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This paper surveys innovative protocols that enhance the programming functionality of the Bitcoin blockchain, a key part of the "Bitcoin Ecosystem." Bitcoin utilizes the Unspent Transaction Output (UTXO) model and a stack-based script language for efficient peer-to-peer payments, but it faces limitations in programming capability and throughput. The 2021 Taproot upgrade introduced the Schnorr signature algorithm and P2TR transaction type, significantly improving Bitcoin's privacy and programming capabilities. This upgrade has led to the development of protocols like Ordinals, Atomicals, and BitVM, which enhance Bitcoin's programming functionality and enrich its ecosystem. We explore the technical aspects of the Taproot upgrade and examine Bitcoin Layer 1 protocols that leverage Taproot's features to program non-fungible tokens (NFTs) into transactions, including Ordinals and Atomicals, along with the fungible token standards BRC-20 and ARC-20. Additionally, we categorize certain Bitcoin ecosystem protocols as Layer 2 solutions similar to Ethereum's, analyzing their impact on Bitcoin's performance. By analyzing data from the Bitcoin blockchain, we gather metrics on block capacity, miner fees, and the growth of Taproot transactions. Our findings confirm the positive effects of these protocols on Bitcoin's mainnet, bridging gaps in the literature regarding Bitcoin's programming capabilities and ecosystem protocols and providing valuable insights for practitioners and researchers.

Digital identity plays a vital role in enabling secure access to resources and services in the digital world. Traditional identity authentication methods, such as password-based and biometric authentications, have limitations in terms of security, privacy, and scalability. Decentralized authentication approaches leveraging blockchain technology have emerged as a promising solution. However, existing decentralized authentication methods often rely on indirect identity verification (e.g. using passwords or digital signatures as authentication credentials) and face challenges such as Sybil attacks. In this paper, we propose BioZero, an efficient and privacy-preserving decentralized biometric authentication protocol that can be implemented on open blockchain. BioZero leverages Pedersen commitment and homomorphic computation to protect user biometric privacy while enabling efficient verification. We enhance the protocol with non-interactive homomorphic computation and employ zero-knowledge proofs for secure on-chain verification. The unique aspect of BioZero is that it is fully decentralized and can be executed by blockchain smart contracts in a very efficient way. We analyze the security of BioZero and validate its performance through a prototype implementation. The results demonstrate the effectiveness, efficiency, and security of BioZero in decentralized authentication scenarios. Our work contributes to the advancement of decentralized identity authentication using biometrics., Comment: 14 pages, 3 figures

The widespread adoption of Network Address Translation (NAT) technology has led to a significant number of network end nodes being located in private networks behind NAT devices, impeding direct communication between these nodes. To solve this problem, a technique known as "hole punching" has been devised for NAT traversal to facilitate peer-to-peer communication among end nodes located in distinct private networks. However, as the increasing demands for speed and security in networks, TCP-based hole punching schemes gradually show performance drawbacks. Therefore, we present a QUIC-based hole punching scheme for NAT traversal. Through a comparative analysis of the hole punching time between QUIC-based and TCP based protocols, we find that the QUIC-based scheme effectively reduces the hole punching time, exhibiting a pronounced advantage in weak network environments. Furthermore, in scenarios where the hole punched connection is disrupted due to factors such as network transitions or NAT timeouts, this paper evaluates two schemes for restoring the connection: QUIC connection migration and re-punching. Our results show that QUIC connection migration for connection restoration saves 2 RTTs compared to QUIC re-punching, and 3 RTTs compared to TCP re-punching, effectively reducing the computational resources consumption for re-punching., Comment: The paper has been accepted for oral presentation at the VTC2024-Fall Conference

