Radiation testing suggests that solar cells made from carbon-based, or organic, materials could outperform conventional silicon and gallium arsenide for generating electricity in the final frontier, a study from the University of Michigan suggests.While previous research focused on how well organic solar cells converted light to electricity following radiation exposure, the new investigation also dug into what happens at the molecular level to cause drops in performance.&nbsp;“Silicon semiconductors aren’t stable in space because of proton irradiation coming from the sun,” said <a href="https://eecs.engin.umich.edu/people/li-yongxi/" target="_blank" rel="noreferrer noopener">Yongxi Li,</a> first author of the study to be published in Joule and a U-M associate research scientist in electrical and computer engineering at the time of the research. “We tested organic photovoltaics with protons because they are considered the most damaging particles in space for electronic materials.”Space missions often land on gallium arsenide for its high efficiency and resistance to damage from protons, but it’s expensive and, like silicon, is relatively heavy and inflexible. In contrast, organic solar cells can be flexible and are much lighter. This study is among those exploring the reliability of organics, as space missions tend to use highly trusted materials.&nbsp;Organic solar cells made with small molecules didn’t seem to have any trouble with protons—they showed no damage after three years worth of radiation. In contrast, those made with polymers—more complex molecules with branching structures—lost half of their efficiency.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1737053221925-1737053221925.png" class="fr-fic fr-dib">“We found that protons cleave some of the side chains, and that leaves an electron trap that degrades solar cell performance,” said <a href="https://eecs.engin.umich.edu/people/forrest-stephen-r/" id="isPasted">Stephen Forrest</a>, the Peter A. Franken Distinguished University Professor of Engineering at U-M, and lead corresponding author of the study.&nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1737053244779-alkyl-trap-healing-opvs-1.jpg" style="width: 100%px;" class="fr-fic fr-dib">The diagram shows the alkyl side chain extending from the organic molecule that converts energy from photons into free electrons. The gray electron hops from carbon to carbon on the way to the electrode. When hit with proton radiation, hydrogen breaks off and leaves behind electron-hungry traps. However, when exposed to temperatures of about 100°C (212°F), the hydrogen can re-bond with the carbon, healing the damage and eliminating the electron traps. Credit: Yongxi Li, Optoelectronic Components and Materials Group, University of MichiganThese traps grab onto electrons freed by light hitting the cell, preventing them from flowing to the electrodes that harvest the electricity.&nbsp;“You can heal this by thermal annealing, or heating the solar cell. But we might find ways to fill the traps with other atoms, eliminating this problem,” said Forrest.It’s plausible that sun-facing solar cells could essentially self-heal at temperatures of 100°C (212°F)—this warmth is enough to repair the bonds in the lab. But questions remain: for instance, will that repair still take place in the vacuum of space? Is the healing reliable enough for long missions? It may be more straightforward to design the material so that the performance-killing electron traps never appear.Li intends to explore both avenues further as an incoming associate professor of advanced materials and manufacturing at Nanjing University in China.The research is funded by Universal Display Corp and the U.S. Office of Naval Research.The devices were built in part at the&nbsp;<a href="https://lnf.umich.edu/" target="_blank" rel="noreferrer noopener">Lurie Nanofabrication Facility</a>, exposed to a proton beam at the&nbsp;<a href="https://mibl.engin.umich.edu/" target="_blank" rel="noreferrer noopener">Michigan Ion Beam Laboratory</a>, and studied at the&nbsp;<a href="https://mc2.engin.umich.edu/" target="_blank" rel="noreferrer noopener">Michigan Center for Materials Characterization</a>.The team has applied for patent protection with the assistance of U-M Innovation Partnerships. Universal Display has licensed the technology from U-M and filed a patent application. Forrest has a financial interest in Universal Display Corp.

Some carbon-based solar cells already show no drop in performance after three years’ worth of radiation, and the cause of degradation in others could be preventable.

Light, flexible and radiation-resistant: organic solar cells for space

The H3 Launch Vehicle (Test Flight No. 2) was launched from JAXA Tanegashima Space Center on February 17, 2024. The flight went as planned and the vehicle successfully delivered two small secondary satellites into orbit. In recent years, many companies around the world have become involved in the space business. Accordingly, the H3 Launch Vehicle has been attracting a great deal of expectation and attention for the development of the space business in Japan.&nbsp;We introduce an overview of the H3 Launch Vehicle, its airframe configuration and form, launch capabilities, and more in this article. Moreover, we look at how the H3 Launch Vehicle achieved flexibility, high reliability, and cost reduction against a background of changing demands and intensifying competition in the international space business. We also explain how electronic components from Murata Manufacturing (hereinafter "Murata") contributed to the cost reduction.&nbsp;There have been more than a few cases of companies facing similar issues in various monozukuri and business operations in addition to the space business in international competition in recent years. How did the H3 Launch Vehicle overcome these difficult issues? The consistent attitude, way of thinking, and efforts of its makers may provide some clues and insights the readers of this site will find useful in your own businesses.<h2 id="isPasted">What Is the H3 Launch Vehicle?</h2><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1736747639422-img-rocket-01.jpg" style="width: 100%px;" class="fr-fic fr-dib">H3 Launch Vehicle flight image ©JAXATo start with, we describe an overview of the H3 Launch Vehicle, the configuration and form of its airframe, examples of its launch capabilities, and more.<h3>Overview of the H3 Launch Vehicle</h3>The H3 Launch Vehicle is a next-generation rocket. It was developed jointly*1 by Mitsubishi Heavy Industries, Ltd. (hereinafter "Mitsubishi Heavy Industries") and the Japan Aerospace Exploration Agency (JAXA). The H3 Launch Vehicle is large for a liquid oxygen/liquid hydrogen rocket. It has a total length of approximately 63 m*2, a length close to the width of a soccer field (64 m), and a diameter of approximately 5.2 m.The H-IIA Launch Vehicle, which has been in operation up to now, features high reliability. On the other hand, it has issues such as in terms of its launch capabilities and costs. Accordingly, to solve these issues, the H3 Launch Vehicle was developed with the aim of it being an "easy-to-use rocket" combining the three elements of flexibility, high reliability, and low cost. This will allow Japan to maintain its independence in terms of means of transportation into space. At the same time, it will maintain and strengthen Japan's industrial base by enhancing its international competitive edge in the commercial satellite launch market.<h3>H3 Launch Vehicle Configuration, Form, and Launch Capabilities</h3>The H3 Launch Vehicle comprises an airframe form with the following parts that have a common airframe design.<ul><li>Number of LE-9 first-stage main engines (2 / 3)</li><li>Number of SRB-3 solid rocket boosters (0 / 2 / 4)</li><li>Size of the fairing (L: long / S: short)</li></ul>The main feature of the H3 Launch Vehicle is that it can flexibly handle launch missions for satellites with a great variety of sizes and masses by rearranging those parts.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1736747660447-img-rocket-02.jpg" style="width: 100%px;" class="fr-fic fr-dib">From the left: LE-9 first stage main engine ©MHI, SRB-3 solid rocket booster and fairing ©JAXAThe H3 Launch Vehicle's airframe form is represented by a combination of the above. For example, "H3-30S" indicates there are three LE-9s, zero SRB-3s (not equipped), and an S (short) fairing. The H3-30S, the smallest form, has the capacity to launch a satellite with a mass of 4 t or more into a sun-synchronous orbit (500 km × 500 km).In addition, it is expected that the H3-22L will be used to launch satellites into a geostationary orbit and to launch even larger satellites. Meanwhile, the H3-24L has higher launch capabilities than the H-IIA. It will be capable of launching large geostationary satellites in the 6 t mass class and multiple small satellites at once. In this way, the H3 Launch Vehicle is capable of flexibly launching small to large satellites with the optimal form and cost for various orbits.<h2>International Competitive Edge Required: Background to the H3 Launch Vehicle Development</h2>We describe here some of the international competition in the background to the H3 Launch Vehicle developing aiming to be highly flexible and reliable at low cost. This includes intensifying competition in rocket launch prices around the world and changing demands.<h3>Intensifying Price Competition in Rocket Launches</h3><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1736747717495-img-rocket-03.jpg" style="width: 100%px;" class="fr-fic fr-dib"><figcaption id="isPasted">H-IIA Launch Vehicle No. 47 ©JAXA</figcaption> Nowadays, there is a need for low-cost rockets even more competitive in price around the world. Costing less than the H-II Launch Vehicle, the H-IIA Launch Vehicle has also entered the commercial launch market with orders received for it from private satellite companies and other organizations both in Japan and overseas. However, since the 2010s, new rockets with features such as low cost and high flexibility have appeared one after another due to companies in the U.S., Europe, and elsewhere around the world entering the space business. That has led to conventional Japanese rockets struggling.In recent years, a major company in the U.S. has been planning to newly enter the commercial launch market with a rocket aiming to reduce costs by about half compared to before. Furthermore, venture businesses in various countries have also been actively developing new small and medium-sized rockets. These rockets may well become competitors in the small satellite launch market.<h3>Changes in Satellite Launch Demands and Cost Issues</h3>In addition to demand for larger satellites, there has been growing demand in recent years for the launch of dozens of small satellites at once. This has led to a need for rockets capable of launching satellites with a larger mass and size at lower cost.The larger communication capacity, the longer life of satellites due to electric propulsion, and other factors have led to an increase in the dimensions and mass of geostationary satellites compared to before. Geostationary satellites with a mass of 7 to 8 t are also being launched. (The H-IIA Launch Vehicle's capacity to launch satellites into a geostationary transfer orbit is up to approximately 6 t.)In particular, there is a need for rockets with large fairings capable of carrying satellites that have larger antennas than before to improve performance for communication and broadcasting satellites. Moreover, it is expected that there will also be an increase in demand in the future for dedicated mounting structures (dispensers) to handle satellite constellations that launch multiple small satellites at once and then operate them in formation.Related articles: <a href="https://article.murata.com/en-global/article/what-is-a-ntn-of-haps-and-satellites" target="_blank">Non-Terrestrial Networks (NTNs) Employing HAPS or Artificial Satellites: Technological Trends Leading to Beyond 5G/6G (1)</a>Large rockets that meet such demands have higher launch costs. On the other hand, there is a pressing need to keep down costs from the perspective of price competition. "Launch satellites with a larger mass than before at lower cost than before." The necessity to meet this contradictory requirement was in the background to the H3 Launch Vehicle development.<h2>3. Efforts Made to Cut Costs on the H3 Launch Vehicle</h2>As mentioned above, the H3 Launch Vehicle was developed with the aim of designing a rocket that can meet global market demands by combining the three elements of flexibility, high reliability, and low cost against a background of price competition, changing demands, and other factors. We introduce here the main efforts relating to the airframe of the H3 Launch Vehicle with a focus on the tough issue of cutting costs that is being demanded at the same time as an improvement in the transportation performance of various satellites. Together with this, we also look at the background that led to the Murata-made components being introduced into the rocket.<h3>Thoroughly Cost Cutting in All Stages</h3>The makers of the H3 Launch Vehicle aimed to thoroughly cut costs. Accordingly, they developed the rocket while staying conscious of how to keep down costs in all stages of conception, development, and operation. Consistent efforts were made as below in each stage.<ul><li>Conception Stage The price requirements for each component were clarified based on the concept of reducing costs. What cost reductions were possible was carefully examined and considered over the entire supply chain (the series of processes from the procurement of materials and components to manufacturing, inventory control, sales, delivery, and consumption).</li><li>Development Stage This technology was developed while adopting low-cost specifications in the design stage. At the same time, cost reduction was also promoted through innovations in the manufacturing method. Costs were evaluated in the design review as well. The project was carried out so that it was possible to achieve the cost determined in the concept at the conception stage.</li><li>Operation Stage The project was operated under a plan to thoroughly cut costs by mass-producing and bulk ordering the rocket and its constituent components and by engaging in ongoing efforts to raise efficiency and make improvements.</li></ul><h3>Reducing the Number of Components and Costs of the LE-9 with 3D Modeling</h3><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1736747756030-img-rocket-04.jpg" style="width: 832px;" class="fr-fic fr-dib"><figcaption id="isPasted">Combustion test with the actual LE-9 engine ©JAXA</figcaption>For example, the expander bleed cycle*3, a result of the accumulation of proprietary technologies in the second-stage engine, has been adopted as a high-thrust engine for the first-stage engine for the first time in the LE-9 with its greater thrust than the LE-7A main engine of the H-IIA in the first-stage main engine of the H3 Launch Vehicle. The LE-9 uses components manufactured with 3D modeling (additive manufacturing) using a 3D printer. This has reduced the number of components by about 20% at the component level compared with the conventional LE-7A. Moreover, the conventional injectors had a structure in which multiple components were manufactured by machining for 500 pieces and then joined before being attached to the main body. Therefore, processing and assembly took a great deal of time and effort. On the other hand, the overall cost was reduced by more than 50% in the LE-9 by using a 3D printer to mold the components that form the core as an integrated unit.<h3>SRB-3 Coupling Points and Cost Reduction</h3><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1736747862698-img-rocket-05.jpg" style="width: 832px;" class="fr-fic fr-dib"><figcaption id="isPasted">Full-scale separation test with the actual SRB-3 ©JAXA</figcaption>The method of coupling the first-stage airframe and the SRB-A solid rocket booster was complicated in the H-IIA/B Launch Vehicle. There was an issue in that the number of coupling points and components used for coupling was extremely large. Therefore, the method of coupling and separating from the first-stage airframe was reviewed and a new, simpler method was adopted for the SRB-3 solid rocket booster in the H3 Launch Vehicle.&nbsp;It became possible to manufacture in Japan the motor case, which was designed based on a U.S. motor previously. With the resulting increase in design flexibility, it was possible to adopt the optimal mechanism for the rocket booster, allowing for a simple coupling and separation mechanism. This drastically reduces the number of components used for coupling and separating. Reducing the weight has improved performance, and has also contributed to cost cutting.<h3>Improving Efficiency, Saving Time, and Reducing Costs with Takt Production</h3>The H-IIA Launch Vehicle was produced in a format called made-to-order production in which it was manufactured after being ordered. On the other hand, the H3 Launch Vehicle saw a manufacturing format being employed close to that of line production for general industrial products, such as automobiles and aircraft, with the time being equalized for component mounting, assembly, and inspection/shipping.&nbsp;In particular, a production method was adopted in which each movement and processing is repeated synchronously with takt production with the rocket being split into four segments. This improved the efficiency of manufacturing, shortened the production time, and reduced costs.<h3>Substantial Cost Cutting with the Introduction of Non-space Components</h3>The valves, sensors, and electrical and electronic equipment used in conventional rockets were developed for space separately from general components. These space components are extremely expensive because they have special specifications and only a few are produced. This was one of the bottlenecks in the goal of cutting costs.Therefore, the decision was made to use components not for space in the H3 Launch Vehicle. Introducing highly reliable and inexpensive non-space components for approximately 90% of all the components led to a significant reduction in costs compared to when using space components. This had the major effect of both improving reliability and reducing costs.When determining the non-space components that would be used, the applicable types of components were selected based on a resistance evaluation with a radiation test. In addition, this relates to component selection standards and safety standards. This meant the evaluation on the use of non-space components was carried out by JAXA and private companies in cooperation. Murata-made non-space components are also being used in the H3 Launch Vehicle. This process has contributed to both high reliability through each private company's technological capabilities and quality control and lower prices through efficient production.1: Mitsubishi Heavy Industries was selected by JAXA in 2014 as the prime contractor to be responsible for H3 Launch Vehicle development and launch transportation services. It is overseeing the development of the airframe.*2: The total length of approximately 63 m refers to where the fairing is L (long). The fairing is located at the top of the H3 Launch Vehicle. It is the part that houses artificial satellites and other devices to be launched. Where the fairing is S (short), the total length of the H3 Launch Vehicle is approximately 57 m.*3: The expander bleed cycle adopted in the LE-9 is a simple mechanism. It uses liquid hydrogen—the fuel—to cool the combustion chamber and nozzle. At the same time, it converts the hydrogen into gas, raises the temperature, and then uses that gas to operate the turbo pump that drives the engine. The efficiency is not as high as that of the two-stage combustion cycle. Nevertheless, this mechanism simplifies the structure—no pre-burner is required, for example—and reduces the number of parts over the entire engine. In addition, it is intrinsically safe in that the entire system is less liable to powering down and entering a catastrophic state such as in the event there is a propellent leak or turbo pump malfunction. Accordingly, it has the advantage of being less liable to enter an abnormal combustion state. This means it can offer both high reliability and low cost at a high level.

