arXiv subjects

Jinwei Zhao

Publications and source records attributed to Jinwei Zhao.

12 recordsLinked to original sources

arXiv2026

A First Look at Starlink Maritime on Cruise Ships

Low-Earth-Orbit (LEO) satellite networks are reshaping maritime connectivity, offering much lower latency and higher capacity than traditional geostationary (GEO) links. As a single user terminal (UT) cannot serve hundreds to thousands of passengers, a cruise ship often deploys several UTs behind an onboard multi-WAN router for aggregated capacity and load balancing. Consequently, passenger connectivity depends not only on the satellite links but also on how the router distributes sessions among UTs. In this paper, we measure Starlink maritime connectivity on two cruise ships of different scales, Utopia of the Seas near the Bahamas in December 2025 and Silver Muse in the Adriatic Sea in July 2026, combining UT telemetry, inside-out measurements from a passenger device, and outside-in measurements from cloud servers near Starlink Points of Presence (PoPs). We find that the onboard network, rather than the satellite links, largely determines the passenger experience, as cruise lines rate limit each passenger device. Moreover, the public IP address of a passenger device changes not only at PoP handovers but also within the same PoP. Furthermore, while the country code reported by the UT reflects the maritime jurisdiction of the ship, the serving PoP follows neither this jurisdiction nor geographic proximity alone, suggesting factors beyond the distance-based PoP boundaries observed in Starlink aviation. To the best of our knowledge, this is the first systematic study of Starlink on commercial cruise ships, complementing prior land mobile and in-flight measurements.

cs.NI↗

arXiv2026

Least-time Gradient Flow

Prescribing the speed of gradient flow on the risk itself, by the dynamics $\dot w=-u(E(w))\nabla E(w)/\abs{\nabla E(w)}^{2}$, makes the risk $e(t)=E(w(t))$ obey $\dot e=-u(e)$ exactly, whatever the landscape~$E$; the time needed to reach zero risk from $e_0$ is $\int_0^{e_0}\dd e/u(e)$. Minimizing this time alone is ill posed, and we study the regularized problem $\inf\{\int_0^{e_0}(\tfrac\lambda2\abs{u'}^{2}+1/u)\,\dd e:\ u\in H^{1}(0,e_0),\ u\ge0,\ u(0)=0\}$, $λ>0$. We prove that the minimizer exists, is unique, and is a linearly scaled cycloid, and we show that the optimal rate behaves like $u^{*}(e)\sim(9/(2λ))^{1/3}e^{2/3}$ near zero risk: the exponent $2/3$ is the one found in \cite{betti2026holder} by a power-law ansatz, and it lies in the Hölder window $(\tfrac12,1)$ where the arrival is in finite time with vanishing weight speed. The proof follows the classical route: existence by the direct method, uniqueness by strict convexity, positivity of the minimizer away from the origin, and the explicit integration of the Euler-Lagrange equation.

cs.LG↗

arXiv2026

Where Does My Call Go? Measuring Google Meet, Zoom, and Microsoft Teams on Starlink

Low-Earth-Orbit (LEO) satellite networks are expanding broadband access, but major Real-Time Communication (RTC) platforms, including Google Meet, Zoom, and Microsoft Teams, still rely on service-point selection mechanisms that appear to be largely shaped by terrestrial-network assumptions. As a result, they may not consistently account for LEO-specific dynamics such as satellite topology and Point-of-Presence (PoP) changes, especially during in-flight connectivity. To characterize RTC service-point selection over Starlink, we measure RTC performance using a Canada-wide testbed and more than 24 hours of in-flight experiments. Our results show clear platform differences. Google Meet tends to align more closely with the serving PoP, consistent with broader observed edge coverage. Zoom follows a regional service-point selection pattern, while Microsoft Teams often remains tied to fixed service points. Moreover, we show that in-flight PoP handovers can shift traffic egress points mid-session, and that RTC platforms respond differently to these changes. In some cases, this interaction leads to suboptimal service-point selections and RTT inflation. Overall, our findings suggest that LEO connectivity can expose mismatches between Starlink egress dynamics and RTC service-point selection, motivating further measurement and LEO-optimized designs for service placement and selection.