With increasing density and heterogeneity in unlicensed wireless networks, traditional MAC protocols, such as carrier-sense multiple access with collision avoidance (CSMA/CA) in Wi-Fi networks, are experiencing performance degradation. This is manifested in increased collisions and extended backoff times, leading to diminished spectrum efficiency and protocol coordination. Addressing these issues, this paper proposes a deep-learning-based MAC paradigm, dubbed DL-MAC, which leverages spectrum sensing data readily available from energy detection modules in wireless devices to achieve the MAC functionalities of channel access, rate adaptation and channel switch. First, we utilize DL-MAC to realize a joint design of channel access and rate adaptation. Subsequently, we integrate the capability of channel switch into DL-MAC, enhancing its functionality from single-channel to multi-channel operation. Specifically, the DL-MAC protocol incorporates a deep neural network (DNN) for channel selection and a recurrent neural network (RNN) for the joint design of channel access and rate adaptation. We conducted real-world data collection within the 2.4 GHz frequency band to validate the effectiveness of DL-MAC, and our experiments reveal that DL-MAC exhibits superior performance over traditional algorithms in both single and multi-channel environments and also outperforms single-function approaches in terms of overall performance. Additionally, the performance of DL-MAC remains robust, unaffected by channel switch overhead within the evaluated range.

The widespread use of the Internet has posed challenges to existing centralized physical infrastructure networks. Issues such as data privacy risks, service disruptions, and substantial expansion costs have emerged. To address these challenges, an innovative network architecture called Decentralized Physical Infrastructure Network (DePIN) has emerged. DePIN leverages blockchain technology to decentralize the control and management of physical devices, addressing limitations of traditional infrastructure network. This article provides a comprehensive exploration of DePIN, presenting its five-layer architecture, key design principles. Furthermore, it presents a detailed survey of the extant applications, operating mechanisms, and provides an in-depth analysis of market data pertaining to DePIN. Finally, it discusses a wide range of the open challenges faced by DePIN.

In this study, we explore an approach aimed at enhancing the transmission or reflection coefficients of absorbing materials through the utilization of joint measurements of entangled photon states. On the one hand, through the implementation of photon catalysis in the reflected channel, we can effectively modify the state of the transmission channel, leading to a notable improvement in the transmission ratio. Similarly, this approach holds potential for significantly amplifying the reflection ratio of absorbing materials, which is useful for detecting cooperative targets. On the other hand, employing statistical counting methods based on the technique of heralding on zero photons, we evaluate the influence of our reflection enhancement protocol for detecting noncooperative targets, which is validated through Monte Carlo simulations of a quantum radar setup affected by Gaussian white noise. Our results demonstrate a remarkable enhancement in the signal-to-noise ratio of imaging, albeit with an increase in mean-square error. These findings highlight the potential practical applications of our approach in the implementation of quantum radar., Comment: 11 pages, 10 figures

In Ethereum, the ledger exchanges messages along an underlying Peer-to-Peer (P2P) network to reach consistency. Understanding the underlying network topology of Ethereum is crucial for network optimization, security and scalability. However, the accurate discovery of Ethereum network topology is non-trivial due to its deliberately designed security mechanism. Consequently, existing measuring schemes cannot accurately infer the Ethereum network topology with a low cost. To address this challenge, we propose the Distributed Ethereum Network Analyzer (DEthna) tool, which can accurately and efficiently measure the Ethereum network topology. In DEthna, a novel parallel measurement model is proposed that can generate marked transactions to infer link connections based on the transaction replacement and propagation mechanism in Ethereum. Moreover, a workload offloading scheme is designed so that DEthna can be deployed on multiple distributed probing nodes so as to measure a large-scale Ethereum network at a low cost. We run DEthna on Goerli (the most popular Ethereum test network) to evaluate its capability in discovering network topology. The experimental results demonstrate that DEthna significantly outperforms the state-of-the-art baselines. Based on DEthna, we further analyze characteristics of the Ethereum network revealing that there exist more than 50% low-degree Ethereum nodes that weaken the network robustness., Comment: Accepted for publication in IEEE INFOCOM 2024

