esa-sceva/satcom-chunk-collection huggingface dataset:
# An Overview of Quantum Machine Learning Research in China
## 6 Conclusions  
As an intersection of quantum computing and machine learning, QML has gradually flourished in the past decade. Despite a relatively late start, China's QML research has developed rapidly in recent years, and many excellent achievements have emerged. This article provides an overview of the QML development in China. As far as we know, this is the first comprehensive review that focuses on QML studies within Chinese research institutions.  
Based on the number of published papers, we divide the development of QML research in China into three stages, i.e., the preliminary stage, transition stage, and explosion stage. In the developing process, the model designing mode has gradually shifted from the quantum replacement mode to the quantum improvement mode, and the learning task type has gradually shifted from unsupervised learning to supervised learning.  
Among China's QML studies, popular algorithms include QSVM, QKNN, and various kinds of QNNs. The applications of QML mainly involve the fields of computer vision, cybersecurity, physical science, and natural language processing. By systematically inspecting the creative QML studies, we have found that there are three major innovation aspects, namely, algorithm/model design, strategy selection, and application scenario. Through these innovations, excellent model performance can be acquired. It is worth noting that in China's cutting-edge work, the overall performance of the proposed QML models has reached worldwide advanced levels, and some models can even achieve top-tier performance among all the analogous models reported globally.  
Finally, we have discussed the major challenges in current QML research and offered some prospects for the future development. Although focusing on the QML work within China, this review is expected to provide inspiration for both China's and global QML-domain progress.  
Conceptualization, L.L., R.S. and J.W.; literature search and data analysis, L.L., X.Z., Z.C. and X.X.; writing--original draft, L.L. and X.Z.; writing--review and editing, L.L. and W.X.; supervision, R.S. and W.X.; funding acquisition, L.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (NSFC) (Grant No. 62475273), National Key R&D Program of China (2022 YFF0504100), Shanghai Rising-Star Program (23 QA1411000), and Natural Science Foundation of Shanghai (22 ZR1472400).  
No new data were created or analyzed in this study.  
The authors would like to thank the Editor-in-Chief, Editor(s), and anonymous Reviewers for their valuable comments.  
The authors declare no conflict of interest.
## 2 Electrohydrodynamic Model
### Electrostatics  
In this model, the electric potential distribution, \(V\), under the presence of a space-charge distribution \(\rho_{c}(\mathbf{r})\), is obtained by solving the Poisson equation  
\[\
abla^{2}\mathrm{V}=-\frac{\rho_{c}(\mathbf{r})}{\varepsilon}, \tag{3}\]  
where \(\varepsilon\) is the plasma permittivity. Consistently, in the same time step, the space-charge density is computed considering the plasma chemistry and the charged species densities by  
\[\rho_{c}(\mathbf{r})=e\Bigl{(}\sum_{j=1}^{N}Z_{j}n_{j}-n_{e}\Bigr{)}, \tag{4}\]  
with \(e\) the absolute value of the electron charge, \(Z_{j}n_{j}\) is the ion charge number, \(n_{j}(\mathbf{r})\) the ion density for species and \(n_{e}(\mathbf{r})\) the electron number density. Finally, the calculation of the electric field, \(\mathbf{E}\), is performed under the relation between this physical quantity and the electric potential distribution  
\[\mathbf{E}=-\boldsymbol{\
abla}V \tag{5}\]  
The Poisson equation is solved self-consistently, giving the electric field at each point in space, showing the characteristics of the corona discharge produced, namely the rapid variation of the electric potential in the vicinity of the electrodes and the verification of quasi-neutrality in the remaining of the thruster volume:  
\[\boldsymbol{\
abla}\boldsymbol{\cdot}\mathbf{J}=0 \tag{6}\]  
where \(\mathbf{J}\) is the total current density vector, including ions plus electrons current densities vectors, \(\mathbf{J}_{i}\) and \(\mathbf{J}_{e}\), respectively.
