Public transportation is a major source of greenhouse gas emissions, highlighting the need to improve bus fuel efficiency. Clustering algorithms assist in analyzing fuel efficiency by grouping data into clusters, but irrelevant features may complicate the analysis and choosing the optimal number of clusters remains a challenging task. Therefore, this paper employs the Gaussian mixture models to cluster the solo fuel-efficiency dataset. Moreover, an integration method that combines the Silhouette index, Calinski-Harabasz index, and Davies-Bouldin index is developed to select the optimal cluster numbers. A dataset with 4006 bus trips in North Jutland, Denmark is utilized as the case study. Trips are first split into three groups, then one group is divided further, resulting in four categories: extreme, normal, low, and extremely low fuel efficiency. A preliminary study using visualization analysis is conducted to investigate how driving behaviors and route conditions affect fuel efficiency. The results indicate that both individual driving habits and route characteristics have a significant influence on fuel efficiency.

Wastewater treatment plants face unique challenges for process control due to their complex dynamics, slow time constants, and stochastic delays in observations and actions. These characteristics make conventional control methods, such as Proportional-Integral-Derivative controllers, suboptimal for achieving efficient phosphorus removal, a critical component of wastewater treatment to ensure environmental sustainability. This study addresses these challenges using a novel deep reinforcement learning approach based on the Soft Actor-Critic algorithm, integrated with a custom simulator designed to model the delayed feedback inherent in wastewater treatment plants. The simulator incorporates Long Short-Term Memory networks for accurate multi-step state predictions, enabling realistic training scenarios. To account for the stochastic nature of delays, agents were trained under three delay scenarios: no delay, constant delay, and random delay. The results demonstrate that incorporating random delays into the reinforcement learning framework significantly improves phosphorus removal efficiency while reducing operational costs. Specifically, the delay-aware agent achieved 36% reduction in phosphorus emissions, 55% higher reward, 77% lower target deviation from the regulatory limit, and 9% lower total costs than traditional control methods in the simulated environment. These findings underscore the potential of reinforcement learning to overcome the limitations of conventional control strategies in wastewater treatment, providing an adaptive and cost-effective solution for phosphorus removal.

Sixth-generation (6G) networks leverage simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) to overcome the limitations of traditional RISs. However, deploying STAR-RISs indoors presents challenges in interference mitigation, power consumption, and real-time configuration. In this work, a novel network architecture utilizing multiple access points (APs) and STAR-RISs is proposed for indoor communication. An optimization problem encompassing user assignment, access point beamforming, and STAR-RIS phase control for reflection and transmission is formulated. The inherent complexity of the formulated problem necessitates a decomposition approach for an efficient solution. This involves tackling different sub-problems with specialized techniques: a many-to-one matching algorithm is employed to assign users to appropriate access points, optimizing resource allocation. To facilitate efficient resource management, access points are grouped using a correlation-based K-means clustering algorithm. Multi-agent deep reinforcement learning (MADRL) is leveraged to optimize the control of the STAR-RIS. Yu Min Park, Sheikh Salman Hassan, Eui-Nam Huh, and Choong Seon Hong are with the Department of Computer Science and Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do 17104, Rep. of Korea, e-mails:{yumin0906, salman0335, johnhuh, cshong}@khu.ac.kr. Yan Kyaw Tun is with the Department of Electronic Systems, Aalborg University, A. C. Meyers Vænge 15, 2450 København, e-mail: ykt@es.aau.dk. Walid Saad is with the Bradley Department of Electrical and Computer Engineering, Virginia Tech, VA, 24061, USA. Additionally, the proposed MADRL approach incorporates convex approximation (CA).

This paper introduces a full solution for decentralized routing in Low Earth Orbit Satellite Constellations (LSatCs) based on continual Deep Reinforcement Learning (DRL). This requires addressing multiple challenges, including the partial knowledge at the satellites and their continuous movement, and the time-varying sources of uncertainty in the system, such as traffic, communication links, or communication buffers. We follow a multi-agent approach, where each satellite acts as an independent decision-making agent, while acquiring a limited knowledge of the environment based on the feedback received from the nearby agents. The solution is divided into two phases. First, an offline learning phase relies on decentralized decisions and a global Deep Neural Network (DNN) trained with global experiences to learn the optimal paths at each possible position and congestion level. Then, the online phase with local, on-board, and pre-trained DNNs requires continual learning to evolve with the environment, which can be done in two different ways: (1) Model anticipation, where the predictable conditions of the constellation, resulting from its orbital dynamics, are exploited by each satellite sharing local model with the next satellite; and (2) Federated Learning (FL), where each agent's model is merged first at the cluster level and then aggregated in a global Parameter Server (PS) at ground or at a geostationary orbit (GEO) satellite. The results show that, without high congestion, the proposed Multi-Agent Deep Reinforcement Learning (MA-DRL) framework achieves the same E2E performance as a shortest-path solution, but the latter assumes intensive communication overhead for real-time network-wise knowledge of the system at a centralized node, whereas ours only requires limited feedback exchange among first neighbour satellites. Moreover, the divergence of models over time is easily tackled by the synergy between anticipation, applied in short-term alignment, and FL, utilized for long-term alignment. F. Lozano-Cuadra (flozano@ic.uma.es) and B. Soret are with the Telecommunications Research Institute, University of Malaga, 29071, Malaga, Spain. I. Leyva-Mayorga and P. Popovski are with the Connectivity Section, Aalborg University, 9220 Aalborg, Denmark. The work of F. Lozano-Cuadra and B. Soret is partially funded by the European Space Agency (ESA) framework SatNEx V (prime contract no. The view expressed herein can in no way be taken to reflect the official opinion of ESA. Through the incremental adoption of the inter-satellite link (ISL), Low Earth Orbit Satellite Constellations (LSatCs) are turning into packet-based Non-Terrestrial Networks (NTN) capable of providing ubiquituous sensing, navigation, positioning, and communication services towards 6G.

