In sections I-IV we formulated the problem considered in this work as
follows: given the *photospheric* vector field
and *chromospheric* line of sight
component satisfying FFF-conditions, reconstruct the
vector field in the 3D space above the photosphere
in which - information is available. Thus, using (i)
analytical FFF solutions for and
``data" and (ii) highly efficient computational
procedures, the algorithm developed here provides results within
less then relative error.

Now, in practice, observed -rather than analytical- and data have to be used. Such data are subject to the following additive sources of uncertainties: (a) projection effects, (b) ambiguity in the azimuth, (c) Faraday rotation of the azimuth and (d) noise in the basic data that is, in the circulary and linearly polarized intensities. Therefore, before these data can be used in the FFF - reconstruction algorithm developed in this paper, the following corrective steps have to be taken:

1. Elimination / minimization of the errors related to the uncertainty sources (a)-(d). (See, e.g. Hagyard 1985, 1988). Note that it is anticipated that space-flight magnetograms will be characterized by polarimetric noise a factor of 10 - 100 smaller than ground based systems (that is about the same as for the normal heliographic component) (see, e.g. Venkatakrishnan & Gary 1989).

2. After the corrective procedures mentioned above are carried out, a variational modification of the ``correct" data such as to fit a FFF- state is required. In this, global constraints characterizing a FFF-state have to be satisfied (see, e.g. Molodensky 1969; Aly 1984, 1989; Semel 1988).

*In conclusion*, we reiterate that the algorithm we
developed is concerned with the basic reconstruction problem,
within the framework of the FFF-model equations. It assumes that the
corrective procedures indicated above have been carefully applied
and that the measurement errors as well as the deviations of the data from a
FFF-state have been reduced to an insignificant level.

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