Ericsson (Finland)

Abstract. Sub-10 nm aerosol particles play a critical role in the atmosphere through their involvement in new particle formation, a key process influencing the availability of cloud condensation nuclei. In recognition of their significance, international observation frameworks such as the Aerosol, Clouds and Trace Gases Research Infrastructure (ACTRIS) engage in the process of integrating their observations into standardized protocols. ACTRIS aims to enhance the understanding of atmospheric processes, including those related to particle dynamics and their interactions with clouds and climate through harmonized long-term observations. To evaluate the performance of current instrumentation for sub-10 nm particle measurements, as well as the procedures for doing so, in November 2023, the ACTRIS Cluster Calibration Center in Helsinki, Finland, held its first workshop on the intercomparison of instrumentation for sub-10 nm aerosol particle number size distribution measurements. In this workshop, three mobility-based systems (GRIMM PSMPS, TSI 1 nm SMPS and TSI 3938N56) and five activation-based systems (three Airmodus A11s and two Airmodus A12s) were evaluated. The focus lay on assessing their number concentration response and size-dependent detection efficiency – including the determination of the diameter with 50 % detection efficiency – and sizing accuracy. In addition to these parameters, the instruments were compared with each other while measuring aerosol particle number size distributions side by side from an aerosol chamber. Beyond the evaluation of instrument performance, the workshop aimed to test and assess calibration and comparison methods to identify aspects of ACTRIS compliance requiring further refinement. Although this work highlights the key strengths of the different measurement techniques and instruments, several challenges remain. Mobility-based systems showed high sizing accuracy, especially for particles larger than 2 nm, while encountering challenges in measuring particles in the lower atmospheric concentration range. Activation-based systems proved more sensitive at lower particle number concentrations and particle sizes, with the drawback of slight unit-to-unit variability. Additionally, a systematic size shift was identified in aerosols generated by 4-way-cross glowing wire generator setups, indicating a need for further investigation on this effect and the development of calibration equipment in the sub-10 nm size range.

Low-cost, resource-constrained, mainte-nance-free, and energy-harvesting (EH) Internet of Things (IoT) devices, referred to as zero-energy devices (ZEDs), are rapidly attracting attention from industry and academia due to their myriad of applications. To date, such devices remain primar-ily unsupported by modern IoT connectivity solutions due to their intrinsic fabrication, hardware, deployment, and operation limitations, while lacking clarity on their key technical enablers and prospects. Herein, we address this by discussing the main characteristics and enabling technologies of ZEDs within the next generation of mobile networks, specifically focusing on unconventional EH sources, multi-source EH, power management, energy storage solutions, manufacturing material and practices, backscattering, and low-complexity receivers. Moreover, we highlight the need for lightweight and energy-aware computing, communication, and scheduling protocols, while discussing potential approaches related to tiny machine learning (TinyML), duty cycling, and infrastructure enablers like radio frequency wire-less power transfer and wake-up protocols. Chal-lenging aspects and open research directions are identified and discussed in all the cases. Finally, we showcase an experimental ZED proof-of-con-cept related to ambient cellular backscattering.

With the recent advances in 5G and 6G communications and the increasing need for immersive interactions due to pandemic, new use cases such as All-Senses meeting are emerging. To realize these use cases, numerous sensors, actuators, and virtual reality devices are used. Additionally, Artificial Intelligence (AI) and Machine Learning (ML) can be used to analyze large amount of data generated by 6G networks and devices to enable new applications and services. While AI/ML technologies are evolving, they do not have the same level of security as well-known information technology components. So, AI/ML threats and their impacts can be overlooked. On the other hand, due to inherent characteristics of AI/ML components and design of AI/ML pipeline, AI/ML services can be a target for sophisticated attacks. In order to provide a holistic security view, the effect of AI/ML components should be investigated, threats should be identified, and countermeasures should be planned. Therefore, in this study, which is a continuation of our previous study [1], we shed the light on the use of AI/ML services in All-Senses meeting use case and their security aspects by carrying out a threat modelling using the STRIDE framework. Additionally, we point out some countermeasures for identified threats.

