esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Subject-Specific Region of Interest on Motor Imagery Brain-Computer Interface
## 4 Discussion  
From the results, it is clear that the ITDs with ROI estimation outperform those without ROI estimation in all classification measurements. Class separability was observed with the selected features, which accommodate outliers and obtain better generalization properties. Thus, it is plausible to say that the classifier results of this paper are in agreement with the results reported by [PERSON] et al. [7]. A comparison between the ITD and DWT with ROIs showed that the ITD with ROIs outperformed DWT with ROIs, by producing a higher  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference and Year** & **Method** & **Accuracy (\%)** \\ \hline [PERSON] et al. [26] & CSP & 88.58 \\ \hline \multirow{4}{*}{This study} & DWT with ROI & \multirow{2}{*}{89.77} \\  & BCI Competition IV dataset & \\ \cline{1-1} \cline{2-3}  & ITD with ROI & \\ \cline{1-1} \cline{2-3}  & BCI Competition IV dataset & 96.87 \\ \cline{1-1} \cline{2-3}  & ITD with ROI Own recorded dataset & 92.70 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison results of feature extraction methods.  
Figure 13: The ROCs of sensitivity vs. 1-specificity for ITD and DWT with ROI.  
accuracy of feature classifications for different motor imagery tasks. The kappa coefficient of ITD increased by 0.0969 than that of DWT, on average; this was significant at \(p<0.001\). The improvements in the classification accuracy achieved in this study are also consistent with the existing ITD studies by [PERSON] et al. [22]. These findings further verify the potential of the proposed ITD method in facilitating an automated technique for developing effective framework assessment strategies. The investigated ITD method was found to outperform those introduced by [PERSON] et al. [3], [PERSON] et al. [25] and [PERSON] et al. [4] by 5.3% to 14.8%. In their work, these authors used fewer subjects (the maximum being 6 subjects) and fewer classes (the maximum having 3 classes).
## 2 Materials and Methods
### Statistical Analysis  
Statistica 13.0 software (StatSoft, Cracow, Polska) was used for statistical analysis conduction. The significance level was set at \(p\) < 0.05. Data normality was confirmed with the Shapiro-Wilk test. Results were expressed as mean \(\pm\) SD and 95% confidence intervals. Further, data variance homogeneity was assessed using [PERSON]'s test, and sphericity was assessed using the Mauchly test. Then, two-way repeated measures ANOVA was performed (time x condition) to identify differences between conditions. The effect sizes were measured using partial eta squared (pn\({}^{2}\)) and categorized as small (<0.01), medium (0.06), or large (\(\geq\)0.14). Next, the Bonferroni post hoc test was introduced to compare the results in detail. For this purpose, dCohen effect size was used and interpreted as small (0.2 \(\geq\) d), medium (d > 0.2; d < 0.79), and large (d \(\geq\) 0.8) [43]. The chi-squared (\(\chi^{2}\)) test assessed the time to peak performance significance. The typical error was calculated based on the absolute difference between the best jump post-CA and baseline values and calculated according to the formula: \(TE=SDdiff/\sqrt{2}\). The method previously employed in sports science experiments [44] categorized participants into three groups based on their jump height changes: (a) responders, if their jump height increased by more than twice the TE value; (b) non-responders, if their jump height change remained within the TE value; and (c) negative responders, if their jump height decreased by more than twice the TE value. However, no one was classified as a negative responder. Therefore, for two categories, the student _t_-test for the independent sample was conducted to compare responders (RS) and non-responders (nRS). To assess the difference, the dCohen effect size was implemented.
# Towards Autonomous Bridge Inspection: Sensor Mounting Using Aerial Manipulators
## 5 Motion Planning
### Model-Based Corrections  
The initial trajectory from Equation (20) is planned without any consideration about the underactuated nature of the multirotor UAV. To obtain the unknowns, namely roll and pitch angles, the initial trajectory can be executed in a simulation environment. The chosen simulation environment is, in our case, _Gazebo_, because it is realistic and supports the robotics operating system (ROS), which is the backbone of our implementation. The simulated aerial manipulator is based on the mathematical model described in Section 3. The standard cascade PID controllers are employed for low-level attitude and high-level position control. The impedance controller is built on top of the position controller and provides a position reference based on the input trajectory. More details about the simulation environment are provided in Section 7.  
