Relative Error Margin Research Articles

A novel method for correcting dew point protection data of heavy-duty diesel vehicles based on random forest

On-board diagnostics (OBD) remote monitoring technology is essential for continuously tracking emissions from heavy-duty diesel vehicles (HDDVs) in operation. However, the dew point protection (DPP) mechanism of nitrous oxides (NOx) sensors frequently results in invalid measurement data, impeding effective NOx emissions monitoring. Current research has yet to offer a practical solution to this challenge. To bridge this gap, a novel machine learning-driven approach has been introduced to correct faulty data. This method focuses on using characteristic data extracted from remote monitoring records to train a NOx concentration prediction model. Consequently, predicted values replace the inaccurate measurements triggered by DPP, enabling self-correction of NOx measurements. To validate the effectiveness of this approach, field tests were conducted on four HDDVs. Among the three compared prediction models, the Random Forest (RF) model demonstrated superior performance on the test dataset, achieving an impressive average R2 value of 0.706, while maintaining low RMSE and MAE values of 0.0335 and 0.0199, respectively (normalized). Post-correction analysis indicated that despite DPP being active for only 10–17% of each month, it accounts for a significant 20–50% of NOx emissions, with a relative error margin of less than 12.3% in our estimation. This research holds pivotal importance in enhancing the quality of remote monitoring data (RMD), thereby enabling seamless and efficient emissions tracking for HDDVs.

Numerical and experimental studies on the heat transfer characteristics and process optimization of the billet soaking furnace

The precision of furnace temperature control in the soaking furnace is critical to the quality of billet heating. This paper develops a three-dimensional(3D) mathematical model of the heating process of steel billets in the soaking furnace, which can be monitored online in real time. The accuracy of the model was verified by black-box experiments. A comparative analysis of the model’s simulated and experimental data revealed a hit rate exceeding 70 % (with a relative error margin within ± 5 %). Leveraging this mathematical model, we propose a direct optimization strategy that effectively saves 24 % of heating time, accelerates the process of achieving temperature uniformity within the billet, and boosts overall heating effectiveness. This approach offers potential for broader application in optimizing the heating processes of various materials.

Binomial Confidence Intervals for Rare Events: Importance of Defining Margin of Error Relative to Magnitude of Proportion

Confidence interval performance is typically assessed in terms of two criteria: coverage probability and interval width (or margin of error). In this article, we assess the performance of four common proportion interval estimators: the Wald, Clopper-Pearson (exact), Wilson and Agresti-Coull, in the context of rare-event probabilities. We define the interval precision in terms of a relative margin of error which ensures consistency with the magnitude of the proportion. Thus, confidence interval estimators are assessed in terms of achieving a desired coverage probability whilst simultaneously satisfying the specified relative margin of error. We illustrate the importance of considering both coverage probability and relative margin of error when estimating rare-event proportions, and show that within this framework, all four interval estimators perform somewhat similarly for a given sample size and confidence level. We identify relative margin of error values that result in satisfactory coverage while being conservative in terms of sample size requirements, and hence suggest a range of values that can be adopted in practice. The proposed relative margin of error scheme is evaluated analytically, by simulation, and by application to a number of recent studies from the literature.

FruitPhenoBox – a device for rapid and automated fruit phenotyping of small sample sizes

