Exploration Of Scenarios Research Articles

Permittivity sensor development for lunar and planetary surface exploration

Permittivity sensors measure the electric permittivity (formerly known as the dielectric constant) of a sample between its electrodes and offer a reliable method to characterize the subsurface of planetary bodies in situ. One potential application is the identification and mapping of water ice at the poles of the Moon. In this paper, the scientific background and heritage of the permittivity sensor concept are discussed, and the implemented electronic architecture is introduced, focusing on the novel patch electrodes. The data processing approach for the measurements is based on Fourier transformation, and numerical simulation setups are used for performance predictions. The calibration of the sensor validates the functionality of the electronics, and the results from both simulations and characterization experiments show that the concept is applicable in the exploration scenario. Considering both engineering and scientific aspects, the results highlight the permittivity sensor’s suitability for lunar and planetary exploration missions, albeit further points for improvement are identified.

Defogger: A Visual Analysis Approach for Data Exploration of Sensitive Data Protected by Differential Privacy.

Differential privacy ensures the security of individual privacy but poses challenges to data exploration processes because the limited privacy budget incapacitates the flexibility of exploration and the noisy feedback of data requests leads to confusing uncertainty. In this study, we take the lead in describing corresponding exploration scenarios, including underlying requirements and available exploration strategies. To facilitate practical applications, we propose a visual analysis approach to the formulation of exploration strategies. Our approach applies a reinforcement learning model to provide diverse suggestions for exploration strategies according to the exploration intent of users. A novel visual design for representing uncertainty in correlation patterns is integrated into our prototype system to support the proposed approach. Finally, we implemented a user study and two case studies. The results of these studies verified that our approach can help develop strategies that satisfy the exploration intent of users.

Decarbonisation roadmap for the building activities: LCA modelling of the renovation lever

Renovation of the existing building stock plays a central role in every decarbonisation scenario. Yet, its impact is rarely addressed in a life-cycle perspective, leading to potential over benefits and neglecting the cross-sectoral impacts that it generates. This article introduces the methodology developed as part of the building activities decarbonisation roadmap to quantify the effect of renovation on operational and embodied greenhouse gas emissions (GHGE). It focuses on describing the modelling tools that are used to create prospective decarbonisation pathways from a georeferenced database. In particular, the macro component approach used to describe the materiality of existing buildings and renovation packages, as well as the energy simulation tool used to quantify energy saving measures are introduced. Explorative scenarios are modelled based on different renovation levels, renovation packages and targeted buildings to investigate the whole-life GHGE by 2030 and 2050. Preliminary static results underline the necessity to consider embodied GHGE and to introduce an optimisation approach to target the buildings with the highest GHGE savings potential.

Deep sequence model-based approach to well log data imputation and petrophysical analysis: A case study on the West Natuna Basin, Indonesia

Well log data imputation is crucial for subsurface geology interpretation, which helps identify the most productive areas for drilling, minimize exploration risks, and maximize hydrocarbon recovery. Obtaining high-quality well data can be challenging due to various issues such as drilling problems, improper logging processes, or operational issues with logging tools. Accurate and reliable imputation methods are essential to address missing or incomplete information in well log data, allowing better decision-making in the oil and gas industry. This study presents a novel well log data imputation method for the West Natuna Basin in Indonesia, using time-series deep learning models. Focusing on the “Kappa” well dataset with missing Vp log data, we trained long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (Bi-LSTM) models using a window-based time step sequence of 10-timesteps intervals as input. The LSTM was observed as the best model, achieving a mean absolute percentage error (MAPE) of about 2.2% and an R-squared (R2) of about 94%. This result suggests that deep sequence model can be effectively used for missing data imputation in well log. Lastly, a petrophysical analysis further underlines the importance of these findings, offering deeper insights into reservoir properties, and value of our research in real-world exploration scenarios. The findings can aid in improving the accuracy and reliability of imputation in well log data, enabling better decision-making in the oil and gas industry.

