esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
(2021) [22] used a combination of convolutional neural networks (CNNs) and long short-term memory (LSTM), along with a support vector machine (SVM) classifier to analyze public sentiment about the Black Lives Matter movement on Twitter. They achieved an accuracy of 87.3%, and the combination of CNNs and LSTMs performed better than using either technique alone; SVM was more effective than other classifiers. The study concludes that this method is effective for sentiment analyses from tweets related to social movements, such as Black Lives Matter. The observational study, authored by [PERSON] et al. (2021) [23], used NLP and machine learning techniques to analyze public attitudes toward COVID-19 vaccines on social media in the UK and the US. A pre-trained deep learning model, bidirectional encoder representations from transformers (BERT), was used to extract features from the text, and a bag-of-words representation and SVM algorithm were used to classify tweets and Facebook posts as positive, negative, or neutral. The overall accuracy of the algorithm was around 80%. In a study conducted by [PERSON] et al. (2022) [24], a combination of traditional machine learning methods and deep learning (DL) techniques were used to classify tweets into different categories. The study employed a hybrid ensemble model that combined state-of-the-art lexicon rule-based and deep learning-based approaches to analyze sentiment trends related to the main vaccines available in the United Kingdom. The aim of the study was to evaluate the frequency and nature of adverse events following immunization (AEFI)-related mentions on social media in the UK and provide insight into public sentiment toward COVID-19 vaccines. The study used a two-step approach to extract and analyze over 121,406 relevant Twitter and Facebook posts. Results indicated an increasing trend in the number of AEFI mentions on social media; public sentiment toward vaccines (and their manufacturers) was largely positive. Studies have suggested that sentiment analysis from Twitter data can be useful in identifying patterns of public sentiment and opinions during civil unrest. Machine learning methods helped in achieving better accuracy [25]. Studies about sentiment analyses provided valuable insight for policymakers and other authorities to enhance their understanding of the dynamics of a given situation and respond in an appropriate manner [26].
## 2 Materials and Methods  
### Sample Preparation  
#### 2.1.1 Semiconductor Quantum Dots  
The semiconductor quantum dots (QDs), which were used in FRET measurement, are the CdSe/ZnS shell/core QDs that have a fluorescence emission peak at 535 nm and strong absorption in the ultraviolet region up to 520 nm. CdSe/ZnS QDs were synthesized by the successive ionic layer absorption and reaction (SILAR) method. Details of the preparation conditions have been reported previously [17,18]. QDs particles are spherical in shape with diameters of approximately 4.0 nm. AFB1 and CdSe/ZnS are compatible as donor-acceptor pairs for FRET measurements where CdSe/ZnS QDs concentrations were maintained and the AFB1 concentration were varied as shown in Table 1.
# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 10 Advancing Medical Diagnostic Videos: Limitations and Future Work
### Preprocessing of Medical Videos  
For the medical domain, important considerations must be taken into account in developing deep learning models. Medical data impose a few challenges that should be handled at the preprocessing step. The first challenge is regarding the format of the medical videos and their resolution. Medical videos often come in a DICOM format with varying resolutions per acquisition. Secondly, processing all pixels in a medical video can lead to excessive computation time and weights to learn. Lastly, medical videos show a slight difference in dissimilar classes; hence, there is a risk of losing vital information if the resolution is reduced.  
To tackle these challenges, the primary prerequisite step is to preprocess the data to standardize the resolution before developing the deep learning models with nontrivial implications. Downsampling can be performed on videos while attempting to avoid the loss of features that are useful in differentiating dissimilar classes. The ideal resolution isone which has a minimal amount of visual information necessary for accurate classification or segmentation. Ideal resolution size can highly depend on the complexity of visual structures and data type. However, in general, there is a rapid increase in computation time with increasing input resolution as the network architecture expands in depth.
