esa-sceva/satcom-chunk-collection huggingface dataset:
# A Two-Stage Kalman Filter-Based Carrier Tracking Loop for Weak GNSS Signals
## 6 Conclusions  
A KF-based two-stage tracking method was proposed, which implemented a coarse-to-fine tracking method to improve the convergence speed and the tracking sensitivity. The coarse tracking stage reduced the large frequency error of the acquisition results for the KF-based fine tracking initialization, thereby achieving faster convergence. The fine tracking stage adopts the KF-based tracking, which can self-adjust KF gains to adapt to the challenging environments.  
Simulation results demonstrated that the proposed algorithm can improve tracking sensitivity by 8-10 dB compared with the conventional 2-stage tracking methods under static conditions. Moreover, compared with the standard KF-based tracking method, the results validate that the proposed method has a shorter convergence time. In addition, under low dynamic weak signal conditions, the proposed method was more robust than the conventional tracking algorithm.  
The proposed algorithm shows better tracking performance in the static and dynamic with pedestrian and low-speed vehicle conditions. This superiority can improve the receiver's sensitivity and enhance the real-time performance, which is very important for positioning and navigation. However, the tracking performance of the proposed method under high dynamic conditions, such as with aircraft and missiles, is unknown. Therefore, the performance of this method under high dynamic situations should be studied in the future. On the other hand, the performance of the 2-stage KF-based tracking highly relies on the knowledge of noise statistics. However, the noise covariance is fixed during the KF iteration, which may not reflect the real variable noise statistics. Therefore, the adaptive Kalman filter can be employed in the tracking loop to further improve the tracking performance under more challenging environments.  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] assisted to perform the experiments; [PERSON] drafted the manuscript.  
**Funding:** This research is supported by the National Natural Science Foundation of China (Grant number: 61471021).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Indistinguishability Operators Applied to Task Allocation Problems in Multi-Agent Systems
## 8 Conclusions and Further Work  
We have shown that the two most famous response functions given in literature are retrieved as particular cases from appropriate indistinguishability operators. This fact opens a wide range of potential applications from a mixed framework based on indistinguishability operators and distances to task allocation problems in multi-agent systems. We have applied the mentioned indistinguishability operators to allocate tasks to a group of robots according to a fuzzy Markov chain. We have shown that the results are very similar whichever indistinguishability operator is applied and, thus, both present equivalent behavior. The simulations extend the results previously obtained in [12] to analyze environments where the tasks are arranged in groups or clusters. The results show that the number of iterations required to converge with fuzzy Markov chains only depend on the placement of tasks in the environment and that they are not affected by the reminder of parameters of the system, whichever indistinguishability operator is applied. In contrast, when probabilistic Markov chains were used, this number of steps also depends on the indistinguishability operator. The theoretical and empirical obtained results in this paper open a wide range of potential applications from a mixed framework based on indistinguishability operators and distances to task allocation problems in multi-agent systems when fuzzy Markov chains are under consideration.  
We plan to propose several families of indistinguishability operators to perform a large number of experiments in order to compare the new results with those provided by the task allocation methods that implement the two aforesaid response functions. An implementation of these methods on real robots is also under consideration.  
This research was funded by the Spanish Ministry of Economy and Competitiveness under Grants DPI2014-57746-C03-2-R, TIN2014-53772-R, TIN2014-56381-REDT (LODISCO), TIN2016-81731-REDT (LODISCO II) and AEI/FEDER, UE funds.  
