Future Launch Research Articles

Preliminary Design of Expendable and Reusable Mixed-Staged Launch Vehicles

One of the very first tasks in launch vehicle design is the preliminary sizing. It is necessary for further design choices, but it should deliver a precise estimate of the launch vehicle’s mass and geometry as possible. Orbital launch vehicles can be either expendable or partially/fully reusable and can assume various stage configurations. Finding an optimal solution under practical constraints is a challenging task, which gives a wide design space for potential future launch vehicles. Hence, a generalized mathematical model of a launch vehicle design has been developed and implemented as a versatile and easily modifiable programming tool for fast and parametric system characterization and optimization. The model uses several basic parameters useful in describing launch vehicles and introduces some new parameters to account for reusability. Analytical and semi-empirical correlations are used to determine the overall system and mission performance of a launch vehicle for a given reference mission, including mass and geometry, and calculate the optimal launcher staging. The implementation of the model also allows coupling with other tools, which forms a design chain with respect to aerodynamics and trajectory simulation. With this design chain, several launch vehicles have been modeled and validated, proving the applicability of the method.

Analytical formulations for nitrogen oxides emissions estimation of a hydrogen fueled Synergetic Air-Breathing Rocket Engine (SABRE) in air-breathing mode

The space sector is undergoing rapid expansion due to the transformations it is experiencing, evolving from government-funded traditional space initiatives to commercially driven NewSpace activities. The surge of government and private investments in the space domain has positioned it as one of the world's fastest-growing sectors, making it crucial to assess the impact of already operational launch assets and to adopt design-to-sustainability strategies for under-development and future launchers. To actively contribute to this transition, this paper proposes a methodology for deriving novel analytical formulations to estimate nitrogen oxide (NOx) emissions of a hydrogen-fueled reusable access-to-space vehicle. Throughout the paper, the Skylon spaceplane and its Synergetic Air Breathing Rocket Engine are used as a case study. In particular, the SABRE engine presents a highly integrated dual propulsion architecture supporting both air-breathing and rocket modes. This study specifically focuses on the former, enabling the Single Stage To Orbit (SSTO) Skylon reaching the hypersonic speed regime before transitioning to rocket mode. This paper proposes novel analytical formulations to be integrated into the Fuel Flow (FF) and P3-T3 methods for estimating NOx Emission Index (EINOx), since the early design phase. These estimation techniques currently enable the calculation of the emission index of pollutants and GreenHouse Gases (GHGs) produced by any subsonic aero-engines powered by traditional fuels (i.e. kerosene) only. The methodology presented in this paper allows for upgrading the original mathematical formulations of the methods to ensure the applicability to high-speed flight and hydrogen fuel conditions. This involves the propulsive modelling of the engine and the chemical-kinetic modelling of the combustion process, representative of various on-ground and in-flight operating conditions, to generate updated and reliable propulsive and emissive databases for the engine. To this end, 0D chemical-kinetic air/hydrogen combustion numerical simulations are employed. The results of the performance analysis and emission modeling of the SABRE engine are reported and discussed. Regarding the calculated EINOx trend for the air-breathing phase of the engine operation, it reaches a peak of 45 gNOx/kgH2burnt under hypersonic conditions. Through the analysis of correlations between the propulsive characteristics of the SABRE and its NOx production, a series of parameters (such as Mach Number, Fuel to Air Ratio, Water to Fuel Ratio, and others) are selected to be integrated into the original formulations of the P3-T3 and FF methods via mathematical interpolation to adapt them to the case study. Finally, the EINOx trends resulting from the application of the new H2-P3T3 and H2-FF methods to the SABRE are graphically reported, along with the corresponding estimation errors relative to the reference trend from the engine emission database. Additionally, a discussion regarding the chemical-physical justifications for the mathematical contributions to the formulations for EINOx of the newly introduced parameters is provided.