## 1 Introduction
Sink mobility has also been exploited to address coverage issues and interested readers may refer to [8, 9, 10, 11] for more details. The rest of this paper is organized as follows: first, the network architecture of mWSNs is described in Section 2. Next in Section 3, the potential advantages that are obtained by exploiting the sink mobility are discussed. Then some challenges for data dissemination that are caused by sink mobility are identified in Section 4. Different mobility patterns exhibited by sinks are discussed in Section 5, as they have a direct impact on the design of a strategy for data delivery towards a mobile sink. A procedure of data delivery to a mobile sink is described in Section 6 to gain more insight into the complexity and the different phases involved when delivering sensed data towards a mobile sink. Based on the sink mobility patterns, the existing mobile sink-based data collection schemes, which are discussed in detail along with their aims, methodologies, strengths and weaknesses in the various sub-sections, are classified into different classes in Section 7. Section 8 organizes existing schemes on the basis of their main goal(s) and provides a comparative study in terms of the various features. Finally, Section 9 concludes our discussion with identification of issues that need to be addressed in pursuit of data delivery to a mobile sink.
## 3 Experimental Results and Discussion
### Experimental Results
#### 3.3.2 Comparison Results  
Table 3 shows the results of the six different types of PCB defect detection, demonstrating the performance comparison of the YOLOv8 model before and after it has been improved, i.e., the YOLO-SUMAS model, in the PCB defect detection task.  
The YOLO-SUMAS model achieves significant optimization in several key performance metrics. First, the precision (P\(\%\)) of the YOLO-SUMAS model is improved from 97.6% to 98.8%, and the recall (Recall\(\%\)) is increased from 90% to 99.2%, i.e., the improved model is more accurate and comprehensive in identifying PCB defects. In addition, the frame rate (FPS) of the model is improved from 338.96 to 383.46, and the processing speed is enhanced, which is more favorable for application scenarios that require real-time processing. In terms of the mean accuracy (mAP@50), YOLO-SUMAS reaches 99.1%, which is a significant improvement compared to 94.4% of YOLOv8, indicating the enhancement of the overall detection performance of the model. Specifically, for each category of defects, YOLO-SUMAS has a lower average accuracy in open-circuit detection, but the performance difference is not significant. mAP@50 values are higher than those of YOLOv8 in the categories of short-circuits, Spurious copper, missing holes, rat bites, and spurs, with the most significant improvement in missing holes and spurs, which have been increased from 98.8% and 86.7% to 99.2% and 99.4%, respectively. The YOLO-SUMAS model improves the accuracy and efficiency when dealing with PCB defect detection tasks and also enhances the model's ability to recognize different types of defects, which provides more reliable technical support in practical industrial inspection applications.
###### Abstract  
A wideband Doppler Effect is a significant challenge for underwater acoustic communications (UAC). This paper proposes a new two-stage structure of direct adaptive multi-resampling turbo equalizer (DAM-TEQ) for solving the problem of large timescale errors in time-varying channels, which uses an innovative adaptive time-domain resampling method for Doppler estimation and compensation. In this equalizer, the received signal is first fed into the first-stage structure, in which an adaptive resampling is performed using equalization coefficient detection to achieve a Doppler rough estimation. After the processing is completed, it is fed into the second-stage structure for joint equalization and decoding, effectively reducing the error of information transmission. Compared with the conventional turbo equalizer (TEQ) based on timescale estimation, the proposed equalizer can avoid the problem of the Doppler Effect not being accurately estimated in time-varying channels, with only a slight increase in complexity. Simulations and lake trails show that the equalizer can effectively perform a Doppler estimation and compensation in time-varying channels, and has a better bit error rate (BER) performance than the traditional timescale-based TEQ.  
