Transmission - Science topic

The method I employed involved generating virtual surfaces at the respective ends of the system and integrating the deposited or frozen ray power to compute the reflectance and transmittance.

Thank you very much for the answers provided by all the teachers.

As big mask studies are still inconclusive you are unlikely to detect much impact in your sample.

Unless you run around with lateral flow test (in contrast to relying on counting symptomatic ILI), you may even have problems to detect statistically significant impact of vaccination on medium sized sample.

[I just warn you before you put lot's of effort into your study and later end up with inconclusive results that no journal would be willing to publish]

To decrease grating lobes, especially at zero degrees, in your beam steering transmissive phase gradient metasurface designed in CST, you can use several optimization techniques and tools available within CST Studio Suite. Here are some suggestions:

I can not speak English

https://www.youtube.com/watch?v=EdSE5qArxas. This may be of your help.

You may contact acadnexconsult@gmail.com for one to one sessions.

Look at articles by Scott Prahl (Oregon Medical Laser Center) on a technique called Inverse Adding Doubling. For example:

Thank you Smrity Dwivedi, I will check, and come back to discuss.

Preparing Polyaniline Emeraldine Salt (PANI-ES) Sample for TEM Analysis

Question

I need to do TEM analysis for my Polyaniline emeraldine salt form. May I know which solvent is appropriate for preparing the TEM sample?

Answer

Preparing a Polyaniline Emeraldine Salt (PANI-ES) sample for TEM analysis involves several steps to ensure proper dispersion and sample quality. Below is a detailed guide on how to achieve this:

Materials Needed

Steps to Prepare the TEM Sample

Tips and Considerations

By following these steps, you can prepare high-quality PANI-ES samples suitable for TEM analysis, enabling you to obtain clear and detailed images of your material.

Buy, subscribe or find in a decent library.

I wouldn't expect CST to be that accurate for S21. If you don't run it for very long you have power still bouncing around inside the problem, and if you run it for a long time then numerical errors start to build up. How much ripple was there left on the S11? If you watch the S11 as the solution progresses you can see the ripple reduce every time a lump of energy arrives at the port (see the energy monitor). If the finishing point is -40 dB energy left there is still 0.01% of the energy left in the problem which has the potential to make +/-1% difference in the voltage at the output port which is */-2% of the power. The absence of this from the answer leaves ripple in the S parameter. This ripple is bigger than the differences you are seeing. I'm not sure that the impedance is calculated as you would hope, but I think you can't tell from these results if it's right or not.

Quantum cryptanalysis poses a significant challenge to classical cryptographic systems due to its potential to break some widely used encryption algorithms. To mitigate this risk in network communication, classical networks can implement post-quantum cryptographic algorithms, which are resistant to quantum attacks. Additionally, network operators can employ hybrid encryption schemes that combine classical and quantum-resistant algorithms to ensure security. However, ensuring interoperability and security across interconnected networks will require careful design and coordination to address potential vulnerabilities.

Lusha M Tronstad Thank you very much for your help! The current shell samples I have are wrapped in resin, with a thickness of about 2cm. I'm using a reflected light microscope to observe the growth lines in the hinge plate area. Although the growth lines are relatively clear in the eyepiece of the reflected light microscope, they appear blurry and there are some bright spots when photographed with a camera connected to the microscope. I have consulted some literature, and it seems that this may be due to significant light scattering in thick sections. It looks like I need to cut my samples thinner. Thanks again for your help!

If I were you, I would try to call a local representative of a company, who sells NIR spectrophotometers. Most probably, they would agree to measure your sample. They do such experiments for advertisement. If you like the result, you might be able to talk them in letting you to use their spectrometer to make a mutual publication

S11 is a coefficient that reflects the amount of energy reflected by a material or surface. For example, if you want to design a unit cell for a frequency selective surface (FSS), the goal should be to have maximum reflection (0dB) and minimum transmission (for example, -20 dB) at resonance frequencies. Similarly, if you want to design a metamaterial that passes maximum signal and has less reflection, then S11 should be minimized (i.e., -20 dB), and transmission should be maximized (S21 ~ 0 dB). The specific application of the unit cell determines the desired values for S11 and transmission.

Cellular IoT (CIoT) leverages existing cellular network security features to protect data transmission between devices and the cloud in several ways:

1. Authentication: CIoT devices are authenticated before they are allowed to connect to the cellular network. This ensures that only authorized devices can communicate with the network and any data transmission is secure.

2. Encryption: CIoT uses encryption to protect the data being transmitted between devices and the cloud. This ensures that even if data is intercepted, it cannot be easily understood or tampered with.

