Mittelstandspresse
07.10.2026
Europe’s Space Sector Needs More Than Reliable Signals – It Needs Meaningful System States
SF2 Systems is developing Space Asset State Intelligence as an additional technology layer connecting telemetry, engineering knowledge, and autonomous systems.
Wien, 07.10.2026 (PresseBox) - Europe’s space sector is evolving into a highly interconnected industrial ecosystem. As space assets become more complex, autonomous and reusable, one question becomes paramount: What is the actual physical state of the system itself?
Vienna-based deep-tech company SF2 Systems is pioneering a technology that turns telemetry, test data, and engineering knowledge into a comparable representation of system state, from individual components and subsystems up to the entire space asset.
Europe’s space industry is no longer a niche market. More than 5,000 companies across the continent are active in space and satellite technologies. According to APEX Ventures, the industry generates approximately €8.8 billion in annual revenue and employs more than 60,000 engineers and scientists.
Recent European space tech ecosystem landscapes illustrate the breadth of this growing sector, spanning satellite manufacturing and launch systems, communications and energy, in-orbit services, space logistics, and space situational awareness.
This last category highlights an important distinction: Space Situational Awareness describes what is happening around a spacecraft. But how can the physical state of the spacecraft itself be described and compared as a holistic system?
Sensors generate telemetry. Avionics carry data. Health-monitoring and fault detection, isolation, and recovery systems (FDIR) monitor technical equipment and respond to predefined conditions and faults. Yet a fundamental gap remains: Is the system still operating within a known, qualified state, or is its overarching behavior beginning to drift?
SF2 Systems calls this additional layer Space Asset State Intelligence.
“A threshold tells us when a parameter has left a defined range. But it does not show how far a complex system as a whole has moved away from a known healthy state,” says Christoph Gretzmacher, Business Development at SF2 Systems. “Sensors measure signals. We want to understand the physical state that those signals collectively describe.”
From Signals to System States
SF2’s Semantic Folding Sensor Fusion technology condenses a wide range of sensor and operational data into a compact State Fingerprint. Measurements such as temperature, current, voltage, pressure, vibration, and motion are evaluated not merely as separate values, but within their shared context. Similar system states produce similar fingerprints.
This makes it possible to determine whether current system behavior corresponds to a known state, whether the system is transitioning between states, whether drift is developing, or whether an entirely new state has emerged.
State Fingerprints are sparse binary representations that can be compared efficiently based on their degree of overlap. The model learns from existing operational and test data without requiring large collections of labeled fault examples. Once a model has been finalized and versioned, runtime evaluations remain strictly deterministic and reproducible. Processing can take place directly at the edge or on-premises via zero-cloud architectures, keeping mission-critical data secure and independent of external cloud services.
SF2 is therefore not positioning its technology as just another anomaly-detection algorithm. Instead, it creates a comparable representation of physical system state that complements engineering, health monitoring, and maintenance decisions, ultimately serving as a foundational layer for autonomous systems.
Sensors tell you what is measured. SF2 tells you what state the system is in.
Putting Manufacturers’ Engineering Knowledge to Work
The core value of this approach lies not only in data analysis but in harnessing existing engineering expertise. The manufacturer that develops and tests a component inherently understands its design, relevant loads, nominal operating conditions, and the states in which technical intervention becomes necessary. In the space industry, development, acceptance, qualification, and testing generate high-quality data that reveal exactly how components and systems behave in the real world.
By encoding engineering-verified states as reference fingerprints, SF2 enables real-time comparison against the asset’s physical operating conditions. Monitoring is no longer restricted to threshold breaches; instead, it continuously evaluates: To what extent does the live system behavior align with states already qualified during testing?
The underlying engineering knowledge remains safely with the manufacturer, but SF2 makes it operational throughout the asset’s wider lifecycle in the form of comparable state references.
SF2 does not replace telemetry, FDIR, health monitoring, engineering, or safety systems. It complements these layers with an objective representation of physical system state.
From Individual Components to the Entire Space Asset
The approach becomes particularly powerful when State Intelligence extends beyond a single component. A State Fingerprint generated at one level can serve as input for the next, creating a hierarchical architecture:
Component → Subsystem → Space Asset → Fleet
Several components can collectively describe the state of a thermal, power, or propulsion subsystem. Multiple subsystem states can then contribute to a higher-level representation of the complete space asset.
This principle is already built into SF2’s architecture for industrial systems: component fingerprints can be processed at the subsystem level, while subsystem states contribute to an overarching system representation. Applied to the space sector, this creates a compelling vision: not merely monitoring thousands of individual measurements, but navigating a hierarchy of comparable system states.
The Economic Value of State Intelligence: Reusability
For reusable space systems, understanding physical system state has immediate economic relevance. Rocket stages, space tugs, and other reusable platforms must be assessed reliably following tests and missions before they can return to service. Inspection, maintenance, and turnaround times are critical factors in determining the commercial viability of reusable systems.
