Scintillation due to atmospheric turbulence is one of the effects challenging the reuse of extremely wideband and high-rate optical satellite-to-satellite communication systems for satellite-to-Earth and Earth-to-satellite transmissions. In this article, we study the possibility of utilizing links in the terahertz (THz) frequency bands for these uplink and downlink transmissions instead. Built upon the physics-based model, originally developed for optical wave propagation, we present a mathematical framework for the THz signal scintillation to analyze atmospheric turbulence's impact on ground-satellite and airplane-satellite connections. Our results indicate that, while the scintillation still significantly impacts the power of the received THz signal (especially at lower elevation angles and under specific weather conditions), the effect is drastically less profound than the extreme losses the optical link will experience in the same weather conditions. We further explore a notable asymmetry of up to 10 dB between uplink and downlink losses. Finally, we illustrate that, even at relatively low airplane altitudes, the airplane-to-satellite link is much less affected than the Earth-to-satellite link, making THz communications a promising candidate technology for future high-rate airplane connectivity systems as a part of 6G and beyond.

The highly heterogeneous ecosystem of Next Generation (NextG) wireless communication systems calls for novel networking paradigms where functionalities and operations can be dynamically and optimally reconfigured in real time to adapt to changing traffic conditions and satisfy stringent and diverse Quality of Service (QoS) demands. Open Radio Access Network (RAN) technologies, and specifically those being standardized by the O-RAN Alliance, make it possible to integrate network intelligence into the once monolithic RAN via intelligent applications, namely, xApps and rApps. These applications enable flexible control of the network resources and functionalities, network management, and orchestration through data-driven intelligent control loops. Recent work has showed how Deep Reinforcement Learning (DRL) is effective in dynamically controlling O-RAN systems. However, how to design these solutions in a way that manages heterogeneous optimization goals and prevents unfair resource allocation is still an open challenge, with the logic within DRL agents often considered as a opaque system. In this paper, we introduce PandORA, a framework to automatically design and train DRL agents for Open RAN applications, package them as xApps and evaluate them in the Colosseum wireless network emulator. We benchmark 23 xApps that embed DRL agents trained using different architectures, reward design, action spaces, and decision-making timescales, and with the ability to hierarchically control different network parameters. We test these agents on the Colosseum testbed under diverse traffic and channel conditions, in static and mobile setups. Our experimental results indicate how suitable fine-tuning of the RAN control timers, as well as proper selection of reward designs and DRL architectures can boost network performance according to the network conditions and demand. Notably, finer decision-making granularities can improve Massive Machine-Type Communications (mMTC)’s performance by$\sim\! 56\%$and even increase Enhanced Mobile Broadband (eMBB) Throughput by$\sim\! 99\%$.

The highly heterogeneous ecosystem of Next Generation (NextG) wireless communication systems calls for novel networking paradigms where functionalities and operations can be dynamically and optimally reconfigured in real time to adapt to changing traffic conditions and satisfy stringent and diverse Quality of Service (QoS) demands. Open Radio Access Network (RAN) technologies, and specifically those being standardized by the O-RAN Alliance, make it possible to integrate network intelligence into the once monolithic RAN via intelligent applications, namely, xApps and rApps. These applications enable flexible control of the network resources and functionalities, network management, and orchestration through data-driven control loops. Despite recent work demonstrating the effectiveness of Deep Reinforcement Learning (DRL) in controlling O-RAN systems, how to design these solutions in a way that does not create conflicts and unfair resource allocation policies is still an open challenge. In this paper, we perform a comparative analysis where we dissect the impact of different DRL-based xApp designs on network performance. Specifically, we benchmark 12 different xApps that embed DRL agents trained using different reward functions, with different action spaces and with the ability to hierarchically control different network parameters. We prototype and evaluate these xApps on Colosseum, the world's largest O-RAN-compliant wireless network emulator with hardware-in-the-loop. We share the lessons learned and discuss our experimental results, which demonstrate how certain design choices deliver the highest performance while others might result in a competitive behavior between different classes of traffic with similar objectives.

Affective Computing is a rather new and multidisciplinary research field that seeks sophisticated automation in emotion detection for later analysis. However, the automated emotion detection and analysis require as well comprehensive data management support, e.g. to keep control of data produced, and to enable its efficient reuse through classification with established terminology. This paper contributes to data management aspects in Affective Computing and to automation support in emotion classification on the basis of a personal traits analysis. Hence, we describe the implementation of a taxonomy management system, derived from requirements of a case study that investigates the relationship between personality and emotions in Affective Computing. The study makes use of machine learning software developed by SenseCare, an EU-funded R&D project that applies Affective Computing to enhance and advance future healthcare processes and systems.

This paper describes a new prototype system for detecting the demeanor of patients in emergency situations using the Intel RealSense camera system [1]. It describes how machine learning, a support vector machine (SVM) and the RealSense facial detection system can be used to track patient demeanour for pain monitoring. In a lab setting, the application has been trained to detect four different intensities of pain and provide demeanour information about the patient's eyes, mouth, and agitation state. Its utility as a basis for evaluating the condition of patients in situations using video, machine learning and 5G technology is discussed.

An unsupervised learning system, implemented as an autonomous agent is presented. A simulation of a challenging path planning problem is used to illustrate the agent design and demonstrate its problem solving ability. The agent, dubbed the ORG, employs fuzzy logic and clustering techniques to efficiently represent and retrieve knowledge and uses innovative sensor modeling and attention focus to process a large number of stimuli. Simple initial fuzzy rules (instincts) are used to influence behavior and communicate intent to the agent. Self-reflection is utilized so the agent can learn from its environmental constraints and modify its own state. Speculation is utilized in the simulated environment, to produce new rules and fine-tune performance and internal parameters. The ORG is released in a simulated shallow water environment where its mission is to dynamically and continuously plan a path to effectively cover a specified region in minimal time while simultaneously learning from its environment. Several paths of the agent design are shown, and desirable emergent behavior properties of the agent design are discussed.

Over the last two years, we have demonstrated the feasibility of applying category-theoretic methods in specifying, synthesizing, and maintaining industrial strength software systems. We have been using a first-of-its-kind tool for this purpose. Kestrel's Specware/sup TM/ software development system. In this paper, we describe our experiences and give an industrial perspective on what is needed to make this technology have broader appeal to industry. Our overall impression is that the technology does work for industrial strength applications, but that it needs additional work to make it more usable. We believe this work marks a turning point in the use of mathematically rigorous approaches to industrial strength software development and maintenance. It is interesting to note that when this technology is applied to software systems whose outputs are designs for airplane parts, the design rationale that is captured is not only software engineering design rationale, but also design rationale from other engineering disciplines (e.g., mechanical, material, manufacturing, etc.). This suggests the technology provides an approach to general systems engineering that enables one to structure and reuse engineering knowledge broadly.

An algorithm for adaptively controlling genetic algorithm parameter (GAP) coding using fuzzy rules is presented. The fuzzy GAP coding algorithm is compared to the dynamic parameter encoding scheme proposed by Schraudolph and Belew. The performance of the algorithm on a hydraulic brake emulator parameter identification problem is investigated. Fuzzy GAP coding control is shown to dramatically increase the rate of convergence and accuracy of genetic algorithms.

This paper briefly describes our initial experiences in applied research of formal approaches to the generation and maintenance of software systems supporting structural engineering tasks. We describe the business context giving rise to this activity, and give an example of the type of engineering problem we have focused on. We briefly describe our approach to software generation and maintenance, and point out the challenges that we appear to face in transferring this technology into actual practice.