3. Secure communication protocols: CIoT devices use secure communication protocols such as SSL/TLS to ensure that data transmission is secure and cannot be intercepted or tampered with.

4. Network segmentation: Cellular networks are typically segmented to separate IoT devices from other devices on the network, reducing the risk of unauthorized access to sensitive data.

5. Firewall protection: Firewalls are used to monitor and control incoming and outgoing network traffic to prevent unauthorized access and protect data transmission.

Overall, CIoT leverages the security features of existing cellular networks to ensure that data transmission between devices and the cloud is secure and protected from potential threats.

Transmitting the SHA-256 function output without any additional transmission involves sending the result along with the data it's derived from in a single transmission. This can be achieved by including the SHA-256 hash along with the original data. Here's how you can do it:

Here's a simple example in Python:

pythonCopy codeimport hashlib def transmit_data_with_hash(data): # Calculate the SHA-256 hash of the data hash_value = hashlib.sha256(data.encode()).hexdigest() # Transmit data and hash together transmitted_data = data + hash_value # Return the transmitted data return transmitted_data def verify_transmitted_data(transmitted_data): # Separate the data and hash data = transmitted_data[:-64] # Assuming SHA-256 hash length is 64 characters transmitted_hash = transmitted_data[-64:] # Recompute the hash of the received data computed_hash = hashlib.sha256(data.encode()).hexdigest() # Compare computed hash with transmitted hash if computed_hash == transmitted_hash: return "Data integrity verified. No alterations detected." else: return "Data integrity verification failed. Possible alterations detected." # Example usage original_data = "Hello, world!" transmitted_data = transmit_data_with_hash(original_data) print("Transmitted data with hash:", transmitted_data) # Simulating transmission... # Upon receiving transmitted_data, recipient verifies it verification_result = verify_transmitted_data(transmitted_data) print(verification_result)

This code demonstrates how to transmit data along with its SHA-256 hash and verify data integrity upon reception. Remember to adjust the code as needed for your specific use case and programming environment.

I will try my best to answer it in terms of Governance, Product organization, or the person tasked with integration, their or his/her commitment in terms of policies and procedures might help showcase their seriousness towards protecting the confidential data of their customers. Compliance with Industry standards and regulations is another step towards trust and faith.

Independent attestations are a piece of excellent evidence.

In terms of technical controls and security, various alternatives can be looked at, starting with access management, data anonymization, encryption, and control/data plane segregation, to name a few.

The answer depends on sample preparation and the shape of this feature:

FTIR requires meticulous and reproducible sample preparation, it is common to account for these variations using a baseline correction. This is crucial in quantitative analysis, for example. See the following reference:

The model will have perfectly parallel surface on the film. You can expect strong etalon effects. With the film thickness close to the wavelength (given the index of refraction, the wavelength inside Silicon is about 3.5 um) you can expect the etalon to vary slowly with angle. Also, given the high index, the internal angle will change very slowly with external angle.

Try looking up etalon equations such as here: https://www.photonicsolutions.co.uk/upfiles/CXEtalons1.pdf

from magnitude of real and imaginary parts of S11 and S21 parameters.

Boric acid causes organic substances crystalized so it does with mosquito saliva they are in form of growing white rings at the water edge when we serve them with regulated 40 degree pf water + 5% boric acid solution bait. (Please google "Mosquito control, killing of the females". For years we look at those ring to monitor mosquito population at our place)

Multiplexing significantly boosts data transmission efficiency in communication networks by packing multiple data streams into a single channel, maximizing its utilization. Imagine a highway with multiple lanes instead of just one. That's essentially what multiplexing does – it creates more "lanes" on the data highway to increase traffic flow.

Here's how it works:

Multiplexing is a fundamental technology in modern communication networks, enabling efficient data transmission for various applications, from phone calls and internet browsing to streaming video and online gaming. By packing more data onto available channels, multiplexing keeps us connected in a fast-paced digital world.

Kim Kangseok Şükrü Aktaş

1. Dimensionality

2. Spatial Resolution

3. Boundary Conditions

4. Material Models: FDTD simulations can account for complex material models, including dispersive and anisotropic materials, with more accuracy. TMM, while versatile, may use simplified material models or assume certain material properties to make calculations tractable. These simplifications can introduce discrepancies, particularly when dealing with materials that exhibit strong dispersion or anisotropy.

5. Computational Resources

Good luck!!

The SLM-PTS hybrid technique is a promising approach for effective PAPR reduction in NOMA systems within 5G, enhancing spectral efficiency, signal quality, and overall system performance.