The central State Intelligence question is: How has the system’s actual multivariate state changed between testing, flight, return, and its next deployment?
Comparing a current State Fingerprint with verified reference states derived from engineering data provides an objective, data-driven basis for inspection and return-to-service decisions.
In the longer term, this could lead to the creation of a State Margin for defined system states. Such a margin would indicate not only that something has changed, but exactly how close an asset is to a defined reference state or intervention threshold. It does not attempt to be a generalized predictive maintenance forecast; instead, it provides a measurable, deterministic relationship between the asset’s current condition and its known reference states.
Starting on the Ground: Assembly, Integration, and Testing
An initial use case does not require a system that is already in orbit. Highly suitable starting points include areas where high-quality technical data is already generated: assembly, integration, and testing (AIT), qualification processes, test benches, ground support equipment, and auxiliary infrastructure (pumps, cooling, propulsion).
Existing historical data can establish reference states and validate specific applications before addressing the rigorous requirements of onboard deployment, such as target hardware optimization and space qualification.
Publicly available space data also provides a valuable proving ground. The ESA Anomaly Detection Benchmark (ESA-ADB) offers a reproducible baseline using annotated real-world telemetry from ESA missions. The next step is application-specific validation using actual telemetry, testing, and qualification data: How accurately can real mission states be represented as State Fingerprints? How reliably can nominal states and transitions be distinguished?
Proven in Mission-Critical Industrial Environments
SF2’s technology is already actively deployed using data from complex industrial equipment. In one municipal water-pump system, SF2 analyzed 52 sensor channels and more than 220,000 data points. Within one of the examined sequences, a significant change in the system’s overall state became visible approximately four days before a documented pump failure. Crucially, model creation took less than 15 minutes on a standard laptop.
This result is not a generic failure forecast. Rather, it demonstrates that evaluating the interaction of multiple physical signals can reveal critical changes in system state that are invisible when individual measurements are viewed in isolation.
For space applications, this proven principle must now be adapted to specific data structures, test environments, and target hardware.
Avionics Deliver the Signals; State Intelligence Delivers the Meaning
TTTech’s recent reorganization illustrates how closely space, defense, and critical infrastructure are converging technologically. Effective 1 October 2026, the Vienna-based technology company is consolidating these areas within the newly established TTTech Systems SE. Its portfolio ranges from components to platform solutions for safety-critical applications, including satellites, spacecraft, and launch vehicles.
TTEthernet forms part of the avionics architecture used in NASA’s Orion spacecraft and Ariane 6. TTTech describes the avionics system as the "nervous system" of the spacecraft – ensuring information reaches its destination reliably and deterministically.
This highlights the need for a complementary architectural layer: A nervous system transports signals reliably. Space Asset State Intelligence explains the physical state those signals collectively represent.
Communication, computing, and state representation perform distinct yet synergistic roles within increasingly autonomous and resilient technical systems.
The Next Step: Bringing State Intelligence into Real Space Architectures
SF2 is developing State Intelligence as a supplementary technological capability for existing components, platforms, and system architectures. For space applications, the next step is applying the technology to real telemetry, test, and qualification data in clearly defined use cases.
This process can begin on a highly focused scale – with a limited dataset, an existing testing process, or a specific engineering challenge.
The long-term vision scales from representing the state of individual components to capturing the operational reality of an entire space asset. SF2 welcomes joint validation projects, integration initiatives, and technology partnerships with organizations that recognize the strategic value of system state transparency.
As space assets become more autonomous, it will no longer be enough to understand what they measure. We must understand the state the system itself is in.
SF2 – Space Asset State Intelligence. From Telemetry to State.
Ansprechpartner
Christoph Gretzmacher
Zuständigkeitsbereich: Business Development
Über SF2 Systems GmbH:
SF2 Systems is a Vienna-based deep-tech company specializing in software-based condition analysis of complex machines, industrial assets, and processes across industry, energy, and critical infrastructure. Vendor- and sensor-agnostic, the technology combines existing operational data into comparable state fingerprints. This makes drift and gradual degradation visible early – reproducibly, transparently, without black-box models, and without requiring large labeled failure datasets.
Designed to complement existing condition-monitoring and automation systems, SF2 is built for brownfield and OT environments. It runs locally at the edge or on-premises with zero cloud dependency, keeping sensitive operational data strictly in-house. This enables early anomaly detection, reduces unplanned downtime, and makes maintenance far more predictable.
Additionally, SF2 is exploring applications for its core technology in biotechnology and life sciences.
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SF2 Space Asset State Intelligence: Multiple sensor and telemetry signals are condensed into a compact State Fingerprint, creating a comparable representation of the system state – from individual components to the complete space asset.
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