underwater acoustic communication; direct adaptive multi-resample; turbo equalizer +
Footnote †: journal: Marine Science and Engineering
## 2 Configuration Design of Truss-Link Mechanism
### Configuration Design
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input Variables** & **Value** & **Output Variables** & **Value** \\ \hline \(\varphi_{1}\) & \(91.55^{\circ}\) & \(\varphi_{2}\) & \(93.11^{\circ}\) \\ \(\varphi_{4}\) & \(129.18^{\circ}\) & \(\varphi_{3}\) & \(91.64^{\circ}\) \\ \(\varphi_{6}\) & \(92.06^{\circ}\) & \(\varphi_{5}\) & \(104.20^{\circ}\) \\ \(L_{1}\) & \(140.00\) mm & \(\varphi_{7}\) & \(27.77^{\circ}\) \\ \(L_{4}\) & \(86.00\) mm & \(L_{2}\) & \(726.28\) mm \\ \(L_{6}\) & \(661.34\) mm & \(L_{3}\) & \(753.58\) mm \\ \(X_{A}\) & \((57.00,-288.00)\) & \(L_{5}\) & \(309.38\) mm \\ \(X_{C}\) & \((43.00,-120.00)\) & \(L_{7}\) & \(129.54\) mm \\ \(X_{D}\) & \((43.00,-640.00)\) & \(X_{B}\) & \((60.79,-148.05)\) \\ \(X_{F}\) & \((21.44,-873.27)\) & \(X_{E}\) & \((97.34,-573.34)\) \\ \(X_{C}\) & \((-69.40,-152.00)\) & \(X_{H}\) & \((-93.18,-812.91)\) \\ \(X_{O}\) & \((-13.00,11.00)\) & \(X_{I}\) & \((-13.20,-813.00)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Values of the input and output design variables of truss-link mechanism. Figure 2: Configuration design of truss-link mechanism in (a) stowed configuration (b) deployed configuration with latching points: B, E, H.  
\[\varphi_{5} =\cos^{-1}(\frac{x_{F}-x_{E}}{L_{5}}) \tag{11}\] \[\varphi_{7} =\cos^{-1}(\frac{x_{F}-x_{H}}{L_{7}}) \tag{12}\]  
For practical design, a 3D configuration design was conducted by CATIA. The 3D model consists of an inner panel, an outer panel, and 12 truss-links, with dimensions of 1620 mm \(\times\) 800 mm in the fully deployed configuration (see Figure 3) and a total mass of 11.45 kg as shown in Table 4. The mass of hinges is included in the mass of the panel, and the mass of the bracket is included in the mass of each link. The truss-links connection was finally realized as illustrated in Figure 4a, with several connection angles, as shown in Figure 4b. The connection angles according to the truss-links are summarized in Table 5. The material of the truss-link is aluminum 6061, which has a Young's modulus of 68.9 GPa, a Poisson's ratio of 0.33, and density of 2700 kg/m\({}^{3}\)[24]. The cross-section of the truss was assumed as a tube-type with an outer diameter of 10 mm and an inner diameter of 8 mm.
## 3 Results and Discussion
### Method Validation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Nr.** & **FAME** & **RT (Mean)** & **R\({}^{2}\)** & **Range (\(\upmu\)g/mL)** & **Detection (LOD), \(\upmu\)g/mL** & **Quantitative (LOQ), \(\upmu\)g/mL** \\ \hline
1. & Methyl butyrate & 10.794 & 0.999 & 6.24–399.6 & 0.51 & 1.54 \\
2 & Meethyl hexanoate & 12.866 & 0.999 & 6.24–399.6 & 0.44 & 1.33 \\
3 & Methyl octanoate & 16.514 & 0.999 & 6.24–399.6 & 0.42 & 1.28 \\
4 & Methyl decanoate & 21.548 & 0.999 & 6.24–399.6 & 0.39 & 1.18 \\
5 & Methyl laurate & 27.024 & 0.999 & 6.24–399.6 & 0.33 & 1.01 \\
6 & Methyl tridecanoate & 29.679 & 0.999 & 3.08–197.5 & 0.32 & 0.98 \\
7 & Methyl myristate & 32.263 & 0.999 & 6.24–399.6 & 0.29 & 0.89 \\
8 & Myristoleic acid methyl ester & 34.256 & 0.999 & 3.12–199.8 & 0.31 & 0.93 \\
9 & Methyl pentadecanoate & 34.706 & 0.999 & 3.12–199.8 & 0.28 & 0.84 \\
10 & Cis-10-pentadecanoic acid methyl ester & 36.649 & 0.999 & 3.09–198.0 & 0.28 & 0.86 \\
11 & Methyl palmitate & 37.09 & 0.999 & 3.65–399.4 & 0.25 & 0.76 \\
12 & Methyl palmitate & 38.629 & 0.999 & 3.12–199.8 & 0.27 & 0.81 \\
13 & Methyl heptadecanoate & 39.292 & 0.999 & 3.11–199.2 & 0.31 & 0.94 \\
14 & Cis-10-heptadecanoic acid methyl ester & 40.815 & 0.998 & 3.12–199.8 & 0.24 & 0.72 \\
15 & Methyl stearate & 41.474 & 0.999 & 6.24–399.6 & 0.22 & 0.68 \\