[PERSON] and [PERSON] developed the theoretical concepts and wrote the theoretical sections. [PERSON] and [PERSON] conceived and designed the experiments and wrote the experimental sections. [PERSON] implemented the simulations. All the authors contributed (wrote, reviewed and supervised) to the final manuscript.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
EPRF Exponential Possibility Response Function  
OPRF Original Possibility Response Function  
RTM Response Threshold Method
## 1 Introduction
To address the above issues, we focus on constructing a lightweight but efficient Conformer speech recognition model, using low-rank approximation decomposition to reduce the number of redundant parameters of the multi-head self-attention module in the Conformer model and the memory size of the model, while keeping the model recognition accuracy and training time largely unchanged. The main innovations in this article are as follows. Firstly, we construct an encoder and decoder of low-rank multi-head self-attention modules, called LMHSA modules, in the Conformer model, and do not need to retrain the overall model during the compression process. Compression of the multi-head attention module using low-rank decomposition reduces the number of redundant parameters and the memory size of the model. Second, to effectively alleviate the long-tail problem in Kazakh, we used a softmax function with penalty factor to replace the original softmax function, called balanced softmax, to improve the accuracy of the model without increasing the training difficulty. Meanwhile, we investigate the best value of penalty factor in the Conformer model. Third, we use CTC as an auxiliary function in the Conformer model to build a hybrid CTC/Attention multi-task-learning training approach to help the model converge quickly. Fourth, we build a lightweight but efficient Conformer model, reducing the number of parameters and the storage space of the model while keeping the training speed and recognition accuracy largely unchanged. Experimental results on the KSC dataset show that the use of an LMHSA module can effectively reduce the parameters of Conformer, and the number of parameters of the model is reduced by 7.4%. The use of the balanced softmax function can effectively alleviate the long-tail problem in the Kazakh language, and the speech recognition word error rate (WER) is reduced by 0.15%. The multi-task efficient Conformer model using hybrid CTC/Attention compresses the final number of parameters of the model by 7.4% and the storage space of the model by 13.5 MB, while the overall training speed and word error rate remain largely unchanged. The rest of this paper is as follows. In Section 2, we briefly introduce the hybrid CTC/Attention end-to-end speech recognition model and its application to Kazakh, while we introduce the decomposition and the speech long-tail problem. The proposed method is described in detail in Section 3. Section 4 presents the environment configuration of the experiments and the introduction of the dataset. Section 5 presents the experiments conducted with our proposed low-rank multi-head self-attention encoder and decoder, balanced softmax, and the final model structure scheme compared to the baseline model Conformer. In Section 6, we summarize the work we have completed and present the outlook.
## 4 Discussion
### Effects of Display Design  
As for different display designs, despite the fact that a reduction in ARS under the air-route-altered condition can be observed in all three display designs, the difference in the 3D-SART scores between the two conditions of air-route-as-planned and air-route-altered was not significant for Interface 2 (center-layout with a medium-salience design), while being significant for Interface 1 (center-layout with a low-salience design) and Interface 3 (off-center layout with a high-salience design). Moreover, the overall operation accuracy and altitude operation accuracy in Interface 2 were also significantly higher than those in Interface 3 (Figure 3c,d), and no differences were found between Interface 1 and 2, nor between Interface 1 and 3. The results of the 3D-SART scores and operational performances suggest that, compared with Interfaces 1 and 3, the design of Interface 2 is more conducive to the maintenance of an operator's SA and flight performance in abnormal or emergency situations [66; 67; 68]. In addition, compared with Interface 3, the increase in average pupil diameter in Interface 2 suggests that the participants paid higher attention during the task completion [69; 70].  
Considering the promoting effect of salience design on SA, the significant difference between Interfaces 2 and 3 suggests that, compared with an off-center-layout design, a central-layout design is beneficial for operators' rapid and accurate capture of the critical decision-making information (such as altitude, manifesting as the decreased fixation rate of altitude AoI in Interface 2), which contributes to operators' good SA and the stability of overall performance in situations of unexpected changes or events [29; 62; 63]. Furthermore, the significant difference between Interfaces 2 and 3 also indicates that the factor of information layout design has a far greater impact on SA and task performance than the factor of salience design in our study. Another point worth noting is that the high-salience design of open-window-designed digital indicators and more immersive pitch attitude design can neutralize the adverse effect of an off-center-layout design on an operator's SA and task performance to a certain extent, which results in the nonsignificant differences between Interfaces 1 and 3. Related studies have pointed out that an SA-oriented display design, such as a high-salience design (focusing on the improvement of information perception and visual attention), is able to play a critical role in facilitating the function of the interaction display and promoting the comprehensive efficiency of human-machine systems [51; 63; 66; 71]. The experimental results of the present study demonstrate that good display designs, such as adopting a center-layout design for the critical decision-making information and using a high-salience design, are helpful to maintaining an operator's SA in dynamic, abnormal and unplanned situations.
# Diet, Polyphenols, and Human Evolution
## 7 Could Polyphenols Have Played a Role in Human Evolution?  
Considering the numerous factors that have simultaneously contributed to the evolution of the human species, it is difficult to know if polyphenols and other dietary antioxidant compounds have played a critical role.  