## 5 Low-Power Cellular Technologies for IoT
### NB-IoT
#### 5.2.4 NB-IoT Mobility Enhancement  
NB-IoT device mobility is limited by the radio resource control (RRC), which is able to re-establish connection with an appropriate cell when a UE moves out of a cell coverage area through cell selection. The connection re-establishment is achieved through the user plane data support. The handover feature is not supported in release 13, and mobility creates issues such as radio communication link failure when a UE moves from one serving cell to another. Release 13 only supports stationary UEs, while release 14 supports low-mobility UEs [65]. Mobility, on the other hand, is an inherent feature of LoRaWAN and the impact of mobility on LoRa communications is the reduction in the packet delivery ratio. A mobility analysis does not apply under this scenario since the UE and LoRa gateway are within a single location (see Figure 3). However, in [68], it was observed that the message size and mobility degrade the LoRa signal propagation and even further in urban areas where high-rise buildings would make the light of sight more difficult to achieve. Applying the NB-IoT for remote monitoring applications presents coverage, low cost, high connection, and other benefits. We identified the following challenges of deploying the NB-IoT:  
* The availability of IoT networks such NB-IoT will depend on the mobile network providers' offering and government regulatory frameworks;
* Most available cellular IoT devices are based on 3 GPP release 13. There is a significant gap that exists in the implementation of new technology features and the technological process;
* To a certain degree, the security and privacy of the NB-IoT solution can rely on the robust security features of LTE. NB-IoT data within the LTE environment is encrypted  
\begin{table}
\begin{tabular}{c c} \hline \hline
**NB-IoT Features** & **Values** \\ \hline NPRACH Detection threshold & 40 dB \\ NPDSCH Repetition and TBS & 1 and 3 \\ UL Subcarrier spacing & 1 \\ Subcarrier & 15 KHz \\ NPDCCH & 8 \\ NPUSDM subcarrier & 12 \\ Frequency carrier & Band 28 \\ Bandwidth & 180 KHz \\ Receive power & \(-\)15 dBm \\ Transmit power & \(-\)3 dBm \\ Sample rate & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 4: NB-IoT network configurations: the coverage level corresponds to NPRACH configurations.  
except for connections between the application and cloud server where other security mechanisms such as TLS can be considered;
* For scalability reasons, NB-IoT modules need to support the IPv6 addressing scheme.
# Chinese Event Detection without Triggers Based on Dual Attention
## 2 Related Work
### Event Detection Based on Trigger Identification
It is evident that document-level information is vital for event detection. Document embedding enhanced bi-directional recurrent neural network (DEEB-RNN) uses document embedding to enhance bi-directional recurrent neural networks, helping identify event trigger words and their types [27]. In Chinese sentences, event trigger words may appear within or between words after word segmentation. This problem can be solved by converting them into character sequence markers [28]. The above problems can be classified as trigger-word mismatch, and the polysemy of Chinese trigger words will affect event classification, which can be remedied by a trigger-aware lattice neural network (TLNN) [10]. To avoid the mismatch of word triggers, TLNN dynamically combines words and characters. Meanwhile, it mitigates the ambiguity of polysemous words by modeling them with the external language knowledge base. In fact, TLNN can only resolve the intersection of multiple words but cannot discriminate between unannotated corpora. The scarcity of annotated data remains a significant challenge for event detection. At the moment, external open domain-triggered knowledge [24], few-shot learning [21], and adversarial generative networks [25] primarily alleviate the effect of the few-shot problem, but low coverage, topic bias, and data noise remain problems.
# MM-Wave Phased Array Quasi-Yagi Antenna for the Upcoming 5G Cellular Communications
## 1 Introduction  
One of the issues expected for the fifth generation (5G) cellular communications is the move to the millimeter-Wave (mm-Wave) bands [1], which has received great attention from many researchers and industry. There has been a lot of reported research efforts on mm-Wave-based 5G communication systems [2]. The 5G mm-Wave communication technology can handle over a thousand times more mobile traffic than previous mobile communication networks such as 4G LTE [3]. Moving to mm-Wave bands would bring new challenges and certainly will require careful consideration on the antenna design for the 5G handheld devices. The 26 GHz is one of the promising frequency bands for mm-Wave 5G communications, which has been proposed by Ofcom, UK [4]. Compact antennas arranged asan array can be employed at different portions of a smartphone PCB to form linear phased arrays with high gain and directional radiation beams. Different from conventional antennas, such as patch, slot, or monopole antennas, to cover the required coverage-space of 5G cellular communications, the end-fire antennas, such as horn, Yagi-Uda, Quasi-Yagi, LTSA, Dipole, and Vivaldi, are more suitable [5; 6; 7; 8; 9; 10; 11]. Among them, the Quasi-Yagi antenna is a promising candidate due to its attractive features, such as compact size, high gain, and easy integration.  