Preparation for CSST: Star-galaxy Classification using a Rotationally Invariant Supervised Machine Learning Method

Most existing star-galaxy classifiers depend on the reduced information from catalogs, necessitating careful data processing and feature extraction. In this study, we employ a supervised machine learning method (GoogLeNet) to automatically classify stars and galaxies in the COSMOS field. Unlike traditional machine learning methods, we introduce several preprocessing techniques, including noise reduction and the unwrapping of denoised images in polar coordinates, applied to our carefully selected samples of stars and galaxies. By dividing the selected samples into training and validation sets in an 8:2 ratio, we evaluate the performance of the GoogLeNet model in distinguishing between stars and galaxies. The results indicate that the GoogLeNet model is highly effective, achieving accuracies of 99.6% and 99.9% for stars and galaxies, respectively. Furthermore, by comparing the results with and without preprocessing, we find that preprocessing can significantly improve classification accuracy (by approximately 2.0% to 6.0%) when the images are rotated. In preparation for the future launch of the China Space Station Telescope (CSST), we also evaluate the performance of the GoogLeNet model on the CSST simulation data. These results demonstrate a high level of accuracy (approximately 99.8%), indicating that this model can be effectively utilized for future observations with the CSST.

Towards robotic on-orbit assembly of large space telescopes: Mission architectures, concepts, and analyses

Imaging systems larger than the James Webb Space Telescope will enable unprecedented astronomy in the future. Larger aperture space-based imagers will also support next-generation Earth Observation missions for national security and disaster monitoring. However, launching and operating such large telescopes in the poses several practical challenges. A key challenge is that mirrors with apertures larger than 3 m cannot be monolithically manufactured so a segmented design is utilized to achieve primary mirror apertures. Even if larger mirrors could be made, it would be impossible to stow them in fairings of most existing and future launch vehicles. Folded-wing designs to deploy segmented primary mirrors, as done with the James Webb Space Telescope, are one approach to overcoming this volumetric challenge but inappropriate for 25 m apertures considered in this study. This paper presents the concept of operations and mission architectures/analysis for on-orbit assembly of a 25 m aperture telescope operating in the visible waveband of the electromagnetic spectrum capable of 1 m spatial resolution from geostationary orbit. Further, a technology demonstration roadmap towards maturing the robotic assembly technology stack is then presented as precursors to the 25 m imaging system.

Plume and wall temperature impact on the subsonic aft-body flow of a generic space launcher geometry

Experimental and numerical simulation of launcher base flows are crucial for future launcher design. In experiments, exhaust plume simulation is often limited to cold or slightly heated gases. In numerical simulations, multi-species reactive flow is often neglected due to limited resources. The impact of these simplifications on the relevant flow features, compared to real flight scenarios, needs to be characterized in order to enhance the design process. Experimental and numerical investigations were carried out within the framework of the SFB/TRR 40 Collaborative Research Centre to study the impact of plume and wall temperature on the base flow of a generic small-scale launcher configuration. Wind tunnel tests were performed in the Hot Plume Testing Facility (HPTF) at DLR, Cologne, using subsonic ambient flow and pressurized air or hydrogen–oxygen combustion as exhaust gases. The tests were numerically rebuilt using the DLR TAU code employing a scale-resolved IDDES approach, including thermal coupling and detailed chemistry. The paper combines the experimental and numerical findings from the SFB/TRR 40 base flow studies and highlights the most prominent influences on the mean flow field, the pressure field, the dynamic wake flow motion, and the resulting aerodynamic forces on the nozzle. High-frequency pressure measurements, high-speed schlieren recordings, and time-resolved CFD results are evaluated using spectral and modal analysis of the one- and two-dimensional flow field data.

CubeSats for space debris removal from LEO: Prototype design of a robotic arm-based deorbiter CubeSat

Space debris has become a growing problem, and various studies indicate that the population of space objects has reached a critical density in low Earth orbits (LEO). Therefore, removing debris from the LEO is key to stabilizing the growth of space objects and making some of the orbits reusable for future launches. Deorbiting larger debris objects using miniaturized satellites, CubeSats is both economically viable and requires relatively less time for design & deployment. In this paper, we present a system-level study of orbital debris removal from LEO using CubeSats, detailing various existing debris tracking, rendezvous, capture, and deorbit mechanisms. A deep learning-based object detection algorithm has been utilized to improve the accuracy of detecting and localizing debris. We also present a discussion on the CubeSat-compatible capture & deorbit mechanisms identified from our study and an upper bound on the debris mass that can be deorbited from a given LEO orbit using CubeSats. We aim to demonstrate a case study for the orbital decay analysis of a CubeSat deorbiting the XSat, a microsatellite launched by Singapore. We detail the design of a 27U CubeSat deorbiter prototype equipped with a pair of robotic arms to capture the XSat. This analysis considers active deorbiting using Hall-effect thrusters, for which the required delta-V & propellant mass have also been computed.