The H3 Launch Vehicle is a next-generation rocket. It was developed jointly by Mitsubishi Heavy Industries, Ltd. (hereinafter "Mitsubishi Heavy Industries") and the Japan Aerospace Exploration Agency (JAXA). The H3 Launch Vehicle is large for a liquid oxygen/liquid hydrogen rocket.

Learning from the H3 Launch Vehicle: Method and Competitiveness of Combining Flexibility, High Reliability, and Thorough Cost Reduction

Orbiting at an altitude of 700 kilometres, satellites of the European Space Agency (ESA) transmit radar signals down to Earth. These are scattered by the planet’s surface and reflected back to the satellite, yielding images of the terrain even when the sky is overcast. Right now, Konrad Schindler and his team are interested in images of Ukraine. “Satellites fly over the country every couple of days, which gives us a kind of slow-motion video,” says Schindler, head of the Institute of Geodesy and Photogram­metry at ETH Zurich. “If the signal hits a heap of rubble where there were once intact buildings, then it bounces back in a different form.” This is because smooth surfaces reflect radio waves more effectively than rough surfaces.Not that the naked eye could make much sense of this stream of freely accessible images. That’s the job of an automatic monitoring system that harnesses the power of artificial intelligence. “The system uses statistics to identify patterns in the data,” Schindler explains. This is an example of machine learning, whereby an Al system is trained with reference images that clearly show, for ex­ample, the difference between a building that has been destroyed and one that is still intact.These reference data were provided by UNOSAT, the United Nations Satellite Centre. Here, experts painstakingly inspect high-resolution images from commercial sources in order to identify damage to buildings and thereby document war crimes such as the bombing of civilian infrastructure. “Our monitoring system is certainly not designed to replace these people,” says Schindler. “But its ability to cover large areas gives them useful guidance on where to take a closer look.”<h2 id="isPasted">Maps for aid organisations</h2>The mapping of war damage is one of two projects on which ETH researchers have collaborated with the International Committee of the Red Cross (ICRC). “The second one sought to answer what might seem like a very simple question – namely, where are the people?” Schindler explains. When the ICRC launches relief operations or vaccination campaigns in the wake of natural disasters, it needs data on population density. In less developed countries, this is often lacking – and that’s when the Red Cross falls back on the population maps that Schindler’s group creates from satellite data.ETH researchers use this powerful cocktail of satellite observation and machine learning across a wide range of areas. In collaboration with the Swiss start-up ExoLabs, for example, they have developed a system that delivers a faster and more accurate assessment of snow cover than that from conventional sources. The researchers began by feeding the system with estimates of snow depth in Switzer­land based on satellite images and digital elevation maps. Using comparisons with precise data gather­ed by aircraft and meteorological ground stations, the system learned how to improve the satellite-­based estimates. Now it provides daily snow depth maps, a service that will soon be launched on a commercial basis.A global map of tree heights has already been made freely available. Also based on satellite data, this is primarily used for environmental purposes. “Obviously, you can’t see the bottom of a tree from space, but the reflected signal contains detailed content, particularly in the infrared range, from which a lot of information about vegetation can be obtained,” Schindler explains. Using reference data from a laser scanner on board the International Space Station (ISS), a computer program has learned how to derive tree heights on the basis of illumination patterns of the area under observation. A laser pulse bouncing back from a treetop takes minimally less time to return to the ISS than one reflected from the ground below. This difference is enough to calculate the height of local vegetation.<h2>Early-warning system for forests</h2>“The global mapping of canopy heights also provides interesting information on the age of the vegetation,” says Verena Griess, Professor of Forest Resources Management and head of the Institute of Terrestrial Ecosystems at ETH Zurich. She and her team are using satellite data to discover more about tree health. “Climate change has propelled us into an era of rapid change,” says Griess. “This also applies to our forests, where problems caused by pests, blowdown, fire and drought are very much on the rise.” Using satellite data, the researchers plan to set up an early-warning system that will alert us to distressed woodland even before such damage is visible to the human eye. This system could also be used to detect the first signs of forest fires in sparsely populated areas, or to show where remed­ial measures are required in Switzerland to safeguard the protection against natural hazards that forestry affords. Satellite data could also identify areas that are suitable for timber extraction or that require conservation due to their high biodiversity. “Our aim is to ensure that the right decision is made for each place,” Griess explains.&nbsp;Researchers are now working on a model that will be able to automatically identify tree species from satellite images. Trained on data from locations where species such as beech, spruce or larch are known to be growing, the model gradually learns to identify the corresponding patterns in satellite images. Using hyperspectral sensors, satellites can also collect information on tree health that is invisible to the human eye. This includes measuring the concentration of certain substances in the leaves and monitoring any changes that might indicate the early stages of a pest infestation.Two-dimensional satellite images can also be enriched with 3D data from laser scanners either on board satellites, aircraft or drones, or located on the ground. ETH recently inaugurated its Alps supercomputer, which will speed up data analysis and improve the training of AI models. “This technology adds yet another string to our bow, alongside our outstanding students and the excellent collaboration between university professorships and other member institutions of the ETH Domain,” says Griess. “Switzerland now has a real wealth of expertise in this area, concentrated to a degree you won’t find anywhere else.”<h2>Gps technology repurposed</h2>The health of woodland trees can also be investigated using signals from GPS satellites. When a GPS receiver is placed under a tree and another one at a distance, the difference in the signals received can serve to calculate how much biomass the tree contains. “It’s amazing what you can do with a technology that was developed for something entirely different – in this case, navigation,” says Benedikt Soja, Professor of Space Geodesy.In collaboration with MeteoSwiss, Soja and his team have installed low-cost GPS receivers at several weather stations located throughout Switzerland. They measure the level of water vapour in the atmosphere by detecting the corresponding delay in the propagation of radio signals transmitted by satellites. This data is then used to calculate the humidity above a weather station and thereby improve rain forecasts.At the same time, the analysis of such data over a longer period of time also yields information on climate change. A warmer atmosphere absorbs more moisture – i.e., more water vapour. “This in turn intensifies climate change, because water vapour is also a greenhouse gas – in fact, the most abundant of all,” Soja explains. “This makes it very easy to determine the local impact of climate change on the atmosphere.” GPS measurements show that, on average, some regions have become wetter and others drier.Researchers are harnessing another technique to track the melting of ice sheets in Greenland and Antarctica. Two satellites flying in tandem measure the Earth’s gravity field, which varies according to how much mass is present at a particular location. “This means we can measure any changes in this mass from space,” explains Soja. This data can be further refined with the help of other satellites that measure altitude. “The polar regions are seeing massive ice melt. We’re losing hundreds of gigatons of ice each year – the equivalent of a gigantic 3,000-metre-high cube the size of the city of Zur­ich – and this is causing sea levels to rise,” says Soja. “Satellite observation is the best way of tracking the impact that climate change is having on the Antarctic and Greenland ice sheets.”

ESA’s Sentinel satellites are civil Earth observation satellites. They are also used by ETH researchers. (Image: ESA)

Earth-observation satellites deliver data for a wealth of applications – from monitoring climate change and documenting war crimes to planning disaster relief and assessing snow depth. ETH researchers are also big beneficiaries.

