esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Method for Document Correction Based on Checkerboard Calibration Pattern
## 3 Proposed Method
### Checkerboard Corner Detection  
To simulate the 3D shape of the document image, we define the corresponding 3D world points \(P_{w}\left(x,\ y,\ z,\ 1\right)\) of the checkerboard pattern. A set of markers are used to recover the shape by estimating the corners of the checkerboard. The checkerboard corners are extracted from each pair of black or white squares that touch each other. For each local corner located in the checkerboard, there are four squares surrounding the corner (upper left \(U_{l}\), upper right \(U_{r}\), lower left \(L_{l}\), lower right \(L_{r}\)) as illustrated in Figure 7. The corner is selected if it satisfies the following condition,  
\[U_{l}=L_{r}=black\ and\ L_{l}=U_{r}=white \tag{3}\]  
Figure 4: Example of a well-defined calibration pattern.  
Figure 5: Example images of a checkerboard calibration patterns.  
Figure 6: Example of selecting the corresponding checkerboard pattern.  
(4)  
All points on the checkerboard lie in \([x\ y]\) plane; we drop the \(z\) component (\(z=0\)) so the world points \(P_{w}\) return an N-by-2 matrix of an N number of \([x\ y]\) coordinates of checkerboard corners. \(P_{w}\) (0, 0) corresponds to the lower-right corner of the upper-left square of the board as shown in Figure 8a. World points \(P_{w}\) are used to match and obtain the corresponding points from the warped document image \(D_{w}\) to recover the 3D shape of the warped document image as shown in Figure 8b. Detected checkerboard corners as illustrated in Figure 8a reflect exactly the warped shape of the document image in Figure 8b. The matrix of \(D_{w}\) is used to solve the warping problem of the document image as follows in the next subsection.
## 4 Results and Discussions
### Result Analysis and Performance Comparison
#### 4.2.1 Baseline  
LR [44]: This model is a linear classifier that transforms weighted feature combinations into probabilities through a sigmoid function. It relies on feature engineering to extract meaningful information and possesses the advantages of high efficiency and strong interpretability, making it a fundamental baseline model.  
FM [6]: This model captures second-order feature interactions through latent vector inner products, effectively addressing feature combination problems in high-dimensional sparse data. It demonstrates computational efficiency and strong scalability.  
Wide&Deep [7]: This model combines the memorization capability of linear models with the generalization advantages of deep networks. The wide component captures explicit feature interactions, while the deep component explores implicit correlations, jointly improving CTR prediction performance.  
GP-BPR [45]: This model integrates Gaussian processes with Bayesian personalized ranking to probabilistically model users' implicit preferences and optimize item ranking.  
DeepFM [8]: This model unifies factorization machines and deep neural networks to jointly capture both low-order feature interactions and high-order implicit patterns. Its end-to-end training eliminates the need for manual feature engineering, significantly improving recommendation accuracy and generalization capability.
## 3 Assessment of Precise MGEX Orbit and Clock Products
### PPP Performance
#### 3.4.2 Kinematic PPP Solutions  
The positioning errors of kinematic PPP are shown for different products at the KAT1 station in Figure 13. The average positioning error RMS values of seven stations are summarized in Table 13. With COD, ESM, GFZ and GRG products, the positioning accuracy of Galileo-only PPP was 17-19 mm in the north and 13-16 mm in the east, which is comparable to that of GPS-only PPP. However, in the up direction, the positioning errors of Galileo-only PPP were about twice as large as those of GPS-only PPP. For BDS-only PPP, WUM products performed the best compared with other products, which is reasonable since it included all the available BDS-2 and BDS-3 satellites. The BDS-only PPP with WUM products achieved accuracies of 14, 14 and 49 mm in the east, north and up directions, respectively. As for the GPS + QZSS PPP, the addition of QZSS satellites slightly improved the positioning accuracy when compared with GPS-only solutions.