docs: refinement chapter (§15), REFINE_ report keys, acknowledgements and in-source credits

- New docs/CPU_DATA_ANALYSIS_REFINEMENT.md (§15): the ML amplitude target and its FFT gradient,
  scale/robust fit/fine solvent mask, D and the variance (work/free), the embedded Monomer Library
  subset and topology, geometry/metal/KL-B restraints, the minimiser, the weight trials, refined maps,
  blobs, GPU/CPU, and a "Not yet" list; with its own References. Linked from TECHNICAL.md and
  CPU_DATA_ANALYSIS.md; §14 no longer says the structure is never refined.
- RUGNUX_REPORT.md: a Refinement (REFINE_*) section - every REFINE_ and REFINE_SCORE_ key, the
  --mode refine input keys and the _blobs.json fields.
- RUGNUX_MODEL_AND_REFERENCE.md: "What it does not do yet"; RUGNUX_ADVANCED.md: --model mentions the
  refinement, --no-refine as in the usage, a --mode refine options table; GPU_DATA_ANALYSIS.md: the
  refinement's CUDA engines and the missing Metal path.
- ACKNOWLEDGEMENT.md: Brunger, Karplus & Petsko 1989 (gradient-balanced starting weight), Nocedal 1980
  (two-loop recursion), Charbonnier et al. 1997 (pseudo-Huber), Jiang & Brunger 1994 (flat solvent
  mask), Kullback & Leibler 1951; all DOIs checked against Crossref.
- In-source credit lines: the weight balancing (replacing a "Brunger (1992)-style" line that named the
  R-free paper), Nocedal 1980, Charbonnier 1997, Jiang & Brunger 1994, Emsley et al. 2010 at
  FindDensityBlobs, Read 1986 at the refined maps. Comments only.
- docs/THIRD_PARTY_NOTICES.md regenerated from the root file with update_version.sh's own step (it
  lacked the CCP4 Monomer Library row).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_015CeEGgpKdfkCTJb3YNMJcu
(cherry picked from commit 09375dba351bae0cc73e9a6f0f79455dc92b3a10)

image_analysis/structure_refinement/ModelScaling.cpp

@@ -147,6 +147,7 @@ struct FixedSolventFit {
 // a model fits badly (a beam-stop shadow, an ice ring, a missing part of the model), which least squares would
 // otherwise let set the scale of all the rest.
 // Following Huber (1964) Ann. Math. Statist. 35, 73-101 for the M-estimator, as iteratively reweighted least squares
+// The smooth (pseudo-Huber) loss of Charbonnier et al. (1997) IEEE Trans. Image Process. 6, 298-311
 void RobustReweight(FixedSolventFit &fit, double &c) {
     std::vector<double> r(fit.points.size());
     for (size_t i = 0; i < fit.points.size(); i++)