BackgroundFruit appearance of apple (Malus domestica Borkh.) is accession-specific and one of the main criteria for consumer choice. Consequently, fruit appearance is an important selection criterion in the breeding of new cultivars. It is also used for the description of older varieties or landraces. In commercial apple production, sorting devices are used to classify large numbers of fruit from a few cultivars. In contrast, the description of fruit from germplasm collections or breeding programs is based on only a few fruit from many accessions and is mostly performed visually by pomology experts. Such visual ratings are laborious, often difficult to compare and remain subjective.ResultsHere we report on a morphometric device, the FruitPhenoBox, for automated fruit weighing and appearance description using computer-based analysis of five images per fruit. Recording of approximately 100 fruit from each of 15 apple cultivars using the FruitPhenoBox was rapid, with an average handling and recording time of less than eleven seconds per fruit. Comparison of fruit images from the 15 apple cultivars identified significant differences in shape index, fruit width, height and weight. Fruit shape was characteristic for each cultivar, while fruit color showed larger variation within sample sets. Assessing a subset of 20 randomly selected fruit per cultivar, fruit height, width and weight were described with a relative margin of error of 2.6%, 2.2%, and 6.2%, respectively, calculated from the mean value of all available fruit.ConclusionsThe FruitPhenoBox allows for the rapid and consistent description of fruit appearance from individual apple accessions. By relating the relative margin of error for fruit width, height and weight description with different sample sizes, it was possible to determine an appropriate fruit sample size to efficiently and accurately describe the recorded traits. Therefore, the FruitPhenoBox is a useful tool for breeding and the description of apple germplasm collections.

Transfer learning-based deep learning models for proton exchange membrane fuel remaining useful life prediction

Proton exchange membrane fuel cells (PEMFCs) offer power generation capabilities for diverse applications including commercial enterprises, industrial sectors, and residential technologies. Nevertheless, the comprehensive integration of PEMFC applications could be improved by challenges related to degradation and durability. The imperative development of efficient performance prognostic models assumes a pivotal role in the prognosis of remaining useful life (RUL), health monitoring, and effective utilization of PEMFCs. This paper centers on the prognostication of critical components within PEMFCs and introduces a transfer learning approach based on variational autoencoder and bi-directional long short-term memory with an attention mechanism (Bi-LSTM-AM) model. This approach combines feature fusion, knee-point detection, and a sophisticated deep-learning-based predictive model. Notably, incorporating the variational autoencoder as the framework for feature fusion introduces a novel perspective previously unexplored. Identifying the knee point and knowing the start point on the training data, facilitates optimized parameter computation. The application of transfer learning facilitates the transfer of optimal model parameters and weights from a source to a target dataset. Conclusively, the estimation of stack voltage degradation and real-time RUL prediction based on the test dataset is executed by implementing our proposed method. The stack voltage prediction findings showcase the Bi-LSTM-AM model’s superior performance relative to comparison models. The proposed online rolling prediction model, utilizing a sliding window technique for RUL prediction, yields significantly enhanced accuracy, culminating in a relative error margin ranging from approximately 1.69% to 5.04%.

Oxygen-bearing organic molecules in comet 67P’s dusty coma: First evidence for abundant heterocycles

The puzzling complexity of terrestrial biomolecules is driving the search for complex organic molecules in the interstellar medium (ISM) and serves as a motivation for many in situ studies of reservoirs of extraterrestrial organics, from meteorites and interplanetary dust particles to comets and asteroids. Comet 67P/Churyumov-Gerasimenko (67P), the best-studied comet to date, has been visited and accompanied for 2 yr by the European Space Agency’s Rosetta spacecraft. Around 67P’s perihelion and under dusty conditions, the high-resolution mass spectrometer on board Rosetta has provided a spectacular glimpse into this comet’s chemical complexity. For this work, we analyzed the O-bearing organic volatiles in unprecedented detail. Through a comparison of 67P’s inventory with molecules detected in the ISM, in other comets, and in soluble organic matter extracted from the Murchison meteorite, we also highlight the (pre)biotic relevance of different chemical groups of species. We report first evidence for abundant extraterrestrial O-bearing heterocycles (with abundances relative to methanol often on the order of 10% and a relative error margin of 30–50%) and various representatives of other molecule classes, such as carboxylic acids and esters, aldehydes, ketones, and alcohols. As with the pure hydrocarbons, some hydrogenated forms seem to be dominant over their dehydrogenated counterparts. An interesting example is tetrahydrofuran, as it might be a more promising candidate for searches in the ISM than the long-sought furan. Our findings not only support and guide future efforts to investigate the origins of chemical complexity in space, but they also strongly encourage the study, in the laboratory as well as by modeling, of such topics as the ratios of unbranched versus branched species and hydrogenated versus dehydrogenated species in astrophysical ice analogs.