The first step is executing the initial trajectory in the aforementioned simulation environment. While executing, the roll and pitch angles are recorded as they are needed for obtaining the full state of the UAV. Rearranging Equation (2) and plugging the unknown roll and pitch angles in the full state of the UAV, the transform of the end-effector in the manipulator base frame can be obtained:  
\[T_{L_{0}}^{ee}=(T_{\text{B}}^{L_{0}})^{-1}\cdot(T_{\text{W}}^{\text{B}})^{-1} \cdot T_{\text{W}}^{ee}. \tag{21}\]  
Usingthe inverse kinematics of the manipulator, joint values \(\mathbf{q}_{\text{M}}\) for the desired end-effector configuration are obtained. This way, the null space of the aerial manipulator is used for the end-effector correction. Note that due to the configuration of the manipulator, an exact solution of the inverse kinematics will not always exist. In such a case, an approximate closest solution is used instead.  
The final trajectory is constructed by replacing the initial \(\mathbf{q}_{\text{M}}\) with the corrected values. This trajectory is afterwards sent to the target aerial manipulator.  
The careful reader should note that the developed three DoF manipulator operates on the \(x\) and \(z\) position in the body frame, as well as the pitch angle. This allows the impedance controller to maintain the orientation perpendicular to the wall, while compensating for the  
Figure 5: Visual comparison between TOPP-RA and the piecewise constant velocity interpolation. Waypoints for both trajectories are kept the same, and around \(t=2.5\) s, the middle waypoint(yellow cross) is reached. Although the dynamical constraints are the same, TOPP-RA takes other degrees of freedom into account and produces a trajectory with overshoot which is not suitable for parameter \(\beta\).  
UAV body motion in the \(x\) and \(z\) axes. However, the system will experience disturbances and control errors which will act on the roll and pitch angle, and the lateral movement along the body \(y\) axis. We can address these issues either with mechanical dampers or by adding additional degrees of freedom to the manipulator, which will be explored in future work.
# Semi-CNN Architecture for Effective Spatio-Temporal Learning in Action Recognition
## 3 Proposed Architecture
### Base Models
#### 3.1.3 Dense Convolutional Networks (DenseNets)  
Another work proposed to build deeper architecture by connecting each convolution layer to all the subsequent layers, forming a densely connected network, DenseNet [21]. Feature maps from previous connected layers were re-used in subsequent layers, hence reducing the number of trainable parameters. Besides, the vanishing gradient problem is also addressed by the skip connections (similar to the shortcut connections in ResNets) between layers, which map the output of a previous layer to the next connecting layer, and allow the gradients from every layer to be accessed during back-propagation. DenseNet has a considerably lower number of parameters when compared with ResNet of the same depth, and yet retains a high training capacity when the model goes deeper.
## 2 CR-SOA  
### Operation Principle  
The CR-SOA incorporates a carrier reservoir (CR) layer adjacent to the active region (AR), as shown in Figure 1[15; 18; 21]. When a bias current is applied, carriers begin to fill the energy levels in both the AR and CR layers. At lower current levels, the quasi-Fermi level is closer to the AR band edge, leading to a higher carrier density in the AR compared to the CR. As the current increases, the quasi-Fermi level shifts upward, resulting in the CR becoming populated. This populated CR region then supplies carriers to the AR, replenishing those depleted by the input optical signal. The CR-SOA's fast response is driven by the ultrafast carrier transition from the CR to the AR, with a typical transition time ranging from 5 to 10 ps [11; 12]. This quick carrier replenishment allows for rapid gain and phase dynamics, similar to QD-SOAs [22; 29; 30]. The faster recovery times in CR-SOAs enhance the speed and efficiency of optical data processing, making them ideal for high-speed optical communication systems. Furthermore, CR-SOAs provide comparable performance to QD-SOAs but are more cost-effective and easier to fabricate, offering significant advantages in practical applications.
## 4 Control Methodology
### Parameter Tuning Adaptive Law Design  
By applying APC to dynamically adjust the \(k_{p}\) and \(k_{d}\) values in the PD controller, the observation error caused by bandwidth selection is compensated. It can not only simplify the controller parameter tuning process, but also improve the control precision of LADRC and the dynamic performance of the system.  
**Remark 2**.: _In order to simplify the adjustment of parameter \(k_{p}\), \(k_{d}\) and avoid errors caused by manual parameter adjustment, we introduce an adaptive law which can adjust \(k_{p}\), \(k_{d}\) timely. Formulas (36) and (37) provide the adaptive law of \(k_{p},k_{d}\)._  
Assumption 1: Assume that a given signal \(x_{d}(t)\) is smooth and bounded such that \(\|x_{d}(t)\|\leq X_{d}\) is true. Here \(X_{d}\) is a known constant.  
