Miracle-dz/UniFAR-FTUs huggingface dataset:
: A

—Distributed systems are extremely difﬁcult to design and implement correctly because they must handle both system correctness and device failures. Most of the work focuses on the ﬁrst aspect, and in particular, on the correctness of security and network conﬁguration. The large demand for availability and reliability for critical services is actually pushing new architectures that tolerate failures, but a-priori analysis of redundancy and recovery features is still limited. To this end, we present a framework to design and formally verify the persistence of network properties, even in case of failures. The solution considers both nodes and links failure, and it is based on a formal model that takes both network topology and network device conﬁgurations into account. In contrast, most of the existing approaches only consider network topology. By analyzing the formal model, the framework can check whether the speciﬁed network services are still available after failures, and in case of success, it outputs a possible conﬁguration of the devices to be used for automatic recovery.
A low complexity NOMA scheme in VLC systems using pulse modulations (invited paper)

Successive interference cancellation (SIC) decoding scheme is commonly utilized in nonorthogonal multiple access (NOMA) scheme to obtain the data of each user under different channel conditions at acceptable complexity. However, the imperfect SIC would lead to error propagation (EP) problem, deteriorating the error performance of the NOMA scheme. In this work, a SIC-free NOMA scheme is proposed in the pulse modulation-based visible light communication (VLC) systems with two users. The user under worse channel condition adopts on-off keying (OOK) and the other employs multiple pulse position modulation (MPPM). Here, we employ the soft decision decoding (SDD) scheme to decode the MPPM signal of the user under the better channel condition, which could fundamentally alleviate the adverse impact of EP on symbol error rate (SER) performance and is easier and faster to implement than the traditional SIC decoding scheme. The expressions of SER for two users are derived and the Monte Carlo (MC) simulation results are further provided to validate analytical results.
Haptic Feedback System of the Additional Hand Position for Multiple Task Situations using a Wearable Robot Arm

Wearable robot arm systems have been proposed that enable various tasks that humans can not do with their only two natural arms. However, many of these do not have a somatosensory feedback system so that users have to confirm the robot's state by looking every time. This active confirmation can often be a ‘switching cost’ of task and reduce the multitasking efficiency performed by natural arms and additional robotic arm. In this paper, we focused on the proprioception among the somatosensation, and propose a “Position haptic belt” which is a new biofeedback system for a wearable robot arm. The system represents the three-dimensional position of a robot hand by the vibrator's placement and vibration frequency. The prototype we developed is able to identify the robot hand position with an accuracy of about 10 cm, and it is used to conduct a case study to evaluate the usability in multiple task situation. The result suggested that the effectiveness of the somatosensory feedback information of robot hand position, even for the wearable robot arm which can be easily confirmed its state on user's own eyes.
Decentralized Robust Connectivity Control in Flocking of Multi-Robot Systems

In this paper, a global connectivity control method for decentralized multi-robot systems is proposed. This method can achieve decentralized connectivity control of multi-robot network under disturbances, which has no effect on the objective flocking control. Based on the gradient between the connectivity and robot positions, the proposed connectivity control method can make each robot move along the desired gradient direction, so as to achieve the control of the global connectivity. Indeed, the flocking method based on the potential of attraction and repulsion can ensure that the distance between robots is stable within the desired range. Then the security of flocking and the stability of communication are guaranteed. It is proved in this paper that both connectivity and objective flocking control have no effect on the stability of each other, under the condition that both controllers are bounded. Therefore, the global connectivity and configuration of the system can achieve the desired states. In addition, a robust control method based on integral sliding mode is designed in this paper, which can counteract the external disturbance and ensure the ideal dynamics of multi-robot systems. Finally, several numerical simulations are given to validate the effectiveness of the proposed control methods.
Couple-group consensus for heterogeneous multi-agent systems with event-triggered methods

A scenario of cooperative and competitive interactions in heterogeneous multi-agent networks is described in this paper. several reasonable control protocols are proposed through event-triggered actuation schemes. According to the knowledge of matrix analysis and algebraic graph theory, the trigger functions for the multi-agent systems are designed. Moreover, the sufficient conditions that enable the particular heterogeneous multi-agent systems to realize couple-group consensus are presented by adopting the Lyapunov stability theorem. Lastly, the validity of the theoretical analysis is confirmed by the simulation.
The Origin of Saturn’s Rings Revisited