The Influence of Filament Orientation on Tensile Stiffness in 3D Printed Structures-Numerical and Experimental Studies.

The present study provides a thorough analysis of the influence of filament orientation on the tensile stiffness of 3D-printed structures. This exploration employs a combination of numerical simulations and experimental trials, providing an extensive understanding of additive manufacturing, particularly 3D printing. This process involves layer-by-layer material deposition to produce three-dimensional objects. The examination specifically targets PLA-based 3D printed structures created using Fused Filament Fabrication (FFF) technology and subjects them to rigorous evaluations using a universal tensile testing machine. Additionally, this approach combines Representative Volume Element (RVE) and Classical Lamination Theory (CLT) techniques to extrapolate the mechanical properties of the test material. Although the initial methodology faces challenges in determining the shear modulus with precision, an in-depth investigation results in enhanced accuracy. Furthermore, this study introduces a parametric RVE numerical method, demonstrating its resilience in handling sensitivity to shear modulus. A comparative study of results derived from both the analytical methods and experimental trials involving five series of samples with varied layups reveals that the newly proposed numerical method shows a stronger correlation with the experimental outcomes, delivering a relative error margin of up to 8%.

Fractal Geometric Model for Statistical Intermittency Phenomenon.

The phenomenon of intermittency has remained a theoretical concept without any attempts to approach it geometrically with the use of a simple visualization. In this paper, a particular geometric model of point clustering approaching the Cantor shape in 2D, with a symmetry scale θ being an intermittency parameter, is proposed. To verify its ability to describe intermittency, to this model, we applied the entropic skin theory concept. This allowed us to obtain a conceptual validation. We observed that the intermittency phenomenon in our model was adequately described with the multiscale dynamics proposed by the entropic skin theory, coupling the fluctuation levels that extended between two extremes: the bulk and the crest. We calculated the reversibility efficiency γ with two different methods: statistical and geometrical analyses. Both efficiency values, γstat and γgeo, showed equality with a low relative error margin, which actually validated our suggested fractal model for intermittency. In addition, we applied the extended self-similarity (E.S.S.) to the model. This highlighted the intermittency phenomenon as a deviation from the homogeneity assumed by Kolmogorov in turbulence.

Comparison of Analytical and Numerical Methods for PMSG Design Applied to D-Type Wind Generators

The objective of this work is to perform a comparative study between analytical and numerical methods (implemented via 2-D and 3-D FEM software) in 3.88 MVA, 710 V, 145 Hz permanent magnet synchronous generator (PMSG) design with a focus on wind power generation, for D-Type wind turbines. For this purpose an analytical solving method is proposed using NdFeB surface permanent magnet rotor. After this step the analytical results were compared with the numerical results and showed results with average relative error margin of about 3% (2-D simulation) and average absolute error of 37.3 mT (3-D simulation). In the second part of the design, AlNiCo PM were used in the rotor, however, the analytical and numerical results showed high average error, making the previous method, used for NdFeB PM, not suitable, requiring changes in the method. It is proposed then, a readjustment of the PM volume calculation, through analytical and numerical methodology. For the development of the method the main contributions were the modification of the table of speeds and frequencies from 60 Hz to 145 Hz, the creation of an extension parameter for the tip of the stator tooth to refine the airgap flux density calculation and the analytical solution of the rotor design when using AlNiCo PM. After these changes, the results obtained with a calculated average error margin of about 4% were satisfactory. The analytical and numerical results were commented and presented at the end of this work for the validation of the methodologies for both of PM (NdFeB and AlNiCo).