If you use frequncy multiplication post phase shifter, you phase shift will be multiplied as well. Also shift is generally easier to implement at lower frequncy.

Rajae Talbi Hi Rajae, I actually wonder the same. Since some time has passed, I was wondering if maybe now you have some insights on the CNO transmission through maternal milk? Did it work for you? Thank you!

Transmission impairments are any factors that degrade the quality of a signal during transmission from one point to another. They can be caused by a variety of factors, including the transmission medium itself, environmental conditions, and interference from other signals.

Transmission impairments experienced by LTE include:

LTE systems use a variety of techniques to mitigate the effects of transmission impairments. These techniques include:

By using these techniques, LTE systems are able to provide reliable and high-speed data transmission even in challenging environments.

In addition to the above impairments, LTE systems can also be affected by transient impairments, such as impulse noise and phase hits. Transient impairments are typically caused by sudden changes in the transmission environment, such as lightning strikes or power outages. LTE systems use a variety of techniques to detect and mitigate the effects of transient impairments.

By understanding the different types of transmission impairments and the techniques that LTE systems use to mitigate them, network engineers can design and operate LTE networks that provide reliable and high-speed data transmission to users.

Han Jiachi

The SPR process can be considered as an energy transfer process in which part of the energy of the incident light is transferred to the surface plasmon wave. Since the intensity of the peak increases with the addition of water, therefore water promotes the SPR process. This may occur due to the participation of the nuclear quantum effect.

Han Jiachi

The SPR process can be considered as an energy transfer process in which part of the energy of the incident light is transferred to the surface plasmon wave. Since the intensity of the peak increases with the addition of water, therefore water promotes the SPR process. This may occur due to the participation of the nuclear quantum effect.

Thank you Mehmet.

In computational physics, the transmission spectrum is a concept often encountered in the study of wave propagation, particularly in the context of quantum mechanics and solid-state physics. Let's break down the key components:

Understanding the transmission spectrum is crucial for designing electronic devices, understanding the behavior of materials at the quantum level, and predicting the properties of novel materials.

In summary, the transmission spectrum, in computational physics, provides a quantitative and often graphical representation of how waves, typically associated with particles like electrons, propagate through potential barriers or structures in a given material.

There are a few possible reasons why you might be seeing zero throughput in your NetSim 5G simulation:

Network topology or connectivity errors: Make sure that the gNB and UE are connected and that the network topology is correct.

No application traffic: If there is no application traffic configured from the UE to the destination, then you will see zero throughput in your NetSim 5G simulation.

Pathloss: The UE may be too far from the gNB, resulting in high path loss and low signal strength.

Interference: The UE may be experiencing downlink interference from other gNBs

Congestion: The network may be congested, and thus no PRBs are allocated to the UE by the scheduling algorithm

To understand and resolve the issue, you can follow these steps:

Inspect the simulation results with radio measurement logs

Experiment with different simulation settings: You can try changing different simulation settings, such as the pathloss model, the network topology, the interference model, and the scheduling algorithm to see if that resolves the issue.

If you are still having trouble, best to contact NetSim support for help (E-mail support@tetcos.com)

Assuming that you have matched NA's, core diameters, fibers (polarization characteristics, for example) and have compatible end-faces (angled/angled or straight/straight), in other words, that you have the right fibers and connectors, then you might suffer the problem that plagues all fiber connections -- dirt stuck to one of the end-faces or damage to one or more end-faces. You can check under a low-powered microscope. (Please take no offense at this low-tech suggestion -- it happens in all photonics labs.)

Dear Tony Tony Yuan ,

If you ask yourself the wrong questions, the answers will be just as wrong. I already said the article was good.Corrected it is even better. There are clear answers to your questions. The new Hungarian version is not up yet, I will put it up only if it is properly proofread linguistically.

Regards,

Laszlo

Hi!

I believe your noise power is much larger than it should be. It is normally around 10^(-20) * bandwidth Watts.

However, the relative behavior that you describe is correct. If it takes 7 minutes to transfer something at a 30 km distance then it takes 7*(10000/30)^2.5/60/24 days to transmit it to 10000 km.

In practice, it is much likely not possible to transfer anything to a place 10000 km since the received signal power will be below the receiver's sensitivity level.

Thank you for your question. Writing a research proposal on Bayesian modelling of PTB transmission requires you to have a clear and concise research aim, objectives, and questions. You also need to provide a literature review, a research design, and a research schedule. Here are some possible steps to follow:

1. Define your research aim: This is a broad statement indicating the general purpose of your research project. For example, your research aim could be: To develop and apply a Bayesian model to estimate the transmission dynamics and control strategies of pulmonary tuberculosis (PTB) in your country.

