I
AE
S In
t
er
na
t
io
na
l J
o
urna
l o
f
Art
if
icia
l In
t
ellig
ence
(
I
J
-
AI
)
Vo
l.
15
,
No
.
3
,
J
u
n
e
20
26
,
p
p
.
2081
~
2
0
9
1
I
SS
N:
2
2
5
2
-
8
9
3
8
,
DOI
: 1
0
.
1
1
5
9
1
/ijai.v
15
.i
3
.
p
p
2
0
8
1
-
2
0
9
1
2081
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
a
i
.
ia
esco
r
e.
co
m
Adv
a
nced
inf
eren
tial sta
tistics
and
da
ta mining
for
c
hlo
ro
phy
ll
distribut
io
n clust
ering
F
elix
Reba
1
,
T
o
ha
Sa
if
ud
in
2,
3
,
Rim
uljo
H
endra
di
4,
5
1
D
o
c
t
o
r
a
l
P
r
o
g
r
a
m
of
M
a
t
h
e
mat
i
c
s
a
n
d
N
a
t
u
r
a
l
S
c
i
e
n
c
e
s,
F
a
c
u
l
t
y
o
f
S
c
i
e
n
c
e
a
n
d
Te
c
h
n
o
l
o
g
y
,
U
n
i
v
e
r
si
t
a
s
A
i
r
l
a
n
g
g
a
,
S
u
r
a
b
a
y
a
,
I
n
d
o
n
e
si
a
2
D
e
p
a
r
t
me
n
t
o
f
M
a
t
h
e
m
a
t
i
c
s
,
F
a
c
u
l
t
y
o
f
S
c
i
e
n
c
e
a
n
d
T
e
c
h
n
o
l
o
g
y
,
U
n
i
v
e
r
si
t
a
s Airl
a
n
g
g
a
,
S
u
r
a
b
a
y
a
,
I
n
d
o
n
e
s
i
a
3
R
e
s
e
a
r
c
h
G
r
o
u
p
o
f
S
t
a
t
i
st
i
c
a
l
M
o
d
e
l
i
n
g
i
n
L
i
f
e
S
c
i
e
n
c
e
s,
F
a
c
u
l
t
y
o
f
S
c
i
e
n
c
e
a
n
d
Te
c
h
n
o
l
o
g
y
,
U
n
i
v
e
r
si
t
a
s
A
i
r
l
a
n
g
g
a
,
S
u
r
a
b
a
y
a
,
I
n
d
o
n
e
si
a
4
I
n
f
o
r
mat
i
o
n
S
y
st
e
ms St
u
d
y
P
r
o
g
r
a
m
,
F
a
c
u
l
t
y
o
f
S
c
i
e
n
c
e
a
n
d
Te
c
h
n
o
l
o
g
y
,
U
n
i
v
e
r
si
t
a
s
A
i
r
l
a
n
g
g
a
,
S
u
r
a
b
a
y
a
,
I
n
d
o
n
e
si
a
5
R
e
s
e
a
r
c
h
G
r
o
u
p
o
f
C
e
n
t
e
r
f
o
r
B
u
s
i
n
e
ss I
n
t
e
l
l
i
g
e
n
c
e
(
C
e
n
B
I
)
,
F
a
c
u
l
t
y
o
f
S
c
i
e
n
c
e
a
n
d
Te
c
h
n
o
l
o
g
y
,
U
n
i
v
e
r
si
t
a
s
A
i
r
l
a
n
g
g
a
,
S
u
r
a
b
a
y
a
,
I
n
d
o
n
e
si
a
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
No
v
8
,
2
0
2
4
R
ev
is
ed
Feb
13
,
2
0
2
6
Acc
ep
ted
Ap
r
20
,
2
0
2
6
Th
is
st
u
d
y
p
ro
p
o
se
s
a
n
i
n
teg
ra
ted
sta
ti
stica
l
fra
m
e
wo
rk
t
o
a
n
a
l
y
z
e
c
h
lo
ro
p
h
y
ll
d
istri
b
u
ti
o
n
i
n
m
a
rin
e
e
n
v
iro
n
m
e
n
ts
b
y
c
o
m
b
in
i
n
g
p
ro
b
a
b
i
li
ty
d
istri
b
u
ti
o
n
m
o
d
e
li
n
g
,
g
o
o
d
n
e
s
s
-
of
-
fit
(G
o
F
)
e
v
a
l
u
a
ti
o
n
,
a
n
d
m
a
c
h
in
e
lea
rn
in
g
-
b
a
se
d
c
lu
ste
rin
g
.
Ei
g
h
t
p
ro
b
a
b
il
i
ty
d
istri
b
u
ti
o
n
m
o
d
e
ls
—
h
a
lf
-
n
o
rm
a
l
,
i
n
v
e
rse
G
a
u
ss
ian
,
Ricia
n
,
Birn
b
a
u
m
–
S
a
u
n
d
e
rs,
Na
k
a
g
a
m
i,
e
x
trem
e
v
a
lu
e
,
t
l
o
c
a
ti
o
n
-
sc
a
le
,
a
n
d
sta
b
le
—
we
re
e
v
a
lu
a
ted
u
sin
g
o
b
s
e
rv
a
ti
o
n
a
l
c
h
lo
ro
p
h
y
ll
-
a
d
a
ta
fro
m
t
h
e
Co
p
e
rn
icu
s
M
a
ri
n
e
S
e
rv
ice
.
M
o
d
e
l
p
e
r
fo
rm
a
n
c
e
wa
s
a
ss
e
ss
e
d
th
ro
u
g
h
th
e
Ko
l
m
o
g
o
r
o
v
–
S
m
irn
o
v
(KS)
a
n
d
An
d
e
rso
n
–
Da
rli
n
g
(AD
)
G
o
F
tes
ts,
a
lo
n
g
with
fiv
e
sta
ti
stica
l
in
f
o
rm
a
ti
o
n
c
ri
teria
.
Th
e
re
su
lt
s
in
d
ica
te
t
h
a
t
t
h
e
i
n
v
e
rse
G
a
u
ss
ian
a
n
d
e
x
trem
e
v
a
lu
e
d
is
tri
b
u
t
io
n
s
c
o
n
siste
n
tl
y
o
ffe
re
d
t
h
e
b
e
st
sta
t
isti
c
a
l
fit
a
n
d
e
c
o
l
o
g
ica
l
re
lev
a
n
c
e
a
c
ro
ss
v
a
ry
in
g
sa
m
p
le
siz
e
s.
Cl
u
ste
rin
g
a
n
a
ly
sis
,
p
e
rf
o
rm
e
d
u
si
n
g
th
e
k
-
m
e
a
n
s
a
lg
o
rit
h
m
a
n
d
v
a
li
d
a
ted
v
ia
th
e
silh
o
u
e
tt
e
i
n
d
e
x
,
fu
r
th
e
r
c
o
n
f
irme
d
th
e
ro
b
u
stn
e
ss
o
f
th
e
se
two
m
o
d
e
ls
in
fo
rm
in
g
sta
b
le
a
n
d
we
ll
-
se
p
a
ra
ted
c
lu
ste
rs.
In
c
o
n
tras
t,
th
e
h
a
lf
-
n
o
r
m
a
l
d
istri
b
u
ti
o
n
s
h
o
we
d
p
o
o
r
p
e
rfo
rm
a
n
c
e
a
n
d
in
sta
b
i
li
ty
,
e
sp
e
c
ially
wit
h
sm
a
ll
e
r
sa
m
p
le
siz
e
s.
Th
e
p
r
o
p
o
se
d
tax
o
n
o
m
y
a
n
d
sp
a
ti
a
l
v
isu
a
li
z
a
ti
o
n
s
e
n
a
b
le
e
m
p
iri
c
a
l
c
las
sifica
ti
o
n
o
f
m
o
d
e
l
b
e
h
a
v
i
o
r
a
n
d
su
p
p
o
rt
i
n
teg
ra
ti
o
n
i
n
to
re
a
l
-
ti
m
e
m
a
rin
e
d
e
c
isio
n
su
p
p
o
rt
sy
ste
m
s
(DSS
)
fo
r
e
c
o
sy
ste
m
m
o
n
it
o
rin
g
.
O
v
e
ra
ll
,
t
h
e
stu
d
y
c
o
n
tri
b
u
tes
t
o
th
e
d
e
v
e
lo
p
m
e
n
t
o
f
a
c
c
u
ra
te,
d
a
ta
-
d
riv
e
n
a
n
a
l
y
ti
c
a
l
to
o
ls
th
a
t
a
id
s
u
sta
in
a
b
le
m
a
rin
e
re
so
u
rc
e
m
a
n
a
g
e
m
e
n
t,
a
li
g
n
e
d
wit
h
s
u
sta
in
a
b
le
d
e
v
e
l
o
p
m
e
n
t
g
o
a
l
(S
DG
)
1
4
o
n
m
a
rin
e
e
c
o
sy
ste
m
p
ro
tec
ti
o
n
.
K
ey
w
o
r
d
s
:
C
h
lo
r
o
p
h
y
ll
C
lu
s
ter
in
g
K
-
m
ea
n
s
Ma
r
in
e
en
v
ir
o
n
m
en
t
Pro
b
ab
ilit
y
d
is
tr
ib
u
tio
n
Sil
h
o
u
ette
in
d
ex
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
T
o
h
a
Saif
u
d
i
n
Dep
ar
tm
en
t o
f
Ma
th
em
atics,
Facu
lty
o
f
Scien
ce
a
n
d
T
ec
h
n
o
l
o
g
y
,
Un
i
v
er
s
itas
Air
lan
g
g
a
Su
r
ab
ay
a,
I
n
d
o
n
esia
E
m
ail:
to
h
asaif
u
d
in
@
f
s
t.u
n
air
.
ac
.
id
1.
I
NT
RO
D
UCT
I
O
N
Mo
d
elin
g
p
r
o
b
ab
ilit
y
d
is
tr
ib
u
t
io
n
s
in
en
v
ir
o
n
m
en
tal
s
tu
d
ies
,
p
ar
ticu
lar
ly
f
o
r
ch
lo
r
o
p
h
y
ll
d
ata
,
p
lay
s
a
cr
itical
r
o
le
in
u
n
d
er
s
tan
d
in
g
th
e
v
ar
iab
ilit
y
an
d
d
y
n
am
ic
s
o
f
n
atu
r
al
o
ce
an
o
g
r
ap
h
ic
p
h
en
o
m
e
n
a
s
u
ch
as
p
r
o
d
u
ctiv
ity
,
s
ea
s
o
n
al
b
lo
o
m
s
,
an
d
ca
r
b
o
n
cy
clin
g
p
r
o
ce
s
s
es
[
1
]
,
[
2
]
.
C
h
lo
r
o
p
h
y
ll
co
n
ce
n
tr
atio
n
is
a
k
ey
in
d
icato
r
o
f
m
a
r
in
e
ec
o
s
y
s
tem
h
ea
lth
,
esp
ec
ially
in
m
o
n
ito
r
i
n
g
p
h
y
to
p
lan
k
to
n
d
y
n
am
ics,
wh
ich
ar
e
c
en
tr
al
to
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
15
,
No
.
3
,
J
u
n
e
20
26
:
2
0
8
1
-
2
0
9
1
2082
th
e
b
io
lo
g
ical
p
u
m
p
an
d
o
ce
a
n
ic
ca
r
b
o
n
f
lu
x
[
3
]
.
T
h
is
h
i
g
h
l
ig
h
ts
th
e
im
p
o
r
tan
ce
o
f
ac
c
u
r
a
tely
ch
ar
ac
ter
izin
g
ch
lo
r
o
p
h
y
ll v
a
r
iab
ilit
y
to
s
u
p
p
o
r
t r
eliab
le
en
v
ir
o
n
m
en
tal
an
a
ly
s
is
an
d
d
ec
is
io
n
-
m
ak
in
g
.
Alth
o
u
g
h
v
a
r
io
u
s
en
v
i
r
o
n
m
e
n
tal
v
ar
iab
les
,
s
u
ch
as
win
d
an
d
r
ain
f
all
,
h
av
e
b
ee
n
s
u
cc
ess
f
u
lly
m
o
d
eled
u
s
in
g
class
ical
p
r
o
b
a
b
ilit
y
d
is
tr
ib
u
tio
n
s
,
i
n
-
d
ep
t
h
s
t
u
d
ies
o
f
th
e
c
o
m
p
lex
an
d
h
ete
r
o
g
en
e
o
u
s
s
p
atial
-
tem
p
o
r
al
p
atter
n
s
o
f
ch
l
o
r
o
p
h
y
ll
d
is
tr
ib
u
tio
n
r
em
ain
lim
ited
[
4
]
,
[
5
]
.
Ma
n
y
p
r
e
v
io
u
s
a
p
p
r
o
ac
h
es
h
av
e
r
elied
o
n
s
im
u
lated
d
atasets
,
wh
ich
o
f
ten
f
ail
to
r
e
f
lect
th
e
r
ea
l
s
p
atio
tem
p
o
r
al
v
ar
iab
ilit
y
o
f
m
a
r
in
e
ch
lo
r
o
p
h
y
ll
[
6
]
.
T
h
is
lim
itatio
n
is
p
ar
ticu
lar
l
y
p
r
o
b
lem
atic
in
ec
o
lo
g
ical
co
n
tex
ts
s
u
ch
as
h
ar
m
f
u
l
alg
al
b
lo
o
m
s
(
HABs
)
,
wh
ich
ar
e
h
ig
h
ly
s
en
s
itiv
e
to
s
m
all
f
lu
ctu
atio
n
s
in
ch
l
o
r
o
p
h
y
ll c
o
n
ce
n
tr
atio
n
[
7
]
.
T
o
o
v
e
r
co
m
e
th
ese
lim
itatio
n
s
,
o
b
s
er
v
atio
n
al
s
atellite
d
atasets
,
s
u
ch
as
th
o
s
e
f
r
o
m
t
h
e
C
o
p
er
n
icu
s
Ma
r
in
e
Ser
v
ice
,
o
f
f
er
h
ig
h
-
r
eso
lu
tio
n
an
d
m
o
r
e
r
e
p
r
esen
t
ativ
e
alter
n
ativ
es
f
o
r
ca
p
t
u
r
i
n
g
d
y
n
am
ic
m
ar
in
e
co
n
d
itio
n
s
[
8
]
,
[
9
]
.
T
h
ese
r
ea
l
-
wo
r
ld
d
atasets
en
h
a
n
ce
ec
o
lo
g
ical
ass
ess
m
en
t
b
y
ac
cu
r
atel
y
r
ef
lectin
g
s
p
atial
an
d
tem
p
o
r
al
v
ar
iatio
n
s
in
ch
l
o
r
o
p
h
y
ll
co
n
ce
n
t
r
atio
n
.
T
h
is
s
tu
d
y
s
ee
k
s
to
f
ill
th
e
r
esear
ch
g
ap
b
y
ev
alu
ati
n
g
th
e
p
er
f
o
r
m
a
n
ce
o
f
ei
g
h
t
p
r
o
b
ab
ilit
y
d
is
tr
ib
u
tio
n
m
o
d
els
—
h
alf
-
n
o
r
m
al
,
i
n
v
er
s
e
Gau
s
s
ian
,
R
ician
,
B
ir
n
b
au
m
–
Sau
n
d
er
s
,
Nak
ag
am
i,
ex
tr
e
m
e
v
alu
e
,
t
lo
ca
tio
n
-
s
ca
le
,
an
d
s
tab
le
—
in
r
ep
r
esen
tin
g
th
e
v
ar
iab
ilit
y
o
f
ch
lo
r
o
p
h
y
ll
-
a
d
ata
[
4
]
,
[
5
]
.
M
o
d
el
ev
alu
atio
n
is
co
n
d
u
cte
d
u
s
in
g
two
well
-
estab
lis
h
ed
g
o
o
d
n
ess
-
of
-
f
it
(
Go
F)
test
s
:
th
e
Ko
lm
o
g
o
r
o
v
–
Sm
ir
n
o
v
(
KS)
an
d
A
n
d
er
s
o
n
–
D
ar
lin
g
(
AD)
test
s
,
wh
ich
ar
e
wid
ely
u
s
ed
in
en
v
ir
o
n
m
en
tal
s
tatis
tics
an
d
tail m
o
d
elin
g
[
1
0
]
–
[
1
2
]
.
