In laser-drive ICF, hybrid drive (HD) combined direct drive (DD) and indirect drive (ID) offers a smoothed HD pressure $P_{HD}$, far higher than the ablation pressure in ID and DD, to suppress hydrodynamic instabilities. In this letter, simulations o
f a new robust HD ignition target show that maximal HD pressure as high as $P_{HD} sim$ 650 Mbar driven by a novel bulldozer effect is achieved, resulting in nonstagnation hotspot ignition at the convergence ratio $C_r sim $23, and finally, fusion energy gain $sim$ 10 in total laser energy = 1.42 MJ. Two-dimensional simulations have confirmed that hydrodynamic instabilities are suppressed. A well-fitted scale of maximal HD pressure $P_{HD}$ (Mbar)= $BE_{DD}^{1/4} T_r$ is found from simulations of different targets and laser energies as long as $T_r> 160$ eV, where B is the constant depending on ablator materials, $E_{DD}$ in kJ is DD laser energy and $T_r$ in 100 eV is radiation temperature depending on ID laser energy $E_{ID}$. $P_{HD}geq$ 450 Mbar is requested for hotspot ignition. This scale from bulldozer effect is also available as $E_{DD}$ is reduced to kJ. Experiments have verified $P_{HD}$ about 3.5 times radiation ablation pressure for CH ablator using $E_{ID}=43$ kJ ($T_r simeq$200 eV) and $E_{DD}$=3.6 kJ, also shown that both backscattering fraction and hot-electron energy fraction for DD laser intensity $sim 1.8 times 10^{15} {rm wcdot cm^{-2}}$ are about a third of the traditional DD laser-plasma interaction
Keyphrases are capable of providing semantic metadata characterizing documents and producing an overview of the content of a document. Since keyphrase extraction is able to facilitate the management, categorization, and retrieval of information, it h
as received much attention in recent years. There are three approaches to address keyphrase extraction: (i) traditional two-step ranking method, (ii) sequence labeling and (iii) generation using neural networks. Two-step ranking approach is based on feature engineering, which is labor intensive and domain dependent. Sequence labeling is not able to tackle overlapping phrases. Generation methods (i.e., Sequence-to-sequence neural network models) overcome those shortcomings, so they have been widely studied and gain state-of-the-art performance. However, generation methods can not utilize context information effectively. In this paper, we propose a novelty Span Keyphrase Extraction model that extracts span-based feature representation of keyphrase directly from all the content tokens. In this way, our model obtains representation for each keyphrase and further learns to capture the interaction between keyphrases in one document to get better ranking results. In addition, with the help of tokens, our model is able to extract overlapped keyphrases. Experimental results on the benchmark datasets show that our proposed model outperforms the existing methods by a large margin.
This paper studies the performance of cache-enabled dense small cell networks consisting of multi-antenna sub-6 GHz and millimeter-wave base stations. Different from the existing works which only consider a single antenna at each base station, the op
timal content placement is unknown when the base stations have multiple antennas. We first derive the successful content delivery probability by accounting for the key channel features at sub-6 GHz and mmWave frequencies. The maximization of the successful content delivery probability is a challenging problem. To tackle it, we first propose a constrained cross-entropy algorithm which achieves the near-optimal solution with moderate complexity. We then develop another simple yet effective heuristic probabilistic content placement scheme, termed two-stair algorithm, which strikes a balance between caching the most popular contents and achieving content diversity. Numerical results demonstrate the superior performance of the constrained cross-entropy method and that the two-stair algorithm yields significantly better performance than only caching the most popular contents. The comparisons between the sub-6 GHz and mmWave systems reveal an interesting tradeoff between caching capacity and density for the mmWave system to achieve similar performance as the sub-6 GHz system.
Wireless networks with directional antennas, like millimeter wave (mmWave) networks, have enhanced security. For a large-scale mmWave ad hoc network in which eavesdroppers are randomly located, however, eavesdroppers can still intercept the confident
ial messages, since they may reside in the signal beam. This paper explores the potential of physical layer security in mmWave ad hoc networks. Specifically, we characterize the impact of mmWave channel characteristics, random blockages, and antenna gains on the secrecy performance. For the special case of uniform linear array (ULA), a tractable approach is proposed to evaluate the average achievable secrecy rate. We also characterize the impact of artificial noise in such networks. Our results reveal that in the low transmit powerregime, the use of low mmWave frequency achieves better secrecy performance, and when increasing transmit power, a transition from low mmWave frequency to high mmWave frequency is demanded for obtaining a higher secrecy rate. More antennas at the transmitting nodes are needed to decrease the antenna gain obtained by the eavesdroppers when using ULA. Eavesdroppers can intercept more information by using a wide beam pattern. Furthermore, the use of artificial noise may be ineffective for enhancing the secrecy rate.