While dust significantly affects the environmental perception of automated agricultural machines, the existing deep learning-based methods for dust removal require further research and improvement in this area to improve the performance and reliability of automated agricultural machines in agriculture. We propose an end-to-end trainable learning network (DedustNet) to solve the real-world agricultural dust removal task. To our knowledge, DedustNet is the first time Swin Transformer-based units have been used in wavelet networks for agricultural image dusting. Specifically, we present the frequency-dominated block (DWTFormer block and IDWTFormer block) by adding a spatial features aggregation scheme (SFAS) to the Swin Transformer and combining it with the wavelet transform, the DWTFormer block and IDWTFormer block, alleviating the limitation of the global receptive field of Swin Transformer when dealing with complex dusty backgrounds. Furthermore, We propose a cross-level information fusion module to fuse different levels of features and effectively capture global and long-range feature relationships. In addition, we present a dilated convolution module to capture contextual information guided by wavelet transform at multiple scales, which combines the advantages of wavelet transform and dilated convolution. Our algorithm leverages deep learning techniques to effectively remove dust from images while preserving the original structural and textural features. Compared to existing state-of-the-art methods, DedustNet achieves superior performance and more reliable results in agricultural image dedusting, providing strong support for the application of agricultural machinery in dusty environments. Additionally, the impressive performance on real-world hazy datasets and application tests highlights DedustNet superior generalization ability and computer vision-related application performance., Comment: arXiv admin note: substantial text overlap with arXiv:2401.04550

Although deep convolutional neural networks have achieved remarkable success in removing synthetic fog, it is essential to be able to process images taken in complex foggy conditions, such as dense or non-homogeneous fog, in the real world. However, the haze distribution in the real world is complex, and downsampling can lead to color distortion or loss of detail in the output results as the resolution of a feature map or image resolution decreases. In addition to the challenges of obtaining sufficient training data, overfitting can also arise in deep learning techniques for foggy image processing, which can limit the generalization abilities of the model, posing challenges for its practical applications in real-world scenarios. Considering these issues, this paper proposes a Transformer-based wavelet network (WaveletFormerNet) for real-world foggy image recovery. We embed the discrete wavelet transform into the Vision Transformer by proposing the WaveletFormer and IWaveletFormer blocks, aiming to alleviate texture detail loss and color distortion in the image due to downsampling. We introduce parallel convolution in the Transformer block, which allows for the capture of multi-frequency information in a lightweight mechanism. Additionally, we have implemented a feature aggregation module (FAM) to maintain image resolution and enhance the feature extraction capacity of our model, further contributing to its impressive performance in real-world foggy image recovery tasks. Extensive experiments demonstrate that our WaveletFormerNet performs better than state-of-the-art methods, as shown through quantitative and qualitative evaluations of minor model complexity. Additionally, our satisfactory results on real-world dust removal and application tests showcase the superior generalization ability and improved performance of WaveletFormerNet in computer vision-related applications.

A decentralized identity system that can provide users with self-sovereign digital identities to facilitate complete control over their own data is paramount to Web 3.0. The accounting system on blockchain is an ideal archetype for realizing Web 3.0 decentralized identity: users can create their accounts without registering with a central agent. Such an identity system is endowed with anonymity property: nobody knows the account's owner because the relationship between an account and the owner is invisible. Thus, user privacy is well protected even though the account's data is public. However, a disadvantage of such complete anonymity is that users can create multiple accounts without authentication to obfuscate their activities on the blockchain. In particular, the current anonymous blockchain account system cannot accurately register the social relationships and interactions between real human users, given the amorphous mappings between users and blockchain identities. Mistrust can be a major hurdle to the large-scale deployment of Web 3.0. This work proposes ZKBID, a zero-knowledge blockchain-account-based Web 3.0 decentralized identity scheme, to overcome endemic mistrust in blockchain account systems. ZKBID links souls (blockchain accounts) to humans (users) in a one-to-one manner to truly reflect the societal relationships and interactions between humans on the blockchain. With ZKBID, the users are accountable for their accounts anonymously, preserving privacy. ZKBID authenticates users using face match and then maps authenticated users to accounts. Zero-knowledge proofs encode the face match results, and user-account mappings employ linkable ring signatures to preserve anonymity. We implemented ZKBID and built a blockchain test network for evaluation purposes. Our tests demonstrate the effectiveness of ZKBID and suggest proper ways to configure ZKBID system parameters., Comment: 16 pages, 13 figures, 8 tables