I
n
ad
d
itio
n
to
th
e
d
is
tr
ib
u
tio
n
al
ev
alu
atio
n
,
th
is
s
tu
d
y
in
tr
o
d
u
ce
s
a
n
o
v
el
m
ac
h
in
e
lear
n
in
g
–
b
ased
clu
s
ter
in
g
ap
p
r
o
a
ch
,
u
s
in
g
th
e
k
-
m
ea
n
s
alg
o
r
ith
m
to
g
r
o
u
p
d
is
tr
ib
u
tio
n
m
o
d
els
b
ased
o
n
Go
F
p
er
f
o
r
m
an
ce
ac
r
o
s
s
m
u
ltip
le
s
am
p
le
s
izes.
C
lu
s
ter
q
u
ality
is
v
alid
ated
t
h
r
o
u
g
h
th
e
s
ilh
o
u
ette
in
d
e
x
,
wh
ich
m
ea
s
u
r
es
th
e
co
h
esio
n
an
d
s
ep
ar
atio
n
o
f
r
esu
ltin
g
clu
s
ter
s
[
1
3
]
.
T
h
is
f
r
am
ewo
r
k
e
n
ab
les
th
e
d
is
co
v
er
y
o
f
b
e
h
av
io
r
al
p
atter
n
s
am
o
n
g
m
o
d
els
th
at
m
ay
n
o
t
b
e
ap
p
ar
en
t
th
r
o
u
g
h
in
d
iv
id
u
al
ev
alu
atio
n
alo
n
e.
R
ec
e
n
t
a
d
v
an
ce
s
i
n
en
v
i
r
o
n
m
e
n
t
al
m
o
n
it
o
r
i
n
g
,
i
n
c
lu
d
i
n
g
t
h
e
i
n
t
er
n
et
o
f
th
in
g
s
(
I
o
T
)
a
n
d
i
n
t
elli
g
en
t
o
c
ea
n
s
e
n
s
o
r
s
,
f
u
r
t
h
e
r
en
h
an
ce
th
e
c
a
p
a
b
il
it
y
t
o
d
et
ec
t
h
id
d
en
p
at
te
r
n
s
i
n
o
ce
an
o
g
r
a
p
h
ic
d
at
a
s
ets
[
1
4
]
.
F
o
r
i
n
s
t
a
n
ce
,
Z
h
a
n
g
et
a
l
.
[
1
5
]
p
r
o
p
o
s
ed
a
cr
o
s
s
-
d
o
m
ain
ad
a
p
tiv
e
d
ee
p
lear
n
in
g
m
eth
o
d
f
o
r
im
p
r
o
v
in
g
o
b
ject
d
etec
tio
n
in
c
o
astal
r
em
o
te
s
en
s
in
g
d
ata,
wh
ile
L
in
g
et
a
l.
[
1
6
]
em
p
h
asized
th
e
r
elev
an
ce
o
f
s
p
ec
tr
al
f
ea
tu
r
es
f
o
r
m
o
n
ito
r
in
g
v
eg
etatio
n
a
n
d
ch
lo
r
o
p
h
y
ll
d
y
n
am
ics.
Su
ch
in
n
o
v
atio
n
s
s
u
p
p
o
r
t
th
e
g
r
o
win
g
in
teg
r
atio
n
o
f
ar
tific
ial
in
tellig
en
ce
(
AI
)
an
d
s
tatis
t
ical
m
o
d
elin
g
in
m
a
r
in
e
s
cien
ce
.
Un
lik
e
p
r
io
r
s
tu
d
ies
th
at
h
av
e
p
r
im
a
r
ily
f
o
c
u
s
ed
o
n
s
atellite
-
b
ased
ch
lo
r
o
p
h
y
ll
esti
m
atio
n
(
e.
g
.
,
m
o
d
er
ate
r
eso
lu
tio
n
im
ag
in
g
s
p
ec
tr
o
r
ad
i
o
m
eter
(
MO
DI
S
)
an
d
s
ea
-
v
iewin
g
wid
e
f
ield
-
of
-
v
iew
s
en
s
o
r
(
SeaWi
F
S
)
)
,
th
is
s
tu
d
y
p
r
o
p
o
s
es
a
s
tr
u
ctu
r
e
-
b
ased
s
tatis
tical
f
r
am
ewo
r
k
th
at
co
m
b
in
es
p
r
o
b
ab
ilit
y
m
o
d
elin
g
,
Go
F
ev
alu
atio
n
,
an
d
clu
s
ter
in
g
—
ad
v
an
ci
n
g
b
e
y
o
n
d
esti
m
atio
n
to
war
d
an
aly
s
is
o
f
s
tatis
tical
d
is
tr
ib
u
tio
n
b
eh
av
io
r
an
d
i
n
ter
-
m
o
d
el
r
e
latio
n
s
h
ip
s
[
1
7
]
.
T
h
e
in
teg
r
ated
f
r
am
ewo
r
k
o
f
f
er
s
s
ev
e
r
al
m
eth
o
d
o
lo
g
ical
co
n
tr
ib
u
tio
n
s
.
First,
it
en
ab
le
s
co
m
p
ar
ativ
e
an
aly
s
is
o
f
d
is
tr
ib
u
tio
n
m
o
d
els
b
ased
o
n
b
o
th
th
eir
in
d
iv
id
u
al
p
er
f
o
r
m
an
ce
an
d
s
h
ar
e
d
b
eh
a
v
io
r
al
p
atter
n
s
th
r
o
u
g
h
clu
s
ter
in
g
—
an
ap
p
r
o
ac
h
r
ar
el
y
ap
p
lied
in
ch
lo
r
o
p
h
y
ll
m
o
d
elin
g
.
Seco
n
d
,
u
s
in
g
th
e
s
ilh
o
u
ette
in
d
ex
i
m
p
r
o
v
es
in
ter
p
r
etab
ilit
y
b
y
e
m
p
ir
ically
g
r
o
u
p
in
g
m
o
d
els
with
s
im
ilar
s
tatis
tical
p
r
o
f
iles
.
T
h
is
tr
an
s
ce
n
d
s
tr
ad
itio
n
al
G
o
F
-
b
ased
r
an
k
i
n
g
b
y
r
e
v
ea
lin
g
d
ee
p
e
r
r
elatio
n
al
p
atter
n
s
am
o
n
g
th
e
ca
n
d
id
ate
m
o
d
els
[
1
3
]
.
L
astl
y
,
ch
lo
r
o
p
h
y
ll'
s
b
r
o
ad
er
ec
o
lo
g
ical
s
ig
n
if
ican
ce
—
as
a
p
r
o
x
y
f
o
r
p
r
im
ar
y
p
r
o
d
u
ctiv
it
y
,
n
u
tr
ie
n
t
cy
clin
g
,
an
d
clim
ate
r
esil
ie
n
ce
—
u
n
d
er
s
co
r
es
th
e
im
p
o
r
tan
ce
o
f
th
is
s
tu
d
y
.
B
etter
u
n
d
er
s
tan
d
in
g
o
f
ch
lo
r
o
p
h
y
ll
v
ar
iab
ilit
y
s
u
p
p
o
r
ts
m
ar
in
e
ec
o
l
o
g
ical
f
o
r
ec
asti
n
g
a
n
d
c
o
n
tr
ib
u
tes
to
s
u
s
tain
a
b
le
m
ar
in
e
r
eso
u
r
ce
m
an
ag
em
en
t
an
d
clim
ate
ad
ap
tatio
n
,
in
lin
e
with
s
u
s
tai
n
ab
le
d
ev
elo
p
m
en
t
g
o
al
(
S
DG)
1
4
o
n
m
ar
in
e
ec
o
s
y
s
tem
p
r
o
tectio
n
[
3
]
.
T
h
i
s
f
u
r
th
er
e
m
p
h
asizes
th
e
n
ee
d
f
o
r
r
o
b
u
s
t
an
d
r
eliab
le
an
aly
t
ical
ap
p
r
o
ac
h
es
to
ca
p
tu
r
e
its
co
m
p
lex
v
ar
iab
ilit
y
.
2.
M
E
T
H
O
D
2
.
1
.
P
r
o
ba
bil
it
y
dis
t
ributio
n
m
o
dels
I
n
th
is
s
tu
d
y
,
eig
h
t
p
r
o
b
ab
ilit
y
d
is
tr
ib
u
tio
n
m
o
d
els
wer
e
s
elec
ted
f
o
r
th
eir
p
o
ten
tial
to
c
ap
tu
r
e
th
e
ch
ar
ac
ter
is
tic
v
ar
iatio
n
s
in
c
h
lo
r
o
p
h
y
ll
-
a
d
ata
with
in
m
ar
i
n
e
en
v
ir
o
n
m
e
n
ts
.
T
h
e
s
e
lec
ti
o
n
w
as
b
ase
d
o
n
a
co
m
p
r
eh
e
n
s
i
v
e
li
te
r
at
u
r
e
r
e
v
ie
w
a
n
d
e
m
p
ir
ic
al
e
v
i
d
e
n
c
e
s
u
p
p
o
r
t
in
g
t
h
e
ir
s
u
it
a
b
ili
ty
f
o
r
e
n
v
i
r
o
n
m
e
n
t
al
d
at
asets
ch
ar
ac
t
er
iz
ed
b
y
h
i
g
h
s
p
a
tia
l
a
n
d
t
em
p
o
r
al
v
a
r
ia
b
i
lit
y
.
T
h
e
s
ele
cte
d
d
is
t
r
i
b
u
ti
o
n
s
i
n
c
lu
d
e:
h
al
f
-
n
o
r
m
al
,
in
v
er
s
e
Gau
s
s
ia
n
,
R
ic
ia
n
,
B
i
r
n
b
a
u
m
–
S
au
n
d
e
r
s
,
N
ak
ag
a
m
i,
e
x
t
r
e
m
e
v
alu
e
,
t
l
o
c
ati
o
n
-
s
ca
le
,
an
d
s
ta
b
l
e
[
1
8
]
–
[
2
2
]
.
E
ac
h
d
is
tr
ib
u
tio
n
p
r
o
v
id
es
s
p
ec
if
ic
s
tr
en
g
th
s
in
m
o
d
eli
n
g
d
is
tin
ct
f
ea
tu
r
es
o
f
c
h
l
o
r
o
p
h
y
ll
-
a
co
n
ce
n
tr
atio
n
s
.
Fo
r
ex
am
p
le,
R
ician
an
d
B
ir
n
b
au
m
–
Sau
n
d
er
s
d
is
tr
ib
u
tio
n
s
ar
e
f
r
e
q
u
en
tl
y
u
s
ed
in
co
n
tex
ts
in
v
o
lv
in
g
o
ce
a
n
ic
d
y
n
am
ics
an
d
b
i
o
lo
g
ical
v
ar
iab
ilit
y
.
T
h
e
ex
tr
em
e
v
alu
e
d
is
tr
ib
u
tio
n
is
well
-
s
u
ited
to
ca
p
tu
r
e
p
ea
k
ev
e
n
ts
s
u
ch
as
a
lg
al
b
lo
o
m
s
.
T
h
e
h
alf
-
n
o
r
m
al
d
is
tr
ib
u
tio
n
is
ap
p
r
o
p
r
iate
f
o
r
m
o
d
elin
g
s
tr
ictly
p
o
s
itiv
e
d
ata,
wh
er
ea
s
th
e
in
v
er
s
e
Gau
s
s
ian
is
ef
f
ec
tiv
e
f
o
r
h
ig
h
ly
s
k
ewe
d
d
atasets
ex
h
ib
itin
g
s
ig
n
if
ican
t
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
A
d
va
n
ce
d
in
feren
tia
l sta
tis
tics
a
n
d
d
a
ta
min
in
g
fo
r
ch
lo
r
o
p
h
yll
d
is
tr
ib
u
tio
n
clu
s
teri
n
g
…
(
F
elix
R
eb
a
)
2083
v
ar
iab
ilit
y
.
Nak
a
g
am
i
an
d
s
tab
le
d
is
tr
ib
u
tio
n
s
ac
c
o
m
m
o
d
ate
asy
m
m
etr
y
a
n
d
h
ea
v
y
-
tailed
b
eh
av
i
o
r
,
wh
ile
th
e
t
lo
ca
tio
n
-
s
ca
le
d
is
tr
ib
u
tio
n
is
k
n
o
wn
f
o
r
its
r
o
b
u
s
tn
ess
to
o
u
tlier
s
.
T
o
ass
ess
th
e
f
itn
ess
o
f
th
ese
m
o
d
els
in
r
ep
r
esen
tin
g
c
h
lo
r
o
p
h
y
ll
-
a
v
ar
iab
ilit
y
,
two
Go
F
test
s
wer
e
ap
p
lied
:
th
e
KS
an
d
AD
test
s
.
I
n
ad
d
itio
n
,
two
m
o
d
el
s
el
ec
tio
n
cr
iter
ia
—
th
e
Ak
aik
e
i
n
f
o
r
m
atio
n
cr
iter
io
n
(
AI
C
)
an
d
th
e
B
ay
esian
in
f
o
r
m
atio
n
cr
iter
io
n
(
B
I
C
)
—
wer
e
em
p
lo
y
e
d
to
g
u
id
e
th
e
i
d
en
ti
f
icatio
n
o
f
t
h
e
m
o
s
t
ap
p
r
o
p
r
iate
m
o
d
els
f
o
r
ch
ar
ac
ter
izin
g
th
e
co
m
p
lex
d
is
tr
ib
u
ti
o
n
al
b
eh
av
io
r
o
f
ch
lo
r
o
p
h
y
ll
-
a
co
n
ce
n
tr
atio
n
s
in
m
ar
in
e
ec
o
s
y
s
tem
s
.
T
o
g
eth
er
,
th
ese
ev
alu
atio
n
m
etr
ics
p
r
o
v
id
e
a
co
m
p
r
eh
en
s
iv
e
f
r
am
e
wo
r
k
f
o
r
co
m
p
a
r
in
g
m
o
d
el
p
er
f
o
r
m
a
n
ce
an
d
en
s
u
r
i
n
g
r
o
b
u
s
t statis
tical
in
f
er
en
ce
.
2
.
2
.
T
est
s
t
a
t
is
t
ics
T
o
e
v
al
u
ate
th
e
f
it
o
f
p
r
o
b
ab
ili
ty
d
is
tr
i
b
u
ti
o
n
m
o
d
els
t
o
th
e
c
h
l
o
r
o
p
h
y
ll
-
a
d
a
tas
et,
tw
o
G
o
F
test
s
w
er
e
ap
p
li
e
d
:
th
e
KS
test
a
n
d
t
h
e
AD
t
est
[
2
3
]
–
[
2
7
]
.
T
h
e
K
S
t
est
m
e
asu
r
es
t
h
e
m
a
x
i
m
u
m
d
is
ta
n
c
e
b
e
twe
en
t
h
e
em
p
i
r
ic
al
c
u
m
u
l
ati
v
e
d
is
t
r
i
b
u
ti
o
n
f
u
n
cti
o
n
(
)
a
n
d
th
e
th
e
o
r
eti
ca
l
d
is
t
r
i
b
u
ti
o
n
0
(
)
,
as
e
x
p
r
ess
e
d
in
(
1
)
.
=
ma
x
∈
ℝ
|
(
)
−
0
(
)
|
(
1
)
A
s
m
aller
v
alu
e
in
d
icate
s
a
clo
s
er
alig
n
m
en
t
b
etwe
en
e
m
p
ir
ical
d
ata
a
n
d
th
e
th
e
o
r
e
tical
m
o
d
el,
s
u
g
g
esti
n
g
a
b
etter
f
it
[
2
4
]
,
[
2
6
]
.
T
h
e
AD
test
co
m
p
lem
en
ts
th
e
KS
tes
t
b
y
p
lacin
g
g
r
ea
ter
weig
h
t
o
n
d
if
f
er
en
ce
s
in
th
e
tails
o
f
th
e
d
is
tr
ib
u
tio
n
—
im
p
o
r
tan
t
f
o
r
d
etec
tin
g
r
ar
e
b
u
t
ec
o
lo
g
ically
s
ig
n
if
ican
t
ev
en
ts
.
T
h
e
AD
s
tatis
tic
i
s
ca
lcu
lated
u
s
in
g
(
2
)
.
2
=
−
−
∑
[
(
2
−
1
)
∗
(
l
og
(
)
+
l
og
(
1
−
(
+
1
−
)
)
)
]
=
1
(
2
)
L
o
wer
2
v
alu
es
in
d
icate
a
b
e
tter
f
it,
p
a
r
ticu
lar
ly
i
n
ca
p
t
u
r
in
g
ex
tr
e
m
e
d
is
tr
ib
u
tio
n
al
b
e
h
av
io
r
,
wh
ich
is
v
ital
in
en
v
ir
o
n
m
e
n
tal
an
d
h
y
d
r
o
lo
g
ical
co
n
tex
ts
[
2
5
]
,
[
2
7
]
.
