Using Satellite Data for CBRN (Chemical, Biological, Radiological, and Nuclear) Threat Detection, Monitoring, and Modelling

Abstract CBRN (Chemical, Biological, Radiological, and Nuclear) threats are becoming more prevalent, as more entities gain access to modern weapons and industrial technologies and chemicals. This has produced a need for improvements to modelling, detection, and monitoring of these events. While there are currently no dedicated satellites for CBRN purposes, there are a wide range of possibilities for satellite data to contribute to this field, from atmospheric composition and chemical detection to cloud cover, land mapping, and surface property measurements. This study looks at currently available satellite data, including meteorological data such as wind and cloud profiles, surface properties like temperature and humidity, chemical detection, and sounding. Results of this survey revealed several gaps in the available data, particularly concerning biological and radiological detection. The results also suggest that publicly available satellite data largely does not meet the requirements of spatial resolution, coverage, and latency that CBRN detection requires, outside of providing terrain use and building height data for constructing models. Lastly, the study evaluates upcoming instruments, platforms, and satellite technologies to gauge the impact these developments will have in the near future. Improvements in spatial and temporal resolution as well as latency are already becoming possible, and new instruments will fill in the gaps in detection by imaging a wider range of chemicals and other agents and by collecting new data types. This study shows that with developments coming within the next decade, satellites should begin to provide valuable augmentations to CBRN event detection and monitoring. Article Highlights There is a wide range of existing satellite data in fields that are of interest to CBRN detection and monitoring. The data is mostly of insufficient quality (resolution or latency) for the demanding requirements of CBRN modelling for incident control. Future technologies and platforms will improve resolution and latency, making satellite data more viable in the CBRN management field

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The Impact of MetOp and Other Satellite Data within the Met Office Global NWP System Using an Adjoint-Based Sensitivity Method

Monthly Weather Review ◽

10.1175/mwr-d-12-00232.1 ◽

2013 ◽

Vol 141 (10) ◽

pp. 3331-3342 ◽

Cited By ~ 55

Author(s):

Sangwon Joo ◽

John Eyre ◽

Richard Marriott

Keyword(s):

Satellite Data ◽

Forecast Error ◽

Weather Prediction ◽

Satellite System ◽

Error Reduction ◽

Forecast Errors ◽

Data Types ◽

Satellite Platform ◽

Atmospheric Sounding ◽

The Impact

Abstract The role of observations in reducing 24-h forecast errors is evaluated using the adjoint-based forecast sensitivity to observations (FSO) method developed within the Met Office global numerical weather prediction (NWP) system. The impacts of various subsets of observations are compared, with emphasis on space-based observations, particularly those from instruments on board the European Organisation for the Exploitation of Meteorological Satellites Meteorological Operational-A (MetOp-A) platform. Satellite data are found to account for 64% of the short-range global forecast error reduction, with the remaining 36% coming from the assimilation of surface-based observation types. MetOp-A data are measured as having the largest impact of any individual satellite platform (about 25% of the total impact on global forecast error reduction). Their large impact, compared to that of NOAA satellites, is mainly due to MetOp's additional sensors [Infrared Atmospheric Sounding Interferometer (IASI), Global Navigation Satellite System (GNSS) Receiver for Atmospheric Sounding (GRAS), and the Advanced Scatterometer (ASCAT)]. Microwave and hyperspectral infrared sounding techniques are found to give the largest total impacts. However, the GPS radio occultation technique is measured as having the largest mean impact per profile of observations among satellite types. This study demonstrates how the FSO technique can be used to assess the impact of individual satellite data types in NWP. The calculated impacts can be used to guide improvements in the use of currently available data and to contribute to discussions on the evolution of future observing systems.

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P-model v1.0: An optimality-based light use efficiency model for simulating ecosystem gross primary production

10.5194/gmd-2019-200 ◽

2019 ◽

Cited By ~ 1

Author(s):

Benjamin D. Stocker ◽

Han Wang ◽

Nicholas G. Smith ◽

Sandy P. Harrison ◽

Trevor F. Keenan ◽

...

