The manipulation of Global Navigation Satellite System (GNSS) signals has grown far beyond local combat zones, becoming a constant and mostly unseen problem. For the first time, data from an experimental Low Earth Orbit (LEO) satellite has mapped the wide reach of active GPS jamming and spoofing from space.

These orbital measurements surprised aerospace engineers by showing that intentional attacks on Positioning, Navigation, and Timing (PNT) data are strong enough to block satellite receivers flying hundreds of miles above Earth.

In the past, electronic warfare tactics such as jamming, which blocks real satellite signals with noise, and spoofing, which sends false coordinates to receivers, were seen as local threats limited to low-altitude areas or specific military regions.

But over the past five years, as global politics have changed, more countries have used electronic countermeasures. This shift has changed how safe Low Earth Orbit (LEO) is from electromagnetic threats.

This ongoing problem now threatens the imaging, weather tracking, and altitude systems used by commercial space networks.

Low earth orbit vulnerability and signal weakness

Modern satellite navigation is vulnerable because of how orbits work. Standard GPS and other global systems operate in Medium Earth Orbit (MEO), more than 12,000 miles (19,000 kilometers) above Earth. Since their signals must travel so far, the radio waves that reach receivers on the ground or in the air are very weak.

This structural fragility makes it is easy for transmitters on the ground to overpower them. Researchers expected that people on the ground and airline flights would be most affected by this interference. However, they did not know how much these jamming fields reached into space until the Pulsar-0 technology demonstrator was launched.

A 30-decibel drop

The mapping breakthrough was achieved by Pulsar-0, a specialized test satellite manufactured by Xona Space Systems. Launched into a 310-mile (500-kilometer) altitude LEO orbit, the spacecraft was built primarily to flight-validate new navigation technologies before the company initiates the mass deployment of its planned 300-satellite constellation later this year.

To ensure full interoperability with existing space architecture, the small probe was equipped with a standard, high-sensitivity commercial GPS receiver.

When flight engineers activated the internal receiver a few months post-launch, the incoming telemetry data revealed an immediate operational anomaly. As Pulsar-0 traversed space corridors extending from Western Europe all the way to the western borders of Pakistan, its onboard GPS capabilities vanished entirely.

In the hardest-hit areas, the strength of the authentic GPS signal at the satellite dropped from the usual 40 decibels to just 10 decibels, making it almost impossible to read.

This sharp 30-decibel drop caused an almost complete blackout of normal positioning data, making it impossible for any satellite over the area to quickly determine its altitude or exact location.

Mitigation via 100x signal amplification

This widespread electromagnetic interference affects much more than just commercial flight delays. Modern observation and remote-sensing satellites need clear GPS signals to aim their cameras or radar correctly before taking high-resolution images of specific places.

Without an active, unjammed PNT lock, these multi-million-dollar imaging platforms suffer severe degradation in altitude determination, leading to significant geometric errors in their data sets. Furthermore, artificial electronic interference is frequently exacerbated by unpredictable space weather.

Major solar events, such as the historic Gannon superstorm in May 2024, have shown that natural forces can disrupt GNSS signals and shut down automated systems across continents for days.

To build a resilient backup infrastructure that withstands both geopolitical jamming and solar anomalies, aerospace engineers are pivoting toward low-altitude, high-power solutions.

Constellations like Xona’s upcoming Pulsar network are designed to broadcast specialized PNT signals from LEO. Because these satellites operate significantly closer to the surface than traditional MEO arrays, they can deliver navigation signals that are roughly 100 times stronger than traditional legacy GPS.

According to empirical simulation profiles, this massive 100-fold boost in signal amplification will alter the economics of electronic warfare: existing terrestrial jammers that currently disrupt hundreds of square miles of airspace will see their effective disruption radius reduced to a mere 5% of their current reach, effectively neutralizing widespread systemic blind spots.

The software architecture governing modern electric vehicles is receiving a critical intelligence upgrade. Working within the European Union-funded Nemo project, a multinational research consortium has developed advanced mathematical models and predictive algorithms designed to dramatically increase the safety, durability, and lifespan of electric vehicle (EV) battery packs.

By shifting EV battery diagnostics from basic external tracking to real-time internal auditing, the system isolates microscopic cell anomalies before they escalate into catastrophic thermal failures.

Just as a symphony orchestra requires a highly synchronized conductor to maintain balance, an EV relies on a Battery Management System (BMS) to regulate power distribution, charging cycles, and energy storage.

Historically, however, standard automotive BMS hardware has been relatively blind to the internal health of individual battery cells. Conventional monitoring has relied almost exclusively on basic external indicators: total voltage, current output, and localized surface temperatures.

Because internal degradation, manufacturing flaws, and localized physical stress cannot be accurately diagnosed solely from surface metrics, vehicle computers have typically been unable to identify micro-damage until a cell suffers a systemic breakdown.

Training algorithms via physical crash stress tests

To bridge this critical observational gap, engineers at the Vehicle Safety Institute at the Graz University of Technology (TU Graz) focused on translating physical impact data into predictive software models.

Inside the university’s specialized Battery Safety Center, researchers subjected individual lithium-ion battery cells to controlled mechanical deformations, perfectly replicating real-world scenarios such as minor fender-benders or low-speed parking lot collisions.

