Changes of State Concerning Matter
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This document defines and provides examples of solids, liquids, and gases. It discusses the particle structure and properties of each state of matter. Solids maintain a definite shape and volume, with particles that are close together and vibrate. Crystalline solids have repeating patterns that melt at distinct temperatures, while amorphous solids soften and then change appearance when heated. Liquids have volume but not a definite shape, with particles that are semi-close and can move. Gases have no definite shape or volume, with particles that separate and fill all available space.
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2. Changes of state like melting, freezing, boiling, and condensation occur when particles gain or lose enough kinetic energy to overcome the forces between them. Melting and boiling happen when particles break away from fixed positions and spread out. Freezing and condensation occur when particles slow down and settle into fixed positions.
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IEEE Aerospace and Electronic Systems Society as a Graduate Student Member
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Recycled Concrete Aggregate in Construction Part III
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TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEM
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
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The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
As digital technology becomes more deeply embedded in power systems, protecting the communication
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represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
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Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
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Changes of State Concerning Matter
- 1. It’s All About Matter Solids Gases Liquids Karen B. Jones
- 2. Table Of Content • Introduction • Define Solid, Liquid, Gas • Examples of Solid, Liquid, Gas • Assessing Your Understanding • Resources
- 3. Solids form a shape and has a volume
- 4. particles in solid • are extremely close • movement is possible • it vibrates or slides back and forth
- 5. crystalline solids • repeating patterns-made out of crystals which are called crystalline solids • things we see or use daily such as snow, salt, and sugar are examples of crystalline solids • however, if heat is applied to crystalline solids it will melt at a distinct temperature
- 6. amorphous solids • are not a regular pattern • will not melt at a distant temperature • materials become soft and then they change appearances
- 7. • things we use and see that are amorphous solids are rubber, plastics, and glass • heated glass will bend
- 8. Liquids have volume but do not form a shape.
- 9. particles in liquids • particles are semi-close • they can move
- 10. surface tension • a pull or force in liquids which causes the molecules on the surface to become closer together • If water was dropped on a leaf it would bead up and pull slightly together because water molecules attract each other
- 11. viscosity • a fluid that resist the force tending to cause the fluid to flow • liquids with a high viscosity flow more slower (ex. honey) • liquids with a low viscosity flow more quicker (ex. vinegar and water)
- 12. Gas do not form a shape or volume.
- 13. Particles in Gas • is considered a fluid • does have pressure • separate as it moves
- 14. volume • the amount of space that matter fills • particles move and fill all available space • value of gas is the same as the volume of its container
- 15. Helium inside a container is compressed or pressed together tightly to fit into a metal tank. If balloons are blown up with this helium it expands to fill them making the volume greater than the volume in the tank
- 16. pressure • The force of an outward push • An object being filled comes from the pressure of the gas
- 17. temperature • Moves faster with hot gas particles • Moves slower with cool gas particles
- 18. Assess Your Understanding • Describe the two kinds of solids. • Give an example of crystalline solid. • Heat applied to this solid causes it to do what. • Liquids have volume but do not form this. • Name a liquid that flow slowly.
- 19. cont. assess your understanding • Describe one way gas may move. • Hot gas particles causes temperature to move how. • The pressure of gas is the force of its what type of push.