Miracle-dz/UniFAR-FTUs huggingface dataset:
Study on Digital Demodulation Technology of All-Optical Cesium Optically Pumped Magnetometer

With the development of science and technology, the digital drive signal generated by the CNC oscillator is turned into an analogue signal to drive the magnetic sensor through a digital to analogue converter, and the output signal from the CNC oscillator to a digital phase amplifier. This process allows the digital demodulation technique to be deeply optimised. Caesium light pump magnetometer is a high precision, high sensitivity weak magnetic field measurement instrument based on the Saiman effect of Caesium atoms in a magnetic field, then combined with light pumping action and magnetic resonance technology. Currently, the digitalisation of the signal detection system of the domestic cesium light pump magnetometer is low and there is a considerable gap with foreign products. This paper discusses the application of the model in the digital demodulation of the all-optical cesium light pump magnetometer based on the all-optical cesium light cluster demodulation technology. The output of the CNC oscillator, the amplitude-frequency response of the low-pass filter circuit and the frequency accuracy of the driving signal in the digital signal generator are tested experimentally, and the waveforms of the collected signals in the digital lock-in amplifier and the accuracy of the phase measurement and amplitude measurement are tested for the readers' reference.
SymSDN: A DRDoS Attack Prevention Approach

Distributed Denial of Service (DDoS) attacks are hostile attempts toward the normal functioning of a system. The attacker exploits vulnerabilities present in various systems to convert them into botnets or bots. These bots generate massive amount of internet traffic towards the victim, choking its bandwidth, and disrupting its connectivity to the internet. Distributed Reflection Denial of Service (DRDoS) attack, a type of DDoS attack, exploit vulnerable servers to generate huge attack traffic towards the target by source IP spoofing, making them even worse. In this paper, we present a mechanism to defend against DRDoS attacks. The mechanism is based on symmetric routing that forces response packets to take the same path as that of request packets. This causes the attack to divert back to the attacker, saving the client from it and also hampering the attacker’s ability to launch further attacks. The proposed methodology, SymSDN, also optimizes flow entries to deal with the overflow of routing tables by DDoS attacks. The percentage of packet loss and the throughput calculation in the result section prove the authenticity of our proposed approach.
A Novel Sequential Monte Carlo Method Based Synthetic Aperture Reconstruction Approach for Real-Time 3D Wireless Local Positioning

Abstract In this paper, we introduce a precise real-time 3D indoor localization concept using a combination of the MISAS (multilateral inverse synthetic aperture secondary radar) approach and SMCM (sequential Monte Carlo method). The synthetic aperture approach is a promising locating technique with effective multipath suppression. However, its practical use is restricted by the huge computational load of the reconstruction algorithms. We use a weighted-particle set – which we reduce subsequently by resampling – to represent the PDF (probability density function) instead of the highly complex, standard, grid-based approach. Simulation runs and experiments, carried out with a 5.8 GHz FMCW secondary radar system with a bandwidth of only 140 MHz, show that a robot TCP (tool center point) can be localized in 3D in the mm-range using a few hundred particles and operating in a dense multipath environment. This novel extension is a promising solution for real-time, 3D MISAS localization applications.
A 13.5-to-28.8GHz 72.3%-Locking Range Multi-Phase Injection-Locked Frequency Tripler with Improved Output Power and Wideband Subharmonic-Spur Rejection in 28nm CMOS

Injection-locked frequency tripler (ILFT) exhibits the potential to improve the PN and power efficiency of mm-wave LO generation, tripling down the operating frequency of the PLL and VCO. Yet, the conventional ILFT only can secure a superior PN performance over a narrow locking range. Recent efforts have been made to pursue locking-range extension for the ILFT to cover a wider spectrum, especially at the mm-wave. For example, adopting a low- Q resonant tank widens the locking range, but at the cost of higher power consumption and lower output power [1]. An alternative is to use injection-current boosting (ICB) technique plus a $6^{\text {th}}$-order resonator [2], but it penalizes the output signal power. Finally, the subharmonic spurs caused by the harmonic-rich injection signal remain severe [1] [2], and the subharmonic-spur rejection provided by the cascade power-hungry buffer is limited and highly frequency dependent.
A Novel GPU Acceleration Algorithm Based on CUDA and MPI for Ray Tracing Wireless Channel Modeling

With the improvement of hardware performance, graphics processing unit (GPU) has been applied to the acceleration of ray tracing (RT) wireless channel modeling. In this paper, a new RT acceleration algorithm based on message passing interface (MPI) and GPU called MPI fused with GPU tree algorithm (MGTree) is proposed. Moreover, the indoor conference room is modeled, and the forward algorithm shooting and bouncing ray (SBR) is used for RT. Considering the conventional reflection and diffraction, the three-dimensional (3D) RT acceleration algorithm based on the unified computing device architecture (CUDA) is used to optimize the conventional algorithm, which combines GPU with MPI to form multi-core distributed computing. Then field information is extracted at the receiving side and the wireless channel simulations are conducted. Through experiments, the new algorithm can effectively improve the upper limit of the acceleration ratio, support more rays, and alleviate the pressure of a single GPU kernel.
Link2speed: VANET speed assessment via link-state analysis