Although an omnivorous diet seems to be the most suitable for the general well-being of the human body (intake of high-quality proteins, vitamin C and vitamin B\({}_{12}\), bioavailable minerals, etc.), a significant leap in evolution occurred when humans started a sedentary life dedicated to agriculture and livestock. The dietary factors that most may have contributed to cognitive development have a mainly vegetable origin e.g., polyunsaturated fatty acids, vitamins and antioxidant molecules. However, fish may also have played a key role, given that they are a source of docosahexaneoic acid. Table 2 shows how _Homo sapiens_ (the modern human species) appeared at exactly the same time as sedentary life. This kind of life led to closer social relationships and the cultivation of numerous plants including cereals, fruit trees, vines, olive trees and nuts. These foods and their derivatives (beer, wine)show a good antioxidant activity, which is provided by some vitamins and other active molecules, the most important of which are polyphenols.  
Table 3 illustrates the content in polyphenols (per 100 g/mL or portion) and the relative antioxidant activity of some foods present in the Mediterranean diet [74]. Foods have been selected taking into account their presence in the Mediterranean basin in ancient times, excluding those foods or beverages that were introduced relatively recently (tea, coffee, soybean, etc.). The polyphenol contents are also expressed as mg/portion to give a more realistic view of dietary intake. The portions refer to a modern diet, which could be very different from those of ancient times. For example, in Roman times, the volume of wine consumed per adult on a daily basis was well above today's recommendation of 125 mL.
# Low-Computational Extended Orthogonal Matched Filter Structure for Multiuser Detection
## 2 Principle of the EOMF
### Formulation of an EOMF
Hence, Equation (7) must hold:  
\[\left|\frac{\sum_{k=1}^{K}N\sqrt{P_{1}}d_{1}Re\!\left[e^{-j\phi_{\lambda}( \theta_{1})}U_{k}^{*}\right]}{\sum_{k=1}^{K}N\sqrt{P_{1}}Re\!\left[e^{-j\phi_{ \lambda}(\theta_{1})}U_{k}^{*}\right]}\right|=|d_{1}|=1. \tag{7}\]  
In this study, Equation (7) is assumed to hold for the sake of simplification. Assuming that the normalized \(y_{\text{MF}_{1}}\) and \(y_{\text{MF}_{i}}\) are \(y_{\text{MF}_{1}}^{\prime}=\frac{y_{\text{MF}_{1}}}{\alpha}\) and \(y_{\text{MF}_{i}}^{\prime}=\frac{y_{\text{MF}_{i}}}{\alpha}\), respectively, the output of the EOMF \(y\) is expressed as follows in Equation (8):  
\[y=y_{\text{MF}_{1}}^{\prime}+\sum_{i=1}^{N-1}w_{i}y_{\text{MF}_{i+1}}^{\prime} =\frac{1}{\alpha}\!\left(y_{\text{MF}_{1}}+\sum_{i=1}^{N-1}w_{i}y_{\text{MF}_ {i}}\right)\ (\alpha\
eq 0). \tag{8}\]  
Thus, the interference is achieved by adaptively controlling the weight vector in such a way that any sum other than the first term approaches zero in the LC. This study uses a normalized least mean square (NLMS) algorithm to update the array antenna and the LC weights, as in previous studies [19]. The NLMS is a method to update the step size \(\mu\) of the LMS algorithm at each sample point, which can converge in the same way faster than the LMS algorithm. The step size of the NLMS is fixed in the range of \(0<\mu<2\). When using the NLMS, the update equations for the array antenna weight vector and the LC weight vector are given by Equations (9)-(11):  
\[{\mathbf{U}}(t+1)={\mathbf{U}}(t)+\frac{\mu{\epsilon_{U}^{*}}(t){\mathbf{x}}(t)}{\left\|{ \mathbf{x}}(t)\right\|^{2}} \tag{9}\]  
\[\mathbf{x}_{k}=\sum_{m=1}^{M}\sqrt{P_{m}}d_{m}\mathbf{c}_{m}e^{-j\phi_{k}(\theta_{m})} +\mathbf{n}_{k} \tag{2}\]0  
\[\mathbf{x}_{k}=\sum_{m=1}^{M}\sqrt{P_{m}}d_{m}\mathbf{c}_{m}e^{-j\phi_{k}(\theta_{m})} +\mathbf{n}_{k} \tag{2}\]1  
Here, the error signals used to update the antenna weights \(e_{U}\) and \(e_{V}\) are given as follows in Equations (12) and (13), respectively:  
\[\mathbf{x}_{k}=\sum_{m=1}^{M}\sqrt{P_{m}}d_{m}\mathbf{c}_{m}e^{-j\phi_{k}(\theta_{m})} +\mathbf{n}_{k} \tag{2}\]2  
\[\mathbf{x}_{k}=\sum_{m=1}^{M}\sqrt{P_{m}}d_{m}\mathbf{c}_{m}e^{-j\phi_{k}(\theta_{m})} +\mathbf{n}_{k} \tag{2}\]3where \(d_{U}(t)\) is the reference signal and \(d_{V}(t)\) is the subtraction of \(d_{U}(t)\) from the received signal. Then, an error signal used to update the LC weights \(e_{\text{LC}}\) is given as follows in Equations (14) and (15):  
\[\mathbf{x}_{k}=\sum_{m=1}^{M}\sqrt{P_{m}}d_{m}\mathbf{c}_{m}e^{-j\phi_{k}(\theta_{m})} +\mathbf{n}_{k} \tag{2}\]4  
\[\mathbf{x}_{k}=\sum_{m=1}^{M}\sqrt{P_{m}}d_{m}\mathbf{c}_{m}e^{-j\phi_{k}(\theta_{m})} +\mathbf{n}_{k} \tag{2}\]5  
where \(y(t)\) and \(I_{\text{replica}}(t)\) are the EOMF output and the replica of the interference component, respectively, and \(Y(t)\) is the hard decision value of \(y(t)\).