Keyword(s):

Primary Production ◽

Satellite Data ◽

Vegetation Type ◽

Meteorological Data ◽

Gross Primary Production ◽

Satellite Monitoring ◽

Light Use Efficiency ◽

Spatial And Temporal Scales ◽

Wide Range ◽

Use Efficiency

Abstract. Terrestrial photosynthesis is the basis for vegetation growth and drives the land carbon cycle. Accurately simulating gross primary production (GPP, ecosystem-level apparent photosynthesis) is key for satellite monitoring and Earth System Model predictions under climate change. While robust models exist for describing leaf-level photosynthesis, predictions diverge due to uncertain photosynthetic traits and parameters which vary on multiple spatial and temporal scales. Here, we describe and evaluate a gross primary production (GPP, photosynthesis per unit ground area) model, the P-model, that combines the Farquhar-von Caemmerer-Berry model for C3 photosynthesis with an optimality principle for the carbon assimilation-transpiration trade-off, and predicts a multi-day average light use efficiency (LUE) for any climate and C3 vegetation type. The model is forced here with satellite data for the fraction of absorbed photosynthetically active radiation and site-specific meteorological data and is evaluated against GPP estimates from a globally distributed network of ecosystem flux measurements. Although the P-model requires relatively few inputs and prescribed parameters, the R2 for predicted versus observed GPP based on the full model setup is 0.75 (8-day mean, 131 sites) – better than some state-of-the-art satellite data-driven light use efficiency models. The R2 is reduced to 0.69 when not accounting for the reduction in quantum yield at low temperatures and effects of low soil moisture on LUE. The R2 for the P-model-predicted LUE is 0.37 (means by site) and 0.53 (means by vegetation type). The P-model provides a simple but powerful method for predicting – rather than prescribing – light use efficiency and simulating terrestrial photosythesis across a wide range of conditions. The model is available as an R package (rpmodel).

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Investigating chemical composition of the troposphere over the Russian Arctic using the "Optik" Tu-134 aircraft laboratory

10.5194/egusphere-egu2020-6795 ◽

2020 ◽

Author(s):

Boris D. Belan ◽

Pavel N. Antokhin ◽

Mikhail Yu. Arshinov ◽

Sergey B. Belan ◽

Denis K. Davydov ◽

...

Keyword(s):

Satellite Data ◽

Land Surface ◽

General Scheme ◽

Water Area ◽

Kara Sea ◽

Atmospheric Composition ◽

The Arctic ◽

Aerosol Composition ◽

The Impact

The need to undertake a comprehensive investigation of the atmospheric composition over the Russian segment of the Arctic is caused by a serious lack and irregularity in obtaining observational data from this regio of the Earth. In addition, a comparison of the aircraft in-situ measurements with satellite data retrieved for the Kara Sea region in 2017 revealed large uncertainties in determining the vertical distribution of greenhouse gas concentrations using remote sensing methods. The development and improvement of the last ones needs at least their periodic verification by means of undertaking precise in-situ aircraft measurements.The general scheme of the proposed experiment is as follows (map is attached): flight from Novosibirsk to Naryan-Mar via Sabetta. From Naryan-Mar, flight to a water area of the Bering Sea (up to 1000 km). Flight from Naryan-Mar to Sabetta. From here, flight to a water area of the Kara Sea (up to 1000 km). Then, flight to Tiksi. Flight from Tiksi to a water area of the Laptev Sea (up to 1000 km). Flight to Chokurdakh or Chersky. From there, flight to a water area of the East Siberian Sea (up to 1000 km). Flight to Cape Schmidt. Flight to a water area of the Chukchi Sea (up to 1000 km). Return route: Cape Shmidt&#8211;Chersky (or Chokurdah)&#8211;Yakutsk&#8211;Bratsk&#8211;Novosibirsk. It will take about 100 hours of flying time to implement the entire aircraft campaign. Campaign period is about 2-3 weeks. It is better to undertake the campaign during summer when the ocean is open. Flights over the land surface are assumed to be undertaken from 0.5 km to 11 km above ground level while above the sea from 0.2 km to 11 km. The flight profile is variable from the maximum possible height to the minimum allowed one. Vertical profiles of gas and aerosol composition will be obtained, including black carbon and organic components, as well as basic meteorological quantities.Satellite data will be verified that do not yet provide acceptable accuracy. For the first time, unique information will be obtained over the least explored region of the Arctic, which is crucial for the whole planet in terms of climate formation and the impact of global warming.