The view from space - and what it tells us

Golden packages on the Moon? Not exactly. This is no extraterrestrial gift depot, but a cutting-edge project in the LUNA hall. Here, the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt; DLR) has been researching how payload boxes, sensors, rovers and astronauts can connect to form an integrated network. These participants, or nodes within the network, exchange signals that facilitate both communication and navigation."Every robot, sensor or astronaut becomes a participant in the network, passing on information to its neighbours. We calculate the distances between participants by measuring the time it takes for radio signals to travel between them. Using this method, we can determine the exact positions of all participants within the network," explains Emanuel Staudinger, project manager at the DLR Institute of Communications and Navigation. "The system is decentralised, requires no infrastructure and is designed to accommodate a large number of participants." The swarm navigation network developed by the institute could be used to explore the surfaces of the Moon and Mars. It is also effective in environments on Earth where conventional systems such as satellite navigation are not available, for example in lava caves or other complex terrains.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1734656795004-image-1000-d6bdda070377af96c5f2c2dc3e88f154.jpeg" style="width: 100%px;" class="fr-fic fr-dib">A rover moves between sensors and stones in the LUNA hall The DLR Institute of Communications and Navigation has carried out an experiment in the LUNA hall, in which payload boxes, sensors and rovers were integrated into a swarm navigation network. In the LUNA hall, network participants navigate across a regolith simulant that closely mimics the properties of lunar regolith. The stones scattered across the surface are modelled on real lunar geology. Credit: DLR (CC BY-NC-ND 3.0)<h2>Robots working together</h2>Staudinger and his team have spent recent years testing the swarm navigation network in diverse terrains, including Mount Etna in Italy and a lava cave on Lanzarote, Spain. Now, for the first time, the swarm navigation network has been successfully tested in the LUNA hall, which simulates a realistic lunar environment. Opened at the DLR site in Cologne in September 2024, LUNA simulates conditions on the lunar surface. "The research centre – a joint project between DLR and the European Space Agency (ESA) – is designed to prepare astronauts and robots for Moon missions," explains Thomas Uhlig from the LUNA project management team. "Our colleagues from the DLR Institute of Communications and Navigation are among the first to conduct research here. We’ve already received numerous requests from a wide range of fields for access to LUNA. The future looks exciting."<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1734656698151-image-1920-40dc969fa8622d505ecc7d68be7684c4.jpeg" style="width: 100%px;" class="fr-fic fr-dib">Twinkling in the LUNA hall The DLR Institute of Communications and Navigation has carried out an experiment in the LUNA hall, in which payload boxes, sensors and rovers were integrated into a swarm navigation network. Within the network, participants exchange information with their neighbours, enabling the precise determination of their positions. The DLR team distributed around 50 sensors throughout the hall, their colourful lights making the space sparkle. Credit: DLR (CC BY-NC-ND 3.0)In the LUNA hall, scientists from the institute in Oberpfaffenhofen demonstrated several scenarios, including one in which a lander arrives on the lunar surface and deploys two rovers. The first rover was tasked with placing radio beacons around the lander. These sensor units immediately then assumed navigation tasks. During its exploratory trips, the rover discovered a crater but realised it could not descend into it, so called for assistance from the second rover. This crater is located in a section of the LUNA hall where the floor has been lowered three metres. Using the navigation system, the second rover found its way to the crater on its own and carried out scientific investigations. As it descended, the first rover assisted from the crater's edge, guiding the second rover with navigation data. Another scenario involved deploying sensor units in an area too steep for a rover to traverse. Here, up to 50 sensors were distributed and interconnected to form a navigation network. The team also tested flying units or dropping the sensors into an inaccessible crater.<h2>3D model of the regolith surface created</h2>In addition to the navigation experiments, the team developed a 3D model of the LUNA floor to support simulations and validate measurement data. A significant portion of the hall is covered with a layer of regolith simulant, or 'Moon dust'. This material mimics the chemical, physical and geological properties of the regolith samples brought back to Earth by the Apollo missions.The research team from the DLR institute will now analyse the data in detail and continue refining the swarm navigation network. The ultimate goal is to provide robust, decentralised navigation and communication capabilities for astronauts and robots exploring the Moon. These advancements could also prove invaluable for terrestrial missions in challenging and inaccessible environments.<iframe width="640" height="360" src="https://www.youtube.com/embed/MEZzv27ha4c?&amp;wmode=opaque&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true">&nbsp;</iframe>Video: Swarm Navigation in LUNA Facility.&nbsp;Various exploration scenarios were successfully tested for the first time in the new LUNA analogue facility in Cologne. To this end, the DLR Institute of Communications and Navigation connected various participants, such as payload boxes, sensors and rovers, to a swarm navigation network in the LUNA hall. These pass information on to their neighbours. The exact positions of the participants are determined using this information. The decentralised radio system requires no infrastructure. This makes navigation and communication possible in unknown terrain, such as on the Moon or Mars. Credit: © DLR. All rights reserved

Gold-coloured packages in the LUNA hall The DLR Institute of Communications and Navigation has carried out an experiment in the LUNA hall, where payload boxes, sensors and rovers connect to form a swarm navigation network. Within the network, participants exchange information with their neighbours, enabling the precise determination of their positions. Credit: DLR (CC BY-NC-ND 3.0)

The German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt; DLR) has been researching how payload boxes, sensors, rovers and astronauts can connect to form an integrated network.

A swarm of sensors, rovers and astronauts explore the 'Moon'

<h2 dir="ltr" id="isPasted">Introduction</h2>Industrial machinery, from robots to crane booms, require sensing and guidance to ensure their effective use. Beyond the industrial world, the latest video cameras detect and compensate for movement and vibration to create high-quality imagery. In fact, accurate sensing of orientation and movement is integral to everything from communication satellites to farming equipment.Inertial measurement units (IMUs) allow the precise measurement of motion and orientation. When combined with accurate positioning information provided by global navigation satellite systems (GNSS), IMUs can provide location and velocity information, even when satellite signals are weak or obstructed.Selecting the right IMU should be easy. Manufacturer’s data sheets provide access to all the information an engineer needs to choose the perfect component for their application. And yet, engineers are designing inaccuracy into their equipment as they are unable to gain a full understanding of how IMUs perform from data sheets alone.In this paper, we will highlight three key challenges that engineers face when specifying an IMU for their next design. We will help them to read between the lines of the data sheet to understand the information they need. We will look at how manufacturers test their products, and we will explain how the environment can affect the performance of IMUs.<iframe width="540" height="370" src="https://c8056756.sibforms.com/serve/MUIFAFzOYRmakTTHP2FX2FyHP3QxXkV_prDOkSjxVq9r73O2ZYgeTBdxzjryZ1uiV-lwmna05gucMW0bsczCM_L1y82QMtCTmxYMAjh47rKxZQYNnUMylX2XNlQ7TpTNyLxACx2rLDFoQXvwdl20h6UYtI005O05w77j-WIyUR8ngfSBpL3p7bGQ8Cu-7pjUFhSkY3xZfKxVGSlQ" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable"></iframe> <h2 dir="ltr">Chapter 1: Getting the Whole Picture</h2>Engineers selecting the components for a new design will rely upon the manufacturer's information. The wealth of information provided by data sheets and catalogues allows the engineer to make decisions based on the component’s predicted performance. The key attributes that must be understood when selecting IMUs are bias instability and angle random walk (ARW). Bias instability is caused by tiny fluctuations in an IMU’s zero-bias level. This is the reading that the sensor delivers when it is at rest, and therefore provides the baseline for an IMU that is stationary. IMUs are affected by a range of external conditions, including variations in temperature and vibration, which reduce the accuracy and stability of the IMU's output. This change is characterized by a slow, long-term drift and is measured in degrees of drift per second (deg/s).In contrast to bias instability, angle random walk is caused by random high-frequency noise inside the IMU which causes variations in the measurement of angular velocity over time. The errors that ARW tries to quantify grow with the square root of time, and so the accumulated angle error will grow over a long period.Both attributes are, therefore, heavily influenced by time, a problem that is made worse by the dynamic conditions that IMUs are intended to measure. However, the data that is typically provided in IMU data sheets does not reflect this dynamic nature, and both bias instability and ARW are often published as a single number. This reduction of a complex system to a single number does not provide the engineer with a clear prediction of its performance over time. By relying on single attributes, engineers run the risk of designing inaccuracies into their device before it is even used.Instead of relying on the single value of the gyro bias instability and ARW, engineers can turn to Allan deviation plots. An Allan deviation plot (or Allan variance plot) is a graphical tool that provides a more useful understanding of the stability and noise characteristics of IMUs. The plot is unique to each IMU and helps identify the effects of different types of noise by showing how the variance of a sensor’s output changes over different averaging times. This is especially useful for assessing the drift and noise behaviour over time.This time dependency is the key advantage of using Allan deviation instead of traditional standard deviation. Standard deviation is a useful tool if the mean of the data is expected to be constant, and the measurements do not drift over time. This is not the case with IMUs. An Allan plot is derived from data collected from the IMU over a period of time, calculated at an interval known as tau (𝜏). This represents the averaging time, and the higher the tau, the more that individual extreme readings will be averaged out.The image below shows a typical Allan deviation plot for an IMU, in which the low point corresponds to the bias instability, whereas the value corresponding to tau of 1 sec (1 second averaging) represents the angular random walk, reflecting the higher frequency fluctuations that define ARW.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1733673242419-banner+epson.jpg" style="width: 100%px;" class="fr-fic fr-dib">Another data point concerns warm-up time. Extreme temperatures can affect the warm-up time of the IMU. This is the period after activation that the IMU needs to reach its operational state. In just one example, after hours exposed to sub-zero temperatures, the warm-up time for an IMU may be considerably longer than published to allow the internal components to stabilize before they can provide accurate readings.To avoid designing undesired inaccuracy into a new device, it is therefore vital for the engineers to access the best possible data. The preferred path for the engineer is to work with an IMU manufacturer that can provide this comprehensive information, for the alternative is for the engineer to conduct their own evaluation, a time-consuming process.While the information presented in data sheets is important, the engineer will only gain a real insight into the performance of their chosen component by understanding the underlying data and how it will affect their application in the long run.<h2 dir="ltr">Chapter 2: IMUs in the Real World</h2>Accurate and comprehensive information is therefore vital, but it is important for the engineer to understand the conditions under which it was obtained. Systems and equipment that use IMUs are regularly deployed beyond the safe environment of the laboratory into the real world. The conditions they encounter can have a significant effect on the performance of IMUs.An example of these tough conditions can be found in modern farming. The agricultural industry is embracing the latest sensing technology, collecting data to maximize efficiency. &nbsp;This data is used to make decisions, from identifying optimum soil conditions to deploying the right resources.&nbsp;Accurate positioning is essential for the smart farm. The ability to precisely control planting, fertilizing and harvesting activities allows the farmer to improve efficiency, yields, and sustainability. The huge growth in the development of autonomous tractors, harvesters, and drones places even more emphasis on navigation and positioning. These systems make use of global navigation satellite systems (GNSS) to allow these machines to operate within centimeters of their intended paths, without the need for human intervention.However, GNSS signals may not always be available as satellite signals can become disrupted or blocked. Atmospheric conditions, interference from structures and geographical features, along with the orbital geometry of the satellites themselves can all cause loss of signals. Without satellite guidance, autonomous machinery cannot obtain the accurate position that is so important to precision farming.Under these circumstances, the IMU becomes the principal means by which the equipment can locate their position. Using a process of dead reckoning, the IMU can measure the movement from the last known position provided by GNSS until the signal can be reacquired. The same method can be employed by a huge range of industries far from the smart farm, from the delivery of medical supplies by drone into remote locations to the automated loading of ships at ports.This reliance on IMUs in the real world means that they must withstand a range of conditions. Some of the most important effects to consider are those caused by shock and vibration. IMUs can be affected by both high and low frequencies. Some frequencies can cause an effect called sensor saturation in which the IMU can no longer work correctly as the vibration exceeds the IMU’s limiting value. Engineers must also be aware of the physical damage that vibration and sudden shock can cause to equipment.Engineers must also understand the effect of extreme temperatures on IMUs. In the earlier example, we looked at the need for accurate positioning on the smart farm. The agricultural environment is one of the harshest in the world, and vehicles are designed to function in an extraordinary range of conditions. Whether deployed to the frozen tundra in the Arctic circle or the unforgiving heat of deserts, equipment is exposed to ambient temperatures from -40°C to near the boiling point of water.This range of temperatures has a significant impact on the accuracy and performance of IMUs. Bias instability changes with temperature, which can lead to cumulative errors, especially for measurements based on integration over time. To complicate matters further, the drift in bias caused by changes in temperature are often nonlinear. This means that the designer needs to calibrate their system over the expected operating temperature range to ensure accuracy.Engineers must therefore understand the environment into which their equipment will be deployed. Identifying harsh conditions on the road or in the factory will allow engineer to choose the correct IMU based on accurate data. This may require information obtained from the IMU manufacture that is not contained in the data sheet. It is essential to understand how temperature will affect warm-up time, and to request a calibration profile that details temperature-dependent drifts.<h2 dir="ltr">Chapter 3: Consistency is the Key</h2>While it is important for the engineer to understand the conditions to which their design will be exposed, it is also crucial to know how the data provided by the manufacturer was created. This has important implications for consistency. Data sheets for IMUs often present typical performance values, without providing clear information about how this information was obtained. In many cases, the published information is an average, but the data sheet will rarely provide details about the statistical methodology used to obtain the average. Without the all-important description of this typical performance, the engineer can only guess how the IMU will perform in their specific application.When evaluating IMUs, it is important to understand the level of statistical confidence behind the performance data. Where some manufacturers will publish performance at a 1-sigma level, others will use 2-sigma. The statistical difference between different sigma levels has important implications for high-performance applications in which consistency is key.Sigma levels are use used in statistics to describe a confidence level in the data provided. In a normal distribution of data, a 1-sigma (1σ) level represents one standard deviation away from the mean. At this level, roughly two-thirds (in fact, 68.27%) of the data values will fall within this one standard deviation from the mean. In real terms, it means that there is a two-thirds probability that any single measurement will fall within this range.However, if a manufacturer bases their data on a 2-sigma (2σ) level, this probability rises to more than 95%. As a result, any one measurement will fall within the predicted range 19 times out of 20, delivering a far higher level of confidence in the data provided. In the context of IMUs, this confidence translates into an expectation of accuracy over time.This variation in predicted consistency is further complicated by the testing regime employed by the manufacturer. Some manufacturers adopt a strategy of batch testing, evaluating only a small number of units to provide the data required. Other manufacturers may conduct multiple tests on a single unit in the expectation that this will provide an accurate prediction of the performance of the entire range. The result is that the listed performance may not accurately reflect the variability between different IMUs from the same production batch. This variability can be significant, particularly for critical characteristics like bias instability.Unfortunately, the only way to truly understand how an IMU will perform in a real-world application is for the engineer to perform their own evaluation. While data sheets can provide a baseline, real-world testing is necessary to understand the variability between units and ensure consistent performance in the field. Working with a trusted manufacturer that publishes clear and comprehensive information can go a long way to helping the engineer with their understanding.<iframe width="540" height="370" src="https://c8056756.sibforms.com/serve/MUIFAFzOYRmakTTHP2FX2FyHP3QxXkV_prDOkSjxVq9r73O2ZYgeTBdxzjryZ1uiV-lwmna05gucMW0bsczCM_L1y82QMtCTmxYMAjh47rKxZQYNnUMylX2XNlQ7TpTNyLxACx2rLDFoQXvwdl20h6UYtI005O05w77j-WIyUR8ngfSBpL3p7bGQ8Cu-7pjUFhSkY3xZfKxVGSlQ" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable"></iframe> <h2 dir="ltr">Conclusion</h2>Selecting the right inertial measurement unit is a complex task. Not only are IMUs required to accurately measure movement, but they are expected to deliver high reliability in some of the most demanding conditions..Engineers need to understand how these conditions will affect the performance of their IMU. The roles played by extremes of temperature and the physical demands of dynamic applications must be considered before the engineer can have confidence on their design. Unfortunately, some manufacturers do not provide easy access to the detailed information that engineers need. Therefore, when selecting an IMU, here are the key considerations for engineers:<ul><li dir="ltr">Remember that the data sheet might not contain the depth of information required. Do not rely on single-number attributes, and obtain Allan deviation plots to gain a full understanding of the device’s performance.</li><li dir="ltr">Device testing takes place in the controlled environment of the laboratory, but the real-world is not so accommodating. Extremes of temperature and physical demands play a huge role in the reliability and accuracy of IMUs. Engineers should ensure that they understand their environment.</li><li dir="ltr">Take the time to understand how IMUs are tested. Confidence in performance comes from extensive testing methods, and clearly published data from the manufacturer. If necessary, engineers should be ready to conduct their own evaluation.</li></ul><a href="https://global.epson.com/products_and_drivers/sensing_system/imu/">Epson</a> manufactures a comprehensive range of IMUs for a huge array of applications, from earthquake detection to the precise positioning of satellites in space. Epson creates highly capable sensing systems that offer outstanding precision, high stability, and low noise to deliver confidence to design engineers in the most demanding industries.