## 3 Materials and Methods
### Cluster Analysis
#### 3.3.1 Correlation Calculation  
They assumed that for any two debris flows, \(i\) and \(j\), each has \(z\) evaluation indicators. The \(k_{th}\) evaluation indicator of the two debris flows can be expressed as \(i_{k}\) and \(j_{k}\). The evaluation indicators of debris flow \(i\) and \(j\) can be combined into a set of data pairs (\(i_{k}\), \(j_{k}\)) (\(1\leq k\leq z\)). For any two data pairs (\(i_{k}\), \(j_{k}\)) and (\(i_{l}\), \(j_{l}\)) in a set, when \(i_{k}>i_{l}\) and \(j_{k}>j_{l}\), or \(i_{k}<i_{l}\) and \(j_{k}<j_{l}\), the data pair is said to be consistent; when \(i_{k}>i_{l}\) and \(j_{k}<j_{l}\), or \(i_{k}<i_{l}\) and \(j_{k}>j_{l}\), the data pair is said to be inconsistent; when \(i_{k}=i_{l}\) and \(j_{k}=j_{l}\), this data pair is neither consistent nor inconsistent. If correlation analysis is conducted on the evaluation indicators between two debris flows, the correlation between any two evaluation indicators can be expressed as [57]:  
\[\tau_{ij}=\frac{2C}{\frac{1}{2}z(z-1)}-1=\frac{4C}{z(z-1)}-1 \tag{16}\]  
Among them, \(C\) is the number of identical order pairs. The value range of \(\tau_{ij}\) is \([-1,+1]\), and when \(\tau_{ij}=1\), it indicates that the two debris flows have the exact level correlation; when \(\tau_{ij}=-1\), it indicates that two debris flows have opposite level correlations; when \(\tau_{ij}=0\), it indicates that the two debris flows are independent of each other. For the risk assessment indicators of debris flow, due to the different dimensions of each indicator, correlation calculation can not only eliminate the impact of different dimensions of each evaluation indicator but also serve as a prerequisite for establishing a correlation matrix [58].
# A Hybrid Multi-Objective Optimization Method and Its Application to Electromagnetic Device Designs
## 3 A Hybrid Multi-Objective Optimization Method
### Adaptive Operators
#### 3.3.1 Crossover Operator  
The commonly used crossover operators include two-point crossover, uniform crossover, differential evolution (DE) crossover [15], the simulated binary crossover (SBX) [16], etc. The two-point crossover is a random setting of two identical crossover points in two parental individuals, wherein the crossover is then performed at the two points where the two parental individuals are located. It has an optimal balance between intrinsic originality and randomness [17]. On the other hand, the SBX operator is a crossover operator that simulates a single-point binary crossover, with an excellent local optimization capability [15].  
Based on this, a dynamic selection crossover operator combining the two-point crossover and the SBX is proposed. Initially, the two-point crossover operator is selected with a higher probability, and a larger search area is proposed to be obtained, while as the iterations proceed, the probability of selecting the SBX operator gradually becomes higher in order to better retain the good individuals. A schematic of the two-point crossover is shown in Figure 1.  
Figure 1: Two-point crossover diagram.  
