ALT We analyze HTML content and hyperlinks from an offline Tor crawl containing 383 onion hosts and 2,067 directed cross-host links. The study combines semantic analysis with degree-controlled quantum–classical link prediction. Two directory groups emit 94.8% of the observed host links. Directed preferential attachment reaches ROC-AUC 0.976 with random non-edges, but falls to 0.550±0.014 under source-conditioned target-degree matching. With disjoint training and test pairs, multimodal logistic regression reaches 0.672±0.021, GAE 0.899±0.016, and VGAE 0.906±0.016. A four-qubit variational quantum classifier (VQC) comparison tests two encodings across 20 disjoint-pair graph splits under exact and 1,024-shot simulation, with classical controls using the same inputs and training examples. Its best exact mean ROC-AUC is 0.549±0.023, compared with 0.551±0.016 for RBF SVC and 0.573±0.029 for a 16-parameter MLP on the same inputs and training examples. These results support predictive signal in co

ALT Open Radio Access Network (O-RAN) enables mobility-management xApps, but a learned controller is not necessarily the most effective or efficient handover policy. This paper compares standard Event A3, a resource- and mobility-aware heuristic, and a Double Deep Q-Network (DDQN) through a common xApp-oriented interface and paired system-level simulation. The heuristic combines radio quality, target-cell resources, UE mobility, and service class, with weights optimized offline using NSGA-II, while DDQN-Multi uses temporal radio and resource observations. All controllers are evaluated on identical UE trajectories, traffic assignments, and ray-traced radio inputs under Base, High-Mobility, and High-UE scenarios. Performance is assessed using service, handover, resource, energy, and fairness KPIs, complemented by CRITIC–TOPSIS ranking and complexity analysis. Results show that the heuristic achieves the lowest outage in all three scenarios and ranks first in the CRITIC–TOPSIS comparison, whi

ALT Concurrent applications (xApps) in the Near-Real-Time RAN Intelligent Controller (Near-RT RIC) of the Open Radio Access Network (O-RAN) can issue control intents that are jointly infeasible when they act on hierarchically coupled radio resources. This paper experimentally studies an interaction between slice-level physical resource block (PRB) allocation and per-user-equipment (UE) scheduling. We implement a utilisation-aware slicing control (the spectral-efficiency xApp) that reclaims a fixed fraction of the baseline allocation from under-utilised slices and redistributes pooled capacity to saturated slices. The control logic is exercised in simulation and on a 5G testbed comprising an OCUDU-based next-generation NodeB (gNB), an Open5GS core, a Universal Software Radio Peripheral (USRP) X310, and five commercial off-the-shelf (COTS) UEs. Because the gNB counters available in this testbed are UE-keyed but not slice-labelled, the slicing control derives a per-slice throughput proxy from

ALT AI-RAN platforms are expected to share CPU and GPU resources between radio workloads and AI services. Aggregate telemetry reports operating conditions, but timing changes may reflect different workloads, processors, or measurement boundaries. We propose workload-aware monitoring that associates radio-work descriptors with processing paths, timing boundaries, and operating context before comparing like with like. In 16 over-the-air downlink experiments, hybrid dispatch (CPU for transport blocks (TBs) below 4,096 bytes and GPU otherwise) has 29.66% and 22.93% lower mean host-callback durations than direct GPU dispatch, without and with a synthetic GPU co-runner, respectively. Grouping locates the separation in small TBs, while large-TB callback-duration ranges overlap. These observations identify a candidate for complementary CPU/GPU use.

