the case of three-level atoms, the three possible configurations (V, Ξ, Λ) are considered. In both protocols, an additional step, consisting on discarding some specific non-valid cases, is found to reduce the bit error ratio (BER). Regarding the security of the protocols, we are able to define secure regions, dependent on the spontaneous emission parameters, within which the protocols are deemed secure due to the violation of Bell-type inequalities. Furthermore, our results show that the choice of three-level atom configuration significantly impacts the protocol performance. We are able to determine that the Λ configuration provides a lower BER than the V and Ξ configurations within their respective secure regions.

Penerapan Metode Grey-Markov(1,1) Untuk Peramalan Penerimaan di Kantor Pengawasan dan Pelayanan Bea Cukai Tipe Madya Pabean Cikarang

The Customs Supervision and Service Office is given a revenue target that must be achieved annually. However, revenue at the Customs Supervision and Service Office tends to fluctuate because it is strongly influenced by various external factors that are difficult to predict. Projections need to be done to see if the given revenue target can be achieved. This study aims to conduct forecasting so that it can be estimated how much revenue will be at the end of the year (December 2023). Research is conducted using the Grey(1,1) and Grey-Markov(1,1) models. The analysis results show that the Grey-Markov(1,1) model provides better forecasting accuracy compared to the Grey(1,1) model with a MAPE value of 5.390541% and a Posterior Error Ratio of 0.190644. These results show that the Grey Markov(1,1) model is more accurate than the Markov(1,1) mode, and that this method (Grey Markov(1,1)) is very good for forecasting with little data.

A physics-informed knowledge distillation model with spatial–temporal attention for energy consumption pre-assessment in sustainable additive manufacturing

To achieve sustainable additive manufacturing (AM), a novel data-driven model termed, physics-informed knowledge distillation (PKD), is proposed to accurately pre-assess the energy consumption at the product design stage. The energy consumption pre-assessment modeling is first constructed in light of the operating characteristics of AM technology. The modeling analysis indicates that physics data plays a pivotal role in determining energy consumption, while current data-driven approaches did not take full advantage of it. Hence, we design a physics-informed architecture with spatial–temporal attention in our PKD to jointly integrate the geometric and physical information from the product design process. Different from classical knowledge distillation, our teacher network and student network utilize an asymmetric physics-informed architecture respectively, encouraging the student to span the knowledge gap over its teacher. A knowledge distillation framework is further devised to enable our PKD to reduce computational costs while retaining pre-assessment performance. Extensive experimental findings show our PKD yields Root Mean Squared Error (RMSE), Mean Error Ratio (MER), and Total Error Rate (TER) as low as 300.16, 5.93%, 1.76%, respectively. It demonstrates that the proposed PKD outperforms state-of-the-art energy consumption pre-assessment methods. The well-distilled PKD is capable of providing referenced energy consumption value for designers and manufacturers to make decisions and manage energy for achieving sustainable additive manufacturing.

A new 3D phase unwrapping method by region partitioning and local polynomial modeling in abdominal quantitative susceptibility mapping.

Accurate phase unwrapping is a critical prerequisite for successful applications in phase-related MRI, including quantitative susceptibility mapping (QSM) and susceptibility weighted imaging. However, many existing 3D phase unwrapping algorithms face challenges in the presence of severe noise, rapidly changing phase, and open-end cutline. In this study, we introduce a novel 3D phase unwrapping approach utilizing region partitioning and a local polynomial model. Initially, the method leverages phase partitioning to create initial regions. Noisy voxels connecting areas within these regions are excluded and grouped into residual voxels. The connected regions within the region of interest are then reidentified and categorized into blocks and residual voxels based on voxel count thresholds. Subsequently, the method sequentially performs inter-block and residual voxel phase unwrapping using the local polynomial model. The proposed method was evaluated on simulation and in vivo abdominal QSM data, and was compared with the classical Region-growing, Laplacian_based, Graph-cut, and PRELUDE methods. Simulation experiments, conducted under different signal-to-noise ratios and phase change levels, consistently demonstrate that the proposed method achieves accurate unwrapping results, with mean error ratios not exceeding 0.01%. In contrast, the error ratios of Region-growing (N/A, 84.47%), Laplacian_based (20.65%, N/A), Graph-cut (2.26%, 20.71%), and PRELUDE (4.28%, 10.33%) methods are all substantially higher than those of the proposed method. In vivo abdominal QSM experiments further confirm the effectiveness of the proposed method in unwrapping phase data and successfully reconstructing susceptibility maps, even in scenarios with significant noise, rapidly changing phase, and open-end cutline in a large field of view. The proposed method demonstrates robust and accurate phase unwrapping capabilities, positioning it as a promising option for abdominal QSM applications.