I
n
ad
d
itio
n
to
th
e
G
o
F
test
s
,
th
is
s
tu
d
y
em
p
lo
y
s
f
i
v
e
s
tatis
tical
in
f
o
r
m
atio
n
cr
iter
ia
to
ass
ess
m
o
d
el
f
it
wh
ile
p
en
alizin
g
co
m
p
l
ex
ity
:
h
a
n
n
an
–
q
u
in
n
cr
iter
io
n
(
HQC),
co
n
s
is
ten
t
A
k
aik
e
in
f
o
r
m
atio
n
cr
iter
i
o
n
(
C
AI
C
)
,
B
I
C
,
co
r
r
ec
ted
A
k
aik
e
in
f
o
r
m
atio
n
cr
iter
io
n
(
AI
C
c)
,
AI
C
.
T
h
ese
cr
iter
ia
en
s
u
r
e
a
b
alan
ce
d
ev
alu
atio
n
b
y
in
teg
r
atin
g
b
o
th
f
it
an
d
p
ar
s
im
o
n
y
,
s
u
p
p
o
r
tin
g
th
e
id
en
tific
atio
n
o
f
th
e
m
o
s
t
ap
p
r
o
p
r
iate
p
r
o
b
a
b
ilit
y
d
is
tr
ib
u
tio
n
m
o
d
el
to
ch
a
r
ac
ter
ize
ch
lo
r
o
p
h
y
ll
-
a
b
eh
av
io
r
in
m
a
r
in
e
ec
o
s
y
s
tem
s
[
2
3
]
,
[
2
7
]
.
2
.
3
.
M
o
del
s
elec
t
io
n ba
s
ed
o
n info
rm
a
t
io
n
-
t
heo
re
t
ic
cr
it
e
ria
I
n
s
tatis
tical
m
o
d
el
s
elec
tio
n
,
in
f
o
r
m
atio
n
-
t
h
eo
r
etic
cr
iter
ia
ar
e
ess
en
tial
to
o
ls
f
o
r
ev
alu
atin
g
an
d
co
m
p
ar
in
g
ca
n
d
id
ate
m
o
d
els
b
y
b
alan
ci
n
g
G
o
F
with
m
o
d
el
co
m
p
lex
ity
.
T
h
e
m
o
s
t
co
m
m
o
n
ly
em
p
lo
y
ed
cr
iter
ia
in
clu
d
e
th
e
HQC,
C
A
I
C
,
B
I
C
,
AI
C
c,
an
d
th
e
AI
C
[
2
8
]
.
E
ac
h
cr
iter
io
n
im
p
o
s
es
a
p
en
alty
o
n
m
o
d
el
co
m
p
lex
ity
to
m
itig
ate
o
v
er
f
i
ttin
g
,
th
o
u
g
h
th
e
s
ev
er
ity
o
f
th
ese
p
en
alties
v
ar
ies.
A
I
C
ap
p
lies
a
m
o
d
er
ate
p
en
alty
an
d
is
g
en
er
ally
s
u
ita
b
le
f
o
r
s
m
aller
s
am
p
le
s
izes,
wh
ile
B
I
C
im
p
o
s
es
a
s
tr
o
n
g
er
p
en
alty
th
at
s
ca
les
with
s
am
p
le
s
ize,
m
ak
in
g
it
m
o
r
e
ap
p
r
o
p
r
iate
f
o
r
lar
g
er
d
atasets
.
AI
C
c
ad
ju
s
ts
AI
C
f
o
r
s
m
all
-
s
am
p
le
b
ias,
im
p
r
o
v
in
g
its
r
eliab
ilit
y
in
li
m
ited
-
d
ata
co
n
tex
ts
.
C
AI
C
a
n
d
HQC,
m
ea
n
wh
ile,
ac
t
as
m
o
r
e
co
n
s
er
v
ativ
e
m
etr
ics,
en
f
o
r
cin
g
s
tr
icter
p
en
alties o
n
o
v
er
ly
c
o
m
p
lex
m
o
d
els.
I
n
t
h
is
s
t
u
d
y
,
all
f
i
v
e
c
r
it
er
i
a
wer
e
a
p
p
li
e
d
t
o
ass
ess
t
h
e
f
i
t
o
f
e
ac
h
p
r
o
b
a
b
i
lit
y
d
is
t
r
i
b
u
t
io
n
m
o
d
e
l
ac
r
o
s
s
f
o
u
r
s
a
m
p
le
s
iz
es
(
n
=
2
0
,
25,
3
0
,
3
5
)
.
I
n
s
t
ea
d
o
f
e
v
a
l
u
at
in
g
p
er
f
o
r
m
a
n
ce
in
d
ep
en
d
e
n
tl
y
at
ea
ch
s
a
m
p
le
s
ize
,
th
e
m
e
a
n
v
a
lu
e
o
f
ea
ch
cr
ite
r
i
o
n
w
as c
o
m
p
u
te
d
ac
r
o
s
s
al
l
s
a
m
p
le
s
i
ze
s
.
T
h
is
a
v
e
r
a
g
i
n
g
m
et
h
o
d
p
r
o
v
i
d
es
a
m
o
r
e
s
ta
b
l
e
a
n
d
e
q
u
ita
b
l
e
f
o
u
n
d
ati
o
n
f
o
r
c
o
m
p
a
r
is
o
n
,
m
in
i
m
iz
in
g
t
h
e
im
p
a
ct
o
f
v
ar
ia
b
il
it
y
d
u
e
t
o
s
a
m
p
le
s
i
ze
.
B
y
i
d
e
n
ti
f
y
i
n
g
m
o
d
els
t
h
a
t
c
o
n
s
is
te
n
t
ly
s
tr
ik
e
a
n
o
p
tim
al
b
al
a
n
ce
b
etwe
e
n
f
it
a
n
d
s
im
p
licit
y
,
t
h
is
a
p
p
r
o
ac
h
im
p
r
o
v
es
g
e
n
e
r
al
iz
ab
ilit
y
a
n
d
r
e
d
u
ce
s
t
h
e
r
is
k
o
f
o
v
e
r
f
itt
in
g
i
n
c
h
l
o
r
o
p
h
y
l
l
-
a
d
is
tr
ib
u
t
io
n
m
o
d
eli
n
g
[
2
8
]
.
2
.
4
.
Clus
t
er
ing
m
et
ho
d a
nd
ev
a
lua
t
io
n
Un
lik
e
tr
ad
itio
n
al
s
tu
d
ies
th
at
clu
s
ter
ch
lo
r
o
p
h
y
ll
d
ata
s
p
atially
o
r
b
ased
s
o
lely
o
n
s
atellite
-
d
er
iv
ed
o
b
s
er
v
atio
n
s
,
th
is
s
tu
d
y
ad
o
p
t
s
a
n
o
v
el
ap
p
r
o
ac
h
b
y
ap
p
ly
i
n
g
k
-
m
ea
n
s
clu
s
ter
in
g
d
ir
ec
tl
y
to
Go
F
m
etr
ics
—
s
p
ec
if
ically
,
th
e
KS
an
d
AD
s
tatis
tic
s
.
T
h
is
m
eth
o
d
en
a
b
les
clu
s
ter
in
g
b
ased
o
n
d
is
tr
ib
u
tio
n
al
b
eh
av
i
o
r
r
ath
e
r
th
an
o
n
r
aw
c
h
lo
r
o
p
h
y
ll
-
a
v
a
lu
es,
o
f
f
er
i
n
g
a
m
o
r
e
r
o
b
u
s
t
an
d
in
f
o
r
m
ativ
e
f
o
u
n
d
atio
n
f
o
r
s
tatis
tical
p
atter
n
r
ec
o
g
n
itio
n
ac
r
o
s
s
v
ar
y
in
g
s
am
p
le
s
izes
[
2
9
]
,
[
3
0
]
.
T
h
is
ap
p
r
o
ac
h
p
r
o
v
i
d
es
d
ee
p
e
r
in
s
ig
h
t
in
to
th
e
co
m
p
ar
ativ
e
p
er
f
o
r
m
an
ce
a
n
d
r
elatio
n
s
h
ip
s
am
o
n
g
ca
n
d
id
ate
d
is
tr
ib
u
tio
n
m
o
d
els.
T
o
im
p
lem
en
t
th
is
ap
p
r
o
ac
h
,
t
h
e
k
-
m
ea
n
s
alg
o
r
ith
m
was
em
p
lo
y
ed
to
g
r
o
u
p
th
e
d
is
tr
ib
u
ti
o
n
m
o
d
els
ac
co
r
d
in
g
to
th
e
s
im
ilar
ity
o
f
th
eir
Go
F
v
alu
es.
T
h
e
alg
o
r
ith
m
ca
lcu
lates
E
u
clid
ea
n
d
is
t
an
ce
b
etwe
en
ea
ch
m
o
d
el’
s
Go
F
v
ec
to
r
an
d
th
e
cl
u
s
ter
ce
n
tr
o
id
,
an
d
iter
ativ
ely
r
ea
s
s
ig
n
s
m
o
d
els
to
clu
s
ter
s
in
o
r
d
er
to
m
in
im
ize
th
e
to
tal
in
tr
a
-
cl
u
s
ter
v
ar
ian
c
e,
co
n
tin
u
in
g
th
is
p
r
o
ce
s
s
u
n
til
co
n
v
e
r
g
en
ce
.
I
n
(
3
)
q
u
a
n
tifie
s
th
e
d
is
tan
ce
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
15
,
No
.
3
,
J
u
n
e
20
26
:
2
0
8
1
-
2
0
9
1
2084
b
etwe
en
a
d
is
tr
ib
u
tio
n
m
o
d
el
—
b
ased
o
n
its
Go
F
v
alu
es
—
a
n
d
th
e
clu
s
ter
ce
n
tr
o
id
in
a
m
u
ltid
im
en
s
io
n
al
Go
F
f
ea
tu
r
e
s
p
ac
e
[
2
9
]
.
=
∑
(
−
)
2
=
1
(
3
)
T
o
ev
alu
ate
clu
s
ter
in
g
q
u
alit
y
,
th
is
s
tu
d
y
em
p
lo
y
ed
t
h
e
s
ilh
o
u
ette
in
d
ex
,
wh
ich
m
ea
s
u
r
es
b
o
th
co
h
esio
n
(
h
o
w
clo
s
e
a
d
is
tr
ib
u
tio
n
is
to
o
th
er
s
with
in
its
o
wn
clu
s
ter
)
an
d
s
ep
ar
atio
n
(
h
o
w
d
is
tin
ct
it
is
f
r
o
m
d
is
tr
ib
u
tio
n
s
in
o
th
e
r
clu
s
ter
s
)
.
T
h
e
in
d
e
x
is
co
m
p
u
ted
as
(
4
)
.
̅
=
1
∑
(
(
)
−
(
)
m
ax
(
(
)
,
(
)
)
)
=
1
(
4
)
W
h
er
e
(
)
is
th
e
av
er
ag
e
in
tr
a
-
clu
s
ter
d
is
tan
ce
f
o
r
d
ata
p
o
in
t
,
an
d
(
)
is
th
e
lo
west
av
er
ag
e
d
is
tan
ce
to
p
o
in
ts
in
o
th
er
clu
s
ter
s
.
Valu
es
clo
s
e
to
1
in
d
icate
well
-
s
ep
ar
ated
cl
u
s
ter
s
,
wh
ile
n
e
g
a
tiv
e
v
alu
es
s
u
g
g
est
p
o
ten
tial m
is
class
if
icatio
n
[
3
1
]
,
[
3
2
]
.
T
h
is
in
teg
r
ated
m
eth
o
d
allo
ws
f
o
r
th
e
id
e
n
tific
atio
n
o
f
r
o
b
u
s
t,
u
n
s
tab
le,
o
r
o
u
tlier
-
p
r
o
n
e
d
i
s
tr
ib
u
tio
n
m
o
d
els
ac
r
o
s
s
v
ar
y
in
g
s
am
p
le
s
izes.
As
s
u
ch
,
clu
s
ter
in
g
in
t
h
is
s
tu
d
y
is
n
o
lo
n
g
er
lim
ited
to
s
p
atial
an
aly
s
is
b
u
t
is
in
s
tead
d
r
iv
e
n
b
y
G
o
F
-
b
ased
b
e
h
av
io
r
al
p
atter
n
s
,
en
ab
lin
g
em
p
ir
ically
g
r
o
u
n
d
ed
g
r
o
u
p
in
g
s
o
f
s
tatis
t
ical
m
o
d
els
[
2
9
]
,
[
3
0
]
,
[
3
3
]
.
T
o
en
h
an
ce
t
r
an
s
p
ar
e
n
cy
an
d
r
e
p
r
o
d
u
cib
ilit
y
,
Fig
u
r
e
1
illu
s
tr
ates
th
e
o
v
er
all
wo
r
k
f
l
o
w
o
f
th
e
m
eth
o
d
o
lo
g
y
—
s
tar
tin
g
f
r
o
m
d
ata
r
etr
iev
al
an
d
d
is
tr
ib
u
tio
n
f
itti
n
g
,
f
o
llo
wed
b
y
Go
F
test
in
g
,
clu
s
ter
in
g
,
an
d
f
in
al
m
o
d
el
e
v
alu
atio
n
.
T
h
is
d
ia
g
r
am
f
ac
ilit
ates
a
clea
r
er
u
n
d
er
s
tan
d
in
g
o
f
t
h
e
an
aly
tical
p
r
o
ce
s
s
an
d
im
p
r
o
v
es in
ter
p
r
etab
ilit
y
f
o
r
r
ea
d
e
r
s
.
Fig
u
r
e
1
.
Simp
lifie
d
wo
r
k
f
l
o
w
o
f
th
e
m
et
h
o
d
o
lo
g
ical
s
tep
s
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
3
.
1
.
Resea
rc
h
d
a
t
a
T
h
is
s
tu
d
y
u
tili
ze
s
ch
lo
r
o
p
h
y
ll
-
an
o
b
s
er
v
atio
n
s
o
b
tain
e
d
f
r
o
m
th
e
C
o
p
er
n
ic
u
s
Ma
r
in
e
Data
Sto
r
e
a
t
h
ttp
s
://ma
r
in
e.
co
p
er
n
ic
u
s
.
eu
/
,
d
o
wn
lo
ad
e
d
in
s
p
r
ea
d
s
h
ee
t
f
o
r
m
at.
T
h
e
d
ataset
r
ep
r
esen
ts
a
v
er
tical
s
n
ap
s
h
o
t,
m
ea
n
in
g
ch
lo
r
o
p
h
y
ll
-
a
v
al
u
es
wer
e
co
llected
at
a
s
in
g
le
ti
m
estam
p
—
1
4
Ma
y
2
0
2
4
,
1
1
:
4
6
UT
C
—
f
r
o
m
a
f
ix
ed
g
e
o
g
r
ap
h
ic
lo
ca
tio
n
in
th
e
n
o
r
th
e
r
n
b
lac
k
s
ea
(
ap
p
r
o
x
im
ately
3
1
.
3
5
7
2
7
°E,
4
1
.
6
9
5
3
5
°N),
co
v
er
in
g
a
v
er
tical
d
ep
t
h
r
a
n
g
e
f
r
o
m
0
t
o
1
9
.
2
m
eter
s
.
T
h
is
v
er
tical
s
am
p
lin
g
d
esig
n
i
n
ten
tio
n
ally
ex
clu
d
es
tem
p
o
r
al
v
ar
iab
ilit
y
,
f
o
c
u
s
in
g
s
o
lely
o
n
th
e
p
r
o
b
a
b
ilis
tic
b
eh
av
i
o
r
o
f
ch
lo
r
o
p
h
y
ll
co
n
ce
n
tr
a
tio
n
s
u
n
d
er
s
tatic
en
v
ir
o
n
m
en
tal
co
n
d
itio
n
s
.
Sam
p
le
s
izes (
n
=2
0
,
25,
3
0
,
3
5
)
wer
e
d
er
iv
ed
b
y
d
r
awin
g
s
u
b
s
ets f
r
o
m
th
e
v
er
tical
p
r
o
f
ile
an
d
a
r
e
ju
s
tifie
d
b
ased
o
n
p
r
io
r
r
em
o
te
s
en
s
in
g
s
tu
d
ie
s
co
n
ce
r
n
in
g
ch
l
o
r
o
p
h
y
ll
r
eso
l
u
tio
n
o
p
tim
izatio
n
an
d
s
p
atial
r
ep
r
esen
tatio
n
u
n
d
er
v
ar
i
o
u
s
o
b
s
er
v
atio
n
al
c
o
n
s
tr
ain
ts
[
7
]
–
[
9
]
,
[
1
4
]
,
[
3
4
]
.
