Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of Average Cavity Dispersion and Spectral Bandwidth on Passively Harmonic Mode Locked L-Band Er-Doped Fiber Laser

We systematically study the impact of average cavity dispersion and pulse spectral bandwidth on harmonic mode locking in the long-wavelength band from an Er-doped fiber laser using carbon nanotubes polyvinyl alcohol (CNTs-PVA) film mode locker. By carrying out pulse energy management through optimizing the average cavity dispersion and optical spectral bandwidth, 2.08 GHz repetition rate corresponding to 201st harmonic with 36.5 dB side mode suppression ratio (SMSR) can be realized under 205 mW pump power at 1597.53 nm. Furthermore, we systematically investigate the characteristics of the harmonically mode locked ultrashort optical pulses with different spectral bandwidths versus pump power under fixed cavity dispersion first. Key laser parameters have been studied including pumping efficiency, duration, energy, SMSR, and time bandwidth product. Enlightened by the experimental results, a simple law on preliminary and fast judgment of pulses frequency under fixed pump power is proposed, namely, by monitoring the pulse spectral bandwidth. The laser source featuring high repetition rate and L-band operation is greatly sought-after in some specific applications such as modern optical communication system etc.
An Enquiry into Digital Inequality Implications for Central Bank Digital Currency

Central banks around the world have pondered the possibility of a central bank digital currency (CBDC). Advantages of such a currency are perceived technological catch-up, and financial inclusion, inter alia. This paper evaluates the implications of digital inequality on CBDC. The paper finds that indeed digital access, a proxy of CBDC, is positively correlated with financial inclusion but is afflicted by digital inequalities with different or contradictory impacts. For example, an increase in digital access benefits financial inclusion, but owing to inequalities, the increase is at a lower level for older people than for young adults. Further, when the income gap worsens, financial inclusion deteriorates at a higher level for the poor than for the rich. The study concludes that CBDC will be vulnerable to the inheritable e-burdens of digital inequalities. The policy implication is that the baffling risk-averse rigidities of older people towards digital access should be addressed in support of CBDC-linked financial inclusion.
Robust Nonlinear Tracking Control of a 2-DOF Helicopter System

A robust nonlinear output feedback control method is presented, which achieves two degrees of freedom (2-DOF) attitude tracking of a helicopter system test bed. The control law is designed to compensate for uncertainty in the helicopter system dynamic model, including input-multiplicative parametric uncertainty. To reduce the computational requirement in the closed-loop system, constant feedforward estimates of the input-multiplicative uncertainty are utilized in lieu of adaptive parameter estimates. Eschewing the high-gain feedback requirement that is characteristic of standard sliding mode observer methods, the proposed control method utilizes a bank of dynamic filters, which operates as a velocity estimator in the closed-loop system. Computer simulation and experimental results are provided to demonstrate the performance of the attitude tracking control method using the Quanser 2-DOF AERO helicopter.
Analysis of the Dynamism in University-Driven Innovation Ecosystems Through the Assessment of Entrepreneurship Role

This paper provides a new way of representing the actors and relationships of a university-type innovation ecosystem based on knowledge. Thanks to this new representation, analysis, and management of knowledge flows are enabled not only qualitatively but also quantitatively, being possible to use well-known tools, such as social network analysis (SNA). Through this contribution, it is possible to analyze both the behavior of the actors individually and the behavior of the ecosystem as a whole in university-type innovation ecosystems, which contributes to adding new knowledge of great relevance for decision-making. Using this new analysis framework, this paper analyzes the dynamism of innovation ecosystems through the assessment of entrepreneurship role, which is tested within the Universidad Politécnica de Madrid (UPM) case. In addition, to show the potential of this new framework, a series of new concepts is introduced, such as connectors and events, which act as activators for the improvement of the structure and dynamism of the innovation ecosystem.
Memetic Based Online Community Detection

Community detection is one of the most important tasks in social networks analysis. This problem becomes more challenging when the structure of the network changes during the time. It is very important to update the structures of the community in a dynamic network without time-consuming procedures. This paper suggests a hybrid evolutionary algorithm for online community detection. The proposed algorithm called Memetic Based Online Community Detection (MBOC) is based on a memetic algorithm with new genetic operators and a novel stochastic local search to assign new nodes to communities and another local search called dense search to modify communities after new assignments. The method is evaluated over several well-known benchmark networks. The results show that the proposed approach outperforms the previous methods in most cases.
Revocable Attribute-Based Encryption Scheme With Efficient Deduplication for Ehealth Systems