Enhanced payload volume in the least significant bits image steganography using hash function

The art of message masking is called steganography. Steganography keeps communication from being seen by any other person. In the domain of information concealment within images, numerous steganographic techniques exist. Digital photos stand out as prime candidates due to their widespread availability. This study seeks to develop a secure, high-capacity communication system that ensures private interaction while safeguarding information from the broader context. This study used the four least significant bits for steganography to hide the message in a secure way using a hash function. Before steganography, the message is encrypted using one of the encryption techniques: Caesar cipher or Vigenère cipher. By altering only the least significant bits (LSBs), the changes between the original and stego images remain invisible to the human eye. The proposed method excels in secret data capacity, featuring a high peak signal-to-noise ratio (PSNR) and low mean square error (MSE). This approach offers significant payload capacity and dual-layer security (encryption and steganography).

A Novel Channel Estimation Framework in MIMO Using Serial Cascaded Multiscale Autoencoder and Attention LSTM with Hybrid Heuristic Algorithm.

In wireless communication, multiple signals are utilized to receive and send information in the form of signals simultaneously. These signals consume little power and are usually inexpensive, with a high data rate during data transmission. An Multi Input Multi Output (MIMO) system uses numerous antennas to enhance the functionality of the system. Moreover, system intricacy and power utilization are difficult and highly complicated tasks to achieve in an Analog to Digital Converter (ADC) at the receiver side. An infinite number of MIMO channels are used in wireless networks to improve efficiency with Cross Entropy Optimization (CEO). ADC is a serious issue because the data of the accepted signal are completely lost. ADC is used in the MIMO channels to overcome the above issues, but it is very hard to implement and design. So, an efficient way to enhance the estimation of channels in the MIMO system is proposed in this paper with the utilization of the heuristic-based optimization technique. The main task of the implemented channel prediction framework is to predict the channel coefficient of the MIMO system at the transmitter side based on the receiver side error ratio, which is obtained from feedback information using a Hybrid Serial Cascaded Network (HSCN). Then, this multi-scaled cascaded autoencoder is combined with Long Short Term Memory (LSTM) with an attention mechanism. The parameters in the developed Hybrid Serial Cascaded Multi-scale Autoencoder and Attention LSTM are optimized using the developed Hybrid Revised Position-based Wild Horse and Energy Valley Optimizer (RP-WHEVO) algorithm for minimizing the "Root Mean Square Error (RMSE), Bit Error Rate (BER) and Mean Square Error (MSE)" of the estimated channel. Various experiments were carried out to analyze the accomplishment of the developed MIMO model. It was visible from the tests that the developed model enhanced the convergence rate and prediction performance along with a reduction in the computational costs.