16 & Trans-9-elaidic acid methyl ester & 42.343 & 0.998 & 3.12–199.8 & 0.21 & 0.64 \\
17 & Cis-9-oleic acid methyl ester & 42.757 & 0.999 & 6.24–399.6 & 0.24 & 0.71 \\
18 & Methyl linoleate & 44.667 & 0.999 & 3.09–197.9 & 0.25 & 0.76 \\
20 & Methyl arachid & 45.518 & 0.998 & 6.24–399.6 & 0.21 & 0.63 \\
21 & Gamma-linolenic acid methyl ester & 46.089 & 0.999 & 3.12–199.8 & 0.25 & 0.77 \\
22 & Methyl eicosanoate & 46.672 & 0.999 & 3.11–199.2 & 0.21 & 0.65 \\
23 & Methyl linolenate & 46.854 & 0.999 & 3.12–199.8 & 0.27 & 0.81 \\
24 & Cis-11,14-eicosadienoic acid methyl ester & 48.475 & 0.999 & 3.12–199.8 & 0.23 & 0.69 \\
26 & Methyl behenate & 49.268 & 0.998 & 6.23–398.8 & 0.26 & 0.79 \\
30 & Cis-11,14,17-eicotrienoic acid methyl ester & 50.507 & 0.999 & 3.03–193.6 & 0.27 & 0.82 \\
33 & Cis-13,16-docosadienoic acid methyl ester & 52.014 & 0.999 & 3.12–199.8 & 0.28 & 0.84 \\
34 & Methyl linoacetate & 52.786 & 0.999 & 6.24–399.6 & 0.26 & 0.79 \\
35 & Methyl cis-5,8,11,14,17-eicosapentaenoate & 52.949 & 0.999 & 3.12–199.8 & 0.33 & 1.01 \\
36 & Methyl nervonate & 53.843 & 0.999 & 3.12–199.8 & 0.29 & 0.87 \\
37 & Cis-4,7,10,3,16,19-docosahexaenoic acid & 57.949 & 0.999 & 3.12–199.8 & 0.54 & 1.63 \\ \hline \hline \end{tabular} Mean of six replications. RT: retention time. R: correlation coefficient. LOD: limit of detection, LOQ: limit of quantification.  
\end{table} 
Table 1: Linearity and homoscedasticity test for calibration plots, results of the system suitability study, and detection and quantitation limit values for fatty acids methyl esters.
## 5 Results
### Experiments with Graph Combinations
Aggregating more data is beneficial from the point of view of having more context during the training process due to the use of deep learning with graphs [32; 33]. On the other hand, the type of features considered span from behavior to transaction types. The improvement opens an opportunity for future works to explore if the combination of certain types of features in conjunction with new entities helps in the correct identification of fraud patterns. Tables 10 and 11 show the percentage of improvement in the results when comparing the use of a single graph versus combinations of graphs (i.e., adding more events) and pairs of graphs versus the complete graph. In addition, Table 12 also shows the percentage of improvement when increasing the number of features. All these results highlight that adding more events (structural information) and features related to transactions/behavior helps to obtain better metric results. Something to note is that previous works related to fraud detection, such as [9] (the only one that uses a TGN approach for fraud detection), do not focus on metric improvements when more data are added (which is relevant in the task of fraud detection on online platforms). In contrast, they did not achieve any improvement for their proposal versus an algorithm on the TGN approach applied to fraud detection. Furthermore, other works cannot be compared because they use DTTG, i.e., they are the snapshot-based case. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Cards + Dev** & **Cards + BA** & **Dev + BA** & **Card + Dev + BA** \\ \hline
**Cards** & 34.55/46.94 & 0.1/16.42 & N/A & 44.45/58.47 \\ \hline
**Dev** & 38.96/0 & N/A & 33.74/3.83 & 56.55/7.40 \\ \hline
**BA** & N/A & 7.95/0 & 73.27/11.59 & 99.93/15.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Percentage of improvement in the results using each single graph vs. the combined graphs. For each pair of values in the table, the first corresponds to the improvement over the \(F_{1}\) score, and the second stands for the AUC. Generally, it can be observed that the aggregation of more events increases the percentage of improvement.
