This paper proposes a paradigm-shifting solution for green telecom infrastructure, addressing 5G deployment challenges through China Mobile's Green Minimalist Digital Machine Room. By integrating digital twin-based cross-vendor orchestration, adaptive AI energy optimization, and modular outdoor hardware, we achieve a 40% reduction in energy consumption and 30% decrease in OPEX-surpassing prior industry efforts in scalability and interoperability. Rigorous case studies across 30 Chinese provinces validate the model's technical feasibility, with ROI$>2.5$within 3 years. This work establishes a replicable framework for carbon-neutral telecom infrastructure under China's “Dual Carbon” policy.

Reservoir monitory technology based on Distributed Acoustic Sensing (DAS) technology provides a new means to characterize and monitor the underground structure, so as to achieve rapid and real-time monitoring and identification of underground target structure changes, which has been widely used in hydraulic fracturing micro-seismic monitoring and precision engineering monitoring. When micro-seismic monitoring technology is used for source location imaging, it is necessary to consider the influence of many factors on the location results, among which the velocity model error is one of the most important factors, so it is a key step to correct the initial velocity model to obtain an accurate velocity model. On the basis of receiving the arrival time data of the perforating source by the distributed optical fiber sensing, the underground horizon is divided according to the acoustic logging curve, the micro-seismic velocity model is corrected by using the perforation event, the travel-time relationship formula based on the velocity model and the propagation path is studied, and a more accurate velocity model is further solved and corrected by the particle swarm optimizatio (PSO) in the global optimization method. The accuracy of the method was validated through numerical simulations, which demonstrated small calibration errors that were almost uniformly below 3%. The method was subsequently applied to real DAS perforation data, achieving satisfactory inversion results.

Fractures play a very important role in hydrocarbon accumulation and migration as well as hydraulic fracturing. Small-scale fractures contain dense sets of fractures that extend from meters to tens of meters in length. They are at risk resulted in drilling loss and fracturing disturbance. It is therefore vital to build a comprehensive fracture system, especially a dedicated description of small-scale fractures is desired. Conventional large-scale fracture identification mostly depends on the post-stack seismic attributes. Ant tracking places ants as seeds on discontinuous anomalies to locate fracture areas. Taking advantage of wide-azimuth pre-stack seismic, we design an azimuthal anisotropy-driven ant-tracking scheme for small-scale fracture detection. First, we fit ellipses to six azimuthal sectors to obtain fracture intensity and strike. Second, we propose an anisotropic Bayes Ant Colony Optimization (ani-Bayes ACO). The novel algorithm is built on an anisotropic transfer mechanism via a Bayesian framework to optimize the detection of small-scale fractures. Specifically, the computed fracture intensity and strike automatically allocate the range of ants’ detection. The anisotropic transfer mechanism strengthens the detection in the fracture-dense zone and constrains ants to track the geometric structure along the fracture. Finally, we combine conventional ant-tracking to construct an across-scale fracture volume. The bandwidth of the resultant fracture system has been enhanced by incorporating the fracture intensity and geometry into tracing. This new theme may accelerate the automated interpretation by simultaneously characterizing the across-scale fractures and fracture zones. The application of field data in the Sichuan Basin, China demonstrated the robustness of our proposed scheme.

Time-delay estimation is a critical step in many geophysical applications. Conventional approaches are mainly based on the cross correlation of waveforms in time domain but show strong distortions with multiple oscillations and side lobes. We propose a band-limited phase-only correlation (POC) algorithm for time-delay estimation. The algorithm involves the following key steps: 1) transforming 1-D waveform into 2-D time–frequency spectra using S-transform; 2) calculating the band-limited POC function of the transformed spectra; and 3) measuring time delays by analyzing POC coefficient. We demonstrate the effectiveness of the proposed algorithm using synthetic and real microseismic fiber-optic distributed acoustic sensing (DAS) datasets. Results show that POC can effectively and accurately estimate the time delays of waveforms. Compared with the performance of the conventional cross correlation method in time domain, the proposed method has three main advantages: 1) better identification of event and noise waveforms; 2) lower uncertainty of narrow correlation peaks; and 3) weaker distortions with small oscillations and side lobes.

Constructing a scientific evaluation system of geological environment and restoration can provide a reliable theoretical basis for mine geological environment protection. In this paper, we firstly constructed the evaluation system of 10 indicators and 30 sub-indicators as the index of ecological environment and restoration. The hierarchical analysis method was used to evaluate each mine according to the actual situation in Chongqing. We took Mengte mine as a study area and obtained the parameters of soil, water, vegetation, and biology by field experiment. Analytic hierarchy process was applied to this paper and the weight of each indicator was calculated based on expert scoring method and the experience method. This research provided technical support for geological environmental assessment in other mines.