Devices and use case heterogeneity in Internet of Things (IoT) make it challenging to streamline the creation of processes that orchestrate devices that work interactively through actuation and sensing (data generation and data processing) toward fulfilling a goal. This raises the question of what is needed to realize this vision using serverless technologies and leveraging semantic description tools that would eventually complement standards to describe such orchestration in a similar way to microservices (e.g., using TOSCA). This study analyzes the different requirements necessary in such automation description and tests them in some practical use cases in a particular application (vertical agriculture). It also provides a set of analyzed instructions that might be incorporated into a description language or become a definition format for an automation orchestration description to provide the necessary process deployment options and interaction chains between multiple devices and data sources. These instructions are analyzed by implementing an execution deployment engine from the intelligence orchestration concept. The practical utilization of such instructions is verified and tested with the use case.

The automotive and railway industries are rapidly transforming with a strong drive toward automation and digitalization, with the goal of increased convenience, safety, efficiency, and sustainability. Since assisted and fully automated automotive and train transport services increasingly rely on vehicle-to-everything communications and high-accuracy real-time positioning, it is necessary to continuously maintain high-accuracy localization, even in occlusion scenes such as tunnels, urban canyons, or areas covered by dense foliage. In this article, we review the 5G positioning framework of the 3rd Generation Partnership Project in terms of methods and architecture and propose enhancements to meet the stringent requirements imposed by the transport industry. In particular, we highlight the benefit of fusing cellular and sensor measurements, and discuss required architecture and protocol support for achieving this at the network side. We also propose a positioning framework to fuse cellular network measurements with measurements by onboard sensors. We illustrate the viability of the proposed fusion-based positioning approach using a numerical example.

Because of the high coverage of international vaccination programs, most people worldwide have been vaccinated against common pathogens, leading to acquired pathogen-specific immunity with a robust memory T-cell repertoire. Although CD8+ antitumor cytotoxic T lymphocytes (CTL) are the preferred effectors of cancer immunotherapy, CD4+ T-cell help is also required for an optimal antitumor immune response to occur. Hence, we investigated whether the pathogen-related CD4+ T-cell memory populations could be reengaged to support the CTLs, converting a weak primary antitumor immune response into a stronger secondary one. To this end, we used our PeptiCRAd technology that consists of an oncolytic adenovirus coated with MHC-I-restricted tumor-specific peptides and developed it further by introducing pathogen-specific MHC-II-restricted peptides. Mice preimmunized with tetanus vaccine were challenged with B16.OVA tumors and treated with the newly developed hybrid TT-OVA-PeptiCRAd containing both tetanus toxoid- and tumor-specific peptides. Treatment with the hybrid PeptiCRAd significantly enhanced antitumor efficacy and induced TT-specific, CD40 ligand-expressing CD4+ T helper cells and maturation of antigen-presenting cells. Importantly, this approach could be extended to naturally occurring tumor peptides (both tumor-associated antigens and neoantigens), as well as to other pathogens beyond tetanus, highlighting the usefulness of this technique to take full advantage of CD4+ memory T-cell repertoires when designing immunotherapeutic treatment regimens. Finally, the antitumor effect was even more prominent when combined with the immune checkpoint inhibitor anti-PD-1, strengthening the rationale behind combination therapy with oncolytic viruses. SIGNIFICANCE: These findings establish a novel technology that enhances oncolytic cancer immunotherapy by capitalizing on pre-acquired immunity to pathogens to convert a weak antitumor immune response into a much stronger one.

The first phase of new radio (NR) standard, which was finalized in December 2017, is called a non‐standalone (NSA) NR as it is based on dual connectivity between an LTE radio access technology (RAT) and the NR RAT, known also as E‐UTRA – NR Dual Connectivity(EN‐DC). This chapter presents an overview of 5G core (5GC) network and functionalities by presenting the specified logical network nodes which are the standardized building blocks for the 5GC and by giving an example of a signaling flow between the nodes for connection establishment. It discusses the different dual connectivity options and related terminology. Two different protocol stacks exist in NR system: control plane protocol stack, carrying control information between user equipment (UE) and gNB or Core Network, and user plane protocol stack, carrying data between different applications in the UE and in the network.