## 3 Results
### Analysis of the Decreasing Orders of Influence  
The effects of fatigue are a continuum with different degrees [13]. It is supposed that higher levels of fatigue are associated with severer effects and contribute to assessing the effects of fatigue more clearly. The subjective fatigue levels and the analysis results (comparison between BAL and AAL) showed that no obvious post-work fatigue was induced by the simple task. It is believed that the key component of fatigue in the AFA situation was the drive to sleep. Among the eight situations, the subjective fatigues level in the AFA situation were the highest. Therefore, the AFA was used to analyze the effects of the fatigue state during work in depth. The ATE was selected to represent the tension state during work. According to the results of the pairwise comparison of BAL vs. AFA and AL vs. FA (Table 3), ten indicators were selected for investigating the effects of fatigue: HR, STT, RTL, RTS, ROA, TDSA, ART, DHR_AVG, RTOS_AVG, and RTOS_SD. According to the results of the pairwise comparison of BAL vs. ATE and AL vs. TE, five parameters were selected to analyze the effects of tension: STT, ART, DHR_AVG, DHR_SD, and RTOS_SD. The objective was to determine the decreasing order of these indicators that were affected by fatigue and tension, respectively, to discuss their influence mechanisms. We used repeated measures and a paired \(t\)-test was used to estimate the levels of the effects. Comparisons of the BSO and AFA situations and the SO and FA states were performed for fatigue and comparisons of the BSO and ATE situations and the SO and TE states were conducted for tension. A higher Cohen's d obtained from the analysis means a bigger effect size. The results of the indicators are shown in Table 4, indicating that the indicators affected by fatigue in decreasing order were HR, RTOS_AVG, STT, RTOS_SD, DHR_AVG, RTL, ROA, RTS, ART, and TDSA, and the indicators affected by tension were DHR_AVG, STT, DHR_SD, RTOS_SD, and ART.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**FA State Indicator** & **T** & \(p\)** & **Cohen’s d** & **TE State Indicator** & **T** & \(p\)** & **Cohen’s d** \\ \hline HR & 8.166 & 0.000 & 1.873 & STT & 3.623 & 0.002 & 0.831 \\ \hline STT & 7.387 & 0.000 & 1.695 & ART & \(-\)2.461 & 0.024 & 0.520 \\ \hline RTL & \(-\)4.336 & 0.000 & 0.995 & DHR\_AVG & \(-\)5.304 & 0.000 & 1.217 \\ \hline RTS & \(-\)3.307 & 0.004 & 0.759 & DHR\_SD & \(-\)3.321 & 0.004 & 0.762 \\ \hline ROA & 4.318 & 0.000 & 0.991 & RTOS\_SD & \(-\)2.946 & 0.009 & 0.676 \\ \hline TDSA & 2.121 & 0.048 & 0.493 & & & \\ \hline \end{tabular}
\end{table}
Table 4: Results of the paired \(t\)-test.  
Figure 10: Means and 95% confidence intervals of the RTOS_AVG and RTOS_SD.
## 2 Power Converter Principle
### Buck Mode Principle  
In the buck operation, power switches, \(Q_{1}\)-\(Q_{4}\), (Figure 3) are replaced with power metal-oxide-semiconductor field-effect transistors (MOSFETs); the synchronous buck circuit with the operating timing is presented in Figure 4. In Figure 4a, \(v_{gs1}\)-\(v_{gs4}\) are the MOSFET gate-to-source pulse width modulation (PWM) driving signals, and \(i_{L}\) represents the inductor current. When the SBBC input voltage, \(V_{in}\), is greater than the HPLDDC output voltage, \(V_{o}\) (\(V_{in}>V_{o}\)), \(v_{gs4}\) is at the high-voltage level, and, consequently, the MOSFET \(Q_{4}\) is always turned on; \(v_{gs3}\) is always at the low-voltage level to turn off the MOSFET \(Q_{3}\). The time intervals, \(t_{k0}\)-\(t_{k1}\) and \(t_{k2}\)-\(t_{k3}\), are dead times that can prevent MOSFETs \(Q_{1}\) and \(Q_{2}\) from concurrently turning on and becoming a short-circuit. \(v_{gs2}\) is changed to the high-voltage level during the time interval, \(t_{k1}\)-\(t_{k2}\), thereby decreasing \(i_{L}\). Figure 4b presents the operating circuit during the time interval, \(t_{k0}\)-\(t_{k2}\).  