Compact block, which replaces transactions in the block with their hashes, is an effective means to speed up block propagation in the Bitcoin network. The compact block mechanism in Bitcoin counts on the fact that many nodes may already have the transactions (or most of the transactions) in the block, therefore sending the complete block containing the full transactions is unnecessary. This fact, however, does not hold in the Ethereum network. Adopting compact block directly in Ethereum may degrade the block propagation speed significantly because the probability of a node not having a transaction in the sending block is relatively high in Ethereum and requesting the missing transactions after receiving the compact block takes much additional time. This paper proposes hybrid-compact block (HCB), an efficient compact block propagation scheme for Ethereum and other similar blockchains. First, we develop a Secondary Pool to store the low-fee transactions, which are removed from the primary transaction pool, to conserve storage space. As simple auxiliary storage, the Secondary Pool does not affect the normal block processing of the primary pool in Ethereum. Second, we design a machine learning-based transaction prediction module to precisely predict the missing transactions caused by network latency and selfish behaviors. We implemented our HCB scheme and other compact-block-like schemes (as benchmarks) and deployed a number of worldwide nodes over the Ethereum MainNet to experimentally investigate them. Experimental results show that HCB performs best among the existing compact-block-like schemes and can reduce propagation time by more than half with respect to the current block propagation scheme in Ethereum.

The advancements of remote sensing (RS) pose increasingly high demands on computation and transmission resources. Conventional ground-offloading techniques, which transmit large amounts of raw data to the ground, suffer from poor satellite-to-ground link quality. In addition, existing satellite-offloading techniques, which offload computational tasks to low earth orbit (LEO) satellites located within the visible range of RS satellites for processing, cannot leverage the full computing capability of the network because the computational resources of visible LEO satellites are limited. This situation is even worse in hotspot areas. In this paper, for efficient offloading via LEO satellite networks, we propose a novel computing-aware routing scheme. It fuses the transmission and computation processes and optimizes the overall delay of both. Specifically, we first model the LEO satellite network as a snapshot-free dynamic network, whose nodes and edges both have time-varying weights. By utilizing time-varying network parameters to characterize the network dynamics, the proposed method establishes a continuous-time model which scales well on large networks and improves the accuracy. Next, we propose a computing-aware routing scheme following the model. It processes tasks during the routing process instead of offloading raw data to ground stations, reducing the overall delay and avoiding network congestion consequently. Finally, we formulate the computing-aware routing problem in the dynamic network as a combination of multiple dynamic single source shortest path (DSSSP) problems and propose a genetic algorithm (GA) based method to approximate the results in a reasonable time. Simulation results show that the computing-aware routing scheme decreases the overall delay by up to 78.31% compared with offloading raw data to the ground to process.

Advances in space exploration have led to an explosion of tasks. Conventionally, these tasks are offloaded to ground servers for enhanced computing capability, or to adjacent low-earth-orbit satellites for reduced transmission delay. However, the overall delay is determined by both computation and transmission costs. The existing offloading schemes, while being highly-optimized for either costs, can be abysmal for the overall performance. The computation-transmission cost dilemma is yet to be solved. In this paper, we propose an adaptive offloading scheme to reduce the overall delay. The core idea is to jointly model and optimize the transmission-computation process over the entire network. Specifically, to represent the computation state migrations, we generalize graph nodes with multiple states. In this way, the joint optimization problem is transformed into a shortest path problem over the state graph. We further provide an extended Dijkstra's algorithm for efficient path finding. Simulation results show that the proposed scheme outperforms the ground and one-hop offloading schemes by up to 37.56% and 39.35% respectively on SpaceCube v2.0.

