1,517 research outputs found

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    The Active CryoCubeSat Technology: Active Thermal Control for Small Satellites

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    Modern CubeSats and Small Satellites have advanced in capability to tackle science and technology missions that would usually be reserved for more traditional, large satellites. However, this rapid growth in capability is only possible through the fast-to-production, low-cost, and advanced technology approach used by modern small satellite engineers. Advanced technologies in power generation, energy storage, and high-power density electronics have naturally led to a thermal bottleneck, where CubeSats and Small Satellites can generate more power than they can easily reject. The Active CryoCubeSat (ACCS) is an advanced active thermal control technology (ATC) for Small Satellites and CubeSats, which hopes to help solve this thermal problem. The ACCS technology is based on a two-stage design. An integrated miniature cryocooler forms the first stage, and a single-phase mechanically pumped fluid loop heat exchanger the second. The ACCS leverages advanced 3D manufacturing techniques to integrate the ATC directly into the satellite structure, which helps to improve the performance while simultaneously miniaturizing and simplifying the system. The ACCS system can easily be scaled to mission requirements and can control zonal temperature, bulk thermal rejection, and dynamic heat transfer within a satellite structure. The integrated cryocooler supports cryogenic science payloads such as advanced LWIR electro-optical detectors. The ACCS hopes to enable future advanced CubeSat and Small Satellite missions in earth science, heliophysics, and deep space operations. This dissertation will detail the design, development, and testing of the ACCS system technology

    Copernicus Cal/Val Solution - D3.2 - Recommendations for R&D on Cal/Val Methods

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    This document presents a gap analysis of the methods used in the calibration and validation of Earth Observation satellites relevant to the Copernicus programme and suggests recommendations for the research and developments required to fulfil this gap when/where possible. The document identifies the gaps and limitations of the CalVal methods, used for calibration and validation (CalVal) activities for the current Copernicus missions. It will also address the development needs for future Copernicus missions. Four types of missions are covered based on the division used in the rest of the CCVS project: optical, altimetry, radar and microwave and atmospheric composition. Finally, it will give a prioritized list of recommendations for R&D activities on the CalVal methods. The information included is mainly collected from the deliverables of work packages 1 and 2 in the CCVS project and from the consortium experts in CalVal activities

    Use of machine learning and principal component analysis to retrieve nitrogen dioxide (NO<sub>2</sub>) with hyperspectral imagers and reduce noise in spectral fitting

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    Nitrogen dioxide (NO2) is an important trace-gas pollutant and climate agent whose presence also leads to spectral interference in ocean color retrievals. NO2 column densities have been retrieved with satellite UV–Vis spectrometers such as the Ozone Monitoring Instrument (OMI) and the Tropospheric Monitoring Instrument (TROPOMI) that typically have spectral resolutions of the order of 0.5 nm or better and spatial footprints as small as 3.6 km × 5.6 km. These NO2 observations are used to estimate emissions, monitor pollution trends, and study effects on human health. Here, we investigate whether it is possible to retrieve NO2 amounts with lower-spectral-resolution hyperspectral imagers such as the Ocean Color Instrument (OCI) that will fly on the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite set for launch in early 2024. OCI will have a spectral resolution of 5 nm and a spatial resolution of ∌ 1 km with global coverage in 1–2 d. At this spectral resolution, small-scale spectral structure from NO2 absorption is still present. We use real spectra from the OMI to simulate OCI spectra that are in turn used to estimate NO2 slant column densities (SCDs) with an artificial neural network (NN) trained on target OMI retrievals. While we obtain good results with no noise added to the OCI simulated spectra, we find that the expected instrumental noise substantially degrades the OCI NO2 retrievals. Nevertheless, the NO2 information from OCI may be of value for ocean color retrievals. OCI retrievals can also be temporally averaged over timescales of the order of months to reduce noise and provide higher-spatial-resolution maps that may be useful for downscaling lower-spatial-resolution data provided by instruments such as OMI and TROPOMI; this downscaling could potentially enable higher-resolution emissions estimates and be useful for other applications. In addition, we show that NNs that use coefficients of leading modes of a principal component analysis of radiance spectra as inputs appear to enable noise reduction in NO2 retrievals. Once trained, NNs can also substantially speed up NO2 spectral fitting algorithms as applied to OMI, TROPOMI, and similar instruments that are flying or will soon fly in geostationary orbit.</p