Economic Effects of Free Trade Zones based on Panel Data and Synthetic Control Methods

To analyze the impact of the establishment of FTZs on economic growth, the study quantitatively evaluates the economic effects of FTZs through the synthetic control method, taking Fujian FTZs as the unit of policy intervention, and combining the panel data with the placebo test method. The mechanism of economic growth effect of the establishment of FTZ is analyzed through the mechanism of mediating effect. The results show that the estimation results of the research method are better and more robust. The difference between the true and composite values of the control variable is relatively small, with the true and composite values of openness being 15.9226 and 15.4561, respectively, with a difference of 0.4665. In 2015, the difference in GDP between Fujian Province and synthetic Fujian Province reached 0.1, and the difference has significantly increased thereafter. After adding control variables, the trend of this difference over the years is consistent with that before adding control variables, i.e., the establishment of FTZ in Fujian Province can significantly promote economic growth. There is a 1/26 probability that other provinces and cities can obtain the same policy effect as Fujian Province. Compared to other synthesized results, the maximum root mean square error ratio in Fujian Province is 6.0, which is 2.8 higher than that in Jiangsu Province, indicating the maximum processing effect. Among them, the root-mean-square error ratios of Shandong Province and Chongqing Municipality are 2.2 and 2.3, respectively, while the root-mean-square error ratio of Fujian Province is 6.0. The research method can effectively analyze the economic effects of FTZ in Fujian Province and deepen the comprehensive understanding of the economic effects of FTZ.

Detection of breakage and impurity ratios for raw sugarcane based on estimation model and MDSC-DeepLabv3.

Broken cane and impurities such as top, leaf in harvested raw sugarcane significantly influence the yield of the sugar manufacturing process. It is crucial to determine the breakage and impurity ratios for assessing the quality and price of raw sugarcane in sugar refineries. However, the traditional manual sampling approach for detecting breakage and impurity ratios suffers from subjectivity, low efficiency, and result discrepancies. To address this problem, a novel approach combining an estimation model and semantic segmentation method for breakage and impurity ratios detection was developed. A machine vision-based image acquisition platform was designed, and custom image and mass datasets of cane, broken cane, top, and leaf were created. For cane, broken cane, top, and leaf, normal fitting of mean surface densities based on pixel information and measured mass was conducted. An estimation model for the mass of each class and the breakage and impurity ratios was established using the mean surface density and pixels. Furthermore, the MDSC-DeepLabv3+ model was developed to accurately and efficiently segment pixels of the four classes of objects. This model integrates improved MobileNetv2, atrous spatial pyramid pooling with deepwise separable convolution and strip pooling module, and coordinate attention mechanism to achieve high segmentation accuracy, deployability, and efficiency simultaneously. Experimental results based on the custom image and mass datasets showed that the estimation model achieved high accuracy for breakage and impurity ratios between estimated and measured value with R2 values of 0.976 and 0.968, respectively. MDSC-DeepLabv3+ outperformed the compared models with mPA and mIoU of 97.55% and 94.84%, respectively. Compared to the baseline DeepLabv3+, MDSC-DeepLabv3+ demonstrated significant improvements in mPA and mIoU and reduced Params, FLOPs, and inference time, making it suitable for deployment on edge devices and real-time inference. The average relative errors of breakage and impurity ratios between estimated and measured values were 11.3% and 6.5%, respectively. Overall, this novel approach enables high-precision, efficient, and intelligent detection of breakage and impurity ratios for raw sugarcane.

Assessment of drying characteristics for whole and sliced red ginseng using short‐wave infrared spectroscopy and mathematical model

AbstractMonitoring the moisture content is crucial for red ginseng quality control. In this study, we established a moisture content prediction model using partial least squares regression (PLSR) based on short‐wave infrared (SWIR) data of different ginseng types (whole and sliced) at various drying temperatures and times. The model parameters, including the R2 values, ratio of prediction deviation, and range error ratio, showed that the developed model was robust. Sliced ginseng showed a constant rate period, followed by a falling rate period, whereas whole ginseng showed only a falling rate period. The falling‐rate period coincided with the formation of a dry zone on the sample surface in the moisture distribution map, indicating that monitoring the moisture distribution map can predict the onset of the falling‐rate period. These results suggest that the SWIR technique is a promising nondestructive method for evaluating the drying process of red ginseng.Practical applicationsMonitoring the moisture content of red ginseng is essential to prevent microbial spoilage during transportation. Traditional monitoring of water content is destructive and time‐consuming. Therefore, using hyperspectral imaging, we aimed to develop an efficient moisture prediction model for the drying process of red ginseng. We believe that our study makes a significant contribution to non‐destructive and rapid quality evaluation in red ginseng production.

