esa-sceva/satcom-chunk-collection huggingface dataset:
# Roll Control of Morphing Aircraft with Synthetic Jet Actuators at a High Angle of Attack
## 2 Dynamic Modeling for Morphing Aircraft with SJA  
### Description of the Model  
To address the poor control efficiency of an aileron at a high AoA, SJA and the morphing span are proposed to achieve the roll control of aircraft. The structural model of morphing aircraft with arrays of actuators is shown in Figure 1, where the fuselage-axis is the axis of symmetry itself. The morphing wing is installed at the tip of the fixed wing, and SJA is located at the middle of the semi-fixed wing, along the spanwise direction. The lengths of the fixed wing, the morphing wing, and the array of SJA are \(L\) = 5 m, \(\Delta L\in\) [0, 1.12 m], and \(L_{\text{jet}}\) = 3 m, respectively. The chordwise position of the actuator in this study was set as \(S_{\text{jet}}\) = 0.12 m, and the chord length as \(c\) = 1 m.  
Figure 1: Top view of morphing aircraft with a synthetic jet actuator (SJA).
# Distributed Visual Crowdsensing Framework for Area Coverage in Resource Constrained Environments
## 3 Methodology
### Data Redundancy Elimination
#### 3.3.1 Local Data Management
For each sub-region \(s\) inside the target area, if the size of Photos(\(s\), \(k\)) > 0, the total utility is updated as shown in line 7, as the utility of Photos(\(s\), \(k\)) is added to the total utility value. The utility of Photos(\(s\), \(k\)) is the area of sub-region \(s\) multiplied by \(\theta\), where \(\theta\) is the effective angle. Steps 6 and 7 are repeated for each sub-region \(s\) in the target area. As an example, sub-region 1 is regarded as full-view covered, providing that the utility of sub-region 1 reaches 2\(\pi\times\) area of sub-region 1. ```
1:Input: Local K-CoverPicTree
2:Total utility = 0
3:Call Algorithm 4
4:for\(s\) = 1:5 do
5:for\(k\) = 1:K do
6:if ((size of Photos(\(s\), \(k\)) > 0)) then
7: Total utility = Total utility + Area of sub-region \(\times\)\(\theta\)
8:endif
9:endfor
10:endfor
```  
**Algorithm 5** Calculating the utility of the photos  
The following is the general sequence of the proposed framework within each sensor:  
1. In accordance with Algorithm 3, using the Boolean K-CoverPicTree, the added value of each photo is computed. 2. The photo with the highest added value is appended to the local K-CoverPicTree. Then, the Boolean K-CoverPicTree is revised based on the new inclusion as updates to the values of \(s\), \(b\), and \(k\) of the photos from values 0 to 1 in the Boolean K-CoverPicTree. 3. In the situation where the highest added value is zero, the next step is to look for photos with a similar sequence of coverage values. Alternatively, the subset of the coverage values of other available photos within the local K-CoverPicTree can be searched. In this regard, if photos with a similar sequence of coverage values or the subset of the coverage values cannot be found, the photo should be appended to the local K-CoverPicTree. 4. Then, utilizing Algorithm 3, the added value of the remaining photos in the device storage is calculated again. The previous steps are repeated until the set of photos is empty. 5. Utilizing Algorithm 4, redundant photos are removed.