    BDS GNSS for Earth Observation

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    For millennia, human communities have wondered about the possibility of observing phenomena in their surroundings, and in particular those affecting the Earth on which they live. More generally, it can be conceptually defined as Earth observation (EO) and is the collection of information about the biological, chemical and physical systems of planet Earth. It can be undertaken through sensors in direct contact with the ground or airborne platforms (such as weather balloons and stations) or remote-sensing technologies. However, the definition of EO has only become significant in the last 50 years, since it has been possible to send artificial satellites out of Earth’s orbit. Referring strictly to civil applications, satellites of this type were initially designed to provide satellite images; later, their purpose expanded to include the study of information on land characteristics, growing vegetation, crops, and environmental pollution. The data collected are used for several purposes, including the identification of natural resources and the production of accurate cartography. Satellite observations can cover the land, the atmosphere, and the oceans. Remote-sensing satellites may be equipped with passive instrumentation such as infrared or cameras for imaging the visible or active instrumentation such as radar. Generally, such satellites are non-geostationary satellites, i.e., they move at a certain speed along orbits inclined with respect to the Earth’s equatorial plane, often in polar orbit, at low or medium altitude, Low Earth Orbit (LEO) and Medium Earth Orbit (MEO), thus covering the entire Earth’s surface in a certain scan time (properly called ’temporal resolution’), i.e., in a certain number of orbits around the Earth. The first remote-sensing satellites were the American NASA/USGS Landsat Program; subsequently, the European: ENVISAT (ENVironmental SATellite), ERS (European Remote-Sensing satellite), RapidEye, the French SPOT (Satellite Pour l’Observation de laTerre), and the Canadian RADARSAT satellites were launched. The IKONOS, QuickBird, and GeoEye-1 satellites were dedicated to cartography. The WorldView-1 and WorldView-2 satellites and the COSMO-SkyMed system are more recent. The latest generation are the low payloads called Small Satellites, e.g., the Chinese BuFeng-1 and Fengyun-3 series. Also, Global Navigation Satellite Systems (GNSSs) have captured the attention of researchers worldwide for a multitude of Earth monitoring and exploration applications. On the other hand, over the past 40 years, GNSSs have become an essential part of many human activities. As is widely noted, there are currently four fully operational GNSSs; two of these were developed for military purposes (American NAVstar GPS and Russian GLONASS), whilst two others were developed for civil purposes such as the Chinese BeiDou satellite navigation system (BDS) and the European Galileo. In addition, many other regional GNSSs, such as the South Korean Regional Positioning System (KPS), the Japanese quasi-zenital satellite system (QZSS), and the Indian Regional Navigation Satellite System (IRNSS/NavIC), will become available in the next few years, which will have enormous potential for scientific applications and geomatics professionals. In addition to their traditional role of providing global positioning, navigation, and timing (PNT) information, GNSS navigation signals are now being used in new and innovative ways. Across the globe, new fields of scientific study are opening up to examine how signals can provide information about the characteristics of the atmosphere and even the surfaces from which they are reflected before being collected by a receiver. EO researchers monitor global environmental systems using in situ and remote monitoring tools. Their findings provide tools to support decision makers in various areas of interest, from security to the natural environment. GNSS signals are considered an important new source of information because they are a free, real-time, and globally available resource for the EO community

    investigating ice microphysical processes by combining multi-frequency and polarimetric Doppler radar observations with Lagrangian Monte-Carlo particle modelling