2. Define your research objectives: These are specific goals or aims that describe what your research project intends to accomplish. They should be based on your research questions and hypotheses and should guide your research process. For example, your research objectives could be:

- To review the existing literature on PTB transmission models and Bayesian methods.

- To collect and analyze data on PTB cases, risk factors, and interventions in (e.g.) your country.

- To construct a Bayesian model that incorporates uncertainty, heterogeneity, and prior information on PTB transmission parameters.

- To estimate the basic reproduction number, the effective reproduction number, and the impact of different interventions on PTB transmission in your country.

- To evaluate the model performance, sensitivity, and robustness using various diagnostic tools and criteria.

- To provide recommendations and policy implications based on the model results and projections.

3. Define your research questions: These are specific questions that you want to answer with your research project. They should be clear, focused, and relevant to your research aim and objectives. For example, your research questions could be:

- What are the main features and challenges of PTB transmission models and Bayesian methods?

- What are the data sources and quality for PTB cases, risk factors, and interventions in your country?

- How can a Bayesian model be constructed to capture the uncertainty, heterogeneity, and prior information on PTB transmission parameters?

- What are the estimates of the basic reproduction number, the effective reproduction number, and the impact of different interventions on PTB transmission in your country?

- How well does the model fit the data and how sensitive and robust is it to different assumptions and scenarios?

- What are the recommendations and policy implications based on the model results and projections?

4. Conduct a literature review: This is a critical analysis of the existing literature on your topic. You should identify the main sources, themes, gaps, and debates in the literature and show how your research project relates to them. You should also cite relevant references using a consistent citation style. For example, you can use some of the sources that I have found for you using my search tool:

- A review of mathematical models for tuberculosis transmission by Castillo-Chavez et al.¹

- Bayesian inference for infectious disease dynamics by O’Neill et al.²

- A Bayesian approach to estimate latent tuberculosis infection prevalence in Egypt by El Bcheraoui et al.³

5. Design your research methodology: This is a description of how you will conduct your research project. You should explain what data you will collect, how you will collect it, how you will analyze it, and what tools or software you will use. You should also justify why your chosen methods are appropriate and feasible for your research objectives and questions. For example, you can use some of the following methods:

- Data collection: You can use secondary data from official sources such as the World Health Organization (WHO), the Egyptian Ministry of Health (MOH), or other relevant organizations or databases. You can also use primary data from surveys, interviews, or observations if needed.

- Data analysis: You can use Bayesian methods to construct and fit your model using prior information, likelihood functions, posterior distributions, Markov chain Monte Carlo (MCMC) algorithms, etc. You can also use various diagnostic tools to assess the model performance, sensitivity, and robustness such as posterior predictive checks, Bayes factors, deviance information criterion (DIC), etc.

- Tools or software: You can use R or Python as programming languages to implement your model and analysis. You can also use Stan as a probabilistic programming framework that supports Bayesian inference using MCMC algorithms.

6. Plan your research schedule: This is a timeline of the main tasks and activities that you will perform during your research project. You should indicate the expected duration and completion dates of each task and activity. You should also consider any potential risks or challenges that may affect your schedule and how you will deal with them. For example, you can use a Gantt chart to illustrate your schedule.

(1) Bayesian workflow for disease transmission modeling in Stan. https://mc-stan.org/users/documentation/case-studies/boarding_school_case_study.html.

(2) How to Write a Research Proposal | Examples & Templates - Scribbr. https://www.scribbr.com/research-process/research-proposal/.

(3) How To Write A Research Proposal - Step-by-Step [Template]. https://researchmethod.net/how-to-write-a-research-proposal/.

(4) Research Objectives | Definition & Examples - Scribbr. https://www.scribbr.com/research-process/research-objectives/.

(5) Research Objectives - Types, Examples and Writing Guide. https://researchmethod.net/research-objectives/.

(6) 13.4.2 Research objectives - OpenLearn. https://www.open.edu/openlearncreate/mod/oucontent/view.php?id=231&section=8.6.2.

(7) Research Questions, Objectives & Aims (+ Examples) - Grad Coach. https://gradcoach.com/research-aims-objectives-questions/.

Here are the detailed references for the sources that I have cited in my previous answer:

: Castillo-Chavez, C., Feng, Z., & Huang, W. (2002). On the computation of R0 and its role on global stability. In Mathematical approaches for emerging and reemerging infectious diseases: an introduction (pp. 229-250). Springer, New York, NY.