This paper explores the potential of wireless power transfer (WPT) in massive multiple input multiple output (MIMO) aided heterogeneous networks (HetNets), where massive MIMO is applied in the macrocells, and users aim to harvest as much energy as po
ssible and reduce the uplink path loss for enhancing their information transfer. By addressing the impact of massive MIMO on the user association, we compare and analyze two user association schemes. We adopt the linear maximal ratio transmission beam-forming for massive MIMO power transfer to recharge users. By deriving new statistical properties, we obtain the exact and asymptotic expressions for the average harvested energy. Then we derive the average uplink achievable rate under the harvested energy constraint.
We investigate beamforming and artificial noise generation at the secondary transmitters to establish secure transmission in large scale spectrum sharing networks,where multiple non-colluding eavesdroppers attempt to intercept the secondary transmiss
ion. We develop a comprehensive analytical framework to accurately assess the secrecy performance under the primary users quality of service constraint. Our aim is to characterize the impact of beamforming and artificial noise generation on this complex large scale network. We first derive exact expressions for the average secrecy rate and the secrecy outage probability.We then derive an easy-to-evaluate asymptotic average secrecy rate and asymptotic secrecy outage probability when the number of antennas at the secondary transmitter goes to infinity. Our results show that the equal power allocation between the useful signal and artificial noise is not always the best strategy to achieve maximum average secrecy rate in large scale spectrum sharing networks. Another interesting observation is that the advantage of beamforming and artificial noise generation over beamforming on the average secrecy rate is lost when the aggregate interference from the primary and secondary transmitters is strong, such that it overtakes the effect of the generated artificial noise.
This paper develops a tractable framework for exploiting the potential benefits of physical layer security in three-tier wireless sensor networks using stochastic geometry. In such networks, the sensing data from the remote sensors are collected by s
inks with the help of access points, and the external eavesdroppers intercept the data transmissions.We focus on the secure transmission in two scenarios: i) the active sensors transmit their sensing data to the access points, and ii) the active access points forward the data to the sinks. We derive new compact expressions for the average secrecy rate in these two scenarios. We also derive a new compact expression for the overall average secrecy rate. Numerical results corroborate our analysis and show that multiple antennas at the access points can enhance the security of three-tier wireless sensor networks. Our results show that increasing the number of access points decreases the average secrecy rate between the access point and its associated sink. However, we find that increasing the number of access points first increases the overall average secrecy rate, with a critical value beyond which the overall average secrecy rate then decreases. When increasing the number of active sensors, both the average secrecy rate between the sensor and its associated access point and the overall average secrecy rate decrease. In contrast, increasing the number of sinks improves both the average secrecy rate between the access point and its associated sink, as well as the overall average secrecy rate.
This paper exploits the potential of physical layer security in massive multiple-input multiple-output (MIMO) aided two-tier heterogeneous networks (HetNets). We focus on the downlink secure transmission in the presence of multiple eavesdroppers. We
first address the impact of massive MIMO on the maximum receive power based user association. We then derive the tractable upper bound expressions for the secrecy outage probability of a HetNets user.We show that the implementation of massive MIMO significantly improves the secrecy performance, which indicates that physical layer security could be a promising solution for safeguarding massive MIMO HetNets. Furthermore, we show that the secrecy outage probability of HetNets user first degrades and then improves with increasing the density of PBSs.
Hexagonal layered crystalline materials, such as graphene, boron nitride, tungsten sulfate, and so on, have attracted enormous attentions, due to their unique combination of atomistic structures and superior thermal, mechanical, and physical properti
es. Making use of mechanical buckling is a promising route to control their structural morphology and thus tune their physical properties, giving rise to many novel applications. In this paper, we employ finite element analysis (FEA), molecular dynamic (MD) simulations and continuum modeling to study the mechanical buckling of a column made of layered crystalline materials with the crystal layers parallel to the longitudinal axis. It is found that the mechanical buckling exhibits a gradual transition from a bending mode to a shear mode of instability with the reduction of slenderness ratio. As the slenderness ratio approaches to zero, the critical buckling strain {epsilon}cr converges to a finite value that is much smaller than the materials mechanical strength, indicating that it is realizable under appropriate experimental conditions. Such a mechanical buckling mode is anomalous and counter-intuitive. The critical buckling strain {epsilon}cr predicted by our continuum mechanics model agrees very well with the results from the FEA and MD simulations for a group of typical hexagonal layered crystalline materials. MD simulations on graphite indicate the continuum mechanics model is applicable down to a scale of 20 nm. This theoretical model also reveals that a high degree of elastic anisotropy is the origin for the anomalous mechanical buckling of a column made of layered crystalline materials in the absence of structural slenderness. This study provides avenues for engineering layered crystalline materials in various nano-materials and nano-devices via mechanical buckling.