Understanding IMU data sheets, manufacturers' testing methods, and the impact of environmental factors on performance.

Three Key Considerations for Selecting IMUs

Visualizing the potential impacts of a hurricane on people’s homes before it hits can help residents prepare and decide whether to evacuate.MIT scientists have developed a method that generates satellite imagery from the future to depict how a region would look after a potential flooding event. The method combines a generative artificial intelligence model with a physics-based flood model to create realistic, birds-eye-view images of a region, showing where flooding is likely to occur given the strength of an oncoming storm.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1732838043094-MIT-AI-Flood-01-press_0.jpg" style="width: 832px;" class="fr-fic fr-dib">Caption:A generative AI model visualizes how floods in Texas would look like in satellite imagery. The original photo is on the left, and the AI generated image is in on the right.&nbsp; Credit: Pre-flood images from Maxar Open Data Program via Gupta et al., CVPR Workshop Proceedings. Generated images from Lütjen et al., IEEE TGRS.As a test case, the team applied the method to Houston and generated satellite images depicting what certain locations around the city would look like after a storm comparable to Hurricane Harvey, which hit the region in 2017. The team compared these generated images with actual satellite images taken of the same regions after Harvey hit. They also compared AI-generated images that did not include a physics-based flood model.The team’s physics-reinforced method generated satellite images of future flooding that were more realistic and accurate. The AI-only method, in contrast, generated images of flooding in places where flooding is not physically possible.The team’s method is a proof-of-concept, meant to demonstrate a case in which generative AI models can generate realistic, trustworthy content when paired with a physics-based model.&nbsp;In order to apply the method to other regions to depict flooding from future storms, it will need to be trained on many more satellite images to learn how flooding would look in other regions.“The idea is: One day, we could use this before a hurricane, where it provides an additional visualization layer for the public,” says Björn Lütjens, a postdoc in MIT’s Department of Earth, Atmospheric and Planetary Sciences, who led the research while he was a doctoral student in MIT’s Department of Aeronautics and Astronautics (AeroAstro). “One of the biggest challenges is encouraging people to evacuate when they are at risk. Maybe this could be another visualization to help increase that readiness.”To illustrate the potential of the new method, which they have dubbed the “Earth Intelligence Engine,” the team has made it <a href="https://climate-viz.github.io/" target="_blank">available</a> as an online resource for others to try.The researchers <a href="https://ieeexplore.ieee.org/document/10758300" target="_blank">report their results today in the journal IEEE Transactions on Geoscience and Remote Sensing</a>.&nbsp;The study’s MIT co-authors include Brandon Leshchinskiy; Aruna Sankaranarayanan; and Dava Newman, professor of AeroAstro and director of the MIT Media Lab; along with collaborators from multiple institutions.<h2>Generative adversarial images</h2>The new study is an extension of the team’s efforts to apply generative AI tools to visualize future climate scenarios.“Providing a hyper-local perspective of climate seems to be the most effective way to communicate our scientific results,” says Newman, the study’s senior author. “People relate to their own zip code, their local environment where their family and friends live. Providing local climate simulations becomes intuitive, personal, and relatable.”For this study, the authors use a conditional generative adversarial network, or GAN, a type of machine learning method that can generate realistic images using two competing, or “adversarial,” neural networks. The first “generator” network is trained on pairs of real data, such as satellite images before and after a hurricane. The second “discriminator” network is then trained to distinguish between the real satellite imagery and the one synthesized by the first network.Each network automatically improves its performance based on feedback from the other network. The idea, then, is that such an adversarial push and pull should ultimately produce synthetic images that are indistinguishable from the real thing. Nevertheless, GANs can still produce “hallucinations,” or factually incorrect features in an otherwise realistic image that shouldn’t be there.“Hallucinations can mislead viewers,” says Lütjens, who began to wonder whether such hallucinations could be avoided, such that generative AI tools can be trusted to help inform people, particularly in risk-sensitive scenarios. “We were thinking: How can we use these generative AI models in a climate-impact setting, where having trusted data sources is so important?”<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1732838099417-MIT-AI-Flood-02-press.jpg" style="width: 100%px;" class="fr-fic fr-dib">Here, the AI model visualizes how flood in North Carolina would look like in satellite imagery. These new visualizations of physics-based flood models might facilitate more intuitive communication of climate risks.&nbsp; Credit: Pre-flood images from Maxar Open Data Program via Gupta et al., CVPR Workshop Proceedings. Generated images from Lütjen et al., IEEE TGRS.<h2>Flood hallucinations</h2>In their new work, the researchers considered a risk-sensitive scenario in which generative AI is tasked with creating satellite images of future flooding that could be trustworthy enough to inform decisions of how to prepare and potentially evacuate people out of harm’s way.Typically, policymakers can get an idea of where flooding might occur based on visualizations in the form of color-coded maps. These maps are the final product of a pipeline of physical models that usually begins with a hurricane track model, which then feeds into a wind model that simulates the pattern and strength of winds over a local region.&nbsp;This is combined with a flood or storm surge model that forecasts how wind might push any nearby body of water onto land. A hydraulic model then maps out where flooding will occur based on the local flood infrastructure and generates a visual, color-coded&nbsp;map of flood elevations over a particular region.“The question is: Can visualizations of satellite imagery add another level to this, that is a bit more tangible and emotionally engaging than a color-coded map of reds, yellows, and blues, while still being trustworthy?” Lütjens says.The team first tested how generative AI alone would produce satellite images of future flooding. They trained a GAN on actual satellite images taken by&nbsp;satellites as they passed over Houston before and after Hurricane Harvey. When they tasked the generator to produce new flood images of the same regions, they found that the images resembled typical satellite imagery, but a closer look revealed hallucinations in some images, in the form of floods where flooding should not be possible (for instance, in locations at higher elevation).To reduce hallucinations and increase the trustworthiness of the AI-generated images, the team paired the GAN with a physics-based flood model that incorporates real, physical parameters and phenomena, such as an approaching hurricane’s trajectory, storm surge, and flood patterns.&nbsp;With this physics-reinforced method, the team generated satellite images around Houston that depict the same flood extent, pixel by pixel, as forecasted by the flood model.“We show a tangible way to combine machine learning with physics for a use case that’s risk-sensitive, which requires us to analyze the complexity of Earth’s systems and project future actions and possible scenarios to keep people out of harm’s way,” Newman says. “We can’t wait to get our generative AI tools into the hands of decision-makers at the local community level, which could make a significant difference and perhaps save lives.”<hr>The research was supported, in part, by the MIT Portugal Program, the DAF-MIT Artificial Intelligence Accelerator, NASA, and Google Cloud.

The method could help communities visualize and prepare for approaching storms.