W
h
ile
d
ep
th
was
r
ec
o
r
d
e
d
,
it
was
n
o
t
tr
ea
ted
as
an
an
aly
tical
v
ar
iab
le.
I
n
s
tead
,
it
s
er
v
ed
as
a
s
tr
u
ctu
r
al
r
ef
er
e
n
ce
f
o
r
s
u
b
s
am
p
le
co
n
s
tr
u
ctio
n
to
e
n
s
u
r
e
ec
o
lo
g
ical
r
ep
r
esen
tativ
en
ess
.
T
h
e
p
r
im
ar
y
a
n
aly
tical
f
o
cu
s
r
e
m
ain
s
o
n
h
o
w
th
e
ch
lo
r
o
p
h
y
ll
-
a
v
alu
es c
o
n
f
o
r
m
to
v
ar
io
u
s
u
n
iv
ar
iate
p
r
o
b
a
b
ilit
y
d
is
tr
ib
u
tio
n
m
o
d
els.
T
o
r
e
d
u
c
e
b
i
a
s
,
o
n
l
y
v
a
l
i
d
o
b
s
e
r
v
a
t
i
o
n
s
t
h
at
s
a
ti
s
f
i
e
d
t
h
e
u
n
d
e
r
l
y
i
n
g
a
s
s
u
m
p
t
i
o
n
s
o
f
t
h
e
t
e
s
t
e
d
d
i
s
t
r
i
b
u
ti
o
n
s
w
e
r
e
i
n
c
l
u
d
e
d
.
Po
t
e
n
t
i
a
l
s
o
u
r
c
e
s
o
f
u
n
c
e
r
t
a
i
n
t
y
—
s
u
c
h
as
a
t
m
o
s
p
h
e
r
i
c
c
o
r
r
e
c
ti
o
n
a
r
t
i
f
a
c
ts
,
c
l
o
u
d
m
a
s
k
i
n
g
,
o
r
i
n
s
t
r
u
m
e
n
t
c
a
li
b
r
a
t
i
o
n
i
s
s
u
es
—
w
e
r
e
a
c
k
n
o
w
led
g
e
d
a
n
d
m
i
t
i
g
a
t
e
d
t
h
r
o
u
g
h
d
a
t
a
s
c
r
ee
n
i
n
g
a
n
d
c
o
n
t
r
o
l
l
e
d
s
a
m
p
li
n
g
p
r
o
c
e
d
u
r
e
s
.
A
lt
h
o
u
g
h
l
i
m
i
t
e
d
t
o
a
s
i
n
g
le
t
e
m
p
o
r
a
l
s
n
a
p
s
h
o
t
,
t
h
is
a
p
p
r
o
a
c
h
p
r
o
v
i
d
e
s
a
r
o
b
u
s
t
f
o
u
n
d
a
t
i
o
n
f
o
r
p
r
o
b
a
b
i
l
i
s
t
i
c
m
o
d
e
l
i
n
g
o
f
c
h
l
o
r
o
p
h
y
l
l
-
a
c
o
n
c
e
n
t
r
a
t
i
o
n
s
i
n
e
c
o
l
o
g
i
c
a
ll
y
d
y
n
a
m
i
c
c
o
a
s
t
al
w
a
te
r
s
.
3
.
2
.
T
est
s
t
a
t
is
t
ic
v
a
lues
I
n
th
is
s
tu
d
y
,
th
e
KS
an
d
AD
test
s
wer
e
ap
p
lied
to
ev
alu
ate
th
e
Go
F
o
f
eig
h
t
d
is
tr
ib
u
tio
n
m
o
d
els
to
ch
lo
r
o
p
h
y
ll
-
a
d
ata
ac
r
o
s
s
v
ar
i
o
u
s
s
am
p
le
s
izes
(
n
=1
5
–
5
0
)
.
T
ab
les
1
an
d
2
r
ep
o
r
t
th
e
r
esu
ltin
g
p
-
v
al
u
es,
with
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
A
d
va
n
ce
d
in
feren
tia
l sta
tis
tics
a
n
d
d
a
ta
min
in
g
fo
r
ch
lo
r
o
p
h
yll
d
is
tr
ib
u
tio
n
clu
s
teri
n
g
…
(
F
elix
R
eb
a
)
2085
h
y
p
o
th
esis
d
ec
is
io
n
s
(
H
0
ac
ce
p
ted
/r
ejec
ted
)
b
ased
o
n
a
s
ig
n
if
ic
an
ce
th
r
esh
o
ld
α
=0
.
0
5
.
T
h
ese
r
esu
lts
p
r
o
v
id
e
a
s
y
s
tem
atic
b
asis
f
o
r
co
m
p
ar
i
n
g
m
o
d
el
p
er
f
o
r
m
a
n
ce
ac
r
o
s
s
d
if
f
er
en
t sam
p
le
c
o
n
d
itio
n
s
.
T
ab
le
1
.
p
-
v
alu
es f
r
o
m
KS a
n
d
AD
test
s
f
o
r
v
ar
io
u
s
d
is
tr
ib
u
tio
n
s
(
n
=2
0
,
2
5
,
3
0
,
3
5
)
D
i
st
r
i
b
u
t
i
o
n
H
y
p
o
t
h
e
s
e
s
n
=
2
0
n
=
2
5
n
=
3
0
n
=
3
5
−
KS
−
AD
−
KS
−
AD
−
KS
−
AD
−
KS
−
AD
R
i
c
i
a
n
H
0
a
c
c
e
p
t
e
d
0
.
3
7
8
0
.
6
7
1
0
.
2
0
5
0
.
3
1
7
0
.
1
4
0
.
3
1
7
0
.
4
8
6
0
.
6
2
3
B
i
r
n
b
a
u
m
–
S
a
u
n
d
e
r
s
H
0
a
c
c
e
p
t
e
d
0
.
3
8
3
0
.
6
3
0
.
2
7
7
0
.
4
0
7
0
.
3
6
8
0
.
5
8
8
0
.
7
7
8
0
.
7
4
1
Ex
t
r
e
me
v
a
l
u
e
H
0
a
c
c
e
p
t
e
d
0
.
4
0
9
0
.
7
8
8
0
.
0
4
8
0
.
1
0
2
0
.
0
8
8
0
.
1
6
4
0
.
3
8
2
0
.
4
1
7
H
a
l
f
-
n
o
r
mal
H
1
(
H
0
r
e
j
e
c
t
e
d
)
0
.
0
0
0
0
.
0
0
0
0
.
0
0
0
0
.
0
0
0
0
.
0
0
0
0
.
0
0
1
0
.
0
0
0
0
.
0
0
0
I
n
v
e
r
se
G
a
u
ss
i
a
n
H
0
a
c
c
e
p
t
e
d
0
.
3
8
3
0
.
6
3
0
.
2
7
7
0
.
4
0
7
0
.
3
7
6
0
.
5
9
4
0
.
7
7
9
0
.
7
4
1
N
a
k
a
g
a
mi
H
0
a
c
c
e
p
t
e
d
0
.
3
7
9
0
.
6
5
8
0
.
2
2
6
0
.
3
4
5
0
.
1
7
3
0
.
3
8
6
0
.
5
7
2
0
.
6
7
8
S
t
a
b
l
e
H
0
a
c
c
e
p
t
e
d
0
.
3
7
9
0
.
6
7
2
0
.
8
7
8
0
.
8
7
7
0
.
7
2
1
0
.
7
3
0
.
4
7
7
0
.
6
1
6
T
l
o
c
a
t
i
o
n
-
s
c
a
l
e
H
0
a
c
c
e
p
t
e
d
0
.
3
7
8
0
.
6
7
1
0
.
8
1
8
0
.
6
7
3
0
.
1
2
3
0
.
2
8
9
0
.
4
7
8
0
.
6
1
6
T
ab
le
2
.
E
x
ten
d
ed
p
-
v
al
u
es f
r
o
m
KS a
n
d
AD
test
s
f
o
r
s
en
s
itiv
ity
an
aly
s
is
(
n
=1
5
,
4
0
,
4
5
,
5
0
)
D
i
st
r
i
b
u
t
i
o
n
H
y
p
o
t
h
e
s
e
s
n
=
1
5
n
=
4
0
n
=
4
5
n
=
5
0
−
KS
−
AD
−
KS
−
AD
−
KS
−
AD
−
KS
−
AD
R
i
c
i
a
n
H
0
a
c
c
e
p
t
e
d
0
.
7
4
5
0
.
8
7
9
0
.
3
6
1
0
.
2
2
6
0
.
6
8
4
0
.
4
1
9
0
.
3
7
2
0
.
4
B
i
r
n
b
a
u
m
–
S
a
u
n
d
e
r
s
H
0
a
c
c
e
p
t
e
d
0
.
8
3
1
0
.
9
4
9
0
.
0
6
1
0
.
0
7
6
0
.
8
5
7
0
.
7
4
3
0
.
1
8
5
0
.
1
0
9
Ex
t
r
e
me
v
a
l
u
e
H
0
a
c
c
e
p
t
e
d
0
.
6
9
6
0
.
6
0
9
0
.
4
3
0
.
5
1
7
0
.
1
0
7
0
.
0
6
3
0
.
0
4
7
0
.
1
0
1
H
a
l
f
-
n
o
r
mal
H
1
(
H
0
r
e
j
e
c
t
e
d
)
0
.
0
0
1
0
.
0
1
0
.
0
0
0
0
.
0
0
0
0
.
0
0
0
0
.
0
0
0
0
.
0
0
0
0
.
0
0
0
I
n
v
e
r
se
G
a
u
ss
i
a
n
H
0
a
c
c
e
p
t
e
d
0
.
8
3
1
0
.
9
4
9
0
.
0
5
9
0
.
0
7
4
0
.
8
5
7
0
.
7
4
5
0
.
1
8
4
0
.
1
0
5
N
a
k
a
g
a
mi
H
0
a
c
c
e
p
t
e
d
0
.
7
7
1
0
.
9
0
6
0
.
2
0
9
0
.
1
6
3
0
.
7
6
2
0
.
5
2
7
0
.
6
3
0
.
5
5
7
S
t
a
b
l
e
H
0
a
c
c
e
p
t
e
d
0
.
9
9
5
0
.
9
9
9
0
.
3
8
0
.
2
3
3
0
.
7
6
9
0
.
8
2
8
0
.
3
2
0
.
3
4
9
T
l
o
c
a
t
i
o
n
-
s
c
a
l
e
H
0
a
c
c
e
p
t
e
d
0
.
7
5
1
0
.
8
8
2
0
.
3
7
7
0
.
2
3
3
0
.
9
1
6
0
.
5
8
9
0
.
3
1
9
0
.
3
5
1
T
h
e
s
tatis
tica
l
o
u
tco
m
es
ar
e
v
is
u
ally
s
u
m
m
ar
ized
in
Fig
u
r
e
2
,
wh
ich
p
r
esen
ts
a
Gr
o
u
p
ed
Vis
u
al
T
ax
o
n
o
m
y
b
ased
o
n
th
e
KS
an
d
AD
ev
alu
atio
n
s
.
T
h
is
f
ig
u
r
e
r
etain
s
b
o
th
th
e
KS
s
ta
tis
tic
v
alu
es
(
as
h
o
r
izo
n
tal
b
ar
len
g
th
s
)
an
d
AD
s
tatis
tic
v
alu
es
(
n
u
m
er
ically
d
is
p
lay
ed
o
n
ea
ch
b
ar
)
,
p
r
o
v
id
in
g
a
co
m
p
r
eh
e
n
s
iv
e
an
d
in
tu
itiv
e
q
u
an
titativ
e
co
m
p
ar
is
o
n
ac
r
o
s
s
m
o
d
els.
T
h
e
tax
o
n
o
m
y
o
r
g
an
izes
all
eig
h
t
d
is
tr
ib
u
tio
n
m
o
d
els
ac
r
o
s
s
f
o
u
r
s
am
p
le
s
izes
(
n
=
2
0
,
25,
30,
3
5
)
with
in
a
2
×
2
m
atr
i
x
lay
o
u
t,
f
ac
ilit
atin
g
clea
r
v
is
u
al
in
ter
p
r
etatio
n
.
Fig
u
r
e
2
.
Gr
o
u
p
e
d
KS
–
AD
ev
alu
atio
n
ac
r
o
s
s
s
am
p
le
s
izes (
n
=2
0
,
2
5
,
3
0
,
3
5
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
15
,
No
.
3
,
J
u
n
e
20
26
:
2
0
8
1
-
2
0
9
1
2086
T
h
is
v
is
u
al
tax
o
n
o
m
y
f
u
n
ctio
n
s
n
o
t
o
n
ly
as
a
co
m
p
ar
ativ
e
f
it
q
u
ality
to
o
l
b
u
t
also
as
a
p
r
o
p
o
s
ed
class
if
icatio
n
f
r
am
ewo
r
k
f
o
r
u
n
d
er
s
tan
d
i
n
g
d
is
tr
ib
u
tio
n
al
b
eh
av
io
r
.
B
y
ex
p
licitly
g
r
o
u
p
i
n
g
m
o
d
els
b
ased
o
n
th
eir
em
p
ir
ical
p
er
f
o
r
m
an
ce
,
it
en
ab
les
r
esear
ch
e
r
s
to
id
en
tify
c
o
n
s
is
ten
t,
u
n
s
tab
le,
o
r
o
u
tlier
-
p
r
o
n
e
d
is
tr
ib
u
tio
n
s
ac
r
o
s
s
v
a
r
y
in
g
s
am
p
le
s
izes.
Fo
r
i
n
s
tan
ce
,
th
e
co
n
s
is
ten
tly
lo
w
KS
an
d
AD
v
alu
es
ass
o
ciate
d
with
th
e
in
v
er
s
e
Gau
s
s
ian
an
d
ex
tr
em
e
v
alu
e
d
is
tr
ib
u
tio
n
s
in
d
icate
th
eir
r
o
b
u
s
tn
ess
in
r
ep
r
esen
tin
g
r
ea
l
-
wo
r
ld
ch
lo
r
o
p
h
y
ll
v
a
r
iab
ilit
y
.
T
h
is
is
esp
ec
ially
im
p
o
r
tan
t
f
o
r
d
ete
ctin
g
ec
o
lo
g
ically
s
ig
n
if
ican
t
p
h
en
o
m
en
a
s
u
ch
as
s
ea
s
o
n
al
alg
al
b
lo
o
m
s
an
d
en
v
ir
o
n
m
en
tal
s
tr
ess
o
r
s
.
I
n
co
n
t
r
ast,
th
e
h
alf
-
n
o
r
m
al
d
is
tr
ib
u
tio
n
ex
h
ib
its
clea
r
s
ig
n
s
o
f
in
s
tab
ilit
y
,
p
ar
ticu
lar
l
y
at
s
m
aller
s
am
p
le
s
izes,
wh
ich
lim
its
it
s
r
eliab
ilit
y
f
o
r
ec
o
l
o
g
ical
in
t
er
p
r
etatio
n
.
I
ts
h
ig
h
AD
v
alu
es
ac
r
o
s
s
m
u
ltip
le
s
am
p
le
s
izes
in
d
icate
p
o
o
r
Go
F
an
d
p
o
ten
tial
m
is
class
if
icatio
n
r
is
k
s
.
Hen
ce
,
th
e
tax
o
n
o
m
y
co
n
tr
ib
u
tes
n
o
t
o
n
ly
to
s
tatis
tical
m
o
d
el
s
elec
tio
n
b
ased
o
n
G
o
F
b
u
t
also
ad
d
s
ec
o
lo
g
ical
r
elev
an
ce
—
d
is
tin
g
u
is
h
in
g
b
etwe
en
m
o
d
els
th
at
ca
p
tu
r
e
s
tab
le
ch
l
o
r
o
p
h
y
ll
b
e
h
av
io
r
an
d
th
o
s
e
t
h
at
s
ig
n
al
h
i
g
h
v
ar
iab
ilit
y
o
r
ec
o
lo
g
ical
ex
tr
e
m
es
(
e.
g
.
,
p
o
ten
tial b
lo
o
m
ev
e
n
ts
)
.
Fig
u
r
e
3
d
is
p
lay
s
th
e
s
p
atial
d
is
tr
ib
u
tio
n
o
f
ch
lo
r
o
p
h
y
ll
s
am
p
lin
g
p
o
in
ts
an
d
th
e
r
esu
ltin
g
d
is
tr
ib
u
tio
n
-
b
ased
clu
s
ter
s
ac
r
o
s
s
s
elec
ted
m
ar
in
e
lo
ca
tio
n
s
.
