Countermeasure against blinding attack for single-photon detectors in quantum key distribution

Quantum key distribution(QKD),rooted in quantum mechanics,offers information-theoretic security.However,practi-cal systems open security threats due to imperfections,notably bright-light blinding attacks targeting single-photon detectors.Here,we propose a concise,robust defense strategy for protecting single-photon detectors in QKD systems against blinding attacks.Our strategy uses a dual approach:detecting the bias current of the avalanche photodiode(APD)to defend against con-tinuous-wave blinding attacks,and monitoring the avalanche amplitude to protect against pulsed blinding attacks.By integrat-ing these two branches,the proposed solution effectively identifies and mitigates a wide range of bright light injection attempts,significantly enhancing the resilience of QKD systems against various bright-light blinding attacks.This method forti-fies the safeguards of quantum communications and offers a crucial contribution to the field of quantum information security.

Effects of Different Storage Methods and Storage Time on the Quality of Codonopsis pilosula

[Objectives]To study the effects of different storage methods and storage time on the quality of Codonopsis pilosula.[Methods]Experiment was performed by setting three different storage methods and times.[Results]When stored for 12 months,the extract content in the natural storage,conventional sealing,and controlled atmosphere storage was slightly lower than 77.32%before storage,and decreased by 2.82%,3.62%,and 1.71%,respectively.When stored for 18 months,the extract content was 74.14%,73.21%,and 72.08%,decreased by 4.11%,5.31%,and 6.78%,respectively compared with that before storage,all of which met the specified content in Chinese Pharmacopoeia.[Conclusions]The three storage times had significant effects on moisture and extract content of C.pilosula,but had no significant effects on atractylenolide III.With the extension of storage time,the moisture,extract,and content declined,so the storage time theoretically should not exceed 12 months.

Unlocking efficient energy storage via regulating ion and electron-active subunits:an(SbS)_(1.15)TiS_(2)superlattice for large and fast Na^(+)storage

Alloying-type metal sulfides with high sodiation activity and theoretical capacity are promising anode materials for high energy density sodium ion batteries.However,the large volume change and the migratory and aggregation behavior of metal atoms will cause severe capacity decay during the charge/discharge process.Herein,a robust and conductive TiS_(2)framework is integrated with a high-capacity SbS layer to construct a single phase(SbS)_(1.15)TiS_(2)superlattice for both high-capacity and fast Na^(+)storage.The metallic TiS_(2)sublayer with high electron activity acts as a robust and conductive skeleton to buffer the volume expansion caused by conversion and alloying reaction between Na+and SbS sublayer.Hence,high capacity and high rate capability can be synergistically realized in a single phase(SbS)_(1.15)TiS_(2)superlattice.The novel(SbS)_(1.15)TiS_(2)anode has a high charge capacity of 618 mAh g^(-1)at 0.2 C and superior rate performance and cycling stability(205 mAh g^(-1)at 35 C after 2,000 cycles).Furthermore,in situ and ex situ characterizations are applied to get an insight into the multi-step reaction mechanism.The integrity of robust Na-Ti-S skeleton during(dis)charge process can be confirmed.This superlattice construction idea to integrate the Na^(+)-active unit and electron-active unit would provide a new avenue for exploring high-performance anode materials for advanced sodium-ion batteries.

SRS-Net: Training object detectors from scratch for remote sensing images without pretraining

Most of the current object detection algorithms use pretrained models that are trained on ImageNet and then fine-tuned in the network,which can achieve good performance in terms of general object detectors.However,in the field of remote sensing image object detection,as pretrained models are significantly different from remote sensing data,it is meaningful to explore a train-fromscratch technique for remote sensing images.This paper proposes an object detection framework trained from scratch,SRS-Net,and describes the design of a densely connected backbone network to provide integrated hidden layer supervision for the convolution module.Then,two necessary improvement principles are proposed:studying the role of normalization in the network structure,and improving data augmentation methods for remote sensing images.To evaluate the proposed framework,we performed many ablation experiments on the DIOR,DOTA,and AS datasets.The results show that whether using the improved backbone network,the normalization method or training data enhancement strategy,the performance of the object detection network trained from scratch increased.These principles compensate for the lack of pretrained models.Furthermore,we found that SRS-Net could achieve similar to or slightly better performance than baseline methods,and surpassed most advanced general detectors.