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    Clouds and precipitation strongly impact society and the earth system by influencing the water cycle, determining fresh water availability or causing natural disasters such as floods or droughts. However, many aspects of precipitation formation are still poorly understood, causing large uncertainties in the prediction of precipitation. Especially the microphysical processes, which describe the nucleation of cloud particle and their growth into precipitation lack understanding. As globally 63% of precipitation originates from the ice phase, increasing the understanding of ice microphysical processes is crucial to improve precipitation forecast. The dendritic growth layer (DGL), located at temperatures between −20 and −10 ° C, plays an important role in the formation of precipitation. Previous studies have found an in particle size and number concentration through depositional growth, aggregation and secondary ice processes. This dissertation investigates ice microphysical processes in the DGL by combining polarimetric and multi-frequency Doppler cloud radar observations with Monte-Carlo Lagrangian particle modelling. Study I presents a statistical analysis of a three-month polarimetric and multi-frequency Doppler radar dataset. This combination of radar measurements allows to observe the full evolution of ice particle growth, as the polarimetric measurements are indicators of depositional growth and possible secondary ice processes, while the multi-frequency approach gives an indication of the increase particle in size through aggregation and riming. The statistical analysis revealed an increase of aggregate size at −15 ° C. The mean size of aggregates is found to be correlated to an updraft with a maximum of approximately 0.1 m s −1 at −14 ° C. The radar observations further indicate the growth of plate-like ice crystals at −15 ° C. Unexpectedly, aggregation is found to increase in the DGL alongside an increase in ice particle number concentration. This simultaneous increase necessitates a source of new ice particles, as aggregation is expected to decrease the total number of ice particles. Secondary ice processes, such as collisional fragmentation provide one explanation for this increase in ice particle size. Another possible explanation might be that small ice particles sediment from colder temperatures into the DGL and enhance the number concentration locally. The third explanation is linked to the observed updraft, as this updraft might increase the super-saturation with respect to ice at −15 ° C, leading to the activation of ice nucleating particles and a subsequent increase in ice particle number and growth of plate-like particles. Unfortunately, radar observations do not observe the formation of particles directly, it is difficult to predict the origin of the particles responsible for the increase in particle concentration and observed polarimetric signatures further. With the observational dataset as a constrain, Study II uses the Monte-Carlo Lagrangian particle model McSnow to investigate the origin of the increase in ice particle number concentration in the DGL further. The comparison of the observations and McSnow simulations indicate that the particles responsible for the polarimetric signatures and increase in number concentration need to be nucleated at temperatures close to −15 ° C. This might indicate that in the observed clouds, sedimenting ice particles into the DGL play a lesser role. The McSnow simulations further indicate that neither collisional fragmentation nor new ice particles due to activation of ice nucleating particles can explain the observed multi-frequency and polarimetric observations. A combination of both processes might explain the observed signatures. This dissertation shows the potential of a combination of radar observations and modelling for increasing the understanding of microphysical processes in clouds. However, further laboratory studies are needed in order to further constrain the processes in the DGL and validate the findings of this dissertation

    Plasmon-exciton coupling for signal amplification and biosensing : fundamentals and application

