esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.2.2 Problem Description  
In the experiment of this paper, let \(D=\left\{\left(x^{(1)},y^{(1)}\right),\cdots,\left(x^{(i)},y^{(i)}\right), \cdots,\left(x^{(m)},y^{(m)}\right)\right\}\) be the training set, \(m\) is the number of ECG recordings, \(x^{(i)}\) is the \(i^{th}\) ECG recording (Note that the lengths of the input ECG recordings are not equal), \(y^{(i)}\in\left\{0,1,2\right\}\) is the class label of the \(i\)th ECG recording, 0 represents a normal rhythm, 1 represents an AF rhythm, and 2 represents the other rhythm. The model output is \(z^{(i)}\) for an ECG recording \(x^{(i)}\). The formula is shown as follows:  
\[z^{(i)}=g\left(x^{(i)};\theta\right) \tag{3}\]  
where \(\theta\) is the relevant parameter in the proposed model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Database** & **Rhythm** &
\begin{tabular}{c} **\#Number of** \\ **Recordings** \\ \end{tabular} & \multicolumn{4}{c}{**Time Length(s)**} \\ \cline{3-6}  & & **Mean** & **Min** & **Max** \\ \hline PhysioNet/CinC & Normal & 5050 & 32.10 & 9.05 & 60.95 \\  & AF & 738 & 32.10 & 9.99 & 60.21 \\ Challenge 2017 & Other & 2456 & 34.39 & 9.13 & 60.86 \\  & Noise & 284 & 24.22 & 9.36 & 60.00 \\ \hline China Physiological Signal & Normal & 918 & 15.43 & 10.00 & 60.00 \\  & AF & 1098 & 15.04 & 9.00 & 74.00 \\ Challenge 2018 & Other & 4861 & 16.25 & 6.00 & 144.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Databases description.  
After calculating the feature vector \(z^{(i)}\) of each class, \(z^{(i)}\) is fed to a linear layer whose output length is the number of categories, and the _softmax_ function is added to output the probability that the ECG recording is a normal rhythm, AF rhythm, or other rhythms. The corresponding _softmax_ function is calculated as follows:  
\[\hat{y}^{(i)}=\frac{\exp(z^{(i)})}{\sum_{j=1}^{c}\exp(z^{(j)})} \tag{4}\]  
where \(c\) is the number of ECG rhythm classes, and \(\hat{y}^{(i)}\) indicates the class probability of the input feature vector \(z^{(i)}\).
# Fault Diagnosis and Prognosis of Satellites and Unmanned Aerial Vehicles: A Review
###### Abstract  
This paper comprehensively analyzes advanced Fault Diagnosis and Prognosis (FDP) techniques employed in aerial and space agents such as satellites, spacecraft, and Unmanned Aerial Vehicles (UAVs). The critical engineering functions of fault diagnostics and prognosis, particularly the emerging field of fault prognosis, emphasize the necessity for further advancement. Integrating these methodologies enriches the system's capacity to diagnose faults in their early stages. Additionally, it enables the prediction of fault propagation and facilitates proactive maintenance to mitigate the risk of severe failure. This paper aims to introduce diverse FDP methods, followed by a discussion on their application and evolution within single and multisatellite/UAV systems. Throughout this review, eighty-five relevant works are analyzed and discussed and their evaluation metrics are expanded upon as well. Within the works analyzed in this review, it was found that data-driven methods constitute 54% and 7% of the methodologies utilized in single- and multiagent FDP, respectively, which underscores the rise of these methods in the field of single-agent FDP and their unexplored potential in multiagent condition monitoring. Finally, this review is brought to a close with a suggested classification scheme of the utilized methodologies in the field, a quantitative analysis of their contributions to the field, and remarks and mentions of the potential gaps in the area.  
fault detection; fault isolation; fault identification; fault prognosis; single/multisatellite/UAV +
Footnote †: journal: Applied sciences
# Design and Performance Analysis of BDS-3 Integrity Concept
## 2 BDS Integrity Concept  
This section first introduces the integrity definition of BDS-3 under the ICAO framework, which includes two concepts: \(\mathrm{P_{sat}}\) and \(\mathrm{P_{const}}\). Secondly, the hybrid alarm mechanism of BDS-3, which has both the ground alarm approach and the satellite alarm approach, is explained. This hybrid design is mainly to make up for the limitations for BDS's failure to deploy ground monitoring stations globally.