Brussels mobility in 2050: Participatory mixed-method scenario building to explore alternative futures

Mobility is deeply embedded in people’s everyday lives and fundamental in shaping society. Therefore, transport policy needs to be embedded in a well-grounded view of the future, while also being resilient to external shocks. One of the difficulties in ensuring the resilience of transportation systems is the large variety of factors of uncertainty that characterizes the field. Scenario building is one method that can address this issue by exploring alternative futures besides a most likely or desired one. But to increase the likelihood of translating exploratory scenarios into policy making, these should be developed in a participatory manner. One of the difficulties encountered when developing scenarios using quantitative methods is that these methods are often a black box for participants, while qualitative methods on their own can lack legitimacy. Mixed-method approaches can therefore be an interesting avenue for research to facilitate participation in transport scenario building, by providing a mathematical structuring of the scenarios, enriched with qualitative stakeholder input. In this paper, we explore how participation can be facilitated in mixed-method scenario building. We further elaborate on the combination of a systems dynamic-approach (cross-impact balance analysis) with creative workshops by testing three approaches to facilitate participation: a Delphi survey, personas, and the use of visual harvesting. We develop four scenarios for the future of urban mobility in Brussels, Belgium, by 2050, and report on the lessons learnt. We find that the use of Delphi as a stakeholder engagement method too rapidly closes down the scenario funnel when used for selecting factors of uncertainty. The use of personas allows for the development of engaging narratives, and could be helpful for the involvement of people less familiar with participatory exercises. Lastly, the use of visuals throughout the workshops was found to stimulate active discussion and resulted in a more transparent scenario development process, by increasing traceability.

Distribution of ecosystem service potential in marginal agroecosystems in a mosaic-type landscape under exploratory scenarios

ABSTRACT Land-use change in agriculturally marginal mosaic-type landscapes involves economic, environmental, and social aspects and can lead to social and economic problems in rural areas. This study focuses on the western part of Vidzeme Uplands, Latvia, which has been heavily impacted by rural depopulation and agricultural land abandonment. The research explores the value of low-intensity agroecosystems in agriculturally marginal landscapes using an ecosystem service approach. Three scenarios were developed: a ‘business as usual’ scenario, a ‘no subsidies’ scenario, and a ‘payments for ecosystem services’ scenario. The ‘business as usual’ scenario shows a pattern of subtle intensification, while the ‘no subsidies’ scenario predicts a significant loss of low-intensity agroecosystems and their services. The ‘payments for ecosystem services’ scenario estimates the safeguarding of essential regulatory ecosystem services, ensuring the potential supply of provisioning services, and maintaining the fragile structure of the marginal mosaic-type landscape.

A Pattern Search Method to Optimize Mars Exploration Trajectories

The Korean National Space Council recently released “Mars Exploration 2045” as part of its future strategic plan. The operations for a Mars explorer can be defined based on domestically available capabilities, such as ground operations, launch, in-space transport and deep space link. Accordingly, all of our exploration scenarios start from the Naro space center, and the pathway to Mars is optimized using an objective function that minimizes the required ∆V. In addition, the entire phase of Mars orbit insertion should remain in contact with our deep space antennas, a measure that is imposed as an operational constraint. In this study, a pattern search method is adopted, as it can handle a nonlinear problem without relying on the derivatives of the objective function, and optimal trajectories are generated on a daily basis for a 15-day launch period. The robustness of this direct search method is confirmed by consistently converged solutions showing, in particular, that the ascending departure requires slightly less ∆V than the descending departure on the order of 10 m/s. Subsequently, mass estimates are made for a Mars orbiter and a kick stage to determine if the desired ∆V is achievable with our eco-friendly in-space propulsion system when launched from our indigenous launch vehicle, KSLV-II.

