## 5 Materials and Methods
### Contrastive Semantic Alignment

Table 8: Micro-F1 scores for multi-source M-FUDA and baseline models across cross-atmospheric tasks. Green background indicates the best accuracy for an individual task; blue background indicates best average accuracy; yellow background and bold font highlight the average performances. Figure 10: Comparison of average micro- and macro-F1 scores for multi-source M-FUDA and baseline models across cross-atmospheric tasks. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{10}{c}{**Macro-F1**} \\ \hline \multicolumn{1}{c}{**Source Domains**} & \multicolumn{1}{c}{**Target Domain**} & \multicolumn{3}{c}{**Proposed Multi-Source Models**} & \multicolumn{3}{c}{**Combined-Source Models**} \\ \hline
**Source 1** & **Source 2** & **Source 3** & **Target 1** & **M-FUDA (MMD)** & **M-FUDA (MK-MMD)** & **M-FUDA (MMD)** & **CORAL [72]** & **Wasserstein [89]** & **MCD [59, 88]** \\ \hline
**De(S1)** & D7(S1) & D8(S2) & **D8(S1)** &





better recognition accuracy and robustness. The model uses a four-stage alignment process with a pre-trained model, domain-specific feature extractors, and classifiers, along with a maximum mean discrepancy (MMD) loss for better performance and simpler architecture. The approach minimizes the discrepancy between classifiers for target samples and uses contrastive semantic alignment loss to improve classification across source-target domain pairs. Evaluations on three public datasets show that M-FUDA outperforms single-source UDA in scenarios where domain samples belong to diverse distributions. However, the model's higher complexity introduces a trade-off between computational cost and performance, limiting its applicability in low-gain, high-cost situations. Future work will explore the model's effectiveness in more challenging scenarios, such as multi-target tracking, using more complex architectures to improve predictive accuracy. Conceptualization, M.H. ; methodology, M.H. ; software, M.H. ; validation, M.H. ; formal analysis, M.H. and T.K. ; investigation, M.H. and T.K. ; resources, M.H. ; data curation, M.H. ; writing--original draft preparation, M.H. and T.K. ; visualization, M.H. ; supervision, M.H. and T.K. ; project administration, M.H. and T.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Ethical review and approval were waived for this study, due to the public availability and prior anonymization of all data used in the study. The data used in this study are publicly available with no personal information about the subjects disclosed. Informed consent was obtained from all subjects involved in the study. The data presented in this study are available at the following uniform resource locator (URL) links: [[https://github.com/parisafm/CSI-HAR-Dataset](https://github.com/parisafm/CSI-HAR-Dataset)]([https://github.com/parisafm/CSI-HAR-Dataset](https://github.com/parisafm/CSI-HAR-Dataset)), [[https://data.mendeley.com/datasets/v38](https://data.mendeley.com/datasets/v38) wjmz6f6]([https://data.mendeley.com/datasets/v38](https://data.mendeley.com/datasets/v38) wjmz6f6) and [https://data.4 tu.nl/datasets/575f95f7-abce-4 be0](https://data.4 tu.nl/datasets/575f95f7-abce-4 be0) -b4d6-29b4a683 cf4c/1 (accessed on 13 January 2025). The authors declare no conflicts of interest. ## References

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