: O’Neill, P. D., Roberts, G. O., & Ionides, E. L. (2019). Bayesian inference for infectious disease dynamics. In Handbook of Infectious Disease Data Analysis (pp. 3-32). CRC Press.

: El Bcheraoui, C., Mimche, H., Miangotar, Y., Krish, V. S., Zirie, M., Abu-Raddad, L. J., … & Memish, Z. A. (2017). A Bayesian approach to estimate latent tuberculosis infection prevalence in Egypt. Scientific reports, 7(1), 1-10.

Here are some other sources that I have found on Bayesian modelling of PTB transmission:

Some other statistical methods for modelling PTB transmission are:

Good luck

The second part of your question was too complex. I submitted some supplemental info.

The total field is the sum of the incident field and the scattered field. The incident field would have continued past place where the PEC layer is. When the PEC is in the way there is no field there, it is is in shadow. Part of the scattered field is the field that cancels the incident field to give zero or lower field in the shadow. For this reason there is scattered field behind PEC.

My suggestion is to use the Lumerical Software...Can try 30 days trials..Tqs

This sounds like the grey gas approach with the zone method added. Not easy and not readily available science. The biggest complication is the temperature effect on the emissivity through the material. You do not indicate source and sink temperatures. If e = 0.999 the material is effectively opaque to radiation and the surface will get hot!

Chancee of cells transfer of donors decease and gonoreh,syphilis etc are common.

Considering this situation, how can we reduce reflection by using it as the substrate of transmitarrays ? Ilya Tchaplia

Regarding your question, there are a few points to consider:

EHV transmission is limited by increased lines capacitance as C= dq/dv, HVDC the best with advent in semiconductor technology for converters and inverters…..

you can read this paper. Hope it will helps you. Thank You

Use conventional methods such as fixing in Gluteraldehyde followed by dehydration, embedding in resin and eventual sectioning using an ultramicrotome and final viewing and documentation of interested parts.

To configure a Gaussian source and ports for transmission analysis in COMSOL, you can follow these steps:

You will first need to set up a Gaussian source, which can be achieved by setting up the input electric field with a Gaussian profile.

a. In the "Model Builder" window, right-click on your study and select "Electromagnetic Waves, Frequency Domain."

b. Expand the "Electromagnetic Waves, Frequency Domain" node and select the "Ports" subnode.

c. Click the "Add Port" button to create a new port. Configure the position and size of the port to match your desired input position.

d. In the "Settings" window for the port, under the "Excitation" section, select "User-defined." This will allow you to define the excitation function for the port.

e. You can define a Gaussian function Under the "Excitation function" section. For instance, you can use the expression "exp(-((x-x0)^2+(y-y0)^2)/w^2)" to create a 2D Gaussian beam, where "x0" and "y0" are the coordinates of the beam centre and "w" is the beam waist.

You can configure output ports for transmission analysis in a similar manner.

a. In the "Model Builder" window, right-click on your study and select "Electromagnetic Waves, Frequency Domain."

b. Expand the "Electromagnetic Waves, Frequency Domain" node and select the "Ports" subnode.

c. Click the "Add Port" button to create a new port. Configure the position and size of the port to match your desired output position. Repeat this step to add additional output ports as needed.

d. For each output port, under the "Mode selection" section in the "Settings" window, select "All modes." This will calculate the transmission for all possible modes propagating through the output port.

a. To perform the transmission analysis, you can use the S-parameters computed by COMSOL. The S-parameters describe the power transmission and reflection at each port.

b. Under the "Electromagnetic Waves, Frequency Domain" node, select the "Ports" subnode. Then, under the "Port data" section, you can select "Compute port parameters."

c. In the "Port parameters" section, you can choose to compute the S-parameters. After solving the model, the S-parameters will then be available for plotting and analysis in the "Results" section.

Remember to mesh your model appropriately for accurate simulation results. Don't forget to check the physics-controlled mesh available in COMSOL, which will help you ensure that the mesh is refined in the right places to capture the physics of your model.

Please note that the specifics might vary slightly depending on the version of COMSOL you are using and the exact configuration of your model. Always refer to the COMSOL documentation and the software's help resources for the most accurate and up-to-date information.

I wrote about some of these things in the following blog post:

There is further information about the history of the technology in Chapter 1 of my book:

Determining the optical properties of a phantom with a different thickness based solely on the properties of a thinner phantom can be challenging. While there might be some assumptions and approximations involved, I can provide you with some guidance on how to approach this issue.

