Initial commit of phase 5

[skyelaird]

This commit implements the core components of Phase 5, which adds signal prediction capabilities to DVOACAP-Python. Phase 5 integrates all previous phases to provide complete HF propagation predictions including signal strength, reliability, and system performance metrics.

Completed Components:

1. Noise Modeling (noise_model.py)

   • Atmospheric noise modeling (ITU-R P.372)
   • Galactic (cosmic) noise calculations
   • Man-made (industrial) noise modeling
   • Combined noise distributions with median and deciles
   • Frequency and time-dependent noise predictions

2. Antenna Gain (antenna_gain.py)

   • Base antenna model class
   • Isotropic antenna (0 dBi reference)
   • Half-wave dipole antenna
   • Vertical monopole antenna
   • Antenna farm management with frequency selection

3. Prediction Engine (prediction_engine.py)

   • Complete signal prediction framework
   • Path loss calculations (free space + absorption)
   • Ground reflection loss (Fresnel coefficients)
   • D-layer and E-layer absorption modeling
   • Signal-to-noise ratio (SNR) computation
   • Circuit reliability calculations
   • Service probability metrics
   • Multipath probability assessment

4. Examples and Documentation

   • Phase 5 example demonstrating noise and antenna modeling
   • Signal budget calculations
   • Integration with previous phases

Version Update:

• Updated from v0.4.0 to v0.5.0 (90% complete)
• Added Phase 5 exports to init.py
• Updated package progress indicators

Technical Details:

• Noise model uses Fourier coefficient maps for atmospheric noise
• Implements Spaulding/Caruana noise combination algorithms
• Antenna models support elevation and azimuth-dependent gains
• Prediction engine integrates all 5 phases of VOACAP

Status:

• Phase 1: Complete - Path Geometry
• Phase 2: Complete - Solar & Geomagnetic
• Phase 3: Complete - Ionospheric Profiles
• Phase 4: Complete - Raytracing
• Phase 5: In Progress - Signal Predictions (Core components complete)

Next Steps:

• Complete prediction engine integration and testing
• Add more antenna patterns (Yagi, Log-periodic, etc.)
• Implement LUF (Lowest Usable Frequency) calculations
• Add complete end-to-end prediction examples

This implementation follows the original VOACAP algorithms from VoaCapEng.pas, NoiseMdl.pas, and AntGain.pas by Alex Shovkoplyas (VE3NEA).