Experimental Investigation on Transmission of Sound in To Metallic Enclosures During Static Firing Tests Of Solid Rocket Motor

Satellite launch vehicle during lift-off generates intense dynamic environment mainly the acoustic noise, which in turn acts on the nearby launch pad systems. Reliable operation of these launch pad ground systems needs to be ensured by safeguarding against these dynamic loads with adequate protection measures. For future launch vehicle programs where the launch turnaround time is a crucial aspect, sensitive systems like ground check-out electronic packages are planned to be placed adjacent to the launch vehicle mounting pedestal on the launch pad to ensure minimal time for preparation. The enclosures housing these electronic packages needs to be configured to minimize the transmission of acoustic levels being imposed from the lift-off induced noise. In the present work, an experimental investigation is carried out on acoustic transmission characteristics of metallic enclosures of different material and thickness during the static firing tests of solid rocket motors. Acoustics generated from the supersonic jet noise has a wide frequency band from 100 to 10,000 Hz with sound power peaking near 1,000 Hz. The electronic components and packages, which are generally vulnerable to the high frequency noise, need to be protected from this lift-off induced acoustics. The enclosures are placed in the far field of this jet noise and the transmission loss measurements are obtained. Variation of transmission loss with material and thickness of the enclosures are studied with specific attention to the internal acoustic modes and the coincidence phenomena. Subsequently, the enclosures treated with acoustic blankets of different thickness are investigated to verify its influence on internal acoustic modes and also the additional reduction in transmission of sound in the dominant frequency band of supersonic jet noise.

ARMA time series prediction model for fault detection of launch vehicle

Spaceflight is a high-risk activity, especially for launch vehicles, and the losses caused by launch failures are considerable. Real-time fault detection is a prerequisite to ensure that future launch vehicles can detect and isolate faults in a timely manner, and ultimately reduce the hazard of fault. In this article, a time series prediction model of axial overload and three-axis angular rate is established using ARMA (Auto Regressive Moving Average) time series prediction model, and the residuals of the prediction model are analyzed and combined with the fault determination decision strategy to determine whether the system has a fault, and a fault tree can be built to further locate the fault location.

Density-based evolutionary model of the space debris environment in low-Earth orbit

Lethal untrackable debris objects pose the highest risk to the sustainability of the space environment, and thus, shall be included in the assessment of the long-term effect of mitigation and remediation measures to the space debris problem. The introduction of centimetre-sized particles in the debris evolutionary models represents a challenge from a computational cost point of view. To answer this need, this work proposes a novel probabilistic debris environment propagator. The method classifies the objects population into intact objects and fragmentation debris. The evolution of the former population is retrieved through an individual definition of each object’s mission profile. A continuum approach is adopted for the characterisation of the fragments, whose density distribution in orbital elements is propagated in time through the continuity equation. The intrinsic computational efficiency of the density-based fragments cloud models is leveraged to make the method agnostic to the lowest fragments size considered. A second classification of the population of intact objects into species, such as payloads, rocket bodies, mission related objects and constellations, ensures a faithful replication of their orbit evolution. Fragmentation debris caused by intact objects explosion and accidental fragments-intact object collision are included in a probabilistic fashion at the detected fragmentation epoch, to account for their feedback effect onto the environment. The model is applied to estimate the evolution of the space debris population in low-Earth orbit up to 200 years from the reference epoch, with and without the inclusion of a future launch traffic pattern, and considering a different fulfilment of the post-mission disposal phase.

NOx emissions estimation methodology for air-breathing reusable access to space vehicle in conceptual design

With an expected global revenue exceeding 1 trillion USD by 2040, the space industry is one of the world's fastest-growing sectors. Given the booming investment in the space industry and the anticipated space tourism era, it is crucial to assess the impact of already operative launch assets as well as to adopt design-to-sustainability strategies for the under-development and future launchers. This paper discloses an integrated methodology to estimate nitrogen oxides emissions of a hydrogen-fuelled air-breathing concept. The proposed strategy combines multi-fidelity propulsive system modelling with 0D chemical-kinetic air/hydrogen combustion numerical simulations to define a high-fidelity emissive database representative of various on-ground and in-flight operative conditions. The propulsive and emissive databases are then used to suggest an analytical formulation able to predict nitrogen oxides emission indices in any in-flight conditions, perfectly fitting the conceptual design needs. Throughout the paper, the Skylon spaceplane and its Synergetic Air Breathing Rocket Engine are used as case study. The methodology disclosed allows proving the high competitiveness of this air-breathing space launchers with respect to famous past and current competitors, as the Space Shuttle and the Falcon 9.