<p>A set of key observations over the Cassini spacecraft's tenure has constrained Saturn's rings' age to be less than a few 100 Myr effectively ruling out currently accepted ring origin scenarios, all of which require that the rings are ancient or primordial. We propose a new scenario motivated from evidence of a comparably recent dynamical instability ~100 Myr ago which would have led to collisions between Saturn's pre-existing mid-size icy moons, opening the door to possible ring formation during that epoch. Successfully testing this scenario requires better<span>&#160; </span>understanding of collisional outcomes. Toward that end, we introduce a new suite of simulations modeling impacts between Saturn's icy moons using the next generation smoothed hydrodynamical and gravity code <em>SWIFT</em>. The unprecedented spatial resolution achieved in these simulations (10<sup>8.5 </sup>particles within the simulation box) allows us to depict the myriad of gravitationally bound objects formed during icy moon collisions which may afterwards evolve both thermally and dynamically to re-accrete or collide with other bodies. Our unprecedented high resolution further allows us to determine a size distribution of fragments which can be used to inform crater impact distributions on newly accreted or remaining moons. </p><p>&#160;</p>
Error-correcting Performance Comparison for Polar Codes, LDPC Codes and Convolutional Codes in High-performance Wireless

In this paper, polar codes, low-density parity-check (LDPC) codes and convolutional codes in the status of high-performance wireless (WirelessHP) are discussed. Those classic code schemes are compared in terms of their encoding and decoding complexity and the error-correcting performance. Results show that, polar codes with successive cancellation list (SCL) decoding is superior to the LDPC codes and the convolutional codes in the condition of WirelessHP. The result has certain significance for the development of next generation communication technology.
StreamingCube: Seamless Integration of Stream Processing and OLAP Analysis

Many organizations require real-time analysis of their business data streams to make instantaneous decisions. Data streams are often multidimensional and at the lowest level of abstraction, yet analysts are interested in their multilevel interactive analyses across several dimensions. Online analytical processing (OLAP) is a technique whose utility has been proven for such an analysis. Traditionally, OLAP over streams has been achieved by coupling a stream processing engine (SPE) with an OLAP engine. However, for many organizations, this is not an effective solution as it results in lower performance, resource wastage, and increased complexity and maintenance costs. Hence, we present StreamingCube, which is a unified framework for stream processing and OLAP analysis. The idea of a unified framework is supported by the observation that the incremental computation in stream processing and the maintenance of the materialized view of OLAP have many similarities. To seamlessly integrate the SPE and OLAP, as well as to maintain the OLAP lattice vertices incrementally, a cubify operator is introduced. StreamingCube supports two types of queries, namely, continuous queries (CQs) and OLAP queries. To demonstrate the effectiveness of the proposed framework, a detailed experimental evaluation is presented herein.
Study on the Influence of High-altitude on Performance of an Opposed-Piston Gasoline Engine

The engine performance test was carried out on the high-performance test bench of opposed-piston engine of UAV (unmanned aerial vehicle) from 0m to 7000m altitudes. The influence of altitudes on power, torque, fuel consumption, and heat balance (cylinder surface temperature and heat flow distribution) of the engine were analyzed. The results showed that power and torque of the engine gradually decrease with the altitudes increasing. The power and torque were reduced by about 11.25% and 12.5% respectively, and the fuel consumption rate increased by 14.4% and 2.8% respectively at low-speed and high-speed with altitude rises 1000m. At the altitude of 0-3000m, the temperature and the surface temperature of the cylinder surface decreased by 18.7 and 7.9 degrees respectively with altitude of sea level per 1000m. At the altitude of 3000m-7000m, the temperature and the surface temperature of the cylinder surface decreased by 31.9 and 21.2. The outlet temperature of cooling water increased first and then decreased with altitudes increasing, and the extent of variation at high-speeds was greater than that at low-speeds. The effective thermal efficiency of the engine is less than 5%, while the percentage of lost heat is over 30% under 3000rpm speed at the altitude of 6000m.
Speech Enhancement and Encoding using SS-VAD and LPC