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    Surface plasmon resonance (SPR) is the collective oscillation of frequency-matched free-space photons and surface electrons at a metal/dielectric interface. Their inherent sensitivity to refractive index changes and ability to couple with exciton species and enhance light-matter interaction make them ideal candidates for low-concentration analyte detection compared to conventional biosensors. The use of metal nanostructures and nanomaterials to excite SPR represents the current state-of-the-art. However, the challenges associated with repeatable synthesis of uniform nanomaterials, complex nanostructure fabrication, low SPR generation efficiency and limited understanding of the mechanism of plasmon-exciton coupling for signal amplification have motivated the search for alternative architectures and procedures. The uniform and repeatable gold nanoslit (NS) and nanoledge (NL) array architectures offers a promising route towards addressing the above issues, and hence this research attempts to take advantage of these platforms to achieve efficient SPR generation and exciton coupling for biosensing applications. The overarching scope of this dissertation extends to the design, fabrication, and optimization of metal NS and NL structures for SPR generation and sensing applications. Emphasis is placed on investigating the mechanism of optical signal enhancement arising from plasmon-exciton coupling (PEC) with particular focus on (a) exploring the role of geometry and size of the nanostructures (b) examining the influence of SPR spectral mode overlap with exciton’s absorption and/or emission energies on the overall optical signal in a NS or NL system, and (c) investigating the analytical sensitivity and signal transduction of the PEC system to biomolecular interactions. The nanoimprinting technique based on soft lithography for NS fabrication, which is used in this work for NS array fabrication, required addressing a critical issue, namely PDMS diffusion into nanoscale patterns for high aspect ratio realization. This was mitigated by curing temperature variation and incubation time to achieve 50 nm-130 nm width NS arrays with an intense, broad spectral response that red-shifts and diminishes with increasing NS width. The 50 nm width structure exhibited ~57× optical enhancement when coupled with acridine orange, a fluorescence dye, whose absorption and emission spectra closely overlaps with plasmonic spectra. A sensitive assay for detecting DNA hybridization was generated using the interaction of the selected SARS-CoV-2 ssDNA and dsDNA with AO to trigger the metachromatic behaviour of the dye to produce a strong optical signal amplification on the formation of AO-ssDNA complex and a quenched signal upon hybridization to the complementary target DNA along with a blue shift in the fluorescence of AO-dsDNA. The SARS-CoV-2 DNA hybridization assay, based on the PEC exhibited 0.21 nM sensitivity to complementary strand target, distinguished 1-, 2-, and 3-base mismatched DNA targets, reusability of ~6 x with 96% signal recovery, stable for up to 10 days at room temperature. Regarding the NL sensing platform, the principle of the sensing mechanism is based on plasmon-mediated extraordinary optical transmission (EOT) whose wavelength red-shifts with increase in refractive index (RI) at near-metal surface. The NL plasmonic-based biosensor fabricated using a patented E-beam writing method exhibited ~ 384.08 nm/RIU sensitivity, limit of detection to cardiac troponin I (TnI) at 0.079 ng/mL, 0.084 ng/mL and 0.097 ng/mL in PBS buffer, human serum, and human blood, respectively. The direct measurement of TnI in whole human blood without any purification or sample preparation step highlights the significance of the sensing platform for point-of-care detection. Thus, this work innovates (a) a tunable SPR to meet the requirement for plasmon-exciton spectral overlap for optical signal amplification, (b) the mechanism of optical enhancements due to PEC in NS arrays, and (c) a new application of PEC in NS and EOT in NL for the sensitive detection of SARS-CoV-2 DNA hybridization and cardiovascular biomarker TnI in human blood, respectively. The enhanced light-matter interactions have a broader impact beyond healthcare to light harvesting for solar cells, heat generation for cancer therapy, and photocatalysis for nanoscale reactions like water splitting

    Satellite based methane emission estimation for flaring activities in oil and gas industry: A data-driven approach(SMEEF-OGI)