Vehicular ad hoc network (VANET) is an emerging technology with a promising future and great challenges. It aims to promote safe driving, improve traffic flow and also enables a variety of entertainment applications. A fundamental need in such a network is the ability to assess vehicular speed. This enables the collection of statistics for the purpose of traffic engineering and long-term planning, and is also critical information for law enforcement groups. Many existing speed assessment technologies suffer from high physical visibility, and relatively expensive hardware. Even those that avoid detection, are inflexible due to being location specific. Therefore, reducing the ability to track and enforce traffic speed and limiting the collection of statistics for traffic engineering. In this paper, we propose a method for vehicle speed assessment, by extracting an induced Communication Connectivity Graph (CCG) from VANET optimized link state routing (OLSR) protocol, and composing an optimization problem for assessing the speed boundaries, using graph hop distance based constraints. We performed evaluation experiments in different traffic scenarios using traffic simulation tool. Our method can provide a cost-effective, easy to implement and hard to uncover solution for vehicles speed assessment on highways.
Converged Service-based Architecture for Next-Generation Mobile Communication Networks

Software Defined Network (SDN) and Network Function Virtualization (NFV) technologies have driven mobile communication networks to evolve toward a Service-Based Architecture (SBA) with great flexibility to meet the highly-dynamic requirements of vertical applications. The SBA has been applied into 5th Generation (5G) core network and is evolving toward service-based end-to-end networks including virtualized Radio Access Networks (vRANs). This paper proposes a converged SBA (cSBA) to decouple access, control, and data planes. Specifically, the access plane with Distributed Units (DUs) functionalities is responsible for accessing User Equipments (UEs); the control plane consists of newly-defined converged services by integrating functionalities of Central Unit Control Planes (CU-CPs) and 5G core network for signaling processing; the data plane comprises Central Unit User Planes (CP-UPs) and User Plane Functions (UPFs), which are responsible for service data transmission. Optional converged services definition, service-based interface implementation, and cognitive service framework are proposed to enable an efficient cSBA. Finally, we evaluate the performance of the cSBA against the monolithic architecture.
Inter-HAP Based Geometrical 3-D Channel Model Operating at 28 to 60 GHz for Future 6G Non-Terrestrial Networks

This paper presents a geometrical 3-D channel model for stationary inter-high altitude platform (HAP) systems operating at millimeter wave (mmWave) frequency bands for the future 6G non-terrestrial networks (NTNs). The motivation of this study is to satisfy such a scenario where a high data rate between two HAPs is desired. In order to do this, the possible reasons for path loss, namely the free space and atmospheric path losses that are severely observed because of the short wavelength of the mmWave signals, are characterized according to 3GPP standards and ITU recommendations. Unlike most of the air-to-air unmanned aerial vehicle (UAV) channel modeling studies where a 2-D channel model is adopted, this study adopts a 3-D approach to characterize the wireless channel between two HAPs to fill the gap in the literature and reflect the actual characteristics of the air-to-air UAV networks. Moreover, this paper introduces the usage of the multiple input multiple output (MIMO) beam-forming technology in the UAV networks to compensate for the path loss effects in mmWave bands. Numerical results show the validation of the proposed 3-D channel model in point-to-point HAP networks at frequencies varying from 28 to 60 GHz.
AI-enabled CSI fingerprinting for indoor localisation towards context-aware networking in 6G

The spatial distribution of cellular networks has made them very promising to use for localization. By knowing the location of a user, cellular networks can provide context-aware services customized to that user. Objects and the dynamic nature of indoor locations result in lots of multipath and non-line-of-sight (NLOS) propagations. In this work, we carry out a novel experimental investigation to improve indoor localization using a grid approach with channel state information (CSI) fingerprinting and artificial intelligence (AI)/ machine learning (ML) methods for determining the location of a mobile device. Experiments are conducted in a standard indoor setting. This paper compares a method for indoor positioning based on received signal strength identifier (RSSI), phase, and CSI using ML to show how the accuracy of indoor localization can be improved. Compared to heuristic approaches like DOA estimation, the precision of ML is superior.
Design of Electric Spindle Control System for High-Grade CNC Machine Tools