The deduplication based on attribute-based encryption can be well used in eHealth systems to save storage space and share medical records. However, the excessive computation costs of existing schemes lead to inefficient deduplication. In addition, the frequent changes of clients’ attribute weaken the forward secrecy of data, and thus, how to achieve the attribute revocation in deduplication is a problem that remains to be solved. In this paper, we propose a variant of the attribute-based encryption scheme that supports efficient deduplication and attributes revocation for eHealth systems. Specifically, an efficient deduplication protocol based on the nature of prime number is used to alleviate the computation burden on the private cloud, and attribute revocation is realized by updating the attribute agent key and the ciphertext. Moreover, outsourcing decryption is introduced to reduce the computation overhead of clients. The security analysis argues that the proposed scheme can reach the desired security requirements, and the visual experiment result indicates the excellent performance of the proposed scheme while realizing deduplication and attribute revocation.
MCF3D: Multi-Stage Complementary Fusion for Multi-Sensor 3D Object Detection

We present MCF3D, a multi-stage complementary fusion three-dimensional (3D) object detection network for autonomous driving, robot navigation, and virtual reality. This is an end-to-end learnable architecture, which takes both LIDAR point clouds and RGB images as inputs and utilizes a 3D region proposal subnet and second stage detector(s) subnet to achieve high-precision oriented 3D bounding box prediction. To fully exploit the strength of multimodal information, we design a series of fine and targeted fusion methods based on the attention mechanism and prior knowledge, including “pre-fusion,” “anchor-fusion,” and “proposal-fusion. ” Our proposed RGB-Intensity form encodes the reflection intensity onto the input image to strengthen the representational power. Our designed proposal-element attention module allows the network to be guided to focus more on efficient and critical information with negligible overheads. In addition, we propose a cascade-enhanced detector for small classes, which is more selective against close false positives. The experiments on the challenging KITTI benchmark show that our MCF3D method produces state-of-the-art results while running in near real-time with a low memory footprint.
Automated Sensor-Fusion Based Emergency Rescue for Remote and Extreme Sport Activities

Even though technological advances changed and improved our daily life in various ways, the risks and dangers of extreme sport activities (ESAs) still persist and the progress of technology had little impact on them. Existing emergency rescue devices for ESAs still require manual activation and do not detect emergency situations autonomously. However, fusing the data feeds of simple sensors can easily enhance the functionalities of those devices and allow for the detection of emergency situations and subsequent rescue in the case of injuries. We identify the difficulties and challenges posed by ESAs, the role and potential value of information technology in such activities and example use cases and scenarios. We further present a prototype device for climbers that can detect potentially dangerous fall events.
The Guided Discovery Learning Principle in Multimedia Learning

The signaling principle, also known as the cueing principle, refers to the finding that people learn more deeply from a multimedia message when cues are added that guide attention to the relevant elements of the material or highlight the organization of the essential material. This chapter reviews the main findings from research on signaling in multimedia learning addressing the effects of incorporating cues into the text, the picture, or both. Text-based cues can consist of sentences that precede the learning materials and highlight their organization. Picture-based cues can consist, for instance, of arrows in which case they are extrinsic in the sense that an element is added to the picture. The chapter considers the design of cues based on eye movements and the effects of using eye movements as cues. The signaling principle may have some relation to other principles identified by the cognitive theory of multimedia learning as well.
Generative Adversarial Networks For Launching and Thwarting Adversarial Attacks on Network Intrusion Detection Systems

Intrusion detection systems (IDSs) are an essential cog of the network security suite that can defend the network from malicious intrusions and anomalous traffic. Many machine learning (ML)-based IDSs have been proposed in the literature for the detection of malicious network traffic. However, recent works have shown that ML models are vulnerable to adversarial perturbations through which an adversary can cause IDSs to malfunction by introducing a small impracticable perturbation in the network traffic. In this paper, we propose an adversarial ML attack using generative adversarial networks (GANs) that can successfully evade an ML-based IDS. We also show that GANs can be used to inoculate the IDS and make it more robust to adversarial perturbations.
Organizational Factors Affecting the Adoption of a Technological Innovation: A Study of the Hospital Information System (HIS)