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**GPS-Only**} & \multicolumn{3}{c}{**BDS-Only**} & \multicolumn{3}{c}{**Galileo-Only**} & \multicolumn{3}{c}{**GPS + QZSS**} \\ \cline{2-13}  & **E** & **N** & **U** & **E** & **N** & **U** & **E** & **N** & **U** & **E** & **N** & **U** \\ \hline COD & 1.6 & 1.1 & 4.4 & 5.2 & 3.4 & 25.6 & 1.8 & 1.4 & 7.7 & 1.4 & 1.0 & 4.2 \\ ESM & 1.6 & 1.1 & 4.4 & 3.0 & 1.4 & 7.4 & 1.7 & 1.3 & 7.4 & 1.4 & 1.0 & 4.1 \\ GFZ & 1.6 & 1.2 & 4.4 & 2.5 & 1.8 & 11.0 & 1.8 & 1.4 & 8.1 & 1.6 & 1.1 & 4.2 \\ GRG & 1.5 & 1.2 & 4.3 & / & / & / & 1.9 & 1.6 & 7.6 & / & / & / \\ JAX & 1.6 & 1.3 & 4.3 & / & / & / & / & / & / & 1.6 & 1.2 & 4.2 \\ SHA & 3.0 & 2.1 & 8.3 & 4.1 & 2.3 & 12.8 & 2.2 & 1.8 & 8.5 & / & / & / \\ WUM & 1.5 & 1.5 & 4.2 & 1.4 & 1.4 & 4.9 & 3.6 & 3.0 & 9.0 & 1.4 & 1.4 & 4.0 \\ \hline \hline \end{tabular}
\end{table}
Table 13: RMS of positioning errors of kinematic PPP with different products (unit: cm).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**GPS-Only**} & \multicolumn{3}{c}{**BDS-Only**} & \multicolumn{3}{c}{**Galileo-Only**} & \multicolumn{3}{c}{**GPS + QZSS**} \\ \cline{2-13}  & **E** & **N** & **U** & **E** & **N** & **U** & **E** & **N** & **U** & **E** & **N** & **U** \\ \hline COD & 0.4 & 0.3 & 0.7 & 0.7 & 0.6 & 5.6 & 0.5 & 0.4 & 1.1 & 0.5 & 0.3 & 0.7 \\ ESM & 0.5 & 0.2 & 0.7 & 0.4 & 0.3 & 1.4 & 0.3 & 0.2 & 1.1 & 0.4 & 0.2 & 0.6 \\ GFZ & 0.5 & 0.3 & 0.7 & 1.5 & 0.4 & 1.8 & 0.3 & 0.4 & 1.1 & 0.4 & 0.3 & 0.7 \\ GRG & 0.4 & 0.4 & 0.6 & / & / & / & / & 0.3 & 0.4 & 1.1 & / & / & / \\ JAX & 0.5 & 0.3 & 0.7 & / & / & / & / & / & / & 0.6 & 0.3 & 0.7 \\ SHA & 1.3 & 0.4 & 0.6 & 1.2 & 0.4 & 3.5 & 0.3 & 0.4 & 1.4 & / & / & / \\ WUM & 0.5 & 0.8 & 0.7 & 0.6 & 0.9 & 0.9 & 0.5 & 0.7 & 1.1 & 0.7 & 0.8 & 0.7 \\ \hline \hline \end{tabular}
\end{table}
Table 12: RMS of positioning errors of static PPP with different products (unit: cm).
###### Abstract  
Very high-resolution satellite imagery (\(\leq\)5 m resolution) has become available on a spatial and temporal scale appropriate for dynamic wetland management and conservation across large areas. Estuarine wetlands have the potential to be mapped at a detailed habitat scale with a frequency that allows immediate monitoring after storms, in response to human disturbances, and in the face of sea-level rise. Yet mapping requires significant fieldwork to run modern classification algorithms and estuarine environments can be difficult to access and are environmentally sensitive. Recent advances in unoccupied aircraft systems (UAS, or drones), coupled with their increased availability, present a solution. UAS can cover a study site with ultra-high resolution (<5 cm) imagery allowing visual validation. In this study we used UAS imagery to assist training a Support Vector Machine to classify WorldView-3 and RapidEye satellite imagery of the Rachel Carson Reserve in North Carolina, USA. UAS and field-based accuracy assessments were employed for comparison across validation methods. We created and examined an array of indices and layers including texture, NDVI, and a LiDAR DEM. Our results demonstrate classification accuracy on par with previous extensive fieldwork campaigns (93% UAS and 93% field for WorldView-3; 92% UAS and 87% field for RapidEye). Examining change between 2004 and 2017, we found drastic shoreline change but general stability of emergent wetlands. Both WorldView-3 and RapidEye were found to be valuable sources of imagery for habitat classification with the main tradeoff being WorldView's fine spatial resolution versus RapidEye's temporal frequency. We conclude that UAS can be highly effective in training and validating satellite imagery.  