Advanced CNC machine tool is one of the high-end equipment to promote the innovation and development of manufacturing industry, and electric spindle is the core component of CNC machine tool. Control system is the brain of CNC machine tools, responsible for receiving user instructions, sensor signals, feedback signals, after data processing and logical operation, send control instructions to the executive parts, so the design of the control system play a crucial role. This paper first constructs the hardware platform of two central control units, and designs the hardware circuit of DSP system, then constructs the three-layer software structure, and designs the flow chart of the main program, distributes the interrupt resources, and finally designs the flow chart of interrupt and fault protection subroutine. The experimental test shows that the maximum temperature rise of the enclosure of the electrical spindle prototype is 15.6 °C when the no-load 20000 rpm is stable, and the maximum radial runout is 1.5 um during the static test. The control system designed in this paper can meet the high-performance control requirements of electric spindle, and lay a good foundation for the later research of high precision control and holding technology.
Statistical CSI Acquisition in Multi-frequency Communication Systems

Multi-frequency communication is a potential strategy to overcome the limitations caused by frequency scarcity. This paper investigates the acquisition of statistical channel state information (CSI) in multi-frequency systems. We first analyze the multi-frequency channel model and reveal the relationship between spatial covariance matrices of different frequency bands. Based on the relationship, we propose a linear autoregressive (AR) method, directly establishing the mapping relationship of covariance elements between different frequency bands. In addition, with the acquired spatial covariance, we estimate APS with the maximum entropy (ME) criterion and use it to benefit downlink transmission. Simulation results verify the accuracy of the AR spatial covariance extrapolation method and show that the ME method can estimate APS with high resolution. Meanwhile, the results validate that the estimated statistical CSI can aid the realization of multi-frequency cooperative robust transmission.
Adaptive Federated Learning for Battery-powered IIoT Devices with Non-IID Data

In this paper, we propose a multi-dimensional resource management scheme for Federated Learning with non-independent and identically distributed (non-IID) data on battery-powered IIoT devices. Firstly, we formulate an optimization problem that aims to maximize the learning efficiency given long-term energy and time constraints to balance training accuracy and learning latency. Secondly, based on the derived lower bound of expected convergence rate with non-IID data, we solve the short-term problems by cyclically manage the resources (i.e., radio, computation and resource block (RB) resources, and device energy). Simulation results validate that the proposed scheme outperforms other baseline schemes, especially in energy shortage scenarios.
Delay-Oriented Knowledge-Driven Resource Allocation in SAGIN-Based Vehicular Networks

Space-air-ground integrated networks (SAGIN) have been envisioned as the promising and key network architecture for the 6G vehicular networks to provide seamless coverage for the connected vehicles. To access the most appropriate network quickly, this paper proposed a knowledge-driven network access approach, where the communication knowledge is explicitly integrated into neural networks, to deal with multiple tasks in SAGIN-based vehicular networks. Specifically, the formulated long-term network access problem is handled by asynchronous advantage actor-critic algorithm (A3C) in reinforcement learning. During this process, the space-time correlation knowledge is introduced to effectively reduce the action space in channel selection and the reward shaping exploiting the problem-specific communication and mathematical knowledge is adopted to solve the sparse reward problem in reinforcement learning. In addition, by modifying the sub-net learning rate of the A3C algorithm with experimental experience, this paper speeds up the network convergence speed by 1.5%. Numerical results also show that integrating knowledge into traditional deep reinforcement learning algorithm can improve the reward by 4%.
Optimized High-efficiency Multi-band RF Energy Harvester

Traditional energy harvesters only work in a single frequency band and cannot make full use of the RF energy in the environment. This paper proposes an optimized multi-band RF energy harvesting system, which can efficiently work in different frequency bands. The structure of this harvester consists of a wideband receiving antenna linking multiple narrowband rectifier branches. Each branch is composed of a bandpass filter, an impedance matching network, and a multi-stage Dickson charge pump circuit. The output power of each branch is summarized by a diode summation network. On the basis of the existing multi-band RF energy harvester, we optimize the impedance matching network, the rectifier, and the diode summation network, which further improves the system performance. The results show that when the input power is 3 dBm, the power conversion efficiency of this system under the simulation conditions at 1 GHz reaches the maximum value of 78.3%. In addition, we also verify the possibility of this multi-band RF energy harvester to provide energy for backscatter communication in the 2.4 GHz ISM band. The simulation results show that the system can reach a power conversion efficiency of more than 50% in the three selected bands.
Low-temperature PEM fuel cell voltage prediction linked to operating conditions for a mission profile

Fuel cell performances are directly linked to the operating conditions, which can fluctuate over time. The aim of this work is to introduce a fuel cell voltage prediction method based on the measured operating conditions while cycling a mission profile. The method is built and tested on analyzing the experimental results obtained during a test campaign while cycling a mission profile for 2030h. Four different configurations are illustrated and compared in this article, considering three different sets of parameters and two formulas.
Growth and Trends of Pulse Production in India