The Quasi-Yagi antenna is a widely used antenna with high gain end-fire radiation patterns. Its configuration consists of a reflector and a driven element along with one or multiple directors. It can provide excellent directional propagation at the higher frequencies and could be used for radar, detecting, and phased array applications [12]. This study investigates the configuration and characteristics of the mm-Wave phased array antenna with Quasi-Yagi radiation elements for use in 5G cellular platforms. Eight Quasi-Yagi antenna elements are placed on the top side of the smartphone PCB and fed by 50-Ohm coax-to-microstriplines [13]. Each element is composed of driving dipole arms with a pair of director elements on the top layer of the substrate. For the handheld devices, the measurement of end-fire microstrip-fed antennas (such as Bow-tie, Dipole, Yagi, Vivaldi, etc.) can only be performed using cable due to the ground plane of smartphone PCBs [14; 15; 16; 17]. We propose here a new coax-to-microstripline feeding technique to achieve a better transition. The designed 5G smartphone antenna has 2 GHz (below \(-10\) dB S\({}_{\text{nn}}\)) in the frequency range of 25 to 27 GHz. Good radiation properties in terms of the antenna's fundamental properties have been obtained for the antenna. In addition, the designed 5G antenna provides sufficient SAR levels with good behavior in the vicinity of the user's hand. The planar phased array configurations of the Quasi-Yagi antenna with \(2\times 2\), \(4\times 4\), and \(8\times 8\) radiation elements were also designed and high directivity radiation beams have been obtained. Simulations were carried out using computer simulation technology (CST) software [18].
# Distinct Signatures of Host Defense Suppression by Plant-Feeding Mites
###### Abstract  
Tomato plants are attacked by diverse herbivorous arthropods, including by cell-content-feeding mites, such as the extreme generalist _Tetranychus urticae_ and specialists like _Tetranychus evansi_ and _Aculops__lyocpersici_. Mite feeding induces plant defense responses that reduce mite performance. However, _T. evansi_ and _A. lycopersici_ suppress plant defenses via poorly understood mechanisms and, consequently, maintain a high performance on tomato. On a shared host, _T. urticae_ can be facilitated by either of the specialist mites, likely due to the suppression of plant defenses. To better understand defense suppression and indirect plant-mediated interactions between herbivorus mites, we used gene-expression microarrays to analyze the transcriptomic changes in tomato after attack by either a single mite species (_T. urticae_, _T. evansi_, _A. lycopersici_) or two species simultaneously (_T. urticae_ plus _T. evansi_ or _T. urticae_ plus _A. lycopersici_). Additionally, we assessed mite-induced changes in defense-associated phytohormones using LC-MS/MS. Compared to non-infested controls, jasmonates (JAs) and salicate (SA) accumulated to higher amounts upon all mite-infestation treatments, but the response was attenuated after single infestations with defense-suppressors. Strikingly, whereas 8 to 10% of tomato genes were differentially expressed upon single infestations with _T. urticae_ or _A. lycopersici_, respectively, only 0.1% was altered in _T. evansi_-infested plants. Transcriptome analysis of dual-infested leaves revealed that _A. lycopersici_ primarily suppressed _T. urticae_-induced JA defenses, while _T. evansi_ dampened _T. urticae_-triggered host responses on a transcriptome-wide scale. The latter suggests that _T. evansi_ not solely down-regulates plant gene expression, but rather directs it back towards housekeeping levels. Our results provide valuable new insights into the mechanisms underlying host defense suppression and the plant-mediated facilitation of competing herbivores.  
Keywords:comparative transcriptomics; defense suppression; dual infestation; facilitation; herbivore; plant defense; plant-mediated interactions; tomato red spider mite (_Tetranychus evansi_); tomato russet mite (_Aculops__lyocpersici_); two-spotted spider mite (_Tetranychus urticae_) +
Footnote †:: journal: J. Mol. Sci. 2018, 19, 3265; doi:10.3390/ijms19103265
# Self-Organizing Control of Mega Constellations for Continuous Earth Observation
## 2 Materials and Methods
### Continuous Coverage Constraint of Mega Constellation
#### 2.1.2 Configuration Requirements for Mega Constellations  
The mega constellation, containing a total of \(N\) satellites, is composed of \(S\) satellites evenly spaced on each of \(P\) orbital plane. The geocentric angle between adjacent coplanar satellites \(\theta\) is \(2\pi/S\). According to Equation (2), the width of SOC of one orbital plane \(\sigma\) is  
\[\sigma=\arccos\biggl{(}\frac{\cos 2\kappa_{\max}}{\cos(2\pi/S)}\biggr{)} \tag{3}\]  
where  
\[S\geq\pi/\kappa_{\max} \tag{4}\]  
To ensure the SOC of every two adjacent co-rotating orbital planes overlaps with each other there exists  
\[\sigma\geq 2\pi/P \tag{5}\]  
From Equations (3) and (5), it can be implied that  
\[\cos\biggl{(}\frac{2\pi}{P}\biggr{)}\cos\biggl{(}\frac{2\pi}{S}\biggr{)}\geq \cos(2\kappa_{\max}) \tag{6}\]  
Therefore, the configuration requirements for mega constellations that satisfy the proposed continuous coverage constraint are given in Equations (4) and (6).  
