Proton Emulator Fix Download
Marion Gwilt
I know that the game is not available on linux but with steam proton linux users still can run and play the game perfectly on linux so i think it will be a good idea to detect if the game is running on Steam Proton (Steam Proton is a wrapper for wine so technically the game is running using wine) to show linux logo.
The DT5800D detector emulator has the ability to generate signal pulses with either a fixed amplitude/energy, with amplitudes that follow a predefined sequence of energies, or with amplitudes in accordance with an energy spectrum.
For the fixed amplitude/energy mode, the amplitude is set via the software interface and can take values between 0 V and 2.2 V (can be set in volts, LSB or if calibrated in keV). In fixed energy mode the emulator behaves similarly to a simple pulser.
When using the sequence mode, the predefined sequence of energy values must be imported as .csv file and formatted as specified in the detector emulator user manual. This mode is particularly suitable for experimental data that needs to emulated exactly how it was recorded (i.e. not pseudo-randomly).
Before using the energy spectrum mode to specify the signal pulse amplitude distribution, the spectrum needed to be defined. The spectrum can either be defined using the inbuilt spectrum editor tool, where the user can choose to draw a spectrum form scratch or load a predefined spectrum from a database of spectra for isotopes, or defined by importing a correctly formatted .csv or CAEN digitiser .dat, spectrum .spectrum or ANSI4242 .xml file. Once imported, the spectrum can be transformed/modified via the software interface. The emulator then uses the defined spectrum and a pseudo-random number generator to generate pulses with different energies with a distribution matching the spectrum. As this mode was used extensively in testing, the details of how the emulator was configured will now be described.
To generate a spectrum for use with the detector emulator, the sample waveforms need to intergrated and put into a histogram. See the figures below shwo an example waveform, a sample of waveforms from a single data file and a generated histogram respectively. If the spectrum is generated as described, from oscilloscope data, the bin width needs to recorded so that the spectrum can be scaled accordingly via the emulator software interface.
When importing a custom signal shape into the detector emulator, the signal cannot be defined in terms of volts. Instead it needs to defined in terms of least significant bit (LSB). In the emulator manual the conversion is stated as $\textnormalLSB = \frac4\textnormal V2^16$, where the 4 V value refers to the voltage range of out put signals (-2 V to 2 V). However, this conversion was not suitable as the imported spectrum did not have a matching range. Therefore a more general equation was used to find the scale for converting from volts to LSB, $\textnormalLSB = \fracV_\textnormalref2^\textnormalbits = \fracV_\textnormalref2^16$, where $V_\textnormalref$ is taken as the voltage of most populated bin (this is easiest identified using the detector emulator GUI).
Unfortunately due to how the detector emulator interpolates between data points when gener- ating a signal, there is an averaging effect between points where $\fracdVdt$ is large and when $\fracdVdt$ changes polarity quickly. i.e. The emulator struggles to reproduce fine details in an imported shape. This was very significant for the emulation of experimental data as by re-representing the waveform at the lower resolution for the correct length signal to be generated, the short pulse is only defined by a few data points. This then meant that the output signal was significantly smaller than required. Therefore the values in the data file needed to be scaled larger. It is difficult to formally and quantitatively describe by how much an imported signal needs to be scaled by as this depends on how detailed the signal is and its magnitude. The orange trace in Fig.14 shows the final form of a waveform that has had all the necessary steps performed so that it is ready to be emulated.
The time distribution of signals generated by the detector emulator can be defined by one or four separate methods; at a constant rate, with a Poisson distribution, a custom user defined distribution or custom user defined sequence of events. Since the sample sequence mode on the oscilloscope was used to record record experiential data, the exact rate and time distribution of the experimental data could not be determined. Therefore, for the testing/evaluation of the custom distribution and sequence time distribution modes, arbitrary distributions and sequences were used to verify how these modes worked.