Cost estimation for launch vehicle families considering uncertain market scenarios

When optimizing the staging of a launch vehicle, usually a reference mission with a fixed payload mass is used to determine the specific launch cost (or a surrogate thereof, e.g. total mass or dry mass). While this approach neglects suboptimal loading, it delivers sufficiently good results for the evaluation of an individual launcher if the design mission is well known. However, this approach does not capture the full range of payloads that a launch vehicle is expected to launch during its operational lifetime. In order to estimate the cost and subsequently optimize the staging of such launch vehicle families, a full launch scenario has to be considered. The cost of an individual launch vehicle loses importance and the deciding factor is the total cost associated with launching the specified scenario. However, estimates of the future market inevitably include large uncertainties. These uncertainties and their impact are ideally considered and quantified when assessing the specific recurring cost of a single launch vehicle. The consideration of a whole launch market scenario is especially relevant when evaluating a family of launch vehicles, an option currently being discussed for future European launchers. In this case, the distribution of the payloads onto the different members of the launch vehicle family has to be optimized in order to reach the lowest possible cost. Within this manuscript, a method that allows the determination of the recurring cost of such a launcher family based on a launch market scenario including uncertainties is proposed. Based on the launch numbers derived from the combinatorial optimization, parametric cost models for recurring and non-recurring cost are adapted and applied to three possible future launch vehicle families.

Establishing the requirements for safe rocket launches with respect to weather

Space Concordia is launching the biggest student built Canadian rocket with the aim of passing the Karman Line. Launching from Churchill, Manitoba, a former military airbase used in the 1950s for suborbital sounding rocket launches, Space Concordia’s ambition is to reinstate the zone as a launch site. Being the first launch out of Churchill in decades, safety is mandatory and must follow the established safety protocols and regulations for suborbital launch vehicles. Space Concordia must structure its own guidelines and requirements leveraging expertise from the Federal Aviation Administration and various agreements with local authorities. To ensure safety, the Flight Performance team runs wind weighting optimization algorithms using Astos, a 6 degree of freedom flight simulation software utilizing wind data from a weather balloon. Launch simulations in Astos are done using historical wind data through a combination of Python and A programming and numeric computing software (MATLAB) analysis scripts. Casualty expectation plays a significant role in the launch safety analysis since the launch site is situated next to a national park and multiple small communities. The probability of casualty is calculated to be 2.61E−8, which is smaller than the 30E-6 Federal Aviation Administration requirement. Establishing a strict set of guidelines while building a rocket creates many difficulties in terms of both design and regulations, however they permit a clearer structure for future launches made by Space Concordia as well as augment predefined understanding of the weather effect on Churchill suborbital rocket launches.

An Augmented Reality Mobile App (Easypod AR) as a Complementary Tool in the Nurse-Led Integrated Support of Patients Receiving Recombinant Human Growth Hormone: Usability and Validation Study

Children with growth hormone deficiency face the prospect of long-term recombinant human growth hormone (r-hGH) treatment requiring daily injections. Adherence to treatment is important, especially at treatment initiation, to achieve positive health outcomes. Historically, telenursing services embedded in patient support programs (PSPs) have been a valid approach to support r-hGH treatment initiation and patient education and facilitate adherence by identifying and optimizing appropriate injection techniques. The development of mobile phones with augmented reality (AR) capabilities offers nurses new tools to support patient education. To investigate experiences among nurses of a new mobile phone app developed to support patient training with a phone-based PSP for r-hGH treatment. In 2020, the Easypod AR mobile app was launched to support nurse-driven telehealth education for patients initiating r-hGH therapy with the Easypod electromechanical auto-injector device. Nurses who were part of PSPs in countries where the Easypod AR app had been launched or where training was provided as part of an anticipated future launch of the app were invited to participate in an online survey based on the Mobile App Rating Scale to capture their feedback after using the app. In total, 23 nurses completed the online questionnaire. They positively rated the quality of the app across multiple dimensions. The highest mean scores were 4.0 for engagement (ie, adaptation to the target group; SD 0.74), 4.1 (SD 0.79) for functionality (navigation) and 4.1 (SD 0.67) for aesthetics (graphics). Responses indicated the potential positive impact of such a tool on enhancing patient education, patient support, and communication between patients and PSP nurses. Some participants also suggested enhancements to the app, including gamification techniques that they felt have the potential to support the formation of positive treatment behaviors and habits. This study highlights the potential for new digital health solutions to reinforce PSP nurse services, including patient education. Future studies could explore possible correlations between any behavioral and clinical benefits that patients may derive from the use of such apps and how they may contribute to support improved patient experiences and treatment outcomes.