drones; unoccupied aircraft systems; RapidEye; WorldView-3; estuarine; wetland; change detection; LiDAR; NERR; habitat mapping +
Footnote †: journal: Remote Sensing, 10
## 2 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Reaction** & **DFT/** & **Zwitterion** & **Salt** & **Iminium Salt** & **Amaal** & **Transition** & **Product** \\  & **Basis Set** & & & & & & **State** & \\ \hline  & B3 LYP/ & 0 & 7.9 & 17.6 & \(-\)5.0 & 18.0 & \(-\)5.1 \\
**A** -> **9** & 6-31+G(d) & \(0\) & 9.2 & 19.7 & 11.1 & 33.8 & 12.4 \\ B3 LYP & B3 LYP-D3/ & 0 & 7.7 & 17.0 & \(-\)12.9 & 7.5 & \(-\)17.5 \\  & 6-31+G(d) & \(0\) & 9.9 & 19.4 & _3.5_ & _24.3_ & _1.3_ \\ \hline
**A** -> **9** & \(\omega\)B97X-Dand/ & 0 & 7.8 & 16.6 & \(-\)17.1 & 6.8 & \(-\)22.5 \\ \(\omega\)B97 XD & 6-31+G(d) & \(0\) & 9.3 & _18.4_ & \(-\)_0.5_ & _22.9_ & \(-\)3.6 \\ \hline  & B3 LYP/ & 0 & 7.8 & 16.4 & \(-\)7.1 & 12.7 & \(-\)3.3 \\
**7** -> **B** & 6-31+G(d) & \(0\) & 9.3 & _18.3_ & _9.9_ & _28.3_ & 12.5 \\ B3 LYP & B3 LYP-D3/ & 0 & 7.9 & 15.9 & \(-\)16.1 & 3.0 & \(-\)14.4 \\  & 6-31+G(d) & \(0\) & 9.4 & _17.4_ & _2.7_ & 19.9 & _4.8_ \\ \hline
**7** -> **B** & \(\omega\)B97X-Dand/ & 0 & 4.2 & 7.6 & \(-\)20.5 & 5.6 & \(-\)19.0 \\ \(\omega\)B97 XD & 6-31+G(d) & \(0\) & 6.1 & _9.4_ & \(-\)3.2 & _22.8_ & \(-\)_0.5_ \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The relative electronic (plain text) and Gibbs free (italics) energies (kcal/mol) of subsequent stages of the tandem Mannich—electrophilic animation reactions calculated by DFT/PCM methods. The structure of ethylenediamine derivative **8b** has been confirmed by single crystal X-ray analysis (Figures 4 and 5). The symmetrical internal quaternary salt **8b** crystallizes as a pentahydrate. The asymmetric part of the unit cell consists of two halves of **8b** occupying special positions of C\({}_{i}\) symmetry, two molecules of **8b** adopting a non-crystallographic C\({}_{i}\)-symmetric conformation and located in general positions and 15 water molecules. The -CH\({}_{2}\)-N-CH\({}_{2}\)-CH\({}_{2}\)-N-CH\({}_{2}\)- fragment of all molecules is fully extended. In crystal, \(\pi\)-\(\pi\) stacking interactions between the quinoline systems of **8b** organize the molecules into two symmetry independent columns along the [11-1] direction. The water molecules forming a 1D assembly via O-H-O hydrogen bond along [11-1] occupy a channel formed between four such columns and bind to the carboxylate groups of **8b** (Figure 5). Since \({}^{1}\)H NMR spectra of compounds **8a**,**b** and **9** reveal single molecules, the absolute configurations at the quaternary nitrogen atoms in **8a** and **9** have been assigned analogously to the _meso_ isomer **8b**, for which 2R2\({}^{\prime}\)S configuration has been proven by single crystal X-ray analysis. However, it cannot be ruled out that the reaction mechanisms that underlay the formation of compounds **8a** and **9** are different to that of **8b**, and hence, these derivatives are obtained as pure enantiomers or their racemic mixtures.