For each iteration of testing the various features of the detector emulator, the signal was output to the Teledyne LeCroy HDO6104 oscilloscope. By using the inbuilt oscilloscope mathematics and graphing functions, it was possible to quickly inspect the output by eye to evaluate if the detector emulator was behaving as expected. The most commonly used inbuilt function/multi-function was to produce an on-the-fly histogram of the signal area. An example of this function in practice can be seen in the figure below, where an acceptable emulation of the experimental data from the 1.98mmCol_RateTest_20kHz run at the Clatterbridge Cancer Centre, 02/08/2016 with all fo the the above described manipulations applied to the spectrum and signal shape.
Artificial noise and interference can applied to an emulation, this is programmable within the detector emulator software interface. These programmable features are: random number noise, white noise, 1/f noise, baseline drift, random walk, shot noise, interference and pile-up. All types of noise emulation, besides baseline drift and interference, can only be programmed by adjusting one/two variables. Baseline drift can be set by either using the GUI to draw how the baseline changes or by importing a .csv file contain this pattern. Interference is set by importing a .csv file with the interference pattern then choosing the time distribution and magnitude of the interference.
Although a PMT has an associated statistical noise known as shot noise, the shot noise feature in the detector emulator will not produce the same effect and should not be used to emulate this. The artificial shot noise feature of the detector emulator randomly generates Gaussian-like pulses of a user defined magnitude with a user defined probability of occurring. This is not the same thing as PMT shot noise. Instead, the better choice of programmable noise to emulate PMT shot noise would be random number or white noise. These would be more suitable because they are more constant and average (closer) to zero. Whereas emulated shot noise is always the same polarity as the signal. The following two figures show the implementations of shot noise and a random number/white noise compariaon respectively. It may be useful to try and emulate PMT shot/statistical noise because the random fluctuations in the signals is lost in the creation of an average waveform. For this to be effective, the magnitude of the programmed random number or shot noise should be very small relative to the signal amplitude.
The main limitation of the DT5800D detector emulator for emulating experimental data of this kind is its ability to generate short and finely detailed pulses. As mentioned before, this is from the relatively coarse resolution of Δt = 6.348 ns between data points for imported signal shapes. This limitation results in a very qualitative trial and error method having to be implemented when scaling the signal shape and a final output signal where the constituent data points can easily be identified.
Another set of limitations, that are more of an inconvenience, are how the some of the features are programmed within the detector emulator software interface. For example not being able to directly specify a probability for pile-up occurring, not being able to change the units in the spectrum display plot from LSB to V but being able to change samples to μs, and that some features sporadically need to selected then deselected and re-selected in order to be implemented.
Overall the detector emulator is capable of emulating experimental data that is comparable to the original data. Therefore verifying it's suitability for being used to test different methods of data collection and on-the-fly data processing without either having the detector running or using a radiation source. The convenience of being able to do this in a laboratory environment is a particular advantage due to the nature of the application of proton therapy beamlines and their relative scarcity. Every experimental run completed during business hours uses time that could be used ot treat patients which, from a hospital's point of view, is a higher priority.
Due to the aforementioned limitations, a better application of the detector emulator may be to use it to drive an LED that injects light into the detector's PMT. Hence, providing another method of stress-testing the detector. This application does not as dependent on the time-step resolution. The detector emulator has been tested and proven to be able to generate the simple pulses that have been previously used to drive an LED in PMT testing. However the stability of the generated signals have not been investigated. Using the detector emulator in role complicates the implementation of programmable noise potential rendering all noise features, bar pile-up, non-applicable.
We calculate the equation of state of asymmetric nuclear matter at finite temperature based on chiral effective field theory interactions to next-to-next-to-next-to-leading order. Our results assess the theoretical uncertainties from the many-body calculation and the chiral expansion. Using a Gaussian process emulator for the free energy, we derive the thermodynamic properties of matter through consistent derivatives and use the Gaussian process to access arbitrary proton fraction and temperature. This enables a first nonparametric calculation of the equation of state in beta equilibrium, and of the speed of sound and the symmetry energy at finite temperature. Moreover, our results show that the thermal part of the pressure decreases with increasing densities.
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