Computing tasks are ubiquitous in space missions. Conventionally, these tasks are offloaded to ground servers for computation, where the transmission of raw data on satellite-to-ground links severely constrains the performance. To overcome this limitation, recent works offload tasks to visible low-earth-orbit (LEO) satellites. However, this offloading scheme is difficult to achieve good performance in actual networks with uneven loads because visible satellites over hotspots tend to be overloaded. Therefore, it is urgent to extend the offloading targets to the entire network. To address the network-wide offloading problem, we propose a metagraph-based computation and transmission fusion offloading scheme for multi-tier networks. Specifically, virtual edges, between the original network and its duplicate, are generated to fuse computation and transmission in single-tier networks. Then, a metagraph is employed to integrate the fusion in multi-tier networks. After assigning appropriate edge weights to the metagraph, the network-wide offloading problem can be solved by searching the shortest path. In addition, we apply the proposed scheme to solve the spatial computation offloading problem in a real multi-tier network. The superiority of the proposed scheme over two benchmark schemes are proved theoretically and empirically. Simulation results show that the proposed scheme decreases the weighted average delay by up to 87.51% and 18.70% compared with the ground offloading scheme and the visible offloading scheme, respectively.

In this manuscript, we present an energy-efficient alternating optimization framework based on the multi-antenna ambient backscatter communication (AmBSC) assisted cooperative non-orthogonal multiple access (NOMA) for next-generation (NG) internet-of-things (IoT) enabled communication networks. Specifically, the energy-efficiency maximization is achieved for the considered AmBSC-enabled multi-cluster cooperative IoT NOMA system by optimizing the active-beamforming vector and power-allocation coefficients (PAC) of IoT NOMA users at the transmitter, as well as passive-beamforming vector at the multi-antenna assisted backscatter node. Usually, increasing the number of IoT NOMA users in each cluster results in inter-cluster interference (ICI) (among different clusters) and intra-cluster interference (among IoT NOMA users). To combat the impact of ICI, we exploit a zero-forcing (ZF) based active-beamforming, as well as an efficient clustering technique at the source node. Further, the effect of intra-cluster interference is mitigated by exploiting an efficient power-allocation policy that determines the PAC of IoT NOMA users under the quality-of-service (QoS), cooperation, SIC decoding, and power-budget constraints. Moreover, the considered non-convex passive-beamforming problem is transformed into a standard semi-definite programming (SDP) problem by exploiting the successive-convex approximation (SCA) approximation, as well as the difference of convex (DC) programming, where Rank-1 solution of passive-beamforming is obtained based on the penalty-based method. Furthermore, the numerical analysis of simulation results demonstrates that the proposed energy-efficiency maximization algorithm exhibits an efficient performance by achieving convergence within only a few iterations.

In this manuscript, we propose an alternating optimization framework to maximize the energy efficiency of a backscatter-enabled cooperative Non-orthogonal multiple access (NOMA) system by optimizing the transmit power of the source, power allocation coefficients (PAC), and power of the relay node under imperfect successive interference cancellation (SIC) decoding. A three-stage low-complexity energy-efficient alternating optimization algorithm is introduced which optimizes the transmit power, PAC, and relay power by considering the quality of service (QoS), power budget, and cooperation constraints. Subsequently, a joint channel coding framework is introduced to enhance the performance of far user which has no direct communication link with the base station (BS) and has bad channel conditions. In the destination node, the far user data is jointly decoded using a Sum-product algorithm (SPA) based joint iterative decoder realized by jointly-designed Quasi-cyclic Low-density parity-check (QC-LDPC) codes. Simulation results evince that the proposed backscatter-enabled cooperative NOMA system outperforms its counterpart by providing an efficient performance in terms of energy efficiency. Also, proposed jointly-designed QC-LDPC codes provide an excellent bit-error-rate (BER) performance by jointly decoding the far user data for considered BSC cooperative NOMA system with only a few decoding iterations., Comment: 30, 7

