• Scholz processes about 30 thousand tons of scrap, about 1 million tons in Austria per year. Sources of scrap: consumers, industry, collection yards of municipalities, repair shops.
• Aluminium, Messing, Kupfer, Kunststoffe (Gummi), Draht – are the problematic components. Bateries and accumulators are also in the scrap and need to be sorted out. Copper is the most important part that must be removed, as this greatly affects the quality of the steel.

• Scrap consists of end-of-life vehicles and whole or crushed and from old household appliances. The cars are disassembled beforehand (tires, fluids, engine, etc.)
• This scrap is used to produce the so-called E40 material, for which there are EU guidelines, but the requirements of the steel producers are higher. The demands of steel and plastics manufacturers must be met. (single-variety) (EU – 2.5cm, Scholz – 10-15cm)
• Criteria in the steel plant: radioactivity, weight, visibility (copper!, plastics, rubber, substances)
• For the E40 material, 1 car and then 2 portions of household scrap are always put into the shredder. The 1:2 blends look about the same as just car scrap. Mixing is done to prevent canting in the shredder. By means of an eddy current separator (use air), the shredder output is divided into the light fraction and the E40 metal fraction. With the help of manual re-sorting, the quality of both outputs is improved. The re-sorting takes place on the two conveyor belts (light fraction, E40 fraction).
• The cameras for data acquisition should be implemented in the area at the hand sorting. If this does not work, a bypass can be created. Also tests can be carried out in the technical center with approx. 5000 stk scrap (Alexia says this is a suitable quantity). 
• technical center in St. Michael owns: NIR, WIS, laser triangulation 
• From next spring, cars will be more partially dismantled before shredding, there will be a re-data collection.

• Libs sensor: very complex and lengthy, has already been carried out at Scholz, silicon and magnesium are detected by it, laser shoots particles out of scrap particles and analyzes the composition
• MBT material: That waste which is removed from the household residual waste with the help of a magnet. This waste goes directly into the shredder, then metal is sorted out again, the rest goes back to the incinerator. (currently not relevant)

•  Moodle access works
•  Visit St. Michael with my own car on 13.06.
• Home Office on 14.06.
• Problems with the SparseDetr Code on my Laptop (running the code is not possible), running the code in the lab on the 3090 is possible

1. Publication
– publicate the collected and labeled data from St. Michael
– generate a GUI for labeling the data 
– the labeling is made by study assistants (about 200.000 Images)

2. Publication
– Conference Paper about Particle Tracking
– Train a network on the basis of the first Publication

eventually 3. Publication Transfer Learning
– use Open source Data for training

Main Question: How does a network learn an efficient representation to be able to build a reasonable model (even with a small amount of training data)?