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The citizen averages are biased towards higher bloom intensity values than the averages of the expert observations, which can be seen in Figures 2, 3 and 4. One possible explanation, but not necessarily the only one, is revealed by comparing the percentages of 'no algae detected' (i.e. observation value '0') by citizen and expert observers.
Rather, as one draws ever larger sarpples, there is a chance that one will come up with an extremely large observation value (such as the 2.94 million value extrapolated from the Pareto distribution of Figure 1.2) that is so large relative to all previous observations that it 'blows' the sample mean to a much higher value.
The observation value of each event is x1, x2, …, x n respectively.
FDIA theory based on nonlinear model is much more complicated because the state variable and observation value has nonlinear relationship.
Firstly, replace indicator function in histogram by smooth kernel function, and then set estimation interval center as sample observation value.
The static solution uses the actual observation value of the lag period of each sequence to determine the next forecast value.
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Observation values were applied on the model.
With this monitoring infrastructure, more than ten million observation values are generated and stored every day.
The first moment, or mean, characterizes the sample's central tendency; it is the sum of observation values divided by the number of observations.
It is called log normal because if one takes logarithms of the observation values, the logarithmic values exhibit a symmetric normal distribution pattern.
The proposed framework is applied to optimize observation values, data weighting coefficients, and the location of sensors for a test problem.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com