cs.NI↗

arXiv2026

A Large-Scale IPv6-Based Measurement of the Starlink Network

Low Earth Orbit (LEO) satellite networks have attracted considerable attention for their ability to deliver global, low-latency broadband Internet services. In this paper, we present a large-scale measurement study of the Starlink network, the largest LEO satellite constellation to date. We first propose an efficient method for discovering active Starlink user routers, identifying approximately 5.98 million IPv6 addresses across 208 regions in 165 countries. Compared to general-purpose IPv6 target generation algorithms, our router-centric approach achieves near-complete coverage and, to the best of our knowledge, yields the most comprehensive known set of active IPv6 addresses for Starlink user routers. Based on the discovered user routers, we further propose an efficient method for mapping the Starlink backbone network and uncover a topology consisting of 49 Points of Presence (PoPs) interconnected by 98 links. We conduct a detailed statistical analysis of active Starlink user routers and PoPs, and further characterize the IPv6 address assignment strategy adopted by the Starlink network. Finally, we analyze the latency of Starlink user routers, propose a method to distinguish different types of users within the same region using outside-in measurement, and identify the ongoing V2 Mini satellite deployment as a potential driver of the performance improvements. The dataset of the Starlink backbone network is publicly available at https://ki3.org.cn/#/starlink-network.

cs.NI↗

arXiv2026

Demystifying Starlink Network Performance under Vehicular Mobility with Dynamic Beam Switching

In the last few years, considerable research efforts have focused on measuring and improving Starlink network performance, especially for user terminals (UTs) in stationary scenarios. However, the performance of Starlink networks in mobility settings, particularly with frequent changes in the UT's orientation, and the impact of environmental factors, such as transient obstructions, has not been thoroughly studied, leaving gaps in understanding the causes of performance degradation. Recently, researchers have started identifying the communicating satellites to evaluate satellite selection strategies and the impact on network performance. However, existing Starlink satellite identification methods only work in stationary, obstruction-free scenarios, as they do not account for UT mobility, obstructions or detect dynamic beam switching events. In this paper, we reveal that the UT can perform multiple dynamic beam switching attempts to connect to different satellites when the UT-satellite link is degraded. This degradation can occur either due to the loss of line-of-sight (LoS) from changes in the FOV or obstructions, or due to poor signal quality, extending UT-satellite handovers beyond the well-known 15-second regular handover interval. We propose a mobility-aware Starlink satellite identification method that detects dynamic beam switching events, and plausibly explain network performance using UT's diagnostic data and connected satellite information. Our findings demystifies the mobile Starlink network performance degradations, which is crucial to enhance the end-to-end performance of transport layer protocols and in diverse application scenarios.

cs.NI↗

arXiv2025

Investigating Web Content Delivery Performance over Starlink

Low Earth Orbit (LEO) satellite ISPs promise universal Internet connectivity, yet their interaction with content delivery remains poorly understood. We present the first comprehensive measurement study decomposing Starlink's web content delivery performance decomposed across Point of Presence (PoP), DNS, and CDN layers. Through two years of measurements combining 225K Cloudflare AIM tests, M-Lab data, and active probing from 99 RIPE Atlas and controlled Starlink probes, we collect 6.1M traceroutes and 10.8M DNS queries to quantify how satellite architecture disrupts terrestrial CDN assumptions. We identify three distinct performance regimes based on infrastructure density. Regions with local content-rich PoPs achieve near-terrestrial latencies with the satellite segment dominating 80-90% of RTT. Infrastructure-sparse regions suffer cascading penalties: remote PoPs force distant resolver selection, which triggers CDN mis-localization, pushing latencies beyond 200 ms. Dense-infrastructure regions show minimal sensitivity to PoP changes. Leveraging Starlink's infrastructure expansion in early 2025 as a natural experiment, we demonstrate that relocating PoPs closer to user location reduces median page-fetch times by 60%. Our findings reveal that infrastructure proximity, not satellite coverage, influences web performance, requiring fundamental changes to CDN mapping and DNS resolution for satellite ISPs.