Data-driven model for divertor plasma detachment prediction

We present a fast and accurate data-driven surrogate model for divertor plasma detachment prediction leveraging the latent feature space concept in machine learning research. Our approach involves constructing and training two neural networks: an autoencoder that finds a proper latent space representation (LSR) of plasma state by compressing the multi-modal diagnostic measurements and a forward model using multi-layer perception (MLP) that projects a set of plasma control parameters to its corresponding LSR. By combining the forward model and the decoder network from autoencoder, this new data-driven surrogate model is able to predict a consistent set of diagnostic measurements based on a few plasma control parameters. In order to ensure that the crucial detachment physics is correctly captured, highly efficient 1D UEDGE model is used to generate training and validation data in this study. The benchmark between the data-driven surrogate model and UEDGE simulations shows that our surrogate model is capable of providing accurate detachment prediction (usually within a few per cent relative error margin) but with at least four orders of magnitude speed-up, indicating that performance-wise, it has the potential to facilitate integrated tokamak design and plasma control. Comparing with the widely used two-point model and/or two-point model formatting, the new data-driven model features additional detachment front prediction and can be easily extended to incorporate richer physics. This study demonstrates that the complicated divertor and scrape-off-layer plasma state has a low-dimensional representation in latent space. Understanding plasma dynamics in latent space and utilising this knowledge could open a new path for plasma control in magnetic fusion energy research.

Research on the lubricated characteristics of journal bearing based on finite element method and mixed method

In order to study the lubricated characteristics of journal bearing, a new numerical method including finite element method (FEM) and mixed method is provided in this paper. Based on the FEM and variation principle, the equivalent integral form of static Reynolds equation is proposed and solved accurately by an iso-parametric element with eight nodes. Moreover, the mixed method including dichotomy and secant method is chosen to calculate the equilibrium position of journal bearing. The calculated results obtained by this new model are validated by comparing with those results calculated by previous model including finite difference method (FDM). The maximum margin of relative error is less than 1.6%. Furthermore, the effects of diameter, lubricant viscosity and external load on the equilibrium position and inner pressure are further researched. Besides, the stiffness and damping coefficients of journal bearing is preliminarily calculated. The theoretical model and calculated results can provide useful references for the design of journal bearing and rotor coupled system.

Zinc Variability in Pecan Orchards: Implications for Leaf Sampling and Nutrient Recommendations

Analysis of composite pecan leaf samples typically used to determine need for nutrient applications does not account for variability among trees in the sampled area. To account for this unmeasured variability, pecan orchard block nutrient standards are greater than actual single tree nutrient requirements. In 2018 and 2019, we measured variability in a pecan orchard block by evaluating nutrient status of all trees in a study area consisting of two cultivars (Wichita and Western) grafted on open-pollinated ‘Ideal’ seedlings. Foliar zinc (Zn) coefficient of variation (cv) ranged from 0.186 to 0.255 within individual cultivars and years but was as high as 0.30 when combining cultivars within a year. The ‘Western’ cultivar had higher foliar Zn concentrations than ‘Wichita’, but Zn concentrations were not consistently associated with other leaf nutrient levels, soil Zn status, or other soil properties. Using observed foliar Zn variability, we determined that it is necessary to sample 35 trees for a composite sample to achieve a relative margin of error of 10% and 95% confidence level in a pecan orchard block with more than 1000 trees. We developed field scale foliar Zn recommendations based on individual tree research that indicates a minimum acceptable leaf Zn concentration of ≈15 mg·kg–1 is needed to maintain optimal photosynthetic function in Zn chelate fertigated pecan trees. Assuming a Zn cv of 0.30 and a composite sample comprised of leaves from 35 trees, the minimum acceptable orchard block Zn level to ensure that less than 5% of trees had suboptimal levels of Zn was 27.6 mg·kg–1. An orchard block Zn level below 23.4 mg·kg–1 indicates that more than 5% of trees in the block had suboptimal foliar Zn concentrations.