# Processing of EMG Signals with High Impact of Power Line and Cardiac Interferences
## 2 Material and Methods
### Filtering of Low-Frequency Spectrum Components  
Band-filtering is the EMG signal's main operation during recording and processing [28]. The basic problem in such processing is the reduction of the impact of superimposed parasitic disturbances. The first antialasing filtering has to be implemented in the front analog circuit. Among numerous sources of disturbances having a significant impact on the EMG signal, the ECG (Electroccardiography) heartbeat activity is one of the most important.  
The maximally flat magnitude filter was proposed in the presented research. The Infinite Impulse Response (IIR) Butterworth filters of different orders for high and band-pass processing were implemented.
# Low-Cost Solution for Kinematic Mapping Using Spherical Camera and GNSS
## 6 Conclusion
### Absolute Accuracy  
To evaluate the cloud's absolute accuracy, i.e., against the coordinate system, identical points were taken from the computed point clouds, and these were compared with the reference points. Table 6 shows these point cloud accuracies. The 2D accuracy was found to be the same for these test areas, reaching 83 mm in both cases. As mentioned above, the reference points for the Lipova street case study were only 2D, so the height component could not be evaluated here. Figure 10 shows the deviations of the individual points using error ellipses.
# Towards Autonomous Bridge Inspection: Sensor Mounting Using Aerial Manipulators
## 7 Simulation  
The environment used for simulating the UAV and manipulator dynamics, as well as the contact with the environment, is the widely accepted Gazebo simulator. It is realistic and highly modular, with a large community and a support for the robot operating system (ROS), which is also the primary implementation environment for impedance control, motion planning and blob detection. Through ROS, Gazebo has a large variety of developed plugins realistically simulating various sensors and actuators. All simulations were conducted with Linux Ubuntu 18.04 operating system and ROS Melodic middleware installed.  
The UAV is modeled as a single rigid body with \(n_{p}\) propellers mounted at the end of each arm. As propulsion units, these propellers generate thrust along the \(z\) axis of the UAV body. To simulate the propeller dynamics, the rotors_simulator package is used. It contains a plugin that models thrust based on the user-provided propeller parameters [33]. Furthermore, to obtain the UAV attitude and position, IMU and odometry plugins are mounted on the vehicle. The manipulator was mounted on the body of the UAV and consists of three joints connected with links. A rod type tool is mounted as the end-effector, with a force-torque sensor required by the impedance controller. Furthermore, a monocular camera with an infrared projector is also mounted for the blob detection.
# A Special Structural Based Weighted Network Approach for the Analysis of Protein Complexes
## 3 Methods
### Identifying Seed Proteins  
The identification of seed protein for the PPI network is essential for the detection of protein complexes. Here, we introduce the concept of weighted degree and cluster coefficient as a strategy for identifying the seed protein. For this, we defined the weighted node degree by  
\[deg_{w}(v)=\sum_{u\in N(v);(v,u)\in E}w(v,u), \tag{10}\]  
where \(deg_{w}(v)\) is the weighted degree of the protein \(v\) and \(w\) is the edge weight stated in the preliminary in Section 2. To determine the seed protein we consider the small world phenomenon model [63; 64] which correspond to local weighted clustering coefficient \(\lambda\). Then, we define \(\lambda_{v}\) of protein \(v\) as measure of its local connectivity among its immediate neighbors and \(\lambda_{w}(v)\) of protein \(v\) as weighted sub-network \(\mathcal{GN}_{v}\) formed by \(N_{v}\) and their corresponding weighted edges. Thus, we can calculate clustering coefficient of protein \(v\) by  
\[\lambda_{w}(v)=\frac{\sum_{u_{l}\in V_{v}}\sum_{u_{j}\in N(u_{j})\cap V_{v}}w(u _{i},u_{j})}{|N_{v}|\times(|N_{v}|-1)}, \tag{11}\]  
where \(\lambda_{w}(v)\) is the clustering coefficient of protein \(v\) and \(\lambda_{w}(v)\in(0,1]\). Using Equation (11), we can calculate the average clustering coefficient of sub-network \(\mathcal{GN}_{v}\) by  
\[Avg(\lambda_{w}(v))=\frac{\sum_{u\in V_{v}}\lambda_{w}(v)}{|V_{v}|}, \tag{12}\]  
where \(\lambda_{w}(v)\) is the average local weighted clustering coefficient of the protein \(v\), \(V_{v}\) is the number of the protein \(v\) and all its local neighbours in a sub-network. With Equations (7) and (12), the seed protein (\(\mathcal{S}\)) is defined as  
\[\mathcal{S}\left(v\right)=\begin{cases}1&if\,\lambda_{w}(v)\geq Avg(\lambda_{ w}(v))\wedge B(v)\leq Avg(B(\mathcal{GN}_{v})),\\ 0&otherwise\end{cases}. \tag{13}\]  
where node \(v\) is a selected seed protein if \(\mathcal{S}\left(v\right)=1\).
