diff --git a/README.md b/README.md index 5698c2abbd38e08eb027c978d17bee6130b7663b..836ac34a114170f9f35045929de635bd56395aed 100644 --- a/README.md +++ b/README.md @@ -1,93 +1,10 @@ # CALIPSO - Combining mAchine Learning and optImization for Planetary remote Sensing missiOns - - -## Getting started - -To make it easy for you to get started with GitLab, here's a list of recommended next steps. - -Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)! - -## Add your files - -- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files -- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command: - -``` -cd existing_repo -git remote add origin https://www.ict.inaf.it/gitlab/roberto.orosei/calipso.git -git branch -M main -git push -uf origin main -``` - -## Integrate with your tools - -- [ ] [Set up project integrations](https://www.ict.inaf.it/gitlab/roberto.orosei/calipso/-/settings/integrations) - -## Collaborate with your team - -- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/) -- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html) -- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically) -- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/) -- [ ] [Set auto-merge](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html) - -## Test and Deploy - -Use the built-in continuous integration in GitLab. - -- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html) -- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing (SAST)](https://docs.gitlab.com/ee/user/application_security/sast/) -- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html) -- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/) -- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html) - -*** - -# Editing this README - -When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thanks to [makeareadme.com](https://www.makeareadme.com/) for this template. - -## Suggestions for a good README - -Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information. - -## Name -Choose a self-explaining name for your project. - ## Description -Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors. - -## Badges -On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge. - -## Visuals -Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method. - -## Installation -Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection. - -## Usage -Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README. - -## Support -Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc. - -## Roadmap -If you have ideas for releases in the future, it is a good idea to list them in the README. - -## Contributing -State if you are open to contributions and what your requirements are for accepting them. - -For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self. - -You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser. +In recent years, more and more attention has been paid from government space agencies such as NASA, ESA and CNSA on the exploration of space beyond Earth. Several missions involved the Moon first, and then Mars, Venus, Jupiter, Saturn and Mercury. Hence, space missions have been increasingly studied from both technical and strategic points of view. -## Authors and acknowledgment -Show your appreciation to those who have contributed to the project. +This research refers to a specific aspect of space mission management, namely the scheduling of the activities of the orbiters. Since space missions are expensive, have limited resources and often exploit old technologies (missions can last for decades), it is really important to make optimized plans for the activities onboard and also to make effective use of the huge amount of data produced from the mission itself. In particular, our attention will be focused on a very common and important task in space missions, the sounding activity (or observation activity) of ground targets. To address this problem, this project aims at integrating two well-known and powerful disciplines of AI, operation research (OR) and machine learning (ML), in order to build an effective, flexible and dynamic scheduler. Techniques from these two research areas will be adapted to deal with the specific characteristics and constraints of spacecrafts and instruments onboard. This strong interconnection between these research areas and their application to the challenging domain of planetary remote sensing represents the strength and originality of the proposal. Hereby, ML will be devoted to the study of data collected from the missions, and specifically about the observations performed by the radar. At the same time, OR algorithms will address the problem of scheduling of observations in the complex context of space exploration, taking into account the outcome of ML algorithms. -## License -For open source projects, say how it is licensed. +The research carried out in the project aims to fulfill four main goals. (i) First, the project aims to develop an effective, flexible and comprehensive optimization algorithm to support the planning work of teams in ground stations that control space missions. (ii) Second, the project aims to exploit the potential of ML to understand some insight of the phenomena happening in space , to both guide the optimization and to retrieve useful information in general. (iii) Third, the project intends to include the uncertainty and dynamicity of space missions in the optimization approach, by developing specific advanced algorithms. (iv) Finally, the project will develop a user-friendly tool, in order to have a concrete impact on the operational activities of controlling space missions. -## Project status -If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers. +To deal with goals (i),(ii) and (iii) we will divide this project into two main parts. In the first one, we will concentrate on a specific mission, active since 2003 and for which we have already collected a large amount of data: Mars Express. Within this scenario, we will study the scheduling of observations of the Marsis radar. Then, we will move on a second mission less defined and with greater uncertainty, with the ambitious goal to adapt and apply the developed tool in a different, challenging context, such as the JUpiter ICy moons Explorer (JUICE) mission.