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Earth Overshoot Day Reaches Record for Earliest Date #society

Earth Overshoot Day Reaches Record for Earliest Date
For 2025, Earth Overshoot Day fell on July 24, the earliest it has been since the event was first calculated in 2006.

"French company offers router capable of redirecting surplus solar to water heating"
#France #SolarPower #Energy #Renewables

French company offers router capable of redirecting surplus solar to water heating
French energy cooperative Solarcoop has launched a solar router that redirects surplus photovoltaic power from self-consumption systems to electric water heaters or other resistive loads, and can also control additional devices such as heat pumps.

Spatial Language Likelihood Grounding Network for Bayesian Fusion of Human-Robot Observations
Supawich Sitdhipol, Waritwong Sukprasongdee, Ekapol Chuangsuwanich, Rina Tse
https://arxiv.org/abs/2507.19947

Spatial Language Likelihood Grounding Network for Bayesian Fusion of Human-Robot Observations
Fusing information from human observations can help robots overcome sensing limitations in collaborative tasks. However, an uncertainty-aware fusion framework requires a grounded likelihood representing the uncertainty of human inputs. This paper presents a Feature Pyramid Likelihood Grounding Network (FP-LGN) that grounds spatial language by learning relevant map image features and their relationships with spatial relation semantics. The model is trained as a probability estimator to capture a…

AI firms are striking partnerships and giving some users in Asia free access to AI tools, in pursuit of larger sources of real-world consumer data for training (Itika Sharma Punit/Rest of World)
https://restofworld.org/2025/ai-data-collection-global-deals/

AI giants race to scoop up elusive real-world data
OpenAI, Google, and Perplexity create partnerships and free offers for a steady stream of consumer data that can't be scraped from the internet.

Channel Estimation in Massive MIMO Systems with Orthogonal Delay-Doppler Division Multiplexing
Dezhi Wang, Chongwen Huang, Xiaojun Yuan, Sami Muhaidat, Lei Liu, Xiaoming Chen, Zhaoyang Zhang, Chau Yuen, M\'erouane Debbah
https://arxiv.org/abs/2507.19812

Channel Estimation in Massive MIMO Systems with Orthogonal Delay-Doppler Division Multiplexing
Orthogonal delay-Doppler division multiplexing~(ODDM) modulation has recently been regarded as a promising technology to provide reliable communications in high-mobility situations. Accurate and low-complexity channel estimation is one of the most critical challenges for massive multiple input multiple output~(MIMO) ODDM systems, mainly due to the extremely large antenna arrays and high-mobility environments. To overcome these challenges, this paper addresses the issue of channel estimation in …

Theoretical modeling and quantitative research on aquatic ecosystems driven by multiple factors
Haizhao Guan, Yiyuan Niu, Chuanjin Zu, Ju Kang
https://arxiv.org/abs/2507.19553 h…

Theoretical modeling and quantitative research on aquatic ecosystems driven by multiple factors
Understanding the complex interactions between water temperature, nutrient levels, and chlorophyll-a dynamics is essential for addressing eutrophication and the proliferation of harmful algal blooms in freshwater ecosystems algal. However, many existing studies tend to oversimplify thse relationships often neglecting the non-linear effects and long-term temporal variations that influence chlorophyll-a growth. Here, we conducted multi-year field monitoring (2020-2024) of the key environmental fa…

A unique solution to overcome the barriers to planetesimal formation at low dust-to-gas ratio
H. Meheut, F. A. Gerosa, J. Bec
https://arxiv.org/abs/2508.20070 https://

A unique solution to overcome the barriers to planetesimal formation at low dust-to-gas ratio
In the incremental growth model, planetesimal formation constitutes the least understood step in the process of planetary formation. The two main difficulties in this regard are the collision/fragmentation and the drift barriers. Numerous solutions have been proposed to overcome these barriers, but often need a conjunction of processes to reach the conditions for planetesimal formation. We present numerical simulations, in which the protoplanetary disk turbulence is fully captured rather than m…

Feature Engineering for Wireless Communications and Networking: Concepts, Methodologies, and Applications
Jiacheng Wang, Changyuan Zhao, Zehui Xiong, Tao Xiang, Dusit Niyato, Xianbin Wang, Shiwen Mao, Dong In Kim
https://arxiv.org/abs/2507.19837

Feature Engineering for Wireless Communications and Networking: Concepts, Methodologies, and Applications
AI-enabled wireless communications have attracted tremendous research interest in recent years, particularly with the rise of novel paradigms such as low-altitude integrated sensing and communication (ISAC) networks. Within these systems, feature engineering plays a pivotal role by transforming raw wireless data into structured representations suitable for AI models. Hence, this paper offers a comprehensive investigation of feature engineering techniques in AI-driven wireless communications. Sp…

Embodied Intelligence for Sustainable Flight: A Soaring Robot with Active Morphological Control
Ghadeer Elmkaiel, Syn Schmitt, Michael Muehlebach
https://arxiv.org/abs/2508.19684

Embodied Intelligence for Sustainable Flight: A Soaring Robot with Active Morphological Control
Achieving both agile maneuverability and high energy efficiency in aerial robots, particularly in dynamic wind environments, remains challenging. Conventional thruster-powered systems offer agility but suffer from high energy consumption, while fixed-wing designs are efficient but lack hovering and maneuvering capabilities. We present Floaty, a shape-changing robot that overcomes these limitations by passively soaring, harnessing wind energy through intelligent morphological control inspired by…

From Token to Rhythm: A Multi-Scale Approach for ECG-Language Pretraining
Fuying Wang, Jiacheng Xu, Lequan Yu
https://arxiv.org/abs/2506.21803 https://

From Token to Rhythm: A Multi-Scale Approach for ECG-Language Pretraining
Electrocardiograms (ECGs) play a vital role in monitoring cardiac health and diagnosing heart diseases. However, traditional deep learning approaches for ECG analysis rely heavily on large-scale manual annotations, which are both time-consuming and resource-intensive to obtain. To overcome this limitation, self-supervised learning (SSL) has emerged as a promising alternative, enabling the extraction of robust ECG representations that can be efficiently transferred to various downstream tasks. W…