Morbidity Patterns and Health Needs of the Geriatric patients in a Tertiary Care Hospital

Background: The aged population of the world is increasing. Old age is associated with increased risk of morbidity and mortality. This puts burden on health services, both financially and in terms of workload. Aim: To determine the morbidity patterns and health needs of this age group. Methods: A descriptive cross¬-sectional study was conducted among the geriatric patients presenting in the outdoor department of services hospital Lahore. The sample size was 220, calculated using the WHO statistical software S size. Nonprobability convenient sampling technique was used. The confidence interval was 95% and relative margin of error was 10%. Chi-square test was used to check the statistical significance. Results: Out of 220 study subjects, 13 had only 1 morbid condition and 137 were suffering from more than 3 morbidities. Hypertension was the most prevalent morbidity (65.9%), followed by eye cataract (61.18%). Other morbid conditions in descending order were diabetes mellitus (47.27%), osteoarthritis (43.63%), coronary heart disease (38.18%), obesity (35.45%), respiratory diseases (30.90%), hearing impairment (25%). Significant association of morbidities with body mass index was observed. Almost 37.7% of respondents reported feeling of loneliness. Most of the geriatric patients were involved in religious activities (88.2%). Spectacles (55%) were the most frequently used supporting aids followed by walking sticks (26.4%). Conclusion: A high proportion of geriatric patients have more than 3 morbidities with hypertension being the most common. Body mass index and smoking were significant risk factors. Geriatrics showed prevalent psychological problems. Keywords: Geriatrics, Morbidity, Pattern, Health needs.

Rain Attenuation Study over an 18 GHz Terrestrial Microwave Link in South Korea

Absorption of microwave radio frequency signal by atmospheric rain and losses is prevalent at frequencies above 5 GHz. The functioning frequencies of 18 GHz are taken for the point-to-point microwave link system. This paper presents studies on rain attenuation at 18 GHz, which specifies minimum performance parameters for terrestrial fixed service digital radio communication equipment. It presents a 3.2 km experimental link at 18 GHz between Khumdang (Korea Telecom, KT station) and Icheon (National Radio Research Agency, RRA station). The received signal data for rain attenuation and the rain rate were collected at 10-second intervals over three year’s periods from 2013 to 2015. During the observation period, rain rates of about 50 mm/hr and attenuation values of 33.38 dB and 21.88 dB occurred for 0.01% of the time for horizontal and vertical polarization. This paper highlights the discussion and comparison of ITU-R P.530-16, Moupfouma, Silva Mello, and Abdulrahman models and proposed an attenuation prediction approach where it presents the relationship between theoretical specific rain attenuation as specified by ITU-R P.838-3, γ%p, and effective specific rain attenuation, γeff. Additionally, it studies 1-minute rain rates derived from higher time integration of 5-minute, 10-minute, 20-minute, 30-minute, and 60-minute instances which are obtained from experimental 1-minute rainfall amounts that are maintained by the Korea Meteorological Administration (KMA). The effectiveness of the proposed approach is further analyzed for 38 and 75 GHz links which shows better prediction capability. Particularly, in an 18 GHz link under horizontal polarization, ITU-R P. 530-16 shows the relative error margin of 71%, 60%, and 38% where as 64%, 49%, and 42% were obtained under vertical polarization for 0.1%, 0.01%, and 0.001% of the time, respectively. The limitation of research lies on the experimental system that is set up in only one location; however, the preliminary results indicate the application of a suitable 1-minute rain attenuation model for a specific site. The method provides useful information for microwave engineers and researchers in making decisions over the choice of the most suitable rain attenuation prediction for terrestrial links operating in the South Korea region, particularly for lower frequency ranges.

A Practical Electronic Health Record-Based Dry Weight Supervision Model for Hemodialysis Patients.