In this paper, an enhancement of noisy speech data and encoding of corrupted and enhanced speech data is presented. An algorithm is proposed which is an amalgamation of spectral subtraction with voice activity detection (SS-VAD) and linear predictive coding (LPC) for speech enhancement and encoding purpose. Firstly, the significance of SS-VAD and LPC methods are studied in detail for various types of noises. In SS-V AD technique, the noisy speech data is considered as an input speech signal which is a combination of clean speech data and noise model. The corrupted speech data is windowed using Hanning window and framed for every 20ms. 50% of overlapping is done while windowing the speech data. The output of SS-VAD is given as an input to the LPC encoder. The coefficients are extracted from the input speech data to design all pole filters. The cross correlation process is also done for differentiating the voiced and unvoiced samples at the analysis step. The pitch information and extracted coefficients are used at the synthesis step. The experiments are conducted for different types of noisy speech data which are degraded by background noise, F16 noise, factory noise, white noise and car noise. The experimental results show that an SS-VAD algorithm significantly improved the signal to noise ratio (SNR) of speech data under various degraded conditions. Therefore, the SS-VAD algorithm is combined with LPC for encoding of enhanced speech data which yields better audibility and intelligibility of speech compared to encoding of noisy speech data.
Window-Based Schedule Synthesis for Industrial IEEE 802.1Qbv TSN Networks

Time-Sensitive Networking (TSN) introduces standardized mechanisms that add real-time capabilities to IEEE 802.1 Ethernet networks. In particular, the Time-Aware Shaper (TAS) can be used to send frames in a deterministic fashion according to a predefined global schedule. Existing methods for generating the global communication schedule enforce isolation either in the time or in the space domain. This extended abstract presents a novel, more flexible window-based scheduling algorithm which removes the previously required isolation constraints for Scheduled Traffic (ST) by integrating worst-case delay analysis to guarantee bounded latency.
Review of Automatic Detection and Classification Techniques for Cetacean Vocalization

Cetaceans have elicited the attention of researchers in recent decades due to their importance to the ecosystem and their economic values. They use sound for communication, echolocation and other social activities. Their sounds are highly non-stationary, transitory and range from short to long sounds. Passive acoustic monitoring (PAM) is a popular method used for monitoring cetaceans in their ecosystems. The volumes of data accumulated using PAM are usually big, so they are difficult to analyze using manual inspection. Therefore different techniques with mixed outcomes have been developed for the automatic detection and classification of signals of different cetacean species. So far, no single technique developed is perfect to detect and classify the vocalizations of over 82 known species due to variability in time-frequency, difference in the amplitude among species and within species’ vocal repertoire, physical environment, among others. The accuracy of any detector or classifier depends on the technique adopted as well as the nature of the signal to be analyzed. In this article, we review the existing techniques for the automatic detection and classification of cetacean vocalizations. We categorize the surveyed techniques, while emphasizing the advantages and disadvantages of these techniques. The article suggests possible research directions that can improve existing detection and classification techniques. In addition, the article recommends other suitable techniques that can be used to analyze non-linear and non-stationary signals such as the cetaceans’ signals. Several research have been dedicated to this topic, however, there is no review of these past results that gives a quick overview in the area of cetacean detection and classification. This review will help researchers and practitioners in the field to make insightful decisions based on their requirements.
Single Chip Broadband Testset for Network Analyzers

This paper presents the design of single chip broadband testset for portable network analyzers. This testset is designed using divider combiner technique. The testset is comprised of an out of phase divider circuit, an intermediate matching network and an in phase combiner circuit. The testset measures the reflection coefficient of an arbitrary load. It has one port for input, second port for arbitrary load (DUT), third and fourth port for reflected and reference power detectors by which we can measure S-parameters of DUT. The testset shows high isolation and dynamic range over broad bandwidth. The dynamic range of the testset is 10 dB to 49.9 dB over frequency range from 1.7 GHz to 12 GHz. The design advantage is, it is an on chip design with area coverage 3.2 × 3.0 mm2. Due to its single chip design it is suitable for compact, portable network analyzers for field testing applications. The design has been realized using UMC 180 nm RF/MM CMOS technology.
Study on Stellar Spectra Classification Based on Multitask Residual Neural Network

At present, large-scale sky surveys have obtained a large volume of stellar spectra. Analyzing these spectra by manual methods cannot meet the actual needs, so it is necessary to explore automatic classification methods of stellar spectra. In this study, a multitask residual neural network is proposed, which adopts the idea of multitask learning to process two tasks simultaneously by using the correlation between the luminosity class and spectral subtype of stars. Moreover, the structure of residual neural network can reduce the parameters in the model to improve the calculation efficiency. We applied the multitask residual neural network to the classification of LAMOST DR4 M-type star spectra, and achieved good results in both luminosity class classification and spectral subtype classification. We further compared the performance of multitask residual neural network in stellar classification with that of random forest (RF) and eXtreme Gradient Boosting (XGBoost). The performance of multitask residual neural network is better than the other two algorithms.
Image Completion Method Considering Content and Sharpness

