The lack of freely available standardized datasets represents an aggravating factor during the development and testing the performance of novel computational techniques in exposure assessment and dosimetry research. This hinders progress as researchers are required to generate numerical data (field, power and temperature distribution) anew using simulation software for each exposure scenario. Other than being time consuming, this approach is highly susceptible to errors that occur during the configuration of the electromagnetic model. To address this issue, in this paper, the limited available data on the incident power density and resultant maximum temperature rise on the skin surface considering various steady-state exposure scenarios at 10$-$90 GHz have been statistically modeled. The synthetic data have been sampled from the fitted statistical multivariate distribution with respect to predetermined dosimetric constraints. We thus present a comprehensive and open-source dataset compiled of the high-fidelity numerical data considering various exposures to a realistic source. Furthermore, different surrogate models for predicting maximum temperature rise on the skin surface were fitted based on the synthetic dataset. All surrogate models were tested on the originally available data where satisfactory predictive performance has been demonstrated. A simple technique of combining quadratic polynomial and tensor-product spline surrogates, each operating on its own cluster of data, has achieved the lowest mean absolute error of 0.058 {\deg}C. Therefore, overall experimental results indicate the validity of the proposed synthetic dataset.

翻译：缺乏免费可用的标准化数据集是暴露评估与剂量学研究领域开发和测试新型计算方法性能时的一个加重因素。这阻碍了进展，因为研究人员需要为每个暴露场景重新使用仿真软件生成数值数据（场分布、功率分布和温度分布）。除了耗时之外，该方法极易在电磁模型配置过程中发生错误。为解决此问题，本文对10–90 GHz稳态暴露场景下有限的入射功率密度及皮肤表面最大温升数据进行了统计建模。从拟合的统计多元分布中采样合成数据，并满足预定的剂量学约束。因此，我们提供了一个综合且开源的数据集，其中包含考虑真实源多种暴露情况的高保真数值数据。此外，基于合成数据集拟合了用于预测皮肤表面最大温升的不同替代模型。所有替代模型均在原始可用数据上进行了测试，并展现出令人满意的预测性能。结合二次多项式与张量积样条替代模型的简单技术（每种模型在其自身数据簇上运行）实现了最低平均绝对误差0.058°C。因此，整体实验结果表明所提出的合成数据集具有有效性。