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The development of sustainable transportation and communication systems requires an increase in both energy density and capacity retention of Li-batteries. Using substrates forming a solid solution with body centered cubic Li enhances the cycle stability of anode-less batteries. However, it remains unclear how the substrate microstructure affects the lithiation behavior. Here, we deploy a correlative, near-atomic scale probing approach through combined ion- and electron-microscopy to examine the distribution of Li in Li-Ag diffusion couples as model system. We reveal that Li regions with over 93.8% at.% nucleate within Ag at random high angle grain boundaries, whereas grain interiors are not lithiated. We evidence the role of kinetics and mechanical constraint from the microstructure over equilibrium thermodynamics in dictating the lithiation process. The findings suggest that grain size and grain boundary character are critical to enhance the electrochemical performance of interlayers/electrodes, particularly for improving lithiation kinetics and hence reducing dendrite formation.

The structural and magnetic properties of Ge0.5Mn0.5Co2O4 (GMCO) have been investigated in detail utilizing neutron powder diffraction (NPD), x-ray diffraction (XRD), DC magnetometry, and heat capacity analysis and compared with GeCo2O4. Despite both compounds exhibiting a cubic structure at room temperature, a substantial difference on low temperature structural properties have been observed for GMCO, indicating the effect of Mn substitution on crystal structure. The magnetic and heat capacity data reveal a ferrimagnetic ordering around 108 K in GMCO. A minor secondary phase is confirmed which undergoes long range AFM ordering at further lower temperatures. This secondary phase remains undetected in XRD due to identical lattice parameters. Furthermore, the analysis of heat capacity data indicates a broadening of the high-temperature transition, attributing to the short-range correlation persisting up to higher temperatures. The estimated magnetic entropy amounts is 63% of the value expected for GMCO. The missing entropy is likely linked with the short-range magnetic correlations persisting well above the transition temperature. Cation distribution at the A and B sites has been estimated in GMCO using NPD. Magnetic structures are also confirmed in the main phase as well as in the secondary phase using NPD analysis. The high-temperature transition corresponds to the ferrimagnetic ordering of A and B site cations in the main phase. A collinear ferrimagnetic arrangement of A and B site spins aligned parallel to c axis is observed. The average values of A and B site moments in the ferrimagnetic phase at 5 K are 2.31(3) and 1.82(3)mB, respectively, with the temperature dependence of moments following the expected power law behavior. The low-temperature ordering is attributed to the antiferromagnetic ordering of B site ions associated with the secondary phase, something similar to GeCo2O4., Comment: 16 pages, 9 figures

This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introduce a principled approach to specifying and constructing the benchmark, which for v0.5 covers only a single use case (an adult chatting to a general-purpose assistant in English), and a limited set of personas (i.e., typical users, malicious users, and vulnerable users). We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0.5 benchmark. We plan to release version 1.0 of the AI Safety Benchmark by the end of 2024. The v1.0 benchmark will provide meaningful insights into the safety of AI systems. However, the v0.5 benchmark should not be used to assess the safety of AI systems. We have sought to fully document the limitations, flaws, and challenges of v0.5. This release of v0.5 of the AI Safety Benchmark includes (1) a principled approach to specifying and constructing the benchmark, which comprises use cases, types of systems under test (SUTs), language and context, personas, tests, and test items; (2) a taxonomy of 13 hazard categories with definitions and subcategories; (3) tests for seven of the hazard categories, each comprising a unique set of test items, i.e., prompts. There are 43,090 test items in total, which we created with templates; (4) a grading system for AI systems against the benchmark; (5) an openly available platform, and downloadable tool, called ModelBench that can be used to evaluate the safety of AI systems on the benchmark; (6) an example evaluation report which benchmarks the performance of over a dozen openly available chat-tuned language models; (7) a test specification for the benchmark.

Autonomous systems are becoming increasingly prevalent in new vehicles. Due to their environmental friendliness and their remarkable capability to significantly enhance road safety, these vehicles have gained widespread recognition and acceptance in recent years. Automated Driving Systems (ADS) are intricate systems that incorporate a multitude of sensors and actuators to interact with the environment autonomously, pervasively, and interactively. Consequently, numerous studies are currently underway to keep abreast of these rapid developments. This paper aims to provide a comprehensive overview of recent advancements in ADS technologies. It provides in-depth insights into the detailed information about how data and information flow in the distributed system, including autonomous vehicles and other various supporting services and entities. Data validation and system requirements are emphasised, such as security, privacy, scalability, and data ownership, in accordance with regulatory standards. Finally, several current research directions in the AVs field will be discussed., Comment: 16 pages, 9 figures