## 1 Introduction
### Machine Learning Algorithms Used in the Interpretation of Cognitive Load
In contrast, as already indicated, unsupervised learning clustering techniques can be used to learn the clustering profile with respect to different indicators of cognitive load without applying a prior classifier [45]. Algorithms can also be used to perform pattern analysis. These include distance analysis through string edit methods [46], which are applied in the study of patterns detected with a scan path or gaze point. Similarly, more traditional statistical algorithms such as Analysis of Variance (ANOVA), Analysis of Covariance (ANCOVA), and Multivariate Analysis of Variance (MANOVA) can be used to determine the significant differences in indicators of cognitive load between different types of users [47]. Table 2 presents a summary of the algorithms and their usefulness in the study of cognitive load. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Contrast Tests** & **Usefulness in Measuring Cognitive Load** \\ \hline Feature selection [19; 37] & The use of feature selection algorithms provides information in a hierarchical order on which features are most relevant. \\ \hline Logistic regression [34] & Helps in the detection of intrinsic cognitive load levels (low, medium, and high) [19]. \\ Probabilistic study (percentile analysis) [36]. & Allows a determination of the level of execution (low, medium, and high). \\ \hline Predictions (linear regression, SVM, Decision tree, Neuronal Networks) & Provide information on cognitive load indicators that may be more relevant in explaining, e.g., learning outcomes. \\ \hline Classification (analysis, discriminant analysis, KNN, SVM, Naive Bayes, Random Forest [38; 39; 40], and Neural networks [41; 42]) & The classifiers provide information on the most important cognitive load indicators with respect to ranking groups of variables in relation to a reference variable, such as academic performance. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Different algorithms and their usefulness for measuring cognitive load.
## 3 Active Guidance Controller Design for IRWs Based on MARL
### MARL-Based Controller Design
#### 3.4.2 Action Space Definition  
During the operation of the IRW vehicle with two independently rotating wheelsets, each agent's output equals half of the torque differential exerted on the left and right IRWs. The input torque of each IRW is subjected to a torque of magnitude \(1\ T_{wi}1\), and the input torque directions are opposite for the left and right IRWs on the same axle. Each agent's action is a one-dimensional vector, and the resulting joint action space is demonstrated in Equation (15).  
\[\begin{array}{l}A=\left[\begin{array}{cc}T_{w1}&T_{w2}\end{array}\right] \\ \text{s.t.}-T_{\text{max}}\leqslant T_{wi}\leqslant T_{\text{max}}(i=1,2)\end{array} \tag{15}\]  
where \(T_{\text{max}}\) is the peak output of the controller, with the control torque to each wheel restricted between \(-T_{\text{max}}\) and \(T_{\text{max}}\).
## 3 Materials and Methods
### Solution Based on Reinforcement Learning  
In this paper, the classic Q-learning algorithm is used to solve the above problem. Q-learning is a reinforcement learning method where an agent takes actions based on the current state and the environment provides corresponding feedback signals based on the actions taken by the agent. The agent updates the Q-values stored in the Q-table according to this feedback and uses these continuous feedback values to determine changes in states. This process is repeated until the agent determines that the state is a final state, at which point the learning ends.  
Simply put, at a given time t, the agent will receive a reward value from the environment and the next state S, and at time t + 1, the agent will provide an action A to the environment based on this information. This process continues iteratively until the agent can make the best decision.  
The Q-learning algorithm selects actions in two ways. One is the greedy strategy, where decisions are made based on the maximum expected value in the Q-table; the other is the \(\varepsilon\)-greedy strategy. The Q-table is updated after each action selection, and its update formula is based on the Bellman equation [34], as shown in Equation (16):  
\[Q(s_{t},a_{t})=(1-\alpha)Q(s_{t},a_{t})+\alpha(R_{t+1}+\gamma\text{max}Q(S_{t+ 1},a)) \tag{16}\]  
The specific algorithm flowchart is shown in Figure 7. This paper mainly introduces randomly generated feasible operation codes as prior knowledge of the algorithm while optimizing the maximum transmission time and satellite task burden. It mainly consists of the following five parts:  
1. State Space. The state space can be defined as a finite set of all jumps. To facilitate the representation of all states, coupling numbers \(S(S_{1},S_{2})\) can be used, where \(S_{1}\) represents the packet number and \(S_{2}\) represents the jump number.  
2. Action Space. The agent selects a satellite for forwarding based on the current state, which includes each hop during forwarding and the satellite situation. In this problem, the total number of satellites is \(M\), so the action space is typically defined as a finite set of the number of satellites, denoted as \(M(M_{1},M_{2},\cdots,M_{m})\).