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Development and implementation of a new biomass burning emissions injection height scheme (BBEIH v1.0) for the GEOS-Chem model (v9-01-01)

Geoscientific Model Development ◽

10.5194/gmd-11-4103-2018 ◽

2018 ◽

Vol 11 (10) ◽

pp. 4103-4116 ◽

Cited By ~ 15

Author(s):

Liye Zhu ◽

Maria Val Martin ◽

Luciana V. Gatti ◽

Ralph Kahn ◽

Arsineh Hecobian ◽

...

Keyword(s):

Biomass Burning ◽

Amazon Basin ◽

Transport Model ◽

Meteorological Data ◽

Atmospheric Composition ◽

Chemical Transport Model ◽

Model Driven ◽

Injection Scheme ◽

Large Fires ◽

The Impact

Abstract. Biomass burning is a significant source of trace gases and aerosols to the atmosphere, and the evolution of these species depends acutely on where they are injected into the atmosphere. GEOS-Chem is a chemical transport model driven by assimilated meteorological data that is used to probe a variety of scientific questions related to atmospheric composition, including the role of biomass burning. This paper presents the development and implementation of a new global biomass burning emissions injection scheme in the GEOS-Chem model. The new injection scheme is based on monthly gridded Multi-angle Imaging SpectroRadiometer (MISR) global plume-height stereoscopic observations in 2008. To provide specific examples of the impact of the model updates, we compare the output from simulations with and without the new MISR-based injection height scheme to several sets of observations from regions with active fires. Our comparisons with Arctic Research on the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) aircraft observations show that the updated injection height scheme can improve the ability of the model to simulate the vertical distribution of peroxyacetyl nitrate (PAN) and carbon monoxide (CO) over North American boreal regions in summer. We also compare a simulation for October 2010 and 2011 to vertical profiles of CO over the Amazon Basin. When coupled with larger emission factors for CO, a simulation that includes the new injection scheme also better matches selected observations in this region. Finally, the improved injection height improves the simulation of monthly mean surface CO over California during July 2008, a period with large fires.

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Monitoring emissions from the 2015 Indonesian fires using CO satellite data

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2017.0307 ◽

2018 ◽

Vol 373 (1760) ◽

pp. 20170307 ◽

Cited By ~ 13

Author(s):

Narcisa Nechita-Banda ◽

Maarten Krol ◽

Guido R. van der Werf ◽

Johannes W. Kaiser ◽

Sudhanshu Pandey ◽

...

Keyword(s):

Satellite Data ◽

El Niño ◽

El Nino ◽

Air Pollutant ◽

Satellite Observations ◽

Total Carbon ◽

Atmospheric Composition ◽

Burned Area ◽

Fire Emissions ◽

The Impact

Southeast Asia, in particular Indonesia, has periodically struggled with intense fire events. These events convert substantial amounts of carbon stored as peat to atmospheric carbon dioxide (CO 2 ) and significantly affect atmospheric composition on a regional to global scale. During the recent 2015 El Niño event, peat fires led to strong enhancements of carbon monoxide (CO), an air pollutant and well-known tracer for biomass burning. These enhancements were clearly observed from space by the Infrared Atmospheric Sounding Interferometer (IASI) and the Measurements of Pollution in the Troposphere (MOPITT) instruments. We use these satellite observations to estimate CO fire emissions within an inverse modelling framework. We find that the derived CO emissions for each sub-region of Indonesia and Papua are substantially different from emission inventories, highlighting uncertainties in bottom-up estimates. CO fire emissions based on either MOPITT or IASI have a similar spatial pattern and evolution in time, and a 10% uncertainty based on a set of sensitivity tests we performed. Thus, CO satellite data have a high potential to complement existing operational fire emission estimates based on satellite observations of fire counts, fire radiative power and burned area, in better constraining fire occurrence and the associated conversion of peat carbon to atmospheric CO 2 . A total carbon release to the atmosphere of 0.35–0.60 Pg C can be estimated based on our results. This article is part of a discussion meeting issue ‘The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications'.