Fair Housing: A Blueprint for Equity in Life: Proceedings from the 2022-2023 High School Big Data ChallengeUnder the patronage of Canadian Commission for UNESCO

In the STEM Fellowship High School Big Data Challenge, students have the opportunity to engage in independent research projects and acquire fundamental data science skills – an essential skill set for a young researcher in the digital age. The program is inquiry-driven and experiential. This year, we invited students to explore issues of Fair Housing at the Individual and Community Levels and to suggest their own evidence-based solutions, using Open Data and the principles of Open Science. Students explored many topics, ranging from a New Framework for Public Rental Housing in Toronto to A Statistical Analysis on Thawing Permafrost and Its Effects on Housing. We designed in-depth learning modules for students as a means of bridging the gap between traditional high school courseware and digital reality and computational science. Students learned how to uncover hidden patterns and trends in structured and unstructured data using a range of data analytics tools and programming languages. Python, R, LaTeX, and machine learning were some of the tools the students learned and used. On behalf of the STEM Fellowship, we extend our sincere congratulations to all students who participated in the challenge, and wish them the best for their future endeavours. We want to express our appreciation to all the mentors and volunteers. This program would not be possible without patronage of CC UNESCO and generous support of our sponsors: RBC Future Launch, Let’s Talk Science, Digital Science, Infor, SCWST, CISCO Networking Academy, Canadian Science Publishing, and the University of Calgary Hunter Hub for Entrepreneurial Thinking. We were privileged to witness first-hand the analytical capabilities of the data-native generation of students, and we are confident they will demonstrate excellence throughout their academic and professional careers.

Stochastic stability of orbital flux

The prediction of the environment in space and the description of its characteristics as well as the use of space debris model data to provide guidance for future launch activities to maintain the long-term sustainability of space have always been important objectives of space debris model use and construction. Because space debris model construction is accompanied by extensive randomness, the stochastic analysis method is adopted in this paper to model the flux data of the MASTER model through the population model. On this basis, stochastic terms are added for further analysis, and different stability classifications are given from the perspective of probability measures to distinguish flux stabilities that are difficult to distinguish by ordinary differential equations. The corresponding characteristics and future launch effects are also quantified. Finally, three different cases, fast convergence, slow convergence and long-term oscillation, are presented.

The roar of the rocket: A hot topics discussion

An increasing number of companies and countries are building and launching space vehicles of different sizes and configurations for diverse purposes. The goal of this Hot Topics presentation is two-fold: first, to introduce a wide-ranging audience of Acoustical Society of America members to principles of rocket noise radiation, propagation, and reception; second, to discuss needs and challenges related to future launch vehicle acoustics research. If attendees leave both mildly entertained and better informed, the presentation will have been a success.

CHES: An astrometry mission searching for nearby habitable planets

The Closeby Habitable Exoplanet Survey (CHES), a proposed space-borne mission to detect Earth-like planets orbiting 100 nearby solar-type stars (∼10 pc or approximately 32 light years from the sun) via micro-arcsecond relative astrometry, is currently being considered by the Chinese Academy of Sciences as a possible space mission for future launch (Figure 1). The discovery of Earth-like planets (or Earth twins, planets with an orbit, mass, and environment similar to Earth) in the habitable zones around nearby solar-type stars will be another “giant leap for mankind” and help us begin to answer essential scientific questions such as “Are we alone in the universe?”, “Is Earth unique?”, and “How do planets become the cradle of life?” Finding such planets could even enable future human visits and identify new habitable places to live.

