ALT Satellite communication systems are increasingly relied upon for connectivity in remote areas and are expected to play a pivotal role in 6G networks, operating under high round-trip times and packet loss where forward error correction is essential. This work evaluates Reed-Solomon (RS) coding, Random Linear Network Coding (RLNC), and a combined RS+RLNC hybrid over a three-hop satellite channel whose loss statistics are derived from orbital geometry and link-budget calculations rather than assumed, comparing RS and RLNC at matched code rates of 0.800. The central finding is the bridging property of RLNC: every RS code rate collapses above a crossover threshold p, while RLNC operates continuously through all failure regions with overhead growing as 1/(1-p). At rate 0.800, p≈ 0.21. Under the derived satcom channel with mean burst duration 166 minutes, RS(255,204) is impractical despite an average loss rate of 1%, while RLNC requires 15.5 Mbit to recover all 1000 packets. An RS+RLNC hybrid

ALT We evaluate ErouVe, a vehicular eco-routing protocol in which roadside units (RSUs) collect per-segment travel-time and CO2 reports from passing vehicles and answer route requests with the lowest-cost alternative, across a range of road architectures and traffic scenarios. We conduct the evaluation with a working implementation of the protocol inside Veins, adapted to drive SUMO's own router rather than a hand-rolled shortest-path search, on a network offering a short, signal-controlled route and a longer, signal-free bypass, under two traffic scenarios. This evaluation reveals clear strengths and weaknesses of the protocol. Under light, uncongested traffic, ErouVe's fleet-wide effect is light. Under traffic dense enough to stress the signalized route's throughput and produce a real, growing queue, the same protocol succeeds a medium fleet-wide CO2 reduction, with diverted vehicles individually saving even more fuels. We use these findings to propose three targeted corrective methods;

ALT Autonomous IoT systems rely on distributed sensing, but sensor faults and communication loss can leave multichannel evidence incomplete or unreliable. Their agents must therefore classify measurements, assess evidence sufficiency, select a sensor to reacquire, and hand off unsafe decisions. We formulate counterfactual sensing: a degraded window defines the factual state, each candidate restoration defines a controlled alternative, and a pre-request utility model estimates loss reduction. Physics-Guided Invariant-Kinematic Fusion (PGIK-Fusion) constructs class evidence, while calibrated risk and cost-sensitive utility select acceptance, sensor request, or handoff from factual information. On the Logistic Activity Recognition Challenge dataset, PGIK-Fusion achieves 0.768 accuracy and a macro-averaged F1 score (Macro-F1) of 0.696, exceeding extreme gradient boosting (XGBoost)–one-dimensional convolutional neural network (1D-CNN) fusion by 2.49 and 2.00 percentage points, respectively. Acr

ALT Lightning routing nodes lend their channel balance for a posted fee. Operators' tools move that fee with the balance around a hand-set level, and which part of the fee drives revenue is unclear. We present Trim, which models a channel as a two-sided inventory with costly refills. Its optimal fee is a level, set by demand and replenishment, plus a balance skew, the shadow price of liquidity. The level is first order: no fee rule beats the ceiling demand and replenishment set, and on exactly solved channels the best flat fee comes within 1.6% of the optimum at the median, whereas a level off by a factor of two loses tens of percent. The skew is second-order insurance: it adds at most what the flow-balancing flat fee loses to refused forwards. So the level should be chosen: Trim's rule traces the revenue–success frontier, posts one level on every channel, and adds a skew only where refills stay frequent and costly. On a 2026 snapshot with synthetic payments, no tuned tool beats that front

ALT The proliferation of orbital sensing data and the real-time demands of emerging global services are pushing low Earth orbit (LEO) satellite networks from bent-pipe relays toward intelligent onboard processing. This shift confronts a fundamental tension, where modern artificial intelligence (AI) training paradigms require intensive computation, abundant memory, and stable connectivity, all of which are scarce under the dynamic topologies, intermittent links, and severe size, weight, and power (SWaP) constraints of satellite networks. Addressing this tension demands rethinking AI workflows, from training and inference to the emerging deployment of large language models (LLMs), under a constraint-aware design philosophy. To the best of our knowledge, this paper provides the first survey that jointly covers all three, whereas prior surveys examine these aspects in isolation. We begin by identifying the deployment challenge of satellite intelligence (SI) and deriving design principles for S