# Robustness of Artificial Neural Networks Based on Weight Alterations Used for Prediction Purposes
## 3 Methodology
### More In-Depth Simulations and Explanations for the Robustness of ANNs Based on Weight Alterations
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Simulation Case** & **Description** \\ \hline Case A: One randomly chosen weight error and one randomly chosen bit alteration. & Firstly, we randomly choose one weight. The weight is changed to binary. After this, we randomly chose one bit to be altered. The bit is changed. We change the altered weight back to decimals and continue with the predictions. \\ \hline Case B: Two randomly chosen weight errors and one randomly chosen bit alteration. & Firstly, we randomly choose two weights. The weights are changed to binary. After this, we randomly chose one bit to be altered for each weight. Both bits are changed. We change the altered weights back to decimals and continue with the predictions. \\ \hline Case C: Two randomly chosen weights and two randomly chosen bits alterations. & Firstly, we randomly choose two weights. The weights are changed to binary. After this, we randomly chose one bit to be altered for each weight. The bits are changed. We changed weights back to decimals and continue with the predictions. \\ \hline Case D: Three randomly chosen weights and one randomly chosen bit alteration. & Firstly, we randomly choose three weights. The weights are changed to binary. After this, we randomly chose one bit to be altered for each weight. The bits are changed. We change the altered weights back to decimals and continue with the predictions. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Explanations for different simulation cases. Figure 5: \(Cont\). Figure 5: Percentage of correct predictions for different weight/bit alteration cases and different ANN sizes. The amount of redundancy that is needed to achieve good levels of robustness is usually high in overheads. If less redundancy is added, this means that fewer faults will be tolerated. A balance between this redundancy and robustness in additional overheads is needed. Based on our research, we attempted to maintain the robustness of ANNs in accepted margins and have as few overheads as possible by only duplicating the most important neurons/connections. Our method is presented in the next section.
## 5 Proposed Method
### Identifying Traffic Conditions
#### 5.1.2 Forming a Traffic Table
3:\(Table_{neighbor}\) \(Table_{traffic}\) Begin
4:for\(i=1\) to \(N\)do
5:if\(\tau=1\)then
6:Vehicle_{i}: Broadcast a _Hello message_ to the neighboring vehicles;
7:endif
8:for\(j=1\) to \(N_{i}\)do
9:if\(Vehicle_{i}\) receives a _Hello message_ from \(Vehicle_{i}\)then
10:Vehicle_{i}: Compare the ID of \(Vehicle_{j}\) with IDs recorded into \(Table_{neighbor}\);
11:if\(Vehicle_{i}\) finds \(ID_{j}\) in \(Table_{neighbor}\)then
12:Vehicle_{i}: Update \(ID_{R}\), \(\left(x_{j},y_{j}\right)\), \(\left(v_{x,j},v_{y,j}\right)\), \(Q_{j}\), \(CQ_{i,j}\), and \(VT_{j}\) into \(Table_{neighbor}\);
13:else
14:Vehicle_{i}: Add a new entry to \(Table_{neighbor}\);
15:Vehicle_{i}: Calculate \(CQ_{i,j}\) using Equation (4);
16:Vehicle_{i}: Insert \(ID_{j}\), \(ID_{R}\), \(\left(x_{i},y_{i}\right)\), \(\left(v_{x,i},v_{y,i}\right)\), \(Q_{j}\), \(CQ_{i,j}\), and \(VT_{j}\) into \(Table_{neighbor}\);
17:endif
18:endif
19:endfor
20:for\(k=1\) to \(N_{R}\)do
21:if\(RSU_{k}\) receives a _Hello message_ from \(Vehicle_{j}\)then
22:Vehicle_{i}: Compare the ID of \(Vehicle_{j}\) with IDs recorded into \(Table_{neighbor}\);
23:if\(RSU_{k}\) finds \(ID_{j}\) in \(Table_{neighbor}\)then
24:\(RSU_{k}\): Update \(ID_{R}\), \(\left(x_{j},y_{j}\right)\), \(\left(v_{x,j},v_{y,j}\right)\), \(Q_{j}\), \(CQ_{i,j}\), and \(VT_{j}\) into \(Table_{neighbor}\);