New AI tool generates realistic satellite images of future flooding

A non-terrestrial network (NTN) is a type of wireless communication network used for applications such as mobile communications. The term refers to multilayered networks linking terrestrial base stations, ships at sea, high-altitude platform stations (HAPS), and communication satellites in space. These characteristics allow an NTN to overcome the limitations of land-based networks, dramatically expanding the coverage of the communication infrastructure to include areas such as mountain valleys and areas of the ocean far from land.Among non-terrestrial networks, this article focuses on wireless communication networks comprising elements at high altitudes, far above the land and the sea. R&amp;D, test beds, and demonstrations are starting to be rolled out as part of plans for the development of non-terrestrial networks in the stratosphere and outer space in preparation for the advent of the next-generation telecommunications standard known as Beyond 5G (5th Generation Mobile Communication System, B5G)/6G (6th Generation Mobile Communication System).<h2 id="isPasted">From the stratosphere to outer space. Configuration and features of non-terrestrial networks (NTNs).</h2><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1732785400292-FIGURE+1.jpg" style="width: 832px;" class="fr-fic fr-dib">Figure 1: Conceptual Diagram Showing Altitude and Coverage Area of Different Types of Transmission Equipment in a Non-Terrestrial NetworkAs shown in Figure 1, a non-terrestrial network deployed in the skies above us would be composed of different types of transmission equipment for which higher altitude corresponds to wider coverage area. Naturally, the greater the distance from the Earth’s surface, the greater the signal delay, and the different types of transmission equipment will be used for different purposes depending on their location, in outer space or the stratosphere, or at ground level. This makes it possible to build a communication infrastructure with a wider coverage range than was previously attainable, such that we will be able to say that “nowhere on Earth is out of range.” Below, we present an overview and feature description of the artificial satellites (GEO and LEO) and unmanned aircraft (HAPS) that compose the non-terrestrial network shown in Figure 1.<h3 id="isPasted">Geostationary Satellite (GEO)</h3>A geostationary satellite or geostationary Earth orbit satellite (GEO) is the component of non-terrestrial networks for B5G/6G that operates at the highest altitude. These artificial satellites are positioned approximately 36,000 km above the Earth’s equator. They orbit the Earth at a speed that matches the Earth’s rate of rotation, so they appear to be stationary above a specific spot on the ground. Generally speaking, weather satellites that predict the weather based on meteorological conditions over wide areas belong to this category of satellites.GEOs used in communications networks operate at a high altitude, allowing them to communicate with a wide area. It is said that three or four such satellites would be able to cover the entirety of the Earth’s surface. However, their distance from the ground means that they have a greater data transfer lag than other types of transmission equipment, and their data transfer speed is said generally to be on the order of several megabits per second (Mbps). Also, since sending signals to the Earth’s surface requires high output, GEOs tend to be larger than low Earth orbit satellites (LEOs), discussed below, and launching them into orbit requires larger rockets.<h3 id="isPasted">Low Earth Orbit Satellite (LEO)</h3>A low Earth orbit satellite (LEO) is a type of artificial satellite that operates in space but in a lower Earth orbit*1 than a GEO, typically at an altitude of several hundred to 2,000 km. Unlike geostationary satellites (GEOs), they are not synchronized with the rotation of the Earth. The Hubble Telescope and the International Space Station (ISS) also operate at a low altitude, using a low Earth orbit of around 400 km, at most. However, it is said that LEOs used in NTNs will be positioned at altitudes of around one thousand several hundred kilometers.LEOs operate at lower altitudes than GEOs, allowing data transfer with low delay and using low-output signals. Such satellites can also have a smaller size. LEOs support communication speeds with the Earth’s surface of several hundred megabits per second (Mbps), so it is said that they could be used for services enabling direct communication between smartphone handsets and satellites. On the other hand, since LEOs operate at lower altitudes than GEOs, the coverage area of each satellite is narrower, and satellites operating in low Earth orbits have a higher orbital speed. This mean that it is necessary to make use of “satellite constellations” consisting of multiple small LEOs operating in coordination in order to provide stable communication under these conditions. The term “constellation” is a reference to constellations of stars and is used to refer to a coordinated group of LEOs.*1 The definition of “Earth orbit” varies among different countries and organizations. For example, the European Space Agency (ESA) defines Earth orbit as orbiting the Earth at an altitude of up to 1,000 km, whereas the Japan Aerospace Exploration Agency (JAXA) specifies an altitude of up to 2,000 km.<h3 id="isPasted">High-Altitude Platform Station (HAPS)</h3>A high-altitude platform station (HAPS), sometimes called a stratospheric communication platform, is an unmanned aircraft (some are like airplanes while others take the form of balloons or airships) flying through the stratosphere at an altitude of around 20 km and that functions as an airborne communication base station. Generally speaking, passenger jetliners can reach altitudes up to around 10 km, and the portion of the stratosphere in which a HAPS operates is about twice that high. At this altitude, air currents and weather conditions are comparatively stable, and there is little air resistance. Since the air density is less than that needed for an unmanned aircraft to achieve the necessary dynamic lift, it is said that operational HAPS models will carry solar panels or batteries that will enable them to remain airborne continuously for several weeks at a time.Whereas terrestrial base stations generally have a coverage area spanning a radius of a few kilometers to a few tens of kilometers, a single HAPS is likely to have a coverage area spanning a radius of around 100 km. Also, since they are closer to the Earth’s surface than artificial satellites operating in space, they are expected to play a major role in the new communication infrastructure for B5G/6G due to their minimal data transfer lag. Nevertheless, since the stratosphere occupies the territorial airspace of different nations, unlike outer space, it can be said that the establishment of a legal framework in each country will be key to the international implementation of HAPS and the provision of communication services.<h2 id="isPasted">Advantages of and Trends in Non-Terrestrial Networks (NTNs) for B5G/6G</h2>In conclusion, we will discuss the advantages of and trends in non-terrestrial networks.<h3>Advantages of Non-Terrestrial Networks (NTNs)</h3>A big advantage of non-terrestrial networks, which unlike terrestrial networks comprise elements operating in the stratosphere and outer space, is that they can provide a communication infrastructure that is relatively unaffected by natural disasters such as earthquakes or tsunamis. In addition, they have the potential to dramatically expand the coverage area to the extent that nowhere on Earth is out of range. This will enable people to use their smartphones to communicate in emergencies even if they find themselves in locations where communication was difficult previously, such as mountain valleys or while at sea.As a result, it will be possible to maintain the communication infrastructure in time of emergencies such as natural disasters or accidents, regardless of location, and to extend the mobile communications network over ever broader areas without interruption, preventing problems from occurring when in the mountains or while traveling by sea.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1732785572794-img-0005.jpg" style="width: 100%px;" class="fr-fic fr-dib">Using a smartphone ina &nbsp;remote montain area.&nbsp;<h3 id="isPasted">Trends in Non-Terrestrial Networks (NTNs)</h3>One big feature of non-terrestrial networks is the remarkable trend toward active involvement by private companies. In the past, most space projects were national initiatives, but nowadays a wide variety of companies around the world, not only in the hardware field but in fields such as communication services as well, are teaming up and taking part in aerospace projects, including the non-terrestrial network business.Examples include the development and manufacture of artificial satellites and HAPSes, components used in them, and telecommunication equipment (payloads) loaded on them. A wide range of companies are involved in these business endeavors, including some that operate rockets that carry artificial satellites into space. In particular, the next-generation mobile communications field called B5G/6G is serving as a starting point for the rapid growth of businesses serving a market that is global in scale.Murata Manufacturing develops and manufactures a variety of wireless communication modules. As part of this effort, we are working to develop products that meet changing needs, including connectivity modules compatible with non-terrestrial networks (NTNs). For details, refer to the page linked to below.

This article highlights non-terrestrial networks at high altitudes, focusing on R&D and testing efforts to support next-generation telecommunications standards, Beyond 5G and 6G.

Non - Terrestrial Networks Employing HAPS or Artificial Satellites: Technological Trends Leading to Beyond 5G

Astronauts can stay in space for extended periods fulfilling their scientific mission, but those extended periods lead to numerous issues for the astronauts — including what to do with human waste.A nationwide consortium of researchers, led by the McKelvey School of Engineering at Washington University in St. Louis, plans to develop an anaerobic digestion process that converts human waste into organic acids and carbon dioxide for use as carbon feedstock. The consortium, Anaerobic Digestion for Advanced Space Technology and Resource Acquisition, or AD ASTRA, is funded by a three-year, $2.5 million grant from NASA.The AD ASTRA team plans to engineer the cyanobacterium&nbsp;Synechococcus 2973, which is capable of photosynthesis and producing oxygen, to create food, nutraceuticals such as beta-carotene, and biopolymers from human waste. They will test this process in simulated micro- and low-gravity conditions, like that on the moon, and integrate the biological systems from anaerobic digestion to their newly developed photo-biorefineries, for example, engineering cyanobacteria and synthetic lichens. Their goal is to produce protein-rich biomass as a food source and other chemicals and to recycle human waste, which ultimately helps to sustain astronauts in space.“This research addresses a critical and neglected area of space life support systems, focusing on an innovative process to convert waste that is essential for extended human spaceflights,” said&nbsp;<a href="https://engineering.washu.edu/faculty/Joshua-Yuan.html">Joshua Yuan</a>, the Lucy and Stanley Lopata Professor and chair of the Department of Energy, Environmental &amp; Chemical Engineering in the McKelvey School of Engineering.&nbsp;Collaborators in the AD ASTRA research include Himadri Pakrasi, the George William and Irene Koechig Freiberg Professor in the Department of Biology in Arts &amp; Sciences; the University of Florida; Saint Louis University; Lincoln University in Missouri, a historically Black university; University of Delaware; and the NASA Lab at Kennedy Space Center. As part of the project, the Kennedy Space Center will host several students and postdoctoral researchers during the summer intern sessions in the 2025 and 2026 fiscal years for a visiting technologist and education experience.In addition, researchers in the McKelvey School of Engineering received a two-year grant from the Air Force Office of Scientific Research (AFOSR) to develop algae-E.coli co-culture as a novel photo-biorefinery. The researchers seek to determine how environmental factors in space affect the algae photosynthesis, metabolic interactions between algae and&nbsp;E.coli, and bio-production of high functional protein materials.“These projects will determine if microgravity can influence the production phenotype of microbial hosts as well as how the environmental variables, such as extreme temperature change or low pressure, impact biomanufacturing,” Yuan said. “Understanding how cells or phototrophic consortia survive and metabolize under space environments will be crucial to engineering resilient and productive microbial cell factories under space conditions.”&nbsp;Both studies aim to explore carbon dioxide-based biomanufacturing via algae, microbial coculture and synthetic lichens, a cyanobacterium-fungus system.In related work,&nbsp;<a href="https://engineering.washu.edu/faculty/Feng-Jiao.html">Feng Jiao</a>, professor of energy, environmental &amp; chemical engineering, was part of a team that was a runner-up for the NASA Deep Space Food Challenge. The team, Nolux, constructed an artificial photosynthetic system that can create plant and fungal-based foods without biological photosynthesis.Collectively, the three projects broaden the impact of the decarbonization research underway in the&nbsp;<a href="https://engineering.washu.edu/news/2024/WashU-to-lead-26-million-decarbonization-initiative.html">Carbon Utilization Redesign for Biomanufacturing-Empowered Decarbonization (CURB) Engineering Research Center</a> (ERC) in the McKelvey School of Engineering.

McKelvey Engineering researchers plan to engineer the cyanobacterium Synechococcus 2973 to create food, nutraceuticals and biopolymers from human waste generated in space. They will test this process in simulated micro- and low-gravity conditions and integrate the biological systems from anaerobic digestion to their newly developed photo-biorefineries, for example, engineering cyanobacteria and synthetic lichens, as shown in this photo. (Courtesy photo)

WashU-led consortium to facilitate integrative research and education for space biology

Researchers aim to develop space biomanufacturing from carbon dioxide, astronaut wastes