3. Reward Function. The optimization goal is to minimize the maximum transmission time and minimize the total task burden. The larger the satellite task burden, the smaller the reward \(R\); the longer the total transmission time of the satellite, the smaller the reward \(R\).
4. Selection Policy. When the agent is in a certain state S, taking an action will, with a probability of \(1-\varepsilon\), choose the action corresponding to the maximum Q-value in the Q-list, and, with a probability of \(\varepsilon\), randomly select an action.
5. Random Generation of Jump Codes. Randomly generate process codes. Under the constraints of the problem, feasible jump codes are randomly generated based on the number of packets and transmission hops as prior knowledge for the algorithm.
## 2 Proposed Prognostic Framework
### Particle Filter Based Prognosis
#### 2.3.1 State Estimation  
At the \(k=1\) step, number of samples/particles are drawn from the initial prior distribution. The initial prior distribution is assumed based on the user's knowledge of the underlying failure mechanism. For subsequent time steps, the posterior distribution of the \((k-1)\)th step is used as the prior distribution of the current time instant \(k\).  
\[p(a_{k}|z_{1:k-1})=\int\,p(a_{k}|a_{k-1})p(a_{k-1}|z_{1:k-1})da_{k-1} \tag{7}\]  
At time \(k\), when new measurement data are available, the posterior distribution obtained is expressed as  
\[p(a_{k}|z_{1:k})=\frac{p(z_{k}|a_{k})p(a|z_{1:k-1})}{\int\,p(z_{k}|a_{k})p(a_{ k}|z_{1:k-1})da_{k}} \tag{8}\]  
Assuming that the measurement noise \(n_{k}\) is normally distributed, the likelihood function can be expressed as  
\[L\Big{(}z_{k}\Big{|}a_{k}^{i},m_{k}^{i},C_{k}^{i}\Big{)}=\frac{1}{z_{k}\sqrt{2 \pi}\delta_{k}^{i}}exp\left[-\frac{1}{2}\left[\frac{lnz_{k}-\lambda_{k}^{i}}{ \delta_{k}^{i}}\right]^{2}\right] \tag{9}\]  
where  
\[\delta_{k}^{i}=\sqrt{ln\left[1+\left[\frac{\sigma}{a_{k}^{i}\big{(}m_{k}^{i},C_{k}^{i}\big{)}}\right]^{2}\right]} \tag{10}\]  
and  
\[\lambda_{k}^{i}=ln\Big{[}a_{k}^{i}\Big{(}m_{k}^{i},C_{k}^{i}\Big{)}\Big{]}- \frac{1}{2}\Big{(}\delta_{k}^{i}\Big{)}^{2} \tag{11}\]
# Human Activity Recognition Method Based on Edge Computing-Assisted and GRU Deep Learning Network
## 6 Conclusions and Future Work  
This paper proposed a novel OI-EleAttG-GRU deep learning model for HAR in an edge computing environment. Using a dynamic scheduling algorithm, we were able to offload wearable computing tasks to the edge server and meet HAR computing resource requirements. Moreover, an improved GRU network model is employed at the edge server to improve the recognition accuracy of HAR by using an inception convolution kernel spliced with a GRU network that possesses Element-Wise-Attention properties. With extensive experiments on different datasets, our proposed method demonstrated superior performance compared to both baseline and state-of-the-art methods. Specifically, we dynamically considered the computing frequency and power consumption of wearable devices and servers in task offloading, which significantly reduces the execution time and energy consumption of task offloading. We use inception convolution in data processing to enhance the ability to analyze multi-scale data. In the process of behavior recognition, the network significantly improves the ability to extract features from spatial-temporal features through the EleAttG-GRU's temporal memory capability and attention mechanism.  
