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For a detailed description of the time-domain envelope modeling process, we refer to [37 39].
and are the path loss exponents, and are Gaussian distributed random variables modeling the shadow fading with each zero mean and variances and respectively, and and are the channel envelope modeling the channel fading.
These observations raise some concerns about the simplistic approach of applying 'standard datasets' of predictors in climate envelope modeling.
Most predictions on the consequences of global warming on marine biodiversity have been based either on climate envelope modeling or on inferences from highly artificial laboratory experiments, rather than from observations or manipulations conducted in real habitats.
The envelope modeling is done by linear prediction in the frequency domain [48, 49].
This happens because some information of the first microphone signal remains in the error signal, since the spectral envelope modeling is not perfect.
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RSPB and NERC supported the initial climatic envelope modelling work.
However, the restricted availability of mapped distributions for climatic envelope modelling and of long-term population monitoring data may restrict the scope for such developments in the immediate future.
Abiotic factors are often incorporated into the most simple modelling approaches (for example, climate envelope modelling) used to predict the future distributions of organisms [ 3].
Large-scale bioclimatic envelope model.
The envelope model for the crustal earthquake of Ho and Kawase (2007) is applied.
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