Heightened interest from nation states to perform content censorship make it evermore critical to identify the impact of censorship efforts on the Internet. We undertake a study of Internet architecture, capturing the state of Internet topology with greater completeness than existing state-of-the-art. We describe our methodology for this, including the tooling we create to collect and process data from a wide range of sources. We analyse this data to find key patterns in nation states with higher censorship, discovering a funnelling effect wherein higher Internet censorship effort is reflected in a constraining effect on a state's Internet routing architecture. However, there are a small number of nation states that do not follow this trend, for which we provide an analysis and explanation, demonstrating a relationship between geographical factors in addition to geopolitics. In summary, our work provides a deeper understanding of how these censorship measures impact the overall functioning and dynamics of the Internet.

Automated Driving Systems (ADSs) are being manufactured at an accelerated rate, leading to improvements in traffic safety, reduced energy consumption, pollution, and congestion. ADS relies on various data streams from onboard sensors, external road infrastructure, and other vehicles to make driving decisions. For effective traffic accident reconstruction, investigators must produce, collect, store, and access real-time data. To ensure meaningful investigation, the data used by investigators must be accurate and maintain its integrity. In this paper, we propose a smart-contract based data integrity and validation tool for automated vehicle incident investigation during road trials, considering uncertainties in a real-world environment., Comment: 11 pages, 5 Figures

Given the imperative for advanced wireless networks in the next generation and the rise of real-time applications within wireless communication, there is a notable focus on investigating data rate performance across various fading scenarios. This research delved into analyzing the effective throughput of the shadowed Beaulieu-Xie (SBX) composite fading channel using the PDF-based approach. To get the simplified relationship between the performance parameter and channel parameters, the low-SNR and the high-SNR approximation of the effective rate are also provided. The proposed formulations are evaluated for different values of system parameters to study their impact on the effective throughput. Also, the impact of the delay parameter on the EC is investigated. Monte-Carlo simulations are used to verify the facticity of the deduced equations., Comment: 18 pages

As the prevalence of Internet-of-Things (IoT) devices becomes more and more dominant, so too do the associated management and security challenges. One such challenge is the exploitation of vulnerable devices for recruitment into botnets, which can be used to carry out Distributed Denial-of-Service (DDoS) attacks. The recent Manufacturer Usage Description (MUD) standard has been proposed as a way to mitigate this problem, by allowing manufacturers to define communication patterns that are permitted for their IoT devices, with enforcement at the gateway home router. In this paper, we present a novel integrated system implementation that uses a MUD manager (osMUD) to parse an extended set of MUD rules, which also allow for rate-limiting of traffic and for setting appropriate thresholds. Additionally, we present two new backends for MUD rule enforcement, one based on eBPF and the other based on the Linux standard iptables. The reported evaluation results show that these techniques are feasible and effective in protecting against attacks and in terms of their impact on legitimate traffic and on the home gateway., Comment: 12 pages, 10 figures, in review currently

Ni$_{2}$Mo$_{3}$O$_{8}$ is a pyroelectric honeycomb antiferromagnet exhibiting peculiar changes of its electric polarization at magnetic transitions. Ni$_{2}$Mo$_{3}$O$_{8}$ stands out from the isostructural magnetic compounds, showing an anomalously low magnetic transition temperature and unique magnetic anisotropy. We determine the magnetic structure of Ni$_{2}$Mo$_{3}$O$_{8}$ utilizing high-resolution powder and single-crystal neutron diffraction. A noncollinear stripy antiferromagnetic order is found in the honeycomb planes. The magnetic space group is \textit{P$_C$na}2$_1$. The in-plane magnetic connection is of the stripy type both for the $ab$-plane and the $c$-axis spin components. This is a simpler connection than the one proposed previously. The ferromagnetic interlayer order of the $c$-axis spin components in our model is also distinct. The magnetic anisotropy of Ni$_{2}$Mo$_{3}$O$_{8}$ is characterized by orientation-dependent magnetic susceptibility measurements on a single crystal, consistent with neutron diffraction analysis. The local magnetoelectric tensor analysis using our magnetic models provides new insights into its magnetoelectric coupling and polarization. Thus, our results deliver essential information for understanding both the unusual magnetoelectric properties of Ni$_{2}$Mo$_{3}$O$_{8}$ and the prospects for observation of exotic nonreciprocal, Hall, and magnonic effects characteristic to this compound family., Comment: 28 pages, 23 figures, 14 tables

