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README.md

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----
-license: other
-license_name: other
-license_link: LICENSE
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+---
+license: other
+license_name: other
+license_link: LICENSE
+---
+
+# Telegraphing Without Wires (1884) — Finite-Difference Simulator and Synthetic Dataset Reconstruction
+
+**Developed by:** DBbun LLC
+**Version:** v1.1
+**Data Formats:** CSV · JSON · NPZ · PNG
+
+---
+
+## Who Should Use This Dataset
+
+This dataset is intended for:
+
+- **Students** studying electromagnetism, signal processing, or numerical simulation
+- **Engineers** exploring signal transmission in conductive media
+- **Data scientists** working with physics-generated structured data
+- **Researchers** interested in executable reconstructions of historical experiments
+- **Educators** integrating computational laboratories into coursework
+
+It provides a reproducible computational environment for studying signal transmission through conductive media, bridging physical modeling, circuit abstraction, and time-domain system behavior.
+
+---
+
+## Abstract
+
+This dataset provides a computational reconstruction of S. J. M. Bear's 1884 experiment *"Telegraphing Without Wires,"* originally presented before the American Institute of Electrical Engineers. The project transforms a pre-digital manuscript — written decades before computers and numerical modeling — into a fully reproducible finite-difference simulation framework. By solving the variable-conductivity Laplace equation, it enables quantitative analysis of electric potential fields, current density distributions, and receiver behavior in conductive media.
+
+The dataset includes multi-scenario simulations with spatially varying conductivity (uniform media, freshwater–brine interfaces, insulating obstacles, conductive paths, and localized plumes). Receiver behavior is modeled using a Thevenin-equivalent formulation with relay resistance and threshold dynamics. Time-domain simulations capture keying signals, electrode polarization effects, and relay actuation.
+
+Beyond historical reconstruction, this resource serves as an educational and research platform:
+
+- **For students:** a hands-on bridge between electromagnetic theory, partial differential equations, numerical methods, and signal processing.
+- **For engineers:** a sandbox for studying signal transmission in conductive environments (e.g., underwater communication, geophysical sensing, bioelectric systems).
+- **For data scientists:** structured, multi-modal datasets suitable for statistical modeling, inverse problems, parameter estimation, surrogate modeling, and machine learning experiments on physics-generated data.
+
+All outputs are provided in CSV, JSON, and compressed NumPy formats to support reproducibility and downstream analysis. The included Python source code regenerates all scenarios.
+
+---
+
+## Dataset Structure
+
+Each experiment generates:
+
+| File | Description |
+|------|-------------|
+| `*_fields.npz` | Spatial field arrays (potential, electric field, current density) |
+| `*_timeseries.csv` | Time-domain signal and relay behavior |
+| `*_summary.json` | Per-experiment scalar results and circuit parameters |
+| `figs/` | Figures directory |
+
+Global outputs:
+
+| File | Description |
+|------|-------------|
+| `experiment_summary_v1p1.csv` | Aggregated scalar results across all experiments |
+| `experiment_summary_v1p1.json` | JSON equivalent of the above |
+| `sweep_sigma_v1p1.csv` | Conductivity sweep results |
+| `sweep_sigma_v1p1.json` | JSON equivalent of the sweep |
+| `manifest_v1p1.json` | File inventory and checksums |
+| `run_meta_v1p1.json` | Run metadata (version, timestamp, grid parameters) |
+
+---
+
+## NPZ File Specification (`*_fields.npz`)
+
+### Material Map
+
+| Array | Type | Description |
+|-------|------|-------------|
+| `sigma` | 2D float | Spatial conductivity map of the tub. Higher values indicate more conductive regions; lower values indicate less conductive or insulating regions. |
+
+---
+
+### Base 1-Volt Sending Condition
+
+| Array | Type | Description |
+|-------|------|-------------|
+| `V_tub_1V` | 2D float | Potential distribution when 1 volt is applied across the sending electrodes. |
