esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this paper, we have proposed a novel adaptive sample-size unscented particle filter with partitioned sampling to track a high-maneuvering target in three-dimensional motion space. Firstly, aiming at the high maneuver of a target, the CS-jerk model was introduced as the motion model to match the target-changing motion by its self-adaptive modification, and to facilitate subsequent processing, the measurement model with coordinate transformation was further introduced to convert the coordinate of measurements obtained by sensors. Next, aiming at the tracking algorithm, a novel adaptive sample-size UPF was proposed, in which the number of samples could be self-adaptively adjusted in real time according to the estimated state variance at each time step, thus effectively satisfying the accuracy and avoiding computational redundancy. Then, the partitioned sampling method was combined with the AUPF presented in Section 3 to reduce the dimension of the maneuver by decomposing the motion space and to take the optimal sample strategy in each subspace, further reducing the computational complexity and increasing the sample diversity. Finally, the simulations in Section 5 verified that the proposed PS-AUPF improves tracking performance in high-maneuvering target tracking scenarios, with at least 6% reduction in terms of RMSE of tracking errors and 86% reduction in terms of running time compared to UPF. In our future work, the target motion model will be further improved to describe the target motion more accurately, which involves attempts to combine multiple-model methods.  
Also, based on an improved motion model, the proposed algorithm would be compared with other new artificial intelligence methods. We also consider to apply the PS-AUPF to the multi-target tracking problem and some other computer vision applications.  
[PERSON] and [PERSON] provided the direction for the algorithm. [PERSON] derived the algorithms, led the experiments and wrote the paper.  
This research was funded in part by the Chinese Academy of Sciences National Defense Innovation Foundation, grant number CXJJ-M215; in part by the Chinese Academy of Sciences Strategic Pilot Science and Technology Project, grant number XDA18040400; and in part by the National Natural Science Foundation of China, grant number 61701473.  
The authors declare no conflict of interest.
## 2 Related Work
### Rumor Detection
Finally, using textual data characteristics, rumors were detected using cleaning data. In the article [16], the author proposed a method of rumor detection based on SDSMOTE and feature selection in 2019. In this paper, they detected rumors in the specific topic using Sina micro blogs, and six new features were added; with guide less words (suspicious, topic, recognition of information, degree of attention to the user, and credit ranking), they used the SMODE algorithm to reduce the impact of unbalanced data. They detected 90% of rumors with an acceptable level of precision. Ref. [17] presented a novel approach for rumor detection that contains new features, including bias potential and measuring the characteristics of the network. They tested their model on a real data set that contains all posts related to health collected from the twitter network. Experimental result showed that using new features correctly detects 90% of rumors with acceptable precision. They also used different classifiers for the selection of different features for rumor detection, which is beneficial for the future selection for best classifier and effective features. Authors in [18] used long short-term memory (LSTM) for the detection of online rumors. They captured the long short-term memory (LSTM) with a neural network for rumor detection based on content that is of a forwarding, spreader, and diffusion structure. For forwarding content, they used a word embedding model for the representation of words. For spreader content, they captured the popularity of content that is forward [19]. For diffusion, they proposed to change the structure of the diffusion layer and change the model between different layers of diffusion [20]. The final data set contains 1623 rumors and 1756 non-rumor. They evaluated the experimental results with the baseline paper by using accuracy and F1 measure as the parameters. This analysis [21] introduced and discussed two types of rumors that spread on social media. One is a long-standing rumor that circulates for a long period of time, and the second is newly emerging rumors spawned during fast-paced events, such as breaking news, where reports are shared without any verification. They provided an overview of a rumor classification system that consists of four components: first is rumor detection, second is rumor tracking, third is rumor stance classification, and the last one is rumor veracity classification.
## 4 Results and Discussion  
### UCS Results of Coal-Grout Composites  
Figure 4 shows the UCS results of coal-grout composites. It can be seen that the 7-day UCS of the coal-MP 364 composite is higher than other chemical grout composites with the same coal-grout ratio. As for the cementitious grout composites with the same coal-grout ratio and curing time, the UCS of  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **Min** & **Max** & **Mean** & **Standard Deviation** \\ \hline Coal-grout ratio & 0.4 & 1.2 & 0.8 & 0.28 \\ Curing time (days) & 0.17 & 28 & 10.03 & 10.26 \\ Grout types & 1.0 & 6.0 & 3.5 & 1.71 \\ UCS (MPa) & 0.51 & 14.08 & 4.79 & 2.77 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistics of parameters.  
