## 1 Introduction and Related Works  
Wireless communication under the sea is very challenging. Radio frequency and optical signals are severely attenuated and therefore unable to cover a great distance, hence their use is restricted to a few specific applications [1]. Acoustic signals, instead, can propagate for several kilometers, and, while characterized by low bandwidth and high propagation delay, at least enable long range communication links, and are considered the most mature underwater wireless communication technology to date. For this reason, underwater acoustic networks (UANs) are widely used in both military and civilian applications, including, but not limited to, coastal surveillance and monitoring, tsunami prevention, and oil and gas pipeline inspection. While sea trials are proven to be the best way to evaluate UANs, their realization is not trivial. In fact, they are very demanding in terms of costs, time, personnel, and equipment, and very prone to external factors that can cause failures of the trial, not only due to equipment issues caused by software faults and hardware damages, but also because of bad sea conditions. For this reason, network simulators are often employed for a preliminary evaluation in order to debug the protocol stack before the final sea trial, hence minimizing the probability of software faults and having an idea on how the new protocol works if compared to other benchmarks. However, in the underwater research community, simulations are still not considered to be a valuable tool to perform the final evaluation of UANs, as channel models are often unable to accurately describe the time varying behavior of a real acoustic channel [2]. The acoustic channel, in fact, depends on a large number of factors. First, changes of temperature, depth of the node, and salinity cause a variation of the sound speed along the water column, and therefore of propagation of the acoustic signal. Second, the presence of water currents, wind, and mobile nodes causes a strong Doppler effect that affects the received signal [3]. Lastly, noise caused by wind waves, rain, snapping shrimps, bubbles brought by tidal inflow, and ship propellers [4] causes the degradation of the signal to noise ratio (SNR). The use of realistic channel models, such as the Bellhop ray tracer [5] where a subset of these parameters can be included, is computationally demanding and hence restricted to networks with a small number of nodes. Given the large number of sea experiments performed by scientists in the last 15 years [6; 7; 8; 9; 10], a wide dataset of time-varying links has been collected, and some measurements are publicly available. Data-driven models have gradually been used to predict the trend of channel performance; for example, in [11] the authors, considering features for the model different environmental characteristics, build a logistic regression network whose Packet Success Rate (PSR) estimates are quite accurate if restricted to the short-term variability of only one of the acoustic link features used to build the regression network. In several works [12; 13; 14], the authors mapped different modems performance figures of PSR versus range in the DESERT network simulator [15]. Although in some cases they have also included the performance degradation due to interference, this model can only be used for a preliminary evaluation of the network, as the channel variability is not considered and the modem performance is assumed constant in time. The ASUNA dataset [6] is a collection of the acoustic link quality time evolution observed during many different sea trials carried out by the Haifa University, Israel, the University of Padova, Italy, and IMDEA Networks, Spain. These experiment have been performed in different locations around Europe and Israel. The authors also show how the time varying links stored in the dataset can be used in a Matlab network simulation in order to reproduce the link quality evolution experienced during those sea trials.