Hospital Information System (HIS) plays a dominant role in improving health processes and in the provision of patient care services. This study seeks potential organizational factors influencing adoption of HIS in Malaysian public hospitals. This study integrates the Technology Organization Environment (TOE) framework and Absorptive Capacity Theory (ACT) in proposing a new theoretical framework for HIS adoption in Malaysia. Specifically, we examined the organizational dimension in the proposed framework and investigated moderating role of hospital size in the adoption of HIS. This research applied a quantitative approach and conducted a nationwide survey involving 137 public hospitals, with different hospital size. A survey method using a web-based questionnaire was conducted to examine the effects of the identified factors on HIS adoption. The logistic regression method is then deployed for testing the research hypotheses. Regarding the hospital size as the only moderator of this study, we found that effects of the significant factors on HIS adoption are the same in different hospital sizes. This analytical study approaches to the TOE framework and extends it by taking into account the absorptive capacity of hospital as the important component of the internal organization. Therefore, the study and its findings are to provide more insight into the process of successful adoption of HIS for public hospitals of Malaysia.
An OAM-Generating Method Using Density-Weighted Circular Array

A generation method of vortex electromagnetic (EM) waves based on the density-weighted circular array is proposed to converge the beams. The density-weighted circular array is presented firstly and the phased modulation method is applied to generate the OAM-carrying beams. Subsequently, the OAM spectrum decomposition is performed to analyze the quality of the radiation field. Moreover, the amplitude-phase modulation method is proposed to calculate the excitation coefficients for array elements. Compared with traditional OAM beams, results indicate that the generated beam has a different intensity distribution but a similar helical wavefront, which can benefit OAM’s applications in remote sensing realms.
Logic In Grammar Polarity Free Choice And Intervention

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A Distributed Adaptive State Feedback Control Scheme For Output Consensus of Multi-Agent Systems

This paper studies an open leader-following output consensus problem of multi-agent systems described by general uncertain linear systems. The control objective is to make the outputs of the follower agents with parametric uncertainties track a desired trajectory given by a virtual leader. For such general agent systems, a new distributed adaptive state feedback control framework is developed for multi-agent output consensus, without restrictive follower-leader matching conditions. This paper solves the state feedback output consensus problem for uncertain multi-agent systems with relative degree one. The adaptive control design has a desirable structure, simple as it is useful for many control applications, and essential as it is extensible to the higher relative degree case and/or the output feedback control case. Such a new distributed adaptive consensus scheme is designed based on model reference adaptive control which is capable of achieving desired tracking and effectively dealing with system parameter uncertainties. It guarantees the desired leader-following output consensus and closed-loop signal boundedness. A simulation study on a multiple-aircraft system verifies the effectiveness of the proposed adaptive multi-agent output consensus scheme.
Stability of Closed Space Biosystems for Algea Ecology

The paper describes the sustainable functioning of the closed self-regulating biological system based on macrophytes Fucus vesiculosus, Fucus inflatus, Laminaria saccharina and Chlorella vulgaris. The authors describe the methods and equipment for monitoring the composition and control of biological experiments with such a system, which is offered to spend on board the International Space Station (ISS). The option of the experiment on board the small satellite is considered also. The main task of automatically controlled experiment is to ensure the long-term functioning of the isolated biological system under the conditions of weightlessness. Based on the analysis of the interaction structure of the main components of biological systems under study, a mathematical model of a closed biological system was developed on the basis of aquatic organisms and holds its computer simulation, with which the conditions for the forecast can be carried out and state control investigated the conditions of maintaining the stable life. The automated control system of a biological system, providing its stable operation, is considered. The main indicator of the functioning of biological systems encouraged to use the amount of oxygen in the air section and dissolved in water. As a biological control system parameters used by the lighting level and temperature of the water environment. In the experiment on autonomous vehicle (microsatellite)it was proposed to manage the dampers in the heat-insulating sheath. The dependencies for defining the threshold oxygen concentration were obtained at which a correction of the control parameters is necessary.
Delay Study in Multi-controller Software Defined Vehicular Network Using OpenDaylight for Emergency Applications

SDN is a promising solution for vehicular networks challenges. Distributing the SDN control plane achieved more scalability and performance. However, synchronization between different controllers of network state generates extra-delay that is not suitable with delay-sensitive applications in vehicular networks like emergency services. In this paper, we investigate the e2e (end-to-end) delay in variable density vehicular scenarios with variable number of controllers. Our experiments prove the benefits of using multi-controllers in terms of delay but also the need of the deployment of the effective number according to the vehicular network density.
A Novel Hybrid Prediction Model for Hourly Gas Consumption in Supply Side Based on Improved Whale Optimization Algorithm and Relevance Vector Machine

