A tensor may be represented as a (potentially multidimensional) array. Just as a vector in an -dimensional space is represented by a one-dimensional array with components with respect to a given basis, any tensor with respect to a basis is represented by a multidimensional array. For example, a linear operator is represented in a basis as a two-dimensional square array. The numbers in the multidimensional array are known as the "scalar components" of the tensor or simply its "components". They are denoted by indices giving their position in the array, as subscripts and superscripts, following the symbolic name of the tensor. For example, the components of an order tensor could be denoted  , where and are indices running from to , or also by . Whether an index is displayed as a superscript or subscript depends on the transformation properties of the tensor, described below. Thus while and can both be expressed as "n" by "n" matrices, and are numerically related via index juggling, the difference in their transformation laws indicates it would be improper to add them together. The total number of indices required to identify each component uniquely is equal to the dimension of the array, and is called the "order", "degree" or "rank" of the tensor. However, the term "rank" generally has another meaning in the context of matrices and tensors.