Define tracking error:  
\[e_{1}=x_{d}-z_{1}. \tag{20}\]  
Derivation can be obtained as follows:  
\[\dot{e}_{1}=\dot{x}_{d}-\dot{z}_{1}=\dot{x}_{d}-z_{2}. \tag{21}\]  
Define the filter tracking error:  
\[\sigma=\left[\xi^{T}\ 1\right]\left[\begin{array}{c}e_{1}\\ \dot{e}_{1}\end{array}\right], \tag{22}\]  
where \(\xi^{T}=a_{1}\) is a suitable gain vector such that when \(\sigma\to 0\), \(e_{1}\to 0\) is satisfied.  
Derivation of \(\sigma\) can be obtained:  
\[\dot{\sigma}=\ddot{x}_{d}-\dot{z}_{2}+\left[0\quad\xi^{T}\right]\left[ \begin{array}{c}e_{1}\\ \dot{e}_{1}\end{array}\right]. \tag{23}\]  
From the output of LESO, we can obtain  
\[\dot{x}_{2}=q(t)+Nu(x)+d(t)=F(x,u(x)). \tag{24}\]  
The input of the control is as follows:  
\[\delta=-\mu\sigma+u_{p}-\left[0\quad\xi^{T}\right]\left[\begin{array}{c}e_ {1}\\ \dot{e}_{1}\end{array}\right], \tag{25}\]  
where \(\mu\) is any positive number.  
In summary, we can obtain  
\[\dot{\sigma}=-\mu\sigma+k_{p}(x_{d}-\hat{z}_{1})+k_{d}(\dot{x}_{d}-\hat{z}_{2}). \tag{26}\]
## 5 Performance Evaluation and Analysis
### Performance Evaluation of Each ACO Model
#### 5.1.1 Control Groups  
In the control groups, the performance of the pure Ant System and Ant Colony System algorithms with no additional algorithms was measured.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline
**Source** & 0 \\
**Destination** & 15 \\
**The least distance of Source and Destination** & 14 hop \\
**Probability of packet loss** & 0\% \\
**The number of packets per evaporation cycle** & 10 \\
**Total cycles per each experiment** & 100 \\
**Total packets (ants) per each experiment** & 1000 \\
**The number of repetitions of the same experiment** & 100 times \\ \hline \end{tabular}
\end{table}
Table 6: Common parameters in experiments.  
Figure 12: NTTnet. The topology which was used in experiments.  
First, this is the experimental result of Ant System. When the pheromone evaporation coefficient was increased from 0.1 to 0.9, it was confirmed that the optimized pheromone evaporation coefficient(\(\rho\)) was about 0.1. The average path length was 22.740940 when it was repeated 100 times. The time series graph of Figure 13a shows that it is slowly approaching the optimal path. The histogram of Figure 13b shows that the frequency of the path length between 21 and 23 is largest.  
Second, this is the experimental result of Ant Colony System. When the pheromone evaporation coefficient was increased from 0.1 to 0.9, it was confirmed that the optimized pheromone evaporation coefficient(\(\rho\)) was about 0.1. The average path length was 24.748670 when it was repeated 100 times. The time series graph of Figure 14a shows that it is not approaching the optimal path. This result is unexpected because the ACS algorithm is known to have the best performance [14]. This result will be explained in a later section. The histogram of Figure 14b shows that the frequency of the path length between 21 and 23 is largest.  
Figure 13: Ant System. (**a**) Time series graph; (**b**) Histogram.
## 3 Reinforcement Learning-Based Results and Discussion
### Learning Multiple Manoeuvres with a Single Network  
This section takes a single network and trains it for two different manoeuvres. The objective is to train the agent with an additional input to encode the manoeuvre that is being demanded. Hence, the manoeuvre is encoded as an 'one-hot' vector in addition to the standard state data. This vector encodes the selected manoeuvre as a single 1 value within a vector of zeroes. As with the turn-around and climb manoeuvres, a truncated state vector is used, containing only the longitudinal state elements. For the purposes of this demonstration, the descent and hover entry manoeuvres, that have been shown in the sections above, were selected.  
