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1519
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ad
e
u
s
er
ex
p
er
ien
ce
s
,
p
ar
ticu
lar
l
y
f
o
r
u
s
er
s
i
n
h
ig
h
-
v
elo
cit
y
s
ce
n
ar
io
s
.
T
h
e
ev
o
lu
tio
n
to
w
a
r
d
6
G
n
et
w
o
r
k
s
w
it
h
an
e
m
p
h
a
s
is
o
n
o
p
er
atin
g
i
n
t
h
e
ter
ah
er
tz
(
T
Hz)
f
r
eq
u
en
c
y
b
an
d
s
,
in
tr
o
d
u
ce
s
m
o
r
e
c
h
alle
n
g
e
s
[
1
]
,
[
7
]
,
[
8
]
.
T
h
is
p
r
o
g
r
ess
io
n
h
i
g
h
lig
h
t
s
t
h
e
n
ee
d
f
o
r
i
n
n
o
v
ati
v
e
s
o
l
u
tio
n
s
to
m
an
a
g
e
m
o
b
il
it
y
e
f
f
ec
ti
v
el
y
as
w
e
m
o
v
e
i
n
to
th
e
er
a
o
f
6
G.
Ho
w
ev
er
,
th
e
co
m
p
lex
it
y
o
f
m
o
b
ilit
y
m
an
a
g
e
m
e
n
t
in
t
h
ese
n
et
w
o
r
k
s
n
ec
e
s
s
ita
tes
i
n
n
o
v
ati
v
e
ap
p
r
o
ac
h
es
to
en
s
u
r
e
ef
f
icie
n
c
y
a
n
d
o
p
ti
m
ize
h
an
d
o
v
er
s
[
9
]
.
A
r
ti
f
icial
i
n
te
lli
g
en
ce
(
A
I
)
e
m
er
g
e
s
as
a
cr
itica
l
en
ab
ler
f
o
r
o
v
er
co
m
i
n
g
th
e
c
h
alle
n
g
es
ass
o
ciate
d
w
it
h
m
o
b
ilit
y
m
a
n
a
g
e
m
e
n
t
in
ad
v
a
n
ce
d
n
et
w
o
r
k
s
b
y
lev
er
a
g
i
n
g
A
I
-
p
o
w
er
ed
p
r
ed
ictiv
e
h
an
d
o
v
er
m
an
a
g
e
m
e
n
t
tech
n
iq
u
e
s
.
T
h
ese
ap
p
r
o
ac
h
es
ca
n
s
ig
n
i
f
ica
n
t
l
y
r
ed
u
ce
laten
c
y
,
m
i
n
i
m
ize
co
s
ts
,
an
d
en
s
u
r
e
a
s
ea
m
le
s
s
u
s
er
e
x
p
er
ien
ce
[
1
0
]
–
[
1
5
]
.
T
h
e
k
e
y
to
ac
h
ie
v
i
n
g
t
h
ese
r
esu
l
ts
lie
s
i
n
u
n
co
v
er
i
n
g
h
id
d
en
co
r
r
elatio
n
s
w
it
h
i
n
a
u
s
er
’
s
s
i
g
n
al
m
ea
s
u
r
e
m
en
t
s
h
is
to
r
y
.
B
y
ca
p
t
u
r
in
g
d
ep
en
d
en
cie
s
i
n
t
h
e
d
ata
[
1
6
]
.
A
I
m
o
d
els
ca
n
ac
cu
r
atel
y
p
r
ed
ict
u
s
er
m
o
b
ilit
y
p
atter
n
s
.
T
h
is
p
ap
er
f
o
cu
s
e
s
o
n
ex
p
lo
r
in
g
A
I
-
d
r
iv
e
n
tech
n
iq
u
e
s
f
o
r
h
an
d
o
v
er
en
h
a
n
ci
n
g
t
h
r
o
u
g
h
m
u
lti
-
s
tep
ah
ea
d
r
ef
er
en
ce
s
i
g
n
al
r
ec
eiv
e
d
p
o
w
er
(
R
S
R
P
)
p
r
ed
ictio
n
in
5
G
n
et
w
o
r
k
s
.
T
h
e
r
est
o
f
th
e
p
ap
er
is
s
tr
u
ct
u
r
ed
as
f
o
llo
w
s
:
s
ec
t
io
n
2
p
r
o
v
id
es
a
co
m
p
r
e
h
en
s
iv
e
liter
at
u
r
e
r
ev
ie
w
an
d
d
etails
t
h
e
m
et
h
o
d
o
lo
g
y
,
i
n
clu
d
i
n
g
t
h
e
k
e
y
s
tep
s
o
f
th
e
p
r
o
p
o
s
ed
m
o
d
el
a
n
d
a
d
escr
ip
tio
n
o
f
t
h
e
d
ata
s
et
s
u
s
ed
.
Sectio
n
3
p
r
esen
t
s
th
e
p
er
f
o
r
m
an
ce
a
n
al
y
s
i
s
,
s
h
o
w
ca
s
in
g
R
S
R
P
p
r
ed
ictio
n
u
s
in
g
en
co
d
er
-
d
ec
o
d
er
lo
n
g
s
h
o
r
t
-
ter
m
m
e
m
o
r
y
(
E
D
-
L
ST
M)
,
b
id
ir
ec
tio
n
al
L
ST
M
(
Bi
-
L
ST
M
)
,
an
d
s
ta
n
d
ar
d
L
ST
M
(S
-
L
ST
M)
m
o
d
els,
f
o
llo
w
ed
b
y
a
co
m
p
ar
ativ
e
ev
alu
a
tio
n
b
ased
o
n
m
ea
n
ab
s
o
lu
te
er
r
o
r
(
MA
E
)
an
d
m
ea
n
ab
s
o
lu
te
p
er
ce
n
tag
e
er
r
o
r
(
MA
P
E
)
m
etr
ics.