cs.NI↗

arXiv2024

Dynamic Decoupling of Placid Terminal Attractor-based Gradient Descent Algorithm

Gradient descent (GD) and stochastic gradient descent (SGD) have been widely used in a large number of application domains. Therefore, understanding the dynamics of GD and improving its convergence speed is still of great importance. This paper carefully analyzes the dynamics of GD based on the terminal attractor at different stages of its gradient flow. On the basis of the terminal sliding mode theory and the terminal attractor theory, four adaptive learning rates are designed. Their performances are investigated in light of a detailed theoretical investigation, and the running times of the learning procedures are evaluated and compared. The total times of their learning processes are also studied in detail. To evaluate their effectiveness, various simulation results are investigated on a function approximation problem and an image classification problem.

cs.LG↗

arXiv2024

An eBPF-Based Trace-Driven Emulation Method for Satellite Networks

System-level performance evaluation over satellite networks often requires a simulated or emulated environment for reproducibility and low cost. However, the existing tools may not meet the needs for scenarios such as the low-earth orbit (LEO) satellite networks. To address the problem, this paper proposes and implements a trace-driven emulation method based on Linux's eBPF technology. Building a Starlink traces collection system, we demonstrate that the method can effectively and efficiently emulate the connection conditions, and therefore provides a means for evaluating applications on local hosts.

cs.NI↗

arXiv2023

Measuring a Low-Earth-Orbit Satellite Network

Starlink and alike have attracted a lot of attention recently, however, the inner working of these low-earth-orbit (LEO) satellite networks is still largely unknown. This paper presents an ongoing measurement campaign focusing on Starlink, including its satellite access networks, gateway and point-of-presence structures, and backbone and Internet connections, revealing insights applicable to other LEO satellite providers. It also highlights the challenges and research opportunities of the integrated space-air-ground-aqua network envisioned by 6G mobile communication systems, and calls for a concerted community effort from practical and experimentation aspects.

cs.NI↗

arXiv2019

A game method for improving the interpretability of convolution neural network

Real artificial intelligence always has been focused on by many machine learning researchers, especially in the area of deep learning. However deep neural network is hard to be understood and explained, and sometimes, even metaphysics. The reason is, we believe that: the network is essentially a perceptual model. Therefore, we believe that in order to complete complex intelligent activities from simple perception, it is necessary to con-struct another interpretable logical network to form accurate and reasonable responses and explanations to external things. Researchers like Bolei Zhou and Quanshi Zhang have found many explanatory rules for deep feature extraction aimed at the feature extraction stage of convolution neural network. However, although researchers like Marco Gori have also made great efforts to improve the interpretability of the fully connected layers of the network, the problem is also very difficult. This paper firstly analyzes its reason. Then a method of constructing logical network based on the fully connected layers and extracting logical relation between input and output of the layers is proposed. The game process between perceptual learning and logical abstract cognitive learning is implemented to improve the interpretable performance of deep learning process and deep learning model. The benefits of our approach are illustrated on benchmark data sets and in real-world experiments.

cs.LG↗

arXiv2019

How to improve the interpretability of kernel learning

In recent years, machine learning researchers have focused on methods to construct flexible and interpretable prediction models. However, an interpretability evaluation, a relationship between generalization performance and an interpretability of the model and a method for improving the interpretability have to be considered. In this paper, a quantitative index of the interpretability is proposed and its rationality is proved, and equilibrium problem between the interpretability and the generalization performance is analyzed. Probability upper bound of the sum of the two performances is analyzed. For traditional supervised kernel machine learning problem, a universal learning framework is put forward to solve the equilibrium problem between the two performances. The condition for global optimal solution based on the framework is deduced. The learning framework is applied to the least-squares support vector machine and is evaluated by some experiments.

cs.LG↗

arXiv2018

How far from automatically interpreting deep learning

In recent years, deep learning researchers have focused on how to find the interpretability behind deep learning models. However, today cognitive competence of human has not completely covered the deep learning model. In other words, there is a gap between the deep learning model and the cognitive mode. How to evaluate and shrink the cognitive gap is a very important issue. In this paper, the interpretability evaluation, the relationship between the generalization performance and the interpretability of the model and the method for improving the interpretability are concerned. A universal learning framework is put forward to solve the equilibrium problem between the two performances. The uniqueness of solution of the problem is proved and condition of unique solution is obtained. Probability upper bound of the sum of the two performances is analyzed.

cs.LG↗