Even though Deep Reinforcement Learning (DRL) showed outstanding results in the fields of Robotics and Games, it is still challenging to implement it in the optimization of industrial processes like wastewater treatment. One of the challenges is the lack of a simulation environment that will represent the actual plant as accurately as possible to train DRL policies. Stochasticity and non-linearity of wastewater treatment data lead to unstable and incorrect predictions of models over long time horizons. One possible reason for the models' incorrect simulation behavior can be related to the issue of compounding error, which is the accumulation of errors throughout the simulation. The compounding error occurs because the model utilizes its predictions as inputs at each time step. The error between the actual data and the prediction accumulates as the simulation continues. We implemented two methods to improve the trained models for wastewater treatment data, which resulted in more accurate simulators: 1- Using the model's prediction data as input in the training step as a tool of correction, and 2- Change in the loss function to consider the long-term predicted shape (dynamics). The experimental results showed that implementing these methods can improve the behavior of simulators in terms of Dynamic Time Warping throughout a year up to 98% compared to the base model. These improvements demonstrate significant promise in creating simulators for biological processes that do not need pre-existing knowledge of the process but instead depend exclusively on time series data obtained from the system.

Phosphorus removal is vital in wastewater treatment to reduce reliance on limited resources. Deep reinforcement learning (DRL) is a machine learning technique that can optimize complex and nonlinear systems, including the processes in wastewater treatment plants, by learning control policies through trial and error. However, applying DRL to chemical and biological processes is challenging due to the need for accurate simulators. This study trained six models to identify the phosphorus removal process and used them to create a simulator for the DRL environment. Although the models achieved high accuracy (>97%), uncertainty and incorrect prediction behavior limited their performance as simulators over longer horizons. Compounding errors in the models' predictions were identified as one of the causes of this problem. This approach for improving process control involves creating simulation environments for DRL algorithms, using data from supervisory control and data acquisition (SCADA) systems with a sufficient historical horizon without complex system modeling or parameter estimation.

Data markets facilitate decentralized data exchange for applications such as prediction, learning, or inference. The design of these markets is challenged by varying privacy preferences as well as data similarity among data owners. Related works have often overlooked how data similarity impacts pricing and data value through statistical information leakage. We demonstrate that data similarity and privacy preferences are integral to market design and propose a query-response protocol using local differential privacy for a two-party data acquisition mechanism. In our regression data market model, we analyze strategic interactions between privacy-aware owners and the learner as a Stackelberg game over the asked price and privacy factor. Finally, we numerically evaluate how data similarity affects market participation and traded data value. A. Context and Motivation In recent years, there has been a surge in Internet of Things (IoT) devices with sensing and computing capabilities, leading to an abundance of IoT data. Shashi Raj Pandey and Petar Popovski are with the Connectivity Section, Department of Electronic Systems, Aalborg University, Denmark. Pierre Pinson has primary affiliation with Dyson School of Design Engineering, Imperial College London, UK. He is also affiliated to the Technical University of Denmark, Department of Technology, Management and Economics, as well as with Halfspace This work was supported by the Villum Investigator Grant "WATER" from the Velux Foundation, Denmark.

When you think about Denmark, you might think about LEGO, Hans Christian Andersen, or the country's considerable contributions to the world of furniture design. But if you've never given the country much thought as a travel destination, you're missing out. From Medieval-era cobblestone streets in urban landscapes to natural wonders you have to see to believe, Denmark offers something for everyone--and particularly for travellers looking to get off the beaten path. Recently, tourism organisation VisitDenmark released an innovative advertising campaign prompting travellers, "Don't be a tourist--be an explorist!" The campaign utilised generative artificial intelligence (AI) to bring widely recognised tourist landmarks like the Mona Lisa and Statue of Liberty to life with a simple message: "Don't come see me.

Earlier this month, Odense Robotics, in collaboration with Aalborg University, opened a robotics hub in Aalborg, Denmark. The fifth and final hub in the country completes Odense Robotics' national setup. Odense Robotics has already established hubs in Aarhus, Copenhagen, Odense and Sonderborg. The hub will provide robotics, automation and drone startups, scaleups and SMEs with opportunities for growth and innovation. It will work with robotics companies in the northern region of Jutland.