Fi
n
all
y
,
th
e
p
ap
er
c
o
n
clu
d
es
w
ith
a
s
u
m
m
ar
y
o
f
f
in
d
i
n
g
s
an
d
s
u
g
g
e
s
tio
n
s
f
o
r
f
u
t
u
r
e
r
esear
ch
d
ir
ec
tio
n
s
.
2.
M
E
T
H
O
D
2
.
1
.
L
it
er
a
t
ure
re
v
iew
T
im
e
-
s
er
ies
f
o
r
ec
ast
in
g
al
g
o
r
i
th
m
s
(
T
SF
A
s
)
ar
e
r
esp
o
n
s
ib
le
f
o
r
g
e
n
er
ati
n
g
r
eliab
le
f
o
r
ec
asts
o
v
er
a
p
r
ed
ef
in
ed
h
o
r
izo
n
b
y
m
o
d
e
llin
g
co
r
r
elatio
n
s
b
et
w
ee
n
e
n
d
o
g
en
o
u
s
v
ar
iab
les,
t
h
e
i
m
p
ac
t
o
f
ex
o
g
e
n
o
u
s
v
ar
iab
les,
an
d
s
tr
u
ctu
r
al
d
ata
p
r
o
p
er
ties
s
u
ch
as
au
to
co
r
r
elatio
n
,
p
er
io
d
icity
,
tr
en
d
,
p
atter
n
,
an
d
ca
u
s
alit
y
[
1
7
]
.
T
SF
A
s
i
n
cl
u
d
e
s
in
g
le
-
s
tep
an
d
m
u
lti
-
s
tep
p
r
ed
ictio
n
task
s
.
W
h
ile
s
in
g
le
-
s
tep
p
r
ed
icts
o
n
l
y
t
h
e
n
e
x
t
v
alu
e
,
m
u
lti
-
s
tep
f
o
r
ec
ast
in
g
ex
te
n
d
s
f
u
r
th
er
,
ca
p
tu
r
in
g
f
u
t
u
r
e
tr
en
d
s
[
1
8
]
,
[
1
9
]
.
Desp
ite
ch
allen
g
e
s
in
p
r
eser
v
i
n
g
ti
m
e
-
d
ep
en
d
en
t p
atter
n
s
,
m
u
l
ti
-
s
tep
f
o
r
ec
asti
n
g
i
s
v
ita
l in
ar
e
as lik
e,
e
n
er
g
y
co
n
s
u
m
p
tio
n
a
n
al
y
s
i
s
,
tr
af
f
ic
lo
ad
d
etec
tio
n
,
an
d
s
to
ck
p
r
ice
p
r
e
d
ictio
n
[
2
0
]
.
Sev
er
al
s
tu
d
ie
s
h
av
e
in
v
es
tig
a
ted
class
ical
au
to
-
r
eg
r
es
s
i
v
e
(
AR
)
m
o
d
el
s
f
o
r
m
u
lti
-
s
tep
ti
m
e
s
er
ies
f
o
r
ec
asti
n
g
,
lev
er
a
g
i
n
g
t
h
eir
ca
p
ab
ilit
y
to
ca
p
tu
r
e
tem
p
o
r
al
d
ep
en
d
en
cies
in
d
ata.
A
p
p
r
o
ac
h
es
lik
e
v
ec
to
r
au
to
r
eg
r
es
s
io
n
(
V
A
R
)
,
au
to
-
r
eg
r
ess
i
v
e
in
teg
r
ated
m
o
v
in
g
av
er
ag
e
(
A
R
I
M
A
)
,
an
d
s
ea
s
o
n
al
au
to
-
r
eg
r
es
s
iv
e
in
te
g
r
ated
m
o
v
i
n
g
av
er
a
g
e
(
S
AR
I
M
A
)
h
a
v
e
b
ee
n
w
id
el
y
a
p
p
lied
.
SA
R
I
M
A
a
n
d
s
u
p
p
o
r
t
v
ec
to
r
r
eg
r
ess
io
n
(
SVR
)
m
o
d
el
s
h
a
v
e
s
h
o
w
n
s
tr
o
n
g
p
er
f
o
r
m
an
ce
in
f
o
r
ec
asti
n
g
e
n
v
ir
o
n
m
e
n
tal
d
ata,
i
n
cl
u
d
i
n
g
te
m
p
er
at
u
r
e
an
d
h
u
m
id
it
y
,
f
o
r
b
o
th
s
h
o
r
t
-
a
n
d
lo
n
g
-
ter
m
p
r
ed
ictio
n
s
[
1
7
]
.
S
im
il
a
r
ly
,
A
R
I
MA
c
o
m
b
i
n
e
d
w
ith
s
e
a
s
o
n
al
d
e
c
o
m
p
o
s
i
t
i
o
n
t
ec
h
n
i
q
u
es
h
as
b
e
e
n
em
p
l
o
y
e
d
t
o
p
r
e
d
i
ct
b
a
n
d
w
id
th
u
t
i
li
z
a
ti
o
n
in
h
ig
h
-
b
an
d
w
id
th
n
e
tw
o
r
k
s
[
2
1
]
.
