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```import sympy as sp

def find_rotation_solutions(a, b, c):
# Define the unknown matrix elements
x, y, w, z, p, q = sp.symbols('x y w z p q', real=True)

# Construct the matrix using the known and unknown values
R = sp.Matrix([
[a, x, y],
[p, q, b],
[w, c, z]
])

# Create the system of equations based on orthogonality and determinant condition
equations = []

# Each row and each column should be a unit vector
for i in range(3):
equations.append(sum(element**2 for element in R.row(i)) - 1)
equations.append(sum(element**2 for element in R.col(i)) - 1)

# Dot product between any two distinct rows and columns should be zero
for i in range(3):
for j in range(i+1, 3):
equations.append(R.row(i).dot(R.row(j)))
equations.append(R.col(i).dot(R.col(j)))

# Cross products for rows
equations.append(R.row(0).cross(R.row(1)) - R.row(2))
equations.append(R.row(1).cross(R.row(2)) - R.row(0))
equations.append(R.row(2).cross(R.row(0)) - R.row(1))

# Cross products for columns
equations.append(R.col(0).cross(R.col(1)) - R.col(2))
equations.append(R.col(1).cross(R.col(2)) - R.col(0))
equations.append(R.col(2).cross(R.col(0)) - R.col(1))

# The determinant should be 1
equations.append(R.det() - 1)

# Solve the system of equations
solutions = sp.solve(equations)

return solutions

a, b, c = 0.4835, 0.1690, 0.3256
solutions = find_rotation_solutions(a, b, c)

for idx, solution in enumerate(solutions, 1):
print(f"Solution {idx}:")
print(solution)
print("---------")

print(f"Total solutions: {len(solutions)}")
```