25:else
26:\(RSU_{k}\): Add a new entry to \(Table_{neighbor}\);
27:\(RSU_{k}\): Compute \(CQ_{i,j}\) using Equation (4);
28:\(RSU_{k}\): Insert \(ID_{j}\), \(ID_{R}\), \(\left(x_{i},y_{i}\right)\), \(\left(v_{x,i},v_{y,i}\right)\), \(Q_{j}\), \(CQ_{i,j}\), and \(VT_{j}\) into \(Table_{neighbor}\);
29:endif
30:endif
31:endfor
32:endfor
33:for\(k=1\) to \(N_{R}\)do
34:if\(T_{TR}=5\)then
35:\(\text{RSU}_{k}\): Extract the ID of the related road (\(R_{North}\), \(R_{South}\), \(R_{East}\), and \(R_{West}\)) from \(Table_{neighbor}\);
36:\(RSU_{k}\): Insert the ID of the related road into \(Table_{traffic}\);
37:\(RSU_{k}\): Calculate the road length (\(L_{R}\)) using Equation (11);
38:\(RSU_{k}\): Compute the road travel time (\(T_{R}\)) based on Equation (12);
39:\(RSU_{k}\): Compute the road congestion (\(\overline{Q}_{R}\)) based on Equation (13);
40:\(RSU_{k}\): Calculate the road connection quality (\(CQ_{R}\)) using Equation (14);
41:\(RSU_{k}\): Update \(L_{R}\), \(T_{R}\), \(D_{R}\), \(\overline{Q}_{R}\), and \(C\bar{Q}_{R}\) using Equation (9) into \(Table_{traffic}\);
42:endif
43:endfor
44:End
```  
**Algorithm 1** Traffic condition detection
## 2 Vehicle Configuration
### Transmission Model  
After the wheel speeds and accelerations are obtained, the output torque \(T_{mf}\) of the front traction motor, the output torque \(T_{mr}\) provided by the right rear in-wheel motor, and the output torque \(T_{ml}\) provided by the left rear in-wheel motor can be calculated under the following assumptions:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Vehicle Property** & **Symbol** & **Value** \\ \hline Frontal area of vehicle [m\({}^{2}\)] & \(A_{f}\) & 1.6 \\ Aerodynamic coefficient & \(C_{d}\) & 0.28 \\ Cornering stiffness of the front tire [N/rad] & \(C_{f}\) & 51,091 \\ Cornering stiffness of the rear tires [N/rad] & \(C_{r}\) & 72,802 \\ Rolling resistance between tire and ground & \(C_{t}\) & 0.01 \\ Height of equivalent aerodynamic point [m] & \(h_{a}\) & 1 \\ Height of mass center [m] & \(h_{m}\) & 0.56 \\ Yaw inertia of vehicle [kg\(\cdot\)m\({}^{2}\)] & \(I_{z}\) & 1200 \\ Distance from mass center to front tire [m] & \(L_{f}\) & 1.433 \\ Distance from mass center to rear tire [m] & \(L_{r}\) & 1.067 \\ Distance between two rear wheels [m] & \(L_{w}\) & 1.46 \\ Total mass of vehicle [kg] & \(M\) & 1813 \\ Sprung mass of vehicle [kg] & \(M_{s}\) & 1753 \\ Gear ratio & \(n_{g}\) & 3 \\ Tire radius [m] & \(r_{t}\) & 0.288 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vehicle Specifications.  
1. The rotor mass is so small compared with the vehicle mass that the rotational inertias of the three traction motors are neglected.
2. The viscous and Coulomb frictions of motors and differentials in the transmission are all neglected. Therefore, when the vehicle accelerates: \[Front\ wheels:\ \ T_{mf}n_{g}=\frac{2I_{w}\dot{\omega}_{f}}{n_{g}}+2F_{xf}r_{t}\] (12) \[Rear\ wheels:\ T_{mi}=I_{w}\dot{\omega}_{i}+F_{xx}r_{t},\ \ \ i=r,\ l\] (13)  
where \(n_{g}\) is the reduction ratio of gearbox; \(I_{w}\) is the wheel inertia; \(\omega_{f}\) is the speed of the front traction motor; \(\omega_{r}\) and \(\omega_{l}\) represent, respectively, the speed of the right and left rear in-wheel motors. In the steady state for a small steer angle and by neglecting frictions on wheel motors, the yaw moment Equation (9) can be simplified as  
\[T_{mr}=T_{ml}+2r_{t}M_{z}/L_{w} \tag{14}\]  
where \(L_{w}\) is the distance between two rear wheels. This equation will be used for determining the real-time torque distribution in the PSO process when the vehicle is cornering. For a straight-line driving, \(M_{z}=0\) and \(T_{mr}=T_{ml}\).