Objective: Dry Weight (DW) is a typical hemodialysis (HD) prescription for End-Stage Renal Disease (ESRD) patients. However, an accurate DW assessment is difficult due to the complication of body components and individual variations. Our objective is to model a clinically practicable DW estimator. Method: We proposed a time series-based regression method to evaluate the weight fluctuation of HD patients according to Electronic Health Record (EHR). A total of 34 patients with 5100 HD sessions data were selected and partitioned into three groups; in HD-stabilized, HD-intolerant, and near-death. Each group’s most recent 150 HD sessions data were adopted to evaluate the proposed model. Results: Within a 0.5 kg absolute error margin, our model achieved 95.44%, 91.95%, and 83.12% post-dialysis weight prediction accuracies for the HD-stabilized, HD-intolerant, and near-death groups, respectively. Within a 1%relative error margin, the proposed method achieved 97.99%, 95.36%, and 66.38% accuracies. For HD-stabilized patients, the Mean Absolute Error (MAE) of the proposed method was 0.17 kg ± 0.04 kg. In the model comparison experiment, the performance test showed that the quality of the proposed model was superior to those of the state-of-the-art models. Conclusion: The outcome of this research indicates that the proposed model could potentially automate the clinical weight management for HD patients. Clinical Impact: This work can aid physicians to monitor and estimate DW. It can also be a health risk indicator for HD patients.

Quality of life predictors for people living with HIV/AIDS in an impoverished region of Brazil.

Quality of life (QoL) is important for people living with HIV/AIDS, particularly as the disease progresses. This study evaluated the QoL of people living with HIV/AIDS (PLWHA), as well as its predictors, in one of the most impoverished regions of Brazil. This cross-sectional study was conducted with patients older than 18 years with HIV/AIDS from two specialized HIV/AIDS care centers in the city of Montes Claros between 2013 and 2014. Sample size was calculated considering the estimates of mean scores for various dimensions of the European Portuguese version of the World Health's Organization Quality of Life Instrument in HIV Infection (WHOQOL-HIV Bref). The following parameters were adopted: CI of 95%, estimated mean scores for QoL equal to 15, estimated variance for QoL scores equal to 16, and 5% relative margin of error. An increase of 20% was established to compensate for possible non-responses or losses, and correcting any design effect, adopting a deff equal to 2.0. Calculations revealed the need to interview at least 221 patients. Therefore, 226 patients living with HIV/AIDS were randomly selected. A total of 226 patients with mean age 43.7 years were evaluated: 51.8% men, 51.8% unemployed, 51.8% with low schooling level, 89.8% used antiretrovirals, and 43.3% experienced depression. Despite this, data indicated that 65% self-assessed their QoL as good and very good. Low QoL was associated with sociodemographic variables and some clinical variables. Depression was the main predictor of low QoL in all domains evaluated.

Integration of commercial CO2 capture plant with primary reformer stack of ammonia plant

The safety of primary reformers is essential to safe operation of large-scale petrochemical processes, especially when carbon dioxide is going to be recovered from an ammonia plant. In this study, a preliminary assessment is made to model an industrial stack as the stack gases are introduced into CO2 capture plant, during ammonia production. A CFD model was first developed in the absence of a commercial carbon dioxide recovery (CDR) unit to validate the model against industrial data under normal operation. At full capacity of the ammonia plan, the results provided by the CFD model match the measurement results well within about 3.87% margin of relative error. The calibrated model was then applied in combination with post-combustion, as part of the process, to verify the process safety constraints in reformer furnace. The effect of starting up and shutting down of CDR plant was explored in the event of emergency operation. From an operational view, in the event of startup or unplanned failure of the CO2 capture plant, the pressure fluctuations do not exceed the maximum allowable pressure of the firebox. Upon reaching the required operating conditions, both subsystems can be integrated operationally to continue production safely.