Desp
ite
t
h
eir
ef
f
ec
ti
v
e
n
ess
f
o
r
s
tatio
n
ar
y
an
d
m
o
d
er
atel
y
v
ar
y
in
g
ti
m
e
s
er
ies,
w
it
h
h
i
g
h
l
y
d
y
n
a
m
ic
d
ata
d
u
e
to
th
eir
ass
u
m
p
tio
n
o
f
lin
ea
r
it
y
a
n
d
r
elian
ce
o
n
s
tatio
n
ar
it
y
.
T
h
ey
o
f
ten
r
eq
u
ir
e
d
if
f
er
en
ci
n
g
tech
n
iq
u
es
to
h
an
d
le
n
o
n
-
s
ta
ti
o
n
ar
y
ti
m
e
s
er
ies,
b
u
t
th
i
s
ap
p
r
o
ac
h
b
ec
o
m
es
i
n
e
f
f
ec
tiv
e
wh
en
ab
r
u
p
t
c
h
an
g
e
s
in
d
ata
o
cc
u
r
.
A
s
a
r
esu
lt,
t
h
ese
m
o
d
els
p
er
f
o
r
m
p
o
o
r
ly
w
h
e
n
f
ac
ed
w
i
th
r
ap
id
f
lu
ct
u
atio
n
s
an
d
ir
r
eg
u
lar
p
atter
n
s
in
d
ata
w
it
h
i
n
tr
icate
n
o
n
-
li
n
ea
r
d
ep
en
d
en
cies
[
1
8
]
,
[
2
2
]
.
I
n
5
G
n
et
w
o
r
k
s
,
R
SR
P
d
ata
p
o
s
e
a
ch
allen
g
e
f
o
r
class
ical
AR
m
o
d
els
d
u
e
to
th
eir
d
y
n
a
m
ic
n
at
u
r
e,
in
f
lu
e
n
ce
d
b
y
u
s
er
m
o
b
ilit
y
,
en
v
ir
o
n
m
e
n
tal
o
b
s
tr
u
ct
io
n
s
,
a
n
d
n
et
w
o
r
k
in
ter
f
er
en
ce
,
r
es
u
ltin
g
in
r
ap
id
an
d
u
n
p
r
ed
ictab
le
v
ar
iatio
n
s
.
T
h
ese
ch
ar
ac
ter
is
tic
s
m
a
k
e
it
d
if
f
icu
lt
f
o
r
tr
ad
itio
n
al
m
o
d
els
to
ac
cu
r
atel
y
ca
p
tu
r
e
lo
n
g
ter
m
d
ep
en
d
e
n
cies
an
d
co
m
p
lex
r
elatio
n
s
h
ip
s
in
h
er
en
t
i
n
5
G
s
ig
n
al
p
r
o
p
ag
atio
n
.
Giv
e
n
th
ese
li
m
itatio
n
s
,
m
o
r
e
ad
v
an
ce
d
m
ac
h
i
n
e
lear
n
in
g
a
n
d
d
ee
p
lear
n
in
g
ap
p
r
o
ac
h
es
ar
e
n
ec
e
s
s
ar
y
to
ef
f
e
ctiv
el
y
m
o
d
el
a
n
d
p
r
ed
ict
R
SR
P
f
l
u
ctu
at
io
n
s
i
n
m
u
lti
-
s
tep
f
o
r
ec
asti
n
g
tas
k
s
.
D
ee
p
lear
n
in
g
m
o
d
els
ar
e
w
i
d
ely
ad
o
p
ted
f
o
r
m
u
l
ti
-
s
tep
a
n
d
s
eq
u
e
n
ce
-
to
-
s
eq
u
e
n
ce
(
Seq
2
Seq
)
ti
m
e
s
er
ies
f
o
r
ec
asti
n
g
,
ca
p
tu
r
i
n
g
lo
n
g
-
ter
m
d
ep
en
d
en
cie
s
an
d
n
o
n
li
n
ea
r
p
atter
n
s
in
co
m
p
le
x
d
ata.
No
tab
ly
,
in
[
2
3
]
,
B
i
-
L
ST
Ms a
n
d
E
D
-
L
ST
Ms o
u
tp
er
f
o
r
m
ed
S
-
L
ST
M
an
d
co
n
v
o
lu
tio
n
al
n
e
u
r
al
n
e
t
w
o
r
k
(
C
NN)
.
Diq
i
et
a
l
.
[
1
9
]
in
teg
r
ated
an
ex
p
o
n
en
t
i
al
m
o
v
i
n
g
av
er
ag
e
(
E
M
A
)
s
m
o
o
th
in
g
alg
o
r
it
h
m
to
i
m
p
r
o
v
e
s
tab
ilit
y
a
n
d
r
o
b
u
s
tn
es
s
in
n
o
i
s
y
an
d
n
o
n
-
s
tatio
n
ar
y
en
v
ir
o
n
m
e
n
ts
,
ad
d
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ess
i
n
g
t
h
e
s
h
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tco
m
i
n
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o
f
co
n
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t
io
n
al
r
ec
u
r
r
en
t
m
o
d
el
s
,
an
d
i
m
p
r
o
v
in
g
co
n
s
i
s
ten
c
y
o
v
er
lo
n
g
er
f
o
r
ec
ast
h
o
r
izo
n
s
.