SF-C composites was the highest followed by coal-P.O 42.5, while coal-P.O 32.5 had the lowest UCS. It can also be observed that the UCS of coal-grout composites decreased with an increasing coal-grout ratio. The possible reason for this is that the coal particles were bonded together by the grout; as the grout decreased, the bonding became weaker [49]. Furthermore, the curing time had different effects on cement-grout and chemical-grout composites. The UCS of coal-chemical grout composites increased and peaked on the seventh day, and then declined. This is due to the rapid chemical reaction of the chemical grout during the early days. However, an obvious shrinkage of the chemical grout was observed after the seventh day, especially in the MP 364 group (shown in Figure 5a). The porous structure affected the overall strength of the chemical grout composites. As for the coal-cement grout composites, the UCS increased with curing time, but the growth rate decreased, which agreed well with much of the literature [50; 51; 52]. In addition, its average early strength (1-day strength) was lower than the 4 h strength of coal-chemical grout composite. In terms of its microstructure, no clear shrinkage between coal and cement was observed, indicating that cementitious grout performs better than chemical grout (Figure 5b).  
Figure 4: Unconfined compressive strength (UCS) results of coal-grout composite specimens: (**a**) coal-chemical grout composite; (**b**) coal-cement grout composite.
## 5 RFI Measurements
### RFI Example at GPS Bands  
Since a GNSS-R instrument is installed in MERITXELL, a measurement of the RFI signals present at GPS L1 band was also performed using the radiometric L-band antenna. Figure 16 shows that some pulsed RFI signals are also observed at the GPS L1 band. These RFI signals can still be seen despite the attenuation introduced by the antenna mismatch present when using the radiometric L-band antenna to measure at GPS L1 band. The effect of RFI signals in GPS navigation application is not as critical as the effect on MWR, due to the inherent protection of the GNSS signals. However, for GNSS-R applications, the presence of RFI signals is much critical as compared to navigation applications [37].  
Figure 16: Capture of RFI signals at GPS L1 band using the L-band radiometric antenna.  
Figure 15: Capture of RFI signals at **(a)** S-; **(b)** C-; **(c)** X-; **(d)** K”-; **(e)** Ka- and **(f)** W-band. All captures are spectrograms with the frequency response compensation procedure already applied. The upper subfigures correspond to V polarization, and the lower ones to H polarization.
# Whole-Body Motion Generation for Balancing of Biped Robot
## 3 Generation of COM Trajectory
### ZMP Modification Through CP Control  
As discussed in Section 2.2, the CP provides a framework for understanding and controlling bipedal locomotion by decomposing the LIPM dynamics. Stabilizing these divergent CP dynamics is crucial because an unstable CP inevitably leads to an unstable COM and potential loss of balance. Therefore, instead of simply tracking a pre-planned ZMP, dynamically modifying the ZMP based on CP feedback allows the robot to better respond to disturbances and maintain stability.  