Several ongoing and planned mega constellation missions are listed in Table 1. Let the minimum elevation angle \(E_{\min}\) takes 20\({}^{\circ}\). The coverage range \(\kappa_{\max}\) can be calculated by Equation (1) together with the orbital altitude. Most of these mega constellations are proven to satisfy the configuration requirements for continuous coverage, i.e., Equations (4) and (6).
# Green Approaches to Carbon Nanostructure-Based Biomaterials
## 2 Green Routes to Prepare Carbon Nanomaterials for Biomedical Applications
### Fullerenes
It is worth noting that fullerenes need to be derivatized to be water-soluble in appreciable concentrations for biomedical applications; therefore, opportunities for the green synthesis of fullerenes may lie in the preparation of those derivatives. For instance, hydrophilic polydopamine and glutathione were used to solubilize fullerenes by simple mixing in water, followed by dialysis and freeze-drying, to then study their antioxidant activity [89]. Sonochemical treatment in water of a mixture of fullerene and gallium oxide yielded nanostructured hybrids with potential applications in the field of sensing [90]. While these methods simply focus on derivatization without addressing the synthesis of the fullerene core structure, a recent development consisted of the catalytic conversion of plastic waste into a magnetic fullerene-based composite, thanks to the key role played by ferrocene, which acted both as a catalyst and magnetic nanoparticle precursor [51].
# Fault Detection in Industrial Equipment through Analysis of Time Series Stationarity
## 3 Materials and Methods
### Experimental Setup  
In the experimental part, the Python programming language was used in a multiplatform open-source integrated development environment (IDE) for scientific programming called Spyder. Additionally, in the development of this project, the following libraries were used: _pandas, numpy, matplotlib, pickle, statsmodels_, and _seaborn_. The specifications of the devices used in this study are an Intel(R) Core(TM) i5-10300H CPU @ 2.50 GHz 2.50 GHz, with approximately 8.00 GB of RAM.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors** & **Model** & **MAPE** & **MAE** \\ \hline [PERSON] et al. [19] & GRU & \textless{}10.00\% & - \\ [PERSON] et al. [20] & GRU & 2.79\% & - \\ [PERSON] et al. [25] & GRU & 1.2\% & - \\ [PERSON] and [PERSON] [15] & LSTM & 5.16\%, 4.16\% & - \\ [PERSON] et al. [16] & LSTM & - & 0.0184 (L\&H), 0.0008 (H) \\ Wisdom and Yar [17] & LSTM & 0.8–6.2\% & - \\ [PERSON] et al. [12] & ARMA & 2.48\% & - \\ [PERSON] et al. [21] & ARIMA & 2.76\% & - \\ [PERSON] et al. [13] & ARIMA, ANN & 2.61\%, 1.80\% & - \\ [PERSON] et al. [21] & ARIMA & - & 0.0962 \\ \hline \hline \end{tabular}
\begin{tabular}{c} GRU—Gated Recurrent Unit, LSTM—Long Short-Term Memory, ARMA—Autoregressive Moving Average, ARIMA—Autoregressive Integrated Moving Average, ANN—Artificial Neural Network. \\ \end{tabular}
\end{table}
Table 1: Comparison of different methods used for time series predictions.