T
h
e
in
teg
r
atio
n
o
f
at
ten
tio
n
m
ec
h
a
n
is
m
s
in
to
L
ST
M
ar
ch
itect
u
r
es
[
2
4
]
en
h
a
n
ce
s
Seq
2
Seq
f
o
r
ec
asti
n
g
b
y
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m
p
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v
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g
f
o
c
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r
ele
v
an
t
ti
m
e
s
tep
s
an
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6930
T
E
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elec
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p
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tr
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l
,
Vo
l.
23
,
No
.
6
,
Dec
em
b
er
20
25
:
1
5
1
8
-
1
527
1520
b
etter
ca
p
tu
r
e
lo
n
g
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r
an
g
e
d
e
p
en
d
en
cies.
T
r
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b
a
s
e
d
ar
ch
itectu
r
e
h
as
b
ee
n
ex
p
l
o
r
ed
in
[
2
5
]
as
an
alter
n
ati
v
e
to
S
-
L
ST
M
m
o
d
els
f
o
r
m
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ed
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i
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tr
an
s
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er
s
w
it
h
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tack
ed
B
i
-
L
ST
M
in
an
en
c
o
d
er
-
d
ec
o
d
er
f
r
am
e
w
o
r
k
.
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h
i
s
m
o
d
el
u
s
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f
-
atten
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h
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p
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ter
m
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cies
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n
p
ar
allel
w
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n
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lay
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s
to
r
ef
in
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te
m
p
o
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elatio
n
s
h
ip
s
.
T
h
is
f
u
s
io
n
ap
p
r
o
ac
h
h
as
b
ee
n
p
a
r
ticu
lar
l
y
e
f
f
ec
ti
v
e
f
o
r
m
u
lti
-
ch
an
n
el
m
u
lti
-
s
tep
s
p
ec
tr
u
m
p
r
ed
ictio
n
,
w
h
er
e
co
m
p
le
x
n
o
n
lin
ea
r
in
ter
f
er
en
ce
p
atter
n
s
ar
e
v
er
y
ch
alle
n
g
in
g
.
T
h
e
ch
allen
g
e
o
f
ac
cu
r
ate
m
u
lti
s
tep
p
r
ed
ictio
n
o
f
u
s
er
tr
aj
ec
to
r
ies
a
r
ea
l
-
w
o
r
ld
u
s
er
tr
aj
ec
to
r
y
d
ataset
co
llected
f
r
o
m
5
G
ce
llu
lar
n
e
t
w
o
r
k
s
w
a
s
ad
d
r
ess
ed
in
[
2
6
]
b
y
in
te
g
r
atin
g
L
ST
M
w
it
h
t
r
a
n
s
f
o
r
m
er
m
o
d
els.
2
.
2
.
P
r
o
po
s
ed
m
o
del
T
h
e
p
r
o
p
o
s
ed
m
et
h
o
d
u
tili
ze
s
an
E
D
-
L
ST
M
n
et
w
o
r
k
to
f
o
r
ec
ast
f
u
tu
r
e
R
SR
P
v
al
u
es
b
as
ed
o
n
p
ast
v
alu
e
s
i
n
a
Seq
2
Seq
ar
ch
itectu
r
e.
Fig
u
r
e
1
illu
s
tr
ates t
h
e
g
en
er
al
ar
ch
itectu
r
e
o
f
a
n
ED
-
L
S
T
M
m
o
d
el.
Fig
u
r
e
1
.
ED
-
L
ST
M
g
en
er
al
m
o
d
el
ar
c
h
itect
u
r
e
−
E
n
co
d
er
:
t
h
e
en
co
d
er
co
n
s
is
ts
o
f
an
L
ST
M
lay
er
th
a
t
tak
es
a
s
in
p
u
t
s
eq
u
e
n
ce
o
f
p
ast
R
S
R
P
v
alu
es.
T
h
is
la
y
er
ex
tr
ac
ts
te
m
p
o
r
al
d
ep
en
d
en
cies
a
n
d
en
co
d
es
th
e
in
f
o
r
m
atio
n
in
to
a
co
n
te
x
t
v
ec
to
r
.
T
h
is
v
ec
to
r
s
er
v
es a
s
a
co
m
p
r
ess
ed
r
ep
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tatio
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o
f
th
e
i
n
p
u
t seq
u
e
n
ce
,
w
h
ic
h
is
p
ass
ed
to
th
e
d
ec
o
d
er
.
−
Dec
o
d
er
:
t
h
e
d
ec
o
d
er
is
an
o
t
h
er
L
ST
M
la
y
er
t
h
at
r
ec
ei
v
e
s
t
h
e
en
co
d
er
’
s
co
n
tex
t
v
ec
to
r
as
its
i
n
itial
s
tate,
th
e
n
g
e
n
er
ates a
s
eq
u
en
c
e
o
f
f
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tu
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e
R
SR
P
v
al
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e
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iter
ati
v
el
y
.
T
o
im
p
r
o
v
e
tr
ain
i
n
g
s
tab
ilit
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an
d
co
n
v
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g
en
ce
,
teac
h
er
f
o
r
c
in
g
is
ap
p
lied
.