Fine-grained, real-time, and accurate monitoring data can better help detect equipment failure and perform traffic engineering. However, existing in-band network telemetry (INT) implementations still exhibit a few drawbacks such as lack of real-time monitoring, relatively high overheads due to per-packet operation, and limited monitoring range. This paper proposes an INT+PDP-based fine-grained real-time telemetry scheme by observing and recording on the programmable data plane (PDP), referred to as O&R. The key idea lies in designing some registers on data plane to observe the states of packets forwarded by it as well as adding a customized header on a normal data packet to record how it is forwarded on its routing path. Except for measuring some conventional performance parameters such as end-to-end delay, jitter, throughput, and packet loss rate, O&R designs a clock offset elimination algorithm to realize the time synchronization of two adjacent switches, based on which we can complete more fine-grained measurement such as queuing delay, processing delay, transmission delay, and propagation delay on any hop. O&R also can measure the queue state that includes real-time queue depth and how many flows share the queue. Extensive experimental results for the K=4 fat-tree data-center network demonstrate the effectiveness of O&R in terms of higher accuracy, better real-time performance, less overheads, and better fine-graining compared to existing schemes. The measurement accuracy of O&R is 46.3% higher than that of INT-like method. The measurement delay of O&R is ~1 ms, while INT-like method needs ~20 ms. The measurement overhead of O&R is only 2.19% of Pingmesh.

Physical traits such as the shape of the hand and face can be used for human recognition and identification in video surveillance systems and in biometric authentication smart card systems, as well as in personal health care. However, the accuracy of such systems suffers from illumination changes, unpredictability, and variability in appearance (e.g. occluded faces or hands, cluttered backgrounds, etc.). This work evaluates different statistical and chrominance models in different environments with increasingly cluttered backgrounds where changes in lighting are common and with no occlusions applied, in order to get a reliable neural network reconstruction of faces and hands, without taking into account the structural and temporal kinematics of the hands. First a statistical model is used for skin colour segmentation to roughly locate hands and faces. Then a neural network is used to reconstruct in 3D the hands and faces. For the filtering and the reconstruction we have used the growing neural gas algorithm which can preserve the topology of an object without restarting the learning process. Experiments conducted on our own database but also on four benchmark databases (Stirling's, Alicante, Essex, and Stegmann's) and on deaf individuals from normal 2D videos are freely available on the BSL signbank dataset. Results demonstrate the validity of our system to solve problems of face and hand segmentation and reconstruction under different environmental conditions.

In 2015, the top 10 largest amusement park corporations saw a combined annual attendance of over 400 million visitors. Daily average attendance in some of the most popular theme parks in the world can average 44,000 visitors per day. These visitors ride attractions, shop for souvenirs, and dine at local establishments; however, a critical component of their visit is the overall park experience. This experience depends on the wait time for rides, the crowd flow in the park, and various other factors linked to the crowd dynamics and human behavior. As such, better insight into visitor behavior can help theme parks devise competitive strategies for improved customer experience. Research into the use of attractions, facilities, and exhibits can be studied, and as behavior profiles emerge, park operators can also identify anomalous behaviors of visitors which can improve safety and operations. In this article, we present a visual analytics framework for analyzing crowd dynamics in theme parks. Our proposed framework is designed to support behavioral analysis by summarizing patterns and detecting anomalies. We provide methodologies to link visitor movement data, communication data, and park infrastructure data. This combination of data sources enables a semantic analysis of who , what , when , and where , enabling analysts to explore visitor-visitor interactions and visitor-infrastructure interactions. Analysts can identify behaviors at the macro level through semantic trajectory clustering views for group behavior dynamics, as well as at the micro level using trajectory traces and a novel visitor network analysis view. We demonstrate the efficacy of our framework through two case studies of simulated theme park visitors.

Subspace-based analysis has increasingly become the preferred method for clustering high-dimensional data. A visually interactive exploration of subspaces and clusters is a cyclic process. Every meaningful discovery will motivate users to re-search subspaces that can provide improved clustering results and reveal the relationships among clusters that can hardly coexist in the original subspaces. However, the combination of dimensions from the original subspaces is not always effective in finding the expected subspaces. In this study, we present an approach that enables users to reconstruct new dimensions from the data projections of subspaces to preserve interesting cluster information. The reconstructed dimensions are included into an analytical workflow with the original dimensions to help users construct target-oriented subspaces which clearly display informative cluster structures. We also provide a visualization tool that assists users in the exploration of subspace clusters by utilizing dimension reconstruction. Several case studies on synthetic and real-world data sets have been performed to prove the effectiveness of our approach. Lastly, further evaluation of the approach has been conducted via expert reviews.

In this article, we present a novel radial visualization of IDS alerts, named IDSPlanet, which helps administrators identify false positives, analyze attack patterns, and understand evolving network conditions. Inspired by celestial bodies, IDSPlanet is composed of Chrono Rings, Alert Continents, and Interactive Core. These components correspond with temporal features of alert types, patterns of behavior in affected hosts, and correlations amongst alert types, attackers and targets. The visualization provides an informative picture for the status of the network. In addition, IDSPlanet offers different interactions and monitoring modes, which allow users to interact with high-interest individuals in detail as well as to explore overall pattern.

This paper describes how a heterogeneous network that comprises of satellite and WLAN systems could be implemented in a high-speed train environment to provide Internet access. The focus of the paper is on the design of the network architecture and mobility management procedures. In particular, the implementation of WLAN to complement satellite coverage in various operating environments is investigated. For mobility management, issues related to location and handover management are described. In addition, the paper also elaborates on the use of location detection procedures and antenna diversity in trains to aid the process of handover.