Miracle-dz/UniFAR-FTUs huggingface dataset:
λ21-cm Interstellar HI Profiles, Critical Ionization Velocities, and Derived Electron Densities

Analysis of neutral hydrogen <inline-formula> <tex-math notation="LaTeX">$\lambda 21$ </tex-math></inline-formula>-cm profiles at high northern and southern Galactic latitudes reveals unexpected properties, connects unanticipated associations, and confirms the fundamental parameters of the interstellar medium. Gaussian decomposition of profiles from the second data release of the Galactic Arecibo <inline-formula> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula>-Band Feed Array (GALFA) confirms earlier results of a pervasive component with a linewidth of order 34 km s<sup>−1</sup>, consistent with the critical ionization velocity (CIV) of helium. The ratio of the column density of this component to the entire profile is proportional to the interstellar helium abundance. The results are comparable to the accepted value of the cosmic abundance, 0.085, in the southern Galactic hemisphere, but different for directions in the northern Galactic hemisphere. Normalizing to the cosmic abundance allows us to estimate how much HI is “missing” from the <inline-formula> <tex-math notation="LaTeX">$\lambda 21$ </tex-math></inline-formula>-cm observations in the northern sky, confirming an asymmetry that has been known for decades. These CIV results also predict the volume density of interstellar electrons along different lines of site, which match values derived from pulsar dispersion measures and <inline-formula> <tex-math notation="LaTeX">$\text{H}\alpha $ </tex-math></inline-formula> observations. These results provide strong support for the hypothesis that interstellar <inline-formula> <tex-math notation="LaTeX">$\lambda 21$ </tex-math></inline-formula>-cm HI profile linewidths are modulated by the CIV plasma phenomenon.
On the Analysis and Optimization of BER Performance of Symmetrical Coding Based NOMA

Non-orthogonal multiple access (NOMA) has been regarded as a promising technique in the future generation wireless communications due to its superior spectral efficiency. However, according to the key conception of NOMA, the distant user (DU) which is far from the base station will be allocated more power than near user (NU) to guarantee fairness. With the utilization of successive interference cancellation (SIC), the signal of NU can be decoded after subtracting the signal of DU, which will deteriorate the performance of NU due to the error bit delivery. In this paper, the analysis and optimization on bit error rate (BER) performance of symmetrical coding (SC) based NOMA (SC-NOMA) scheme is studied under Nakagami-m fading channel. First, closed-form BER expressions of DU and NU are derived simultaneously in SC-NOMA scheme. Second, based on the closed-form expressions, a power allocation algorithm is proposed in SC-NOMA to achieve a better BER performance, and the trade-off between BER performances of NU and DU can be achieved with the proposed algorithm. Finally, simulation results are conducted to verify the validity of analysis. Results show that the proposed SC-NOMA can achieve a better BER performance compared with conventional NOMA.
mmWave on the Road: Investigating the Weather Impact on 60 GHz V2X Communication Channels

We study the impact of a diverse set of weather conditions on 60 GHz communication channels. In this context, we are primarily interested in next-generation Vehicle-to-everything (V2X) communication using mmWave frequencies. Given the application constraints of this scenario, we focus on rain and snow - and different rain and snow intensities. We extended the MilliCar ns-3 model to incorporate standardized rain and snow based attenuation models as suggested, for example, by the ITU. We cross-validated our simulator using as a reference the results obtained from the validated-well-known NYUSIM channel model simulator. In the core of this paper, we present an extensive simulation study on the impact of rain and snow in a realistic vehicular network at 60 GHz channels. Our results show that weather conditions can reduce the communication distance significantly (by up to 60 m).
Multi-relay selection of hybrid satellite-terrestrial cooperative network based on CR-NOMA in AF mode