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Development and assessment of the performance of a shared ventilatory system that uses clinically available components to individualize tidal volumes

10.1101/2020.12.09.20246165 ◽

2020 ◽

Author(s):

David M. Hannon ◽

Tim Jones ◽

Jack Conolly ◽

Conor Judge ◽

Talha Iqbal ◽

...

Keyword(s):

Invasive Mechanical Ventilation ◽

Ventilation System ◽

List Type ◽

Test Lung ◽

Minimal Impact ◽

Wide Range ◽

The Impact ◽

Ventilator Settings

AbstractObjectivesTo develop and assess the performance of a system for shared ventilation that uses clinically available components to individualize tidal volumes under a variety of clinically relevant conditions.DesignEvaluation and in vitro validation study.SettingVentilator shortage during the SARS-CoV-2 global pandemic.ParticipantsThe design and validation team consisted of intensive care physicians, bioengineers, computer programmers, and representatives from the medtech sector.MethodsUsing standard clinical components, a system of shared ventilation consisting of two ventilatory limbs was assembled and connected to a single ventilator. Individual monitors for each circuit were developed using widely available equipment and open source software. System performance was determined under 2 sets of conditions. First, the effect of altering ventilator settings (Inspiratory Pressure, Respiratory rate, I:E ratio) on the tidal volumes delivered to each lung circuit was determined. Second, the impact of altering the compliance and resistance in one simulated lung circuit on the tidal volumes delivered to that lung and the second lung circuit was determined. All measurements at each setting were repeated three times to determine the variability in the system.ResultsThe system permitted accurate and reproducible titration of tidal volumes to each ‘lung circuit’ over a wide range of ventilator settings and simulated lung conditions. Alteration of ventilator inspiratory pressures stepwise from 4-20cm H2O, of respiratory rates from 6-20 breaths/minute and I:E ratio from 1:1 to 1:4 resulted in near identical tidal volumes delivered under each set of conditions to each simulated ‘lung’. Stepwise alteration of compliance and resistance in one ‘test’ lung circuit resulted in reproducible alterations in tidal volume to the ‘test’ lung, with little change to tidal volumes in the ‘control’ lung (a change of only 6% is noted). All tidal volumes delivered were highly reproducible upon repetition.ConclusionsWe demonstrate the reliability of a simple shared ventilation system assembled using commonly available clinical components that allows individual titration of tidal volumes. This system may be useful as a temporary strategy of last resort where the numbers of patients requiring invasive mechanical ventilation exceeds supply of ventilators.Article SummaryStrengths and limitations of this studyThis solution provides the ability to safely and robustly ventilate two patients simultaneously while allowing differing tidal volumes in each limb.The designed solution uses equipment readily available in most hospitals.Accurate and reproducible titration of tidal volumes to each ‘lung’ was possible over a wide range of ventilator settings.Alteration of one simulated ‘lung’ conditions had minimal impact on the tidal volumes delivered to the unaffected lungThe system relies on patients being sedated and paralysed.We have not yet tested this solution in vivo, on COVID-19 patients.

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Development and implementation of a new biomass burning emissions injection height scheme for the GEOSChem model

10.5194/gmd-2018-93 ◽

2018 ◽

Cited By ~ 1

Author(s):

Liye Zhu ◽

Maria Val Martin ◽

Arsineh Hecobian ◽

Luciana V. Gatti ◽

Ralph Kahn ◽

...

