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Fig. 4 Supply of cobalt, copper, and nickel by mine type and scenario.
First, each year, each mine type in each region increases its capacity by 3%%.
It gives the amount of ore extracted in mine type m in the different regions and it has the same length as C. G m is the ore grades matrix of mine type m.
Additional details of the clusters' contents per mine type and per burial depth are shown in Fig. 9 b, c.
Typically, depending on the mine type and burial depth, the mine signature may extend over 40 200 depth values, i.e., it may cover no more than 10% of the extracted data cube.
It gives the average ore grade of the different metals (cobalt, copper, and nickel in our case) in the ore of mine type m in the different regions and has dimension number of metals studied times the number of regions.
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hoodie, sat in the seat in front of mine, typing on his phone.
In the context of landmine detection, variations in the class of mines may be caused by the different mine types, burial depth, soil type, and moisture.
In [5, 6], hidden Markov modeling was proposed for detecting both metal and nonmetal mine types using data collected by a moving vehicle-mounted GPR system.
To match supply and demand for cobalt, we apply an optimization routine that determines in which regions and in which mine types resources are extracted and where new mining capacity will be installed.
Twenty eight distinct mine types that can be classified into four categories: anti-tank metal (ATM), anti-tank with low metal content (ATLM), anti-personal metal (APM), and anti-personal with low metal content (APLM) were used.
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