ALT Radio access network (RAN) telemetry reveals radio conditions, protocol behavior, and their consequences for applications. Evaluating its usefulness requires controlled impairments and observations of the same execution. We present C and Python channel brokers and a fault injection workflow for software 5G testbeds connecting an OCUDU base station and a software UE through ZeroMQ. The brokers apply scheduled, directional transformations to I/Q samples and restore the link, while the workflow jointly records applied faults, native and programmable (codelet) telemetry, and application delay. We demonstrate this capability through 45 comparative trials of brief uplink interruptions and stepped additive noise. These use cases distinguish recognizing a radio change from measuring its service consequence: native missing-feedback events retrospectively identify interruptions, whereas noise changes decoding-error measurements without producing those events. Median on-time round-trip times rema

ALT Researchers need blockchain experimental environments with configurable workloads, controllable network conditions, and observable outcomes. Public blockchains and testnets limit experimental control, model-based simulators omit real client implementations, and real-client environments face challenges in combining network control with resource-efficient deployment. We present ChainSEED, a scalable, high-fidelity blockchain emulator built on the SEED Internet Emulator. It separates blockchain configuration from client placement on a configurable routed network. For Ethereum PoS, it organizes separately configured execution, consensus, and validator clients and provides APIs for customizing genesis state, peer connections, and client software. A transaction generator informed by Ethereum mainnet activity, application interfaces, and chain- and node-level monitoring support experiments. ChainSEED also supports Monero and Solana. We examine beacon-node memory reduction, execution-client me

ALT Unmanned Aerial Vehicles (UAVs) in Flying Networks (FNs) increasingly require mechanisms to translate high-level natural language objectives into executable missions. Although Large Language Models (LLMs) have demonstrated the feasibility of language-driven UAV control, existing approaches typically rely on static model configurations and overlook mission-aware performance variability. This paper presents AERIAL, a benchmark-driven framework for adaptive LLM selection in hybrid edge-cloud UAV control. AERIAL separates systematic model benchmarking from mission execution, deriving empirical scenario-model mappings and applying them at runtime for mission-aware selection. Evaluation across 720 automated simulation runs spanning navigation, spatial reasoning, and algorithm invocation revealed significant performance variability depending on scenario-model pairing. A case study integrating AERIAL with a FNs placement algorithm further validated the framework's ability to interface with dom

ALT Ambient Internet of Things (A-IoT) devices operate under stringent energy constraints, relying on harvested power in the μW-to-mW range and limited energy storage. The A-IoT air interface for active devices is still under development; we therefore use Narrowband Internet of Things (NB-IoT) as an implementable reference for evaluating autonomous Uplink (UL) access under harvested energy. Under these conditions, the standard NB-IoT contention-based Random Access (RA) procedure incurs substantial per-packet overhead, making UL access in dense A-IoT networks increasingly energy-expensive. Context-retaining UL access schemes reduce this overhead, but their energy-latency trade-offs are insufficiently studied in literature under realistic harvesting and protocol constraints. To address this gap, we extend the LENA-NB ns-3 module with solar energy harvesting, supercapacitor storage, and a hysteretic brown-out mechanism, and propose Hybrid Scheduling Request (HSR), a hybrid UL access scheme co

ALT Network failures degrade Multipath Reliable Connection (MRC) in direct-connect optical datacenters. We propose Optical MRC (OMRC), which remaps collectives and circuits, preserving 95.7% of training throughput, restoring stalled inference, and achieving 4.7x MRC's simulated bandwidth.

ALT Distributed quantum computing (DQC) is a promising near-term approach for executing circuits that exceed the capacity of a single quantum processing unit (QPU). Prior work has made significant progress on circuit partitioning and reconstruction, but practical DQC requires more than cutting a circuit into smaller pieces. A runtime system must determine whether to execute the circuit intact, cut it for sequential execution on one QPU, or distribute it across multiple QPUs, while accounting for queueing, communication, reconstruction, and hardware constraints. We present a resource-aware scheduler for DQC that compares these execution alternatives at runtime using explicit models of QPU execution, communication, and host-side reconstruction, together with hardware-informed timing derived from simulated IBM QPUs. By treating communication and reconstruction as first-class scheduling costs, the scheduler selects execution plans using a more realistic view of end-to-end overhead than cutting