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However, the task of delivering content in such dynamic network is far from trivial.
With the properties of simplicity and flexibility, the Kalman filter [17] has been widely applied in such dynamic systems.
In fact, it is not easy to have an accurate estimating approach in such dynamic multi-hop wireless networks.
In such dynamic environments there is a need for adapting the ways of collaboration to reflect the current conditions.
In such dynamic environment, a transport layer protocol can suffer from variation in delay and available bandwidth due to spectrum sensing and mobility.
In such dynamic network settings the mentioned optimization models might cause many handovers between APs to accommodate for user arrivals or mobility.
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Indeed, the main goal in defining such dynamic algorithm is to show that conspicuous improvement in system performance can be achieved even by applying simple dynamic clusters.
This paper aims to introduce four methods in determining such dynamic axle loads from bridge responses.
Due to the hardly predictable nature of complex ecosystems and, in particular, such dynamic value creation processes, the exchange value of goods and services tends to be more volatile and unsure.
Our existing sequence-based computational infrastructure may have to evolve to an infrastructure in which such dynamic graphs can be built, interrogated and analysed.
One approach to achieve dynamic adaptation is to offer middleware that facilitates component migration between modalities in response to such dynamic changes.
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