CyberWire Daily -  AWS in Orbit: Automated Satellite Management. [T-Minus Space]

Episode Date: May 26, 2025

While our team is observing Memorial Day in the United States, please enjoy this episode from our team from T-Minus Space Daily recorded recently at Space Symposium. You can learn more about AWS in Orbit at space.n2k.com/aws. Our guests on this episode are Dax Garner, CTO at Cognitive Space and Ed Meletyan, AWS Sr Solutions Architect. Remember to leave us a 5-star rating and review in your favorite podcast app. Be sure to follow T-Minus on LinkedIn and Instagram. Selected Reading AWS Aerospace and Satellite Want to hear your company in the show? You too can reach the most influential leaders and operators in the industry. Here’s our media kit. Contact us at [e-mail] to request more info. Want to join us for an interview? Please send your pitch to [e-mail] and include your name, affiliation, and topic proposal. T-Minus is a production of N2K Networks, your source for strategic workforce intelligence. © 2023 N2K Networks, Inc. Learn more about your ad choices. Visit megaphone.fm/adchoices

Thank you. I'm Maria Varmazas, host of t-minus space daily, and this is AWS in Orbit, automated satellite management with cognitive space. Today we're bringing you the next installment of the AWS in Orbit podcast series from the 40th Space Symposium. In this episode I'm speaking with representatives from Cognitive Space and AWS Aerospace and Satellite. And we're going to be speaking about automated satellite operations. Gentlemen, welcome. Good to see you both. Let's start with a round of intros please. Dax, could you start? Sure. My name is Dax Garner. I'm start with a round of intros please. Dax, could you start? Sure.

My name is Dax Garner. I'm the CTO at Cogniz Space. I'm an aerospace engineer by trade. I worked as a contractor for a NASA Johnson Space Center in that arena. My background is in guidance navigation control, which I really think about as an analog to machine learning and all of the AI ML that we have today. I spend a lot of time working on flight control algorithms, doing simulation, embedded flights learning and all of the AI ML that we have today. I spend a lot of time working on flight control algorithms,

doing simulation embedded flight software. Did that for about 10 years. I had a little stint at Firefly where I really cut my teeth on being at a startup and loved it. I learned a whole bunch there in terms of what it meant to be at a startup. I went to Lockheed Martin for a minute and then Cognitive Space got started about five years ago and I got hired there as the first engineer. So as the first engineer there, just writing a whole bunch of software and getting the company off the ground in that way. And then from there grew the team, the engineering team in particular before becoming CTO.

Awesome. Thank you. Ed. Hey, I'm Ed Militian. the engineering team in particular, before becoming CTO. Awesome, thank you. Ed. Hey, I'm Ed Militian. I'm a Solutions Architect at AWS. Also aerospace nerd by trade. I do a little bit of cloud now. So love to kind of bridge the gap between customers

like Cognitive Space and AWS and show customers what they can do on the cloud. And I had worked on a few different missions from NASA to Space Force or other national security partners. I mostly focused on mission management, mission planning, scheduling, also in the design phase, trying to figure out what's kind of the optimal way to build this mission to accomplish its goals. So now I've kind of transferred to doing that on the cloud and doing that at scale. I'm excited to talk with you all today about it. Thank you so much. I'm looking forward to learning more. Dax, I feel like this would be a great time to tell me a little bit

about Cognitive Space and y'all's mission and the problems y'all are solving. Yeah, definitely. In fact, I'll kind of start a little bit about, you know, why I even joined cognitive space exactly. So I feel like I'm a very mission driven person. I became an aerospace engineer because I want to go into space. I went to NASA JSC because I want to put humans into space. But after working there for a while,

I realized that there's an entire infrastructure that needs to go into space that will allow all of us to go into space one day. And kind of combine that with watching the AI ML space grow and advance. It occurred to me that space is hard and AI and ML technologies can really make it easier. And that's a key component in getting infrastructure and eventually humans in the space.

So I joined Cognitive Space because that was essentially the mission. It wasn't, it's not human space flight, but it is the technologies that are going to allow us to manage all the assets that we need on ground and in space that will allow humans to fly if they want to in the future. So that was one of my primary motivations for joining Cognitive Space. Cognitive Space's mission is to empower the use of space assets, particularly developing AI and ML algorithms for proliferated systems and mission management of those assets.

So when I talk about proliferated systems, that's hundreds of satellites all working together to achieve a mission. That could be taking pictures of the Earth. It could be establishing a mesh network around, a global mesh network around the world. But we've got to work together in order to achieve those missions. And that becomes a huge optimization and combinatorial space. And that's where our algorithms come in. Tell me more about that.

That is a heavy lift, but at the same time, the technology that's available now, I imagine, is just massively enabling that. And I'd love to hear more about what that looks like. Right. So when you're solving combinatorial optimization problems, the idea there is that, I guess, let me use an example. Sure. So if you have 100 satellites in the sky and their job is to take pictures of the Earth

every single day, you want to make sure that you take the best picture for each one of those targets. But you have multiple satellites that could fly over that target at any time. And so you have a choice which satellite is going to take that picture. So it becomes an optimization problem. But when you have a huge combinatorial space, so many options, it can become very difficult to optimize that effectively with traditional constraint-based

or other operations research-type algorithms. It's an MPR problem. It takes a long time to solve, if it can be solved at all. Conversely, and historically because of that problem, people tend to use heuristics, which are just simple. The first satellite to go over that target takes the picture. It's a simple algorithm. It just gets the job done.

But you lose a lot of optimality with these simple rules. And the sweet spot is training and designing ML models that can run at the speeds of heuristics. The heuristics run really quickly, but you can buy back a lot of that optimality, a lot of that performance in your mission and get a lot of that optimality, a lot of that performance in your mission and get a lot more pictures.

There's another layer of complexity there, right? When your schedule is dynamic, what if you're losing tasks or getting new tasks or what if the thing that you thought was going to take the picture actually can't because of some hardware failure? Now you have to redo all the planning that you've done and re-optimize all the system, otherwise you're dropping collections. Yes, yes, exactly. Another good example is ground communication planning.

They plan like long two-week cycles and that becomes their reference schedule. But the plan you made two weeks out isn't going to take into account that that antenna has decided not to work today. And now you have to re-plan, but that optimization algorithm that you use to generate a two-week schedule takes three, five hours to generate, and you don't have that time to re-plan. And that's where technologies like ML can come in.

You can re-optimize very quickly. Yeah. The word speed and scale has come up a lot lately, and that's what everybody wants, is what we're moving towards, but then it becomes, we have that added complexity, and how do we enable that speed and scale without having to rely on these algorithm-stake hours, which we don't have? So what you're talking about, I imagine,

would also enable a lot of really crucial missions that are going on right now, as well as future. Can you tell me a bit about that? So what you're talking about, I imagine, would also enable are helping them optimize their link schedules. They have their own assets that they do mission planning with, but they want to complement those capabilities with what commercial providers are doing. Planet, Umbra, ISI, Capella, etc. Airbus. And we can help them understand that capacity and make predictions about whether they can fulfill certain requests on the commercial side.

Yeah, I kind of want to highlight what you said there with the geospatial insights. A lot of these folks have very tight latency requirements where they have to shorten the time from a collection down to a dissemination. And sometimes these plans are great when you have all the antennas that you want. And so, you know, if I take an image here,

I'll have a downlink opportunity in 10 minutes and I'll get all my data down. It's not always the case because if that antenna, like you said, is gone, it would have actually been better to have a satellite that's lagging do the collection because now the original satellite has to go all the way around the earth to the next contact,

which could increase your latency by an hour or two hours. Yeah, yeah. So that's why the optimality and also the reacting to new stimulus is really, really important. So I'm going to go back to the speed and scale. I'm so curious how AWS technology comes in here to enable all these incredible missions that you all are doing, because I'm so curious how AWS technology comes in here to enable all these incredible missions that you all are doing

because I'm just thinking about the heavy lift involved to make all this happen. And I'd love to hear a little bit more about that. all of our ML algorithms and standing up our applications in their cloud environment. Yeah, and I can add a little more to that. Sure. In terms of the architecture that they've chosen, it scales really well to train the models and also to execute the models when you're planning. Like you said, scale is really important, both in the sense of just the total amount of compute I have, but also being able to onboard new constellations, new missions,

and not having to re-architect the whole system. That's why deployment like this is really, really crucial for these critical missions that our governments have. Because as they want to integrate constellations, they don't want to go back to the drawing board and have to figure out all these different interfaces that now have to be made. have because as they want to integrate constellations, they don't want to go back to the drawing board and have to figure out all these different interfaces that now have to be made. They just want it to work.

Yeah. And I would imagine security is also extremely important, given the customers that you've mentioned. Having that baked in is just a given. Yeah. Absolutely. Yeah. These services all can run in both our our GovCloud regions as well as our most classified

secret and top secret regions. Jax, I would love to know about real world impact if we have any examples. I mean, obviously not from the national security customers, but just in terms of like, do we have any numbers about efficiencies, like anything like that in data set? Yeah, so as we benchmark our ML algorithms, we benchmark them against heuristics in terms of understanding solve time performance, and then we also benchmark them against traditional operations research constraint programming type solutions. So that way we understand how much performance we are gaining back in terms of and so depending on the domain, whether it's network management, sensor planning, we tend

to see that our approaches can run a little bit slower than heuristics, because it is still an ML model that's running. And then, but we can gain back about where heuristics might perform at like 50, 60% of optimality. We're really gaining back to like 90, 95% of the optimum solutions, depending on the objective and the constraints. What that means for operational real-time performance

is that we're planning in minutes. You're planning hundreds of satellites in minutes, whether that's establishing link schedules or planning sensor management and collecting pictures on the ground. Yeah, and doing that very quickly is important if you want to keep track of a lot of different areas of interest, right?

So if you're interested in imaging the entire earth with hundreds of satellites, this is a big problem. And so being able to run it very closely to a heuristic model is really important. And on top of that, 90 to 95 percent is really impressive. Yeah. I'm curious, where do you see, with AI developing all the time, where do you see things going from here? Yeah. So we've focused a lot on these specialized ML algorithms that solve big combinatorial space optimization problems.

But fundamentally, I think that those are just tools for agentic systems. What is your constraint today might be your objective tomorrow. And when we talk to operators, they're planning reference schedules to just make sure that they're going to meet their operational needs. But then as dynamic things come in, they might need to just get, I guess, a good example as data latency, like you had mentioned, can be paramount. And so you want to minimize your general data latency, but as resources get constrained because a network node goes down or whatnot, you just want to minimize your latency.

So what was just a constraint, a fundamental operations constraint, becomes an objective function. And why that matters is because we will train machine learning algorithms on an objective function, on different objective functions, and different constraints. And when you combine those models, those optimization models, and pair them with an agentic system, an agent gets to... The idea is that an agent will start to decide, I'm going to use this model today because my operator is asking for this

and I recognize that the node is down. So this model might be the best way to resolve and re-plan. Maybe I have time to generate a reference schedule so I can take the time to run a constraint programming solution. Maybe I don't have any time and the greedy heuristic is the best to get a skits plan out right now Because I don't care about maximizing performance And so it's providing agents these tools Yeah, the agentic piece is really interesting and One one thing cloud is really good at isn't integrating these systems together in a common platform. So

is integrating these systems together in a common platform. So like now that you have this model, it's much easier to build these agentic workflows on top of that. And that's something that we had really focused on building out our cloud mission operations center concept, which is mission operations in the cloud, like the name suggests. So what that is, is being able to run the various subsystems

of a mission operations system at scale. And so that's flight dynamics, mission planning, command and control, data processing, as well as orchestration. The benefit of the Cloud Mission Operations Center is you can run best of breed solutions at scale, like cognitive solution. So if you have a big problem,

you can scale up your mission planning. You don't need to scale up everything simultaneously, and you don't need to worry about how big will my system have to be over the next 20 years. You're just solving the problem that you have today, and you know that it'll grow to meet your needs. The other benefit of running your mission operations on the cloud is that you're today. And you know that it'll grow to meet your needs. The other benefit of running your mission operations on the cloud is that you're always getting access

to the best underlying infrastructure. You don't have to worry about provisioning new GPUs, new CPUs, and you don't have to worry about getting rid of your old stuff, which in classified systems is a big problem because that stuff has classified data on it. You can't just wheel it out and throw it in the trash bin. In classified systems is a big problem because that stuff has classified data on it. You can't just wheel it out and throw it in the trash bin.

There's a long process to get rid of it and procure new hardware. With the Cloud Mission Operations Center, you are automatically getting the best available technology under the hood, and at the application layer. We definitely see AWS as key to helping us move our technology from unclassed to classified. Supporting common cloud-native infrastructure has been key. I appreciate that. I know that we're coming up on time. I want to make sure that I give you both an opportunity to have the floor, have a wrap up. Is there anything you wanted to add, Dax, about what Cognitive Space is doing or what

you're looking at for future missions? Yeah, I think just looking forward, constellations are going to keep increasing. That is just a trend that is absolutely true. So do customers want to continue to worry about buying more and more infrastructure, doing trades on, you know, do we bring this stuff in? How do we grow? AWS is going to be focused on reducing that burden on customers so they can focus on mission as well as providing a platform for our partners like Cognitive to build out, to meet the customer where they're at and provide those critical mission services.

Dax? Yeah, I think I will add that we're very focused on the US government at the moment. They're building the most proliferated systems and I think these technologies will be developed there. But the key is to enable an entire space economy. And as startups, there's many companies out there that want to also fly constellations of satellites to do really cool missions. But they're startups, they're focused on putting their first spacecraft into space.

They're not thinking about how to manage a proliferated constellation. And that's where a system like ours They're focused on putting their first spacecraft in space. ready to onboard their proliferated systems as they fly their first, second, and then eventually become their full-scale constellations. That's really the future, I think, in our collaboration for sure. Well, gentlemen, thank you both. It's been a pleasure. Thank you. That's it for this episode of AWS in Orbit by N2K Space. We'd love to know what you think of this podcast. You can email us at space at n2k.com or submit the survey in the show notes. Your feedback ensures that we deliver the information that keeps you a step ahead in

the rapidly changing space industry. This episode was produced by Laura Barber for AWS Aerospace and Satellite, and by N2K producer Liz Stokes and senior producer Alice Carouse. Mixing by Elliot Peltzman and Trey Hester with original music and sound design by Elliot Peltzman. Our executive producer is Jennifer Iben. Our publisher is Peter Kilpea.

And I've been your host, Maria Varmazes. Thank you for joining us.