functions and of the effective bandwidth are reported in [22] for the main source geometries and receiving manifold (including planar, cylindrical, and spherical). According to the bandlimited function theory, the number of (spatial) samples is given by the space-spatial bandwidth product, i.e., W'E/r, wherein E is the extension of the observation domain in the s variable and W' is called "the effective angular spatial bandwidth of the field" [21]. Equation 2.13 includes the role of the geometry of the source and of the observation domain in the field representation in a simple and elegant formula. In spite of the relative simplicity, this formula allows to subtract the effect of the geometry around the observation point with an amazing efficiency, giving a number of spatial samples almost equal to NDF. It is understood that thanks to the choice of the su parameterization the sampling points are placed at a constant distance (equal to As = /W') along s, but are nonuniformly placed along the curvilinear abscissa S. In summary, the reduced field admits a bandlimited approximation with an angular bandwidth practically equal to W' and a Nyquist step along the observation curve which is constant in the parameterization s(s) but non-constant in the curvilinear abscissa S. The common application of the bandlimited representation of the functions is the interpolation from the samples. Strictly speaking, in our case prolate spheroidal functions must be used as interpolation functions [67], since sinc functions are orthogonal only on unlimited domains, while the observation domain in s is always finite. The solution followed by Bucci and co-workers is to enlarge the bandwidth [22], accepting a slight oversampling able to reduce the truncation error to a negligible value. However, the problem discussed in this chapter differs from standard interpola- tion. As previously mentioned, the problem studied by Bucci essentially concerns a sphere covering problem, i.e., the minimum number of spheres that allow to cover the set Y, a