## 1 Introduction  
The use of Wheatstone bridges for measuring minute resistance changes in strain gauges and other resistive transducers is prevalent in engineering and scientific research. They play a key role in gauging physical parameters like strain, pressure, and displacement [1; 2; 3]. Accurate and real-time acquisition of strain data is crucial in engineering projects and scientific research, which require a comprehensive and efficient measurement system. Consequently, high-precision measurement systems are highly valuable. In practical industrial production, strain measurement systems usually combine resistance strain gauges with Wheatstone bridges to translate minor deformations into electrical resistance changes, to facilitate strain measurement and analysis through electrical signal variations. However, resistance strain gauges generate only minor resistance changes upon force application, and it is necessary to ensure the signal processing system's accuracy for precise strain-signal measurement. Baseline drift, especially temperature-induced baseline drift, is one of the most important factors affecting the accuracy of signal acquisition, and it eventually leads to poor stability and reduced reliability of the  
methods, some time-series analysis methods have also been proven to be effective in removing noise from signals. For example, the discrete wavelet transform (DWT) has been shown to be an effective tool for eliminating clinical motion artifacts in thoracic electrical impedance tomography, including common artifacts like baseline drift [10]. A sensor drift detection method based on the DWT and the grey model has been demonstrated to be effective both in analog temperature sensor output data from a continuous stirred-tank reactor sensor model and in measurements from a physical temperature sensor at a nuclear power control test facility [11]. Empirical mode decomposition (EMD), which offers a higher degree of adaptability compared to wavelet variation, has also been shown to be effective in removing power line interference and baseline wander noise from electrocardiogram signals [12]. An EMD-based fusion algorithm has been demonstrated to be effective in eliminating the effects of external environments and accurately compensating for temperature drift in MEMS gyroscopes [13]. However, such methods do not perform well in the face of highly randomized drift [14; 15] and are difficult to apply in real-time systems [16]. In recent years, some improved methods have also been proposed. For example, empirical wavelet transform has shown good results in handling non-stationary signals and has been successfully applied to eliminate noise from electromyography signals [17]. With the continuous development of neural networks, various models have become powerful tools for processing time series data and have achieved significant results in dealing with signal drift [18; 19; 20; 21]. Therefore, using deep learning is a viable solution for complex signal modeling tasks like drift elimination. The Recurrent Neural Network (RNN) is a classic neural network architecture designed to capture temporal information in time series data. However, RNNs suffer from problems like gradient explosion and gradient vanishing, limiting their ability to learn long-term dependencies [22]. To solve these problems, several RNN variants like Long Short-Term Memory Networks (LSTMs) and Gated Recurrent Units (GRUs) have been developed [23; 24]. Though these variants mitigate the gradient issue to a degree, they do not completely resolve it, nor do they allow for focusing on variable information at distinct time steps [25]. Moreover, to more effectively solve sequence-to-sequence problems, an encoder-decoder architecture based on the attention mechanism has been presented [26]. This architecture is now extensively used in time series processing and shows improved efficacy in removing signal drifts [27; 28; 29]. However, the problem of distance dependence in these systems remains unresolved.