T
h
is
in
v
o
lv
e
s
s
h
i
f
ti
n
g
t
h
e
g
r
o
u
n
d
tr
u
t
h
v
al
u
es
as
in
p
u
t
s
to
th
e
d
ec
o
d
er
d
u
r
in
g
tr
ai
n
in
g
r
ath
er
th
a
n
u
s
i
n
g
o
n
l
y
th
e
m
o
d
el
’
s
o
w
n
p
r
ed
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n
s
.
A
Den
s
e
la
y
er
w
i
t
h
a
s
ig
m
o
id
ac
ti
v
atio
n
f
u
n
ctio
n
m
ap
s
th
e
d
ec
o
d
er
o
u
tp
u
ts
to
th
e
f
in
al
p
r
ed
icted
R
S
R
P
v
alu
e
s
.
T
h
e
m
o
d
el
is
tr
ain
ed
u
s
in
g
t
h
e
m
ea
n
s
q
u
a
r
ed
er
r
o
r
(
MSE
)
lo
s
s
f
u
n
ctio
n
,
w
it
h
th
e
A
d
a
m
o
p
tim
izer
an
d
u
s
in
g
an
8
0
to
2
0
tr
ain
-
te
s
t sp
lit a
n
d
v
alid
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n
lo
s
s
is
m
o
n
ito
r
ed
to
p
r
ev
en
t o
v
er
f
it
tin
g
.
2
.
3
.
Da
t
a
s
et
s
des
cr
iptio
n
T
h
e
f
ir
s
t
d
ataset
is
an
o
p
en
ac
ce
s
s
d
ataset
f
r
o
m
[
2
7
]
co
n
tain
s
r
ea
l
n
et
w
o
r
k
d
ata
co
llected
f
r
o
m
d
r
iv
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test
m
ea
s
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r
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m
e
n
ts
i
n
a
r
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l
d
ep
lo
y
m
en
t
in
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elo
Ho
r
izo
n
t
e,
B
r
az
il,
w
ith
a
d
ata
g
r
an
u
l
ar
it
y
o
f
1
m
s
,
th
e
av
er
ag
e
v
elo
cit
y
d
u
r
in
g
t
h
e
m
ea
s
u
r
e
m
e
n
t
ca
m
p
ai
g
n
w
a
s
5
0
k
m
/
h
.
T
h
e
d
ataset
co
m
p
r
is
es
8
8
9
6
d
ata
p
o
in
ts
ca
p
tu
r
in
g
v
ar
io
u
s
m
etr
ics
t
h
a
t
ass
es
s
n
e
t
w
o
r
k
p
er
f
o
r
m
a
n
c
e
an
d
co
v
er
ag
e.
T
h
e
d
r
iv
e
t
est
d
ataset
f
ea
tu
r
e
s
co
n
tain
g
eo
g
r
ap
h
ic
p
ar
a
m
ete
r
s
(
lo
n
g
it
u
d
e
an
d
latit
u
d
e)
alo
n
g
s
id
e
n
et
w
o
r
k
p
er
f
o
r
m
an
ce
m
etr
ics
(
R
SR
P
,
r
ef
er
en
ce
s
i
g
n
a
l
r
ec
eiv
ed
q
u
alit
y
(
R
S
R
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)
,
an
d
s
ig
n
al
-
to
-
in
ter
f
er
en
ce
-
p
l
u
s
-
n
o
i
s
e
r
atio
(
SIN
R
)
)
.
I
t
also
in
cl
u
d
es a
d
d
itio
n
al
f
ea
tu
r
e
s
t
h
at
p
r
o
v
id
e
m
o
r
e
in
f
o
r
m
at
io
n
o
n
m
ea
s
u
r
e
m
e
n
ts
.
T
h
e
s
ec
o
n
d
d
ataset
[
2
8
]
co
n
s
is
ts
o
f
d
r
iv
e
tes
t
m
ea
s
u
r
e
m
e
n
t
s
d
er
iv
e
d
f
r
o
m
o
r
g
an
ized
m
e
asu
r
e
m
en
t
d
r
iv
es
co
v
er
in
g
a
2
5
k
m
lo
n
g
Au
s
tr
ia
n
h
i
g
h
w
a
y
s
ec
tio
n
.
T
h
e
s
ec
tio
n
co
n
s
is
ts
o
f
a
m
i
x
o
f
u
r
b
an
h
i
g
h
w
a
y
n
ea
r
th
e
cit
y
o
f
Salzb
u
r
g
a
n
d
r
u
r
al
h
ig
h
w
a
y
i
n
t
h
e
Salzk
a
m
m
er
g
u
t
r
eg
io
n
.
T
h
e
d
r
iv
e
test
s
w
er
e
co
n
d
u
cted
d
u
r
in
g
r
u
s
h
h
o
u
r
w
h
e
n
h
i
g
h
ce
ll
lo
ad
s
co
u
ld
b
e
ex
p
ec
ted
.
W
ith
2
6
7
1
9
9
d
ata
p
o
in
ts
f
r
o
m
w
h
ic
h
we
u
s
ed
7
.