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    Klimaendringer, delvis utlĂžst av klimagassutslipp, utgjĂžr en kritisk global utfordring. Metan, en svĂŠrt potent drivhusgass med et globalt oppvarmings potensial pĂ„ 80 ganger karbondioksid, er en betydelig bidragsyter til denne krisen. Kilder til metanutslipp inkluderer olje- og gassindustrien, landbruket og avfallshĂ„ndteringen, med fakling i olje- og gassindustrien som en betydelig utslippskilde. Fakling, en standard prosess i olje- og gassindustrien, antas ofte Ă„ vĂŠre 98 % effektiv ved omdannelse av metan til mindre skadelig karbondioksid. Nyere forskning fra University of Michigan, Stanford, Environmental Defense Fund og Scientific Aviation indikerer imidlertid at den allment aksepterte effektiviteten pĂ„ 98 % av fakling ved konvertering av metan til karbondioksid, en mindre skadelig klimagass, kan vĂŠre unĂžyaktig. Denne undersĂžkelsen revurderer fakkelprosessens effektivitet og dens rolle i metankonvertering. Dette arbeidet fokuserer pĂ„ Ă„ lage en metode for uavhengig Ă„ beregne metanutslipp fra olje- og gassvirksomhet for Ă„ lĂžse dette problemet. Satellittdata, som er et nyttig verktĂžy for Ă„ beregne klimagassutslipp fra ulike kilder, er inkludert i den foreslĂ„tte metodikken. I tillegg til standard overvĂ„kingsteknikker, tilbyr satellittdata en uavhengig, ikke-pĂ„trengende, rimelig og kontinuerlig overvĂ„kingstilnĂŠrming. PĂ„ bakgrunn av dette er problemstillingen for dette arbeidet fĂžlgende "Hvordan kan en datadrevet tilnĂŠrming utvikles for Ă„ forbedre nĂžyaktigheten og kvaliteten pĂ„ estimering av metanutslipp fra faklingsaktiviteter i olje- og gassindustrien, ved Ă„ bruke satellittdata fra utvalgte plattformer for Ă„ oppdage og kvantifisere fremtidige utslipp basert pĂ„ maskinlĂŠring mer effektivt?" For Ă„ oppnĂ„ dette ble fĂžlgende mĂ„l og aktiviteter utfĂžrt. * Teoretisk rammeverk og sentrale begreper * Teknisk gjennomgang av dagens toppmoderne satellittplattformer og eksisterende litteratur. * Utvikling av et Proof of Concept * ForeslĂ„ en evaluering av metoden * Anbefalinger og videre arbeid Dette arbeidet har tatt i bruk en systematisk tilnĂŠrming, som starter med et omfattende teoretisk rammeverk for Ă„ forstĂ„ bruken av fakling, de miljĂžmessige implikasjonene av metan, den nĂ„vĂŠrende «state-of-the-art» av forskning, og «state-of-the-art» i felt for fjernmĂ„ling via satellitter. Basert pĂ„ rammeverket utviklet i de innledende fasene av dette arbeidet, ble det formulert en datadrevet metodikk, som benytter VIIRS-datasettet for Ă„ fĂ„ geografiske omrĂ„der av interesse. Hyperspektrale data og metandata ble samlet fra Sentinel-2 og Sentinel-5P satellittdatasettet. Denne informasjonen ble behandlet via en foreslĂ„tt rĂžrledning, med innledende justering og forbedring. I dette arbeidet ble bildene forbedret ved Ă„ beregne den normaliserte brennindeksen. Resultatet var et datasett som inneholdt plasseringen av kjente fakkelsteder, med data fra bĂ„de Sentinel-2 og Sentinel-5P-satellitten. Resultatene understreker forskjellene i dekningen mellom Sentinel-2- og Sentinel-5P-data, en faktor som potensielt kan pĂ„virke nĂžyaktigheten av metanutslippsestimater. De anvendte forbehandlingsteknikkene forbedret dataklarheten og brukervennligheten markant, men deres effektivitet kan avhenge av fakkelstedenes spesifikke egenskaper og rĂ„datakvaliteten. Dessuten, til tross for visse begrensninger, ga kombinasjonen av Sentinel-2 og Sentinel-5P-data effektivt et omfattende datasett egnet for videre analyse. Avslutningsvis introduserer dette prosjektet en oppmuntrende metodikk for Ă„ estimere metanutslipp fra fakling i olje- og gassindustrien. Den legger et grunnleggende springbrett for fremtidig forskning, og forbedrer kontinuerlig presisjonen og kvaliteten pĂ„ data for Ă„ bekjempe klimaendringer. Denne metodikken kan sees i flytskjemaet nedenfor. Basert pĂ„ arbeidet som er gjort i dette prosjektet, kan fremtidig arbeid fokusere pĂ„ Ă„ innlemme alternative kilder til metan data, utvide interesseomrĂ„dene gjennom industrisamarbeid og forsĂžke Ă„ trekke ut ytterligere detaljer gjennom bildesegmenteringsmetoder. Dette prosjektet legger et grunnlag, og baner vei for pĂ„fĂžlgende utforskninger Ă„ bygge videre pĂ„.Climate change, precipitated in part by greenhouse gas emissions, presents a critical global challenge. Methane, a highly potent greenhouse gas with a global warming potential of 80 times that of carbon dioxide, is a significant contributor to this crisis. Sources of methane emissions include the oil and gas industry, agriculture, and waste management, with flaring in the oil and gas industry constituting a significant emission source. Flaring, a standard process in the Oil and gas industry is often assumed to be 98% efficient when converting methane to less harmful carbon dioxide. However, recent research from the University of Michigan, Stanford, the Environmental Defense Fund, and Scientific Aviation indicates that the widely accepted 98% efficiency of flaring in converting methane to carbon dioxide, a less harmful greenhouse gas, may be inaccurate. This investigation reevaluates the flaring process's efficiency and its role in methane conversion. This work focuses on creating a method to independently calculate methane emissions from oil and gas activities to solve this issue. Satellite data, which is a helpful tool for calculating greenhouse gas emissions from various sources, is included in the suggested methodology. In addition to standard monitoring techniques, satellite data offers an independent, non-intrusive, affordable, and continuous monitoring approach. Based on this, the problem statement for this work is the following “How can a data-driven approach be developed to enhance the accuracy and quality of methane emission estimation from flaring activities in the Oil and Gas industry, using satellite data from selected platforms to detect and quantify future emissions based on Machine learning more effectively?" To achieve this, the following objectives and activities were performed. * Theoretical Framework and key concepts * Technical review of the current state-of-the-art satellite platforms and existing literature. * Development of a Proof of Concept * Proposing an evaluation of the method * Recommendations and further work This