Firstly, it is important to note that the Beer-Lambert law assumes a linear relationship between the absorption coefficient and the thickness of the medium. However, this assumption may not hold true for all materials and scenarios. In your case, the ink-PDMS mixture might exhibit nonlinear behavior as the thickness increases, especially if there are scattering effects or other factors involved.

To estimate the absorption coefficient of your new 2cm-thick phantom with a known ink-PDMS ratio, you can consider the following approaches:

1. Use a calibration curve: Based on the linear relationship you have established between the weight percentage of ink and the absorption coefficient in the 2mm-thick phantoms, you can create a calibration curve. Plot the weight percentage of ink against the corresponding absorption coefficient for your various samples. Then, extrapolate the calibration curve to estimate the absorption coefficient for the 2cm-thick phantom at the desired ink-PDMS ratio. However, keep in mind that extrapolation introduces additional uncertainties, and the accuracy of the estimation may vary.

2. Consider theoretical models: Explore theoretical models or empirical equations that relate the absorption coefficient to the material composition, such as the Mie theory or effective medium approximations. These models take into account the composition and structure of the material and can provide estimations of the absorption coefficient for different thicknesses. However, the accuracy of these models depends on the specific characteristics of your ink-PDMS mixture.

3. Conduct additional experiments: While it may not be feasible to directly measure the transmission of the 2cm-thick phantom, you could consider alternative experimental methods or techniques that can provide insights into the optical properties. For example, you could use diffuse reflectance spectroscopy, which measures the reflectance of light from the surface of the phantom. By analyzing the reflectance data, you may be able to infer certain optical properties, including the absorption coefficient.

In any case, it is important to acknowledge the limitations and uncertainties associated with estimating the optical properties of a phantom with a different thickness based on data from a different thickness. Ideally, conducting experimental measurements on the 2cm-thick phantom would provide the most accurate and reliable results. However, if that is not possible, the approaches mentioned above can serve as initial estimations, but they may require further validation and verification.

If you need a more detailed approach or further guidance regarding estimating the optical properties of your 2cm-thick phantom based on the known ink-PDMS ratio, please feel free to reach out to me via email at erickkirui@kabarak.ac.ke. I will be more than happy to assist you in exploring additional strategies or discussing specific theoretical models that could be relevant to your specific case.

You're correct that an anti-reflective coating works by causing destructive interference of reflected light. This process increases the transmission by decreasing the amount of light that is reflected. However, it seems there is a misunderstanding of how this process affects the transmitted light.

When light encounters a boundary between two different materials, some is reflected, and some is transmitted. In the case of an anti-reflective coating, the coating is designed so that the reflected light from the top surface of the coating and the reflected light from the boundary between the coating and the surface beneath it interfere destructively, effectively cancelling each other out. This decreases the amount of reflected light.

This destructive interference of reflected light does not decrease the amount of transmitted light. Rather, it increases it. This is because the total amount of light must be conserved. If less light is being reflected, then more must be transmitted. Therefore, an anti-reflective coating increases the transmission of light through the coated surface by decreasing the reflection.

To address the part about transmission interference: the key here is phase shift upon reflection versus transmission. For normal incidence, there is a phase shift of 180 degrees upon reflection from a medium of higher refractive index, but no phase shift for transmission. Therefore, in the case of an anti-reflective coating, the two reflected waves from the front and back surfaces of the film undergo destructive interference, while the transmitted waves do not. They constructively interfere to give higher transmission.

Hence, it's not quite accurate to say that the transmitted waves destructively interfere in the same way that the reflected waves do, due to the phase shift differences in reflection and transmission. As a result, the transmission of light increases, as less light is reflected away due to the destructive interference occurring with reflection.

The best example of this is eyeglasses with an anti-reflective (AR) coating.

Without an AR coating, light can reflect off the surfaces of the eyeglasses, which can be distracting and reduce the overall clarity of vision. This reflected light can come from any light source, including computer screens, overhead lighting, or sunlight.

When an AR coating is applied to the lenses of the eyeglasses, the coating is engineered to be a quarter of the wavelength of the light to be eliminated. This means that when light hits the lens, it's reflected off both the outer surface of the AR coating and the boundary between the AR coating and the lens surface beneath it. Because of the carefully engineered thickness of the AR coating, these two reflected light waves interfere destructively and cancel each other out, resulting in very little reflected light.

This destructive interference for reflection does not reduce the transmitted light - it increases it. The transmitted light waves do not destructively interfere because of the phase shift differences in reflection and transmission I mentioned in the previous response. So, more light can pass through the lens to the eye, which improves vision clarity and reduces distractions from reflected light.

