相关论文: Enhancing Carbon Emission Reduction Strategies usi…

Advances in Earth observation (EO) foundation models have unlocked the potential of big satellite data to learn generic representations from space, benefiting a wide range of downstream applications crucial to our planet. However, most…

We present a new procedure to measure the large-scale carbon monoxide (CO) emissions across cosmic history. As a tracer of large-scale structure (LSS), the CO gas content as a function of redshift can be quantified by its three-dimensional…

The Ocean Color Monitor-3 (OCM-3) sensor is instrumental in Earth observation, achieving a critical balance between high-resolution imaging and broad coverage. This paper explores innovative imaging methods employed in OCM-3 and the…

To improve privacy and ensure quality-of-service (QoS), deep learning (DL) models are increasingly deployed on Internet of Things (IoT) devices for data processing, significantly increasing the carbon footprint associated with DL on IoT,…

The use of Internet of Things (IoT) sensors for air pollution monitoring has significantly increased, resulting in the deployment of low-cost sensors. Despite this advancement, accurately calibrating these sensors in uncontrolled…

Accurate and robust vehicle localization in highly urbanized areas is challenging. Sensors are often corrupted in those complicated and large-scale environments. This paper introduces GNSS-FGO, an online and global trajectory estimator that…

Mitigating climate change requires transforming agriculture to minimize environ mental impact and build climate resilience. Regenerative agricultural practices enhance soil organic carbon (SOC) levels, thus improving soil health and…

Multi-modal co-learning is emerging as an effective paradigm in machine learning, enabling models to collaboratively learn from different modalities to enhance single-modality predictions. Earth Observation (EO) represents a quintessential…

This paper addresses the environmental impacts linked to hazardous emissions from gas turbines, with a specific focus on employing various machine learning (ML) models to predict the emissions of Carbon Monoxide (CO) and Nitrogen Oxides…

Environment mapping is an important computing task for a wide range of smart city applications, including autonomous navigation, wireless network operations and extended reality environments. Conventional smart city mapping techniques, such…

One of the aspects currently holding back commercial scale deployment of carbon capture and storage (CCS) is an accurate understanding of the thermodynamic behaviour of carbon dioxide and relevant impurities during the pipeline transport…

Following the Paris Agreement of $2015$, most countries have agreed to reduce their carbon dioxide (CO$_2$) emissions according to individually set Nationally Determined Contributions. However, national CO$_2$ emissions are reported by…

Recent computing needs have lead technology companies to develop large scale, highly optimized data centers. These data centers represent large loads on electric power networks which have the unique flexibility to shift load both…

The COSMO-RS (Conductor-like Screening Model for Real Solvents) is a predictive thermodynamic model that has found diverse applications in various domains like chemical engineering, environmental chemistry, nanotechnology, material science,…

Over the past decade, climate change has become an increasing problem with one of the major contributing factors being carbon dioxide (CO2) emissions; almost 51% of total US carbon emissions are from factories. Current materials used in CO2…

Estimation of the soil organic carbon content is of utmost importance in understanding the chemical, physical, and biological functions of the soil. This study proposes machine learning algorithms of support vector machines, artificial…

This paper introduces a new approach for fine-tuning the predictions of structured state space models (SSMs) at inference time using real-time recurrent learning. While SSMs are known for their efficiency and long-range modeling…

Accurate and timely hyperlocal weather predictions are essential for various applications, ranging from agriculture to disaster management. In this paper, we propose a novel approach that combines hyperlocal weather prediction and anomaly…

The significant carbon footprint of the ICT sector calls for methodologies to contain carbon emissions of running software. This article proposes a novel framework for implementing, configuring and assessing carbon-aware interactive…

Floating Car Observers (FCOs) extend traditional Floating Car Data (FCD) by integrating onboard sensors to detect and localize other traffic participants, providing richer and more detailed traffic data. In this work, we explore various…