Satellite sensors are used for Earth observation, navigation and positioning, telecommunications, military missions, and scientific research. Custom sensors have specialized capabilities, but commercial off-the-shelf (COTS) products that use advanced technologies can reduce development and launch costs.&nbsp;If you’re selecting satellite sensors, consider these four factors.<ol start="1" type="1"><li>Sensor type</li><li>Performance specifications</li><li>Environmental considerations</li><li>Integration with satellite systems</li></ol>The following sections explain.<h2>#1 Sensor Types</h2>Satellites are equipped with a variety of sensors, including some that are redundant to ensure continued operations in the event of device failure. The type of sensor to select is a function of your application requirements and the device’s capabilities.<h3>Optical Sensors</h3><a href="https://www.mouser.com/c/sensors/optical-sensors/?srsltid=AfmBOopLoBeb_e87pPGBojAgJIghB1oK08NvEPMH4Vkz5TKRXeVkyrv-">Optical sensors</a> detect light within specific spectral ranges, such as visible and infrared (IR), for imaging, monitoring, and navigation. They are highly accurate, compact, lightweight, and can support high-bandwidth transmissions. For satellites with directional antennas, optical sensors are used for positioning, tracking, and alignment. For laser communications with ground stations, these satellite sensors support very fast transmissions. &nbsp;<h3>Radar Sensors</h3><a href="https://www.earthdata.nasa.gov/learn/backgrounders/what-is-sar">Synthetic aperture radar (SAR)</a> sensors combine multiple radar signals collected over time as a satellite moves in orbit. They support high-resolution imaging without the use of a large antenna. Unlike optical sensors, SAR operates in the microwave range of the electromagnetic spectrum. Among their advantages, radar sensors can capture images regardless of lighting or cloud cover.<h3>Gyroscopes and Accelerometers</h3><a href="https://escies.org/download/webDocumentFile?id=1056#:~:text=Guidance%2C%20navigation%20and%20control%20systems,and%20stabilization%20of%20its%20attitude.">Gyroscopes</a> measure angular velocity and help satellites maintain their orientation in flight.<a href="https://metromatics.com.au/applications-for-accelerometers/#:~:text=Accelerometers%20in%20Space&text=Accelerometers%20measure%20the%20vibration%20and,of%20the%20vehicles%20during%20operation.">&nbsp;</a><a href="https://metromatics.com.au/applications-for-accelerometers/#:~:text=Accelerometers%20in%20Space&text=Accelerometers%20measure%20the%20vibration%20and,of%20the%20vehicles%20during%20operation.">Accelerometers</a> measure linear acceleration and can determine shock and vibration levels during launch, flight, and landing. There are also piezoelectric accelerometers, capacitive accelerometers, mechanical gyroscopes, ring laser gyroscopes (RLGs), and fiber optic gyroscopes (FOGs), each suited to specific applications.<h3>Magnetometers</h3><a href="https://www.pnisensor.com/understanding-magnetometers-and-their-uses/">Magnetometers</a> are sensors that measure changes in magnetic fields. They are essential for navigation and are used in conjunction with gyroscopes and accelerometers for attitude determination and motion tracking. Subtypes include fluxgate, optically pumped, SQUID, hall effect, magneto-resistive (MR), and Lorentz force magnetometers.<h3>Thermal Infrared Sensors</h3><a href="https://up42.com/blog/introduction-to-thermal-infrared">Thermal infrared sensors (TIR)</a> measure land surface temperatures and detect heat signatures. Unlike near-infrared (NIR) and shortwave infrared radiation (SWIR) devices, TIR technology senses emitted energy rather than reflected energy. With their high spatial resolution, TIR sensors can attribute temperature values to specific features, such as urban heat islands.<h3>Radio Frequency Sensors</h3>Radio frequency (RF) sensors detect and measure RF signals for applications that include signals intelligence (SIGINT) and environmental monitoring. By triangulating signals from multiple orbital points,<a href="https://newspaceeconomy.ca/2023/09/05/what-are-radio-frequency-rf-monitoring-satellites/#:~:text=Advanced%20RF%20sensors%20aboard%20satellites,classification%20and%20detection%20of%20patterns.">&nbsp;</a><a href="https://newspaceeconomy.ca/2023/09/05/what-are-radio-frequency-rf-monitoring-satellites/#:~:text=Advanced%20RF%20sensors%20aboard%20satellites,classification%20and%20detection%20of%20patterns.">RF sensors</a> for SIGINT can identify distinct emitters and find their precise location. For environmental monitoring, RF sensors can track signals regardless of weather conditions.<h3>GNSS Sensors</h3>Global navigation satellite system (<a href="https://www.geo-instruments.com/technology/gnss-sensors/">GNSS</a>) sensors provide autonomous geo-spatial positioning. As satellites orbit the Earth, they transmit a unique signal along with orbital parameters. Ground-based receivers use this information to calculate a user’s exact location. GNSS encompasses a variety of satellite-based systems, including the U.S. global positioning system (GPS).<h2>#2 Performance Specifications</h2>Satellite sensors differ in terms of performance specifications. Depending on the specific application, some specs may be more important than others. For example, Earth observation requires sensors with high spatial resolution to distinguish small objects on the ground from their surroundings.<h3>Frequency Range</h3>Frequency range is the range of frequencies over which a satellite sensor is designed to operate. It determines the sensor’s ability to capture specific signals, perform remote sensing tasks, or transmit and receive data. Different sensors are designed to operate within different frequency bands, such as the ultra-high frequency (UHF) band within the RF portion of the electromagnetic spectrum.<h3>Resolution</h3>Resolution refers to a sensor’s ability to distinguish between closely spaced objects or fine details. There are different types of resolution, including spatial, spectral, temporal, radiometric, and angular resolution. Satellite sensors with higher resolution can acquire more data, but they also need more storage and processing power. In addition, higher resolution sensors are more expensive.<h3>Sensitivity</h3>Sensitivity refers to the ability of a sensor to detect small changes or weak signals in the quantity it’s designed to measure. Depending on the type of space sensor, this quantity could be IR or visible light, magnetic fields, temperatures, or RF signals. With SAR sensors, sensitivity determines how well the radar can detect small, reflected signals from objects on Earth’s surface. &nbsp; &nbsp; &nbsp;&nbsp;<h3>Power Consumption</h3>Sensor power consumption refers to the amount of electrical power that a sensor uses. It includes the power that’s consumed by the sensor itself for signal detection and the power used by supporting electronics for signal amplification, processing, or transmission. Because satellites have limited power availability, sensors with low power consumption are preferred.<h3>Weight and Size</h3>Sensor weight and size are important because satellites have strict payload limitations. These specifications can also affect larger design decisions and overall sensor performance. For example, the size of an optical sensor that’s used with a satellite’s camera determines how much light the camera has available to create an image. In turn, this affects image quality.<h2>#3 Environmental Considerations</h2>When selecting a satellite sensor, consider the environment in which it will be used. Outer space exposes sensors to extreme conditions, and repairing or replacing components can be challenging.&nbsp;<ul><li>Radiation Tolerance: Space environments expose sensors to high levels of ionizing radiation, which can degrade performance over time. Choose sensors and other electronic components that are radiation-hardened.</li><li>Temperature Variations: Satellites face extreme temperature fluctuations, and sensors need to function reliably under these changing conditions. Depending on whether a satellite is in sunlight or shadow, temperatures can range from -270°C to +200°C.</li><li>Outgassing and Vacuum: Choose sensors that are built from materials that do not outgas in the vacuum of space. Outgassing, the process by which materials release trapped gasses when exposed to vacuum pressure, can cloud optical lenses and interfere with signals.</li></ul><h2>#4 Integration with Satellite Systems</h2>When selecting satellite sensors, consider how the device will integrate with larger systems that are essential for continuous operations. There are several systems that are especially critical.<h3>Data Handling and Communications</h3>Command and data handling (CDH) systems acquire, process, store, format and downlink data to ground-based assets. They include various subsystems, such as telemetry and telecommunications (TTC). For real-time or delayed data transmissions, choose sensors that are compatible with the satellite’s onboard data handling system.<h3>Pointing and Stabilization</h3><a href="https://www.satnow.com/search/antenna-pointing-mechanisms">Antenna-pointing mechanisms</a> direct a satellite’s antenna toward a specific communication target. A control system receives data that’s acquired from sensors, calculates the required rotation of the antenna, and stabilizes the device. Depending on the sensor type and application, pointing and stabilization may also be used with high-resolution cameras.&nbsp;<h3>Power Monitoring</h3>Satellites have multiple subsystems, each of which requires different amounts of power. Each subsystem must receive the correct voltage and current, and without power spikes or other anomalies that can jeopardize performance. Power monitoring systems for energy generation and storage must work with satellite sensors and support the use of onboard solar panels and batteries.Are you looking for satellite sensors? <a href="https://www.mouser.com/c/sensors/?srsltid=AfmBOoroprtuwE_0UkwpJGlpiyyQxfRCl337OHfi9U8LZ_YkVUuPqpRZ">Visit Mouser Electronics.</a>

The Arctic Weather Satellite is collecting data in one of the regions of our planet that is most severely affected by climate change. ©OHB Sweden

Key Considerations for Selecting Satellite Sensors: Types, Performance, Environment, and Integration

How to Select Satellite Sensors: Five Key Considerations

AI is revolutionizing our ability to train cars to drive safely and to respond to unexpected events. | Shutterstock/Scharfsinn

An authority on autonomous robotic systems talks about the impact of AI on his field here on Earth and growing aspirations for autonomy in space.

The future of autonomous vehicles

Space deployment comes with a series of significant engineering challenges for the electronic and electrical systems that are going to be utilised. Engineers must therefore specify components for their designs that have all the necessary properties – particularly in relation to coping with the harsh environments they are going to be situated in, alongside the acute size, weight and power (SWAP) constraints involved. In this article, we will look at exactly what this will entail and give a detailed use case example to illustrate the points raised too.<h2 dir="ltr">Unique aspects of the application setting</h2>Though this must still be adequately addressed, it is not actually space itself that poses the biggest threat to component reliability. Instead, it is the process of getting it there that is likely to be the problematic part. During launch, a rocket’s payload can easily be subjected to 4-5g accelerations. On top of that, there are certain to be intense vibrational forces experienced. There is the potential for hardware to be exposed to very high levels of heat too. Should any damage occur at this stage, then the space deployed equipment might not be able to carry out its assigned functions as intended and the mission will end up being a failure.&nbsp;Once the hardware is finally in orbit, there will be other considerations. Among the most prominent is the temperature cycling that components will have to deal with – going from severe cold when traversing the dark side of the Earth, followed by periods in the Sun’s harsh thermal radiation. Another consideration is whether the chosen components will be detrimentally affected by the vacuum of space. Outgassing means that chemical substances could be emitted from these components, due to seepage from cracks or internal gaps. This might result in delicate instrumentation in close proximity to them being impaired in some way by the presence of unwanted contaminants. Once again, the mission could be jeopardised, meaning that all the expense and engineering effort that had gone into it would count for nothing. &nbsp;&nbsp;<h2 dir="ltr">Sourcing interconnects for space&nbsp;</h2>The various factors just described make it very clear that the connectors and cabling being selected for space deployment must have superior operational characteristics. Consequently, it is vital that they are able to withstand prolonged mechanical stresses (in terms of both shock and vibration), as well as extreme temperatures. The remote nature of the application of course means that once deployed there is no opportunity to replace faulty interconnects. This is why chances cannot be taken on choosing anything other than a solution with the highest degrees of reliability and resilience to the most challenging of ambient environments.Having collaborated on a multitude of different projects in this sector over several decades, Harwin offers an extensive portfolio of space-qualified connectors and cables. Each of these has a heavy duty construction, so that prolonged reliability is assured. They can be provided with built-in shielding (both backshells and braids) to protect them from cosmic radiation.<h3 dir="ltr">Use case example</h3>A recent venture, on which Harwin worked with the Group of Astrodynamics for the Use of Space Systems&nbsp;(GAUSS), underlines the need for sourcing appropriate connectors and cabling. GAUSS is an established provider of microsatellite deployment solutions, as well as designing and manufacturing its own satellite hardware. The Italian company’s UNISAT platform enables the in-orbit-release of third-party pico/nano-satellites, with numerous CubeSats and PocketQubes being carried into space on each mission and then getting deployed directly from a purpose-built spacecraft. This approach has made it much more convenient and cost-effective for academic research groups to undertake space-based experiments and monitoring work, as they can reduce the upfront expense normally associated with ‘rideshare’ activities.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1729852467851-wendy-1.jpg" style="width: 832px;" class="fr-fic fr-dib">Figure 1: The UNISAT-7 pico/nano-satellite deployment platform from GAUSS (images courtesy of Gauss Srl)When the GAUSS team embarked upon the development of their latest UNISAT spacecraft, there were some key objectives that they wanted to achieve. The most important of these was to be able to expand the payload capacity. This would mean that a greater quantity of CubeSats and PocketQubes could be deployed. The engineering team at GAUSS looked at ways that they could make UNISAT-7 construction more lightweight than the previous models, so more of the assigned launch mass could be taken up by the payload.&nbsp;For it to be accommodated in the rocket, the UNISAT-7 spacecraft’s overall volume had to be kept as low as possible. Its total launch mass had to be kept down to no more than 32kg. All of the constituent electronics would be contained within an aerospace-grade aluminium honeycomb framework of 50mm x 50mm x 50mm dimensions. This would have carbon fibre sheets attached to it. The spacecraft’s power would be generated by banks of photovoltaic panels mounted onto its exterior.&nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1729852521555-wendy+3.jpg" style="width: 100%px;" class="fr-fic fr-dib">Figure 2: View of rocket used to deploy multiple satellites (image courtesy of GK Launch Services)Given that the UNISAT-7 needed to remain operational for up to 5 years, the ongoing integrity of the interconnect technology had to be beyond doubt. After sampling and testing connectors from a variety of different manufacturers, GAUSS engineers decided that going with units featured in the Harwin Datamate series would be the best tactic. Datamate and Datamate Mix-Tek connectors have shown themselves to be very effective when used in spaceborne equipment. These high-reliability (Hi-Rel) components are robust enough to cope with the difficult conditions posed by space launches. They can withstand 20G vibrations and 100G shocks, plus up to 125°C temperature levels. Their compact form factor means that they take up only minimal board space, leaving plenty of room for other devices.The 4-finger Beryllium Copper contacts incorporated into Harwin Datamate connectors allow them to always maintain a connection with their corresponding mating surfaces. The contacts in a standard Datamate mating pair carry up to 3A of current, while Datamate Mix-Tek contacts are 40A rated. The advanced locking features employed mean that industry-leading interconnect retention is attained.The low outgassing that these connectors exhibit is another important benefit. For instance, the moulded plastic used in them has a 1.01% total mass loss (TML) and a collected volatile condensable material (CVCM) figure of less than 0.01% when placed in the vacuum of space.Complementing their low profile, the availability of right-angle configuration Datamate versions addresses limited gaps between PCBs in space hardware designs. It means that connectors can be positioned at the edge of these PCBs, thereby avoiding the need for cabling to reach further into these boards. The upshot is that cables and connectors are much easier to access when the hardware is being assembled. Furthermore, cables do not need to be bent to such great angles – with less chance of them breaking in the process. &nbsp;Having been successfully trialled out by GAUSS, the selected Harwin connectors would play an integral role in the communication and data processing elements of the UNISAT-7, allowing all the related data to be transported. They would also prove essential to the spacecraft’s power systems.&nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1729852579004-wendy+4.jpg" style="width: 100%px;" class="fr-fic fr-dib">Figure 3: Connectors visible in the PCB stack of a small satellite (image courtesy of Gauss Srl)<h2>Conclusion</h2>Space hardware must be able to rely on the quality of interconnect technology that has been specified for it. Any cutting of corners here could lead to projects that may have taken years to prepare for and large amounts of money to fund falling short of the goals that they were originally set and not meeting expectations. That is why engaging with an expert in this area is definitely advised. This will mean the connectors and cabling can be recommended that are fully optimised for such purposes.