How Much Data are Good Enough? Using Simulation to Determine the Reliability of Estimating POMR for Resource-Constrained Settings.

Perioperative mortality rate (POMR) is a suggested indicator for surgical quality worldwide. Currently, POMR is often sampled by convenience; a data-driven approach for calculating sample size has not previously been attempted. We proposed a novel application of a bootstrapping sampling technique to estimate how much data are needed to be collected to reasonably estimate POMR in low-resource countries where 100% data capture is not possible. Six common procedures in low- and middle-income countries were analysed by using population database in New York and California. Relative margin of error by dividing the absolute margin of error by the true population rate was calculated. Target margin of error was ±50%, because this level of precision would allow us to detect a moderate-to-large effect size. Target margin of error was achieved at 0.3% sampling size for abdominal surgery, 7% for fracture, 10% for craniotomy, 16% for pneumonectomy, 26% for hysterectomy and 60% for C-section. POMR may be estimated with fairly good reliability with small data sampling. This method demonstrates that it is possible to use a data-driven approach to determine the necessary sampling size to accurately collect POMR worldwide.

A quantitative seismic prediction technique for the brittleness index of shale in the Jiaoshiba Block, Fuling shale gas field in the Sichuan Basin

In this paper, a quantitative seismic prediction technique of multi-parameter shale brittleness index suitable for the environment with complex structural stress was developed in order to confirm highly brittle intervals and favorable fracturing zones in the shale reservoirs in the Jiaoshiba Block, Fuling shale gas field, Sichuan Basin. Firstly, the effect of structural compression stress on the brittleness characteristics of rocks were figured out by analyzing structures, mineral composition, development degree of fractures in cores, well logging and seismic data comprehensively. Secondly, according to the Rickman formula, a new brittleness index prediction model based on Young's modulus, Poisson's ratio and shear modulus × density was established by introducing the shear modulus which reflects lateral shear stress–strain. Finally, the quantitative prediction technique of multi-parameter shale brittleness index suitable for the environment with complex structural stress was developed by virtue of the superiority of an elastic rock brittleness index method based on mineral composition to accurately calculate the brittleness index of a full hole. Field application shows that this technique is reliable, since its prediction results coincide with the calculated brittleness index of exploratory wells which are not used for modeling, with a relative error margin below 4%; and that the brittleness index of good shale of Upper Ordovician Wufeng Fm–Lower Silurian Longmaxi Fm in the Jiaoshiba Block increases from the top to the bottom and is stably distributed laterally. Particularly, the Wufeng–Longmaxi 11 is the highest in brittleness index, so it is the most favorable interval for penetrating and fracturing of horizontal wells.

Rain attenuation over terrestrial microwave links in South Korea

Absorption of radio frequency signal by atmospheric rain and losses is prevalent at frequencies above 10 GHz. This study presents studies on rain attenuation at 18 and 38 GHz microwave link systems, which specifies the minimum performance parameters for terrestrial fixed service digital radio communication. The 3.2 km experimental link at 18 and 38 GHz are used for studying rain propagation effects. The received signal data for rain attenuation and rain rate were collected at 10 s intervals over a 3-year period from 2013 to 2015. The record shows rain rate of about 50 mm/h and attenuation values of 33.38, 21.88 and 20.89 dB occurred for 0.01% of the time under 18 GHz horizontal, vertical and 38 GHz vertical polarisation, respectively. This study highlights the discussion and comparison of ITU-R P.530-16, Moupfouma, Da Silva Mello and Abdulrahman models. Relative error margin of about 71, 60, 38%; 64, 49, 42%; 3, 38, 42% were obtained for 0.1, 0.01, 0.001% of the time in 18 GHz horizontal, vertical and 38 GHz vertical polarisation, respectively, from ITU-R P. 530-16 which have been analysed along with suitable approach to characterise rain attenuation in microwave link whose efficiency have been compared with prominent rain attenuation models.