Her
e,
c
lu
s
ter
1
in
clu
d
es
o
n
ly
th
e
h
alf
-
n
o
r
m
al
d
is
tr
ib
u
tio
n
an
d
is
v
is
u
ally
an
d
s
tatis
tically
d
is
tin
ct
f
r
o
m
c
lu
s
ter
2
,
wh
ich
co
m
p
r
is
es
th
e
r
em
ain
in
g
s
ev
en
m
o
d
els.
I
n
s
et
m
a
p
s
f
u
r
th
e
r
h
i
g
h
lig
h
t
two
f
o
cu
s
r
e
g
io
n
s
—
th
e
B
lack
Sea
an
d
W
est
Af
r
ica
—
em
p
h
asizin
g
th
e
d
is
tr
ib
u
tio
n
o
f
s
am
p
le
s
izes
u
s
ed
ac
r
o
s
s
d
if
f
er
en
t
m
ar
in
e
e
n
v
ir
o
n
m
en
ts
.
T
h
is
s
p
atial
clu
s
ter
in
g
v
is
u
aliza
tio
n
r
ein
f
o
r
ce
s
th
e
in
te
r
p
r
etatio
n
o
f
m
o
d
el
b
eh
a
v
io
r
u
n
d
er
v
ar
y
in
g
en
v
ir
o
n
m
en
tal
co
n
d
itio
n
s
an
d
s
u
p
p
o
r
ts
its
ap
p
licatio
n
in
r
e
g
io
n
al
c
h
lo
r
o
p
h
y
ll m
o
n
ito
r
in
g
s
tr
ateg
ies.
Fig
u
r
e
3
.
C
h
lo
r
o
p
h
y
ll
-
a
s
am
p
l
in
g
lo
ca
tio
n
s
an
d
clu
s
ter
in
g
r
e
s
u
lts
with
r
eg
io
n
al
in
s
ets
3
.
3
.
E
v
a
lua
t
i
o
n o
f
m
o
del c
o
m
plex
it
y
a
nd
f
it
us
ing
info
r
m
a
t
io
n c
rit
er
ia
T
o
c
o
m
p
l
e
m
e
n
t
t
h
e
G
o
F
r
e
s
u
l
t
s
,
t
h
i
s
s
t
u
d
y
e
m
p
l
o
y
e
d
f
i
v
e
i
n
f
o
r
m
a
t
i
o
n
c
r
i
t
e
r
i
a
—
H
Q
C
,
C
A
I
C
,
A
I
C
c
,
B
I
C
,
a
n
d
A
I
C
—
t
o
e
v
a
l
u
a
t
e
t
h
e
t
r
a
d
e
-
o
f
f
b
e
t
w
e
e
n
m
o
d
e
l
f
i
t
a
n
d
c
o
m
p
l
e
x
i
t
y
.
T
h
e
s
e
c
r
i
t
e
r
i
a
s
u
p
p
o
r
t
t
h
e
i
d
e
n
t
i
f
i
c
a
t
i
o
n
o
f
m
o
d
e
l
s
t
h
a
t
a
c
h
i
e
v
e
b
o
t
h
h
i
g
h
a
c
c
u
r
a
c
y
a
n
d
p
a
r
s
i
m
o
n
y
,
t
h
e
r
e
b
y
m
i
n
i
m
i
z
i
n
g
t
h
e
r
i
s
k
o
f
o
v
e
r
f
i
t
t
i
n
g
.
T
a
b
l
e
3
p
r
e
s
e
n
t
s
t
h
e
a
v
e
r
a
g
e
i
n
f
o
r
m
a
t
i
o
n
c
r
i
t
e
r
i
o
n
v
a
l
u
e
s
a
c
r
o
s
s
f
o
u
r
s
a
m
p
l
e
s
i
z
e
s
(
n
=
2
0
,
2
5
,
3
0
,
3
5
)
.
T
h
e
in
v
e
r
s
e
Gau
s
s
ian
an
d
e
x
tr
em
e
v
al
u
e
d
is
tr
ib
u
tio
n
s
c
o
n
s
is
ten
tly
y
ield
ed
t
h
e
lo
wes
t
s
co
r
es,
in
d
icatin
g
s
tr
o
n
g
p
er
f
o
r
m
a
n
ce
in
b
o
th
s
tatis
tical
f
it
an
d
m
o
d
el
p
ar
s
im
o
n
y
.
T
h
ese
m
o
d
els
also
p
er
f
o
r
m
ed
well
u
n
d
e
r
th
e
m
o
r
e
s
tr
in
g
en
t
p
en
alties im
p
o
s
ed
b
y
C
AI
C
an
d
HQC,
r
ein
f
o
r
cin
g
th
eir
r
o
b
u
s
tn
ess
.
I
n
co
n
tr
ast,
th
e
h
alf
-
n
o
r
m
al
d
i
s
tr
ib
u
tio
n
ex
h
ib
ited
th
e
h
ig
h
e
s
t
(
i.e
.
,
least
f
av
o
r
ab
le)
v
alu
es
ac
r
o
s
s
all
cr
iter
ia,
r
ef
lectin
g
its
lim
ited
s
u
itab
ilit
y
f
o
r
m
o
d
elin
g
c
h
lo
r
o
p
h
y
ll
-
a
d
ata
—
p
ar
ticu
lar
ly
in
s
ce
n
ar
io
s
in
v
o
lv
in
g
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
A
d
va
n
ce
d
in
feren
tia
l sta
tis
tics
a
n
d
d
a
ta
min
in
g
fo
r
ch
lo
r
o
p
h
yll
d
is
tr
ib
u
tio
n
clu
s
teri
n
g
…
(
F
elix
R
eb
a
)
2087
o
u
tlier
s
o
r
d
ata
h
eter
o
g
e
n
eity
.
T
h
ese
f
in
d
in
g
s
ar
e
co
n
s
is
ten
t w
ith
th
e
ea
r
lier
Go
F a
n
aly
s
is
a
n
d
co
n
f
ir
m
in
v
er
s
e
Gau
s
s
ian
an
d
ex
tr
em
e
v
alu
e
as
th
e
m
o
s
t
r
eliab
le
ca
n
d
id
ate
m
o
d
els.
Su
ch
co
n
s
is
ten
cy
f
u
r
th
er
s
u
p
p
o
r
ts
th
eir
s
elec
tio
n
in
th
e
s
u
b
s
eq
u
en
t c
l
u
s
ter
in
g
an
d
class
if
icatio
n
s
tag
es.
T
ab
le
3
.
Av
e
r
ag
e
in
f
o
r
m
atio
n
cr
iter
ia
v
alu
es f
o
r
c
h
lo
r
o
p
h
y
ll d
is
tr
ib
u
tio
n
m
o
d
els
D
i
st
r
i
b
u
t
i
o
n
A
v
e
r
a
g
e
c
r
i
t
e
r
i
a
v
a
l
u
e
(
n
=
2
0
,
2
5
,
3
0
,
3
5
)
A
I
C
B
I
C
A
I
C
c
C
A
I
C
HQC
R
i
c
i
a
n
-
9
3
.
7
2
9
-
9
1
.
1
4
3
-
1
3
.
6
1
-
1
3
.
3
2
4
-
9
2
.
9
7
B
i
r
n
b
a
u
m
–
S
a
u
n
d
e
r
s
-
9
5
.
6
7
7
-
9
3
.
0
9
1
-
9
4
.
6
6
9
-
9
4
.
3
8
3
-
9
4
.
9
1
8
Ex
t
r
e
me
v
a
l
u
e
-
8
8
.
0
4
6
-
8
5
.
4
6
-
9
5
.
0
3
5
-
9
4
.
7
4
9
-
8
7
.
2
8
7
H
a
l
f
-
n
o
r
mal
-
2
5
.
9
2
4
-
2
3
.
3
3
8
-
9
1
.
9
0
3
-
9
1
.
6
1
7
-
2
5
.
1
6
5
I
n
v
e
r
se
G
a
u
ss
i
a
n
-
9
5
.
6
8
4
-
9
3
.
0
9
9
-
9
3
.
5
7
6
-
9
3
.
2
9
1
-
9
4
.
9
2
5
N
a
k
a
g
a
mi
-
9
4
.
6
3
-
9
2
.
0
4
4
-
9
3
.
0
5
1
-
9
2
.
7
6
6
-
9
3
.
8
7
1
S
t
a
b
l
e
-
9
1
.
5
2
6
-
8
6
.
3
5
5
-
4
6
.
4
8
1
-
4
5
.
4
5
1
-
9
0
.
0
0
8
T
l
o
c
a
t
i
o
n
-
sc
a
l
e
-
9
2
.
0
7
6
-
8
8
.
1
9
7
-
9
5
.
0
3
-
9
4
.
4
3
6
-
9
0
.
9
3
8
3
.
4
.
Clus
t
er
ing
a
na
ly
s
is
T
h
is
s
ec
tio
n
ap
p
lies
k
-
m
ea
n
s
clu
s
ter
in
g
to
g
r
o
u
p
p
r
o
b
ab
il
ity
d
is
tr
ib
u
tio
n
m
o
d
els
b
ased
o
n
th
eir
KS
an
d
AD
s
tatis
tics
.
C
lu
s
te
r
q
u
ality
is
ass
e
s
s
ed
u
s
in
g
th
e
s
ilh
o
u
ette
in
d
ex
,
wh
er
e
v
alu
es
ap
p
r
o
ac
h
in
g
1
in
d
icate
well
-
s
ep
ar
ated
an
d
c
o
h
esiv
e
clu
s
ter
s
.
Un
lik
e
tr
a
d
i
tio
n
al
m
o
d
el
-
by
-
m
o
d
el
e
v
alu
atio
n
,
th
is
ap
p
r
o
ac
h
em
p
h
asizes
co
llectiv
e
d
is
tr
ib
u
tio
n
al
b
e
h
av
io
r
ac
r
o
s
s
v
a
r
y
i
n
g
d
ata
co
m
p
lex
ities
,
o
f
f
er
i
n
g
a
m
o
r
e
in
te
g
r
ativ
e
p
er
s
p
ec
tiv
e
o
n
m
o
d
el
p
er
f
o
r
m
an
ce
.
C
lu
s
ter
in
g
was
in
itially
p
er
f
o
r
m
ed
u
s
in
g
s
am
p
le
s
izes
o
f
n
=2
0
,
25,
3
0
,
an
d
3
5
,
an
d
s
u
b
s
eq
u
en
tly
ex
t
en
d
ed
to
n
=1
5
,
40,
4
5
,
an
d
5
0
to
ev
alu
ate
th
e
r
o
b
u
s
tn
ess
o
f
th
e
r
esu
lts
.
As
s
h
o
wn
in
T
ab
le
4
,
th
e
in
v
er
s
e
Gau
s
s
ian
an
d
ex
tr
em
e
v
alu
e
d
is
tr
ib
u
tio
n
s
co
n
s
is
ten
tly
ap
p
ea
r
ed
in
well
-
d
ef
in
e
d
clu
s
ter
s
ac
r
o
s
s
a
ll
s
am
p
le
s
ize
s
,
d
em
o
n
s
tr
atin
g
s
tr
o
n
g
s
tab
ilit
y
an
d
h
ig
h
s
ilh
o
u
ette
in
d
ex
s
co
r
es.
I
n
co
n
tr
ast,
th
e
Half
-
n
o
r
m
al
d
is
tr
ib
u
tio
n
was
r
ep
ea
ted
ly
is
o
lated
in
c
lu
s
ter
1
,
ex
h
ib
itin
g
lo
w
SH
v
al
u
es,
wh
ich
co
n
f
ir
m
s
its
in
s
tab
ilit
y
wh
en
ev
alu
ated
u
s
in
g
th
e
co
m
b
in
ed
KS
–
AD
m
etr
ics.
T
ab
le
4
.
Sil
h
o
u
ette
in
d
ex
v
alu
es f
r
o
m
K
-
m
ea
n
s
clu
s
ter
in
g
ac
r
o
s
s
d
is
tr
ib
u
tio
n
m
o
d
els
D
i
st
r
i
b
u
t
i
o
n
K
-
me
a
n
s
c
l
u
s
t
e
r
e
v
a
l
u
a
t
i
o
n
w
i
t
h
S
H
n
=
1
5
v
=
2
0
n
=
2
5
n
=
3
0
n
=
3
5
n
=
4
0
n
=
4
5
n
=
5
0
R
i
c
i
a
n
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
B
i
r
n
b
a
u
m
–
G
a
u
n
d
e
r
s
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
Ex
t
r
e
me
v
a
l
u
e
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
H
a
l
f
-
n
o
r
mal
C
l
u
st
e
r
1
C
l
u
st
e
r
1
C
l
u
st
e
r
1
C
l
u
st
e
r
1
C
l
u
st
e
r
1
C
l
u
st
e
r
1
C
l
u
st
e
r
1
C
l
u
st
e
r
1
I
n
v
e
r
se
G
a
u
ss
i
a
n
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
N
a
k
a
g
a
mi
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
S
t
a
b
l
e
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
T
l
o
c
a
t
i
o
n
-
s
c
a
l
e
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
C
l
u
st
e
r
2
Dis
tr
ib
u
tio
n
s
s
u
ch
as
B
ir
n
b
au
m
–
Sau
n
d
er
s
an
d
Nak
a
g
a
m
i
d
is
p
lay
ed
m
o
d
er
ate
c
o
n
s
is
ten
cy
—
f
r
eq
u
e
n
tly
ass
ig
n
ed
to
c
lu
s
ter
2
,
b
u
t
with
less
d
is
tin
ct
s
ep
ar
atio
n
co
m
p
a
r
ed
to
th
e
to
p
-
p
er
f
o
r
m
in
g
m
o
d
els.
T
h
ese
f
in
d
i
n
g
s
u
n
d
er
s
co
r
e
th
e
ef
f
ec
tiv
e
n
ess
o
f
u
s
in
g
KS a
n
d
AD
s
tatis
tics
a
s
clu
s
ter
in
g
attr
ib
u
tes an
d
p
r
o
v
id
e
ad
d
itio
n
al
in
s
ig
h
t
in
to
d
is
tr
ib
u
tio
n
al
b
eh
av
io
r
b
ey
o
n
d
in
d
i
v
id
u
al
m
o
d
el
f
it.
T
h
is
in
d
icate
s
th
at
th
ese
m
o
d
els
o
f
f
er
r
ea
s
o
n
ab
le
p
e
r
f
o
r
m
an
ce
,
alth
o
u
g
h
th
ey
ar
e
less
s
tab
le
co
m
p
ar
ed
to
th
e
b
est
-
p
er
f
o
r
m
in
g
d
is
tr
ib
u
tio
n
s
.
T
o
v
is
u
alize
th
ese
f
in
d
in
g
s
,
Fig
u
r
e
4
co
m
p
ar
es e
m
p
ir
ical
ch
l
o
r
o
p
h
y
ll
-
a
d
ata
(
g
r
ay
h
is
to
g
r
a
m
)
ag
ain
s
t
f
itted
d
is
tr
ib
u
tio
n
s
f
o
r
f
o
u
r
s
a
m
p
le
s
izes
(
n
=
2
0
–
3
5
)
,
wh
er
e
ea
ch
s
u
b
p
l
o
t
s
h
o
ws
th
e
o
v
er
la
y
o
f
em
p
ir
ical
d
ata
an
d
f
itted
PDFs
o
f
eig
h
t
ca
n
d
id
ate
d
is
tr
ib
u
tio
n
s
u
n
d
er
d
if
f
er
en
t
s
am
p
lin
g
co
n
d
itio
n
s
:
Fig
u
r
e
4
(
a
)
n
=2
0
,
Fig
u
r
e
4
(
b
)
n
=2
5
,
Fig
u
r
e
4
(
c)
n
=3
0
,
an
d
Fig
u
r
e
4
(
d
)
n
=3
5
.
I
n
v
er
s
e
Gau
s
s
ian
an
d
ex
tr
em
e
v
alu
e
clo
s
ely
f
o
llo
w
th
e
em
p
ir
ical
d
ata
an
d
ca
p
tu
r
e
asy
m
m
etr
y
an
d
ex
tr
e
m
es,
wh
ile
h
alf
-
n
o
r
m
al
c
o
n
s
is
ten
tly
d
ev
iates
at
th
e
d
is
tr
ib
u
tio
n
tails
,
co
n
f
i
r
m
in
g
p
o
o
r
ec
o
lo
g
ical
f
it.
Oth
er
m
o
d
els
lik
e
B
ir
n
b
au
m
–
Sau
n
d
er
s
an
d
Na
k
ag
am
i
p
er
f
o
r
m
r
ea
s
o
n
a
b
ly
b
u
t a
r
e
less
p
r
ec
is
e.
