unstructuredio/SCORE-Bench · Datasets at Hugging Face

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Source: https://www.industrydocuments.ucsf.edu/docs/fhhd0346
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JIMA PFAS APPENDIX01 2023/09/14 Japan Inspection Instruments Manufacturer’s Association (JIMA)
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The cable with transducer shown in Photo 2 is composed of about 200 coaxial wires in order to transmit and receive about 200 signals.
The inside of the red circle cable in Photo 2 is as shown in Photo 3.
Photo 4 shows a cross-sectional view of one of Photo 3.
The performance required for these coaxial wires is (electric insulation / low dielectric constant), heat resistance, and extrusion suitability. PFA/FEP is used as a material that satisfies these three elements.


Table 15 Comparison with alternative candidate material PEEK shows the comparison results with the alternative candidate material PEEK.
In order to obtain a diagnostic image with high accuracy, the attenuation of coaxial wire must be 2 dB / m or less in terms of the size shown in Table 15 Comparison with alternative candidate material PEEK, and the required performance is not satisfied unless PFA/FEP is applied.
In order to satisfy the attenuation, it is necessary to reduce the capacitance, and for this purpose, the dielectric constant must be 2.1 or less.
The cross-sectional view of one coaxial wire is shown in Photo 4, and the red arrow part is the insulating layer and the green arrow part is the outer skin layer.
Both layers require thickness control of 0.05 mm or less, and PEEK cannot be controlled, especially for the outer layer because it does not stretch.
In order to evenly cover the outer layer without destroying the shield layer, stretching is necessary.
PEEK meets only heat resistance requirements.


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PFA PEEK Required characteristics
electrical characteristics dielectric constant 2.1 ○ 3.15 × ≦2.1
Attenuation*1 1.75 ○ ≧2 × @10MHz <2d B/ m
heat resistance Rated temperature ≧200○ ≧200 ○ 200° C or higher
extrusion suitability Coating thickness control GOOD ○ BAD × ≦0.05㎜t
Non-stop extrusion time GOOD ○ BAD × ≧2hr
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*1Conductor 48AWG (7/0.012), OD 0.18mm Φ
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Table 14 Comparison with alternative candidate material PEEK
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conductor
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Photo4
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Figure 8 Cross-sectional view of one coaxial wire
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16 / 20
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Source: https://www.industrydocuments.ucsf.edu/docs/fhhd0346
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IRJETS
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e-ISSN: 2582-5208
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International Research Journal of Modernization in Engineering Technology and Science
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( Peer-Reviewed, Open Access, Fully Refereed International Journal )
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Volume:05/Issue:04/April-2023
Impact Factor- 7.868
www.irjmets.com
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usage for efficient crop growth weather data, crop data, irrigation data clustering, deep learning yield, reduces water usage, lowers costs
5 Livestock Health Monitoring and managing animal health Sensor data, health records, weather data Classification, clustering, anomaly detection Early detection of disease, reduced mortality, increased productivity
6 Harvest Planning Optimizing harvest logistics and planning Weather data, soil data, crop data, machinery data Regression, clustering, deep learning Maximizes efficiency, minimizes waste, reduces labor costs
7 Weather Forecasting Predicting weather patterns for farming operations Weather data Regression, time-series analysis Helps with planting decisions, crop management, risk mitigation
8 Crop Disease Identifying and preventing crop diseases Sensor data, weather data, crop data, disease data Classification, clustering, anomaly detection Early detection, targeted treatment, reduces crop loss
9 Harvest Quality Predicting crop quality at harvest time Sensor data, weather data, crop data Regression, clustering, deep learning Minimizes post-harvest losses, maximizes profit potential
10 Food Traceability Tracking food products from farm to consumer Sensor data, supply chain data, weather data Classification, clustering, anomaly detection Ensures food safety, reduces waste, builds consumer trust
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Machine Learning algorithms-
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Precision agriculture utilizes advanced technologies, including remote sensing, GIS, IoT, and machine learning algorithms, to enhance crop yields, minimize waste, and reduce costs. Various machine learning algorithms are employed in precision agriculture [50-52], such as:Regression Analysis: This algorithm models the relationship between different variables to anticipate the outcome of an event. It is applied in precision agriculture to forecast crop yields by analyzing factors such as weather, soil type, and irrigation. The statistical technique of regression analysis is employed to determine the correlation between two or more variables. Within precision agriculture, regression analysis models the interrelationship between factors like weather patterns, soil properties, and irrigation techniques and their impact on crop production. By utilizing regression analysis, farmers can forecast future crop yields based on past data, thus enabling them to make informed decisions about planting and harvesting.Decision Trees: A classification algorithm that categorizes data into different groups. Decision trees can be employed in precision agriculture to classify crops based on growth patterns and determine the optimal time for harvesting. Decision trees are a classification algorithm in machine learning that categorize data based on a set of decision rules. In precision agriculture, decision trees can be utilized to classify crops according to their growth patterns and identify the optimal time for harvesting. Additionally, decision trees can identify crops that are more vulnerable to pests and diseases.Neural Networks: This type of algorithm is used for image classification and recognition. In precision agriculture, neural networks analyze images of crops to identify any diseases or pests that might be affecting them. Inspired by the functioning of the
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www.irjmets.com @International Research Journal of Modernization in Engineering, Technology and Science
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[4386]
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--------------------------------------------------- Unstructured Plain Text Format 1.0.4
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Source: https://www.industrydocuments.ucsf.edu/docs/fhhd0346

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--------------------------------------------------- Unstructured Plain Text Format 1.0.4

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JIMA PFAS APPENDIX01 2023/09/14 Japan Inspection Instruments Manufacturer’s Association (JIMA)

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The cable with transducer shown in Photo 2 is composed of about 200 coaxial wires in order to transmit and receive about 200 signals.