This paper investigates the impact of relay selection on cognitive radio non-orthogonal multiple access (CR-NOMA) based satellite-ground cooperation network. By analyzing the signal-to-interference-plus-noise ratio (SINR) at the User 1, we propose a two-stage relay selection scheme, and derive the exact outage probability expression and the progressive probability expression of the primary user. Finally, theoretical simulations are used to verify the correctness of the deduced results and the impact of related variables on system performance.
Research on Staring Beamforming Algorithm of Spaceborne Phased Array

In the traditional satellite fixed beam scheme, the beam direction of the satellite is not adjustable, which requires frequent intra satellite beam switching. The staring beam scheme changes the beam direction by constantly updating the weight of phased array elements, which can greatly reduce the internal beam switching. This paper mainly studies the beamforming algorithm of spaceborne phased array staring at a single area and multiple area. The digital phase configuration algorithm is used as the gaze beam shaping algorithm. Aiming at the problem of high sidelobe in the beam pattern, the formation of the phased array antenna is improved. And the round caliber Taylor amplitude weighting method is used to reduce the sidelobe, which can improve the main sidelobe ratio. Also we have achieved multi-beamforming. Then the gaze process of single target area and multi-target area is simulated. Within the 3dB beam range, effective coverage can be achieved.
Co-training For Image-Based Malware Classification

A malware detection model based on semi-supervised learning is proposed in the paper. Our model includes mainly three parts: malware visualization, feature extraction, and classification. Firstly, the malware visualization converts malware into grayscale images; then the features of the images are extracted to reflect the coding patterns of malware; finally, a collaborative learning model is applied to malware detections using both labeled and unlabeled software samples. The proposed model was evaluated based on two commonly used benchmark datasets. The results demonstrated that compared with traditional methods, our model not only reduced the cost of sample labeling but also improved the detection accuracy through incorporating unlabeled samples into the collaborative learning process, thereby achieved higher classification performance.
CeVRicale: A VR app for Cervical Rehabilitation

We propose CeVRicale, a cervical rehabilitation application based on the use of virtual reality (VR). CeVRicale is smartphone-based, thus it may be available to larger shares of population when compared to those applications that are implemented for head mounted displays such as HTC Vive or Oculus Rift. The app exploits a smartphone’s sensor to track head movements in five exergames inspired by rehabilitation exercises. This project is the first step in a study to evaluate the effectiveness and efficiency of a low cost VR application in the treatment of cervical musculoskeletal disorders.
Fabry-Perot Antenna Employing Artificial Magnetic Conductors and Phase Gradient Metasurface for Wideband Monostatic RCS Reduction and High Gain Tilted Beam Radiation

Platforms with low radar signatures require compatible antennas (low scattering) for communication purposes. Under this context, the article presents two Fabry-Perot (FP) Cavity antennas (Ant-1 and Ant-2). Ant-1’s aperture is an integrated design of artificial magnetic conductor (AMC) surface and a phase gradient metasurface (PGM), incorporating three unit-cell structures. The antenna achieves reduced monostatic scattering over broad bandwidth, and simultaneously obtains high gain as well as tilted beam peak radiation. Patch radiating at 7 GHz forms excitation source of the cavity, and cavity height is <inline-formula> <tex-math notation="LaTeX">$0.33\lambda $ </tex-math></inline-formula>, smaller than the heights of conventional cavity designs (<inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula>/2). RCS reduction is obtained over a bandwidth of 105.8% (4-13 GHz) for arbitrary polarizations, covering C/X bands. Antenna gain is 12 dB, and peak radiation tilt is −60° in elevation plane. Simulations have been verified through fabricated prototype. Ant-2 is a modification of Ant-1. Ant-2’s aperture is constructed from two unit-cell structures, and its aperture size is 41% smaller than Ant-1. Its cavity height is <inline-formula> <tex-math notation="LaTeX">$0.3\lambda $ </tex-math></inline-formula>. Ant-2 also achieves wideband backscattering reduction; however, due to asymmetric layout of unit-cell elements over the aperture, radar cross section (RCS) reduction bandwidth is different for the two polarizations. For vertically polarized (VP) incident wave, RCS reduction bandwidth is 105.8% (4-13 GHz), but for horizontally polarized (HP) wave, it is 85.7% (5.2-13 GHz). Nevertheless, broadband RCS reduction is still achieved for both polarizations. Peak gain is 11.73 dB, achieved at −37° tilt angle. Simulations are presented for Ant-2. Compared to literature, both antennas show various improved results.
CFN-dyncast: Load Balancing the Edges via the Network