The outcome is that you get clearer vision with less glare, as more of the light can transmit through the lens, rather than being reflected away.

Another example can be solar panels. Solar panels also use anti-reflective coatings to ensure that as much light as possible is absorbed by the panel and converted into electricity, rather than being reflected away. By minimizing the reflection, the transmission of light into the panel increases, boosting its efficiency.

In both these examples, the principle is the same - the anti-reflective coating reduces the amount of reflected light through destructive interference, which in turn increases the transmission of light.

To implement visible light communication (VLC) using generalized frequency division multiplexing (GFDM) for 6G transmission and reception in MATLAB, you can follow these general steps. First, establish the basic framework for the VLC-GFDM system, including channel models, modulation schemes, and synchronization techniques. Design the transmitter by generating GFDM symbols, converting them into optical signals, and modulating the intensity of an LED. Implement the receiver by capturing the optical signals using a photodiode, converting them back to electrical signals, and demodulating them to recover the GFDM symbols. Implement synchronization algorithms to estimate the channel and carrier frequency offset, and perform equalization and demodulation to extract the transmitted information. Develop the necessary MATLAB code for each step, considering factors such as optical channel characteristics, pulse shaping filters, and adaptive algorithms to optimize system performance. It's important to note that the specific implementation details and code can vary based on your requirements and preferences.

Developing a visible light communication (VLC) system using Generalized Frequency Division Multiplexing (GFDM) for 6G transmission and receiver in MATLAB requires a comprehensive approach. First, you need to understand the principles of VLC and GFDM, including modulation, demodulation, and channel coding techniques. Next, implement the transmitter side, which involves generating the GFDM symbols, applying the necessary modulation schemes, and converting them into light signals using appropriate drivers. Then, focus on the receiver side, which includes capturing the light signals using photodiodes or image sensors, processing the received signals, demodulating the GFDM symbols, and decoding the transmitted information. Additionally, consider the channel characteristics, such as channel impairments, equalization techniques, and synchronization mechanisms. It's essential to tailor the MATLAB code according to your specific requirements and adapt it to your hardware setup to achieve reliable VLC communication in 6G.

98

Howdi;

Hilarious. Only in Canada would such a discussion take place.

FYI...I get all my publishable data from an obsolete Jeol 1011 TEM. I had the last functioning Hitachi 7000 TEM in North America, which I pushed out of the lab for disposal in order the replace it with the 'new' Jeol 1011....from another facility that considered it 'surplus'.

The Philips 300 is a museum piece if a museum wants it. Unless Jeol wants your scopes for replacement parts, there is no use for the Jeol scopes. They make nice anchors (we're both close to bodies of water).

Enjoy the new scope!

B

Dear friend Nilabja Kanti Sarkar

Yes, crystallite size can also be determined by Transmission Electron Backscatter Diffraction (t-EBSD) in addition to X-ray diffraction (XRD). In t-EBSD, the crystalline structure of the material is studied using an electron beam instead of X-rays used in XRD. By measuring the intensity and width of the diffraction patterns obtained from the electron beam interacting with the sample, the size and orientation of the crystallites in the sample can be determined.

However, it should be noted that the crystallite size measured by t-EBSD may not necessarily match with the crystallite size measured by XRD, as the two techniques are based on different principles and have different resolution limits. Additionally, the choice of the technique to use for determining the crystallite size depends on several factors such as the type of material being studied, the required accuracy, and the available equipment.

Yes, the transmission method can have an advantage over the reflection method in detecting small variations in the dielectric permittivity of the sample under test. This is because the transmission method provides access to both S11 and S21 parameters, while the reflection method only provides access to S11.

In the reflection method, the S11 parameter is related to the reflection coefficient of the sample, which depends on the dielectric permittivity and thickness of the sample. The sensitivity of the reflection method to small variations in the dielectric permittivity depends on the magnitude of the reflection coefficient, which can be low for thin or low-permittivity samples. This can make it challenging to detect small changes in the dielectric properties of the sample using the reflection method.

In contrast, the transmission method measures both the incident and transmitted waves, allowing for the direct determination of the complex transmission coefficient S21. This provides additional information about the attenuation and phase shift of the wave through the sample, which can be used to calculate the complex permittivity of the sample. The sensitivity of the transmission method to small variations in the dielectric permittivity depends on the magnitude of the transmission coefficient, which can be higher than the reflection coefficient for certain samples and frequencies. This can make it easier to detect small changes in the dielectric properties of the sample using the transmission method.