The inside of the red circle cable in Photo 2 is as shown in Photo 3.

Photo 4 shows a cross-sectional view of one of Photo 3.

The performance required for these coaxial wires is (electric insulation / low dielectric constant), heat resistance, and extrusion suitability. PFA/FEP is used as a material that satisfies these three elements.

Table 15 Comparison with alternative candidate material PEEK shows the comparison results with the alternative candidate material PEEK.

In order to obtain a diagnostic image with high accuracy, the attenuation of coaxial wire must be 2 dB / m or less in terms of the size shown in Table 15 Comparison with alternative candidate material PEEK, and the required performance is not satisfied unless PFA/FEP is applied.

In order to satisfy the attenuation, it is necessary to reduce the capacitance, and for this purpose, the dielectric constant must be 2.1 or less.

The cross-sectional view of one coaxial wire is shown in Photo 4, and the red arrow part is the insulating layer and the green arrow part is the outer skin layer.

Both layers require thickness control of 0.05 mm or less, and PEEK cannot be controlled, especially for the outer layer because it does not stretch.

In order to evenly cover the outer layer without destroying the shield layer, stretching is necessary.

PEEK meets only heat resistance requirements.

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PFA PEEK Required characteristics

electrical characteristics dielectric constant 2.1 ○ 3.15 × ≦2.1

Attenuation*1 1.75 ○ ≧2 × @10MHz <2d B/ m

heat resistance Rated temperature ≧200○ ≧200 ○ 200° C or higher

extrusion suitability Coating thickness control GOOD ○ BAD × ≦0.05㎜t

Non-stop extrusion time GOOD ○ BAD × ≧2hr

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*1Conductor 48AWG (7/0.012), OD 0.18mm Φ

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Table 14 Comparison with alternative candidate material PEEK

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conductor

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Photo4

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Figure 8 Cross-sectional view of one coaxial wire

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16 / 20

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Source: https://www.industrydocuments.ucsf.edu/docs/fhhd0346

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--------------------------------------------------- Unstructured Plain Text Format 1.0.4

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IRJETS

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e-ISSN: 2582-5208

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International Research Journal of Modernization in Engineering Technology and Science

--------------------------------------------------- Unstructured Title End

--------------------------------------------------- Unstructured Sub-Title Begin

( Peer-Reviewed, Open Access, Fully Refereed International Journal )

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Volume:05/Issue:04/April-2023

Impact Factor- 7.868

www.irjmets.com

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usage for efficient crop growth weather data, crop data, irrigation data clustering, deep learning yield, reduces water usage, lowers costs

5 Livestock Health Monitoring and managing animal health Sensor data, health records, weather data Classification, clustering, anomaly detection Early detection of disease, reduced mortality, increased productivity

6 Harvest Planning Optimizing harvest logistics and planning Weather data, soil data, crop data, machinery data Regression, clustering, deep learning Maximizes efficiency, minimizes waste, reduces labor costs

7 Weather Forecasting Predicting weather patterns for farming operations Weather data Regression, time-series analysis Helps with planting decisions, crop management, risk mitigation

8 Crop Disease Identifying and preventing crop diseases Sensor data, weather data, crop data, disease data Classification, clustering, anomaly detection Early detection, targeted treatment, reduces crop loss

9 Harvest Quality Predicting crop quality at harvest time Sensor data, weather data, crop data Regression, clustering, deep learning Minimizes post-harvest losses, maximizes profit potential

10 Food Traceability Tracking food products from farm to consumer Sensor data, supply chain data, weather data Classification, clustering, anomaly detection Ensures food safety, reduces waste, builds consumer trust

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Machine Learning algorithms-

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Precision agriculture utilizes advanced technologies, including remote sensing, GIS, IoT, and machine learning algorithms, to enhance crop yields, minimize waste, and reduce costs. Various machine learning algorithms are employed in precision agriculture [50-52], such as:Regression Analysis: This algorithm models the relationship between different variables to anticipate the outcome of an event. It is applied in precision agriculture to forecast crop yields by analyzing factors such as weather, soil type, and irrigation. The statistical technique of regression analysis is employed to determine the correlation between two or more variables. Within precision agriculture, regression analysis models the interrelationship between factors like weather patterns, soil properties, and irrigation techniques and their impact on crop production. By utilizing regression analysis, farmers can forecast future crop yields based on past data, thus enabling them to make informed decisions about planting and harvesting.Decision Trees: A classification algorithm that categorizes data into different groups. Decision trees can be employed in precision agriculture to classify crops based on growth patterns and determine the optimal time for harvesting. Decision trees are a classification algorithm in machine learning that categorize data based on a set of decision rules. In precision agriculture, decision trees can be utilized to classify crops according to their growth patterns and identify the optimal time for harvesting. Additionally, decision trees can identify crops that are more vulnerable to pests and diseases.Neural Networks: This type of algorithm is used for image classification and recognition. In precision agriculture, neural networks analyze images of crops to identify any diseases or pests that might be affecting them. Inspired by the functioning of the

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www.irjmets.com @International Research Journal of Modernization in Engineering, Technology and Science

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[4386]

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--------------------------------------------------- Unstructured Plain Text Format 1.0.4

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