## 6 Results and Discussion
### Comparison with PR and SRK EoS Results  
The results obtained by the MLP-LMA model were compared with values calculated using thermodynamic models based on PR and SRK EoS.  
Tables 4 and 5 provide the CO\({}_{2}\) solubility and thermodynamic properties respectively. Furthermore, Table 6 summarizes a comparison, where the RMSEs and R\({}^{2}\) of the best-proposed model, i.e., MLP-LMA, and PR and SRK EoS were mentioned. To perform this comparison, two samples of ILs that had not been included in the training phase of the intelligent models were considered. These samples included [bmim][Tf2N] and [bmim][PF6] ILs. Further description is given in Tables 4 and 5. All three models (MLP-LMA, and the PR and SRK EoS) gave accurate descriptions with \(R^{2}\) above 0.98. For the two ILs, the MLP-LMA model had a higher coefficient of determination and lower RMSE compared to the PR and SRK EoS, in the following order of reliability: MLP-LMA > SRK > PR in the case of [bmim][PF6], and MLP-LMA > PR > SRK in the case of [bmim][Tf2N].  
Figures 4 and 5 exhibited the comparison performed between PR and SRK EoS and the MLP-LMA model, and at the same time, the evolution of CO\({}_{2}\) solubility with pressure in the two ILs at a constant temperature. As it is clear in the figures, the solubility of CO\({}_{2}\) increased with the increase in pressure for the two investigated ILs. [PERSON]'s law can explain this phenomenon, which supposes that the gas  
Figure 3: Cross plots of the proposed predictive models.
## 4 LLR Calculation under Rayleigh Fading Channel
After eliminating the constant term \(\frac{\sigma_{p}^{2}}{2\sigma^{2}\Bigl{(}\sigma_{p}^{2}\sum\limits_{k=1}^{N} \sum\limits_{i=1}^{16}\left|\mathbf{s}_{y_{i}}\right|^{2}+\sigma^{2}\Bigr{)}}\) in (26), the LLR can be obtained:  
\[\zeta_{m}=\max\limits_{\mathbf{S}_{y}:c_{m}=0,l}\left|\sum\limits_{k=1}^{N}w_{ k,\delta_{l}}\right|^{2}-\max\limits_{\mathbf{S}_{y}:c_{m}=1,l}\left|\sum \limits_{k=1}^{N}w_{k,\delta_{l}}\right|^{2},\,l\in\{1,\,2,\,3\},\,m\in\{0,\,1, \,\,\cdots,4N-1\} \tag{27}\]  
Comparing (22) with (7), we find that when there is no coding, the decision statistics are \(\left|\left\langle\mathbf{R},\mathbf{S}\right\rangle\right|\) and \(\left|\left\langle\mathbf{R},\mathbf{S}\right\rangle\right|^{2}\) respectively, which are obviously equivalent. Therefore, the decision result is not affected by the fading multiplicative \(\rho\) without coding.
## 4 Proposed DQN-Based Offloading Decision Algorithm
### Deep Reinforcement Learning  
In theory, the reinforcement learning is a method where an agent learns the set of actions which purposes maximizing cumulative summation of rewards. The reward is defined as a return value that the agent can get. The agent receives the reward and information about the changed state at time step \(t+1\) from the environment. Using this reward and observation, the agent learns a policy \(\pi\), and we say the policy as optimal policy \(\pi^{*}\) when the reward is always optimal through the policy [28; 29]. The reward value can be calculated using Q-function. \(\hat{Q}(s,a)\) is a reward of current state and it is a summation of return value \(r\) and \(\hat{Q}(s^{\prime},a^{\prime})\) which is a maximum reward value expected to be received in the future [30]. The corresponding mathematical formulation is as follows:  
\[\hat{Q}(s,a) \leftarrow r+\max\hat{Q}(s^{\prime},a^{\prime}) \tag{3}\] \[\hat{Q}(s,a) \leftarrow (1-\alpha)\hat{Q}(s,a)+\alpha\left[r+\gamma\max_{a^{\prime}}\hat {Q}(s^{\prime},a^{\prime})\right] \tag{4}\]  
Figure 3: Execution process for the proposed DQN-based novel offloading compression decision algorithm.  
where (4) is an equation that contains a learning rate \(\alpha\) and a discounting rate \(\gamma\) to (3).  