Two-dimensional (2D) materials van der Waals heterostructures (vdWHs) provides a revolutionary route towards high-performance solar energy conversion devices beyond the conventional silicon-based pn junction solar cells. Despite tremendous research progress accomplished in recent years, the searches of vdWHs with exceptional excitonic solar cell conversion efficiency and optical properties remain an open theoretical and experimental quest. Here we show that the vdWH family composed of MoSi$_2$N$_4$ and WSi$_2$N$_4$ monolayers provides a compelling material platform for developing high-performance ultrathin excitonic solar cells and photonics devices. Using first-principle calculations, we construct and classify 51 types of MoSi$_2$N$_4$ and WSi$_2$N$_4$-based [(Mo,W)Si$_2$N$_4$] vdWHs composed of various metallic, semimetallic, semiconducting, insulating and topological 2D materials. Intriguingly, MoSi$_2$N$_4$/(InSe, WSe$_2$) are identified as Type-II vdWHs with exceptional excitonic solar cell power conversion efficiency reaching well over 20%, which are competitive to state-of-art silicon solar cells. The (Mo,W)Si$_2$N$_4$ vdWH family exhibits strong optical absorption in both the visible and ultraviolet regimes. Exceedingly large peak ultraviolet absorptions over 40%, approaching the maximum absorption limit of a free-standing 2D material, can be achieved in (Mo,W)Si$_2$N$_4$/$\alpha_2$-(Mo,W)Ge$_2$P$_4$ vdWHs. Our findings unravel the enormous potential of (Mo,W)Si$_2$N$_4$ vdWHs in designing ultimately compact excitonic solar cell device technology., Comment: 30 pages, 7 figures

Despite numerous prior attempts to boost transaction per second (TPS) of blockchain systems, many sacrifice decentralization and security. This paper proposes a bodyless block propagation (BBP) scheme for which the blockbody is not validated and transmitted during block propagation, to increase TPS without compromising security. Nodes in the blockchain network anticipate the transactions and their ordering in the next upcoming block so that these transactions can be pre-executed and pre-validated before the block is born. For a network with $N$ nodes, our theoretical analysis reveals that BBP can improve TPS scalability from $O(1/log(N))$ to $O(1)$. Ensuring consensus on the next block's transaction content is crucial. We propose a transaction selection, ordering, and synchronization algorithm to drive this consensus. To address the undetermined Coinbase address issue, we further present an algorithm for such unresolvable transactions, ensuring a consistent and TPS-efficient scheme. With BBP, most transactions require neither validation nor transmission during block propagation, liberating system from transaction-block dependencies and rendering TPS scalable. Both theoretical analysis and experiments underscore BBP's potential for full TPS scalability. Experimental results reveal a 4x reduction in block propagation time compared to Ethereum blockchain, with TPS performance being limited by node hardware rather than block propagation.

Two-dimensional (2D) semiconductors offer a promising prospect for high-performance and energy-efficient devices especially in the sub-10 nm regime. Inspired by the successful fabrication of 2D $\beta$-TeO$_2$ and the high on/off ratio and high air-stability of fabricated field effect transistors (FETs) [Nat. Electron. 2021, 4, 277], we provide a comprehensive investigation of the electronic structure of monolayer $\beta$-TeO$_2$ and the device performance of sub-10 nm metal oxide semiconductors FETs (MOSFETs) based on this material. The anisotropic electronic structure of monolayer $\beta$-TeO$_2$ plays a critical role in the anisotropy of transport properties for MOSFETs. We show that the 5.2-nm gate-length n-type MOSFET holds an ultra-high on-state current exceeding 3700 {\mu}A/{\mu}m according to International Roadmap for Devices and Systems (IRDS) 2020 goals for high-performance devices, which is benefited by the highly anisotropic electron effective mass. Moreover, monolayer $\beta$-TeO$_2$ MOSFETs can fulfill the IRDS 2020 goals for both high-performance and low-power devices in terms of on-state current, sub-threshold swing, delay time, and power-delay product. This study unveils monolayer $\beta$-TeO$_2$ as a promising candidate for ultra-scaled devices in future nanoelectronics., Comment: 21 pages, 6 figures