Wavelet transform based image enhancement: A noise reduction approach

<span lang="EN-US">This paper introduces a new thresholding function that mixes the soft thresholding functions and Smoothly Clipped Absolute Deviation (SCAD) for denoising images using the decimated wavelet transform technique, which is widely popular in various applications. The proposed method is applied to denoise noisy images contaminated with additive white Gaussian noise, employing the Top rule. The efficiency of this new thresholding function is also evaluated within the context of the Translation Invariant method. The results are compared with existing methods such as SCAD, soft thresholding, and the Wiener filter-based denoising approach. Parameters such as Root Mean Square Error (RMSE) and Peak Signal Noise Ratio (PSNR) are employed to assess the quality of denoising.</span>

Nanoparticle Incorporation to Enhance Titanium Alloy Electric Discharge Machining Capabilities

The primary objective of this study was to examine the effects of the Powder-blended Micro Electric Discharge Machining (PMEDM) technique on micromachining applications, specifically when sea water is employed as the dielectric medium. Tiny apertures with a width of 200µm were punctured over Ti-6Al-4V plates. In the initial round of experimentation, the machining performance was evaluated by subjecting sea water to various process variables without the inclusion of any other ingredients. The effects of input variables, including electrode material, hole voltage, current, Pulse-on-time, and Duty factor, on the Material Removal Rate (MRR), Tool Wear Rate (TWR), Overcut (OC), Circularity Error (CE), and Taper Ratio (TR) were analysed by conducting tests in accordance with Taguchi's L18 plan design. The method employed to ascertain the optimal parametric configuration for multi-objective enhancement involved utilising the strategy of soliciting inclination based on similarity to an ideal arrangement. The present study aimed to investigate the effects of foreign materials on the dielectric-based miniature electrical discharge machining (EDM) process in sea water. This was achieved by utilising powders with varying weight concentrations and molecule sizes, including non-conductive (Al2O3), semi-conductive (SiC), and conductive (Al) powders. The experimental setup ensured that the variable boundaries were maintained at their ideal settings. The results indicate that the choice of tool has a notable influence on the functioning of micro EDM when sea water is employed as the dielectric, without the inclusion of extraneous particles. The performance metric of multi-objective execution in PMEDM is influenced by the conductivity of supplementary compounds. An 83.18 percent increase in Material Removal Rate (MRR) was observed when SiC added compounds were utilised in Powder Mixed Electrical Discharge Machining (PMEDM). Conversely, there was a drop in Tool Wear Rate (TWR) by 36.42 percent, Open Circuit voltage (OC) by 21.48 percent, Current Efficiency (CE) by 45.15 percent, and Tool Roughness (TR) by 22.87 percent.

Denoising of Raman Spectra Using a Neural Network Based on Variational Mode Decomposition, Empirical Wavelet Transform, and Encoder-Bidirectional Long Short-Term Memory

