Similarity value is different between equivalent atoms about dscribe HOT 5 CLOSED

from dscribe.descriptors import SOAP
from ase.build import bulk

soap_descriptor = SOAP(species=['Cu'], n_max=2, l_max=2, r_cut=5, periodic=True)

system_1x1 = bulk("Cu", "fcc")
features_1x1 = soap_descriptor.create(system_1x1, centers=[0])

system_3x3 = bulk("Cu", "fcc") * [3, 3, 3]
features_3x3 = soap_descriptor.create(system_3x3, centers=[0])

diff = features_1x1-features_3x3
print(diff.max())

unit_cell_mono, super_cell_mono          =  'ZrO2_mono_111.xyz', 'ZrO2_mono_222.xyz'
system_1x1     = ase.io.read(unit_cell_mono,':')
system_2x2     = ase.io.read(super_cell_mono,':')

metric_soap    = AverageKernel(metric="polynomial",gamma=None, degree=4, coef0=1.0, normalize_kernel=True)
soap_descriptor = SOAP(species=['Zr','O'], rcut=2.5, nmax=10, lmax=10, sigma=0.5, periodic=True, rbf='polynomial',crossover=True, sparse=False)

features_1x1 = soap_descriptor.create(system_1x1)
features_2x2 = soap_descriptor.create(system_2x2)

diff = features_1x1[0] - features_2x2 [0] 
#diff is not equal to zero

CUB_MONO = []
for j in range(0,8):
    sim_kernel = metric_soap.create([features_1x1[0].reshape(1, -1), features_2x2[j].reshape(1, -1)])
    CUB_MONO.append(sim_kernel[0][1])
    print(j, sim_kernel[0][1])
print(np.mean(CUB_MONO))

import numpy as np
import ase.io
from dscribe.descriptors import SOAP
from dscribe.kernels import AverageKernel

unit_cell_mono = 'ZrO2_mono_111.xyz'
system_1x1 = ase.io.read(unit_cell_mono, 0)
system_2x2 = system_1x1 * [2, 2, 2]

metric_soap = AverageKernel(
    metric="polynomial",
    gamma=None,
    degree=4,
    coef0=1.0,
    normalize_kernel=True
)
soap_descriptor = SOAP(
    species=['Zr','O'],
    r_cut=2.5,
    n_max=10,
    l_max=10,
    sigma=0.5,
    periodic=True,
    rbf='polynomial',
    compression={"mode": "crossover"},
    sparse=False
)

features_1x1 = soap_descriptor.create(system_1x1)
features_2x2 = soap_descriptor.create(system_2x2)

max_diff = np.max(np.abs(features_1x1[0] - features_2x2[0]))
print(f"Maximum difference in SOAP desciptor: {max_diff}")

CUB_MONO = []
for j in range(0, 8):
    sim_kernel = metric_soap.create([features_1x1[0].reshape(1, -1), features_2x2[j].reshape(1, -1)])
    CUB_MONO.append(sim_kernel[0][1])
    print(j, sim_kernel[0][1])
print(np.mean(CUB_MONO))