Ov
er
all,
th
is
clu
s
ter
in
g
f
r
am
ewo
r
k
p
r
o
v
id
es
d
ee
p
er
in
s
ig
h
t
in
to
m
o
d
el
b
eh
av
io
r
,
im
p
r
o
v
in
g
r
eliab
ilit
y
in
s
elec
tio
n
an
d
e
n
h
an
cin
g
ec
o
lo
g
ical
in
ter
p
r
et
atio
n
.
Mo
d
els
f
o
r
m
in
g
d
is
tin
ct,
s
tab
le
clu
s
ter
s
—
p
ar
ticu
lar
ly
in
v
er
s
e
Gau
s
s
ian
an
d
ex
tr
e
m
e
v
al
u
e
—
ar
e
b
etter
s
u
ited
to
ca
p
tu
r
e
c
h
lo
r
o
p
h
y
ll
v
ar
iab
ilit
y
r
elev
a
n
t
to
p
h
en
o
m
en
a
lik
e
b
lo
o
m
s
o
r
n
u
tr
ien
t
u
p
wellin
g
.
I
n
c
o
n
tr
ast,
u
n
s
tab
le
m
o
d
els
lik
e
h
al
f
-
n
o
r
m
al
m
ay
lac
k
ec
o
lo
g
ical
s
ig
n
if
ican
ce
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
15
,
No
.
3
,
J
u
n
e
20
26
:
2
0
8
1
-
2
0
9
1
2088
(
a)
(
b
)
(
c)
(
d
)
Fig
u
r
e
4
.
C
o
m
p
a
r
is
o
n
o
f
em
p
i
r
ical
ch
lo
r
o
p
h
y
ll
-
a
d
ata
(
g
r
ay
h
is
to
g
r
am
)
an
d
f
itted
p
r
o
b
a
b
ilit
y
d
en
s
ity
f
u
n
ctio
n
s
(
PDFs
)
o
f
eig
h
t c
an
d
id
ate
d
is
tr
ib
u
tio
n
s
ac
r
o
s
s
d
if
f
er
en
t sam
p
le
s
izes: (
a)
n
=2
0
,
(
b
)
n
=
2
5
,
(
c)
n
=3
0
,
an
d
(
d
)
n
=
3
5
3
.
4
.
1
.
M
et
ho
do
lo
g
ica
l
co
ns
idera
t
io
ns
a
nd
po
licy
inte
g
ra
t
i
o
n
A
k
ey
lim
itatio
n
o
f
th
is
s
tu
d
y
lies
in
it
s
r
elian
ce
o
n
u
n
iv
ar
iat
e
p
r
o
b
ab
ilit
y
d
is
tr
ib
u
tio
n
m
o
d
els,
wh
ich
d
o
n
o
t
ac
c
o
u
n
t
f
o
r
s
p
atial
d
e
p
en
d
en
cies
o
r
r
eg
io
n
al
v
ar
iab
ilit
y
in
m
ar
in
e
ch
lo
r
o
p
h
y
ll
d
a
ta.
W
h
ile
clu
s
ter
in
g
m
eth
o
d
s
s
u
ch
as
k
-
m
ea
n
s
a
n
d
th
e
s
ilh
o
u
ette
in
d
e
x
p
r
o
v
id
e
v
alu
ab
le
in
s
ig
h
ts
in
to
d
is
tr
ib
u
tio
n
al
b
e
h
av
io
r
,
th
e
y
o
v
er
lo
o
k
s
p
atial
r
elatio
n
s
h
ip
s
am
o
n
g
s
am
p
lin
g
p
o
in
ts
—
p
o
ten
tially
lim
itin
g
th
eir
ec
o
lo
g
ical
r
ep
r
esen
tativ
en
ess
.
No
n
eth
ele
s
s
,
s
p
ec
if
ic
d
is
tr
ib
u
tio
n
s
—
s
u
ch
as
th
e
in
v
er
s
e
Gau
s
s
ian
,
ex
tr
em
e
v
alu
e
,
an
d
B
ir
n
b
au
m
–
Sau
n
d
er
s
—
ef
f
ec
tiv
ely
ca
p
tu
r
e
k
ey
f
ea
tu
r
es
lik
e
s
k
ewn
ess
,
s
tab
ilit
y
,
an
d
h
e
a
v
y
tails
,
wh
ic
h
ar
e
o
f
ten
ass
o
ciate
d
with
en
v
ir
o
n
m
en
tal
s
tr
ess
o
r
s
(
e.
g
.
,
alg
al
b
lo
o
m
s
)
.
I
n
teg
r
atin
g
th
ese
m
o
d
els
in
to
p
r
ed
ictiv
e
f
r
am
ewo
r
k
s
m
ay
im
p
r
o
v
e
ch
lo
r
o
p
h
y
ll
f
lu
ctu
atio
n
esti
m
ates
an
d
s
u
p
p
o
r
t
ec
o
s
y
s
te
m
-
b
ased
s
tr
ateg
ies,
in
clu
d
in
g
f
is
h
er
ies p
lan
n
in
g
a
n
d
eu
tr
o
p
h
icatio
n
m
itig
atio
n
.
T
o
en
h
a
n
ce
b
o
th
m
o
d
el
r
o
b
u
s
tn
ess
an
d
ec
o
lo
g
ical
r
ele
v
an
ce
,
f
u
tu
r
e
r
esear
ch
s
h
o
u
ld
ex
p
lo
r
e
m
u
ltiv
ar
iate
o
r
s
p
atially
ex
p
licit
ap
p
r
o
ac
h
es,
s
u
ch
as
g
eo
s
tatis
tical
m
o
d
els
(
e.
g
.
,
k
r
ig
i
n
g
)
,
s
p
atial
au
to
r
eg
r
ess
io
n
,
o
r
s
p
atio
tem
p
o
r
al
m
ac
h
in
e
lear
n
i
n
g
tech
n
iq
u
es.
T
h
ese
m
eth
o
d
s
ca
n
ac
co
u
n
t
f
o
r
s
p
atial
au
to
co
r
r
elatio
n
an
d
f
ac
ilit
ate
in
teg
r
atio
n
with
r
em
o
te
s
en
s
in
g
an
d
g
eo
g
r
ap
h
ic
in
f
o
r
m
a
tio
n
s
y
s
te
m
lay
er
s
,
s
u
p
p
o
r
tin
g
r
ea
l
-
tim
e
m
o
n
ito
r
i
n
g
an
d
lo
ca
lized
m
ar
i
n
e
ec
o
s
y
s
tem
m
an
ag
em
en
t.
I
n
ad
d
itio
n
,
th
e
clu
s
ter
in
g
f
r
am
ewo
r
k
c
o
u
ld
b
e
s
tr
en
g
t
h
en
ed
b
y
in
co
r
p
o
r
atin
g
alter
n
ativ
e
Go
F
s
tatis
tics
—
s
u
ch
as
th
e
C
r
am
ér
–
v
o
n
Mises
,
L
ilief
o
r
s
,
o
r
Sh
a
p
ir
o
–
W
ilk
test
s
—
wh
ich
p
r
o
v
id
e
c
o
m
p
lem
en
tar
y
p
er
s
p
ec
tiv
es
o
n
d
is
tr
ib
u
tio
n
al
f
it.
T
h
ese
test
s
ar
e
p
ar
ticu
lar
ly
u
s
ef
u
l
in
s
m
all
-
s
am
p
le
co
n
tex
ts
o
r
wh
en
tar
g
etin
g
s
p
ec
if
ic
d
is
tr
ib
u
tio
n
al
ch
ar
ac
ter
is
tics
,
th
er
eb
y
en
r
ich
i
n
g
th
e
clu
s
ter
in
g
f
ea
tu
r
e
s
p
ac
e
an
d
im
p
r
o
v
in
g
m
o
d
el
d
is
cr
im
in
atio
n
.
Fro
m
an
o
p
e
r
atio
n
al
p
er
s
p
ec
tiv
e,
th
e
p
r
o
p
o
s
ed
m
o
d
elin
g
f
r
am
ewo
r
k
h
o
ld
s
s
tr
o
n
g
p
o
ten
tial
f
o
r
in
teg
r
atio
n
in
to
s
atellite
-
b
ased
d
ec
is
io
n
s
u
p
p
o
r
t
s
y
s
tem
s
(
DSS).
B
y
co
m
b
in
in
g
s
tatis
ti
ca
l
m
o
d
elin
g
with
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
A
d
va
n
ce
d
in
feren
tia
l sta
tis
tics
a
n
d
d
a
ta
min
in
g
fo
r
ch
lo
r
o
p
h
yll
d
is
tr
ib
u
tio
n
clu
s
teri
n
g
…
(
F
elix
R
eb
a
)
2089
clu
s
ter
in
g
,
it
en
a
b
les
th
e
d
elin
ea
tio
n
o
f
ec
o
l
o
g
ical
zo
n
es,
s
en
s
o
r
ca
lib
r
atio
n
,
a
n
d
ea
r
ly
war
n
in
g
s
y
s
te
m
en
h
an
ce
m
e
n
t.
Ma
r
i
n
e
s
ta
k
e
h
o
l
d
e
r
s
—
s
u
c
h
as
f
is
h
er
ies
ag
e
n
ci
es,
p
o
li
cy
m
ak
er
s
,
an
d
e
n
v
ir
o
n
m
e
n
ta
l
m
a
n
a
g
e
r
s
—
ca
n
ap
p
l
y
t
h
is
f
r
a
m
e
wo
r
k
to
s
u
p
p
o
r
t
d
at
a
-
d
r
i
v
e
n
,
e
c
o
l
o
g
ic
all
y
i
n
f
o
r
m
ed
m
a
r
i
n
e
r
es
o
u
r
c
e
g
o
v
er
n
a
n
c
e.
4.
CO
NCLU
SI
O
N
T
h
is
s
tu
d
y
p
r
o
p
o
s
ed
an
in
te
g
r
ated
s
tatis
tica
l
f
r
am
ewo
r
k
f
o
r
ev
alu
atin
g
an
d
clu
s
ter
in
g
p
r
o
b
ab
ilit
y
d
is
tr
ib
u
tio
n
m
o
d
els
to
an
al
y
ze
ch
lo
r
o
p
h
y
ll
-
a
v
ar
iab
ilit
y
i
n
m
ar
in
e
en
v
ir
o
n
m
e
n
ts
.
E
ig
h
t
ca
n
d
id
ate
d
is
tr
ib
u
tio
n
s
wer
e
ass
es
s
ed
u
s
in
g
Go
F
test
s
an
d
clu
s
ter
in
g
tech
n
iq
u
es
b
ased
o
n
m
o
d
el
p
er
f
o
r
m
an
ce
m
etr
ics.
T
h
e
r
esu
lts
in
d
icate
th
at
th
e
in
v
er
s
e
Gau
s
s
ian
an
d
ex
tr
em
e
v
alu
e
d
is
tr
ib
u
tio
n
s
co
n
s
is
ten
tly
p
r
o
v
i
d
e
d
th
e
m
o
s
t
ac
cu
r
ate
r
ep
r
esen
tatio
n
s
o
f
ch
lo
r
o
p
h
y
ll
d
is
tr
ib
u
tio
n
ac
r
o
s
s
v
ar
y
in
g
s
am
p
le
s
izes.
T
h
ese
m
o
d
els
al
s
o
ex
h
ib
ited
s
tr
o
n
g
clu
s
ter
in
g
p
er
f
o
r
m
an
ce
,
as
r
ef
lecte
d
b
y
h
ig
h
s
ilh
o
u
ette
in
d
ex
s
co
r
es,
in
d
icatin
g
th
eir
s
tab
ilit
y
an
d
clea
r
s
ep
ar
atio
n
f
r
o
m
o
th
er
m
o
d
els
.
I
n
co
n
tr
ast,
th
e
h
alf
-
n
o
r
m
al
d
is
tr
ib
u
tio
n
d
em
o
n
s
tr
ated
s
u
b
s
tan
tial
m
is
f
it
an
d
in
s
tab
ilit
y
,
p
ar
ticu
lar
ly
at
s
m
aller
s
am
p
le
s
izes.
Me
an
wh
ile,
m
o
d
els
s
u
ch
as
B
ir
n
b
au
m
–
Sau
n
d
er
s
an
d
Nak
ag
am
i
s
h
o
wed
m
o
d
e
r
ately
s
tr
o
n
g
r
esu
lts
an
d
m
a
y
s
er
v
e
as
co
m
p
lem
en
tar
y
o
p
tio
n
s
f
o
r
f
u
tu
r
e
e
x
p
lo
r
atio
n
.
B
ey
o
n
d
m
eth
o
d
o
lo
g
ical
in
s
ig
h
ts
,
th
e
p
r
o
p
o
s
ed
f
r
am
ew
o
r
k
o
f
f
er
s
p
r
ac
tical
r
elev
an
ce
f
o
r
in
teg
r
atio
n
in
to
s
atellite
-
b
ased
DSS
—
s
u
p
p
o
r
tin
g
en
h
a
n
ce
d
m
ar
in
e
m
o
n
it
o
r
in
g
a
n
d
e
v
id
en
ce
-
b
ased
p
o
licy
-
m
ak
in
g
.
B
y
em
p
h
asizin
g
r
o
b
u
s
t
s
tatis
tica
l
m
o
d
el
s
elec
tio
n
,
th
e
f
in
d
i
n
g
s
co
n
t
r
ib
u
te
to
d
ata
-
d
r
iv
e
n
m
ar
i
n
e
r
eso
u
r
ce
m
an
ag
em
en
t,
p
ar
ticu
lar
l
y
in
d
etec
tin
g
HABs
an
d
o
th
er
ec
o
lo
g
ically
s
ig
n
if
ican
t
p
h
en
o
m
en
a.
T
h
is
r
esear
ch
alig
n
s
with
g
lo
b
al
s
u
s
tain
ab
ilit
y
g
o
als
—
p
ar
ticu
lar
ly
SDG
1
4
,
w
h
ich
em
p
h
asizes
th
e
p
r
o
tectio
n
an
d
s
u
s
tain
ab
le
u
s
e
o
f
m
ar
in
e
ec
o
s
y
s
tem
s
.
F
UNDING
I
NF
O
R
M
A
T
I
O
N
T
h
is
r
esear
ch
was
s
u
p
p
o
r
ted
b
y
th
e
E
d
u
ca
tio
n
Fu
n
d
Ma
n
ag
em
en
t
Ag
en
c
y
(
L
PDP),
Min
is
tr
y
o
f
Fin
an
ce
o
f
th
e
R
ep
u
b
lic
o
f
I
n
d
o
n
esia,
t
h
r
o
u
g
h
th
e
I
n
d
o
n
e
s
ian
E
n
d
o
wm
e
n
t
Fu
n
d
f
o
r
E
d
u
ca
tio
n
s
ch
o
la
r
s
h
ip
p
r
o
g
r
a
m
u
n
d
er
c
o
n
tr
ac
t n
o
.
L
OG
-
6
6
0
3
/LPDP/LPDP.3
/2
0
2
3
.
AUTHO
R
CO
NT
RI
B
UT
I
O
NS ST
A
T
E
M
E
N
T
T
h
is
jo
u
r
n
al
u
s
es
th
e
C
o
n
tr
ib
u
to
r
R
o
les
T
ax
o
n
o
m
y
(
C
R
ed
iT)
to
r
ec
o
g
n
ize
in
d
iv
id
u
al
au
th
o
r
co
n
tr
ib
u
tio
n
s
,
r
ed
u
ce
au
th
o
r
s
h
ip
d
is
p
u
tes,
an
d
f
ac
ilit
ate
co
llab
o
r
atio
n
.
Na
m
e
o
f
Aut
ho
r
C
M
So
Va
Fo
I
R
D
O
E
Vi
Su
P
Fu
Felix
R
eb
a
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
T
o
h
a
Saif
u
d
i
n
✓
✓
✓
✓
✓
✓
✓
✓
R
im
u
ljo
Hen
d
r
ad
i
✓
✓
✓
✓
✓
✓
✓
✓
C
:
C
o
n
c
e
p
t
u
a
l
i
z
a
t
i
o
n
M
:
M
e
t
h
o
d
o
l
o
g
y
So
:
So
f
t
w
a
r
e
Va
:
Va
l
i
d
a
t
i
o
n
Fo
:
Fo
r
mal
a
n
a
l
y
s
i
s
I
:
I
n
v
e
s
t
i
g
a
t
i
o
n
R
:
R
e
so
u
r
c
e
s
D
:
D
a
t
a
C
u
r
a
t
i
o
n
O
:
W
r
i
t
i
n
g
-
O
r
i
g
i
n
a
l
D
r
a
f
t
E
:
W
r
i
t
i
n
g
-
R
e
v
i
e
w
&
E
d
i
t
i
n
g
Vi
:
Vi
su
a
l
i
z
a
t
i
o
n
Su
:
Su
p
e
r
v
i
s
i
o
n
P
:
P
r
o
j
e
c
t
a
d
mi
n
i
st
r
a
t
i
o
n
Fu
:
Fu
n
d
i
n
g
a
c
q
u
i
si
t
i
o
n
CO
NF
L
I
C
T
O
F
I
N
T
E
R
E
S
T
ST
A
T
E
M
E
NT
T
h
e
au
t
h
o
r
s
d
ec
lar
e
th
at
th
e
y
h
av
e
n
o
k
n
o
wn
c
o
m
p
etin
g
f
in
an
cial
in
ter
ests
o
r
p
er
s
o
n
al
r
el
atio
n
s
h
ip
s
th
at
co
u
ld
h
av
e
ap
p
ea
r
ed
t
o
in
f
lu
en
ce
th
e
wo
r
k
r
e
p
o
r
te
d
in
t
h
is
p
ap
er
.
