YouTube has become the most successful Internet website providing a new generation of short video sharing service since its establishment in early 2005. great impact on traffic nowadays, yet itself is suffering from severe problem scalability. Therefore, understanding characteristics and similar sites essential to network engineering their sustainable development. To this end, we have crawled site for four months, collecting more than 3 million videos' data. In paper, present systematic...

Established in 2005, YouTube has become the most successful Internet website providing a new generation of short video sharing service. Today, alone consumes as much bandwidth did entire year 2000 . Understanding features and similar sites is thus crucial to their sustainable development network traffic engineering. In this paper, using traces crawled 1.5-year span (from February 2007 September 2008), we present an in-depth systematic measurement study on characteristics videos. We find that...

Established in 2005, YouTube has become the most successful Internet site providing a new generation of short video sharing service. Today, alone comprises approximately 20% all HTTP traffic, or nearly 10% traffic on Internet. Understanding features and similar sites is thus crucial to their sustainable development network engineering. In this paper, using traces crawled 3-month period, we present an in-depth systematic measurement study characteristics videos. We find that videos have...

The recent three years have witnessed an explosion of networked video sharing, represented by YouTube, as a new killer Internet application. Their sustainable development however is severely hindered the intrinsic limit their client/server architecture. A shift to peer-to-peer paradigm has been widely suggested with success already shown in live streaming and movie-on-demand. Unfortunately, our latest measurement demonstrates that short clips exhibit drastically different statistics, which...

Wind farms are often located at high latitudes, which entails a risk of icing for wind turbine blades. Traditional anti-icing methods rely primarily on manual observation, the use special materials, or external sensors/tools, but these limited by human experience, additional costs, and understanding mechanical mechanism. Model-based approaches heavily prior knowledge subject to misinterpretation. Data-driven can deliver promising solutions require large datasets training, might face...

Accurate and reliable optical remote sensing image-based small-ship detection is crucial for maritime surveillance systems, but existing methods often struggle with balancing performance computational complexity. In this article, we propose a novel lightweight framework called HSI-ShipDetectionNet that based on high-order spatial interactions (HSIs) suitable deployment resource-limited platforms, such as satellites unmanned aerial vehicles. includes prediction branch specifically tiny ships...

The sea-state estimation is a fundamental problem in the development of autonomous ships. Traditional methods such as wave buoy, satellites, and radars are limited by locations, clouds, costs, respectively. Model-based prone to incorrect estimations due their high dependence on mathematical models As previous data-driven studies for consider only height use motion data from dynamic positioning (DP) vessels, this article introduces new, deep neural network (SSENET) estimate sea state light...

Wind energy is of great importance for future development. In order to fully exploit wind energy, farms are often located at high latitudes, a practice that accompanied by risk icing. Traditional blade icing detection methods usually based on manual inspection or external sensors/tools, but these techniques limited human expertise and additional costs. Model-based highly dependent prior domain knowledge prone misinterpretation. Data-driven approaches can offer promising solutions require...

Wind farms are usually located in high-latitude areas, which bring a high risk of icing. Traditional methods anti-blade-icing limited by extra costs and potential damages to the original mechanical structure. Model-based heavily dependent on mathematical models blade icing, prone produce erroneous estimation. As data-driven better able achieve competitive performances for icing estimation, this article proposes temporal attention-based convolutional neural network (TACNN). This novel model...

Wind farms are typically located at high latitudes, resulting in a risk of blade icing. Data-driven approaches offer promising solutions for icing detection, but they rely on considerable amount data. Data exchange between multiple wind would improve the performance detection models, due to spatio-temporal dependencies capable reflecting different meteorological conditions. The traditional centralized approach faces many challenges, including requirement storage and computational capacity...

Wind energy is a fast-growing renewable but faces blade icing. Data-driven methods provide talented solutions for icing detection, considerable amount of Internet Things data needs to be collected central server, which may lead the leakage sensitive business data. To address this limitation, article proposes <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BLADE</i> , Blockchain-empowered imbalanced federated learning (FL) model detection....

Measurement while drilling (MWD) data reflect the rig–rock mass interaction; they are crucial for accurately classifying rock ahead of tunnel face. Although machine-learning methods can learn relationship between MWD and mechanics parameters to support classification, most current models do not consider impact continuous drilling-sequence process, thereby leading rock-classification errors, small unbalanced field datasets result in poor model performance. We propose a novel deep neural...

Sustainable urban water management is crucial for meeting the growing demands of populations. This study presents a novel approach that combines time series clustering, seasonal analysis, and entropy analysis to uncover residential consumption patterns their drivers. Using three-year dataset from SmartH2o project, encompassing 374 households, we identify nine distinct through leveraging Dynamic Time Warping (DTW) as optimal similarity measure. Multiple linear regression reveals key household...

In modern Internet of Things-enhanced wind power systems, most existing data-driven fault diagnosis approaches for turbines (WTs) are performed under a centralized paradigm that ignores data privacy. Recently, federated learning (FL) presented solution to enable edge WTs located at isolated sites collaboratively learn shared model without accessing local privacy-sensitive data. However, the practical issues label heterogeneity among clients and scarcity labeled still severely impede...

To build a compact data-driven ship motion model for offshore operations that require high control safety, it is necessary to select the most influential parameters and analyze uncertainty of input parameters. This paper proposes framework sensitivity analysis data. The consists four components: data cleaning, surrogate model, analysis, results visualization. Data cleaning focuses on removal noise, transformation easy analysis. An artificial neural network (ANN) based constructed basis...

Semantic segmentation is of great importance and a challenge in computer vision. One its main problems how to efficiently obtain rich information (geometric structure) identify useful features from higher dimensions. A light field camera, due special microlens array structure, can completely record the angular-spatial scenes, which attractive has potential improve performance semantic task. Inspired by this, we propose an end-to-end network that process macro-pixel image robustly extract...

Blades icing will seriously affect the performance of wind turbines with respect to power loss and dynamic load increase. detection technique becomes necessary advance de-icing maintenance. Extracting effective features from supervisory control data acquisition (SCADA) has become a challenging task during operating conditions under icing. Current research work lacks for integration physical information insufficient analysis evolution in process feature extraction. In order eliminate these...

After adulthood, as a person grows older, the secretion of sex hormones in body gradually decreases, and risk periodontitis increases. But relationship between is still controversial.

The conventional approach to mitigating wind turbine blade icing is associated with high costs, and farms are susceptible icing-related challenges. In pursuit of enhanced prediction, this study introduces a data-driven solution known as the graph temporal attention network (GTAN) model. This model incorporates feature extractor module aimed at enhancing distinctions among various categories raw sensor data. addition, it integrates (TA) mechanism heighten sensitivity characteristics. Baseline...

Urban energy demand aggregation (UEDA) is a key aspect of urban sustainability, as it can help to improve the efficiency systems and reduce their environmental impacts. However, UEDA challenging task, involves aggregating heterogeneous diverse demands individual buildings into collective at given spatial scale. This paper proposes novel entropy-based method for that quantifies information loss or distortion resulting from this process. The also identifies optimal scale minimizes distortion,...