esa-sceva/satcom-chunk-collection

SatCom Chunk Collection

A large-scale dataset of 1,900,085 text chunks was constructed from satellite communication (SatCom) research papers. These chunks are extracted from the SatCom corpus.

Each chunk is enriched with structured metadata (e.g., publication details and authorship information), domain relevance scores, and precomputed vector embeddings using Qwen3-Embedding-4B to enable efficient semantic retrieval and downstream RAG-based generation.

Dataset Structure

The dataset is organized into two subsets:

chunks (default)

Text content and metadata. Lightweight and previewable in the HuggingFace dataset viewer.

Column	Type	Description
id	int64	Unique identifier for each chunk
content	string	The text content of the chunk
title	string	Title of the source paper
authors	string	Authors of the source paper
doi	string	Digital Object Identifier of the source paper
url	string	URL to the source paper
journal	string	Journal or venue where the paper was published
publisher	string	Publisher of the source paper
year	float64	Publication year (ranges from 1929 to 2026)
score	float64	Domain relevance score produced by UltraRM, a reward model. Represents how closely the chunk's content relates to the satellite communication domain. Higher values (closer to 0) indicate stronger relevance
file_id	string	Internal file identifier
original_file_name	string	Original filename of the source document
chunk_name	string	Name/identifier of the chunk within its source document

embeddings

Precomputed embedding vectors, joinable with chunks via the id column.

Column	Type	Description
id	int64	Unique identifier (matches chunks.id)
vector	fixed_size_list<float32>[2560]	2560-dimensional embedding vector

Usage

from datasets import load_dataset

# Load text and metadata (default)
chunks = load_dataset("esa-sceva/satcom-chunk-collection")

# Load embeddings
embeddings = load_dataset("esa-sceva/satcom-chunk-collection", "embeddings")

# Merge on id when you need both text and vectors
import pandas as pd
chunks_df = chunks["train"].to_pandas()
embeddings_df = embeddings["train"].to_pandas()
merged = chunks_df.merge(embeddings_df, on="id")

Chunking Strategy

Documents were split using hierarchical chunking, which preserves the logical structure of research papers (sections, subsections, paragraphs) rather than splitting at arbitrary token boundaries. This ensures that each chunk captures a coherent unit of information. The maximum token length per chunk is 1,048 tokens.

Dataset Statistics

• Total rows: 1,900,085
• Max chunk length: 1,048 tokens
• Chunking method: Hierarchical
• Embedding dimensions: 2,560 (float32)
• Publication years: 1929–2026
• Source: Satellite communication research literature

Score Details

The score column is a domain relevance score computed using UltraRM-13b, a reward model developed by OpenBMB. Each chunk was scored based on how closely its content aligns with the satellite communication domain. Scores are negative floats where values closer to zero indicate higher relevance to SatCom.