readme

Author: roberto_esaclear

Makefile

@@ -1,4 +1,4 @@
-.PHONY: build build-wheels bump-version check-deps-platforms check-dist check-universal-wheel clean dev format help install lint lock publish-pypi publish-testpypi test
+.PHONY: build build-wheels bump-version check-deps-platforms check-dist check-universal-wheel clean dev format help install lint lock publish publish-pypi publish-testpypi test
 
 UV := uv
 DIST_FILES := dist/*
@@ -74,11 +74,14 @@ publish-testpypi: check-dist
 
 publish:
 	@$(MAKE) bump-version
+	@$(MAKE) lock
 	@$(MAKE) check-dist
 	@pypi_token="$$(awk -F= '/^[[:space:]]*pypi_token[[:space:]]*=/ {sub(/^[^=]*=[[:space:]]*/, ""); print; exit}' .env)"; \
 	if [ -z "$$pypi_token" ]; then echo "Missing pypi_token in .env"; exit 1; fi; \
 	TWINE_USERNAME=__token__ TWINE_PASSWORD="$$pypi_token" $(UV)x --from twine twine upload --repository pypi $(DIST_FILES)
 
+publish-pypi: publish
+
 clean:
 	@rm -rf .venv build dist *.egg-info .pytest_cache .coverage .mypy_cache .ruff_cache
 
@@ -97,5 +100,6 @@ help:
 	@echo "  check-universal-wheel - Verify wheel tag is py3-none-any"
 	@echo "  check-dist        - Build and validate wheel/sdist metadata with twine"
 	@echo "  publish-testpypi  - Build, validate, and upload dist files to TestPyPI"
-	@echo "  publish-pypi      - Bump version, build, validate, and upload dist files to PyPI"
+	@echo "  publish           - Bump version, refresh uv.lock, build, validate, and upload dist files to PyPI"
+	@echo "  publish-pypi      - Alias for publish"
 	@echo "  clean    - Remove local build and cache artifacts"

README.md

@@ -5,9 +5,9 @@
 <p align="center">
   <a href="https://github.com/ESA-PhiLab/pynas/actions/workflows/ci.yml"><img alt="CI" src="https://github.com/ESA-PhiLab/pynas/actions/workflows/ci.yml/badge.svg" /></a>
   <a href="https://www.python.org/downloads/"><img alt="Python 3.11+" src="https://img.shields.io/badge/python-3.11+-blue.svg" /></a>
-  <a href="https://github.com/ESA-PhiLab/pynas"><img alt="Version" src="https://img.shields.io/badge/version-0.1.0-informational.svg" /></a>
+  <a href="https://github.com/ESA-PhiLab/pynas"><img alt="Version" src="https://img.shields.io/badge/version-0.1.2-informational.svg" /></a>
   <a href="https://www.apache.org/licenses/LICENSE-2.0"><img alt="License: Apache 2.0" src="https://img.shields.io/badge/License-Apache_2.0-blue.svg" /></a>
-  <a href="https://doi.org/10.5281/zenodo.15332053"><img alt="DOI" src="https://zenodo.org/badge/DOI/10.5281/zenodo.15332053.svg" /></a>
+  <a href="https://zenodo.org/"><img alt="Zenodo DOI pending" src="https://img.shields.io/badge/Zenodo-DOI_pending-lightgrey" /></a>
 </p>
 
 # PyNAS
@@ -33,7 +33,7 @@ PyNAS provides building blocks for evolutionary NAS experiments, with an emphasi
 | Item | Status |
 | --- | --- |
 | Package name | `esa-pynas` |
-| Version | `0.1.0` |
+| Version | `0.1.2` |
 | Python support | `>=3.11,<3.15` |
 | Distribution | Local checkout; PyPI publication planned |
 | License | Apache 2.0 |
@@ -127,23 +127,30 @@ Spaceborne edge computing enables AI-capable CubeSats to process data onboard, r
 
 PyNAS explores evolutionary Neural Architecture Search for this setting. The framework is designed around compact segmentation architectures and hardware-aware optimization for CubeSat-class platforms such as NVIDIA Jetson AGX Orin and Intel Movidius Myriad X targets.
 
+The research motivation and validation details are described in the accompanying Scientific Reports article, "Optimizing deep learning models for on-orbit deployment through neural architecture search" ([DOI: 10.1038/s41598-025-21467-8](https://doi.org/10.1038/s41598-025-21467-8)).
+
 ## Documentation
 
 - Project documentation: [sirbastiano.github.io/pynas-docs](https://sirbastiano.github.io/pynas-docs/)
 - Source repository: [github.com/ESA-PhiLab/pynas](https://github.com/ESA-PhiLab/pynas)
+- Scientific Reports paper: [Optimizing deep learning models for on-orbit deployment through neural architecture search](https://www.nature.com/articles/s41598-025-21467-8)
 - ESA Phi-lab: [philab.esa.int](https://philab.esa.int)
 - Little Place Lab: [littleplace.com](https://www.littleplace.com)
 
 ## Citation
 
-If you use PyNAS in academic work, cite the Zenodo record below. A paper-specific citation will be added when the associated manuscript is available.
+If you use PyNAS in academic work, cite the Scientific Reports paper below. Add the Zenodo software citation as well once the project DOI is published.
 
 ```bibtex
-@software{pynas_zenodo_15332053,
-  title = {PyNAS},
-  publisher = {Zenodo},
-  doi = {10.5281/zenodo.15332053},
-  url = {https://doi.org/10.5281/zenodo.15332053}
+@article{delprete2025pynas,
+  title = {Optimizing deep learning models for on-orbit deployment through neural architecture search},
+  author = {Del Prete, Roberto and Thind, Parampuneet Kaur and Mazzeo, Andrea and Whitley, Matthew and Papa, Lorenzo and Long{\'e}p{\'e}, Nicolas and Meoni, Gabriele},
+  journal = {Scientific Reports},
+  volume = {15},
+  pages = {37783},
+  year = {2025},
+  doi = {10.1038/s41598-025-21467-8},
+  url = {https://doi.org/10.1038/s41598-025-21467-8}
 }
 ```
 

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "esa-pynas"
-version = "0.1.2"
+version = "0.1.3"
 description = "PyNAS, a Python package for Neural Architecture Search experiments"
 readme = {file = "README.md", content-type = "text/markdown"}
 requires-python = ">=3.11,<3.15"