## 4 Results and Discussion
### Calculation of the Normalized Edge Displacement Using Dynamic and Static Analysis
#### 4.4.2 Investigating Torsional Radius and Radius of Gyration on Torsional Response
The spectrum parameter \(A_{t}\) is a predictor for the accidental torsion response. Through examination of the accidental torsion response data that were presented graphically in Figures 5c, 6c, 7c, 8c, 9c, 10c, 11c, 12c and 13c, the following \(A_{t}\) response spectrum values are proposed:  
1. The accidental torsion response factor, \(A_{t}\) will have a constant value for frequency ratios ranging from \(\Omega=0\) to \(\Omega=1\). The value of \(A_{t}\) is equal to the maximum response of the buildings for a specific \(b/r\) ratio. This a conservative simplification is shown in Figure 17, where the drop in response at \(\Omega=1\) is avoided. 2. The maximum response parameter, \(A_{t}\), will then will then decrease linearly to zero as the frequency ration \(\Omega\) increase up to \(\Omega=\Omega_{c}=1.8\). As shown in Figures 11c and 13c, the normalized accidental torsion response at a frequency ratio of 1.8 is very small, as such buildings are torsionally stiff. This frequency ratio as characteristic of torsionally stiff buildings was also reported in the literature [6]. The two points above are summarized in Equation (11). \[\hat{u}_{b/2}= \begin{cases}A_{t}&\text{where }0\leq\Omega\leq 1\\ \left[(A_{t}-1)\times\left(\frac{\Omega_{c}-\Omega}{\Omega_{c}-1}\right)\right] +1&\text{where }1<\Omega<\Omega_{c}\\ 0&\text{where }\Omega>\Omega_{c}\end{cases}  \tag{11}\]Through statistical analysis of the normalized accidental torsion response data presented in Figures 5-13, Equation (12) is developed to correlate the response parameter \(A_{t}\) as a function of the floor plan ratio \(b\)/\(r\). Equation (12) is represented graphically by Figure 18.  
\[A_{t}=0.8263\mathrm{e}^{0.1873(b/r)}, \tag{12}\]
# Macro-Micro-Coupled Simulations of Dilute Viscoelastic Fluids
## 2 Governing Equations  
### Macroscopic Flow Equations  
At the macroscale, considering an unsteady, incompressible viscoelastic flow and in the absence of external body forces, the flow is governed by the conservation of mass and the conservation of momentum equations  
\[\mathbf{\ddot{\
abla}}\cdot\mathbf{\dot{u}} = 0, \tag{1}\] \[\rho\bigg{(}\frac{\partial\mathbf{\ddot{u}}}{\partial\mathbf{\dot{t} }}+\mathbf{\dot{u}}\cdot\mathbf{\boldsymbol{\
abla}}\mathbf{\ddot{u}}\bigg{)} = -\mathbf{\boldsymbol{\
abla}}\tilde{p}+\eta_{s}\mathbf{\boldsymbol {\
abla}}^{2}\mathbf{\ddot{u}}+\mathbf{\boldsymbol{\
abla}}\cdot\mathbf{ \boldsymbol{\tau}}, \tag{2}\]  
where \(\mathbf{\ddot{u}}\) is the velocity vector, \(\tilde{p}\) is the pressure, \(\mathbf{\boldsymbol{\tau}}\) is the extra stress tensor, \(\eta_{s}\) is the solvent viscosity, and \(\rho\) is the density.  
We scale the conservation equations as follows:  
\[t=\mathbf{\dot{t}}\cdot\bigg{(}\frac{U}{L}\bigg{)}, \tag{3}\]  
Here, \(L\) is a characteristic macroscopic length, \(U\) is a characteristic velocity, \(\eta_{p}\) is the zero shear rate polymeric viscosity, and \(\lambda\) is the longest relaxation time. With this scaling, the conservation equations become  
\[\mathbf{\boldsymbol{\
abla}}\cdot\mathbf{u} = 0, \tag{4a}\] \[\frac{\partial\mathbf{u}}{\partial t}+\mathbf{u}\cdot\mathbf{ \boldsymbol{\
abla}}\mathbf{u} = -\mathbf{\boldsymbol{\
abla}}p+\frac{1}{Re}\bigg{[}\beta\mathbf{ \boldsymbol{\
abla}}^{2}\mathbf{u}+\frac{1-\beta}{De}\mathbf{\boldsymbol{ \
abla}}\cdot\mathbf{\boldsymbol{\tau}}\bigg{]}. \tag{4b}\]  
Several nondimensional groups arise from this scaling:  
* The Reynolds number, \(Re=\frac{\rho UL}{\eta}\), which is the ratio of inertial to viscous forces.
* The ratio of the solvent to total zero-shear viscosity: \(\beta=\frac{\eta_{s}}{\eta_{s}+\eta_{p}}\).
* The Deborah number, \(De=\lambda\,\frac{U}{L}\), which is the ratio of the fluid relaxation time to a characteristic time for the macroscopic flow. Note that in the case of viscometric flow, in which \(U/L=\dot{\gamma}_{0}\), this is also referred to as the Weissenberg number.