Multi-access Edge Computing (MEC) is a promising business paradigm in the 5G network. The principle "the nearest is the best" may not apply in some cases. How to break the boundaries among individual MEC sites and leverage the computing resources as an integrity, is the key to improve the user experience and improve the usage efficiency of computing and network resources. This paper proposes CFN-dyncast, a distributed technique that dispatches clients’ demands to an optimal site according to the load of each computing site and the network status. This paper also introduces the related design considerations, the implementation and the evaluation comparing to other load balancing techniques.
Energy-Efficient Beamforming for Beamspace HAP-NOMA Systems

In this letter, we study an energy-efficient algorithm for beamspace high altitude platform with non-orthogonal multiple access (HAP-NOMA) systems over Rician fading channels. We first derive the non-line-of-sight (NLOS) theorem of beamspace HAP channel in large scale array regime according to the asymptotic property of the Toeplitz matrix. Then, we propose the beamspace HAP-NOMA scheme based on the derived NLOS theorem, which can be partitioned into two stages, i.e. user grouping scheme and power optimization scheme. The statistical channel state information (CSI) is only required in the proposed algorithm, which avoids acquiring instantaneous CSI. NOMA can serve users in each beam simultaneously to compensate for the loss of spectral efficiency due to the beamspace HAP. The digital precoder can be obtained by zero forcing (ZF) based on the equivalent channel. Numerical results show the performance enhancement of the proposed beamspace HAP-NOMA algorithm.
A Network Intrusion Detection Approach at the Edge of Fog

In addition to the feature of real-time analytics, fog computing allows detection nodes to be located at the edges of the network. On the other hand, intrusion detection systems require prompt and accurate attack analysis and detection. These systems must promptly respond appropriately to an event. Increasing the speed of data transfer and response requires less bandwidth in the network, reducing the data sent to the cloud and increasing information security as some of the advantages of using detection nodes at the edges of the network in fog computing. The use of neural networks in the analyzer engine is important for the low consumption of system resources, avoidance of explicit production of detection rules, detection of known deformed attacks, and the ability to manage noise and outlier data. The current paper proposes and implements the architecture of network intrusion detection nodes in fog computing, in addition to presenting the proposed fog network architecture. In the proposed architecture, each node can, in addition to performing intrusion detection operations, observe the nodes around it, find the compromised node or intrusion node, and inform the nodes close to it to disconnect from that node.
The Effects of a Stressful Physical Environment During Virtual Reality Height Exposure

Virtual reality height exposure is a reliable method of inducing stress with low variance across age and demographics. As the virtual environment’s quality of rendering fidelity increases dramatically, it leads to the neglect or simplification of the physical environment. This paper presents an experiment that explored the effects of an elevated physical platform with a virtually heightened environment to induce stress. Fifteen participants experienced four different conditions of varying physical and virtual heights. Participants reported significantly higher stress level when physically elevated regardless of the virtual height, which suggests that the inherent elevation will induce more stress within participants.
Dynamic Projection Mapping with 3D Images Using Volumetric Display

Recently, projection mapping has become popular that it is used in various entertainments. In the research field, dynamic projection mapping has been studied to change the appearance of moving and deforming objects. Although it seamlessly connects the real world and the virtual world, complex equipment surrounds the object to achieve advanced projection and it is an unnecessarily visually disturbing factor. Furthermore, projection images are occluded when the target object is grasped and manipulated by hand. In this study, we propose a novel dynamic projection mapping with invisible projection devices with 3D images using a volumetric display and retro-transmissive optics.
Dual-band OFDM Signal Excitation based RFID Channel Estimation and Single-station Ranging

