LIM-RPS and DELL Evaluation Guide

This guide explains how to evaluate sensor data using the LIM-RPS and DELL algorithms with visualizations. The `evaluate_with_limrps.py` script: 1. Loads sensor data from CSV files 2. Processes data through LIM-RPS (Lipschitz-constrained Implicit-Map for Recursive Proximal Synthesis) 3. Optionally processes through DELL (Dual-Equilibrium Latent Learning) 4. Generates comprehensive visualizations The script generates plots in the specified `--plots-dir` directory (default: `evals/plots/`): 1. **Accelerometer X, Y, Z over time** - Raw accelerometer data 2. **Accelerometer Magnitude** - L2 norm of acceleration vector 3. **Motion Energy** - Computed motion energy over time 4. **Gyroscope Data** - Angular velocity (if available) 5. **LIM-RPS Convergence** - Residual convergence plot (if LIM-RPS is used) 6. **3D Accelerometer Trajectory** - 3D visualization of acceleration space 1. **Encoding**: Converts raw sensor data (accelerometer, gyroscope, energy) into latent representations 2. **Fusion**: Solves a fixed-point equation to find the equilibrium state: