Human in the Loop

Look up at the night sky, and you might spot a satellite streaking across the darkness. What you won't see is the invisible minefield surrounding our planet: more than 28,000 tracked objects hurtling through orbit at speeds exceeding 28,000 kilometres per hour, according to the European Space Agency. This orbital debris population includes defunct satellites, spent rocket stages, fragments from explosions, and the shrapnel from collisions that happened decades ago. They're still up there, circling Earth like a swarm of high-velocity bullets.

The problem isn't just that there's a lot of junk in space. It's that tracking all of it has become a monumentally complex task that's pushing human analysts to their breaking point. With thousands of objects to monitor, predict trajectories for, and assess collision risks from, the traditional approach of humans staring at screens and crunching numbers simply doesn't scale anymore. Not when a single collision can create thousands of new fragments, each one a potential threat to operational satellites worth hundreds of millions.

Enter machine learning, the technology that's already transformed everything from facial recognition to protein folding prediction. Can these algorithms succeed where human analysts are failing? Can artificial intelligence actually solve a problem that's literally growing faster than humans can keep up with it?

The answer, it turns out, is complicated. And fascinating.

The Scale of the Tracking Crisis

To understand why we need machine learning in the first place, you need to grasp just how overwhelming the space debris tracking problem has become. According to NASA's Orbital Debris Program Office, there are approximately 28,160 objects larger than 10 centimetres currently being tracked by the US Space Surveillance Network. That's just what we can see with current ground-based radar and optical systems.

The actual number is far worse. ESA estimates there are roughly 900,000 objects larger than one centimetre orbiting Earth right now. At orbital velocities of around 28,000 kilometres per hour, even a paint fleck can strike with the force of a hand grenade. A 10-centimetre piece of debris? That's enough to catastrophically destroy a spacecraft. The International Space Station needs special shielding just to protect against anything larger than one centimetre.

Here's the truly horrifying part: we can only track about three per cent of the actual debris population. The other 97 per cent is invisible to current detection systems, but very much capable of destroying satellites that cost hundreds of millions to build and launch.

Tim Flohrer, head of ESA's Space Debris Office, has stated that collision avoidance manoeuvres have increased dramatically. In 2020 alone, ESA performed 28 manoeuvres, more than double the number from just a few years earlier. Each one requires careful analysis, fuel expenditure, and operational disruption.

These aren't trivial decisions. Every manoeuvre consumes precious fuel that satellites need to maintain their orbits over years or decades. Run out of fuel early, and your multi-million-pound satellite becomes useless junk. Operators must balance immediate collision risk against long-term operational life. Get it wrong, and you either waste fuel on unnecessary manoeuvres or risk a catastrophic collision.

The calculations are complex because orbital mechanics is inherently uncertain. You're trying to predict where two objects will be days from now, accounting for atmospheric drag that varies with solar activity, radiation pressure from the sun, and gravitational perturbations from the moon. Small errors in any of these factors can mean the difference between a clean miss and a collision.

The Union of Concerned Scientists maintains a database showing there are currently over 7,560 operational satellites in orbit as of May 2023. With companies like SpaceX deploying mega-constellations numbering in the thousands, that number is set to explode. More satellites mean more collision risks, more tracking requirements, and more data for analysts to process.

And it's not just the number of satellites that matters. It's where they are. Low Earth orbit, between 200 and 2,000 kilometres altitude, is getting crowded. This is prime real estate for satellite constellations because signals reach Earth quickly with minimal delay. But it's also where most debris resides, and where collision velocities are highest. Pack thousands of satellites into overlapping orbits in this region, and you're creating a high-speed demolition derby.

Human analysts at organisations like the US Space Force's 18th Space Defense Squadron and ESA's Space Debris Office are drowning in data. Every tracked object needs its orbit updated regularly as atmospheric drag, solar radiation, and gravity alter trajectories. For each of the 28,000+ objects, analysts must calculate where it will be hours, days, and weeks from now. Then they must check if any two objects might collide.

The maths gets ugly fast. Each new object doesn't just mean one more thing to track. It means checking if that object might hit any of the thousands of existing objects. With 28,000 objects, there are potentially hundreds of millions of collision checks to perform each day.

When a potential collision is identified, analysts must determine the probability of collision, decide whether to manoeuvre, and coordinate with satellite operators, often with only hours of warning. A probability of 1 in 10,000 might sound safe until you realise thousands of such assessments happen daily. Different operators use different thresholds, but there's no universal standard.

It's a system that's fundamentally broken by its own success. The better we get at launching satellites, the worse the tracking problem becomes. Each successful launch eventually adds derelict objects to the debris population. Even satellites designed for responsible end-of-life disposal sometimes fail to deorbit successfully.

Consider the economics. Launching satellites generates revenue and provides services: communications, navigation, Earth observation, weather forecasting. These are tangible benefits that justify the costs. Tracking the resulting debris? That's a pure cost with no direct revenue. It's a classic collective action problem: everyone benefits from better tracking, but no individual operator wants to pay for it.

The result is that tracking infrastructure is chronically underfunded relative to the challenge it faces. The US Space Surveillance Network, the most capable tracking system in the world, operates radar and optical systems that are decades old. Upgrades happen slowly. Meanwhile, the number of objects to track grows exponentially.

How Machine Learning Entered the Orbital Battlespace

Machine learning didn't arrive in space debris tracking with fanfare and press releases. It crept in gradually, as frustrated analysts and researchers realised traditional computational methods simply couldn't keep pace with the exponentially growing problem.

The tipping point came around 2015-2016. Computational power had reached the point where training complex neural networks was feasible. Datasets from decades of debris tracking operations were large enough to train meaningful models. And crucially, the tracking problem had become desperate enough that organisations were willing to try unconventional approaches.

The traditional approach relies on physics-based models. You observe an object's position at multiple points in time, then use equations that describe how things move under gravity and other forces to predict where it will be next. These methods work brilliantly when you have good observations and plenty of time.

But space debris tracking doesn't offer those luxuries. Many objects are observed infrequently or with poor accuracy. Small debris tumbles unpredictably. Atmospheric density varies with solar activity in ways that are hard to model precisely. For thousands of objects, you need predictions updated continuously, not once a week.

Machine learning offers a different approach. Instead of modelling all the forces acting on an object from scratch, these algorithms learn patterns directly from data. Feed them thousands of examples of how objects actually behave in orbit, including all the messy effects, and they learn to make predictions without needing to model each force explicitly.

Early applications focused on object classification. When radar detects something in orbit, is it a large piece of debris, a small satellite, or a cloud of fragments? This isn't just curiosity. Classification determines tracking priority, collision risk, and even legal responsibility.

The algorithms, particularly neural networks designed for image recognition, proved remarkably good at this task. Researchers at institutions including the Air Force Research Laboratory showed that these systems could classify objects from limited data with accuracy matching or exceeding human experts.

The breakthrough came from recognising that radar returns contain patterns these networks excel at detecting. A tumbling rocket body produces characteristic reflections as different surfaces catch the radar beam. A flat solar panel looks different from a cylindrical fuel tank. A dense cluster of fragments has a distinct signature. These patterns are subtle and difficult for humans to categorise consistently, especially when the data is noisy or incomplete. But they're exactly what neural networks were designed to spot.

It's similar to how these same networks can recognise faces in photos. They learn to detect subtle patterns in pixel data that distinguish one person from another. For debris tracking, they learn to detect patterns in radar data that distinguish a rocket body from a satellite bus from a fragment cloud.

The next frontier was trajectory prediction. Researchers began experimenting with neural networks designed to handle sequential data, the kind that tracks how things change over time. These networks could learn the complex patterns of how orbits evolve, including subtle effects that are hard to model explicitly.

Perhaps most crucially, machine learning proved effective at conjunction screening: identifying which objects might come dangerously close to each other. Traditional methods require checking every possible pair. Machine learning can rapidly identify high-risk encounters without computing every single trajectory, dramatically speeding things up.

The Algorithms That Are Changing Orbital Safety

The machine learning techniques being deployed aren't exotic experimental algorithms. They're mostly well-established approaches proven in other domains, now adapted for orbital mechanics.

Object identification: The same neural networks that power facial recognition are being used to identify and classify debris from radar returns. Space debris comes in all shapes: intact rocket bodies, fragmented solar panels, clusters of collision debris. These networks can distinguish between them with over 90 per cent accuracy, even from limited data. This matters because a large, intact rocket body on a predictable orbit is easier to track and avoid than a cloud of small fragments.

Trajectory prediction: Networks designed to understand sequences, like how stock prices change over time, can learn how orbits evolve. Feed them the history of thousands of objects and they learn to predict future positions, capturing effects that are hard to model explicitly.

Atmospheric density at orbital altitudes varies with solar activity, time of day, and location in complex ways. During solar maximum, when the sun is most active, the upper atmosphere heats up and expands, increasing drag on satellites. But predicting exactly how much drag a specific object will experience requires knowing its cross-sectional area, mass, altitude, and the precise atmospheric density at that location and time.

A network trained on years of actual orbital data can learn these patterns without needing explicit atmospheric models. It learns from observation: when solar activity increased by this much, objects at this altitude typically decelerated by this amount. It's not understanding the physics, but it's pattern matching at a level of complexity that would be impractical to model explicitly.

Collision risk assessment: Algorithms that combine multiple decision trees can rapidly estimate collision probability by learning from historical near-misses. They're fast, understandable, and can handle the mix of data types that characterise orbital information.

Manoeuvre planning: Newer approaches use reinforcement learning, the same technique that teaches computers to play chess. When a collision risk is identified, operators must decide whether to manoeuvre, when, and how much. Each manoeuvre affects future collision risks and consumes precious fuel. These algorithms can learn optimal strategies by training on thousands of simulated scenarios.

ESA's CREAM project, the Collision Risk Estimation and Automated Mitigation system, represents one of the most advanced operational deployments of machine learning for debris tracking. Announced in 2025, CREAM uses machine learning algorithms to automate collision risk assessment and recommend avoidance manoeuvres. According to ESA documentation, the system can process conjunction warnings significantly faster than human analysts, enabling more timely decision-making.

The key advantage these algorithms offer isn't superhuman intelligence. It's speed and consistency. A well-trained neural network can classify thousands of objects in seconds, predict trajectories for the entire tracked debris population in minutes, and screen for potential conjunctions continuously. Human analysts simply cannot maintain that pace.

But there's another advantage: consistency under pressure. A human analyst working a 12-hour shift, processing hundreds of conjunction warnings, will get tired. Attention wanders. Mistakes happen. An algorithm processes the 500th conjunction warning with the same careful attention as the first. It doesn't get bored, doesn't get distracted, doesn't decide to cut corners because it's nearly time to go home.

This doesn't mean algorithms are better than humans at everything. Humans excel at recognising unusual situations, applying contextual knowledge, and making judgment calls when data is ambiguous. But for high-volume, repetitive tasks that require sustained attention, algorithms have a clear advantage.

Where the Algorithms Struggle

For all their promise, machine learning algorithms haven't solved the space debris tracking problem. They've just shifted where the difficulties lie.

The first challenge is data. Machine learning needs thousands or millions of examples to learn effectively. For common debris scenarios, such data exists. Decades of tracking have generated vast datasets of observations and near-misses.

But space is full of rare events that matter enormously. What about objects in highly unusual orbits? Debris from a recent anti-satellite test? A satellite tumbling in a novel way? These AI systems learn from past examples. Show them something they've never seen before, and they can fail spectacularly.

A model that's 99 per cent accurate sounds impressive until you realise that one per cent represents hundreds of potentially catastrophic failures when screening tens of thousands of objects daily. Traditional physics-based models have a crucial advantage: they're based on fundamental laws that apply universally. Newton's laws don't suddenly stop working for an unusual orbit. But a neural network trained primarily on low-Earth orbit debris might make nonsensical predictions for objects in very different orbits.

The second challenge is interpretability. When a machine learning model predicts a high collision probability, can it explain why? For some algorithms, you can examine which factors were most important. For deep neural networks with millions of parameters, the reasoning is essentially opaque. It's a black box.

Satellite operators need to understand why they're being asked to manoeuvre. Is the risk real, or is the model seeing patterns that don't exist? For a decision that costs thousands of pounds in fuel and operational disruption, “the algorithm said so” isn't good enough. There's a fundamental trade-off: the most accurate models tend to be the least explainable.

The third challenge is adversarial robustness. Space debris tracking is increasingly geopolitical. What happens when someone deliberately tries to fool your models?

Imagine a satellite designed to mimic the radar signature of benign debris, approaching other satellites undetected. Or spoofed data fed into the tracking system, causing incorrect predictions. This isn't science fiction. Researchers have demonstrated adversarial attacks on image classifiers: add carefully crafted noise to a photo of a panda, and the system confidently identifies it as a gibbon. The noise is imperceptible to humans, but it completely fools the algorithm.

Similar attacks could theoretically target debris tracking systems. An adversary could study how your classification algorithms work, then design satellites or debris to exploit their weaknesses. Make your reconnaissance satellite look like a dead rocket body to tracking algorithms, and you could position it undetected. Feed false observational data into the tracking network, and you could cause operators to waste fuel on phantom threats or ignore real ones.

This is particularly worrying because machine learning models are often deployed with their architectures published in research papers. An adversary doesn't need to hack into your systems; they can just read your publications and design countermeasures.

The fourth challenge is the feedback loop. These models are trained on historical data about how objects moved and collided. But their predictions influence behaviour: satellites manoeuvre to avoid predicted conjunctions. The future data the models see is partially determined by their own predictions.

If a model over-predicts risks, operators perform unnecessary manoeuvres, generating data that might reinforce the model's bias. If it under-predicts, collisions occur that could be misinterpreted as evidence that risks were lower than thought. The model's own deployment changes the data it encounters.

The Hybrid Future: Humans and Machines Together

The most successful approaches to space debris tracking aren't pure machine learning or pure traditional methods. They're hybrids that combine the strengths of both.

Physics-informed neural networks represent one promising direction. These systems incorporate known physical laws directly into their structure. A network predicting orbital trajectories might include constraints ensuring predictions don't violate conservation of energy or momentum.

Think of it as giving the algorithm guardrails. A pure machine learning model might predict that an object suddenly accelerates for no reason, because that pattern appeared in noisy training data. A physics-informed model knows that objects don't spontaneously accelerate in orbit. Energy must be conserved. Angular momentum must be conserved. Any prediction that violates these laws is automatically rejected or penalised during training.

This hybrid approach reduces the training data needed, improves performance on novel situations, and increases trust. The model isn't learning arbitrary patterns; it's learning how to apply physical laws in complex scenarios where traditional methods struggle. Researchers at institutions including the University of Colorado Boulder have demonstrated these hybrids can predict orbits with accuracy approaching traditional methods, but orders of magnitude faster. Speed matters when you need to continuously update predictions for thousands of objects.

Another hybrid approach uses machine learning for rapid screening, then traditional methods for detailed analysis. An algorithm quickly identifies the 100 most worrying conjunctions out of millions, then human analysts examine those high-risk cases in detail.

ESA's CREAM system exemplifies this philosophy. Machine learning automates routine screening, processing conjunction warnings and calculating collision probabilities. But humans make final decisions on manoeuvres. The algorithms handle the impossible task of continuously monitoring thousands of objects; humans provide judgment and accountability.

This division of labour makes sense. Algorithms can rapidly identify that objects A and B will pass within 200 metres with a collision probability of 1 in 5,000. But deciding whether to manoeuvre requires judgment: How reliable is the orbital data? How valuable is the satellite? How much fuel does it have remaining? What are the operational consequences of a manoeuvre? These are questions that benefit from human expertise and contextual understanding.

These systems are also learning to express uncertainty. A model might predict two objects will pass within 500 metres, with confidence that the actual distance will be between 200 and 800 metres. This uncertainty information is crucial: high collision probability with low uncertainty is very different from high probability with high uncertainty.

Some systems use “active learning” to improve themselves efficiently. The algorithm identifies cases where it's most uncertain, requests human expert input on those specific cases, then incorporates that expertise to refine future predictions. Human knowledge gets deployed where it matters most, not wasted on routine cases.

The Race Against Exponential Growth

Here's the uncomfortable reality: even with machine learning, we might be losing the race against debris proliferation.

The debris population isn't static. It's growing. The 2007 Chinese anti-satellite test destroyed the Fengyun-1C weather satellite, creating more than 3,000 trackable fragments and increasing the catalogued population by 25 per cent in a single event. The 2009 collision between Iridium 33 and Cosmos 2251 generated over 2,300 more.

These are permanent additions to the orbital environment, each capable of triggering further collisions. This is Kessler Syndrome: the point where collisions generate debris faster than atmospheric drag removes it, creating a runaway cascade. We may already be in the early stages.

Here's why this is so insidious. In low Earth orbit, atmospheric drag gradually pulls objects down until they burn up on reentry. But this process is slow. An object at 800 kilometres altitude might take decades to deorbit naturally. At 1,000 kilometres, it could take centuries. During all that time, it's a collision hazard.

If collisions are creating new debris faster than natural decay is removing it, the total population grows. More debris means more collisions. More collisions mean even more debris. It's a runaway feedback loop.

ESA projections suggest that even if all launches stopped tomorrow, the debris population would continue growing through collisions in certain orbital regions. The only way to stabilise things is active debris removal: physically capturing and deorbiting large objects before they collide.

Algorithms make tracking more efficient, but removing debris requires physical missions. Better predictions enable better avoidance manoeuvres, yet every manoeuvre consumes fuel, ultimately shortening satellite lifetimes.

ESA's ClearSpace-1 mission, scheduled to launch in 2025, will attempt the first commercial debris removal by capturing a rocket adapter left in orbit in 2013. This 100-kilogram object is relatively large, in a well-known orbit, with a simple shape. It's a proof of concept, not a scalable solution.

Stabilising the orbital environment would require removing thousands of objects, at a cost running into billions. Machine learning might help identify which debris poses the greatest risk and should be prioritised, but it can't solve the fundamental problem that removal is expensive and difficult.

Meanwhile, launch rates are accelerating. SpaceX alone has launched over 5,000 Starlink satellites, with plans for tens of thousands more. Amazon's Project Kuiper, OneWeb, and Chinese mega-constellations add thousands more.

Each satellite is a potential future debris object. Even with responsible disposal practices, failures happen. Satellites malfunction, deorbit burns fail. Batteries that should be depleted before end-of-life still hold charge and can explode. With thousands being launched, even a small failure rate produces significant debris.

SpaceX has committed to deorbiting Starlink satellites within five years of mission end, and the latest generation is designed to burn up completely on reentry rather than producing fragments. That's responsible behaviour. But enforcing such practices globally, across all operators and countries, is a different challenge entirely.

This creates a tracking burden that grows faster than our capabilities, even with machine learning. The US Space Surveillance Network can track objects down to about 10 centimetres in low Earth orbit. Improving this to track smaller objects would require major infrastructure investments: bigger radars, more sensitive receivers, more powerful optical telescopes, more processing capability.

These systems squeeze more information from existing sensors, predicting more accurately from sparse observations. But they can't observe objects too small for sensors to detect. The 97 per cent we can't currently track remains invisible and dangerous. A one-centimetre bolt moving at 15 kilometres per second doesn't care whether you can track it or not. It'll still punch through a satellite like a bullet through paper.

What Needs to Happen Next

If machine learning is going to meaningfully help, several things need to happen quickly.

Better data sharing: Debris tracking data is fragmented across organisations and countries. The US maintains the most comprehensive catalogue, but Russia, China, and European nations operate independent systems. Machine learning performs best on large, diverse datasets. A global, open debris database aggregating all observations would enable significantly better models.

Purpose-built infrastructure: Current space surveillance systems were designed primarily for tracking operational satellites and monitoring for missile launches. Purpose-built systems optimised for debris would provide better data. This includes improved ground-based radar and optical systems, plus space-based sensors that can observe debris continuously from orbit.

Several companies and agencies are developing space-based space surveillance systems. The advantage is continuous observation: ground-based systems can only see objects when they pass overhead, but a sensor in orbit can track debris continuously in nearby orbital regimes. The US Space Force has deployed satellites for space surveillance. Commercial companies are proposing constellations of debris-tracking satellites. These systems could provide the continuous, high-quality data that machine learning models need to reach their full potential.

Targeted research: We need machine learning research specifically tackling debris tracking challenges: handling sparse, irregular data; quantifying uncertainty in safety-critical predictions; maintaining performance on unusual cases; providing interpretable predictions operators can trust. Academic research tends to focus on clean benchmark problems. Debris tracking is messy and safety-critical.

Stronger regulations: Tracking and prediction algorithms can't prevent irresponsible actors from creating debris through anti-satellite tests or failed disposal. International agreements like the UN Space Debris Mitigation Guidelines exist but aren't binding. Nations can ignore them without consequences.

The 2007 Chinese anti-satellite test, the 2019 Indian anti-satellite test, and the 2021 Russian anti-satellite test all created thousands of trackable fragments. These tests demonstrate capabilities and send political messages, but they also contaminate the orbital environment for everyone. Debris doesn't respect national boundaries. Fragments from the Chinese test still threaten the International Space Station, a multinational facility.

Stronger regulations with actual enforcement mechanisms would reduce new debris generation, buying time for tracking and removal technologies to mature. But achieving international consensus on space regulations is politically fraught, especially when debris-generating activities like anti-satellite tests are seen as demonstrations of military capability.

Sustained funding: Space debris is a tragedy of the commons. Everyone benefits from a clean orbital environment, but individual actors have incentives to launch without fully accounting for debris costs. This requires collective action and sustained investment over decades.

The challenge is that the benefits of debris mitigation are diffuse and long-term, while the costs are concentrated and immediate. Spend billions on improved tracking systems and debris removal, and the benefit is avoiding catastrophic collisions that might happen years or decades from now. It's hard to generate political enthusiasm for preventing hypothetical future disasters, especially when the spending must happen now.

Yet the alternative is grim. Without action, we risk making certain orbital regimes unusable for generations. Low Earth orbit isn't infinite. There are only so many useful orbits at optimal altitudes. Contaminate them with debris, and future generations lose access to space-based services we currently take for granted: satellite communications, GPS navigation, Earth observation for weather forecasting and climate monitoring.

The economic value of the space industry is measured in hundreds of billions annually. Protecting that value requires investment in tracking, mitigation, and removal technologies, with machine learning as a crucial enabling tool.

The Verdict: Necessary but Not Sufficient

Can machine learning solve the space debris tracking problem that overwhelms human analysts? Yes and no.

The technology has made debris tracking more efficient, accurate, and scalable. Algorithms can process vastly more data than humans, identify patterns in complex datasets, and make predictions fast enough for thousands of objects simultaneously. Without these systems, tracking would already be unmanageable. They've transformed an impossible task into something tractable, enabling analysts to focus on high-risk or unusual cases rather than routine processing, whilst making screening fast enough to keep pace with growth.

But this isn't a silver bullet. Current sensors still miss countless objects. Debris already in orbit still needs physical removal. New debris generation continues unchecked. And the technology introduces fresh challenges around data quality, interpretability, robustness, and validation.

The real solution requires algorithmic assistance as part of a broader strategy: better sensors, active debris removal, international cooperation, stronger regulations, sustained investment. We're still racing against exponential growth. We haven't achieved the combination of tracking capability, removal capacity, and prevention needed to stabilise the orbital environment. Better tools are here, but the outcome is far from certain.

The future is hybrid: algorithms and humans working together, each contributing unique strengths to a problem too large for either alone. Machines handle the impossible task of continuous monitoring and rapid screening. Humans provide judgment, accountability, and expertise for the cases that matter most.

It's not as satisfying as a purely technological solution. But it's probably the only approach with a chance of working.

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Sources and References

1. European Space Agency. “About Space Debris.” ESA Space Safety Programme. Accessed October 2025. https://www.esa.int/Space_Safety/Space_Debris/About_space_debris

2. European Space Agency. “Space Debris by the Numbers.” ESA Space Debris Office. Accessed October 2025.

3. European Space Agency. “ESA Commissions World's First Space Debris Removal.” 9 December 2019. https://www.esa.int/Safety_Security/Space_Debris/ESA_commissions_world_s_first_space_debris_removal

4. European Space Agency. “CREAM: Avoiding Collisions in Space Through Automation.” 12 August 2025.

5. NASA Orbital Debris Program Office. Johnson Space Center, Houston, Texas. Accessed October 2025. https://orbitaldebris.jsc.nasa.gov

6. NASA. “10 Things: What's That Space Rock?” NASA Science. 21 July 2022, updated 5 November 2024.

7. Union of Concerned Scientists. “UCS Satellite Database.” Updated 1 May 2023. Data current through 1 May 2023. https://www.ucsusa.org/resources/satellite-database

8. Kessler, D.J., and Cour-Palais, B.G. “Collision Frequency of Artificial Satellites: The Creation of a Debris Belt.” Journal of Geophysical Research, vol. 83, no. A6, 1978, pp. 2637-2646.

9. United Nations Office for Outer Space Affairs. “Space Debris Mitigation Guidelines of the Committee on the Peaceful Uses of Outer Space.” 2010.

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

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