Keyword(s):

Biomass Burning ◽

Amazon Basin ◽

Transport Model ◽

Meteorological Data ◽

Atmospheric Composition ◽

Chemical Transport Model ◽

Model Driven ◽

Injection Scheme ◽

Large Fires ◽

The Impact

Abstract. Biomass burning is a significant source of trace gases and aerosols to the atmosphere, and the evolution of these species depends acutely on where they are injected into the atmosphere. GEOS-Chem is a chemical transport model driven by assimilated meteorological data that is used to probe a variety of scientific questions related to atmospheric composition, including the role of biomass burning. This paper presents the development and implementation of a new global biomass burning emissions injection scheme in the GEOS-Chem model. The new injection scheme is based on monthly gridded Multi-Angle Imaging Spectro Radiometer (MISR) global plume-height stereoscopic observations in 2008. To provide specific examples of the impact of the model updates, we compare the output from simulations with and without the new MISR-based injection height scheme to several sets of observations from regions with active fires. Our comparisons with ARCTAS aircraft observations show that the updated injection height scheme improves the ability of the model to simulate the vertical distribution of peroxyacetyl nitrate (PAN) and carbon monoxide (CO) over North American boreal regions in summer. We also compare a simulation for October 2010 and 2011 to vertical profiles of CO over the Amazon Basin. When coupled with larger emission factors for CO, a simulation that includes the new injection scheme also better matches selected observations in this region. Finally the improved injection height also improves the simulation of monthly mean surface CO over California during July 2008, a period with large fires.

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Influence of a Carrington-like event on the atmospheric chemistry, temperature and dynamics

Atmospheric Chemistry and Physics ◽

10.5194/acp-12-8679-2012 ◽

2012 ◽

Vol 12 (18) ◽

pp. 8679-8686 ◽

Cited By ~ 15

Author(s):

M. Calisto ◽

P. T. Verronen ◽

E. Rozanov ◽

T. Peter

Keyword(s):

Atmospheric Chemistry ◽

Total Ozone ◽

Fold Increase ◽

Solar Proton ◽

Solar Proton Event ◽

Atmospheric Composition ◽

Proton Event ◽

Solar Energetic Particle Event ◽

The Impact ◽

Near Future

Abstract. We have modeled the atmospheric impact of a major solar energetic particle event similar in intensity to what is thought of the Carrington Event of 1–2 September 1859. Ionization rates for the August 1972 solar proton event, which had an energy spectrum comparable to the Carrington Event, were scaled up in proportion to the fluence estimated for both events. We have assumed such an event to take place in the year 2020 in order to investigate the impact on the modern, near future atmosphere. Effects on atmospheric chemistry, temperature and dynamics were investigated using the 3-D Chemistry Climate Model SOCOL v2.0. We find significant responses of NOx, HOx, ozone, temperature and zonal wind. Ozone and NOx have in common an unusually strong and long-lived response to this solar proton event. The model suggests a 3-fold increase of NOx generated in the upper stratosphere lasting until the end of November, and an up to 10-fold increase in upper mesospheric HOx. Due to the NOx and HOx enhancements, ozone reduces by up to 60–80% in the mesosphere during the days after the event, and by up to 20–40% in the middle stratosphere lasting for several months after the event. Total ozone is reduced by up to 20 DU in the Northern Hemisphere and up to 10 DU in the Southern Hemisphere. Free tropospheric and surface air temperatures show a significant cooling of more than 3 K and zonal winds change significantly by 3–5 m s−1 in the UTLS region. In conclusion, a solar proton event, if it took place in the near future with an intensity similar to that ascribed to of the Carrington Event of 1859, must be expected to have a major impact on atmospheric composition throughout the middle atmosphere, resulting in significant and persistent decrease in total ozone.

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FC20-04 -’Who is on my side?’ - qualitative analysis of ethnic minorities experiences of mental health related stigma and discrimination

European Psychiatry ◽

10.1016/s0924-9338(11)73628-8 ◽

2011 ◽

Vol 26 (S2) ◽

pp. 1924-1924

Author(s):

G. Shefer ◽

C. Henderson ◽

D. Rose ◽

S. Evans-Lacko

Keyword(s):