Data Generation with GAN Networks for Sidescan Sonar in Semantic Segmentation Applications

In the realm of underwater exploration, particularly within the domain of autonomous detection, sidescan sonar stands as a pivotal sensor apparatus. Autonomous detection models necessitate a substantial volume of scanned sonar image data for optimal training, yet the challenges and costs associated with acquiring such data pose significant limitations on the deployment of autonomous detection models in underwater exploration scenarios. Consequently, there arises a demand for the development of cost-effective data augmentation techniques. In the present investigation, an initial collection of scanned sonar image data was conducted during lake trials, encompassing diverse environmental regions, including rocky terrain, shadowed areas, and aquatic bodies. Subsequently, a proprietary generative adversarial network (GAN) model was devised for the purpose of synthesizing scanned sonar data. The synthesized data underwent denoising and underwent post-processing via algorithmic methods. Subsequently, similarity metrics were computed to gauge the quality of the generated scanned sonar data. Furthermore, a semantic segmentation model was meticulously crafted and trained by employing authentic data. The generated data were subsequently introduced into this semantic segmentation model. The output outcomes demonstrated that the model exhibited preliminary labeling proficiency on the generated image data, requiring only minimal manual intervention to conform to the standards of a conventional dataset. Following the inclusion of the labeled data into the original dataset and the subsequent training of the network model utilizing the expanded dataset, there was an observed discernible enhancement in the segmentation performance of the model.

AOR: Adaptive opportunistic routing based on reinforcement learning for planetary surface exploration

Planetary surface exploration mobile ad-hoc networks (PSEMANET) show the characteristics of routing void caused by craters and electromagnetic interference, relatively high dynamics caused by node heterogeneity, and small node capacity caused by load constraints, which is the reason for the increase of communication delay and packet loss. Adaptive opportunistic routing based on reinforcement learning (AOR) is an opportunistic routing protocol that determines the forwarding node based on the current coordinates, queue length, and the number of neighbors. The full forwarding areas are used to avoid routing void, and the dual competition mechanism is designed to avoid the node hidden problem. The Q-value obtained by reinforcement learning is used to design the dynamic delay cost (DDC) so that nodes in the forwarding areas can compete to achieve adaptive path switching to the environment. Compared with representative proactive routing OLSR, reactive routing AODV, geographic routing GPSR and opportunistic routing BLR, AOR and AOR with memory(AOR-M) have high packet delivery ratio (PDR) and low end-to-end delay in planetary surface exploration scenarios implemented by our simulation system. And the AOR-M protocol shows the lowest expected end-to-end delay and highest channel utilization in both high and low speed scenarios. The result shows that the AOR-M provides efficient and robust routing in planetary surface exploration mobile ad-hoc networks with a complex environment, highly dynamic nodes, and performance constraints.

Development of research skills and formative research in higher education.

The university has been using the concept of formative research for the fulfillment of one of its functions within the framework of the law. Since then we have conducted research on the development of research skills in the university classroom, we present activities developed by professors, which bring us closer to the use of tools that allow the development of research skills from the training process. Material and methods: it was based on an exploratory descriptive descriptive research, as well as on the documentary analysis carried out at different times. First: institutional diagnosis on formative research, second: design of the conceptual model and third: construction of exploratory scenarios.

How, where, and when to charge electric vehicles – net-zero energy system implications and policy recommendations

A coordinated Charging Infrastructure (CI) strategy could accelerate the adoption of Battery Electric Vehicles (BEVs). Policymakers need to understand the tradeoffs between several types of CI developments. To support decision-makers, we apply the Swiss TIMES Energy system Model, which we extended with heterogeneous consumer segments with four trip types and several CI options. The novelty of this work lies in the interplay of such method advancements, representing BEV charging options with various CI types that can be accessed based on their location type at an hourly intraday temporal resolution. In explorative scenario analyses, we evaluate the effects of CI on car fleet deployment and their energy system implications in achieving net-zero CO2 emissions in Switzerland by 2050. Our analysis shows that the BEV share makes up 39%–77% of the fleet by 2050, and each BEV needs about 5 kW total charging capacity, split into 1.6–2.6 BEVs per private charger and 18–25 BEVs per public charger. Providing overnight charging access through private home chargers or public chargers in residential areas facilitates a 12%–20% increased BEV penetration compared to the reference scenario. For consumers without private home charging, improved public CI in non-residential areas increases BEV uptake by 24%. While low-power slow CI is cost-effective at home, high-power fast CI in commercial areas supports integration of solar PV. We highlight the need for coordinated CI policies and provide a variety of policy options based on our analysis and international insights.