Accurate short-term prediction of the natural gas load is of great significance to the operation and allocation of the pipeline network. Because the short-term natural gas load has obvious nonlinearity and randomness, the traditional regression model is difficult to predict accurately. Therefore, this paper proposes a hybrid prediction model that integrates an improved whale swarm algorithm (IWOA) and relevance vector machine (RVM). In addition, empirical mode decomposition (EMD), approximate entropy (ApEn), and C-C method are introduced to aid the calculation. In this paper, the IWOA is used to test the four functions and compared with the other five algorithms. The results show that the convergence accuracy and convergence speed of the new algorithm are higher than other algorithms, indicating that it has better global optimization ability. Second, the IWOA-RVM model is used to predict the supply data of two natural gas stations in Anhui Province, China. The prediction results are compared with the five algorithms including RBFNN, GRNN, ELMANNN, LSSVM, and SMOSVM. The results show that: 1) through the test of four functions, IWOA has better ability to jump out of local optimum, has higher optimization performance, and the calculation speed is at a medium level and 2) compared with other models, the IOWA-RVM model has higher prediction accuracy when the amount of data is larger or smaller, but the calculation time is relatively long, but the calculation time is acceptable in engineering.
Rotationally Unconstrained Region Proposals for Ship Target Segmentation in Optical Remote Sensing

The segmentation of ship targets in remote sensing images is of great importance in both military and civil fields. The existing methods often suffer from a low overlap between region proposals, which are constrained to be the horizontal rectangles and the ground truth. This enhances the problems of background noise and results in more false positives and a lower recall. Hence, we propose a method to generate the rotated bounding boxes that are more appropriate for ship segmentation. The proposed method is based on the Mask R-CNN framework, and the key contribution lies in the approach to predefine the rotated bounding boxes and generate the rotationally unconstrained region proposals. Specifically, for a double-stage proposal generation, a multiangle region proposal layer is designed; following this, an adapted alignment approach is proposed to extract the features for each proposal. In addition to the above-mentioned two proposed methods, we adopt a second stage to refine the proposals with rotation regression and mask prediction. We evaluated the proposed method using a remote sensing dataset with extensively labeled ship targets, and the experimental results show that the proposed method performs better than its competitors. The proposed method significantly increases the recall of densely arranged ships while substantially reducing the number of false positives.
Wearable Bioimpedance Measurement for Respiratory Monitoring During Inspiratory Loading

Bioimpedance is an unobtrusive noninvasive technique to measure respiration and has a linear relation with volume during normal breathing. The objective of this paper was to assess this linear relation during inspiratory loading protocol and determine the best electrode configuration for bioimpedance measurement. The inspiratory load is a way to estimate inspiratory muscle function and has been widely used in studies of respiratory mechanics. Therefore, this protocol permitted us to evaluate bioimpedance performance under breathing pattern changes. We measured four electrode configurations of bioimpedance and airflow simultaneously in ten healthy subjects using a wearable device and a standard wired laboratory acquisition system, respectively. The subjects were asked to perform an incremental inspiratory threshold loading protocol during the measurements. The load values were selected to increase progressively until the 60% of the subject’s maximal inspiratory pressure. The linear relation of the signals was assessed by Pearson correlation ( ${r}$ ) and the waveform agreement by the mean absolute percentage error (MAPE), both computed cycle by cycle. The results showed a median greater than 0.965 in ${r}$ coefficients and lower than 11 % in the MAPE values for the entire population in all loads and configurations. Thus, a strong linear relation was found during all loaded breathing and configurations. However, one out of the four electrode configurations showed robust results in terms of agreement with volume during the highest load. In conclusion, bioimpedance measurement using a wearable device is a noninvasive and a comfortable alternative to classical methods for monitoring respiratory diseases in normal and restrictive breathing.
Extended Fuzzy Adaptive Event-Triggered Compensation Control for Uncertain Nonlinear Systems With Input Hysteresis

This paper addresses the problem of adaptive compensation event-triggered control for uncertain nonlinear systems with input hysteresis. The communication resources cannot be saved effectively for the considered systems, as traditional event-triggered mechanisms do not consider input hysteresis with nonlinear gain. In a network control system, how to simultaneously overcome the problem of input hysteresis and save communication resources remains a challenge. In this paper, an extended fuzzy approximation method is proposed to estimate uncertain items for control design. The method considers the time-varying error of the approximation instead of viewing the error as a bounded constant. Furthermore, in combination with the extended fuzzy approximation method, an adaptive event-triggered compensation control method for uncertain nonlinear systems with input hysteresis is designed. Under the proposed event-triggered mechanism, the control method can effectively compensate for the input hysteresis and simultaneously save communication resources. Finally, this method is verified through two simulation experiments that the occupation of communication source has been reduced and the stability of the considered system can be guaranteed effectively.