This paper examines the growth and trend of pulse production in India using time series data on area, production, yield and trade pertaining to the period 1980–81 to 2008–09. The period has been divided into two parts viz., before (1980–81 to 1994–95) and after (1995–96 to 2008–09). Linear, semi-log growth function and compound growth function were tried to examine the growth and trend. The compound growth was, however, found to have best fitting with the data. The positive percent change in area, production and yield of pigeonpea, chickpea and total pulse have been observed during both before and after the launch of TMOP except incase of lentil there was negative percent change in area of chickpea and yield of pigeonpea also showed negative percent change before the launch of TMOP. The pigeonpea showed positive growth in area and production to the time of 0.50 and 0.20 percent annually while its yield has shown downtrend with0.30 percent annually. Inchickpea, the positive of 0.20, 0.50 and 0.30 percent annually in area, production and yield during post period of TMOP have been found. The area and production of lentil have grown up by 1.80 and 2.50 percent annually, respectively during the entire period of study, while its yield observed negative growth rate 0.20 percent during post period of TMOP. For total pulse production and yield have risen by 0.50 and 0.70 percent annually, respectively, during the entire period of study while its area has been down by 0.20 percent per annum. The import and export of pulses have also grown up substantially per annum to the tune of 8.90 land 23.40 percent annually during the entire period. Effective increase in area and yield has mattered in general in fetching differential production of pulses during post period. Area of pulses has been most stable with few exceptions as compared to production and yield of pulse corps indicating thereby technological interaction in the pulse production. On other hand, instability in export and import has, however, been quite high during the period of study.
FRAVaR: A Fast Failure Recovery Framework for Inter-DC Network

Along with the development of 5G and IoT technologies in recent years, Inter Data Center (Inter-DC) network is facing an explosive growth of geographically distributed user data, which needs to be duplicated among DCs in a real-time manner. Transmission-based applications require high availability that is going beyond 99.99%. However, with the expansion of Inter-DC network scale, link failures are also growing, which seriously affects data transmission efficiency, so fast link failure recovery is then urgently needed. Many previous works have been done to achieve fast failure recovery, but most of them ignore two key points, 1) the cost of deploying recovery strategies, and 2) the side-effect of re-transmission to network availability. These two factors make the existing failure recovery process too slow to be practical in real-time online industrial environments. To achieve realistic fast recovery from Inter-DC network failures, we propose a failure recovery framework FRAVaR, which achieves high network availability with very little deployment overhead. Particularly, FRAVaR reduces the deployment overhead by a novel incremental routing strategy to isolate link failures. In other words, it only needs to shuffle a tiny amount of traffic within a small failure isolation domain. On this base, FRAVaR further adopts a risk assessment theory named Value-at-Risk (VaR) to control flow re-transmission. We implement a prototype of FRAVaR and conduct a series of experiments on 4 real InterDC network topologies (ATT North America, IBM, GlobalCenter, AGIS). Experiment results show that FRAVaR outperforms state-of-the-art solutions on the recovery speed by 70.2%. 1
A Novel Approach based on Improved Naive Bayes for 5G Air Interface DDoS Detection

The network security architecture of 5G defined by 3rd Generation Partnership Project (3GPP) in version 15, is vulnerable to the attack on wireless transmission due to its imperfect security design. The Distributed Denial of Service (DDoS) Attack is currently one of the biggest threats to air interface security of 5G. Attackers use controlled equipment to send a large number of authentication signaling to target base station, forming an instantaneous DDoS attack to achieve the purpose of destroying available resources. DDoS attack is simple to operate, hard to detect, and hugely harmful. From the perspective of signaling changes, this paper focuses on analyzing the authentication process that is most prone to DDoS attack, and extracts seven features based on the signaling change of the authentication process. We innovatively establish a classifier to detect DDoS attack using a novel improved naive Bayes algorithm. The algorithm not only comprehensively considers the independency and dependency between features through structural improvement, but also uses genetic algorithm to solve the optimal combination of attributes for category weights. At the end of the paper, we give simulation results which show the effectiveness of the proposed algorithm in detecting DDoS attack.
Synchronous Die-to-Die Signaling Using Aeonic Connect

This paper presents a system providing accurate clock alignment for on-die and die-to-die synchronous circuits. A low frequency reference clock provides an accurate timing reference with low power consumption, while distributed delay lines align the endpoints of loosely constrained clock trees. For on-die clocks, this synchronization strategy severs the traditional relationship between power and skew. For die-to-die interconnect, this allows single-cycle latency flop-to-flop synchronous communication with lower power and lower complexity than traditional clock-forwarded schemes. Our testchip in TSMC 12nm demonstrates a power efficiency of 0.2pJ/bit without aggressive scaling of the pad ESD.