A big data exploration approach to exploit in-vehicle data for smart road maintenance

In modern Smart Cities, pervasive collection of sensor-based and IoT data streams is a challenging opportunity for improving mobility resilience. Among the potential applications, sensor-based data streams provide valuable information about the quality of the area-wide road surface. Modern vehicle black boxes are also able to estimate the type of anomaly (e.g., bump, hole, rough ground, depression), based on real-time analysis of acceleration data streams. Road maintainers may use all this information to improve monitoring and maintenance activities. However, the volume of data streams, the variety of road network and different degrees of seriousness of detected anomalies call for methods to support maintainers in the exploration of available data. To this aim, in this paper, we propose a methodological approach, based on big data exploration techniques. The approach is grounded on: (i) a multi-dimensional model, apt to organise data streams according to different dimensions and enable data exploration; (ii) data summarisation techniques, based on an incremental clustering algorithm, to simplify the overall view over massive data streams and to cope with their dynamic nature; (iii) a measure of relevance, to focus the attention on road portions that present critical conditions. The innovative contributions regard the formalisation of the exploration methodology, the definition of exploration scenarios, based on road maintainers’ goals and the measure of relevance, and an extensive experimentation on a real world case study, addressed in a research project on smart and resilient mobility. Experimental results show how relevance evaluation is able to efficiently attract the road maintainers’ attention on road portions that present the most critical conditions and the proposed incremental clustering algorithm outperforms existing algorithms in the literature.

Nonoxidative methane coupling in a micro‐DBD with enhanced secondary electron emission

AbstractThe conversion of methane into transportable and storable materials is crucial in the petrochemical sector. In this study, a specially constructed AC‐driven dielectric barrier discharge (DBD) that has charge injector pyramids on one of the electrodes and runs at ambient temperature and pressure was used to evaluate noncatalytic methane conversion. The obtained result was compared with the traditional flat electrode DBD. It was discovered that the product selectivity in direct nonoxidative methane conversion depended on the discharge conditions. Pyramid electrode plasma sources convert methane up to 50 more than flat electrode plasma due to the appearance of more microdischarges. Pyramid electrode plasma generally has a greater production efficiency than flat electrode plasma, while requiring more operational power. The turnkey solutions offered by the sustainable methane coupling method discussed here may be advantageous for the long‐term small‐scale ethylene exploration scenario.

Indirect Optimization of Optimal Continuous Trajectory for Electric Sails With Refined Thrust Model in the Multi-Target Exploration Scenario

Electric sail-based propulsion is an innovative propellant-less propulsion technology that generates continuous thrust through the interaction between an artificial electric field and the solar wind. In an electric sail, the propulsive acceleration is adjusted by controlling the attitude of its normal plane and the coefficient determining the maximum thrust. However, the attitude adjustment speed of the electric sail is relatively small and the direction of the normal vector is constrained. Consequently, the electric sail is required to maintain a continuous propulsive acceleration vector when flying by the intermediate targets in multi-target interplanetary exploration. Therefore, an indirect optimization of three-dimensional optimal continuous interplanetary trajectory for electric sails with refined thrust model is investigated in this study. First, the optimal propulsive angles and thrust adjustment coefficient of electric sails with a refined thrust model are derived using Pontryagin's minimum principle. Second, a homotopy function is introduced in the process of trajectory optimization with an indirect method to approximate the step of the thrust adjustment coefficient to improve the accuracy of numerical integration. Additionally, the initial costates of the electric sail are transformed from the numerical simulation results of the Bezier shaping approach (BSA) and integrative Bezier shaping approach (IBSA) using the Karush-Kuhn-Tucker (KKT) condition. The numerical simulation results for the Earth-Mars rendezvous mission and the multi-target flyby mission reveal that the initial values of the costates are effective to implement an indirect optimization process of the optimal continuous trajectory for the rapid convergence of the electric sail. According to the numerical simulation results for multi-target mission, the indirect method is on average 2.5% better than the Gauss pseudospectral method (GPM) in overall index, and the calculation time is only 1.06% of GPM.

