upload samplEth simulator with data and README

DAS/samplEth/README.md

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+# samplEth Simulator
+
+`samplEth` is a simulator designed to model and analyze peer-to-peer network connections, custodianship behaviors, and different node configurations. It allows you to tweak various parameters to simulate different conditions like connection establishment, data poisoning, and query growth. 
+
+## How to Run the Simulator
+
+To start the simulator, run the following command in your terminal:
+
+```bash
+python3 samplEth.py
+```
+
+Once the simulation is complete, check the results folder for the output.
+
+## Customizable Parameters
+
+The following parameters can be adjusted to simulate different network configurations. Each section describes the purpose of the parameter and how you can modify it.
+
+### 1. connections_per_peer
+
+This parameter defines the range of connections each peer will have. It is set to simulate peer connections with varying numbers.
+
+```bash
+connections_per_peer = [[50, 100], [100, 150], [150, 200]]
+```
+This means each peer will have a random number of connections, selected from the specified ranges. You can add, remove, or modify the ranges as needed.
+
+### 2. Custody Parameters
+These parameters control the minimum custody and validator custody of peers in the network.
+
+
+```bash
+min_custody = 2
+val_custody = 2
+flat_custody = False
+```
+
+- `min_custody`: This is the baseline number of rows and columns every node is required to store, ensuring even the smallest nodes contribute to data custody.
+
+- `val_custody`: This represents the additional rows and columns assigned per validator on a node. It is a network-wide parameter and can take fractional values, allowing nodes to increase custody incrementally based on their validator count. For example, a node with two validators would be responsible for one additional row/column if val\_custody = 0.5.
+- `flat_custody`: If set to True, all peers will have uniform custody values. Set to False for varied custody values.
+
+### 3. Poisoning Nodes
+You can poison nodes based on a specific value or by percentage. This simulates potential network attacks or anomalies.
+
+Poison by Value:
+```bash
+row_number = 3  # 1 -> Country, 2 -> Company, 3 -> ISP
+row_value = 'prysm'
+```
+
+- `row_number`: The column you want to poison (1 for Country, 2 for Company, 3 for ISP).
+- `row_value`: The value to poison in the specified column (e.g., 'prysm').
+
+Poison by Percentage:
+```bash
+poison_by_percentage = True
+poison_percentage = 90
+```
+
+`poison_by_percentage`: Set to True to poison a percentage of the nodes.
+poison_percentage: The percentage of nodes to poison (e.g., 90%).
+
+### 4. Query Growth Methods
+Simulate different query growth patterns in the network.
+
+Exponential Growth:
+```bash
+exponential_growth = False
+```
+When set to True, the number of queries increases exponentially in each subsequent round: 1, 2, 4, 8, 16, 32, .... 
+
+Linear Growth:
+```bash
+linear_growth = True
+```
+The number of queries increases linearly in each round: 1, 10, 20, 30, 40, 50, ... 
+
+Linear Constant Growth:
+```bash
+linear_constant_growth = False
+```
+Queries increase linearly up to a fixed maximum and remain constant thereafter: 1, 10, 20, 30, 40, 40, 40, ... 
+
+Hybrid Growth:
+```bash
+hybrid_growth = False
+```
+Combines features of exponential and linear sampling: 1, 2, 4, 8, 16, 32, 64, 74, 84, 94, 104, 104, ... 
+
+Exponential Constant Growth:
+```bash
+exponential_constant_growth = False
+```
+Similar to exponential sampling but capped at a maximum query count: 1, 2, 4, 8, 16, 32, 32, 32, ... 
+
+Linear Growth Constant:
+```bash
+linear_growth_constant = 10
+```
+Define a constant growth rate for linear growth.
+
+### 5. Results Folder
+After running the simulation, the results will be saved in the results folder. Check this folder to analyze the output of the simulation.
+
+
+## Conclusion
+You can adjust these parameters to simulate various peer connection behaviors, node poisoning scenarios, and query growth patterns in a peer-to-peer network. Experimenting with these settings will help you analyze different network conditions for your research or experimentation needs.
+