ALT We study the vulnerability of beam allocation approaches in large low-Earth-orbit (LEO) satellite constellations to adversarial demand inflation resulting in denial-of-service attacks. We consider an attack model in which an adversary places (injects) additional terminals and evaluate these attacks against greedy and max-min-fair beam allocation algorithms, each combined with either equal or demand-proportional intra-cell capacity sharing. Vulnerability is measured using mean demand satisfaction, coverage, and the fraction of legitimate users receiving at least a threshold fraction of their requested capacity under several attack heuristics. There is a trade-off for the system between robustness and performance, meaning the algorithm with the most vulnerability to the attacks (greedy-proportional) has the highest performance before the attacks. Demand-proportional sharing increases the fraction of under-served users up to 34% under both allocation algorithms, whereas equal sharing limi

ALT Emerging 6G control planes would compose Large Language Model (LLM) agents that exchange interpreted conclusions, so hallucinations can propagate even over error-free transport. Recent architectures audit each message locally against the sender's observation mask, but isolated audits miss collective behavior. Modeling the agent society as a cellular sheaf, we establish three laws valid on arbitrary topologies. First, the composition law: network-wide divergence exceeds the sum of per-agent audit scores by exactly their hidden total correlation. This blind spot masks up to 23% of joint divergence and, because the declared signal cancels in the cognitive signal-to-noise ratio (SNR), overstates it by 1.14 dB purely through topology; the resulting trust inflation preserves valid consensus states but slows convergence. Second, the scaling law: reconciliation decouples across metric counters, so the worst-connected dimension sets the pace. Consistency time grows for a society size n as Ω(n²/

ALT Inferring directional handover traffic from aggregate mobility observations remains challenging because population density evolution does not fully describe the movement of individual users across cell boundaries. This paper develops a kinetic framework for geometry-induced serving-region crossings in nonstationary cellular networks, connecting boundary-normal velocity statistics with fluid spatiotemporal stochastic geometry (F-STSG) for density-based transport reconstruction. The framework distinguishes signed net population transfer from gross bidirectional crossings and reveals two information limits: density evolution does not uniquely determine physical pairwise transport, while first-order transport alone cannot resolve counter-moving crossings. We establish exact recovery conditions for potential transport and develop coherent and Gaussian mobility closures for estimating directional crossing rates. Sharp moment-constrained bounds further characterize the remaining uncertainty u

ALT The Invisible Internet Project (I2P) is a peer-to-peer anonymity network whose routing and peer-selection mechanics underlie a growing family of state-sponsored covert relay architectures. Studying these mechanics demands a controlled, reproducible environment. We present Garlic-in-a-Box, an installable emulator that deploys a fully isolated network of unmodified I2P Java routers on a single host or cluster of hosts. Each router runs in its own Linux network namespace. A filesystem bootstrap removes the dependency on signed reseed servers, and synthetic geographic diversity activates the same location-aware peer-selection code paths present on the live network. A fifty-router deployment across five geographic locations stabilises in about twenty-five minutes and uses roughly 21 GB of memory. To demonstrate the platform's value, we model I2P peer selection as a two-stage process and conduct a reputation attack. Across five independent trials, the attack promotes the adversary into the v

ALT This paper studies a reconfigurable intelligent surface (RIS)-assisted indoor architecture for extended reality (XR) applications, where multiple access points (APs) simultaneously serve multiple users over a shared frequency channel under a centralized AP controller. We formulate a non-convex sum-rate maximization problem with coupled AP precoders and RIS phase shifts, and develop a centralized alternating optimization (AO) framework for joint design. We rigorously prove that the proposed AO guarantees monotonic improvement of the sum rate that converges to a coordinate-wise stationary point, despite the non-convexity of the resulting subproblems. Although AO provides reliable performance, its iterative nature limits real-time applicability as the system dimension grows. To overcome this limitation, we propose a supervised learning-assisted approach that leverages AO-generated solutions to significantly accelerate convergence. Simulation results demonstrate that the proposed framework