As the performance for deep learning on HAR in an edge computing environment still has room for improvement, in the future, we will further study the following limitations of edge computing: (i) limited computing resources of some servers; (ii) privacy security protection; and (iii) network and bandwidth delays. In terms of deep learning, improving the recognition accuracy of different continuous actions on larger and more complex datasets is also an important aspect that we need to consider in our future work. We believe that the outcomes of such efforts can benefit various domains, including human health and biomedical applications.  
Figure 11: Confusion matrix for two methods in one test. (**a**) The confusion matrix for RNN. (**b**) The confusion matrix for the proposed method.  
**Author Contributions:** Conceptualization, Y.Y. and X.H.; methodology, X.H.; software, X.H.; validation, Y.Y. and X.H.; formal analysis, C.Z.; investigation, Y.G.; resources, C.C.; data curation, Y.G.; writing--original draft preparation, X.H.; writing--review and editing, L.Y. and X.H.; visualization, C.Z.; supervision, L.Y.; project administration, C.C.; funding acquisition, Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Natural Science Foundation of Zhejiang Province (No. LGG21F010005), the National Natural Science Foundation of China under Grant (No. 61602137, 61702144).  
Not applicable.  
**Informed Consent Statement:** Written informed consent was obtained from the patients to publish this paper.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
# A Deep Multi-Frame Super-Resolution Network for Dynamic Scenes
## 4 Results
### Ablation Studies  
We conducted three experiments with 5 input frames to demonstrate the effectiveness of the SSA module and the DW module. As shown in Table 2, model 1, equipped with both the SSA module and the DW module, had the best performance, whose PSNR reaches 28.50 db.  
Compared with model 1, model 2 removes the SSA module and had a substantial drop of 0.31 db, which may have been caused by the loss of the subpixel information in standard low-resolution grid warping operation. This means subpixel-wise alignment was essential in MFSR tasks, especially in optical-flow-based methods. Therefore, we can confirm the effectiveness of the SSA module through this comparison.  
In addition, model 1 used the DW module to decrease errors caused by the optical flow or the alignment operation before fusion operation. In contrast, model 3 replaces the DW module with the average operation and gets a performance drop of 0.57 db compared with model 1. It is not strange that, without the DW module, the fusion operation in our method introduced many errors into the restoration procedure. This comparison demonstrates that the DW module was indispensable in our method. Surprisingly, model 3 behaved even worse than model 4, which indicated that errors introduced by optical flow were harmful to the restoration if they were not carefully processed.  
We also tested our method with different numbers of input frames to justify the advantages of our framework for being able to handle variant numbers of inputs. Comparing model 1 with models 4 and 5, we found that the more low-resolution frames participated in restoration, the better performance the model had. It shows that our model was capable of computing useful temporal information between different input frames. However, when the input number increased to 9, as shown in model 6, the performance gain became tiny. This may have been caused by the decrease of the overlapping area when the motion range between frames was large.
## 3 Solution Procedure  
Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm introduced by [PERSON] and [PERSON] ([PERSON] and [PERSON] (1977); [PERSON] and [PERSON] (1977). Because of its easy implementation, robustness, and fast convergence ability, PSO has been applied in solving many combinatorial problems. In the PSO system, each individual, called a particle, represents a point and the population, called a swarm, represents a set of points in the search space. A swarm of initial particles are generated and the particles fly through the search space using a velocity function. The \(i\)-th particle in \(d\)-dimension solution space is denoted by \(X_{i}=(x_{i1},x_{i2},\ldots,x_{id})\). The \(i\)-th particle is assigned a randomized velocity \(V_{i}=(v_{i1},v_{i2},\ldots,v_{id})\) and is iteratively moved through the problem space. During the evolution phase, each particle follows the two best values: the local best solution, which is the best solution that the \(i\)-th particle has achieved so far, and the global best solution, which is the best solution obtained by the population so far. Therefore, the velocity function is continually updated in accordance with the particle's previous experience and the group's experience. In spite of the simple concept, reasonable computational time, and few parameters to adjust, the shortcoming of PSO is that it is easily trapped into local optima and premature convergence. Since the positions and velocities of standard PSO are real-valued, it is designed for solving continuous functions. However, our problem is set in a discrete space and the standard PSO is not suitable, and we should modify the position representation of PSO from continuous to discrete. Thus, we introduce the crossover operator in the classical GA into the PSO to help the individuals to share information among particles. Tabu search (TS), proposed by [PERSON] in 1986, is a famous local search algorithm used to deal with combinational optimization problems [29]. TS uses the Tabu list to prevent being trapped into local optima and the aspiration rule to exploit a prohibited resolution.  