Space deployment presents significant engineering challenges for electronic and electrical systems, requiring engineers to specify components with properties suited to harsh environments and acute size, weight, and power (SWAP) constraints.

Implementing Effective Interconnects into Space System Designs

Recently we launched new Edge Impulse functionality that shows just one of the powerful ways that LLMs can be utilized on the edge: <a href="https://www.edgeimpulse.com/blog/llm-knowledge-distillation-gpt-4o/" rel="noreferrer">using GPT-4o to train a 2,000,000x smaller model</a>, that runs directly on device.Our initial demo showed how this tool could be used to label images for binary classification (presence/absence) but there are more complex labeling tasks possible.In this post and video we explore how visual data from a satellite could be labeled both for cloud type and for coverage, generating two useful edge models from the same dataset very quickly:<iframe width="640" height="360" src="https://www.youtube.com/embed/iXdyrDrY9Zs?&amp;wmode=opaque&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true">&nbsp;&nbsp;</iframe><h3 id="isPasted">The challenge of AI for remote applications</h3>Satellites are resource constrained — power, compute and storage must all be managed carefully as expansion is rarely possible. Another issue is data transfer to Earth, which can be costly and has bandwidth restrictions. Current LLMs can only run in large data centers due to their unwieldy size (billions of parameters) so to utilize their power, raw data must be sent over the air to be computed.Edge AI is perfectly positioned to address these issues. By developing a model that can run on the device where data collection occurs, you can reduce data transmission to just the desired insight. For example, if you’re only interested in seeing clear images but a particular satellite image is obscured by clouds, it doesn't need to be sent down to earth for inspection. These methods can also be useful for improving existing edge models — sending raw data for classifications on-device that return an “unsure” or “anomaly” result to an LLM for labeling can be a good way to improve model performance over time.<a href="https://form.jotform.com/242533284499365"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1728891710283-5+Rising+Trends+for+AI+Adoption+in+Manufacturing+%283%29.png" style="width: 100%px;" class="fr-fic fr-dib"></a><h3>Cloud type and coverage labeling examples</h3>To show the power of LLM labeling tools we have created two models from one raw dataset by designing two different LLM prompts for labeling. In the above video, I accessed an existing <a href="https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/BSETKZ" rel="noreferrer">Harvard cloud dataset</a>. Using ChatGPT, I can ask questions about the images in the dataset, such as what type of cloud is shown or how much of the frame is covered by clouds. The latest LLMs excel at this type of image analysis, but running this procedure has high latency, and incurs significant cloud costs, especially when in a production environment with many source cameras and a high frame-rate.Here's how we can create a smaller model to perform this same tasks, on-device:<h3>Project walkthrough</h3><ol><li>Data collection: I downloaded the open <a href="https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/BSETKZ" rel="noreferrer">Harvard cloud satellite-image dataset</a> (originally curated for cloud removal with spatiotemporal networks, a more complex task). Taking only the RGB images, I then uploaded them without labels into an Edge Impulse project. This unlabeled dataset forms the bases for both the cloud type classification and cloud coverage models we want to create.</li><li>Designing a prompt: I first experimented with labeling a few images in ChatGPT itself, noting the setting specific output options that worked best for each tasks. • Cloud Classification: My final prompt was “What cloud type is visible in this satellite picture? Respond only with “cirrus”, “cirrocumulus”, “cirrostratus”, “altocumulus”, “altostratus”, “nimbostratus”, “stratocumulus”, “stratus”, “cumulus”, “cumulonimbus”. If there is no cloud respond with "no cloud" or "unsure" if you're not sure. <img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1728891780952-Untitled--4--2.png" style="width: 100%px;" class="fr-fic fr-dib">•&nbsp;Cloud Coverage: My final prompt was “Look at this image from a satellite and respond with a percentage of how much of the image is obscured by clouds. Respond only with a percentage not including "%" or "unsure" if the image is corrupted, or it is not an image from a satellite.”</li></ol><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1728891803331-Untitled--5-.png" style="width: 100%px;" class="fr-fic fr-dib">3. Labeling with GPT-4o: Using the new transformation block "Label image data using GPT-4o" inside each Edge Impulse project, I asked GPT-4o to label the images with my prompt. The block discards any blurry or uncertain images, providing a clean dataset. It also provides natural language reasoning for every labeling decision — a powerful tool for debugging model performance.4. Model Training: For each model type, I designed an impulse with the desired output format. I used a Transfer Learning Image classifier block for the cloud classification task and a Regression block for the cloud coverage task: •&nbsp;Cloud Classification: I trained a small MobileNetV1&nbsp;classification model with these images. The model was only 1.6MB in size, significantly smaller than the original LLM. •&nbsp;Cloud Coverage: I trained a small convolutional neural network regression model with these images. The model was only 49.1K in size, significantly smaller than the original LLM.5. Deployment: This model could be deployed on MCU hardware from Cortex M4 upwards, for example an Arduino Nicla Vision. The model accurately detects cloud types on-device, in real-time, without requiring cloud services.<table><tbody><tr><td><h3>New whitepaper: Discover the 5 AI Trends Transforming Manufacturing</h3><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1728891888961-1728891888961.png" class="fr-fic fr-dii">The whitepaper, "5 Rising Trends for AI Adoption in Manufacturing," offers key insights into how artificial intelligence is transforming manufacturing processes. It explores survey data from over 150 industry leaders, revealing that optimizing production and improving product quality are top goals driving AI adoption. The report also discusses the rise of AI solutions for worker safety, real-time insights to boost profitability, and the growing importance of internal expertise in implementing AI. Download the full whitepaper to gain a deeper understanding of these trends and how AI/ML is shaping the future of manufacturing.<a href="https://form.jotform.com/242533284499365" target="_blank" rel="nofollow">DOWNLOAD YOUR FREE COPY</a></td></tr></tbody></table><h3>Results</h3>These two models were created in an incredibly short time. Data labeling is often the most time-consuming and error-prone part of training a model for the edge. By utilizing LLMs you can create your first-pass model much more quickly, and the power of having natural language reasons for each label makes debugging model datasets even easier.LLMs are still flawed and don’t perform perfectly for every use-case, but they are a great tool to assist subject matter experts in developing models faster. As generative AI continues to accelerate in popularity and complexity these techniques will only get better.<h3>References</h3><ul><li>Jan Jongboom <a href="https://www.edgeimpulse.com/blog/llm-knowledge-distillation-gpt-4o/" rel="noreferrer">Bringing Large Language Models to the Edge with GPT-4o and NVIDIA TAO</a></li><li>Met Office <a href="https://www.metoffice.gov.uk/weather/learn-about/weather/types-of-weather/clouds/cloud-names-classifications" rel="noreferrer">Cloud names and classifications</a></li><li>Edge Impulse <a href="https://studio.edgeimpulse.com/public/430153/latest" rel="noreferrer">GPT-4o Cloud Type Experiment</a></li><li>Edge Impulse <a href="https://studio.edgeimpulse.com/public/417680/latest" rel="noreferrer">GPT-4o Cloud Coverage Experiment</a></li><li>Harvard Dataverse <a href="https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/BSETKZ" rel="noreferrer">Dataset From: Cloud Removal from Satellite Images using Spatiotemporal Generator Networks</a></li></ul><hr><h3 id="isPasted">Download the whitepaper: 5 AI Trends Transforming Manufacturing</h3><div data-message-author-role="assistant" data-message-id="a9ce89dd-4eb9-44c7-9d28-e3c7d10f7094" dir="auto" data-message-model-slug="gpt-4o"><div data-message-author-role="assistant" data-message-id="1c2c7603-add5-4aa4-84c9-81aa2c1371a2" dir="auto" data-message-model-slug="gpt-4o">Stay ahead of the curve on how AI is transforming businesses with the whitepaper "5 Rising Trends for AI Adoption in Manufacturing." The guide offers valuable insights to help leaders implement AI strategies that optimize production, improve product quality, and drive innovation. Download your free copy now:<iframe id="JotFormIFrame-242533284499365" title="Edge Impulse Form" allowtransparency="true" src="https://form.jotform.com/242533284499365" frameborder="0" style="min-width:100%;max-width:100%;height:900px;border:none;" scrolling="no" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true">&nbsp;&nbsp; </iframe></div></div>

Recently we launched new Edge Impulse functionality that shows just one of the powerful ways that LLMs can be utilized on the edge: using GPT-4o to train a 2,000,000x smaller model, that runs directly on device.

Using LLMs to Analyze and Label Satellite Imagery in Edge Impulse

GPS is a critical tool in modern society, and there’s more that can be done to improve the coverage, safety, and security of the technology. | iStock/aapsky

An expert in global positioning technologies looks ahead to expand GPS’s capabilities to the moon and, perhaps, beyond.

The Future of GPS: Innovations and Impacts on Navigation

For humans throughout history, the sky has evoked thoughts of a vast emptiness, of a great vacant dome punctuated during the day by the sun, and at night by numerous tiny spots of light (and, periodically, by the moon). As we have ventured into space, both physically, with spacecraft, and optically, with a range of telescopic technologies, we now know that there is quite a lot of stuff up there.<iframe width="640" height="360" src="https://www.youtube.com/embed/MPXj6pBBrD0?&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe>Simulations at Caltech model a new way for autonomous spacecraft to avoid collisions. This technology holds promise for autonomous vehicles on Earth as well as in space. Credit: James RaganThis discovery has profound implications for the aerospace industry. Imagine, for example, a multibillion-dollar autonomous spacecraft that has been carefully designed and engineered for years is launched into space with precision calculations only to lose one of its thrusters and go sailing into an asteroid.Historically, engineers have dealt with the possibility of equipment failure on board spacecraft in two main ways: First, by having a "safe mode" in which the spacecraft can do the least amount of damage to itself while scientists on the ground look at the data, make a diagnosis, and develop a solution; and second, by equipping autonomous vehicles with redundant systems. These allow a spacecraft to, for example, shut off a malfunctioning thruster and start using backup thrusters.However, dangerous situations may crop up in space with little warning and insufficient time for space-to-ground communications. And though redundant systems have been quite effective, they add to the expense and heft of autonomous spacecraft.This is why experiments are being conducted in the laboratory of Soon-Jo Chung, Bren Professor of Control and Dynamical Systems and senior research scientist at JPL, which Caltech manages for NASA, to streamline emergency features on autonomous vehicles such that they can diagnose and safely respond to encounters with other objects in real time. With new algorithms on board, spacecraft can test their own equipment and predict which future actions are most likely to keep them operating safely.As one of the supervisors for this project, Fred Hadaegh, research professor in aerospace at Caltech and former JPL chief technologist, explains: "Having redundant systems is not always practical. It means the spacecraft has to be bigger, heavier, and more expensive than it would be otherwise. So, the idea here is that when a spacecraft encounters a problem, it can figure out what's not working and correct or adapt to that specific fault."Chung's lab houses, among other things, an advanced multispacecraft dynamics simulator facility. "The simulator occupies a large room with a really flat floor," explains James Ragan, a PhD student in the Graduate Aerospace Laboratories of the California Institute of Technology (GALCIT) and lead author of this paper. "The model spacecraft uses air bearings so that it moves across the floor with near zero friction. At rest, it seems to be floating, and if you push it in one direction, it will keep going until it hits something, which is what space dynamics are like."Ragan has programmed the robotic spacecraft simulator with what he and his co-authors call s-FEAST: Safe Fault Estimation via Active Sensing Tree Search. "Our s-FEAST algorithm rapidly 'dreams' about numerous possible futures that could result from actions it takes now," Ragan says. "Because the system is noisy, these futures are uncertain. There are multiple possible outcomes, which leads to a tree of possible branching futures. Each branch represents one possible way the future might happen, based on things the spacecraft controls—the test actions it selects—and also things it doesn't, such as observations coming from faulty sensors."Chung adds: "What is innovative about our s-FEAST method is that we systematically solve the chicken and egg problem of estimating vehicle states, such as positions and velocities, and inferring failures or degradations, which are intrinsically coupled to one another."When the spacecraft detects unexpected data, it turns to the s-FEAST algorithm, which runs test actions "similar to how you might carefully test your muscles when you feel an unexpected pain, and you want to figure out just what hurts and how to avoid actions that might further injure you," Ragan explains.s-FEAST simultaneously spins out a range of possible futures and from those selects the course of action that appears most likely to diagnose what went wrong and also to avoid danger. In the case of this model, danger amounts to a collision course with an asteroid.<iframe width="640" height="360" src="https://www.youtube.com/embed/aJ04dlgaP0o?&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe>"The key idea here is that s-FEAST isn't replacing all spacecraft operations. It's your emergency response," Ragan says. "The spacecraft receives an internal signal that something is wrong, so s-FEAST takes over all the spacecraft's computing power to quickly assess what's going on and take remedial action. Once the danger is pinpointed and addressed, s-FEAST hands control back to the spacecraft's ordinary computing environment."s-FEAST can also be used proactively. Say an autonomous spacecraft is about to take on a particularly risky or mission-critical action; s-FEAST can run a testing cycle to ensure that all systems are working properly before this action.Chung and his co-authors envision that the proposed method will establish a new way of making expensive space exploration safer and more cost effective. "Space systems make autonomous operations necessary since we cannot grab and fix spacecraft and Mars helicopters operating in a world far away from us," Chung says. "Space is our ultimate ‘proving ground' for any autonomy research we do for Earth-based vehicle systems."Not surprisingly, the s-FEAST algorithm that worked on the spacecraft simulator was adapted by the team to work on a ground track vehicle as well. Both experiments were successful, so s-FEAST technology holds great promise for autonomous vehicles on Earth as well as in space."<a href="https://www.science.org/doi/10.1126/scirobotics.adn4722" target="_blank">Online Tree-based Planning for Active Spacecraft Fault Estimation and Collision Avoidance</a>" is published in&nbsp;Science Robotics as an online feature article. A preliminary version of this paper received the&nbsp;<a href="https://www.eas.caltech.edu/news/jimmy-ragan-and-benjamin-rivi%C3%A8re-receive-2023-aiaa-guidance-navigation-and-control-best-graduate-student-paper-award" target="_blank">2023 American Institute of Aeronautics and Astronautics (AIAA) Guidance, Navigation, and Control Best Graduate Student Paper Award</a>. Co-authors are Ragan, Caltech postdoc Benjamin Rivière (PhD '24), Hadaegh, and Chung. This work is funded by the Aerospace Corporation, JPL, the Defense Advanced Research Projects Agency Learning Introspective Control program, and the Technology Innovation Institute.