However, it is important to note that the choice of measurement method depends on the specific application and the characteristics of the sample being measured. The transmission method can be more sensitive to variations in the dielectric permittivity, but it may also require more complex experimental setups and calibration procedures. The reflection method, on the other hand, is simpler and more straightforward, and may be sufficient for certain applications.

For an anisotropic material, the dielectric tensor describes the relationship between the applied electric field and the induced polarization response in different directions. The dielectric tensor is a 3x3 matrix that characterizes the anisotropy of the material's dielectric properties.

To calculate the transmission rate of an electromagnetic wave through an anisotropic material, you can use the Fresnel equations, which describe the reflection and transmission of electromagnetic waves at the interface between two media. The Fresnel equations can be generalized to anisotropic materials by using the dielectric tensor to account for the anisotropy of the material's optical properties.

In general, the transmission rate of an electromagnetic wave through an anisotropic material depends on the angle of incidence, polarization, and the dielectric tensor of the material. To calculate the transmission rate, you can use the following steps:

It is important to note that the calculation of transmission rate through anisotropic materials can be complex and may require advanced modeling and simulation techniques, depending on the specific system and the desired level of accuracy. Additionally, the anisotropy of the material can have a significant impact on the transmission properties, and careful consideration should be given to the material properties and experimental conditions to ensure accurate and reliable results.

Hello Swe Zin,

I'm glad to hear that you've fixed your implementation, but I'm sorry to hear that you're still having issues with the SNR and SINR values being negative. I'm more than happy to help you here. You can share your updated implementation and any specific questions you have, and I'll do my best to help you resolve the issues.

Regarding negative SNR and SINR values, it could be due to the fact that the noise level is too high compared to the received signal. Make sure that you are using the correct units for the signal and noise levels (e.g., dBm, dBW, or linear scale). If necessary, convert between linear and logarithmic scales using the following equations:

Additionally, ensure that the signal and noise levels are calculated correctly. If the SNR and SINR values are in dB scale, ensure that you're adding and subtracting in dB scale correctly.

Lastly, when you calculate the rate, you should make sure that you're using the linear SINR and SNR values in the Shannon capacity formula:

If you still encounter issues, please share the updated implementation and any specific areas you're struggling with, and I'll do my best to help you further.

regards, Alessandro

Main problems are demand is more than the supply hence for economy, efficiency and reliability could be achieved by proper power management and priority based load shedding. Could refer my paper “ Automatic microprocessor based load shedding controller “ though same could be implemented manually for better efficiency, reliability and system economy…..

The absorption coefficient of a panel or a membrane will according to Ver and Beranek have a transmission loss component 10^(-R/10) + a panel or mambrane dependant term. It is briefly described in this article draft with the reference:

This may be relevant eg for outdoor «stage bubbles», »bubble tennis courts», tents, or simply for light walls with somewhat low sound insulation. At low frequencies the transmission term of the »absorption» may be 0,1 or even higher.

Digital logic design using carbon nanotubes field effect transistors by

Debaprasad das, Subhajit das

You can see https://tetcos.com/help/v13.2/Technology-Libraries/Internetworks.html#hidden_node_behaviour (takes a few seconds to load though) for an understanding of simulating the hidden node problem in NetSim.

The answer to your question is dependent on many things, not the distance only. In addition to the distance, it also heavily dependent on the RF frequency band, the propagation condition, the Tx/Rx techniques, and so on. For instance, regarding the propagation condition, is it used in-door or out-door ? Is there a line of sight (LOS) propagation path, or no LOS path at all ? Is the Tx/Rx in fixed location or in moving ? Talking about the RF frequency band, for instance, if it is working in the millimeter wave band (e.g. 60-GHz band), there is a significant extra power loss in the air. Regarding to the used techniques, what kind of modulation scheme is used ? Is there any error correction coding technique used ? These things greatly affect the answer to your question. Good luck.

Nondimensionalization is the partial or complete removal of physical dimensions from an equation involving physical quantities by a suitable substitution of variables. The paper you're referring to talks about the population movement in different classes with different dimensions concerning the total population. To make it simpler, the authors proportionalized the population into different classes with respect to the total population N. If you see the B, k, and b also depend on the infected population I and, therefore, need to be done.

Dear Sabah al-ithawi ,

Please follow this weblink to comprehend polarizations, i.e., TE and TM in the directional coupler. https://s3.amazonaws.com/fip-3/Full-Explorer/index.html

May you please give a reference to your formula of ER mentioned in the document?