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Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
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Sampling Continuous Time Signal01:11

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In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
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Stream Function01:20

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In two-dimensional incompressible fluid flow, the continuity equation is essential for ensuring mass conservation, meaning that any change in fluid entering or exiting a region is balanced by a corresponding change elsewhere. For incompressible flow, where density remains constant, this requirement simplifies to the condition that the divergence of the velocity field must be zero. Mathematically, this is expressed as,
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Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
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Signal Flow Graphs01:18

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Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
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Basic Continuous Time Signals01:22

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Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
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Related Experiment Video

Updated: Jul 20, 2025

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
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A Real-Time Streaming System for Customized Network Traffic Capture.

Adrian-Tiberiu Costin1, Daniel Zinca1, Virgil Dobrota1

  • 1Communications Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania.

Sensors (Basel, Switzerland)
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Summary
This summary is machine-generated.

This study introduces an innovative network traffic logging tool for real-time analysis. It efficiently transfers network packets to Apache Kafka and ksqlDB for swift data storage and management.

Keywords:
Apache KafkaLibtinsksqlDBnetwork traffic logger

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Area of Science:

  • Computer Science
  • Network Engineering
  • Data Management

Background:

  • Network traffic logging is crucial for data flow analysis and troubleshooting.
  • Real-time querying and pattern matching are essential for advanced network traffic analysis.
  • Existing methods may lack efficiency in handling and storing large volumes of network data.

Purpose of the Study:

  • To present an innovative network traffic logging tool.
  • To demonstrate the integration of modern technologies for network traffic analysis.
  • To showcase efficient real-time data storage and retrieval.

Main Methods:

  • Developing a network traffic logging tool.
  • Implementing real-time network packet transfer to Apache Kafka.
  • Utilizing ksqlDB for data processing and querying.

Main Results:

  • Successful real-time transfer of network packets to Apache Kafka.
  • Dependable storage of network packets in Apache Kafka topics.
  • Demonstrated effectiveness of the tool in network traffic analysis.

Conclusions:

  • Modern technologies significantly enhance network traffic analysis.
  • The developed tool provides swift and reliable network packet storage.
  • Employing Apache Kafka and ksqlDB is beneficial for network management.