DATA AV
AI
L
AB
I
L
I
T
Y
T
h
e
d
ata
th
at
s
u
p
p
o
r
t th
e
f
i
n
d
i
n
g
s
o
f
th
is
s
tu
d
y
wer
e
o
b
tain
e
d
f
r
o
m
t
h
e
C
o
p
er
n
ic
u
s
Ma
r
in
e
Ser
v
ice
at
h
ttp
s
://d
ata.
m
ar
in
e.
co
p
e
r
n
icu
s
.
eu
/ a
n
d
a
r
e
o
p
e
n
ly
ac
ce
s
s
ib
le
to
th
e
p
u
b
lic
.
RE
F
E
R
E
NC
E
S
[
1
]
T.
Zh
a
n
g
,
L.
S
o
n
g
,
H
.
Y
u
a
n
,
B
.
S
o
n
g
,
a
n
d
A
.
N
g
a
n
d
o
,
“
A
c
o
mp
a
r
a
t
i
v
e
st
u
d
y
o
n
h
a
b
i
t
a
t
mo
d
e
l
s
f
o
r
a
d
u
l
t
b
i
g
e
y
e
t
u
n
a
i
n
t
h
e
I
n
d
i
a
n
o
c
e
a
n
b
a
se
d
o
n
g
r
i
d
d
e
d
t
u
n
a
l
o
n
g
l
i
n
e
f
i
s
h
e
r
y
d
a
t
a
,
”
F
i
s
h
e
r
i
e
s
O
c
e
a
n
o
g
ra
p
h
y
,
v
o
l
.
3
0
,
n
o
.
5
,
p
p
.
5
8
4
–
6
0
7
,
2
0
2
1
,
d
o
i
:
1
0
.
1
1
1
1
/
f
o
g
.
1
2
5
3
9
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
15
,
No
.
3
,
J
u
n
e
20
26
:
2
0
8
1
-
2
0
9
1
2090
[
2
]
J.
Zh
a
i
,
Z.
L
i
,
R
.
W
a
n
,
S
.
Ti
a
n
,
P
.
S
o
n
g
,
a
n
d
H
.
Li
n
,
“
Ef
f
e
c
t
s
o
f
e
st
u
a
r
i
n
e
e
n
v
i
r
o
n
m
e
n
t
a
l
h
e
t
e
r
o
g
e
n
e
i
t
y
o
n
t
h
e
h
a
b
i
t
a
t
o
f
g
o
b
i
i
d
e
a
sp
e
c
i
e
s
l
a
r
v
a
e
,
”
M
a
r
i
n
e
a
n
d
C
o
a
s
t
a
l
Fi
sh
e
ri
e
s
,
v
o
l
.
1
5
,
n
o
.
3
,
2
0
2
3
,
d
o
i
:
1
0
.
1
0
0
2
/
mcf2
.
1
0
2
4
1
.
[
3
]
A
.
W
a
n
g
a
n
d
H
.
S
u
,
“
S
p
a
t
i
o
-
t
e
m
p
o
r
a
l
n
e
i
g
h
b
o
r
s
a
d
a
p
t
i
v
e
l
e
a
r
n
i
n
g
w
i
t
h
t
w
o
-
p
o
i
n
t
d
i
f
f
e
r
e
n
c
e
s
f
o
r
o
c
e
a
n
su
b
s
u
r
f
a
c
e
t
e
m
p
e
r
a
t
u
r
e
r
e
c
o
n
st
r
u
c
t
i
o
n
f
r
o
m
1
9
6
0
t
o
2
0
2
2
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
D
i
g
i
t
a
l
E
a
r
t
h
,
v
o
l
.
1
8
,
n
o
.
1
,
2
0
2
5
,
d
o
i
:
1
0
.
1
0
8
0
/
1
7
5
3
8
9
4
7
.
2
0
2
5
.
2
5
0
0
5
2
5
.
[
4
]
M
.
W
a
l
l
n
e
r
,
“
A
h
a
l
f
-
n
o
r
ma
l
d
i
s
t
r
i
b
u
t
i
o
n
s
c
h
e
me
f
o
r
g
e
n
e
r
a
t
i
n
g
f
u
n
c
t
i
o
n
s
,
”
E
u
r
o
p
e
a
n
J
o
u
r
n
a
l
o
f
C
o
m
b
i
n
a
t
o
ri
c
s
,
v
o
l
.
8
7
,
2
0
2
0
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
e
j
c
.
2
0
2
0
.
1
0
3
1
3
8
.
[
5
]
H
.
M
.
A
l
i
,
M
.
Z.
A
h
me
d
,
R
.
E.
K
h
a
m
i
ss,
a
n
d
A
.
K
a
r
i
m,
“
Ev
a
l
u
a
t
i
o
n
o
f
n
i
n
e
t
w
o
-
p
a
r
a
m
e
t
e
r
p
r
o
b
a
b
i
l
i
t
y
d
i
st
r
i
b
u
t
i
o
n
s
f
o
r
m
o
d
e
l
i
n
g
w
i
n
d
s
p
e
e
d
d
a
t
a
a
t
t
h
r
e
e
s
i
t
e
s
i
n
W
e
st
e
r
n
M
o
u
n
t
a
i
n
-
L
i
b
y
a
,
”
i
n
2
0
2
2
I
EEE
2
n
d
I
n
t
e
r
n
a
t
i
o
n
a
l
M
a
g
h
r
e
b
M
e
e
t
i
n
g
o
f
t
h
e
C
o
n
f
e
re
n
c
e
o
n
S
c
i
e
n
c
e
s
a
n
d
T
e
c
h
n
i
q
u
e
s
o
f
A
u
t
o
m
a
t
i
c
C
o
n
t
r
o
l
a
n
d
C
o
m
p
u
t
e
r
E
n
g
i
n
e
e
ri
n
g
(
MI
-
S
T
A)
,
S
a
b
r
a
t
h
a
,
Li
b
y
a
,
2
0
2
2
,
p
p
.
7
6
7
-
7
7
1
,
d
o
i
:
1
0
.
1
1
0
9
/
M
I
-
S
TA
5
4
8
6
1
.
2
0
2
2
.
9
8
3
7
7
3
1
.
[
6
]
A
.
K
a
r
a
k
u
s,
E.
E.
K
u
r
u
o
ğ
l
u
,
a
n
d
A
.
A
c
h
i
m
,
“
A
g
e
n
e
r
a
l
i
z
e
d
g
a
u
ss
i
a
n
e
x
t
e
n
s
i
o
n
t
o
t
h
e
r
i
c
i
a
n
d
i
s
t
r
i
b
u
t
i
o
n
f
o
r
S
A
R
i
ma
g
e
mo
d
e
l
i
n
g
,
”
I
E
EE
T
r
a
n
sa
c
t
i
o
n
s
o
n
G
e
o
sc
i
e
n
c
e
a
n
d
R
e
m
o
t
e
S
e
n
s
i
n
g
,
v
o
l
.
6
0
,
2
0
2
2
,
d
o
i
:
1
0
.
1
1
0
9
/
TG
R
S
.
2
0
2
1
.
3
0
6
9
0
9
1
.
[
7
]
S
.
D
r
o
z
d
,
N
.
K
u
ssu
l
,
a
n
d
A
.
S
h
e
l
e
st
o
v
,
“
I
mp
r
o
v
i
n
g
s
p
a
t
i
a
l
r
e
s
o
l
u
t
i
o
n
o
f
a
q
u
a
M
O
D
I
S
a
n
d
G
C
O
M
-
C
c
h
l
o
r
o
p
h
y
l
l
-
a
d
a
t
a
f
o
r
c
y
p
r
u
s
c
o
a
st
a
l
w
a
t
e
r
s
mo
n
i
t
o
r
i
n
g
,
”
Eu
r
o
p
e
a
n
J
o
u
r
n
a
l
o
f
Re
m
o
t
e
S
e
n
si
n
g
,
v
o
l
.
5
8
,
n
o
.
1
,
2
0
2
5
,
d
o
i
:
1
0
.
1
0
8
0
/
2
2
7
9
7
2
5
4
.
2
0
2
5
.
2
5
5
1
0
2
3
.
[
8
]
S
.
R
a
d
f
a
r
,
P
.
G
a
l
i
a
t
sa
t
o
u
,
a
n
d
T.
W
a
h
l
,
“
A
p
p
l
i
c
a
t
i
o
n
o
f
n
o
n
s
t
a
t
i
o
n
a
r
y
e
x
t
r
e
me
v
a
l
u
e
a
n
a
l
y
si
s
i
n
t
h
e
c
o
a
s
t
a
l
e
n
v
i
r
o
n
me
n
t
-
a
sy
st
e
ma
t
i
c
l
i
t
e
r
a
t
u
r
e
r
e
v
i
e
w
,
”
We
a
t
h
e
r a
n
d
C
l
i
m
a
t
e
E
x
t
r
e
m
e
s
,
v
o
l
.
4
1
,
2
0
2
3
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
w
a
c
e
.
2
0
2
3
.
1
0
0
5
7
5
.
[
9
]
Y
.
X
u
,
L.
L
u
,
L
.
Y
a
n
,
a
n
d
H
.
K
.
Z
h
a
n
g
,
“
A
r
e
v
i
e
w
o
f
r
e
m
o
t
e
se
n
si
n
g
i
n
c
o
a
s
t
a
l
a
q
u
a
c
u
l
t
u
r
e
:
d
a
t
a
,
g
e
o
g
r
a
p
h
i
c
h
o
t
s
p
o
t
s,
m
e
t
h
o
d
s
,
a
n
d
c
h
a
l
l
e
n
g
e
s,”
G
I
S
c
i
e
n
c
e
&
Re
m
o
t
e
S
e
n
s
i
n
g
,
v
o
l
.
6
2
,
n
o
.
1
,
2
0
2
5
,
d
o
i
:
1
0
.
1
0
8
0
/
1
5
4
8
1
6
0
3
.
2
0
2
5
.
2
5
7
3
5
2
9
.
[
1
0
]
G
.
F
a
l
l
a
h
g
o
u
l
,
H
.
H
a
ssa
n
,
a
n
d
G
.
Lo
e
p
e
r
,
“
M
o
d
e
l
l
i
n
g
t
a
i
l
r
i
s
k
w
i
t
h
t
e
mp
e
r
e
d
st
a
b
l
e
d
i
st
r
i
b
u
t
i
o
n
s
:
a
n
o
v
e
r
v
i
e
w
,
”
A
n
n
a
l
s
o
f
O
p
e
r
a
t
i
o
n
s
Re
se
a
r
c
h
,
v
o
l
.
2
9
9
,
p
p
.
1
2
5
3
–
1
2
8
0
,
2
0
2
1
,
d
o
i
:
1
0
.
1
0
0
7
/
s
1
0
4
7
9
-
0
1
9
-
0
3
2
0
4
-
3.
[
1
1
]
X
.
Li
a
n
d
J.
M
a
,
“
N
o
n
-
c
e
n
t
r
a
l
st
u
d
e
n
t
-
t
m
i
x
t
u
r
e
o
f
s
t
u
d
e
n
t
-
t
p
r
o
c
e
sses
f
o
r
r
o
b
u
s
t
r
e
g
r
e
ssi
o
n
a
n
d
p
r
e
d
i
c
t
i
o
n
,
”
i
n
I
n
t
e
l
l
i
g
e
n
t
C
o
m
p
u
t
i
n
g
T
h
e
o
ri
e
s
a
n
d
Ap
p
l
i
c
a
t
i
o
n
,
C
h
a
m
,
S
w
i
t
z
e
r
l
a
n
d
:
S
p
r
i
n
g
e
r
,
2
0
2
1
,
p
p
.
4
9
9
–
511
,
d
o
i
:
1
0
.
1
0
0
7
/
9
7
8
-
3
-
0
3
0
-
8
4
5
2
2
-
3
_
4
1
.
[
1
2
]
M
.
A
.
U.
H
a
q
,
G
.
S
.
R
a
o
,
M
.
A
l
b
a
ss
a
m,
a
n
d
M
.
A
s
l
a
m
,
“
M
a
r
s
h
a
l
l
–
o
l
k
i
n
p
o
w
e
r
l
o
ma
x
d
i
st
r
i
b
u
t
i
o
n
f
o
r
m
o
d
e
l
i
n
g
o
f
w
i
n
d
s
p
e
e
d
d
a
t
a
,
”
E
n
e
r
g
y
R
e
p
o
rt
s
,
v
o
l
.
6
,
p
p
.
1
1
1
8
–
1
1
2
3
,
2
0
2
0
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
e
g
y
r
.
2
0
2
0
.
0
4
.
0
3
3
.
[
1
3
]
K
.
S
.
G
u
e
d
e
s
,
C
.
F
.
D.
A
n
d
r
a
d
e
,
P
.
A
.
C
.
R
o
c
h
a
,
R
.
D.
S
.
M
a
n
g
u
e
i
r
a
,
a
n
d
A
.
D.
M
o
u
r
a
,
“
P
e
r
f
o
r
ma
n
c
e
a
n
a
l
y
s
i
s
o
f
met
a
h
e
u
r
i
st
i
c
o
p
t
i
m
i
z
a
t
i
o
n
a
l
g
o
r
i
t
h
ms
i
n
e
st
i
mat
i
n
g
t
h
e
p
a
r
a
me
t
e
r
s
o
f
sev
e
r
a
l
w
i
n
d
s
p
e
e
d
d
i
s
t
r
i
b
u
t
i
o
n
s,
”
Ap
p
l
i
e
d
E
n
e
rg
y
,
v
o
l
.
2
6
8
,
2
0
2
0
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
a
p
e
n
e
r
g
y
.
2
0
2
0
.
1
1
4
9
5
2
.
[
1
4
]
X
.
P
.
N
g
u
y
e
n
,
T
.
D
.
N
g
u
y
e
n
,
D
.
N
.
N
g
u
y
e
n
,
R
.
H
i
d
a
y
a
t
,
T
.
T
.
H
u
y
n
h
,
a
n
d
D
.
T.
N
g
u
y
e
n
,
“
A
r
e
v
i
e
w
o
n
t
h
e
i
n
t
e
r
n
e
t
o
f
t
h
i
n
g
(
I
o
T)
t
e
c
h
n
o
l
o
g
i
e
s
i
n
c
o
n
t
r
o
l
l
i
n
g
o
c
e
a
n
e
n
v
i
r
o
n
m
e
n
t
,
”
E
n
e
r
g
y
S
o
u
r
c
e
s
,
P
a
rt
A
:
Re
c
o
v
e
ry,
U
t
i
l
i
z
a
t
i
o
n
,
a
n
d
E
n
v
i
ro
n
m
e
n
t
a
l
Ef
f
e
c
t
s
,
v
o
l
.
4
7
,
n
o
.
1
,
p
p
.
1
0
0
6
4
–
1
0
0
8
2
,
2
0
2
1
,
d
o
i
:
1
0
.
1
0
8
0
/
1
5
5
6
7
0
3
6
.
2
0
2
1
.
1
9
6
0
9
3
2
.
[
1
5
]
Z.
Z
h
a
n
g
,
G
.
R
e
n
,
P
.
W
u
,
Y
.
H
u
,
S
.
L
i
u
,
a
n
d
F
.
Z
h
a
n
g
,
“
S
A
A
-
V
M
a
m
b
a
:
c
r
o
ss
-
d
o
m
a
i
n
a
d
a
p
t
i
v
e
r
e
m
o
t
e
s
e
n
si
n
g
r
e
c
o
g
n
i
t
i
o
n
m
e
t
h
o
d
o
f
S
o
n
n
e
r
a
t
i
a
a
p
e
t
a
l
a
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
D
i
g
i
t
a
l
E
a
r
t
h
,
v
o
l
.