work has adopted a systematic approach, starting with a comprehensive theoretical framework to understand the utilization of flaring, the environmental implications of methane, the current state-of-the-art of research, and the state-of-the-art in the field of remote sensing via satellites. Based upon the framework developed during the initial phases of this work, a data-driven methodology was formulated, utilizing the VIIRS dataset to get geographical areas of interest. Hyperspectral and methane data were aggregated from the Sentinel-2 and Sentinel-5P satellite dataset. This information was processed via a proposed pipeline, with initial alignment and enhancement. In this work, the images were enhanced by calculating the Normalized Burn Index. The result was a dataset containing the location of known flare sites, with data from both the Sentinel-2, and the Sentinel-5P satellite. The results underscore the disparities in coverage between Sentinel-2 and Sentinel-5P data, a factor that could potentially influence the precision of methane emission estimates. The applied preprocessing techniques markedly enhanced data clarity and usability, but their efficacy may hinge on the flaring sites' specific characteristics and the raw data quality. Moreover, despite certain limitations, the combination of Sentinel-2 and Sentinel-5P data effectively yielded a comprehensive dataset suitable for further analysis. In conclusion, this project introduces an encouraging methodology for estimating methane emissions from flaring activities within the oil and gas industry. It lays a foundational steppingstone for future research, continually enhancing the precision and quality of data in combating climate change. This methodology can be seen in the flow chart below. Based on the work done in this project, future work could focus on incorporating alternative sources of methane data, broadening the areas of interest through industry collaboration, and attempting to extract further features through image segmentation methods. This project signifies a start, paving the way for subsequent explorations to build upon. Climate change, precipitated in part by greenhouse gas emissions, presents a critical global challenge. Methane, a highly potent greenhouse gas with a global warming potential of 80 times that of carbon dioxide, is a significant contributor to this crisis. Sources of methane emissions include the oil and gas industry, agriculture, and waste management, with flaring in the oil and gas industry constituting a significant emission source. Flaring, a standard process in the Oil and gas industry is often assumed to be 98% efficient when converting methane to less harmful carbon dioxide. However, recent research from the University of Michigan, Stanford, the Environmental Defense Fund, and Scientific Aviation indicates that the widely accepted 98% efficiency of flaring in converting methane to carbon dioxide, a less harmful greenhouse gas, may be inaccurate. This investigation reevaluates the flaring process's efficiency and its role in methane conversion. This work focuses on creating a method to independently calculate methane emissions from oil and gas activities to solve this issue. Satellite data, which is a helpful tool for calculating greenhouse gas emissions from various sources, is included in the suggested methodology. In addition to standard monitoring techniques, satellite data offers an independent, non-intrusive, affordable, and continuous monitoring approach. Based on this, the problem statement for this work is the following “How can a data-driven approach be developed to enhance the accuracy and quality of methane emission estimation from flaring activities in the Oil and Gas industry, using satellite data from selected platforms to detect and quantify future emissions based on Machine learning more effectively?" To achieve this, the following objectives and activities were performed. * Theoretical Framework and key concepts * Technical review of the current state-of-the-art satellite platforms and existing literature. * Development of a Proof of Concept * Proposing an evaluation of the method * Recommendations and further work This work has adopted a systematic approach, starting with a comprehensive theoretical framework to understand the utilization of flaring, the environmental implications of methane, the current state-of-the-art of research, and the state-of-the-art in the field of remote sensing via satellites. Based upon the framework developed during the initial phases of this work, a data-driven methodology was formulated, utilizing the VIIRS dataset to get geographical areas of interest. Hyperspectral and methane data were aggregated from the Sentinel-2 and Sentinel-5P satellite dataset. This information was processed via a proposed pipeline, with initial alignment and enhancement. In this work, the images were enhanced by calculating the Normalized Burn Index. The result was a dataset containing the location of known flare sites, with data from both the Sentinel-2, and the Sentinel-5P satellite. The results underscore the disparities in coverage between Sentinel-2 and Sentinel-5P data, a factor that could potentially influence the precision of methane emission estimates. The applied preprocessing techniques markedly enhanced data clarity and usability, but their efficacy may hinge on the flaring sites' specific characteristics and the raw data quality. Moreover, despite certain limitations, the combination of Sentinel-2 and Sentinel-5P data effectively yielded a comprehensive dataset suitable for further analysis. In conclusion, this project introduces an encouraging methodology for estimating methane emissions from flaring activities within the oil and gas industry. It lays a foundational steppingstone for future research, continually enhancing the precision and quality of data in combating climate change. This methodology can be seen in the flow chart below. Based on the work done in this project, future work could focus on incorporating alternative sources of methane data, broadening the areas of interest through industry collaboration, and attempting to extract further features through image segmentation methods. This project signifies a start, paving the way for subsequent explorations to build upon