In this study, we propose a hybrid discrete algorithm combining the merits of PSO and the TS algorithm to solve this problem. In the proposed algorithm, PSO is used for the global searching process and TS is employed for the local searching process. The proposed algorithm consists of two phases; the first phase of the hybrid algorithm is the PSO algorithm, whose solution is treated as the initial solution for the Tabu search algorithm in the second phase. The framework of the proposed algorithm is shown in Figure 3. The pseudo-code of the hybrid algorithm is shown in Figure 4.
## 4 OOB Effect Correction  
For Case 1 water (Table 2 and Figure 5), two cameras had consistent OOB correction factors, with a percentage difference of less than 2.8%. Specifically, the correction factors for the blue band were the closest to 1.0 (0.956-1.007) among the three CZI visible bands, implying the most insignificant OOB effect, which is consistent with the OOB evaluation results described above. Not surprisingly, the values for the red band were found to be the lowest (0.786-0.809), indicating the most significant OOB effect, due to the weak water-leaving signal at this band. The values of correction factors for the green band have the largest variation magnitude (0.879-1.197), and a close correlation between the correction factor and Chla (R\({}^{2}\) = 0.99) was found.  
As Chla can be remotely sensed from the blue-to-green band ratios of CZI R\({}_{\rm rs}(\lambda)\), the OOB correction factor for the green band can be estimated from the ocean color data itself (Figure 6), as shown in Equations (7) and (8). The results for the two cameras (Equation (7) vs. Equation (8)) have a slight difference (\(\sim\)2%).  
\[{\rm OOB}(B2\_Camera\_1)\ =\ -0.0468\ X^{2}-\ 0.2659\rm X\ +\ 1.1485 \tag{7}\]  
Figure 5: OOB correction factors for the three CZI visible bands in Case 1 water with chlorophyll a concentrations from 0.01 mg/m\({}^{3}\) to 10 mg/m\({}^{3}\). The two CZI cameras had almost the same correction factors (relative difference \(<\)2.8%), so results from only one camera is shown here.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \multicolumn{2}{c}{\(\Delta\lambda\) **(nm)**} & \multicolumn{2}{c}{**OOB (a) \({}^{*}\)**} & \multicolumn{2}{c}{**OOB (\%)**} & \multicolumn{2}{c}{**Correction Factor**} \\ \hline  & B1 & 2.4(\(-\)2.5) \(\pm\) 3.8 & 0.00003 (\(-\)0.00001) \(\pm\) 0.00105 & \(-\)0.13 \(\pm\) 0.37 & 0.9823 \(\pm\) 0.0729 \\ Camera 1 & B2 & \(-\)13.7(\(-\)1.5) \(\pm\) 44.3 & \(-\)0.00046 (\(-\)0.00007) \(\pm\) 0.00231 & \(-\)0.69 \(\pm\) 0.33 & 1.0875 \(\pm\) 0.0560 \\  & B3 & \(-\)15.5(\(-\)4.0) \(\pm\) 49.0 & 0.00017 (0.00004) \(\pm\) 0.00184 & 1.56 \(\pm\) 1.45 & 0.9616 \(\pm\) 0.0623 \\ \hline  & B1 & 3.1 (3.5) \(\pm\) 4.0 & 0.00006 (\(-\)0.00001) \(\pm\) 0.00145 & \(-\)0.17 \(\pm\) 0.46 & 0.9776 \(\pm\) 0.0744 \\ Camera 2 & B2 & \(-\)13.7(\(-\)1.5) \(\pm\) 44.3 & \(-\)0.00068 (\(-\)0.00009) \(\pm\) 0.00360 & \(-\)0.68 \(\pm\) 1.00 & 1.0985 \(\pm\) 0.0618 \\  & B3 & \(-\)13.9(\(-\)2.5) \(\pm\) 47.6 & 0.00019 (0.00004) \(\pm\) 0.00195 & 2.06 \(\pm\) 2.69 & 0.9671 \(\pm\) 0.0993 \\ \hline \end{tabular} \({}^{*}\) The numbers in the parentheses are the median.  
