## 1 Introduction  
Uncertainty related to experiments with scale models is an issue which is currently being discussed by the international scientific community. The reason for this is that codes of practice do not give experimental protocols [1, 2] and that scientific literature focuses on specific cases. Error propagation due to the small scale of experimental models is known to affect results at the prototype scale [3, 4, 5, 6, 7, 8].  
The most common experimental errors can be grouped in three families: instrumental error, inaccuracy error and random repetitiveness error. Instrumental error is due to the limits of the instrument and is checked before the experiment is set up [3]. This is an awareness error and is commonly taken into account. Inaccuracy errors can be due to several aspects such as the inaccuracy of the model, inaccuracy when performing experiments and others. The small scale of the models [9] used for wind tunnel tests generally causes the most common inaccuracy errors. Small scales may cause loss of important details or undesired asymmetries or scaling inaccuracy due to the Reynolds number effects. However, the modern technology in the field of the 3D printer and control systems reduces the inaccuracy due to the model geometry. The situation for the Reynolds number effects is different because this is a long-standing problem that should be investigated by repeating tests many times.  
The last family of experimental errors, random repetitiveness errors, is the hardest to check or to plan before experiments because it depends primarily on the amount of time and economic resources available for experiments. It is well known that the number of repetitions of experiments affects results at the prototype scale [3, 4, 5]. This is particularly relevant in the case of experiments that cannot be totally automated. [PERSON] and [PERSON] (2018) [4] showed that the number of repetitions affects flutter critical velocity estimation during wind tunnel aeroelastic tests. [PERSON] et al. (2020) [3] showed that the number of experiment repetitions affects dynamic identification of an aeroelastic model and consequently affects the prediction of magnitudes at the prototype scale.  
Acquisition time is another important aspect to take into account during dynamic experiments. Acquisition time in the wind tunnel is a relevant issue for both pressure and force tests and for aeroelastic tests. This dependence is discussed in literature with particular relevance to peak statistics. The maxima and minima of a random process depend on the recorded time history length. Most analytical models used to predict peak factors are affected by this aspect [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20]. In the case of dynamic experiments, where structural response is affected by nonlinearity and vorticity, model vibration is related to acquisition time length even if recording processes in the wind tunnel are mostly stationary.  
This paper discusses the acquisition time dependence of wind-induced floor acceleration on a scale model high-rise building. Individual values, eigenvectors and floor acceleration correlations between different levels and wind angles are assumed as significant magnitudes in order to have a measure of variability due to acquisition time length.  
The complete recorded processes of floor accelerations at different levels of a high-rise building scale model were subdivided into random sub-processes. The variability of the singular values, eigenvectors and correlation coefficients is discussed.  
Sections 2 and 3 discuss the structural setup of the prototype, the experimental model scaling and construction procedure and, finally, the wind tunnel aeroelastic tests. Section 4 discusses the acquisition time dependence of the individual values and of the wind induced floor acceleration correlation coefficient between both different levels of the building and different wind directions.