5
%
w
o
r
t
h
2
0
0
0
0
d
ata
p
o
in
ts
f
ea
tu
r
i
n
g
g
eo
g
r
ap
h
ic
lo
ca
tio
n
,
n
e
t
w
o
r
k
p
er
f
o
r
m
a
n
ce
m
etr
ic
s
(
R
SR
P
,
R
SR
Q
a
n
d
SIN
R
)
alo
n
g
s
id
e
d
ata
r
ate
an
d
o
th
er
f
ea
tu
r
es,
th
is
d
ataset
p
r
ese
n
ts
m
o
r
e
c
h
alle
n
g
e
s
in
ter
m
s
o
f
m
o
b
ilit
y
m
a
n
a
g
e
m
e
n
t
d
u
e
to
h
ig
h
s
p
ee
d
d
r
iv
in
g
an
d
ce
ll
lo
ad
w
h
ich
m
a
k
es
it
i
n
ter
esti
n
g
to
ev
alu
a
te
th
e
r
o
b
u
s
t
n
ess
o
f
th
e
AI
m
o
d
el
s
.
A
cc
o
r
d
in
g
to
3
r
d
g
en
er
atio
n
p
ar
tn
er
s
h
ip
p
r
o
j
ec
t
(
3
G
P
P
)
s
tan
d
ar
d
s
[
2
9
]
,
[
3
0
]
,
h
an
d
o
v
er
(
HO)
tr
ig
g
er
i
n
g
r
elies
s
o
lel
y
o
n
th
e
r
ec
eiv
ed
s
ig
n
al
co
n
d
itio
n
,
i.e
.
,
m
ai
n
l
y
o
n
R
S
R
P
,
R
S
R
Q
an
d
SIN
R
m
ea
s
u
r
e
m
e
n
t
s
.
Am
o
n
g
th
ese,
R
SR
P
h
as
b
ee
n
s
elec
ted
as
th
e
k
e
y
i
n
p
u
t
m
etr
ic
f
o
r
th
e
m
o
d
el
d
u
e
to
it
s
f
u
n
d
a
m
en
ta
l
r
o
le
in
ass
e
s
s
i
n
g
s
ig
n
al
s
tr
e
n
g
t
h
.
T
ab
le
1
p
r
o
v
id
es
a
s
tat
is
tical
s
u
m
m
ar
y
o
f
th
e
R
S
R
P
f
o
r
b
o
th
d
atasets
.
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
E
n
h
a
n
ci
n
g
h
a
n
d
o
ve
r
ma
n
a
g
e
men
t in
5
G
n
etw
o
r
ks w
ith
en
co
d
er
-
d
ec
o
d
er
LS
TM
fo
r
…
(
Zi
n
eb
Zia
n
i
)
1521
T
ab
le
1.
Statis
tical
s
u
m
m
ar
y
o
f
th
e
R
S
R
P
f
o
r
b
o
th
d
atasets
D
a
t
a
se
t
P
a
r
a
me
t
e
r
M
i
n
.
M
a
x
.
M
e
a
n
S
t
a
n
d
a
r
d
d
e
r
v
i
a
t
i
o
n
D
a
t
a
se
t
1
R
S
R
P
-
1
0
1
-
40
-
75
9
.
5
7
D
a
t
a
se
t
2
R
S
R
P
-
1
2
2
-
51
-
85
1
5
.
3
4
T
h
e
m
i
n
i
m
u
m
an
d
m
a
x
i
m
u
m
v
alu
e
s
in
d
icate
th
e
r
an
g
e
o
f
s
i
g
n
al
s
tr
e
n
g
th
v
ar
iatio
n
s
.
W
h
il
e
th
e
m
ea
n
r
ep
r
esen
ts
t
h
e
av
er
a
g
e
r
ec
eiv
ed
p
o
w
er
ac
r
o
s
s
t
h
e
d
ataset
s
.
T
h
e
s
tan
d
ar
d
d
ev
iatio
n
r
ef
l
ec
ts
th
e
d
e
g
r
ee
o
f
f
l
u
ctu
a
tio
n
i
n
R
S
R
P
v
al
u
es.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
o
ev
atu
ate
th
e
p
er
f
o
r
m
a
n
ce
o
f
t
h
e
E
D
-
L
ST
M
m
o
d
el
an
d
co
m
p
ar
e
it
to
B
i
-
L
ST
M
an
d
S
-
L
ST
M
m
o
d
el
s
w
e
p
r
esen
t
in
th
i
s
s
ec
tio
n
th
e
r
ea
l
v
s
.
p
r
ed
icted
R
SR
P
v
alu
e
s
p
lo
ts
f
o
r
b
o
th
d
atasets
alo
n
g
s
id
e
lo
s
s
cu
r
v
e
s
o
f
th
e
th
r
ee
m
o
d
els,
w
e
a
ls
o
co
m
p
ar
e
t
h
e
m
o
d
el
s
in
ter
m
s
o
f
M
A
E
an
d
M
AP
E
ac
r
o
s
s
d
if
f
er
e
n
t
p
r
ed
ictio
n
h
o
r
izo
n
s
r
an
g
in
g
f
r
o
m
1
s
tep
a
h
ea
d
to
3
0
s
tep
s
ah
ea
d
.
T
h
e
r
ea
l
v
s
.
p
r
e
d
i
c
t
e
d
R
SR
P
p
l
o
t
s
in
F
ig
u
r
e
2
d
em
o
n
s
t
r
a
t
e
t
h
e
g
o
o
d
p
e
r
f
o
r
m
an
c
e
o
f
th
e
E
D
-
L
S
T
M
m
o
d
e
l
in
c
a
p
tu
r
in
g
s
ig
n
a
l
v
a
r
ia
t
i
o
n
s
f
o
r
b
o
t
h
d
a
t
a
s
e
ts
.