# Acceleration of Service Life Testing by Using Weibull Distribution on Fiber Optical Connectors
## 4 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(n_{i}\) & \(Y_{i}\) & \(\mu_{y}\) & \(R(t)\) & \(t_{oi}\) & \(\sigma_{2i}\) & \(\sigma_{1i}\) \\  & **Equation (12)** & **Equation (13)** & **Equation (14)** & **Equation (15)** & **Equation (16)** & **Equation (17)** \\ \hline
1 & \(-2.156\) & \(-0.359\) & 0.891 & 0.312 & 2.509 & 25.803 \\
2 & \(-1.175\) & \(-0.196\) & 0.734 & 0.523 & 4.262 & 15.188 \\  & \(-\textbf{1.008}\) & \(-\textbf{0.544}\) & **0.694** & **0.580** & **4.670** & **13.861** \\
3 & \(-0.602\) & \(-0.100\) & 0.578 & 0.722 & 5.812 & 11.138 \\
4 & \(-0.147\) & \(-0.025\) & 0.422 & 0.924 & 7.429 & 8.712 \\
5 & 0.282 & 0.047 & 0.266 & 1.165 & 9.370 & 6.908 \\
6 & 0.794 & 0.132 & 0.109 & 1.536 & 12.361 & 5.237 \\ \hline \hline \end{tabular} BOLD: The failure times from service life testing.  
\end{table} 
Table 6: Weibull service life program testing statistics analysis. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(n_{i}\) & \(Y_{i}\) & \(\mu_{y}\) & \(R(t)\) & \(t_{oi}\) & \(\sigma_{2i}\) & \(\sigma_{1i}\) \\  & **Equation (12)** & **Equation (13)** & **Equation (20)** & **Equation (15)** & **Equation (16)** & **Equation (17)** \\ \hline  & \(-\textbf{3.504}\) & \(-\textbf{1.402}\) & **0.970** & **0.246** & **6.639** & **109.550** \\
1 & \(-1.753\) & \(-0.438\) & 0.841 & 0.496 & 13.377 & 54.371 \\
2 & \(-0.717\) & \(-0.179\) & 0.614 & 0.751 & 20.247 & 35.922 \\
3 & \(-0.050\) & \(-0.013\) & 0.386 & 0.980 & 26.432 & 27.516 \\
4 & 0.609 & 0.152 & 0.159 & 1.276 & 34.405 & 21.139 \\ \hline \hline \end{tabular} BOLD: The failure times from vibration ALT.  
\end{table} 
Table 7: Weibull vibration ALT statistics analysis. consumed is defined by \(1-0.298=0.702\). It was determined that the service life testing does consume 70.2% of the total fiber optic connector's life. Equivalently, this implies that the observed life without considering the service life load (only vibration) is 3.352 times higher than when the service life testing load is applied. Therefore, we recommend conducting service life program testing instead of vibration testing only to determine the reliability and remnant life of the product.
# Unsupervised Adaptation of Deep Speech Activity Detection Models to Unseen Domains
## 1 Introduction
In some specific scenarios, obtaining labelled data can be significantly expensive or even impossible, which is why unsupervised domain adaptation techniques are an active research topic [21; 22]. Domain adaptation techniques aim to transfer the knowledge obtained from a source domain and transfer it to a target domain. In addition, unsupervised domain adaptation techniques work under the constraint that no labels are available in this new target domain. Inspired by our previous experiences participating in the Fearless Steps Challenge [23; 24], that introduced a new audio domain in the research community, in this paper we aim to explore unsupervised domain adaptation techniques in the context of the SAD task. Considering a SAD model trained on different well-known domains in the SAD task, such as broadcast or meetings, with huge amounts of labelled data available, we evaluate three possible ways to adapt the SAD model to a new unseen domain in an unsupervised way. Results are presented using the data provided in the Fearless Steps Challenge; however, the techniques and methods described in this paper are described in a general way so that domain adaptation could be done on any possible scenario. Unlike the work presented in [25], where the key idea is to perform a pretraining process on DNN models using unlabelled data, seeking to obtain a shared representation, this work aims to perform unsupervised domain adaptation directly on the model space, with the objective of fine tuning a given model trained previously with labelled out of domain data. The remainder of the paper is organised as follows. Section 2 introduces the domain adaptation problem, presents some approximations found in the literature on how to solve it, and introduces different methods evaluated. Section 3 presents the experimental setup, describing the neural network architecture, datasets considered and metrics used in the evaluation. In Section 4, results for the baseline SAD system and unsupervised adaptation techniques are described. Finally, a summary of the work and conclusions are presented in Section 5.