Here, DQN is a way to learn the Q-function using a deep neural network. Through layers, the output informs all or the possible actions and the corresponding reward of each action [31]. The application of deep neural network overcomes the size of memory that has been the limitation of Q-table in Q-learning; and an efficient function output is possible for large amounts of data. The expressions (5) show that it minimizes a difference between the optimal value and real value (i.e., predicted value). The optimal value is a target value that the agent aims to get. The equation form is similar to the _costfunction_ of linear regression problem:  
\[\min_{\theta}\sum_{t=0}^{T}\left[\hat{Q}(s_{t},a_{t}|\theta)-\left(r_{t}+\gamma \max_{a^{\prime}}\hat{Q}(s_{t+1},a^{\prime}|\theta)\right)\right]^{2} \tag{5}\]  
However, since both the target and predicted values use the same network and similar learning data, DQN has a problem with correlation between samples and non-stationary target. The change of target value when performing network update in \(Q_{pre}\) is the cause of the non-stationary target problem. Furthermore, because of that problem, it is impossible to make the predicted value similar to the target value, and the agent will learn policies in a different direction than the target. To solve this problem, we can use DQN. It can store data collected by agent in reply buffer and use only data that is extracted from the buffer. In addition, in DQN, the pretend network and the target network are separated. Therefore, the updates of the pretend network \(\theta\) cannot make an impact to the target network. According to these characteristics of DQN, the agent can get appropriate policies [32; 33; 34].
## 6 Prediction of the Ground Settlement of Pipe Jacking  
### Peck Theoretical Formula Regression Analysis  
To better predict the surface response when deviations occur in the jacking parameters of pipe jacking, and to combine the prediction with numerical simulation results to provide guidance for the subsequent construction of the project, the classic Peck prediction formula was selected for predicting surface settlement during pipe jacking. [PERSON] [28] analysed a large amount of measured data and reported that, under undrained conditions, the lateral settlement trough caused by tunnel construction approximates a normal distribution. The primary reason for surface settlement is the loss of ground, described by the following formula:  
\[S(x)=S_{\max}\exp\left(\frac{-x^{2}}{2i^{2}}\right) \tag{1}\]  
\[S_{\max}=\frac{V_{loss}}{\sqrt{2\pi i}}=\frac{\sqrt{\pi}R^{2}v_{l,s}}{\sqrt{2i}} \tag{2}\]  
\[I=\frac{Z}{\sqrt{2\pi\tan(45^{\circ}-\frac{\pi}{2})}} \tag{3}\]  
where \(S(x)\) is the ground settlement amount at the monitoring point located at a distance from the tunnel axis, m; \(S_{\max}\) is the maximum surface settlement amount directly above the tunnel axis, m; \(V_{loss}\) is the volume of soil loss; \(i\) is the trough width coefficient; \(R\) is the excavation radius, m; \(v_{l,s}\) is the formation loss rate; \(Z\) is the distance from the tunnel axis to the surface, m; and \(\varphi\) is the internal friction angle of the soil. Linear regression analysis was performed on the Peck formula, taking the logarithm of both sides to transform it into a linear equation. \[\ln S(x)=\ln S_{\max}+\frac{1}{i^{2}}(\frac{-x^{2}}{2}) \tag{4}\]  
Let \(\ln S(x)\) be the constant term and let \(\frac{1}{i^{2}}\) be the linear coefficient, yielding  
\[S_{\max}=\exp(a),i=\frac{1}{\sqrt{b}} \tag{5}\]  
where:  
\[b=\frac{\sum\left[(\frac{-x^{2}}{2})\ln S(x)\right]-\frac{1}{n}\Sigma\left( \frac{-x^{2}}{2}\right)\sum\ln S(x)}{\sum\left(\frac{-x^{2}}{2}\right)^{2}- \frac{1}{n}(\Sigma\frac{-x^{2}}{2})^{2}} \tag{6}\]  
Figure 14: Normalized vertical surface displacement caused by parameter deviation. \[a=\frac{1}{n}\sum\ln S(x)-[\frac{1}{n}\sum(\frac{-x^{2}}{2})]b \tag{7}\]  
Regression analysis was performed on the monitoring data at monitoring section D\({}_{0}\). The arrangement of the surface monitoring points is shown in Figure 1, and the calculations can be performed using Equations (6) and (7).
