Add TsFile (converted from nuerrr/robot-sensor-telemetry)

README.md

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+---
+license: mit
+task_categories:
+- robotics
+- time-series-forecasting
+language:
+- en
+tags:
+- tsfile
+- timeseries
+- robotics
+- embedded-systems
+- ultrasonic
+- autonomous-robot
+- PIC16F877A
+- time-series
+- format:tsfile
+pretty_name: Robot Sensor Telemetry Dataset (TsFile)
+size_categories:
+- n<1K
+configs:
+- config_name: default
+  data_files:
+  - split: robot_sensor_telemetry
+    path: robot_sensor_telemetry.tsfile
+---
+
+# Robot Sensor Telemetry Dataset (TsFile)
+
+Apache TsFile version of [`nuerrr/robot-sensor-telemetry`](https://huggingface.co/datasets/nuerrr/robot-sensor-telemetry).
+
+## Overview
+
+This dataset contains real-time sensor telemetry collected from an autonomous robot built for CIE-349/408 Embedded Systems at Zewail City University of Science and Technology (Spring 2026).
+
+- **Tables:** 1 TsFile table(s), 81 rows in total.
+  - `robot_sensor_telemetry`: 81 rows, 1970-01-01 00:00:00 → 1970-01-01 00:00:20 (UTC)
+
+## Schema (TsFile structure)
+
+### `robot_sensor_telemetry.tsfile`
+
+- **Time** (INT64, milliseconds) — the source `timestamp_ms` column.
+- **TAG**: none — the table is stored as a single device.
+- **FIELD** (8): `front_cm` (int64), `rear_cm` (int64), `left_cm` (int64), `right_cm` (int64), `motor_state` (string), `face_verified` (int64), `tilt_warning` (int64), `fall_detected` (int64)
+
+## Conversion notes
+
+- One CSV, one TsFile, single robot: no TAG column.
+- Time = the source `timestamp_ms` (milliseconds since the telemetry session started, 250 ms sampling) stored as INT64 milliseconds.
+- No column is dropped.
+
+## Usage
+
+Install the Apache TsFile Python SDK (`pip install tsfile`) and read a converted file:
+
+```python
+from pathlib import Path
+from tsfile import TsFileReader
+
+path = Path("robot_sensor_telemetry.tsfile")
+with TsFileReader(str(path)) as reader:
+    schemas = reader.get_all_table_schemas()
+    print("tables:", list(schemas))
+    table_name = next(iter(schemas))
+    table = schemas[table_name]
+    columns = [column.get_column_name() for column in table.get_columns()]
+    print("columns:", columns)
+    field_names = [
+        column.get_column_name()
+        for column in table.get_columns()
+        if column.get_column_name() not in {"Time", "time"}
+    ]
+    if field_names:
+        with reader.query_table(table_name, field_names[:3], batch_size=1024) as result:
+            batch = result.read_arrow_batch()
+            if batch is not None:
+                print(batch.to_pandas().head())
+```
+
+## Source & license
+
+- Original dataset: [`nuerrr/robot-sensor-telemetry`](https://huggingface.co/datasets/nuerrr/robot-sensor-telemetry)
+- Source card title / publisher: Robot Sensor Telemetry Dataset
+- License: mit (as declared by the original dataset).

source_dataset_card.md

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+---
+license: mit
+task_categories:
+- robotics
+- time-series-forecasting
+language:
+- en
+tags:
+- robotics
+- embedded-systems
+- ultrasonic
+- autonomous-robot
+- PIC16F877A
+pretty_name: Robot Sensor Telemetry Dataset
+size_categories:
+- n<1K
+---
+
+# Robot Sensor Telemetry Dataset
+
+## Description
+
+This dataset contains real-time sensor telemetry collected from an autonomous robot built for CIE-349/408 Embedded Systems at Zewail City University of Science and Technology (Spring 2026).
+
+The robot uses a PIC16F877A microcontroller reading 4x HC-SR04 ultrasonic sensors at 250ms intervals. Telemetry is streamed over UART to an ESP32 NodeMCU, forwarded via UDP to a Raspberry Pi 3B, and served to a Flutter mobile app over HTTP.
+
+## Dataset Structure
+
+| Column | Type | Description |
+|--------|------|-------------|
+| timestamp_ms | int | Milliseconds since robot boot |
+| front_cm | int | Front ultrasonic sensor distance (cm) |
+| rear_cm | int | Rear ultrasonic sensor distance (cm) |
+| left_cm | int | Left ultrasonic sensor distance (cm) |
+| right_cm | int | Right ultrasonic sensor distance (cm) |
+| motor_state | string | Current motor command: FORWARD/BACKWARD/TURN_LEFT/TURN_RIGHT/STOP/IDLE |
+| face_verified | bool | Whether biometric face recognition lock is verified |
+| tilt_warning | bool | MPU6050 tilt > 30 degrees detected |
+| fall_detected | bool | MPU6050 tilt > 60 degrees detected |
+
+## System Architecture
+
+The robot operates as a 4-tier distributed pipeline:
+
+1. **PIC16F877A** — Hard real-time controller, reads sensors via Timer1, drives motors via PWM
+2. **ESP32 NodeMCU** — WiFi bridge, forwards UART telemetry to Raspberry Pi over UDP
+3. **Raspberry Pi 3B** — Flask server, face recognition security gate, UDP auto-discovery
+4. **Flutter Android App** — Mobile dashboard with D-pad control and radar display
+
+## Face Recognition Security
+
+Motor commands are blocked unless a verified face is detected by the `face_recognition` library (dlib-based deep learning model) running on the Raspberry Pi. Face encodings are stored persistently in a `.pkl` file (biometric database).
+
+## Safety Features
+
+- Software obstacle cutoff: front sensor < 10cm → force STOP
+- Tilt/fall detection via MPU6050 accelerometer
+- Biometric lock: commands blocked until face verified
+
+## Team
+
+- Nouran Essam (202300627)
+- Menna Khaled (202301665)
+- Shrooq Amin (202300112)
+
+Zewail City University of Science and Technology — CIE-349/408 Spring 2026