Raman spectroscopy has been widely applied in numerous fields including bioanalysis, disease diagnosis, and molecular recognition, owing to its unique advantages of being non-invasive, rapid, and highly specific. However, the acquisition of Raman spectral data is often susceptible to various noise interferences, such as shot noise from the internal detector and dark current noise from the instrument. As a result, the weak Raman signals typically become arduous to discern, which affects the localization and identification of characteristic spectral peaks. This study investigates variational mode decomposition (VMD), empirical wavelet transform (EWT), and integrated encoder-bidirectional long short-term memory (EBiLSTM) modules to propose a neural network algorithm for adaptive denoising of Raman spectra. By combining VMD and EWT, the Raman spectra are decomposed into several sub-sequences, which solves the problem of mode mixing between high-frequency signals and noise in empirical mode decomposition, and significantly reduces the complexity of the original Raman spectral lines. The correlation coefficient between each modal component and the original signal is calculated along with the zero-crossing rate index to categorize the noise and signal sequences. Leveraging the linear differences between the ideal spectral lines and the noisy spectral curves, an encoder-bidirectional long short-term memory (EBiLSTM) denoising network is constructed for hierarchical denoising to extract valid spectral feature information from the high-frequency components, realizing refined adaptive denoising of the Raman spectra. Industry standard objective evaluation metrics on the signal-to-noise ratio and root mean square error are utilized to conduct simulation experiments comparing state-of-the-art algorithms, including empirical mode decomposition, VMD, sliding window averaging, and wavelet thresholding. The experimental results demonstrate that the Raman spectral denoising algorithm combining variational mode decomposition and neural networks improves the denoising performance by 13.38% to 72%, exhibiting higher accuracy and reliability.

Si Mach-Zehnder Modulator for PAM-4, QAM-OFDM, and DMT Transmission at C-Band

A silicon (Si) Mach-Zehnder modulator (MZM) is demonstrated for 10-km SMF Link at C-band (1550 nm), which allows the pre-emphasized 4-level pulse amplitude modulation (PAM-4) data transmission at 50 GBaud (100 Gbit/s) per channel. By employing the pre-emphasis to the PAM-4 data over different links, the channel dispersion-induced power fading can be compensated. The PAM-4 data with improved spectral usage efficiency in limited bandwidth is employed to encode the Si MZM, and the maximal transmission capacities of 50 GBaud for back-to-back (BtB), 50 GBaud for 2-km SMF, and 26 GBaud for 10-km DSF under KP4-FEC criterion are respectively obtained for providing 100/100/52 Gbit/s data transmission with qualified bit error ratios of 1.5×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> /2×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> /1.4×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> . The penalties on the sensitivity of the receiving power are 0.32 dB for 50-GBaud PAM-4 data after 2-km-SMF link and 0.64 dB for 26-GBaud PAM-4 data over 10-km DSF as compared to the BtB case. Such a scheme takes advantages including the lower power consumption with less circuit complexity than traditional push-pull driven operation, which certifies the 50-GBaud PAM-4 data capability per channel for inter-data-center applications. In addition, this Si MZM device can also modulate the pre-emphasized 16-QAM OFDM data at 140 Gbit/s and the bit-loaded DMT data at 153 Gbit/s.

Optimizing Instrument Transformer Performance through Adaptive Blind Equalization and Genetic Algorithms

In real-world scenarios, deviations in the frequency response of instrumentation transformers can lead to distorted harmonic measurements, highlighting the critical role harmonic measurement plays in assessing power quality. The blind channel equalization technique offers a potential solution to improve the frequency response of a large number of instrumentation transformers already installed in substations. These transformers were designed to accurately measure only the fundamental phasor component. Therefore, in order to use them for harmonic phasor measurement, methodologies for reducing frequency distortion must be applied. In this work, we propose a novel approach to improve the frequency response of the instrument transformer using adaptive blind equalization. The blind technique can compensate for distortions caused by voltage and current transducers without requiring prior knowledge of input signals or circuit characteristics. The proposed methodology uses a Linear Prediction Filter to convert the colored noise present at the channel output into white noise. Furthermore, a genetic algorithm is used to find a pole to cancel possible zeroes present in the frequency response of some transducers. The main advantage of blind equalization with the genetic algorithm is its independence, operating without clear information about the channel or the input signal. Through extensive experimentation, we demonstrate the effectiveness of the proposed methodology in significantly reducing the absolute error in ratio and phase caused by current and voltage transformers. Simulated and laboratory experiments are presented in this paper.