Operationalizing the Nature Futures Framework for ecological infrastructure

AbstractScenarios are useful for considering development pathways under different future conditions. To manage a functioning ecological infrastructure (EI) as a network of natural and semi-natural habitats that can promote biodiversity and provide nature’s contributions to people (NCPs), one needs to understand future biophysical and socio-economic influences on its development. However, scenarios often do not incorporate the reciprocity of biophysical and societal changes. This has prompted new proposals from the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) on the importance of creating nature-centered multiscale scenarios that include a normative dimension accounting for diverse human–nature relationships. In this contribution, we operationalize the Nature Futures Framework (NFF), developed under IPBES, into integrated normative and exploratory scenarios framing the development of a functioning EI in Switzerland until 2060. We follow a two-stage participatory approach methodologically aligned with the IPBES development. First, we elaborated positive visions for future EI with stakeholders in four regions of Switzerland. We then collaborated with experts to create integrated normative and exploratory scenarios through a process comprising literature research, workshops and a survey. By positioning status quo and future scenarios within the Nature Futures Framework, we demonstrate the diversity of nature values in a specific context of EI in Switzerland, thus contributing to the global set of Nature Futures scenarios. Integrating both plausible and desirable developments, these scenarios will serve as a valuable tool in the planning of long-term measures to ensure a functioning EI in Switzerland.

A probabilistic cross‐impact methodology for explorative scenario analysis

AbstractAs one of the approaches to scenario analysis, cross‐impact methods provide a structured approach to building scenarios as combinations of outcomes for selected uncertainty factors. Although they vary in their details, cross‐impact methods are similar in that they synthesize expert judgments about probabilistic or causal dependencies between pairs of uncertainty factors and seek to focus attention on scenarios that can be deemed consistent. Still, most cross‐impact methods do not associate probabilities with scenarios, which limits the possibilities of integrating them in risk and decision analysis. Motivated by this recognition, we develop a cross‐impact method that derives a joint probability distribution over all possible scenarios from probabilistically interpreted cross‐impact statements. More specifically, our method (i) admits a broad range of probabilistic statements about the realizations of uncertainty factors, (ii) supports the process of eliciting such statements, (iii) synthesizes these judgments by solving a series of optimization models from which the corresponding scenario probabilities are derived. The resulting scenario probabilities can be used to construct Bayesian networks, which expands the range of analyses that can be carried out. We illustrate our method with a real case study on the impacts of three‐dimensional (3D)‐printing on the Finnish Defense Forces. The scenarios, their probabilities, and the associated Bayesian network resulting from this case study helped explore alternative futures and gave insights into how the Defence Forces could benefit from 3D‐printing.

Colonization of urban futures in the Global South: lessons from the case of Isfahan 2040

PurposeDue to the limitations of conventional urban planning, it is essential to develop novel techniques of urban futruing. This paper aimed to use the scenario technique to create four plausible narratives of the future of Isfahan. Also, the authors described the problems of city foresight in the Global South.Design/methodology/approachThis paper chronicles the Schwartzian steps taken to build explorative scenarios of Isfahan City in Iran in 2040. After using a STEEPV (Social, Technological, Environmental, Economic, Political, Value) analysis, the authors prioritized the collected variables by combining influence diagrams, the iceberg metaphor and an expert-based survey. Once the key uncertainties were derived, four scenarios were developed and discussed.FindingsThrough thematic analysis of the official visions of Isfahan’s future and the juxtaposition of these narratives with insight yielded in the scenario-development process, the paper concludes that the Northernness of the prevailing urban imaginaries, uncritical mimetic benchmarking, depoliticization of urban futures and the decorative reductionistic visions colonize urban futures in Isfahan/Iran. Critical/deconstructive city foresight and application of discomfort/ignorance criteria in the generation of scenarios can improve the rigor and quality of city foresight in the Global South.Originality/valueThe application of city foresight in the Global South has been limited. The paper is a step toward bridging this gap and providing some recommendations on how city foresight in the Global South might differ from its counterparts in the Global North.