# Tulip-Shaped Pattern Imprinting for Omni-Phobic Surfaces Using Partially Cured Photopolymer
## 2 Process Design and Experiments  
### Design of The Tulip-Shaped Pattern Imprinting Process  
The tulip-shaped pattern imprinting process is designed to produce an omni-phobic surface, as shown in Figure 1. The designed imprint process comprises six steps focusing on three UV curing processes. In the first to third steps of the process, PUA resin (MINS-311 RM, Minuta Tech, Osan-si, Korea) is applied to a polyethylene terephthalate (PET) film in the same manner as that used for nano-imprinting. The PUA resin is then imprinted with a micro-scale pattern by sequentially applying pressure and UV curing. In the process, PDMS (RT 623, WACKER, Munich, Germany) is used as the mold material; this yields a partially cured layer from the inherent and permeated oxygen that reacts with the resin surface. The depth of the partially cured layer is affected by the UV irradiation energy; for equal process times, lower UV energies yield deeper partially cured layers. As a result, significant deformation occurs during pressurized UV curing in the fourth step of the process. This deformation increases the diameter of the upper end of the pattern, decreases the diameter and height of the lower end, and increases the surface roughness of the upper surface of the pattern. The deformed pattern has a tulip shape, and the hydrophobicity and oleophobicity of the surface are improved by the air pocket effects and increased roughness [17; 19; 27; 35; 36; 37; 38; 39; 40; 41]. In the final step, the process is completed by final curing using ultra high-power UV to improve the wear resistance and omni-phobicity of the tulip structure.  
Figure 1: Flow diagram of designed tulip-shaped pattern imprinting process.
## 4 Simulation Results
### Preprocessing of Data and Parameters
#### 4.1.2 Error Compensation Model  
The range of outputs in error compensation model is shown in Table 8. The range of inputs is the same as that of the mechanism model, which is shown in Table 7. Since the error compensation model is established based on the general LSTM network, the normalized range can be all selected as \([0,1]\), without considering the sign of outputs. The LSTM network structure is set to 1-1-1, that is, an input layer, one hidden layer with 90 neurons and an output layer. The learning rate is set to 0.001, and the threshold of gradient is set to one. The loss function is defined as  
\[L_{G}^{cc}=\sum_{l=0}^{T}L_{G}^{cc}(t)=\sum_{t=0}^{T}\frac{1}{2}\left(\widetilde {y}_{G}^{hy}(t)-y_{G}(t)\right)^{T}\left(\widetilde{y}_{G}^{hy}(t)-y_{G}(t) \right), \tag{21}\]  
where \(\widetilde{y}_{G}^{hy}(t)=\widetilde{y}_{G}^{mc}(t)+\widetilde{y}_{G}^{cc}(t)\). The \(\widetilde{y}_{G}^{cc}\) and \(\widetilde{y}_{G}^{hy}\) represent the predictive outputs of error compensation model and that of digital twin hybrid model of block \(G\), respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** & **Parameter** & **Value** & **Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(H_{lk}\) & 41 & \(H_{rk}\) & 276 & \(T_{g}\) & 30 & \(C_{pa}\) & 1.022 & \(C_{cf}\) & 1.3 \\ \(H_{g}\) & 56.4 & \(K_{g}\) & 100 & \(K_{cf}\) & 2.5 & \(K_{cc}\) & \(-600\) & \(H_{sd}\) & 200 \\ \(K_{T}\) & 1667 & \(w_{p214}^{2}\) & 0.001 & \(b_{p24}^{2}\) & 0.001 & \(K_{r}\) & \(2.97\times 10^{4}\) & \(C_{gs}\) & 1.5 \\ \(V_{0}\) & 5.25 & \(\rho_{sat}\) & 1.416 &
## 2 Materials and Methods
### Kinetic Corrosion Analysis  
Corrosion of pure titanium can produce several anodic and cathodic reactions, including Ti species that interact with hydrogen protons and hydroxide ions depending on the solution pH [29; 31; 32; 36]. In this work, the corrosion kinetic investigation was conducted utilizing non-linear fitting applied to experimental potentiodynamic curves, employing the superposition model based on mixed potential theory. This approach aligns with the methodology outlined in our prior research, where the total current density can be expressed as follows [32; 35; 37]:  
\[i=i_{O_{2}}+i_{H_{2}}+i_{Ti} \tag{2}\]  
where \(i\) represents the total current density; \(i_{O_{2}}\) and \(i_{H_{2}}\) denote the partial reduction current densities for ORR and HER, respectively; and \(i_{Ti}\) denotes the partial oxidation current density for TOR. Although direct experimental measurements of \(i_{O_{2}}\), \(i_{H_{2}}\), and \(i_{Ti}\) are not feasible, their values can be deduced using a fitting methodology to determine the kinetic parameters of each reaction.  
