Reliability is essential for ensuring efficiency in LLM training. However, many real-world reliability issues remain difficult to resolve, resulting in wasted resources and degraded model performance. Unfortunately, today's collective communication libraries operate as black boxes, hiding critical information needed for effective root cause analysis.

Entering the “NewSpace” era, satellite Internet constellation (SIC) is expanding rapidly. However, while operating a large number of broadband satellites in free space enables great opportunities for ubiquitous and low-latency Internet services, it also involves new threats that were previously ignored to the energy-limited satellite systems. In this paper, we investigate the feasibility and impact of a new class of real risk in emerging SICs: energy drain attack. To this end, we play the role of an attacker and propose Starmelt,an energy drain attack mechanism that persistently injects malicious traffic that goes through the victim satellite from various geo-distributed locations, preventing the victim from hibernating and overusing the satellite battery to cut its lifetime. Further, we design a series of techniques to: (i) handle the path ambiguity challenge caused by various access selection and routing schemes to guarantee that the deliberately crafted traffic can precisely pass through the victim; (ii) leverage the tail energy consumption characteristics to reduce the detectability as well as the traffic cost of an attack. Extensive simulations based on real constellation knowledge demonstrate that Starmeltcan substantially increase on-board energy consumption, reducing up to 76% lifetime of the victim satellite under various power models and traffic patterns.

The rapid development of the Internet of Things (IoT) boosts the spread of intelligent spaces. Biometrics-based user identification has gained great popularity recently, among which gait analysis offers a stable, user-friendly, and economical solution. Thanks to the advancement in wireless sensing technologies, capturing gait characteristics using WiFi signals has become a promising new paradigm. The identification process is contactless, insensitive to lighting conditions, and can reuse the incumbent WiFi infrastructure. In this article, we present a gait-based dual-user identification framework named WiWalk to tackle the difficulty where users walk closely together with mixed effects on WiFi signals. The core of WiWalk is to train a deep neural network that can separate and recover individual signals from the mixed ones. Since the separation process inevitably causes information loss, we carefully design a series of algorithms for interference elimination, segmentation, and feature extraction, to enhance the identification accuracy. We conduct extensive experiments to evaluate WiWalk at different locations and times with users of different ages, genders, clothing, and walking behaviors. WiWalk can reach an accuracy of 94.44%, which is suitable for smart homes or offices with a small user base.

Low-earth-orbit (LEO) satellite mega-constellations promise broadband, low-latency network infrastructure from space for terrestrial users in remote areas. However, they face new QoS bottlenecks from infrastructure mobility due to the fast-moving LEO satellites and earth’s rotations. Both cause frequent space-ground link churns and challenge the network latency, bandwidth, and availability at the global scale. Today’s LEO networks mask infrastructure mobility with fixed anchors (ground stations) but cause single-point bandwidth/latency bottlenecks. Instead, we design LBP to remove the LEO network’s QoS bottlenecks from infrastructure mobility. LBP removes remote terrestrial fixed anchors via geographic addressing for shorter latencies and more bandwidth. It adopts local, orbit direction-aware geographic routing to avoid global routing updates for high network availability. LBP further shortens the routing paths by refining handover policies by satellites’ orbital directions. Our experiments in controlled testbeds and trace-driven emulations validate LBP’s 1.64× network latency reduction, 9.66× more bandwidth, and improve network availability to 100%.

With recent advances in the study of biometrics, gait analysis has drawn much attention for its potential use in forensics, surveillance, and legal systems. In this paper, we present WIAGE, a contactless and non-intrusive gait-based age estimation system, which leverages wireless sensing to perform gait analysis to infer the age of individuals. Traditional age estimation systems either require users to carry wearable devices that are inconvenient or rely on image processing that is computationally intensive and sensitive to lighting conditions and occlusion. In contrast, WIAGE utilizes the incumbent WiFi infrastructure to infer the age of users with minimal interference to their activities. We adopt a series of signal processing techniques to recover clear gait patterns from the noisy WiFi signals and extract the most relevant features from steps that can be used for robust age estimation. The experimental results show that WIAGE can achieve an age estimation accuracy of 95.2% for 23 users, which demonstrates the feasibility and effectiveness of our proposed system.