The agent is trained on both manoeuvres simultaneously, alternating between them, with the manoeuvre encoding input and the reward function changing for each episode. This allows the single network agent to be exposed equally to training data for both manoeuvres.  
The agent was trained on both descent and entry into the hover using the settings as detailed in Table 8. The simulation results from this can be seen in Figure 10. Starting from identical flight conditions, the aircraft can be seen transitioning into the hover and into a descent depending on the the 'one-hot' state input used.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Timesteps & 500,000 \\ \(x_{\text{lim}}\) & 30 m \\ \(u_{\text{lim}}\) & 25 m s\({}^{-1}\) \\ \(u_{\theta}\) & 0 \\ Use reduced observation & True \\ \hline \hline \end{tabular}
\end{table}
Table 7: Run E: Climb metadata used.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Timesteps & 1,000,000 \\ \(x_{\text{lim}}\) & 30 m \\ \(u_{\text{lim}}\) & 25 m s\({}^{-1}\) \\ \(u_{\theta}\) & 0 \\ Use reduced observation & True \\ Network depth & 3 \\ Network width & 64 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Run F: Multi-manoeuvre metadata used.  
Figure 9: Run E: Simulation results showing the Climb manoeuvre response.  
In this case, though, 'bleed' can be seen between the manoeuvres where, for example, the descent manoeuvre ended with the aircraft at a very high angle of attack, whereas a low pitch angle and alpha response was desirable. During experimentation, it was found that increased network depth improved the discrimination between manoeuvres. Therefore, a depth of 3, one additional layer, was used for the multi-manoeuvre cases.  
In the following example, the pitch weight was used to penalise the high pitch at the end of the descent manoeuvre, to aid the agent in discriminating between the required behaviours. Following this, the two manoeuvres from a single agent were linked together sequentially.
## 3 Basic Methodology
### Hierarchical Dirichlet Process Vector Auto-Regressive Hidden Markov Models
Given this (K+1)-dimensional probability vector \(\beta\), we simplify the HDP-VAR-HMM transition distributions \(\pi_{j}\) for each state \(j\) from a Dirichlet distribution with mean equal to \(\beta\) and variance governed by concentration parameter \(\alpha>0\)  
\[log\ p(y_{t}|\theta_{k},y_{t-1}) =log\ p(y_{t}|A_{k},\Sigma_{k},y_{t-1})=log\ \mathcal{N}(y_{t}|A_{k}y_{t-1},\Sigma_{k}) \tag{7}\]3  
Generally, we define the starting probability vector \(\pi_{0}\) from the same prior, but with much smaller variance, \(\pi_{0}\sim Dir(\alpha_{0}\beta)\) with \(\alpha_{0}\gg\alpha\), which denotes that few starting states are observed. To capture the sufficient dynamics of given time series \(n\) with \(T_{n}\) observations, our goal is to interpret these observations with the hidden state \(z_{j}\). However, instead of assigning variable states in a short time period, we should treat each observation in the context of its predecessors for improving the temporal consistency. A \"sticky\" parameter \(\kappa\) was introduced that favors self-transition by assigning extra prior mass on the transition probability \(\pi_{jj}\). Thus, the transition representation will operate in a temporally consistent way, that is  
\[log\ p(y_{t}|\theta_{k},y_{t-1}) =log\ p(y_{t}|A_{k},\Sigma_{k},y_{t-1})=log\ \mathcal{N}(y_{t}|A_{k}y_{t-1},\Sigma_{k}) \tag{7}\]4  
where \(\kappa>0\) governs the degree of self-transition bias. Informally, what we are doing is increasing the expected probability of self-transition for maintaining the dynamical phenomenon consistency for many time steps. After constructing the sHDP-VAR-HMM model, we use the state-of-the-art variational inference algorithm for learning the parameters, which are known as Split-Merge Monte Carlo method by [PERSON] et al. More information can be found in [53].
# Evolution of Access Network Sharing and Its Role in 5G Networks
## 5 Fixed Access Network Sharing Economics
### New Ownership Models
This can significantly reduce human intervention in the business transaction part of the service chain, thus reducing service provisioning time and increasing the network revenue.