Climate-Related Sea Level Rise and Coastal Wastewater Treatment Infrastructure Futures: Landscape Planning Scenarios for Negotiating Risks and Opportunities in Australian Urban Areas

Around the world, human populations and their supporting infrastructures are concentrated in coastal areas. With rising sea levels, these settlements and urban infrastructures are at risk of service interruptions, lasting damage and frequent climate-related hazards. Wastewater systems are especially vulnerable due to their proximity to coastlines. Despite the seriousness of sea-level-rise-induced challenges, a clear understanding of the risks and potential adaptations of coastal wastewater treatment systems and their associated landscapes in Australia has been overlooked. Further, there is a lack of urgency and awareness concerning this issue. In this study, we consider how scenario-based landscape design approaches might enhance current debates and approaches related to coastal change with particular reference to wastewater treatment systems and associated environmental landscapes. Adelaide is used as a case study, and a range of landscape planning exploratory scenarios are developed and evaluated to assess the possible consequences of different courses of action in uncertain contexts. We find that whilst wastewater treatment plants are threatened by climate-related hazards, there is an opportunity for landscape-scale environmental planning to manage risks and opportunities and improve ecological and economic outcomes. We also find that for wicked multidimensional problems, such as sea level rise, landscape scenario design testing can assist in identifying a number of creative adaptation approaches that are not immediately apparent. We find that approaches such as retreat, defense and accommodation are not mutually exclusive but can each share elements and strategies. The strategic potential of a more creative, scenario-based approach can therefore form a productive part of the sea level rise adaptation of coastal infrastructure landscapes in Australia and elsewhere.

Creating Flexible Plans for an Uncertain Future: From Exploratory Scenarios to Adaptive Plans With Real Options

Scenario planning is increasingly used to manage uncertainty, but such planning often struggles to influence decision making and help communities navigate multiple futures. This article proposes a framework for planning practice that integrates scenario planning and real option theory to identify adaptation options that make plans or projects responsive to multiple futures. The framework is explained through a demonstration case, Plan Bay Area 2050 and Link21, based on document content analysis and expert interviews. The findings show that exploratory scenarios generate opportunities for real options reasoning and adaptive planning, by making uncertainties explicit when thinking about the future.

Scenarios of environmental deterioration in the Paraopeba River, in the three years after the breach of B1 tailings dam in Brumadinho (Minas Gerais, Brazil)

The collapse of B1 dam at the Córrego do Feijão mine of Vale, S.A., located in the Ferro-Carvão stream watershed (Brazil), released 11.7 Mm3 of tailings rich in iron and manganese, and 2.8 Mm3 entered the Paraopeba River 10 km downstream. Seeking to predict the evolution of environmental deterioration in the river since the dam break on January 25, 2019, the present study generated exploratory and normative scenarios based on predictive statistical models, and proposed mitigating measures and subsides to ongoing monitoring plans. The scenarios segmented the Paraopeba into three sectors: “anomalous” for distances ≤63.3 km from the B1 dam site, “transition” (63.3–155.3 km), and “natural” (meaning unimpacted by the mine tailings in 2019; >155.3 km). The exploratory scenarios predicted a spread of the tailings until reaching the “natural” sector in the rainy season of 2021, and their containment behind the weir of Igarapé thermoelectric plant located in the “anomalous” sector, in the dry season. Besides, they predicted the deterioration of water quality and changes to the vigor of riparian forests (NDVI index) along the Paraopeba River, in the rainy season, and a restriction of these impacts to the “anomalous” sector in the dry season. The normative scenarios indicated exceedances of chlorophyll-a in the period January 2019–January 2022, but not exclusively caused by the rupture of B1 dam as they also occurred in areas not affected by the accident. Conversely, the manganese exceedances clearly flagged the dam failure, and persist. The most effective mitigating measure is likely the dredging of the tailings in the “anomalous” sector, but currently it represents solely 4.6 % of what has entered the river. Monitoring is paramount to update the scenarios until the system enters a route towards rewilding, and must include water and sediments, the vigor of riparian vegetation, and the dredging.