6.13Indicatornoisegainestimate.
.................. 92
6.14Indicatortrendslope........................ 92
6.15Indicatornoisegainslope.
....................
92
6.16Sigmoidprototype.
........................ 93
6.17
Calibration
set
with
approximated
using
evolutionary
opti-
mization............................... 96
6.18
Calibration
set
with
approximated
using
Trust
Region
with
simpleinitialguess......................... 96
6.19
Calibration
set
with
Trust
Region
residual
spectrum
validation
model.
............................... 98
6.20
Calibration
set
with
iteratively
approximated
sigmoid
model.
.
99
6.21
Calibration
set
with
sigmoid
approximate.
. . . . . . . . . . . 100
6.22Indicatordecomposition.
.....................102
6.23Indicatornoisegainestimate.
..................102
6.24
Indicatortrendslope..
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.102
6.25Indicatornoisegainslope.
....................102
6.26Sourcesplitteroverview.
.....................107
6.27Indicatordecomposition.
.....................108
6.28Indicatornoisegainestimate.
..................108
6.29
Indicatortrendslope..
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.108
6.30Indicatornoisegainslope.
....................108
6.31
Decomposition
of
synthetically
generated
dataset.
.
.
.
.
.
.
.
110
6.32
Decomposition
of
synthetically
generated
dataset.
.
.
.
.
.
.
.
111
6.33
Decomposition
of
synthetically
generated
dataset.
.
.
.
.
.
.
.
112
7.1
Indicator
parametrization
overview.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
116
7.2
Componentstateclassi.cation.
.................118
7.3
Case2MODrawanddecomposed.
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