The CP dynamics described by Equation (7) and the COM dynamics described by Equation (4) are in a cascaded form, as shown in Figure 3. Since \(\omega\) is a positive constant, the CP dynamics are naturally unstable. If the CP dynamics are controlled to be stable, the COM dynamics will naturally become stable. To stabilize the CP dynamics, the control rule is defined based on error dynamics. This type of error dynamics is well-established in CP-based control for achieving asymptotic stability [32]:  
\[\triangle\mathbf{\dot{\xi}}=k_{cp}\triangle\mathbf{\xi} \tag{12}\]  
where \(\triangle\mathbf{\xi}=\mathbf{\xi}-\mathbf{\xi}^{ref}\) and \(\triangle\mathbf{\dot{\xi}}=\frac{d}{dt}(\triangle\mathbf{\xi})\). From Equation (6) with \(\triangle\mathbf{P}=\mathbf{P}^{des}-\mathbf{P}^{ref}\),  
\[\triangle\mathbf{\dot{\xi}}=\omega(\triangle\mathbf{\dot{\xi}}-\triangle\mathbf{P}) \tag{13}\]  
by Equations (12) and (13),  
\[\triangle\mathbf{P}=\triangle\mathbf{\xi}-\frac{1}{\omega}\triangle\mathbf{ \dot{\xi}}=\triangle\mathbf{\xi}-\frac{k_{cp}\triangle\mathbf{\xi}}{\omega} \tag{14}\] \[\mathbf{P}^{des}=\mathbf{P}^{ref}+\left(1-\frac{k_{cp}}{\omega}\right) \triangle\mathbf{\xi} \tag{15}\]  
For \(k_{cp}<0\), \(\triangle\mathbf{\xi}\to 0\) as \(t\rightarrow\infty\) by the control rule (12). The ZMP is modified through CP control, which enables the naturally diverging CP to track its reference.
# TSNetIQ: High-Resolution DOA Estimation of UAVs Using Microphone Arrays
## 2 System Model and Problem Formulation  
### Signal Model  
DOA estimation is usually achieved by antenna array reception. Consider a uniform circular array (UCA) composed of \(M\) antenna elements with element spacing; its structure is shown in Figure 1. Suppose that the origin of coordinates is set at the center, \(O\), and the angle between the line between the \(m\)th array element and the center and the \(x\)-axis is \(\phi_{m}=2\pi m/M\). Let a narrow-band plane wave be incident on the UCA in the direction \((\phi,\theta)\), where the azimuth angle \(\phi\in[0,2\pi]\) of the signal is the angle projected from the \(x\)-axis counterclockwise to the signal incident direction on the array plane, and the pitch angle \(\theta\in[0,\pi/2]\) is the angle between the \(z\)-axis and the signal incident direction.  
There are \(L(L<M)\) independent narrowband signals from different directions \(\theta_{1},\theta_{2},\ldots,\theta_{L}\) indicated on the array. Taking the first array element as a reference, the \(i\)th signal can be expressed as \(s_{i}(n)\); thus, the signal received by the \(m\)th array element is given by  
\[x_{m}(n)=\sum_{i=1}^{L}s_{i}(n)e^{-i\frac{2\pi}{\lambda}(m-1)R\cos{(\phi_{i}- \phi_{m})}}+\delta_{m}(n) \tag{1}\]  
where \(\lambda\) denotes the wavelength corresponding to the signal carrier frequency and \(\delta_{m}(n)\) represents the noise added to the \(m\)th array element.
## 5 Analytical Modeling  
In this section, the analysis of the proposed reliable transport process is presented. In the first and second sub-sections below, the transmission model of coded packets during one cycle and the backpropagation model of ACKs are given, respectively. Then we can model the sequence of cycles to obtain the file round-trip delay (the average delay for the source to receive ACK 0) and energy consumption defined as the total number of transmissions in the third sub-section. Based on the model, the maximization of the network performance which takes the energy consumption and round-trip delay into account can be achieved by optimizing the system parameters \(\{M_{r},T_{s,r},T_{w,r}\}_{r=1}^{K}\), which will be presented in next section.
# Multi-Layer Progressive Face Alignment by Integrating Global Match and Local Refinement
## 1 Introduction
### The Local Methods  
Instead of modeling holistic appearance, local methods merely learn a local texture model for each landmark and restrict all the local decisions according to the global shape to match an image. The earlier Active Shape Models and their improvements have led to the construction of a generative model which measures the variations around a landmark to adjust the model parameters [15; 16]. Constrained Local Models (CLMs) describe the local variations by map image, and optimize the parameter set by maximizing the sum of responses [17]. [PERSON] et al. [18] use deep CNN networks to produce a local response map which is just like CLM. The independent landmark detectors are trained by learning the discriminative local models, and the landmark positions are updated based on the detection response results [19; 20].  