## 2 Materials and Experiment
### Anti-Icing Asphalt Mixture
#### 2.2.1 Design of Ingrediient
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Technical Indicators** & \begin{tabular}{c} **Residual** \\ **Stability** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Freeze-Thaw** \\ **Splitting Strength** \\ **Ratio (\%)** \\ \end{tabular} & \begin{tabular}{c} **Dynamic** \\ **Stability** \\ **(times/mm)** \\ \end{tabular} & \begin{tabular}{c} **Maximum** \\ **Fleural-Tensile** \\ **Stain (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Standard** \\ **Flying Loss** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Flooding** \\ **Flying Loss** \\ **(\%)** \\ \end{tabular} \\ \hline anti-icing asphalt mixture with V-260 & 89.7 & 87.2 & 8579 & 4834 & 1.6 & 2.51 \\ \hline anti-icing asphalt mixture with IGD & 90.14 & 87.8 & 7969 & 4257 & 1.8 & 2.72 \\ \hline Conventional asphalt mixture & 96.5 & 92.6 & 6997 & 4531 & 1.69 & 1.73 \\ \hline Standard values [28] & 85 & 80 & 2800 & 2500 & 15 & 15 \\ \hline Test method [26] & T0709 & T0729 & T0719 & T0715 & T0733 & T0733 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Test results of asphalt mixture performance. Figure 2: Aggregate gradation figure. Asphalt mixture type and asphalt-aggregate ratio are measured as test variables in this section. Many characteristic parameters were measured, such as void volume, aggregate voids filled with asphalt, voids in mineral aggregate, Marshall stability, flow value, residual stability, freeze-thaw splitting strength, dynamic stability, maximum flexural-tensile strain and so on. There are 81 specimens in total. Table 7 compares with the water stability, high-temperature stability and low-temperature crack resistance between conventional asphalt mixture and the anti-icing asphalt mixture. It can be observed that the addition of anti-icing filter reduced the mixture's residual stability and tensile strength ratio and simultaneously enhanced the maximum flexural-tensile strain. The residual stability and freeze-thaw splitting strength ratio of anti-icing asphalt mixture with IGD are slightly larger than anti-icing asphalt mixture with V-260. The reason is that the salt content of IGD is lower than that of V-260, and the salt is soaked in water, the change of void volume of anti-icing asphalt mixture with IGD is relatively small, and it is denser than anti-icing asphalt mixture with V-260.
## 4 Scheduling Policy
### Integration of Job Packing  
Job packing improves the utilisation of a GPU allocated to train a small model by efficiently using the remaining resources to accommodate additional jobs. However, it is crucial to ensure that the memory requirement of job combinations does not exceed the GPU memory. The memory required for each job is determined by the model size and batch size. Therefore, we propose to automatically override the user-specified batch size to overcome the limitation of GPU memory and train both models simultaneously on the same GPU.  
In addition, job packing helps to alleviate queuing delays by enabling the simultaneous training of multiple models. However, it might introduce some interference that leads to a reduction in job throughput. Job packing is, hence, considered when the cluster load is high (i.e., still having jobs in the queue awaiting resources). In this work, scheduling decisions are initially made without job packing, and if there are more jobs in the queue, the scheduler seeks opportunities to implement job packing.  
Previous work indicates that combining at most two jobs is the most effective in terms of throughput [8]. More importantly, the throughput of job combinations varies depending on the chosen models and their batch size. As a consequence, the model-similarity-based scheduling policy is applied to identify the optimal job combinations that maximise job throughput.  
Given a list of scheduling decisions for jobs made in each round, a job running alone in a single GPU can be packed with another job in the queue if the GPU memory allows it.  
We compute the similarity measure for this job and find the closest reference model in the database. From this, we compute the list of combined throughput between the reference model and all the pending jobs. The scheduler then selects job combinations based on two criteria: minimising a reduction in throughput for the original job and maximising the combined throughput of job combination. Due to the memory constraint, the constraint on the arrival time of jobs in the queue is relaxed, which means any jobs can be chosen to pack. The procedure of job packing is detailed in Algorithm 3.  
```
1:\(Sj\leftarrow\) scheduled jobs in the current round
2:while\(J\) not empty do
3:for\(sj\) in \(Sj\)do
4:if\(sj\) is trained alone in a GPU then
5:\(jp_{m}\leftarrow\) potential jobs in the queue to pack with \(sj\)
6:\((sj,j\in j\eta_{m})\leftarrow\) combinations that \(max(throughput_{sj})\) and \(max(sum(throughput_{(sj,j)}))\)
7: Packing \((sj,j)\)
8: Delete \(j\) from queue
9:endif
10:endfor
11:endwhile
```  
**Algorithm 3** Algorithm of job packing.