In order to solve the problems of low accuracy of traditional Radio Frequency Identification (RFID) ranging and time overhead of frequency hopping technology that developed recently. In this paper, we propose a ranging system based on dual-band Orthogonal Frequency Division Multiplexing (OFDM) backscatter signals. First, the structure of OFDM signal is de-signed according to the characteristics of RFID communication. Then, the correlation method in time-frequency domain is used for RFID signal detection, without recovering the RFID signal. After that, the RFID channel is estimated by channel difference method. Finally, we use the Phase Difference of Arrival (PDOA) and Chinese remainder theorem to calculate the cycle ambiguity corresponding to the distance. The extensive simulation results show that the proposed method can achieve centimeter-level ranging accuracy with a single station as well as maintains a low time cost.
Preparation, evaluation, and application of a novel reversed-phase/zwitterionic/hydrophilic interaction liquid chromatographic mixed-mode stationary phase

ABSTRACT The present study described the preparation and application of a reversed-phase/zwitterionic/hydrophilic interaction liquid chromatography stationary phase, named as SIL-PS. The SIL-PS was prepared through a four-step reaction, chemical bonding, nucleophilic addition, SN1 substitution, and sulfonation on the silica matrix. It was featured with C12 alkyl chain, quaternary ammonium, tertiary amine, and sulfonate groups. After SIL-PS was packed into the stainless steel column (150 × 2.1 mm i.d.), chromatographic parameters, including acetonitrile content, pH, and ionic strength of the mobile phase, and the column temperature, were systematically investigated to study the retention mechanism. Electrostatic adsorptive/repulsive, partition, and hydrogen-bonding interactions were demonstrated to contribute to the retention. The stability of the SIL-PS was satisfactory, with relative standard deviations of retention factors of 1.93, 2.08, and 1.90% for loxoprofen, adenosine, and liquiritin, respectively. Additionally, to investigate the separation selectivity, the non-steroidal anti-inflammatory drugs, nucleobases/nucleotides, and alkaloids/glycosides were separated; the HPLC fingerprinting of the Cortex phellodendri extract was also conducted, and the separation performance was superior to that of the C18 column in terms of peak shape, resolution, and analytical time. The results revealed that the prepared SIL-PS possessed multifunctionalities for multiretention and could be promising for complicated samples. GRAPHICAL ABSTRACT
Performance Metric System for Malicious URL Data using Revised Random Forest Algorithm

Phishing alludes to drawing techniques utilized by character cheats to angle for individual data in a lake of clueless Internet clients. Phishers use ridiculed email, phishing programming to take Phishing costs, Internet clients, billions of dollars for every year. It alludes to attracting techniques utilized by character cheats to angle for individual data in a lake of clueless Internet clients. Phishers use satirize email, phishing programming to take budgetary record subtleties, and individual data, for example, usernames and passwords. This paper manages techniques for distinguishing phishing Web destinations by investigating different highlights of benevolent and phishing URLs by Machine learning calculations. We talk about the techniques utilized for the recognition of phishing Web locales dependent on have properties, axical highlights, and page significance properties. the Proposed model has been assessed utilizing five distinctive AI calculations provided the best performance and results. The tests were led with a few (angled and symmetrical) random forest (RF) method used to classy the data for site acknowledgment
Stock Price Prediction Based on Morphological Similarity Clustering and Hierarchical Temporal Memory