Occlusion poses a major challenge for person re-identification (ReID). Existing approaches typically rely on outside tools to infer visible body parts, which may be suboptimal in terms of both computational efficiency and ReID accuracy. In particular, they may fail when facing complex occlusions, such as those between pedestrians. Accordingly, in this paper, we propose a novel method named Quality-aware Part Models (QPM) for occlusion-robust ReID. First, we propose to jointly learn part features and predict part quality scores. As no quality annotation is available, we introduce a strategy that automatically assigns low scores to occluded body parts, thereby weakening the impact of occluded body parts on ReID results. Second, based on the predicted part quality scores, we propose a novel identity-aware spatial attention (ISA) module. In this module, a coarse identity-aware feature is utilized to highlight pixels of the target pedestrian, so as to handle the occlusion between pedestrians. Third, we design an adaptive and efficient approach for generating global features from common non-occluded regions with respect to each image pair. This design is crucial, but is often ignored by existing methods. QPM has three key advantages: 1) it does not rely on any outside tools in either the training or inference stages; 2) it handles occlusions caused by both objects and other pedestrians;3) it is highly computationally efficient. Experimental results on four popular databases for occluded ReID demonstrate that QPM consistently outperforms state-of-the-art methods by significant margins. The code of QPM will be released.

As a disruptive technology that originates from cryptocurrency, blockchain provides a trusted platform to facilitate industrial IoT (IIoT) applications. However, implementing a blockchain platform in IIoT scenarios confronts various security challenges due to the rigorous deployment condition. To this end, we present a novel design of secure blockchain based on trusted computing hardware for IIoT applications. Specifically, we employ the trusted execution environment (TEE) module and a customized security chip to safeguard the blockchain against different attacking vectors. Furthermore, we implement the proposed secure IIoT blockchain on the ARM-based embedded device and build a small-scale IIoT network to evaluate its performance. Our experimental results show that the secure blockchain platform achieves a high throughput (150TPS) with low transaction confirmation delay (below 66ms), demonstrating its feasibility in practical IIoT scenarios. Finally, we outline the open challenges and future research directions.

We studied the linear electro-optic effect of chiral topological semimetal RhSi which is characterized by high-fold chiral fermions separated in energy space. We identify that the general second order conductivity $\sigma^{(2)}_{xyz}(\omega=\omega_{1}+\omega_{2};\omega_{1},\omega_{2})$ includes a real symmetric component and an imaginary antisymmetric component, which are from the inter-band shift and intra-band injection current with frequency $\omega$, respectively. The $\sigma^{(2)}_{xyz}$ is significantly enhanced by the high electron velocity and nontrivial band topology of chiral fermion, and modifies the phase velocity of light wave. We also predict that the electro-optic coefficient $\chi_{xyz}^{(2)}(\omega;\omega,0)$ of chiral crystal RhSi is about 7000 pm/V at photon energy 0.01 eV and 1.1 eV, which is about 200 times that of widely used LiNbO$_{3}$ crystal. The giant electro-optic coefficient renders a relatively low half-wave voltage in order of hundreds volt, and demonstrates potential application as electro-optic crystals for the wavelength in the second telecom window of optical fiber communications., Comment: 6 pages, 3 figure

Properties of iron oxides at the extreme conditions are of essential importance in condensed matter physics and Geophysics. The recent discovery of a new type of iron oxide, Fe4O5, at high pressure and high temperature of Earth's deep interior attracts great interests. In this paper, we report the electronic structure and the magnetic properties of Fe4O5 predicted by the density functional theory plus dynamic mean field theory (DFT+DMFT) approach. We find that Fe4O5 stays metallic from ambient pressure to high pressure. The magnetic moments of iron atoms at the three different crystallographic positions of Fe4O5 undergo position-dependent collapse as being compressed. Such site-selective magnetic moment collapse originates from the shift of energy levels and the consequent charge transfer among the Fe-3d orbits under compression., Comment: Main Text: 5 pages, 6 figures; Supplementary Information: 3 pages, 4 figures; The article has been accepted by Physics Letters A