The data collected from these laboratory stress tests was used to train specialized machine learning algorithms. This allows the upgraded BMS software to independently recognize the exact electrical signatures of internal structural damage.

Tracking internal cell expansion to block short circuits

To extract high-fidelity data from deep within active battery cells without destructive testing, the research team integrated an advanced sensor technology, Electrochemical Impedance Spectroscopy (EIS).

Operating directly within the moving vehicle, EIS measuring tools continuously pulse small electrical signals through the cells to chart shifting internal resistance profiles. This provides an active window into the microscopic chemical changes that occur as a battery ages.

To complement the EIS sensor array, the researchers engineered a predictive physical-volume model. Lithium-ion cells naturally expand and contract slightly during high-volume charging and discharging cycles. If individual cells swell excessively, the internal pressure in the sealed battery pack increases rapidly, leading to microcracks, torn separators, and internal short circuits.

By predicting these exact physical volume fluctuations in real time, the new algorithm intelligently throttles power delivery to minimize structural strain, effectively curbing the risk of internal short circuits and preventing dangerous thermal spikes before they can manifest.

Extending battery longevity via real-time adjustments

Historically, assessing battery degradation required a vehicle to be plugged into a stationary diagnostic terminal, yielding a static percentage showing total capacity loss relative to factory standards.

The new algorithms completely bypass these external limitations by providing continuous, granular insights into internal chemical shifts as aging occurs. This allows the vehicle’s computer to make minute, real-time adjustments to individual cell workloads.

By dynamically balancing the power draw across healthy versus aging cells, the system dramatically extends the overall commercial service life of the entire battery pack. Despite this massive leap in compute capability, researchers noted that the upgraded BMS does not add any meaningful size or weight penalties to the vehicle, though it does require specialized sensor configurations.

Moving beyond conventional, screen-based digital interfaces, an engineering team at the University of Adelaide has developed an innovative wearable device that maps personal data directly onto physical sensations.

The functional prototype, named ThermoPhy, utilizes a localized combination of micro-heating hardware and 3D-printed physical indicators to allow wearers to literally feel complex datasets.

Developed at the university’s Australian Research Centre for Interactive and Virtual Environments (IVE), the platform explores a new paradigm in data visualization by prioritizing private tactile responses over traditional visual graphs.

Modern health and wellness tracking apps typically generate complex dashboards filled with abstract numerical charts that can feel cold and disconnected from human experience.

By utilizing “data humanism“—a design philosophy that makes raw information more relatable and narrative-driven—the ThermoPhy project aims to cultivate a more empathetic, embodied relationship with personal telemetry. The hardware achieves this by taking sensitive or emotionally charged metrics and converting them into distinct, carefully calibrated thermal zones across a user’s hand.

Integrating micro-heating elements with tactile tokens

The physical layout of the ThermoPhy glove relies on two distinct feedback loops: thermal stimulation and tangible structural tokens. Integrated into the internal lining of the glove’s fingers are arrays of miniature electronic heating elements.

These micro-resistors are wired to a central microcontroller capable of modulating temperatures precisely to deliver subtle, gradated sensations of warmth to the wearer’s fingertips.

On the exterior surface of the glove, researchers mounted attachable 3D-printed tokens engineered to resemble miniature physical bar charts, line graphs, and heatmaps. By merging these physical shapes with variable thermal signals, the device can simultaneously convey multi-layered datasets.

For instance, a user tracking their sleep architecture can feel the physical height of a 3D bar to measure total sleep duration, while the internal heating elements concurrently warm up or cool down to signal their psychological mood or stress level upon waking.

Isolating private telemetry and simulating crowd comfort

A primary technical advantage of using thermal feedback over traditional visual or auditory alerts is its inherent privacy. Because thermal energy transfers via direct conduction, only the person physically wearing the glove can perceive the shifting temperature profiles.

This makes the system uniquely suited for displaying sensitive, human-centered metrics—such as biometric anxiety tracking, mood fluctuations, and personal wellness logs—out of the prying eyes of external onlookers or surrounding screens.

In one practical testing scenario, researchers mapped the glove’s thermal engine to environmental crowding and ambient comfort indices. As a user simulated navigating a heavily congested space, the glove’s internal temperature scaled upward proportionally.

Higher physical temperatures were programmed to represent acute levels of environmental discomfort or heightened situational stress, translating abstract density calculations into an immediate, intuitive physiological warning system.

Designing for radical affordability and future AR tracking

Beyond individual self-tracking, the research team notes that the device holds significant potential for cultivating interpersonal empathy. Because the software architecture allows data profiles to be shared across a network, one user can equip the glove to directly experience the real-time stress, biometric fatigue, or psychological state of another person, providing a completely non-verbal platform for shared storytelling and emotional communication.

A standout feature of the ThermoPhy prototype is its extreme cost efficiency. While medical-grade haptic garments often cost thousands of dollars to manufacture, the Adelaide engineering team constructed their prototype using off-the-shelf electronic parts and raw components totaling just $19.68 USD.

Moving forward, the research team plans to conduct extensive user-group testing to catalog exactly how humans interpret varying degrees of thermal feedback over extended periods.

Additionally, the team is working on software patches to integrate the haptic glove directly into emerging augmented reality (AR) and virtual reality (VR) simulation suites, laying the groundwork for immersive, screen-free computing environments.