Predicting stock prices through historical data is a hot research topic. Stock price data is considered to be a typical time series. Recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent units (GRU) have been commonly employed to handle this type of data. However, most studies focus on the analysis of individual stocks, thus ignoring the correlation between similar stocks in the entire stock market. This paper proposes a clustering method for mining similar stocks, which combines morphological similarity distance (MSD) and kmeans clustering. Subsequently, Hierarchical Temporal Memory (HTM), an online learning model, is used to learn patterns from similar stocks and make predictions at last, denoted as C-HTM. The experiments on the price prediction show that 1) compared with HTM which has not learned similar stock patterns, C-HTM has better prediction accuracy, 2) in terms of short-term prediction, the performance of C-HTM is better than all baseline models.
Caching Strategy Based on Data Freshness with Helpers in D2D Wireless Network

Recently, device-to-device (D2D) wireless network has become a research hotspot, which can reduce the traffic through the network. In the network, by caching popular files in the memory of end users, the users can obtain requested contents with lower energy consumption. Helper is defined as a dedicated device with large caching capacity, with helpers in a D2D wireless networks, the performance can be significantly improved. However, the data freshness has not been considered in previous works using helpers in D2D wireless network. In this paper, taking data freshness into consideration and aiming at reducing energy consumption, the access route for user content requests is formulated and a content access and updating strategy with helpers is proposed. In this strategy, a helper can improve service quality for adjacent users with low energy consumption and high data freshness. Simulation results demonstrate that our proposed strategy can reduce the system energy consumption while ensuring the content freshness.
Implementation and Evaluation of SMT-based Real-time Communication Scheduling for IEEE 802.1Qbv in Next-generation in-vehicle network

The explosive growth of automotive electronics and self-driving prompts the expansion of in-vehicle networks and the revolution of E/E architecture. A flexible, scalable, lightweight, and service-oriented architecture based on Ethernet is widely approved. Time Sensitive Networks (TSNs) is introduced to satisfy the demanding requirements for deterministic and low end-to-end delay in in-vehicle real-time communication networks. This paper focuses on the traffic scheduling of in-vehicle network through gate control lists (GCLs) based on IEEE 802.1Qbv. We extend the model and implementation of GCLs based on Satisfiability Modulo Theories (SMT) with the coexistence of massive data flow and high frequency data flow. The simulative assessments and validation are complemented for the promising next-generation in-vehicle communication architecture using OMNeT++.
Evaluating Non-Hierarchical Overflow Loss Systems Using Teletraffic Theory and Neural Networks

The Information Exchange Surrogate Approximation (IESA) is a powerful tool for estimating the blocking probability of non-hierarchical overflow loss systems (NH-OLSs), but can exhibit significant approximation errors in some cases. This letter proposes a new method of evaluating the blocking probability of generic NH-OLSs by combining machine learning with IESA. Specifically, we modify IESA by using neural networks (NN) to tune a newly introduced parameter in the IESA algorithm. Extensive numerical results for a simple NH-OLS show that our new hybrid method, which we call IESA+NN, is more accurate and robust than both base IESA and direct NN-based approximation of NH-OLS blocking probability, while remaining much more computationally efficient than computer simulation. Furthermore, due to the generic nature of our technique, IESA+NN is also easily extensible to more specialized stochastic models for communications and service systems, where base IESA has previously been applied.
A Novel Superpixels Image Segmentation Algorithm for PCBA Components

The visual location accuracy of assembly robot is affected directly by the accuracy of image segmentation. Most traditional algorithms couldn’t meet the industrial manufacture rhythm of Printed Circuit Board Assembly (PCBA) components. As a result, this paper proposed a novel superpixels image segmentation algorithm for PCBA components. Firstly, the salient feature was extracted by Histogram-based Contrast (HC) algorithm. Then, the salience map was extracted according to the Simple Non-Iterative Clustering (SNIC) superpixels segmentation algorithm. A new method was proposed to obtain information entropy by creating a new superpixels image. Ultimately, the segmented image was obtained according to the information entropy. According to the experimental results, the F-measure, the MAE and the time were 0.7979, 0.040567 and 179.12851s respectively, showing that the proposed algorithm was better than the other six algorithms.