For two parent individuals \(x_{1}(x_{11},\cdots,x_{1n})\) and \(x_{2}(x_{21},\cdots,x_{2n})\), the offspring \(y_{1}(y_{11},\cdots,y_{1n})\) and \(y_{2}(y_{21},\cdots,y_{2n})\) can be obtained using the SBX operator by (9).  
\[\begin{cases}y_{1i}=0.5\times\left[(1+\beta)x_{1i}+(1-\beta)x_{2i}\right]\\ y_{2i}=0.5\times\left[(1-\beta)x_{1i}+(1+\beta)x_{2i}\right]\end{cases} \tag{9}\]  
where \(\beta\) is determined by a random number \(rand\in(0,1)\).  
\[\beta=\begin{cases}\quad(2 rand)^{\frac{1}{1+\beta\kappa}},\quad rand\leq 0.5\\ \left[1/(2-2 rand)\right]^{\frac{1}{1+\kappa}},\; otherwise\end{cases} \tag{10}\]  
where \(\mu_{c}\) is a user defined parameter, usually taken as 20. The larger the value of \(\mu_{c}\), the greater the probability that the superior individual will be preserved.
## 4 Discussion  
Our current study was conducted based on the latest nationally representative survey of NSCH, 2018-2019, with children aged 3-17 years. The study aim was as follows: (i) to investigate the risk factors of the children with ADHD; and (ii) to predict the children with ADHD. The current diagnostic process for ADHD is time-consuming and complicated by behavioral symptom overlaps. Since the incidence rate of ADHD is high, it is necessary to provide a tool that can swiftly and correctly predict the risk of ADHD. There were some ML-based works in previous studies to correctly detect and predict ADHD [28; 29; 64; 65; 66] and children with ADHD who received treatment [32]. Our current study expands these previous works by implementing an LR-based model for the risk factor extraction method and eight ML-based classifiers for the prediction of the children with ADHD. LR results illustrated that several factors (child's age, child's sex, mother's age, allergies, asthma, anxiety, depression, alcohol, insurance, race, family structure, very LBW, premature child, and poverty) were identified as the high-risk predictors of the children who had ADHD. This present study also adopted eight ML-based classifiers for prediction. Eight ML-based classifiers for the prediction of children with ADHD gave an accuracy range of 69.8% to  
Figure 1: ROC curves of eight ML-based classifiers for children with ADHD.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier Types** & **Accuracy (\%)** & **SE (\%)** & **SP (\%)** & **AUC** \\ \hline RF & 85.5 & 84.4 & 86.4 & 0.94 \\ NB & 69.8 & 77.3 & 65.3 & 0.78 \\ DT & 84.6 & 83.4 & 86.0 & 0.90 \\ XGBoost & 77.0 & 76.8 & 77.3 & 0.86 \\ KNN & 84.0 & 82.6 & 85.6 & 0.92 \\ MLP & 79.9 & 80.1 & 79.6 & 0.88 \\ SVM-RBF & 71.9 & 72.9 & 70.9 & 0.80 \\
1D CNN & 72.1 & 80.1 & 84.4 & 0.80 \\ \hline \hline \end{tabular}  
* SE: Sensitivity; SP: Specificity; AUC: Area under the curve.  
\end{table}
Table 5: Performances of ML-based classifiers for predicting children with ADHD.  
85.5% and an AUC of 0.78 to 0.94. RF-based classifiers correctly predicted the children with ADHD with an excellent accuracy of 85.5% and also an excellent AUC of 0.94.
# Towards Efficiently Provisioning 5G Core Network Slice Based on Resource and Topology Attributes
## 4 Heuristic 5G Core Network Slice Provisioning Algorithm Design
### Heuristic Slice Provisioning
#### 4.2.3 Slice Provisioning  
When the \(i\)th slice request \(SR_{i}=(G^{S}_{i},t^{a}_{i},t^{l}_{i})\) arrives at the slice provisioning system, the system first checks the already provisioned slices whose lifetime ends at time \(t^{a}_{i}\) and releases the physical resources they occupied. Then, slice nodes and slice links of \(G^{S}_{i}\) are, respectively, provisioned according to the above heuristic node provisioning and the link provisioning algorithms. The slice is only accepted by the slice provisioning system if the nodes and the links are both provisioned successfully. The slice provisioning algorithm RT-CSP+ is described in Algorithm 3. The only difference between RT-CSP and RT-CSP+ is that RT-CSP uses the basic \(k\)-shortest path-based slice link provisioning while RT-CSP+ uses the _minMaxBWUtilHops_ based slice link provisioning.