Employing higher density lower reliability weather data from the Global Historical Climatology Network monitors to generate serially complete weather data for watershed modelling

AbstractHydrological models require complete and accurate weather data time series to represent watershed‐scale responses adequately. The Global Historical Climatology Network (GHCN) is the most comprehensive weather database used in hydrological modelling studies globally. Since higher‐density, lower‐reliability precipitation measurements from private citizens collected by the Community Collaborative Rain, Hail, and Snow (CoCoRaHS) network data were integrated into the GHCN, hydrological modellers in the United States have access to a much greater amount of weather data. However, the benefit of using CoCoRaHS data has not been assessed. The objectives of this work were to develop a method for generating a complete weather data time series based on the combination of data from multiple GHCN monitors and to assess several methods for the estimation of missing weather data. Weather data from GHCN monitors located within a specific radius of a watershed were obtained and interpolated using three estimation methods (Inverse Distance Weighting (IDW), Inverse Distance and Elevation Weighting (IDEW) and Closest Station), creating a seamless time series of weather observations. To evaluate the performance of the methodologies, weather data obtained from each estimation method was used to force the Soil and Water Assessment Tool (SWAT) and Thornthwaite‐Mather models for 21 US Department of Agriculture‐Conservation Effects Assessment Project watersheds in different climate regions to simulate daily streamflow for 2010–2021. Except for three watersheds, all of the SWAT models had Nash‐Sutcliffe Efficiency above 0.5, the ratio of the root mean square error to the standard deviation of observations below 0.7, and percent bias from −25% to 25% with a satisfactory performance rating. IDEW and IDW performed similarly, and the Closest Station method resulted in the poorest streamflow simulation. A comparison with published SWAT model results further corroborated improved model performance using novel combined GHCN data with all Closest Station, IDW and IDEW methods.

The Reference Wideband Inductive Current Transformer

The aim of this paper is to show that the developed inductive current transformer may ensure the required wideband transformation accuracy and it may be applied, as the reference source, in the measuring system for the evaluation of the transformation accuracy of inductive current transformers for harmonics of distorted current. This device ensures 5 A and 1 A RMS secondary currents to provide the opportunity to use the differential measuring setup. Such solutions are characterized by the significantly reduced measurement uncertainty in relation to the comparative measurements made between two current/voltage channels. The problems required to ensure the high wideband transformation accuracy, including the self-generation phenomenon of the low order higher harmonics to the secondary current and a too-low frequency range of operation, were overcome in the design process. The values of its ratio error and the phase displacement of the developed reference wideband inductive current transformer did not exceed ±0.2%/° up to 1 kHz, ±0.4%/° from 1 kHz up to 1.5 kHz and ±0.5%/° from 1.5 kHz up to 3 kHz, as is required to perform the test procedure in accordance with the optional requirements for the inductive current transformers defined in the new edition of the standard IEC 61869-1.

Image Steganography Using LSB and Hybrid Encryption Algorithms

In today’s era of widespread web technology and cloud computing, ensuring data security has become a crucial concern across various industries. Instances of data breaches and vulnerabilities in cloud storage have emphasized the need for robust data protection and communication protocols, particularly in sectors like social media, military, and research. This research proposes a Multi-Level Steganography (MLS) algorithm that employs two encryption algorithms, AES and Blow-Fish, to secure the cover image and embed encryption keys as key images within the stego image. The proposed MLS algorithm incorporates a robust pixel randomization function to enhance the security of the encrypted data. Experimental results demonstrate that the proposed algorithm effectively protects data with high Peak Signal-to-Noise Ratio (PSNR) and low Mean Square Error (MSE) values, ensuring superior image quality, reliable encryption, and decryption of secret messages. The utilization of hybrid encryption with AES and BlowFish algorithms further strengthens the algorithm’s security by augmenting the complexity of the encryption process.