    Jupiter science Enabled by ESA's Jupiter Icy Moons Explorer

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    ESA's Jupiter Icy Moons Explorer (JUICE) will provide a detailed investigation of the Jovian system in the 2030s, combining a suite of state-of-the-art instruments with an orbital tour tailored to maximise observing opportunities. We review the Jupiter science enabled by the JUICE mission, building on the legacy of discoveries from the Galileo, Cassini, and Juno missions, alongside ground- and space-based observatories. We focus on remote sensing of the climate, meteorology, and chemistry of the atmosphere and auroras from the cloud-forming weather layer, through the upper troposphere, into the stratosphere and ionosphere. The Jupiter orbital tour provides a wealth of opportunities for atmospheric and auroral science: global perspectives with its near-equatorial and inclined phases, sampling all phase angles from dayside to nightside, and investigating phenomena evolving on timescales from minutes to months. The remote sensing payload spans far-UV spectroscopy (50-210 nm), visible imaging (340-1080 nm), visible/near-infrared spectroscopy (0.49-5.56 Όm), and sub-millimetre sounding (near 530-625 GHz and 1067-1275 GHz). This is coupled to radio, stellar, and solar occultation opportunities to explore the atmosphere at high vertical resolution; and radio and plasma wave measurements of electric discharges in the Jovian atmosphere and auroras. Cross-disciplinary scientific investigations enable JUICE to explore coupling processes in giant planet atmospheres, to show how the atmosphere is connected to (i) the deep circulation and composition of the hydrogen-dominated interior; and (ii) to the currents and charged particle environments of the external magnetosphere. JUICE will provide a comprehensive characterisation of the atmosphere and auroras of this archetypal giant planet
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