A regression function is trained to directly map face appearance to the shape residuals [21; 22]. Combining a series of weak regressors in a cascaded manner can progressively reduce the error and achieve higher accuracy [23; 24]. The highly discriminative local features are extracted and encoded, followed by the global linear regression [25; 26]. Consequently, the local features are integrated under the constraint of the global variation [27; 28; 29]. Similar approaches combine cascaded regression with the project in order to update the shape parameters [30].  
Face alignment under various poses and occlusions is achieved by discriminatively training cascaded deformable part models and optimizing part mixtures [31]. The appearance model combined with head pose tracking by introducing 3D depth information can handle the variations in pose [32; 33]. In the composite component graph-matching model [34], the graph structure represents the global attributes and the graph vertex represents the local attribute of key regions. [PERSON] et al. [35] built a local block model to realize face detection and alignment by linear regression. [PERSON] et al. [36] combined the holistic model and the part-based model.  
The local methods focus on local feature representation and search for a single landmark within the local neighborhood, which can achieve the best performance when the landmark positions are close to the ground-truths. Its advantage is fast computation and good representation for local details. However, because there is no strong mutual constraint between the landmarks across the whole texture, each landmark is aligned separately. A single landmark can be attracted by local noise to drift from the ground-truth. Partial occlusion can cause the loss of local information and detection of the wrong convergence location. Once the initial position is far from the ground-truth, the face shape deforms or drifts as a whole. In sum, this method is greatly influenced by the initial position, image noise and local occlusion, which tend to converge to the local optimum and cause misalignment.
# Generating Robotic Speech Prosody for Human Robot Interaction: A Preliminary Study
## 3 Method
### Protocols  
#### 3.3.1 Experiment A  
We recruited the participants in the University, and 21 undergraduate students (1 female), aged from 20 to 22 (M:21.10, SD: 1.41), participated in the 5-min interaction session. The data was collected in accordance with the Chubu University ethical guidelines for research (approved number of study on contacting with NAO: 270099). Informed consent was obtained from all subjects involved in the study. All students are familiar enough with NAO robots to avoid the apprehension of a new environment. Each participant entered a room with a NAO placed on a table and was instructed to sit on a chair at the table. The subject enters the experiment room to find the robot called NAO sitting on a table. The user sits on the chair, and the experiment starts with the experimenter explaining the session, saying \"This robot is called NAO, and it will tell you some stories. You can note the stories by using this pen and paper.\" The participants were allowed to note the information from NAO in order to avoid the additional burden of memorization. The participants engaged in 3 sessions: (1) neutral(default) session, (2) positive(joy) session, (3) negative(sadness) session. All participants started from the neutral session, and subsequent positive and negative sessions proceeded in a randomized order.  
The pitch of the NAO speech was varied. The neutral voice of NAO used the default value of 291.58 Hz. The pitch values of joy and sadness were 314.46 Hz and 251.36 Hz, respectively, as noted in Table 1.  
The contents of the NAO speech were as follows:  
_I will tell you tomorrow's weather. Hokkaido will be snowy due to low pressure. Tohoku will be snowy due to cold weather. Kanto will be cloudy with the influence of cold. Chubu will be covered with high pressure and it will be sunny. Kinki will be rainy due to the influence of an atmospheric pressure valley. Chugoku will be rainy due to cold air. Shikoku will be covered with high pressure and it will be sunny. Kyushu will be rainy due to moist air. Okinawa will be cloudy with the influence of moist air. It was the weather forecast._  
The information given was different in each session in order to avoid the learning effect. After listening to [PERSON], participants answered a questionnaire that included questions, such as \"What is the weather of Hokkaido?\", \"Was [PERSON]'s speech easy to understand? If it wasn't, why?\", and \"Does it sound like emotional speech? If it does, what kinds of emotions did it sound like?\" A yes/no and open-ended questions were employed. This test was to see if there was a difference in the understanding of the participants between positive and negative sessions, and whether the default value could be used as the neutral voice of [PERSON].