Simulations at Caltech model a new way for autonomous spacecraft to avoid collisions. This technology holds promise for autonomous vehicles on Earth as well as in space.

A New Approach to Autonomous Vehicle Safety

On 5 September 2024 at 03:50 CEST (22:50 local time), the new Sentinel-2C satellite was successfully launched from Kourou, French Guiana, aboard the last Vega rocket in its original configuration.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1726012698757-image-2000-9ce0a3184832e9d2b679e4ad8caf68f9.jpeg" style="width: 100%px;" class="fr-fic fr-dib">The Sentinel-2C satellite is lifted out of its transport container at Europe’s Spaceport in Kourou, French Guiana&nbsp; Sentinel-2C is equipped with an optical multispectral instrument with a high resolution of up to 10 metres, that scans a swath 290 kilometres wide. This makes the satellite ideally suited for detecting changes in vegetation, compiling crop forecasts, mapping forests and determining the growth of wild and cultivated plants. The instrument is also used to monitor algae growth in coastal and inland waters, for example, as well as track sediment input in river deltas. Credit: ESA-CNES-ARIANESPACE/Optique vidéo du CSG–S. MartinThe Sentinel-2 mission consists of two identical satellites in the same Earth orbit. They complete one full orbit every 100 minutes, capturing all land surfaces as well as inland and coastal waters every five days. Sentinel-2C now replaces Sentinel-2A, which has been in space since 2015, making the satellite a crucial component in the long-term data provision for numerous applications.&nbsp;In Germany, the Federal Ministry for Digital and Transport (BMDV) is the principal authority responsible for Copernicus. The German Space Agency at DLR (Deutsches Zentrum für Luft- und Raumfahrt) supports the European Earth observation programme on behalf of the BMDV at the European level and promotes its utilisation in Germany through specific measures. The Sentinel-2C satellite was built by Airbus in Germany.Sentinel-2C is equipped with an optical multispectral instrument offering a high resolution of up to 10 metres and scanning a swath 290 kilometres wide. This makes the satellite ideally suited for detecting changes in vegetation, enabling crop forecasting, mapping forests and determining the growth of wild and cultivated plants. The instrument is also used to monitor algae growth in coastal and inland waters, as well as track sediment input in river deltas.<h2>Researching climate change, improving disaster protection</h2>The data collected by Copernicus is crucial for researching and monitoring climate change, as well as supporting key areas such as disaster management and security policy. The data also serves as a foundation for significant international agreements on environmental and climate protection, for example as part of the United Nations (UN) Climate Change Conferences and Sustainable Development Goals.Volker Wissing, Germany's Federal Minister for Digital and Transport emphasises the programme's range of applications: "With the launch of Sentinel-2C, Germany is demonstrating its leading role in global Earth observation. Its innovative instrument will play a crucial role in understanding climate change, combating it and dealing with its consequences. Our commitment to the Copernicus programme is an investment in cutting-edge technology and a sustainable future, as the satellite's precise data will support decision-makers worldwide in environmental policy measures."Walther Pelzer, DLR Executive Board Member and Director General of the German Space Agency at DLR, adds, "Copernicus is the largest and most successful Earth observation programme in Europe today, demonstrating the efficient cooperation between the European Union, European Space Agency (ESA) and national space agencies. With Sentinel-2C, we are securing its future and at the same time strengthening Germany's lasting position in the space sector."<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1726012832312-ezgif-7-5fa2653c65.png" style="width: 100%px;" class="fr-fic fr-dib">Snow and a calving glacier in Greenland&nbsp; This Sentinel-2 image shows an icy glacier tongue extending into a fjord (left). The mountainous landscape of northern Greenland is only partially covered by snow and ice, with ice floes breaking off from the glacier and floating on the water. Also clearly visible are the brownish, sediment-rich tributaries flowing into the fjord (right side of image). Due to the low position of the Sun at these high latitudes, even in summer, dark mountain shadows can be seen (left side of the image). Monitoring snow and ice masses is crucial for understanding climate change and predicting potential sea level changes. The image was captured on 21 July 2024. Credit: European Union, Copernicus Sentinel-2<h2>Copernicus – an EU and ESA partnership</h2>Copernicus is a collaborative initiative between the European Union (EU) and the European Space Agency (ESA). Through Copernicus, the EU operates satellite-based information services for land (CLMS), oceans (CMEMS), atmosphere (CAMS), disaster and crisis management (CEMS), climate change (C3S) and civil security (CSS). An increasing number of German government agencies, researchers and innovative companies are also working with Copernicus data. The foundation of these applications and services lies in six satellite constellations, known as the Sentinels – meaning 'guardians' – operated by ESA in collaboration with the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). In addition, a further six missions are currently being prepared, which will gradually expand the Copernicus fleet over the coming years. Among these is the Copernicus Anthropogenic Carbon Dioxide Monitoring Mission (CO2M), set to measure global greenhouse gas emissions from 2026 onwards. Copernicus also integrates satellite data from third parties, including from the German satellites TerraSAR-X and TanDEM-X. Data portals such as the Copernicus Data and Exploitation Platform – DE (CODE-DE for short) ensure users have easy access to Earth observation data and processing tools.

On 5 September 2024 at 03:50 CEST (22:50 local time), the new Sentinel-2C satellite was successfully launched from Kourou, French Guiana, aboard the last Vega rocket in its original configuration. Sentinel-2C is equipped to recognise changes in vegetation, enabling crop forecasting, forest mapping and determining the growth of wild and cultivated plants, as well as monitoring algae growth in coastal and inland waters, as well as tracking sediment input in river deltas. Credit: ESA–S. Corvaja 

On 5 September 2024 at 03:50 CEST (22:50 local time), the new Copernicus Sentinel-2C satellite was launched from Kourou, French Guiana. Sentinel-2C is the third of four Sentinel-2 satellites and will replace Sentinel-2A, which is now almost ten years old.

A new era for Copernicus: the Sentinel-2C satellite launches

Spacebit-Fri Jun 30 2023 23:59:59 GMT+0200 (Central European Summer Time)

Spacebit-Mon Jul 31 2023 23:59:59 GMT+0300 (Eastern European Summer Time)

Spacebit-Thu Aug 31 2023 23:59:59 GMT+0300 (Eastern European Summer Time)

Spacebit-Thu Aug 31 2023 23:59:59 GMT+0200 (Central European Summer Time)

Spacebit-Sat Sep 30 2023 23:59:59 GMT+0300 (Eastern European Summer Time)

NASA-Fri Jun 30 2023 23:59:59 GMT+0200 (Central European Summer Time)

NASA-Mon Jul 31 2023 23:59:59 GMT+0300 (Eastern European Summer Time)

NASA-Thu Aug 31 2023 23:59:59 GMT+0300 (Eastern European Summer Time)

NASA-Thu Aug 31 2023 23:59:59 GMT+0200 (Central European Summer Time)

NASA-Sat Sep 30 2023 23:59:59 GMT+0300 (Eastern European Summer Time)

Pulsar Fusion-Fri Jun 30 2023 23:59:59 GMT+0200 (Central European Summer Time)

Pulsar Fusion-Mon Jul 31 2023 23:59:59 GMT+0300 (Eastern European Summer Time)

Pulsar Fusion-Thu Aug 31 2023 23:59:59 GMT+0300 (Eastern European Summer Time)

Pulsar Fusion-Thu Aug 31 2023 23:59:59 GMT+0200 (Central European Summer Time)

Pulsar Fusion-Sat Sep 30 2023 23:59:59 GMT+0300 (Eastern European Summer Time)

AON3D-Fri Jun 30 2023 23:59:59 GMT+0200 (Central European Summer Time)

AON3D-Mon Jul 31 2023 23:59:59 GMT+0300 (Eastern European Summer Time)

AON3D-Thu Aug 31 2023 23:59:59 GMT+0300 (Eastern European Summer Time)

AON3D-Thu Aug 31 2023 23:59:59 GMT+0200 (Central European Summer Time)

AON3D-Sat Sep 30 2023 23:59:59 GMT+0300 (Eastern European Summer Time)

DLR-Fri Jun 30 2023 23:59:59 GMT+0200 (Central European Summer Time)

DLR-Mon Jul 31 2023 23:59:59 GMT+0300 (Eastern European Summer Time)

DLR-Thu Aug 31 2023 23:59:59 GMT+0300 (Eastern European Summer Time)

DLR-Thu Aug 31 2023 23:59:59 GMT+0200 (Central European Summer Time)

DLR-Sat Sep 30 2023 23:59:59 GMT+0300 (Eastern European Summer Time)

Unitree Robotics-Thu Aug 31 2023 23:59:59 GMT+0300 (Eastern European Summer Time)

Unitree Robotics-Thu Aug 31 2023 23:59:59 GMT+0200 (Central European Summer Time)

Unitree Robotics-Sat Sep 30 2023 23:59:59 GMT+0300 (Eastern European Summer Time)

Franka Emika-Fri Jun 30 2023 23:59:59 GMT+0200 (Central European Summer Time)

Franka Emika-Mon Jul 31 2023 23:59:59 GMT+0300 (Eastern European Summer Time)

Franka Emika-Thu Aug 31 2023 23:59:59 GMT+0300 (Eastern European Summer Time)

Franka Emika-Thu Aug 31 2023 23:59:59 GMT+0200 (Central European Summer Time)

Franka Emika-Sat Sep 30 2023 23:59:59 GMT+0300 (Eastern European Summer Time)

READY Robotics-Fri Jun 30 2023 23:59:59 GMT+0200 (Central European Summer Time)

READY Robotics-Mon Jul 31 2023 23:59:59 GMT+0300 (Eastern European Summer Time)

READY Robotics-Thu Aug 31 2023 23:59:59 GMT+0300 (Eastern European Summer Time)

READY Robotics-Thu Aug 31 2023 23:59:59 GMT+0200 (Central European Summer Time)

READY Robotics-Sat Sep 30 2023 23:59:59 GMT+0300 (Eastern European Summer Time)

space

James Webb Space Telescope aims to take us to the unexplored realm of our cosmic origins. From observing the formation of the first stars and galaxies to looking for the possibility of life on other planets, the telescope will play a major role in the future of space exploration.

Space

Profiles