Learning power Gaussian modeling loss for dense rotated object detection in remote sensing images

Object detection in Remote Sensing(RS)has achieved tremendous advances in recent years,but it remains challenging for rotated object detection due to cluttered backgrounds,dense object arrangements and the wide range of size variations among objects.To tackle this problem,Dense Context Feature Pyramid Network(DCFPN)and a powerα-Gaussian loss are designed for rotated object detection in this paper.The proposed DCFPN can extract multi-scale information densely and accurately by leveraging a dense multi-path dilation layer to cover all sizes of objects in remote sensing scenarios.For more accurate detection while avoiding bottlenecks such as boundary discontinuity in rotated bounding box regression,a-Gaussian loss,a unified power generalization of existing Gaussian modeling losses is proposed.Furthermore,the properties ofα-Gaussian loss are analyzed comprehensively for a wider range of applications.Experimental results on four datasets(UCAS-AOD,HRSC2016,DIOR-R,and DOTA)show the effectiveness of the proposed method using different detectors,and are superior to the existing methods in both feature extraction and bounding box regression。

Observation of room‐temperature out‐of‐plane switchable electric polarization in supported 3R‐MoS_(2) monolayers

Two‐dimensional(2D)ferroelectrics have attracted considerable attention due to their potential in the development of devices of miniaturization and multifunction.Although several van der Waals(vdW)‐layered materials show ferroelectricity,the experimental demonstrations of ferroelectric behavior in monolayers are very limited.Here we report the observation of room‐temperature out‐of‐plane switchable electric polarization in supported MoS_(2) monolayers exfoliated from 3R‐stacked bulk crystals under ambient conditions.Using in situ piezoelectric force microscopy and Kelvin probe force microscopy in a glovebox,we reveal that trapped water/ice molecules are responsible for this switchable electric polarization and this conclusion is strongly supported by theoretical simulations.It is worth noting that the water/ice trapping in the monolayers exfoliated from 2H‐stacked MoS_(2) crystals is not as much as that in 3R monolayers and,consequently,the out‐of‐plane electric polarization is missing there.Our findings indicate that monolayers with a trapped single layer of polar molecules might be emerging alternatives to 2D ferroelectrics.Furthermore,the stacking sequences may bring new properties and applications to 2D vdW materials not only when we stack them up but also when we thin them down.

Anti-adhesive Property of Maize Leaf Surface Related with Temperature and Humidity

The anti-adhesive surfaces have always aroused great interest of worldwide scientists and engineers. But in practical ap- plications, it often faces the threat and impact of temperature and humidity. In this work, the excellent anti-adhesive perform- ance of maize leaf under high temperature and humidity were investigated in detail. Firstly, the adhesion forces of the maize leaf surface under different temperature and humidity were measured by using Atomic Force Microscopy （AFM）. The temperature of the substrate was varied between 23 ~C to 100 ~C, and the ambient relative humidity is from 18% to 100%. It was found that the adhesion force of maize leaf decreased with the increase of temperature and humidity. The mechanism of its excellent anti-adhesive performance of maize leaf under high temperature and relative humidity was revealed. The transverse and lon- gitudinal ridges on maize leaf surface interlace with each other, forming small air pockets, which reduces the actual contact area between the object and the maize leaf. With the increase of humidity, the liquid film will be formed in the air pockets gradually and so much water vapor is produced with increase of tempera^tre. Then the air flow rate increases though the wavy top of transverse ridges, inducing the dramatic decrease of adhesion force. Inspired by this mechanism, four samples with this bionic structure were made. This functional ＂biomimetic structure＂ would have potential value in the wide medical equipments such as high frequency electric knife with anti-adhesion surface under high temperature and high humidity.

Micro/nano-scale Characterization and Fatigue Fracture Resistance of Mechanoreceptor with Crack-shaped Slit Arrays in Scorpion

The nocturnal scorpion Heterometrus petersii uses Basitarsal Compound Slit Sensilla (BCSS) as mechanoreceptor to detect mechanical signal (e.g. substrate vibration, cyclic loads caused by walking) without fatigue failure such as initiation of fatigue crack and further propagation of crack-shaped slit. The outstanding perceptive function has been discovered for over half a century. However, it is not yet clear about the microstructure, material composition and micromechanical property which are all important factors that determine the fatigue fracture resistance of the BCSS. Here, the microscopic characteristics of the BCSS were thoroughly studied. The results dicate that anti-fatigue resilin and stiff chitinous cuticle form multilayered composite as the main body of the BCSS. Meanwhile, the pre-existing slit as mechanosensory structure is covered by cuticular membrane which has different mechanical property with the epicuticle. Theoretical analysis shows that the structure-composition-property synergistic relations of composites confer on the BCSS with extreme fatigue fracture tolerance.