Sentence examples for error varying from inspiring English sources

Hence the one way time transfer of the GPS is affected, inturn resulting in pseudorange error varying from few meters to tens of meters at zenith.

IOA returns the degree of model prediction error, varying between 0 and 1, with a higher value indicating better agreement between the model predictions and observations, while a lower value indicates worse agreement.

The conventional approaches for development of LC MS/MS methods are usually via trial and error, varying intentionally the experimental factors which is time consuming and interactions between experimental factors are not considered.

This sample size was calculated considering the estimation of a prevalence of 50%, 95% confidence intervals (95% CI), a sampling error varying from 4%to5%5%, and an design effect (deff) of 2. The sample of households was drawn in two stages.

The simulation experiment results are denoted in Figure10, indicating the phase errors varying with each parameter errors.

Examining reasonable random errors, we simulate a case of systematic errors varying linearly with a given parameter, where the number of unknown parameters is reduced to one for simplification.

For FPS errors varying from 10 to 25°, the misfit angle can range from 30 to 45° (Michael, 1991), indicating that stress inversion results with misfits lower than this are considered acceptable and that the stress field is approximately homogeneous.

The 630.0-nm 630.0-nmobservairglowat Shigaraki, Japan, by FP02–FP04 observations, 2009, give thermospheric wind velocities with ratdom errorShigaraki from 2 m s−1 to more than 50 m s−1.

The test set data reveal that SVM is able to predict damage intensity and damage location with errors varying from 0.28% to 4.57% and 0%to20.3%3%, respectively, when there is no noise in the data.

These characteristics grant to the modelling processes and methods, requirements associated with pre-processing techniques used to eliminate as much as possible the sampling errors, varying sampling density, and registration errors, i.e., find feasible modelling processes departing from unorganized 3D laser scanning point clouds.

Whereas the existing models predicted with relative errors varying between 10 and 127% (depending on product and pipeline conditions), the new developed model resulted in predictions within 24% accuracy for a wide range of scale-up conditions, which provides better reliability and a narrower range of predictions, more suitable for industrial scale up requirements.