When \(i_{L}\) is lower than the current command inside the SBBC controller at \(t_{k2}\), \(v_{gs2}\) is changed to the low-voltage level, thus, turning off \(Q_{2}\), and \(v_{gs1}\) is changed to the high-voltage level at \(t_{k3}\), thus, increasing \(i_{L}\). The operating circuit during the time interval, \(t_{k3}\)-\(t_{k4}\), is depicted in Figure 4c. When \(i_{L}\) is greater than the current command inside the SBBC controller at \(t_{k4}\), \(v_{gs1}\) is changed to the low-voltage level, thus, turning off \(Q_{1}\).  
Figure 4: Buck mode principle: (**a**) \(v_{gs1}\)-\(v_{gs4}\) and \(i_{L}\) timing charts; (**b**) operating circuit in \(t_{k1}\)–\(t_{k2}\); and (**c**) operating circuit in \(t_{k3}\)–\(t_{k4}\).  
Figure 3: Synchronous buck–boost converter.
# Unsupervised Domain Adaptation with Coupled Generative Adversarial Autoencoders
## 3 Proposed Approach
### Style Transfer and GAN  
Style-transferred synthetic images can be generated by changing the combination of encoders and decoders. More specifically, let \(DEC_{T}\) take the output of \(ENC_{S}\), and let \(DEC_{S}\) take the output of \(ENC_{T}\), thus we are able to change the style of images between domains.  
When training an autoencoder, element level penalties such as squared error, is the classic choice. However, as discussed in [34], they are actually not ideal for image generation, and the generated images are always blurred. Therefore, we combine autoencoder with GAN in our method. By jointly training an autoencoder and a GAN, we can generate better images with the feature level metric expressed by the discriminator. In our method CGAA, the \(ENC_{S}\), \(DEC_{T}\) and \(D_{T}\) constitute a generative adversarial network. During training, \(DEC_{T}\) takes the output of \(ENC_{S}\), mapping an input source domain image \(x^{S}\) into a target domain style synthetic image \(x^{S\to T}\), and discriminator \(D_{T}\) is trained to distinguish between synthetic images \(x^{S\to T}\) and real images \(x^{T}\) from the target domain. Similarly, \(ENC_{T}\) and \(DEC_{S}\) generate synthetic source-style images \(x^{T\to S}\) conditioned on the target domain images \(x^{T}\) and \(D_{S}\) is trained to distinguish between real source domain images \(x^{S}\) and synthetic images \(x^{T\to S}\). With this pair of GANs, our goal is to minmax the following object:  
\[\begin{split}\min_{ENC_{S},ENC_{T},DEC_{S},DEC_{T},\mathcal{C}} &\max_{D_{S},D_{T}}&\alpha\mathcal{L}_{GAN_{S}}(ENC_{ T},DEC_{S},D_{S})+\beta\mathcal{L}_{REC_{S}}(ENC_{S},DEC_{S})\\ &\alpha\mathcal{L}_{GAN_{T}}(ENC_{S},DEC_{T},D_{T})+\beta \mathcal{L}_{REC_{T}}(ENC_{T},DEC_{T})\end{split} \tag{3}\]  
where \(\alpha\) and \(\beta\) are weights that balance the GAN loss and the reconstruction loss. \(\mathcal{L}_{GAN_{S}}\) and \(\mathcal{L}_{GAN_{T}}\) represent the GAN loss:  
\[\mathcal{L}_{GAN_{S}}(ENC_{T},DEC_{S},D_{S}) =\mathbb{E}_{X^{S}}[\log D_{S}(x^{S})]+\mathbb{E}_{X^{T}}[\log(1- D_{S}(DEC_{S}(ENC_{T}(x^{T}))))] \tag{4}\] \[\mathcal{L}_{GAN_{T}}(ENC_{S},DEC_{T},D_{T}) =\mathbb{E}_{X^{T}}[\log D_{T}(x^{T})]+\mathbb{E}_{X^{S}}[\log(1- D_{T}(DEC_{T}(ENC_{S}(x^{S}))))] \tag{5}\]