The current image completion method can complete the missing image well, but they often fail to achieve better results when the missing area is at the edge of the image. In order to overcome this defect, this paper proposes a novel image completion method considering content and sharpness based on a generation adversarial network (GAN) and region growing algorithm. This model uses trained content and pixel joint discriminator to distinguish between real and generated images. The content discriminator focus on the entire image content to assess whether it is as coherent as a whole, and the pixel discriminator focus on the sharpness of the entire image to assess whether it is as same as the original image in sharpness based on multi-focus image fusion. A variety of missing blocks can be completed well by our method in dataset of Places2. In addition, compared to the other image completion method, this method can complete edge-loss blocks more excellently.
Discovery of Key Nodes in Social Networks Premised on PageRank Algorithm

By abstracting the complex structure of social network into undirected graph, nodes into pages and edges into hyperlinks, and combining PageRank algorithm with the discovery of key nodes in social networks, this thesis comes up with a new algorithm of key nodes premised on improved PageRank algorithm, and finally employs microblog data as data set. Through the experiment of comparing KeyRank algorithm with TIPR algorithm, it can be concluded that the PageRank algorithm proposed in this thesis is comparatively suitable for discovering key nodes. Under the same data set, the efficiency of identifying key nodes is raised by 30% in comparison with the other two algorithms.
A Study on Extreme Learning Machine for Gasoline Engine Torque Prediction

This research presents an extreme learning machine (ELM) based neural network modeling technique for gasoline engine torque prediction. The technique adopts a single-hidden layer feedforward neural network (SLFN) structure which has the potential to approximate any continuous function with high accuracy. To verify the robustness of this technique, over 3300 data points collected from a real-world gasoline engine are used to train, validate, and test the model. These data points cover a wide spectrum of normal engine operating conditions, with the engine speed from 1000 rpm to 4500 rpm, and the engine torque from idle to full load. The experiment results demonstrate that the model can predict the gasoline engine torque with high accuracy. Moreover, this research proposes a weight factor approach to further improve the prediction accuracy of the model in the desired data regions without modifying the input data set. The evaluation shows that the weight factor approach can reduce the overall prediction errors in the regions significantly. This feature is particularly useful in tuning the performance of the model when the significance of the individual data points varies, or when the distribution of the data points is imbalanced. In practice, the modeling approaches presented in this research will help reduce the engine test and verification time.
Functional Qos Metric For Lorawan Applications In Challenging Industrial Environment

Industry 4.0 applications rely upon timely and accurate data about plant and process within a production site. Whilst modern facilities tend to have this capability as a matter of course, older equipment may lack network connectivity. A lack of data-gathering capability represents a significant barrier-to-entry when undertaking any data-driven investigation or improvement programs. Wireless sensor networks (WSNs) can be used as a flexible and low-disruption technique to acquire data at the point of interest, however the data stream is often lossy when deployed in harsh conditions without costly adaptations to the environment.This paper introduces the F-QoS metric which is able to classify the quality of the data stream from a WSN (using only packet reception timestamps), at user-defined sampling rates with a constraint placed upon the maximum amount of missing data. The resulting classifications can be used in an offline fashion to select periods of high-quality data for modelling, or, in an online manner to assess the realtime performance of a WSN.The F-QoS metric is applied to a LoRaWAN network in a large commercial bakery with a low-disruption installation-the network links are strained by large metal obstructions and the endpoints are installed inside metal cabinets. Each node transmits on a 10s cycle, and the analysis shows that >70% of the data is suitable for sampling at a 30s rate. The results indicate that LoRaWAN is capable of data acquisition in an unadapted and challenging environment, with the recommendation that the raw sample rate should be triple the desired final sample rate.
Tightening Up Security In Low Power Deterministic Networks

The unprecedented pervasiveness of IoT systems is pushing this technology into increasingly stringent domains. Such application scenarios become even more challenging due to the demand for encompassing the interplay between safety and security. The IEEE 802.15. 4 DSME MAC behavior aims at addressing such systems by providing additional deterministic, synchronous multi-channel access support. However, despite the several improvements over the previous versions of the protocol, the standard lacks a complete solution to secure communications. In this front, we propose the integration of TAKS, an hybrid cryptography scheme, over a standard DSME network. In this paper, we describe the system architecture for integrating TAKS into DSME with minimum impact to the standard, and we venture into analysing the overhead of having such security solution over application delay and throughput. After a performance analysis, we learn that it is possible to achieve a minor impact of 1% to 14% on top of the expected network delay, depending on the platform used, while still guaranteeing strong security support over the DSME network.