I
t
s
o
b
s
e
r
v
a
b
l
e
o
n
t
h
e
l
ef
t
s
i
d
e
o
f
th
e
F
ig
u
r
e
s
2
(
a
)
a
n
d
(
b
)
t
h
at
th
e
m
o
d
el
p
r
e
d
i
c
t
i
o
n
s
clo
s
e
ly
f
o
l
l
o
w
th
e
a
ct
u
a
l
R
SR
P
v
a
l
u
e
s
,
in
d
i
ca
t
in
g
i
ts
ab
ilit
y
to
lear
n
te
m
p
o
r
al
d
ep
en
d
en
cies.
T
h
e
tr
ain
in
g
an
d
v
alid
atio
n
lo
s
s
cu
r
v
e
s
Fig
u
r
e
s
2
(
c
)
an
d
(
d
)
s
tab
ilize
af
ter
2
0
ep
o
ch
s
,
in
d
icati
n
g
e
f
f
ec
ti
v
e
co
n
v
er
g
en
ce
an
d
g
o
o
d
g
en
er
aliza
tio
n
w
it
h
o
u
t s
ig
n
s
o
f
o
v
er
f
i
tti
n
g
.
(
a)
(
b
)
(
c)
(
d
)
Fig
u
r
e
2
.
P
er
f
o
r
m
a
n
ce
ev
al
u
at
io
n
o
f
th
e
E
D
-
L
ST
M
m
o
d
el
o
n
b
o
th
d
atasets
;
(
a)
r
ea
l v
s
.
p
r
e
d
icted
R
SR
P
f
o
r
d
ataset
1
,
(
b
)
r
ea
l v
s
.
p
r
ed
icte
d
R
SR
P
f
o
r
d
ataset
2
,
(
c)
tr
ain
in
g
v
s
.
v
alid
atio
n
lo
s
s
f
o
r
d
ata
s
et
1
,
an
d
(
d
)
tr
ain
in
g
v
s
.
v
alid
atio
n
lo
s
s
f
o
r
d
ataset
2
Fig
u
r
e
3
p
r
e
s
en
ts
th
e
r
e
a
l
v
s
.
p
r
e
d
i
c
t
e
d
R
SR
P
p
l
o
t
s
o
f
th
e
B
i
-
L
S
T
M
m
o
d
e
l
f
o
r
b
o
t
h
d
at
as
e
ts
,
h
ig
h
-
l
i
g
h
t
in
g
i
ts
a
b
i
l
i
ty
t
o
ca
p
tu
r
e
t
r
e
n
d
s
b
u
t
w
it
h
g
r
e
at
e
r
d
ev
i
at
i
o
n
s
th
an
th
e
E
D
-
L
S
T
M
.
T
h
e
d
e
v
i
at
i
o
n
b
e
tw
ee
n
th
e
r
e
a
l
an
d
p
r
e
d
i
c
te
d
v
al
u
es
is
cl
ea
r
l
y
o
b
s
er
v
ed
o
n
Fi
g
u
r
e
s
3
(
a)
an
d
(
b
)
.
T
h
e
lo
s
s
cu
r
v
es
F
ig
u
r
e
s
3
(
c
)
an
d
(
d
)
in
d
icate
ef
f
ec
ti
v
e
co
n
v
er
g
en
c
e,
w
it
h
v
alid
atio
n
lo
s
s
clo
s
el
y
f
o
llo
w
i
n
g
tr
ain
i
n
g
lo
s
s
a
f
te
r
ar
o
u
n
d
1
5
to
2
0
ep
o
ch
s
.
Desp
ite
m
o
d
er
ate
p
r
ed
ictiv
e
p
er
f
o
r
m
a
n
ce
,
th
e
B
i
-
L
ST
M
m
o
d
el
s
tr
u
g
g
les
w
i
th
lo
n
g
-
ter
m
f
o
r
ec
asts
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6930
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
,
Vo
l.
23
,
No
.
6
,
Dec
em
b
er
20
25
:
1
5
1
8
-
1
527
1522
(
a)
(
b
)
(
c)
(
d
)
Fig
u
r
e
3
.
P
er
f
o
r
m
a
n
ce
ev
al
u
at
io
n
o
f
th
e
B
i
-
L
ST
M
m
o
d
el
o
n
b
o
th
d
atasets
;
(
a)
r
ea
l v
s
.
p
r
ed
icted
R
SR
P
f
o
r
d
ataset
1
,
(
b
)
r
ea
l v
s
.
p
r
ed
icte
d
R
SR
P
f
o
r
d
ataset
2
,
(
c)
tr
ain
in
g
v
s
.
v
alid
atio
n
lo
s
s
f
o
r
d
ata
s
et
1
,
an
d
(
d
)
tr
ain
i
n
g
v
s
.
v
a
lid
atio
n
lo
s
s
f
o
r
d
ataset
2
T
h
e
r
ea
l
v
s
.
p
r
ed
icted
R
SR
P
p
lo
ts
Fig
u
r
e
s
4
(
a)
an
d
(
b
)
o
f
t
h
e
S
-
L
ST
M
m
o
d
el
in
Fig
u
r
e
4
r
esem
b
le
th
e
B
i
-
L
ST
M
m
o
d
el
r
esu
lt
s
a
n
d
d
e
m
o
n
s
tr
ate
t
h
at
S
-
L
ST
M
p
er
f
o
r
m
s
w
ell
to
ca
p
tu
r
e
th
e
g
en
er
al
tr
en
d
s
i
n
t
h
e
d
ata,
b
u
t
s
tr
u
g
g
le
s
to
ac
cu
r
ately
f
o
llo
w
t
h
e
r
ap
id
f
lu
ctu
a
tio
n
s
in
R
SR
P
,
lead
in
g
to
in
cr
ea
s
e
d
p
r
ed
ictio
n
er
r
o
r
s
.