To achieve this, the electrochemical reactions of TOR and HER are considered to be controlled by charge transfer kinetics, while the ORR is controlled by a mixed-charge diffusion mechanism, according to the following expressions [35]:  
\[i_{H_{2}}=i_{0,H_{2}}\times exp\left(-\frac{2.303\times\eta_{H_{2}}}{t_{H_{2}} }\right) \tag{3}\]  
\[i_{O_{2}}=i_{0,O_{2}}\times exp\left(-\frac{2.303\times\eta_{O_{2}}}{t_{O_{2}} }\right)\times\left(1+\frac{i_{0,O_{2}}\times exp\left(-\frac{2.303\times\eta _{O_{2}}}{t_{O_{2}}}\right)}{i_{I,O_{2}}}\right)^{-1} \tag{4}\]  
\[i_{Ti}=i_{0,Ti}\times exp\left(\frac{2.303\times\eta_{Ti}}{t_{Ti}}\right) \tag{5}\]  
where the \(\eta_{O_{2}}=E-E_{eq,O_{2}}\), \(\eta_{H_{2}}=E-E_{eq,H_{2}}\), and \(\eta_{Ti}=E-E_{eq,Ti}\) are the ORR, HER, and TOR overpotentials, respectively; \(i_{0,O_{2}}\), \(i_{0,H_{2}}\), and \(i_{0,Ti}\) are the exchange current densities for ORR, HER, and TOR, respectively; \(i_{I,O_{2}}\) is the limiting current density for ORR; \(t_{O_{2}}\), \(t_{H_{2}}\), and \(t_{Ti}\) are the Tafel slopes for ORR, HER, and TOR, respectively; and \(E_{eq,O_{2}}\), \(E_{eq,H_{2}}\), and \(E_{eq,Ti}\) are the equilibrium potentials which are equal to 752 mV, \(-\)479 mV, and \(-\)1804 mV for ORR, HER, and TOR, respectively, assuming that the main anodic reaction is Ti \(\rightarrow\) Ti\({}^{2+}+2\)e\({}^{-}\)[34]. The root mean square error (RMSE) was used to evaluate the quality of curve fitting.
## 4 Evaluation
### Results and Discussion
\begin{table}
\begin{tabular}{l c c c c c} \hline
**ID** & **acc\_at\_1** & **acc\_at\_2** & **acc\_at\_3** & **acc\_at\_4** & **acc\_at\_5** \\ \hline A1 & 0.4487 & **0.6367** & 0.7094 & **0.7948** & 0.8461 \\ A2 & 0.4530 & 0.6239 & **0.7222** & 0.7692 & 0.8504 \\ A3 & **0.4744** & 0.6282 & 0.7179 & 0.7905 & **0.8589** \\ A4 & 0.4444 & 0.5940 & 0.6965 & 0.7735 & 0.8247 \\ A5 & 0.4402 & 0.5982 & 0.7136 & 0.7820 & 0.8418 \\ A6 & 0.4487 & 0.6068 & 0.7136 & 0.7905 & 0.8376 \\ A7 & 0.4572 & 0.6153 & 0.7094 & 0.7820 & 0.8376 \\ A8 & 0.4529 & 0.5811 & 0.7051 & 0.7735 & 0.8376 \\ A9 & 0.4102 & 0.5940 & 0.7008 & 0.7777 & 0.8247 \\ \hline \end{tabular}
\end{table} 
Table 3: Architecture experiments: Accuracy for different number of predictions.