Energy efficient large-scale storage of liquid hydrogen

The world’s largest liquid hydrogen storage tanks were constructed in the mid-1960s at the NASA Kennedy Space Center. These two vacuum-jacketed, perlite powder insulated tanks, still in service today, have 3,200 m3 of useable capacity. In 2018, construction began on an additional storage tank at Launch Complex 39B. This new tank will give an additional storage capacity of 4,700 m3 for a total on-site storage capacity of roughly 8,000 m3. NASA’s new Space Launch System (SLS) heavy lift rocket for the Artemis program includes an LH2 tank that makes up the bulk of the vehicle, holding 2,033 m3 of LH2 in its 8.4-m diameter by 40-m height. The new storage tank includes two new energy-efficient technologies: a glass bubbles insulation system in lieu of perlite, and an Integrated Refrigeration and Storage (IRAS) heat exchanger for controlled storage capability. The evacuated glass bubbles insulation system is based on the prior two decades of research to prove the thermal performance benefits as well as the mechanical and vacuum integrity; and has been shown to reduce LH2 boiloff by 46% versus perlite in field demonstrations. The IRAS capability is centered on a heat exchanger system that is built within the inner vessel to reject heat from the bulk liquid through the future implementation of an external helium refrigerator. Controlled storage via IRAS, when fully implemented, will provide full control of the ullage pressure, zero boiloff, and even production of densified LH2 pending its adoption on future launch vehicles. The design basics are described along with main construction and testing processes involved. The key features of the new technology items and implications on simplified operations and long-term energy savings are addressed.

Myths, misconceptions, and hesitancy in people residing in Qatar toward mRNA COVID-19 vaccines: An experience exchange from Qatar University health center.

The hesitancy in taking COVID-19 vaccines is a complex process influenced by several factors, including individual, social, and cultural. Health literacy and community awareness around mRNA COVID-19 vaccines are critical for successfully combating the pandemic. Healthcare professionals, including family physicians and nurses, can help increase community awareness and mitigate some misconceptions and hesitancy regarding mRNA COVID-19 vaccines in people's attitudes. Therefore, in this study, we aimed to explore how the interaction between an individual's social identities such as gender, ethnicity, culture, knowledge, and belief impact their hesitancy and attitudes toward mRNA COVID-19 vaccines. We aimed to describe our experience in dealing with people residing in Qatar from the perspective of healthcare practitioners from the Qatar University Health Center during the period when mRNA COVID-19 vaccines was introduced in a time frame of 6 months (April to October, 2021).We identified several factors associated with the reluctance to receive mRNA COVID-19 vaccines once vaccination services were available, affordable, and accessible to everyone in Qatar (Table 1). Most individuals were hesitant and refused to take mRNA COVID-19 vaccines owing to the unjustified myths and fear about potential side effects of vaccines in general and unknown long-term effects of vaccination, especially among women who were uneducated. We believe we have been able to put forth a fair, unbiased, and balanced argument between an individual's right to take or refuse the vaccine and the overall benefits to the public and community health in terms of the overall community immunity when the vast majority of the population will be vaccinated. Our experience could assist in developing culturally sensitive and tailored community outreach programs to increase community awareness as it is the cornerstone on which public health can fight the irrational myths, fear, misconceptions, vaccine hesitancy, and improve vaccination coverages. Moreover, our shared experiences might be able to better prepare future launching of pandemic vaccination campaigns in order to minimize vaccine hesitancy.

Changing the sources and usage of energy for a better and sustainable future for all: Proceedings from the 2021-2022 High School Big Data Challenge

STEM Fellowship’s High School Big Data Challenge is an inquiry-driven experiential learning program that provides students an opportunity to learn and apply the fundamentals of data science – a crucial skill set for a young researcher in the digital age – through independent research projects. The COVID-19 pandemic disrupted high school education, at the same time creating a “fertile ground” for interdisciplinary, student-driven STEM education. This year, we invited students to explore issues of Affordable and Clean Energy at the Individual and Community Levels and to suggest their own evidence-based solutions, using Open Data and the principles of Open Science. Students explored many topics, ranging from Greenhouse Gas Emissions of School Buses to Legitimacy of Electric Vehicles to be the Greener Alternative We developed in-depth learning modules designed to bridge the gap between traditional high school courseware and digital reality and computational science. The students learnt a broad range of data analytics tools and programming languages which are useful for uncovering hidden patterns, trends in structured and unstructured data. Some of the tools the students learnt and used include Python, R, LaTeX, and machine learning. On behalf of the STEM Fellowship, we extend our sincere congratulations to all students who participated in the challenge, and wish them the best for their future endeavours. We want to express our appreciation to all the mentors and volunteers. This program would not be possible without patronage of CC UNESCO and generous support of our sponsors: RBC Future Launch, Let’s Talk Science, Digital Science, Infor, SCWST, CISCO Networking Academy, Canadian Science Publishing, and the University of Calgary Hunter Hub for Entrepreneurial Thinking. It has been a privilege for us to witness the analytical capabilities of the data-native generation of students first hand, and we are certain all entrants will continue to demonstrate excellence in their respective academic and professional careers.