T
h
e
tr
ain
in
g
v
s
.
v
alid
atio
n
l
o
s
s
cu
r
v
es
p
r
ese
n
ted
i
n
F
ig
u
r
e
s
4
(
c)
an
d
(
d
)
,
r
esp
ec
tiv
el
y
,
s
h
o
w
s
u
cc
es
s
f
u
l
co
n
v
er
g
e
n
ce
,
s
tab
iliz
in
g
af
ter
ap
p
r
o
x
im
a
tel
y
1
0
–
1
5
ep
o
ch
s
w
it
h
o
u
t si
g
n
s
o
f
o
v
er
f
itti
n
g
.
(
a)
(
b
)
(
c)
(
d
)
Fig
u
r
e
4
.
P
er
f
o
r
m
a
n
ce
ev
al
u
at
io
n
o
f
th
e
S
-
L
ST
M
m
o
d
el
o
n
b
o
th
d
atasets
;
(
a)
r
ea
l v
s
.
p
r
ed
icted
R
SR
P
f
o
r
d
ataset
1
,
(
b
)
r
ea
l v
s
.
p
r
ed
icte
d
R
SR
P
f
o
r
d
ataset
2
,
(
c)
tr
ain
in
g
v
s
.
v
alid
atio
n
lo
s
s
f
o
r
d
ata
s
et
1
,
an
d
(
d
)
tr
ain
in
g
v
s
.
v
alid
atio
n
lo
s
s
f
o
r
d
ataset
2
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
E
n
h
a
n
ci
n
g
h
a
n
d
o
ve
r
ma
n
a
g
e
men
t in
5
G
n
etw
o
r
ks w
ith
en
co
d
er
-
d
ec
o
d
er
LS
TM
fo
r
…
(
Zi
n
eb
Zia
n
i
)
1523
A
ll
o
f
th
e
t
h
r
ee
m
o
d
els
d
e
m
o
n
s
tr
ate
g
o
o
d
co
n
v
er
g
en
ce
a
n
d
ac
h
iev
e
lo
w
f
in
al
lo
s
s
v
al
u
e
s
,
in
d
icatin
g
ef
f
ec
tiv
e
lear
n
i
n
g
w
it
h
o
u
t
s
i
g
n
s
o
f
o
v
er
f
itti
n
g
.
T
h
e
tr
ain
i
n
g
a
n
d
v
alid
atio
n
lo
s
s
es
cu
r
v
es
r
e
m
ai
n
clo
s
el
y
alig
n
ed
ac
r
o
s
s
ep
o
ch
s
f
o
r
ea
ch
m
o
d
el,
s
u
g
g
e
s
ti
n
g
s
tr
o
n
g
g
e
n
er
aliza
tio
n
p
er
f
o
r
m
a
n
c
e.
Ho
w
e
v
er
,
w
h
e
n
co
m
p
ar
i
n
g
th
e
ac
tu
a
l
v
er
s
u
s
p
r
ed
icted
R
SR
P
p
lo
ts
,
th
e
E
D
-
L
ST
M
m
o
d
el
clea
r
l
y
p
r
o
v
i
d
es
m
o
r
e
co
n
s
is
te
n
t
an
d
ac
cu
r
ate
tr
ac
k
i
n
g
o
f
s
i
g
n
al
v
ar
iatio
n
s
o
v
er
ti
m
e,
o
u
tp
er
f
o
r
m
in
g
b
o
th
t
h
e
B
i
-
L
ST
M
an
d
S
-
L
ST
M
m
o
d
els.
Fig
u
r
e
5
illu
s
tr
ates
t
h
e
p
er
f
o
r
m
a
n
ce
ev
a
lu
at
io
n
o
f
E
D
-
L
ST
M,
B
i
-
L
ST
M,
an
d
S
-
L
S
T
M
ac
r
o
s
s
p
r
ed
ictio
n
h
o
r
izo
n
s
f
r
o
m
1
to
30
-
ti
m
e
s
tep
s
,
u
s
i
n
g
M
A
E
a
n
d
MA
P
E
as
m
etr
ics.
I
t
is
ev
id
en
t
th
at
t
h
e
E
D
-
L
ST
M
co
n
s
i
s
te
n
tl
y
o
u
tp
er
f
o
r
m
s
th
e
o
th
er
m
o
d
els
ac
r
o
s
s
all
p
r
ed
ictio
n
h
o
r
izo
n
s
.
T
h
e
m
o
d
el
m
a
in
tai
n
s
a
r
elativ
el
y
lo
w
s
tab
le
MA
E
an
d
MA
P
E
d
em
o
n
s
tr
ati
n
g
s
u
p
er
io
r
g
en
er
aliza
tio
n
ca
p
ab
ilit
y
,
w
it
h
M
A
E
r
an
g
i
n
g
b
et
w
ee
n
1
.
9
2
an
d
2
.
2
3
f
o
r
d
at
aset
1
(
Fig
u
r
e
5
(
a)
)
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Fig
u
r
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Evaluation Warning : The document was created with Spire.PDF for Python.