Specific emitter identification for satellite communication using probabilistic neural networks

Electromagnetic signal emitted by satellite communication (satcom) transmitters are used to identify specific individual uplink satcom terminals sharing the common transponder in real environment, which is known as specific emitter identification (SEI) that allows for early indications and warning (I&W) of the targets carrying satcom furnishment and furthermore the real time electromagnetic situation awareness in military operations. In this paper, the authors are the first to propose the identification of specific transmitters of satcom by using probabilistic neural networks (PNN) to reach the goal of target recognition. We have been devoted to the examination by exploring the feasibility of utilizing the Hilbert transform to signal preprocessing, applying the discrete wavelet transform to feature extraction, and employing the PNN to perform the classification of stationary signals. There are a total of 1000 sampling time series with binary phase shift keying (BPSK) modulation originated by five types of satcom transmitters in the test. The established PNNs classifier implements the data testing and finally yields satisfactory accuracy at 8 dB(±1 dB) carrier to noise ratio, which indicates the feasibility of our method, and even the keen insight of its application in military.     

Spreading and Curing Behaviors of a Thermosetting Droplet-Silicone on a Heated Surface

Thermosetting materials are widely used as encapsulation in the electrical packaging to protect the core electronic components from external force, moisture, dust, and other factors. However, the spreading and curing behaviors of such kind of fluid on a heated surface have been rarely explored. In this study, we experimentally and numerically investigated the spreading and curing behaviors of the silicone(OE6550 A/B, which is widely used in the light-emitting diode packaging) droplet with diameter of ～2.2 mm on a heated surface with temperature ranging from 25 ℃ to 250 ℃. For the experiments, we established a setup with high-speed camera and heating unit to capture the fast spreading process of the silicone droplet on the heated surface. For the numerical simulation, we built a viscosity model of the silicone by using the Kiuna's model and combined the viscosity model with the Volume of Fluid(VOF) model by the User Defined Function(UDF) method. The results show that the surface temperature significantly affected the spreading behaviors of the silicone droplet since it determines the temperature and viscosity distribution inside the droplet. For surface temperature varied from 25 ℃ to 250 ℃, the final contact radius changed from ～2.95 mm to ～1.78 mm and the total spreading time changed from ～511 s to ～0.15 s. By further analyzing the viscosity evolution of the droplet, we found that the decreasing of the total spreading time was caused by the decrease of the viscosity under high surface temperature at initial spreading stage, while the reduction of the final contact radius was caused by the curing of the precursor film. This study supplies a strategy to tuning the spreading and curing behavior of silicone by imposing high surface temperature, which is of great importance to the electronic packaging.     

Evaluation of bismuth doped La2-xBixNiO4+δ (x = 0, 0.02 and 0.04) as cathode materials for solid oxide fuel cells

Bismuth doped La_2-xBi_xNiO_4+δ (x = 0, 0.02 and 0.04) oxides are investigated as SOFC cathodes. The effects of Bi doping on the phase structure, thermal expansion, electrical conduction behavior as well as electrochemical performance are studied. All the samples exist as a tetragonal Ruddlesden-Popper structure. Bi-doped LBNO-0.02 and LBNO-0.04 have good chemical and thermal compatibility with LSGM electrolyte. The average TEC over 20–900°С was 13.4 × 10^?6 and 14.2 × 10^?6 K^?1 for LBNO-0.02 and LBNO-0.04, respectively. The electrical conductivity was decreasing with the rise of Bi doping content. EIS measurement indicates Bi doping can decrease the ASR values. At 750 °C, the obtained ASR for LBNO-0.04 is 0.18 Ωcm², which is 56% lower than that of the sample without Bi doping, suggesting Bi doping is beneficial to the electrochemical catalytic activity of LBNO cathodes.     

基于区块链技术的公益时间银行系统

在现有的时间银行系统中，时间币的发行功能和结算功能完全集中到一个中心节点上。这种极度中心化的功能结构，不仅存在容易发生中心节点单点失效、数据容易被篡改等信息安全问题，还存在着时间币的发行和流通缺乏透明度以及时间币的结算依赖中心化的结算机构等问题。针对这些问题，提出了一种基于区块链技术的解决方法。首先，将时间币的发行功能和结算功能从中心节点上分离出来；然后，利用具有分布式去中心化、集体维护和不可篡改等特性的区块链技术，将分离出来的发行功能逐步去中心化，将分离出来的结算功能去中心化，形成公益时间链（PWTB）；最终，PWTB利用区块链技术以去中心化的方式将时间银行系统由单个节点维护账本变成由集体维护一个分布式的共享账本，使时间币的发行和流通公开透明，时间币的结算不依赖某个中心化的节点。安全分析表明所设计的PWTB能够实现安全的信息传输与存储，以及数据的共享。     

PIXE study on recovery of making-technology of Chinese Longquan celadon made in the Southern Song Dynasty (1127–1279 CE)

Proton induced X-ray emission (PIXE) technique and factor analysis were used to study the recovery of making-technology of Chinese Longquan celadon made in the late Southern Song Dynasty (1127–1279 CE). Based on factor analysis, for the raw material for body and the recipe for glaze, there is a close but slightly different relationship between these Longquan celadons unearthed from the core area of Dayao and the non-core area, despite all the Longquan celadons belong to the high-calcium-glaze system. The chemical compositions of most of the tentative body and glaze samples are very close to those of Longquan celadons unearthed from the core area of Dayao and the non-core area. This study can provide a guidance for recovery of making-technology of Longquan celandon.     

生姜主要病虫害及其防治技术分析

生姜的根状茎为使用部分,其中包含姜油酮、姜油醇、姜油酚、蛋白质、粗脂肪、糖,并且钙、铁的含量也较为丰富,是人们日常生活当中不可或缺的调味品,还能够加工成为姜片、糖姜片和盐姜片等诸多食品,其在医药方面的应用也较为广泛,备受人们的推崇与喜爱。我国在生姜栽培方面的历史较为悠久,技术也较为成熟,每年生姜出口贸易都能为我国带来非常可观的经济效益。     

Using electroencephalogram to continuously discriminate feelings of personal thermal comfort between uncomfortably hot and comfortable environments

Thermal comfort is an important factor for the design of buildings. Although it has been well recognized that many physiological parameters are linked to the state of thermal comfort or discomfort of humans, how to use physiological signal to judge the state of thermal comfort has not been well studied. In this paper, the feasibility of continuously determining feelings of personal thermal comfort was discussed by using electroencephalogram (EEG) signals in private space. In the study, 22 subjects were exposed to thermally comfortable and uncomfortably hot environments, and their EEG signals were recorded. Spectral power features of the EEG signals were extracted, and an ensemble learning method using linear discriminant analysis or support vector machine as a sub-classifier was used to build the discriminant model. The results show that an average discriminate accuracy of 87.9% can be obtained within a detection window of 60 seconds. This study indicates that it is feasible to distinguish whether a person feels comfortable or too hot in their private space by multi-channel EEG signals without interruption and suggests possibility for further applications in neuroergonomics.