1
8
,
n
o
.
2
,
2
0
2
5
,
d
o
i
:
1
0
.
1
0
8
0
/
1
7
5
3
8
9
4
7
.
2
0
2
5
.
2
5
6
4
2
6
0
.
[
1
6
]
S
.
W
.
Li
n
g
,
H
.
W
a
n
g
,
a
n
d
J.
Zh
a
n
g
,
“
A
r
e
v
i
e
w
o
f
m
a
n
g
r
o
v
e
d
e
g
r
a
d
a
t
i
o
n
a
ssessme
n
t
u
si
n
g
r
e
m
o
t
e
se
n
si
n
g
:
a
d
v
a
n
c
e
s,
c
h
a
l
l
e
n
g
e
s
,
a
n
d
o
p
p
o
r
t
u
n
i
t
i
e
s
,
”
G
I
S
c
i
e
n
c
e
& R
e
m
o
t
e
S
e
n
si
n
g
,
v
o
l
.
6
2
,
n
o
.
1
,
2
0
2
5
,
d
o
i
:
1
0
.
1
0
8
0
/
1
5
4
8
1
6
0
3
.
2
0
2
5
.
2
4
9
1
9
2
0
.
[
1
7
]
M
.
M
.
B
a
d
r
,
“
G
o
o
d
n
e
ss
-
of
-
f
i
t
t
e
s
t
s
f
o
r
t
h
e
c
o
mp
o
u
n
d
r
a
y
l
e
i
g
h
d
i
s
t
r
i
b
u
t
i
o
n
w
i
t
h
a
p
p
l
i
c
a
t
i
o
n
t
o
r
e
a
l
d
a
t
a
,
”
H
e
l
i
y
o
n
,
v
o
l
.
5
,
n
o
.
8
,
2
0
1
9
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
h
e
l
i
y
o
n
.
2
0
1
9
.
e
0
2
2
2
5
.
[
1
8
]
A
.
M
.
C
.
D.
S
o
u
z
a
,
F
.
A
r
i
s
t
o
n
e
,
W
.
A
.
F
e
r
n
a
n
d
e
s,
A
.
P
.
G
.
O
l
i
v
e
i
r
a
,
Z
.
O
l
a
o
f
e
,
a
n
d
M
.
A
b
r
e
u
,
“
A
n
a
l
y
s
i
s
o
f
o
z
o
n
e
c
o
n
c
e
n
t
r
a
t
i
o
n
s
u
si
n
g
p
r
o
b
a
b
i
l
i
t
y
d
i
s
t
r
i
b
u
t
i
o
n
s,”
O
zo
n
e
:
S
c
i
e
n
c
e
&
En
g
i
n
e
e
r
i
n
g
,
v
o
l
.
4
2
,
n
o
.
6
,
p
p
.
5
3
9
–
5
5
0
,
2
0
2
0
,
d
o
i
:
1
0
.
1
0
8
0
/
0
1
9
1
9
5
1
2
.
2
0
2
0
.
1
7
3
6
9
8
7
.
[
1
9
]
W
.
Zh
e
n
g
,
D
.
La
i
,
a
n
d
N
.
G
o
u
l
d
,
“
A
si
mu
l
a
t
i
o
n
s
t
u
d
y
o
f
a
c
l
a
ss
o
f
n
o
n
p
a
r
a
me
t
r
i
c
t
e
s
t
st
a
t
i
st
i
c
s
:
a
c
l
o
se
l
o
o
k
o
f
e
m
p
i
r
i
c
a
l
d
i
s
t
r
i
b
u
t
i
o
n
f
u
n
c
t
i
o
n
-
b
a
s
e
d
t
e
s
t
s,”
C
o
m
m
u
n
i
c
a
t
i
o
n
s
i
n
S
t
a
t
i
st
i
c
s
-
S
i
m
u
l
a
t
i
o
n
a
n
d
C
o
m
p
u
t
a
t
i
o
n
,
v
o
l
.
5
2
,
n
o
.
3
,
p
p
.
1
1
3
3
–
1
1
4
8
,
2
0
2
3
,
d
o
i
:
1
0
.
1
0
8
0
/
0
3
6
1
0
9
1
8
.
2
0
2
1
.
1
8
7
4
9
8
7
.
[
2
0
]
S
.
B
e
n
c
h
i
h
a
,
A
.
I
.
A
l
-
O
mari,
a
n
d
M
.
A
l
o
ma
n
i
,
“
G
o
o
d
n
e
ss
-
of
-
f
i
t
t
e
s
t
s
f
o
r
w
e
i
g
h
t
e
d
g
e
n
e
r
a
l
i
z
e
d
q
u
a
s
i
-
l
i
n
d
l
e
y
d
i
st
r
i
b
u
t
i
o
n
u
si
n
g
S
R
S
a
n
d
R
S
S
w
i
t
h
a
p
p
l
i
c
a
t
i
o
n
s
t
o
r
e
a
l
d
a
t
a
,
”
A
x
i
o
m
s
,
v
o
l
.
1
1
,
n
o
.
1
0
,
2
0
2
2
,
d
o
i
:
1
0
.
3
3
9
0
/
a
x
i
o
ms
1
1
1
0
0
4
9
0
.
[
2
1
]
Ž.
L
u
k
i
ć
a
n
d
M
.
M
i
l
o
še
v
i
ć
,
“
C
h
a
r
a
c
t
e
r
i
z
a
t
i
o
n
-
b
a
se
d
a
p
p
r
o
a
c
h
f
o
r
c
o
n
st
r
u
c
t
i
o
n
o
f
g
o
o
d
n
e
ss
-
of
-
f
i
t
t
e
st
f
o
r
l
é
v
y
d
i
st
r
i
b
u
t
i
o
n
,
”
S
t
a
t
i
st
i
c
s:
A
J
o
u
r
n
a
l
o
f
T
h
e
o
re
t
i
c
a
l
a
n
d
A
p
p
l
i
e
d
S
t
a
t
i
s
t
i
c
s
,
v
o
l
.
5
7
,
n
o
.
5
,
p
p
.
1
0
8
7
–
1
1
1
6
,
2
0
2
3
,
d
o
i
:
1
0
.
1
0
8
0
/
0
2
3
3
1
8
8
8
.
2
0
2
3
.
2
2
3
8
2
3
6
.
[
2
2
]
H
.
M
.
G
o
u
a
l
a
n
d
H
.
Y
o
u
so
f
,
“
V
a
l
i
d
a
t
i
o
n
o
f
b
u
r
r
X
I
I
i
n
v
e
r
se
r
a
y
l
e
i
g
h
mo
d
e
l
v
i
a
a
m
o
d
i
f
i
e
d
c
h
i
-
sq
u
a
r
e
d
g
o
o
d
n
e
ss
-
of
-
f
i
t
t
e
st
,
”
i
n
J
o
u
rn
a
l
o
f
Ap
p
l
i
e
d
S
t
a
t
i
s
t
i
c
s
,
2
0
2
0
,
p
p
.
3
9
3
–
4
2
3
,
d
o
i
:
1
0
.
1
0
8
0
/
0
2
6
6
4
7
6
3
.
2
0
1
9
.
1
6
3
9
6
4
2
.
[
2
3
]
H
.
S
.
B
a
k
o
u
c
h
,
T.
H
u
ss
a
i
n
,
C
.
C
h
e
sn
e
a
u
,
a
n
d
T.
Jó
n
á
s,
“
A
n
o
t
a
b
l
e
b
o
u
n
d
e
d
p
r
o
b
a
b
i
l
i
t
y
d
i
s
t
r
i
b
u
t
i
o
n
f
o
r
e
n
v
i
r
o
n
m
e
n
t
a
l
a
n
d
l
i
f
e
t
i
m
e
d
a
t
a
,
”
E
a
rt
h
S
c
i
e
n
c
e
I
n
f
o
rm
a
t
i
c
s
,
v
o
l
.
1
5
,
n
o
.
3
,
p
p
.
1
6
0
7
–
1
6
2
0
,
2
0
2
2
,
d
o
i
:
1
0
.
1
0
0
7
/
s
1
2
1
4
5
-
022
-
0
0
8
1
1
-
w.
[
2
4
]
M
.
M
a
h
mo
u
d
,
A
.
I
.
B
o
g
h
d
a
d
y
,
A
.
R
.
A
.
E
l
-
F
i
k
k
y
,
a
n
d
M
.
H
.
A
l
y
,
“
S
t
a
t
i
s
t
i
c
a
l
s
t
u
d
i
e
s
u
si
n
g
g
o
o
d
n
e
ss
-
of
-
f
i
t
t
e
c
h
n
i
q
u
e
s
w
i
t
h
d
y
n
a
mi
c
u
n
d
e
r
w
a
t
e
r
v
i
si
b
l
e
l
i
g
h
t
c
o
mm
u
n
i
c
a
t
i
o
n
c
h
a
n
n
e
l
m
o
d
e
l
i
n
g
,
”
I
EEE
Ac
c
e
ss
,
v
o
l
.
9
,
p
p
.
5
6
3
7
8
–
5
6
3
9
1
,
2
0
2
1
,
d
o
i
:
1
0
.
1
1
0
9
/
A
C
C
ESS
.
2
0
2
1
.
3
0
7
2
6
8
9
.
[
2
5
]
A
.
B
a
n
i
b
a
y
a
t
,
H
.
G
.
K
h
a
r
a
z
i
,
H
.
Es
l
a
m
i
,
S
.
K
h
o
s
h
n
a
v
a
z
,
a
n
d
B
.
D
a
h
a
n
z
a
d
e
h
,
“
D
r
o
u
g
h
t
m
o
n
i
t
o
r
i
n
g
i
n
b
i
v
a
r
i
a
t
e
p
r
o
b
a
b
i
l
i
st
i
c
f
r
a
mew
o
r
k
f
o
r
t
h
e
ma
x
i
mi
z
a
t
i
o
n
o
f
w
a
t
e
r
u
s
e
e
f
f
i
c
i
e
n
c
y
,
”
I
ra
n
i
a
n
J
o
u
r
n
a
l
o
f
S
c
i
e
n
c
e
a
n
d
T
e
c
h
n
o
l
o
g
y
,
T
r
a
n
sa
c
t
i
o
n
s
o
f
C
i
v
i
l
En
g
i
n
e
e
ri
n
g
,
v
o
l
.
4
6
,
n
o
.
2
,
p
p
.
4
9
9
–
5
1
4
,
2
0
2
2
,
d
o
i
:
1
0
.
1
0
0
7
/
s4
0
9
9
6
-
0
2
1
-
0
0
5
8
9
-
9.
[
2
6
]
H.
-
Y
.
C
h
o
,
“
N
o
r
m
a
l
i
t
y
t
e
st
o
f
t
h
e
w
a
t
e
r
q
u
a
l
i
t
y
m
o
n
i
t
o
r
i
n
g
d
a
t
a
i
n
h
a
r
b
o
u
r
,
”
J
o
u
r
n
a
l
o
f
t
h
e
K
o
r
e
a
n
S
o
c
i
e
t
y
o
f
C
o
a
st
a
l
a
n
d
O
c
e
a
n
En
g
i
n
e
e
rs
,
v
o
l
.
3
3
,
n
o
.
2
,
p
p
.
5
3
–
6
2
,
2
0
2
1
,
d
o
i
:
1
0
.
9
7
6
5
/
k
sc
o
e
.
2
0
2
1
.
3
3
.
2
.
5
3
.
[
2
7
]
C
.
E
.
-
S
a
n
d
o
v
a
l
,
“
M
i
x
t
u
r
e
p
r
o
b
a
b
i
l
i
t
y
d
i
st
r
i
b
u
t
i
o
n
s
f
o
r
l
o
w
-
f
l
o
w
f
r
e
q
u
e
n
c
y
a
n
a
l
y
si
s
i
n
m
e
x
i
c
o
:
i
m
p
l
i
c
a
t
i
o
n
s
f
o
r
e
n
v
i
r
o
n
m
e
n
t
a
l
i
mp
a
c
t
a
ssess
me
n
t
,
d
r
o
u
g
h
t
ma
n
a
g
e
me
n
t
,
a
n
d
r
e
g
i
o
n
a
l
w
a
t
e
r
p
o
l
i
c
y
,
”
E
n
v
i
r
o
n
m
e
n
t
s
,
v
o
l
.
1
2
,
n
o
.
1
2
,
2
0
2
5
,
d
o
i
:
1
0
.
3
3
9
0
/
e
n
v
i
r
o
n
me
n
t
s
1
2
1
2
0
4
5
0
.
[
2
8
]
S
.
E
o
,
H
.
P
a
r
k
,
a
n
d
Y
.
R
.
K
i
m,
“
P
r
e
d
i
c
t
i
v
e
&
r
e
p
r
e
s
e
n
t
a
t
i
v
e
c
o
m
p
o
s
i
t
e
s
c
o
r
e
,
”
Q
u
a
l
i
t
y
a
n
d
Q
u
a
n
t
i
t
y
,
v
o
l
.
5
7
,
n
o
.
1
,
p
p
.
1
–
1
4
,
2
0
2
3
,
d
o
i
:
1
0
.
1
0
0
7
/
s1
1
1
3
5
-
0
2
2
-
0
1
3
4
5
-
5.
[
2
9
]
A
.
M
.
I
k
o
t
u
n
,
A
.
E.
Ez
u
g
w
u
,
L.
A
b
u
a
l
i
g
a
h
,
B
.
A
b
u
h
a
i
j
a
,
a
n
d
J.
H
e
mi
n
g
,
“
K
-
mea
n
s
c
l
u
s
t
e
r
i
n
g
a
l
g
o
r
i
t
h
ms
:
a
c
o
m
p
r
e
h
e
n
si
v
e
r
e
v
i
e
w
,
v
a
r
i
a
n
t
s
a
n
a
l
y
s
i
s,
a
n
d
a
d
v
a
n
c
e
s
i
n
t
h
e
e
r
a
o
f
b
i
g
d
a
t
a
,
”
I
n
f
o
rm
a
t
i
o
n
S
c
i
e
n
c
e
s
,
v
o
l
.
6
3
0
,
p
p
.
1
1
9
–
1
4
7
,
2
0
2
3
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
i
n
s
.
2
0
2
2
.
1
1
.
1
3
9
.
[
3
0
]
B
.
S
a
d
e
g
h
i
,
“
C
l
u
st
e
r
i
n
g
i
n
g
e
o
-
d
a
t
a
sci
e
n
c
e
:
n
a
v
i
g
a
t
i
n
g
u
n
c
e
r
t
a
i
n
t
y
t
o
se
l
e
c
t
t
h
e
m
o
st
r
e
l
i
a
b
l
e
me
t
h
o
d
,
”
O
re
G
e
o
l
o
g
y
Re
v
i
e
w
s
,
v
o
l
.
1
8
1
,
2
0
2
5
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
o
r
e
g
e
o
r
e
v
.
2
0
2
5
.
1
0
6
5
9
1
.
[
3
1
]
M
.
N
.
N
.
S
h
u
t
a
y
w
i
a
n
d
N
.
N
.
K
a
c
h
o
u
i
e
,
“
S
i
l
h
o
u
e
t
t
e
a
n
a
l
y
si
s
f
o
r
p
e
r
f
o
r
ma
n
c
e
e
v
a
l
u
a
t
i
o
n
i
n
ma
c
h
i
n
e
l
e
a
r
n
i
n
g
w
i
t
h
a
p
p
l
i
c
a
t
i
o
n
s
t
o
c
l
u
st
e
r
i
n
g
,
”
E
n
t
ro
p
y
,
v
o
l
.
2
3
,
n
o
.
6
,
2
0
2
1
,
d
o
i
:
1
0
.
3
3
9
0
/
e
2
3
0
6
0
7
5
9
.
[
3
2
]
A
.
M
.
B
a
g
i
r
o
v
,
R
.
M
.
A
l
i
g
u
l
i
y
e
v
,
a
n
d
N
.
S
u
l
t
a
n
o
v
a
,
“
F
i
n
d
i
n
g
c
o
mp
a
c
t
a
n
d
w
e
l
l
-
se
p
a
r
a
t
e
d
c
l
u
s
t
e
r
s:
c
l
u
st
e
r
i
n
g
u
s
i
n
g
si
l
h
o
u
e
t
t
e
c
o
e
f
f
i
c
i
e
n
t
s,
”
P
a
t
t
e
r
n
Re
c
o
g
n
i
t
i
o
n
,
v
o
l
.
1
3
5
,
2
0
2
3
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
p
a
t
c
o
g
.
2
0
2
2
.
1
0
9
1
4
4
.
Evaluation Warning : The document was created with Spire.PDF for Python.