+| `Ex_1V` | 2D float | Horizontal component of the electric field under the 1-volt sending condition. |
+| `Ey_1V` | 2D float | Vertical component of the electric field under the 1-volt sending condition. |
+| `Emag_1V` | 2D float | Magnitude of the electric field at each spatial location. |
+| `Jx_1V` | 2D float | Horizontal component of current density. |
+| `Jy_1V` | 2D float | Vertical component of current density. |
+| `Jmag_1V` | 2D float | Magnitude of current density. |
+
+---
+
+### Delivered Voltage Fields
+
+| Array | Type | Description |
+|-------|------|-------------|
+| `V_delivered_no_pol` | 2D float | Potential distribution accounting for battery internal resistance and contact resistance, without polarization. |
+| `V_delivered_pol` | 2D float | Potential distribution including steady-state electrode polarization effects. |
+
+---
+
+### Receiver-Port Solution
+
+| Array | Type | Description |
+|-------|------|-------------|
+| `V_port_1V` | 2D float | Potential distribution when 1 volt is applied directly across the receiver electrodes. Used to estimate the receiver's effective resistance. |
+
+---
+
+### Electrode Masks
+
+Binary arrays (0 or 1):
+
+| Array | Description |
+|-------|-------------|
+| `tx_plus` | Positive sending electrode region |
+| `tx_minus` | Negative sending electrode region |
+| `rx1` | First receiver electrode |
+| `rx2` | Second receiver electrode |
+
+---
+
+## Time-Series CSV Specification (`*_timeseries.csv`)
+
+| Column | Description |
+|--------|-------------|
+| `t` | Simulation time in seconds |
+| `key` | Telegraph key state (1 = pressed, 0 = released) |
+| `Vpol` | Electrode polarization voltage |
+| `Vtub` | Voltage delivered across the tub |
+| `Isource` | Current supplied by the battery |
+| `Vth` | Effective voltage at the receiver |
+| `Irelay` | Current flowing through the relay |
+| `relay_state` | Relay state (1 = closed, 0 = open) |
+
+---
+
+## Per-Experiment Summary JSON Fields (`*_summary.json`)
+
+| Field | Description |
+|-------|-------------|
+| `name` | Experiment identifier |
+| `sigma_map_name` | Conductivity scenario used |
+| `battery_voltage` | Battery voltage |
+| `battery_internal_resistance_ohm` | Internal battery resistance |
+| `electrode_contact_resistance_ohm` | Electrode contact resistance |
+| `relay_resistance_ohm` | Relay resistance |
+| `relay_pull_in_current` | Current required to activate relay |
+| `relay_release_current` | Current below which relay releases |
+| `Vth_per_1V` | Receiver voltage scaling factor |
+| `Rth_ohm_like` | Effective resistance at receiver port |
+| `delivered_tub_voltage_no_pol` | Tub voltage without polarization |
+| `delivered_tub_voltage_with_pol` | Tub voltage with polarization |
+| `Irelay_no_pol` | Relay current without polarization |
+| `Irelay_with_pol` | Relay current with polarization |
+| `relay_click_no_pol` | Relay activation without polarization (bool) |
+| `relay_click_with_pol` | Relay activation with polarization (bool) |
+| `tub_resistance_ohm_like` | Effective tub resistance |
+| `source_current_no_pol` | Battery current without polarization |
+| `source_current_with_pol` | Battery current with polarization |
+
+---
+
+## Conductivity Sweep Dataset (`sweep_sigma_v1p1.csv`)
+
+| Column | Description |
+|--------|-------------|
+| `sigma` | Uniform conductivity value |
+| `R_tub` | Effective tub resistance |
+| `Vth_per_1V` | Receiver scaling factor |
+| `Rth` | Effective receiver resistance |
+| `Vtub_no_pol` | Delivered voltage without polarization |
+| `Irelay_no_pol` | Relay current without polarization |
+| `click_no_pol` | Relay activation without polarization (0/1) |
+| `Vtub_pol` | Delivered voltage with polarization |
+| `Irelay_pol` | Relay current with polarization |
+| `click_pol` | Relay activation with polarization (0/1) |
+
+---
+
+## Reproducibility
+
+Run:
+
+```bash
+python Bear-1884-Code-v1.1.py
+```
+
+Outputs are generated under:
+
+```
+output/
+output/figs/
+```
+
+**Dependencies:**
+
+- `numpy`
+- `scipy`
+- `matplotlib`
+
+---
+
+## Contribution
+
+Developed by **DBbun LLC**, this project demonstrates how a historical scientific experiment can be transformed into a structured, reproducible computational laboratory suitable for education, engineering analysis, and data-driven research.