# A 12-20 GHz Wideband High-Power SP2T Switch Based on Gap Waveguide Technology
## 2 WR-62 to Groove Gap Waveguide Transition
By removing the step, the parametric sweep analysis was performed on the step height (Step\({}_{h}\)), pin height (P\({}_{h}\)), and window width (W\({}_{w}\)) to investigate the effects of these parameters on the transition performance, as shown in Figure 3. According to Figure 3a, the step height has a significant effect on the wave rotation and coupling to the GGW. By removing the step, the input wave reflects back to the input port and cannot be coupled to the GGW. The effects of the pin height and window width on the bandwidth enhancement are apparent in Figure 3b,c. Figure 3: Simulated S-parameters of WR62 to GGW transition.
# Low Dose Ionising Radiation-Induced Hormesis: Therapeutic Implications to Human Health
## 4 The Phenomena of LDIR
### Adaptive Response
Epidemiological studies from Guangdong, China (2018), have reported that adaptive responses, such as increasing DNA damage-repair capacity and antioxidant production could contribute to the lower cancer mortality rate in those low-dose high-background-radiation areas [105]. The blood samples of male resident (50-59 years old) analysis data also indicated a lower expression of pro-apoptotic gene mRNA, enhanced cell-survival gene expression and lesser DNA oxidative damage index 8-Oco-2\({}^{\prime}\)deoxyguanosine (8-OHdG) in high-background-radiation areas, as compared to the control population. In another recent epidemiology study from India (2016), individuals exposed to >5.0 mGy/year showed a significant decrease in chromosome damage, as indicated by cytokinesis-block micronucleus assay, when their blood samples were further challenged to 1.0 Gy and 2.0 Gy doses, as compared to individuals from normal-level natural radiation area [106]. Recently, in vivo animal studies have demonstrated that there are three dose thresholds for protective adaptive responses to be initiated. The upper dose threshold is where low radiation doses increase to the point where they no longer elicit a protective response; however, the lower dose threshold below which the dose would no longer be able to initiate the cellular protective mechanisms (as it has not been irradiated) seen at the more elevated low doses [107]. [PERSON] et al. (2006) were the first to demonstrate a non-linear chromosomal inversions mutation in the prostate cells of mice following exposure to various ultra-low doses of 0.005-0.01 mGy X-ray doses. It was reported that a dose of 1 mGy produced a protective response and reduced spontaneous inversion, but a dose of 0.01 mGy did not, and actually increased spontaneous inversion frequency [108]. Recently, [PERSON] et al. (2015) performed a gene profiling characterisation of AR in AGO1522 human fibroblasts exposed to 5 cGy (priming dose), followed by 2 Gy of X-rays, identifying gene transcripts or pathways associated with reduced micronuclelei. mRNA and microRNA microarrays were used to detect altered genes and they also demonstrated that cell communication and intercellular signalling transduction played important roles after low-dose irradiation [109]. Taken together, studies of chronic LDIR exposure both in in vitro and in vivo have demonstrated LDIR as an important priming dose to induce radio-adaptive response in cells against subsequent exposure of higher radiation doses.
###### Abstract  
To assist in the broadcasting of time-critical traffic information in an Internet of Vehicles (IoV) and vehicular sensor networks (VSN), fast network connectivity is needed. Accurate traffic information prediction can improve traffic congestion and operation efficiency, which helps to reduce commute times, noise and carbon emissions. In this study, we present a novel approach for predicting the traffic flow volume by using traffic data in self-organizing vehicular networks. The proposed method is based on using a probabilistic generative neural network techniques called deep belief network (DBN) that includes multiple layers of restricted Boltzmann machine (RBM) auto-encoders. Time series data generated from the roadside units (RSUs) for five highway links are used by a three layer DBN to extract and learn key input features for constructing a model to predict traffic flow. Back-propagation is utilized as a general learning algorithm for fine-tuning the weight parameters among the visible and hidden layers of RBMs. During the training process the firefly algorithm (FFA) is applied for optimizing the DBN topology and learning rate parameter. Monte Carlo simulations are used to assess the accuracy of the prediction model. The results show that the proposed model achieves superior performance accuracy for predicting traffic flow in comparison with other approaches applied in the literature. The proposed approach can help to solve the problem of traffic congestion, and provide guidance and advice for road users and traffic regulators.  
deep belief network; historical time traffic flows; restricted Boltzmann machine; optimization; traffic flow prediction 18 August 2018; Accepted: 10 October 2018; Published: 15 October 2018