We present a molecular orbital candidate Ba$_{4}$TaMn$_{3}$O$_{12}$ with a face-sharing octahedra trimer, by comparing it with a related compound Ba$_{4}$NbMn$_{3}$O$_{12}$. The synthesis of the polycrystalline powder is optimized by suppressing the secondary impurity phase via x-ray diffraction. Magnetic susceptibility measurements on the optimized samples reveal a weak magnetic hysteresis with magnetic transitions consistent with heat capacity results. The effective magnetic moments from susceptibility indicate a strongly coupled $S=2$ antiferromagnetic trimer at around room temperature, whereas the estimated magnetic entropy from heat capacity suggests the localized $S=3/2$ timer. These results can be explainable by a partial molecular orbital state, in which three $t_{2g}$ electrons are localized in each Mn ion and one $e_{g}$ electron is delocalized over two-end Mn ions of the trimer based on density functional theory calculations. This unconventional 3$d$ orbital state is comprehended as a consequence of competition between the hybrid interatomic orbitals within the Mn trimer and the local moment formation by on-site Coulomb correlations., Comment: 15 pages, 12 figures, 4 tables

Advancements in wireless communication and the increased accessibility to low-cost sensing and data processing IoT technologies have increased the research and development of urban monitoring systems. Most smart city research projects rely on deploying proprietary IoT testbeds for indoor and outdoor data collection. Such testbeds typically rely on a three-tier architecture composed of the Endpoint, the Edge, and the Cloud. Managing the system's operation whilst considering the security and privacy challenges that emerge, such as data privacy controls, network security, and security updates on the devices, is challenging. This work presents a systematic study of the challenges of developing, deploying and managing urban monitoring testbeds, as experienced in a series of urban monitoring research projects, followed by an analysis of the relevant literature. By identifying the challenges in the various projects and organising them under the V-model development lifecycle levels, we provide a reference guide for future projects. Understanding the challenges early on will facilitate current and future smart-cities IoT research projects to reduce implementation time and deliver secure and resilient testbeds., Comment: 25 pages, under review

Interactions between lecturers and students are the key to learning in the higher education environment. In this paper, the investigation pursues two different contexts to understand these interactions and the impact of anonymity and privacy in different interactions in the Computer Science (CS) department. The first context "different interaction between a lecturer and students" is investigated using phenomenological research approach by interviewing lecturer in CS ($N_a = 5$). The second context "the significance of anonymity and privacy in interactions" is investigated using a quantitative and qualitative questionnaire-based research method using an online student questionnaire ($N_b = 53$). The study finds a large gap between students' perception of preferred communication methods and the use of the same communication method. From the second context study, it is evident that "anonymity and privacy" in online surveys and module evaluations are preferred by all student participants, thus supporting diversity and inclusivity., Comment: 15 pages

Edge computing has gained attention in recent years due to the adoption of many Internet of Things (IoT) applications in domestic, industrial and wild settings. The resiliency and reliability requirements of these applications vary from non-critical (best delivery efforts) to safety-critical with time-bounded guarantees. The network connectivity of IoT edge devices remains the central critical component that needs to meet the time-bounded Quality of Service (QoS) and fault-tolerance guarantees of the applications. Therefore, in this work, we systematically investigate how to meet IoT applications mixed-criticality QoS requirements in multi-communication networks. We (i) present the network resiliency requirements of IoT applications by defining a system model (ii) analyse and evaluate the bandwidth, latency, throughput, maximum packet size of many state-of-the-art LPWAN technologies, such as Sigfox, LoRa, and LTE (CAT-M1/NB-IoT) and Wi-Fi, (iii) implement and evaluate an adaptive system Resilient Edge and Criticality-Aware Best Fit (CABF) resource allocation algorithm to meet the application resiliency requirements using Raspberry Pi 4 and Pycom FiPy development board having five multi-communication networks. We present our findings on how to achieve 100% of the best-effort high criticality level message delivery using multi-communication networks, Comment: 14 pages, Under review

Medical AI has tremendous potential to advance healthcare by supporting the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving provider and patient experience. We argue that unlocking this potential requires a systematic way to measure the performance of medical AI models on large-scale heterogeneous data. To meet this need, we are building MedPerf, an open framework for benchmarking machine learning in the medical domain. MedPerf will enable federated evaluation in which models are securely distributed to different facilities for evaluation, thereby empowering healthcare organizations to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status, and our roadmap. We call for researchers and organizations to join us in creating the MedPerf open benchmarking platform.