\end{table}
Table 3: CZI OOB and correction factor in the turbid waters. \(\Delta\lambda\) represents the shift of the effective central wavelength \(\lambda_{E}\) relative to nominal one \(\lambda_{N}\).  
For the turbid waters, the OOB correction factors (Table 3) for the CZI blue, green, and red bands ranged from 0.962 to 1.099, and the two cameras also had consistent results.
## 3 Results
### Feature Extraction and Selection
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature** & **Feature Name** & **_p_-Value** & **Achieved** & **SNR Value** \\
**Number** & **(Abbreviation)** & **(ANOVA)** & **Statistical Power** & \\ \hline
1 & SD ACC V & 0.000 & 0.99816 & 0.42581 \\
2 & SD ACC AP & 0.000 & 0.99999 & 0.37793 \\
4 & Skew ACC V & 0.050 & 0.79921 & 0.17291 \\
10 & RMS ACC V & 0.000 & 0.99810 & 0.42377 \\
11 & RMS ACC AP & 0.000 & 0.99999 & 0.37786 \\
17 & FD Freq AP & 0.002 & 0.99990 & 0.35230 \\
19 & Power 3 dB V & 0.001 & 0.90132 & 0.40920 \\
20 & Power 3 dB AP & 0.004 & 0.82464 & 0.39399 \\
26 & Flo 3 dB AP & 0.001 & 0.99994 & 0.35812 \\
29 & Fri 3 dB AP & 0.002 & 0.99981 & 0.33804 \\
31 & Power all V & 0.000 & 0.98057 & 0.41361 \\
32 & Power all AP & 0.000 & 0.9999 & 0.53191 \\
33 & Power all ML & 0.038 & 0.80151 & 0.15502 \\
36 & BW all ML & 0.002 & 0.91135 & 0.33509 \\
42 & Fri all ML & 0.002 & 0.91121 & 0.32780 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of statistically significant features and signal-to-noise ratio (_SNR_) value. * Band power on 99% occupied bandwidth on vertical acceleration (Power all V): calculated from 99% occupied bandwidth of the result from Fast Fourier Transform on vertical acceleration data during whole duration of level walking [8,23]. SVM training, coupled with 10-fold cross-validation, was performed with four kernels (linear, Gaussian, quadratic, and cubic). Thereafter, a set of test data was used to train the models. The results derived for the models and performance assessment are shown in Table 3. The performance index table obtained from the cross-validation of the models showed that the SVM classifier with the quadratic kernel had the highest accuracy (93.46%), whereas the SVM classifier with the cubic kernels had the lowest accuracy (90.65%). However, testing the trained models by using different test datasets revealed that both the quadratic kernel and cubic kernel had the highest accuracy (96.55%), whereas the linear and Gaussian kernels had the lowest accuracy (89.29%). Additionally, the model with the linear SVM kernel had the highest sensitivity (92.16%) on train/validation dataset; meanwhile, the quadratic and cubic kernels had the highest sensitivity (94.4%) on the testing dataset. The specificity assessment results revealed that all SVM kernels achieved  
Figure 3: Frequency domain characteristic of acceleration from young (**a**), elderly (**b**), and stroke (**c**) subjects on three different directions. 100% specificity, indicating that the four models can avoid misclassification of normal and gait data.