Mental Health ◽

Mental Illness ◽

Ethnic Minority ◽

Minority Groups ◽

List Type ◽

Service Users ◽

Ethnic Minority Groups ◽

Minority Communities ◽

Wide Range ◽

The Impact

IntroductionThe Time to Change (TTC) anti-stigma campaign, launched in January 2009 in England, intends to make fundamental improvements across England in: public knowledge, attitudes and discriminatory behaviour in relation to people with mental illness. To be effective and valid the campaign must reach a wide range of diverse audiences. This study explores attitudes of people from ethnic minority communities in relation to mental health.ObjectivesThe study investigates:1)General attitudes and perceptions about mental illness in ethnic minority communities2)How we might increase awareness about mental wellbeing and decrease stigma in ethnic minority communities.MethodsTen focus groups with members of ethnic minority groups were conducted. Five groups consisted of service users and five were composed of non-service users. Two groups comprised participants from an Indian origin, two Somali origin, two Afro-Caribbean origin and the other groups were mixed.ResultsWe will present findings regarding the ways in which traditional perceptions of mental health and personal experiences of ethnic minority service users affect their perceptions of sources of support such as family, friends, medical staff and religion and how this feedback could inform ant-stigma interventions.ConclusionThe study suggests that in order to maximise the impact of anti-stigma campaigns, attention should be given to sources of discrimination and traditional perceptions of mental illness which are emphasised by ethnic minority groups. When planning anti-stigma campaigns it is important to incorporate experiences and perceptions from a wide range of audiences.

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Intelligent Waterflood Optimization Advisory System – A Step Change Towards Digital Transformation

10.2118/207946-ms ◽

2021 ◽

Author(s):

Samat Ramatullayev ◽

Muzahidin Muhamed Salim ◽

Muhammad Ibrahim ◽

Hussein Mustapha ◽

Obeida El Jundi ◽

...

Keyword(s):

Oil Production ◽

Successful Implementation ◽

Generation Process ◽

Data Types ◽

Production Forecast ◽

Wide Range ◽

Monitoring And Surveillance ◽

End To End ◽

Reservoir Simulator ◽

The Impact

Abstract In this paper, we discuss the development of an end-to-end waterflood optimization solution that provides monitoring and surveillance dashboards with artificial intelligence (AI) and machine learning (ML) components to generate and assess insights into waterflood operational efficiency in an automated manner. The solution allows for fast screening of waterflood performance at diverse levels (reservoir, sector, pattern, well) enabling prompt identification of opportunities for immediate uptake into an opportunity management process and for evaluation in AI-driven production forecast solution and/or a reservoir simulator. The process starts with the integration of a wide range of production and reservoir engineering data types from multiple sources. Following this, a series of monitoring and surveillance dashboards of key units and elements of the entire waterflood operations are created. The workflows in these dashboards are framed with key waterflood reservoir and production engineering concepts in mind. The optimization opportunity insights are then extracted using automated traditional and AI/ML algorithms. The identified opportunities are consolidated in an optimization action list. This list is passed to an AI-driven production forecast solution and/or a reservoir simulator to assess the impact of each scenario. The system is designed to improve the business-time decision-making cycle, resulting in increased operational performance and lower waterflood operating costs by consolidating end-to-end optimization workflows in one platform. It incorporates both surface and subsurface aspects of the waterflood and provides a comprehensive understanding of waterflood operations from top-down field, reservoir, sector, pattern and well levels. Its AI/ML components facilitate understanding of producer-injector relationships, injector dynamic performance, underperformance of patterns in the sector as well as evaluating the impact of different optimization scenarios on incremental oil production. The data-driven production forecast component consists of several ML models and is tailored to assess their impact on oil production of different scenarios such as changes in voidage replacement ratio (VRR) in reservoir, sector, pattern and well levels. Opportunities are also converted into reservoir simulator compatible format in an automated manner to assess the impact of different scenarios using more rigorous numerical methods. The scenarios that yield the highest impact are passed to the field operations team for execution. The solution is expected to serve as a benchmark, upon successful implementation, for optimizing injection schemas in any field or reservoir. The novelty of the system lies in automating the insights generation process, in addition to integrating with an AI/ML production forecasting solution and/or a reservoir simulator to assess different optimization scenarios. It is an end-to-end solution for waterflood optimization because of the integration of various components that allow for the identification and assessment of opportunities all in one environment.

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