Miracle-dz/UniFAR-FTUs huggingface dataset:
Goal-Oriented Water-Filling for Dynamic Management of Edge-Assisted OFDM Communications

Goal-oriented communications aim to deploy efficient learning architectures at the wireless edge, by parsimoniously using transmission resources, while possibly guaranteeing prescribed learning accuracy and delay. Aim of this paper is to propose a practical goal-oriented communications framework, which exploits an OFDM physical layer to cope with frequency selective fading channels. Targeting an image classification task, where images are scheduled according to an unknown arrival process, we propose a dynamic and goal-oriented data-loading of the wireless OFDM link that, exploiting banks of convolutional encoders at the user equipment, is capable to extract data features, with time-adaptive low dimension, that are meaningful to the classification task performed at the edge-server. The transmission of these features to the edge-server is dynamically optimized by exploiting knowledge of the channel state, computation load and images backlog, resorting to a Lyapunov stochastic optimization framework, which jointly optimizes the communication resources, the computation energy, and the encoders to be used. Specifically, we propose a minimum energy strategy under prescribed delay and accuracy of the edge-server learning task. Simulation results testify the effectiveness of the proposed strategy.
Intelligent Reflecting Surface Enhanced Full-Duplex Wireless-Powered Communication Network

In this paper, we consider an intelligent reflecting surface (IRS)-aided full-duplex (FD) wireless-powered communication network (WPCN), where a hybrid access point (HAP) operating in FD mode sends information signals to a device in the downlink (DL) and meanwhile receives energy signals from a power station (PS) in the uplink (UL) with the help of an IRS. Our objective is to maximize the achievable data rate from the HAP to the device by jointly optimizing the transmit covariance matrix at the PS, the transmit beamforming vector at the HAP, and the phase shift vector at the IRS. The optimal transmit beamformer at the HAP is derived in closed from, and the joint optimization of the transmit covariance matrix at the PS and the phase shift vector at the IRS results in an intractable non-convex problem. To tackle this challenge, we propose an efficient penalty-based algorithm consisting of two layers. In the inner layer, we iteratively increase the device's signal-to-interference-plus-noise ratio (SINR) by applying the Dinkelbach's transform. While in the outer layer, we gradually decrease the penalty parameter. Numerical results demonstrate the superiority of our proposed design over benchmark schemes, and also unveil the necessity of the joint design of passive IRS beamforming and active beamforming for achieving better WPCN performance.
Super-Resolution Range and Velocity Fast Estimation for OFDM-Based RadCom System

In order for the future vehicular radar and vehicular communication to integrate well, and aiming at the low real-time performance of the range and velocity estimation method, accurate and fast range-velocity estimation needs to be realized. To achieve this goal, this paper proposes a joint range and velocity fast estimation method based on the integrated signal of radar and communication (RadCom) system. First, we conceive of the orthogonal frequency division multiplexing (OFDM)-based integrated echo signal model, which is preprocessed to reduce the influence of communication data on the echo signal and achieve decoherence processing for the echo signal model. Then, the estimation of signal parameters via rotational invariance techniques-multiple signal classification (ESPRIT-MUSIC) algorithm is used to obtain fast estimation of range and velocity. The computer simulation results illustrate the superiority of our proposed method.
Optimal Energy Management System for Grid-Tied Microgrid: An Improved Adaptive Genetic Algorithm

Grid-tied microgrids play a crucial role by connecting renewable energy sources to the main power grid, contributing to sustainability and resilience in a balanced and effective manner. However, the dynamic interplay between the intermittent nature of renewable energy sources and the volatility of load fluctuations presents a multifaceted array of intricate energy management complexities. This study aims to formulate optimization techniques for energy management systems based on renewable energy resources and standalone diesel systems. The proposed system consists of a wind turbine, a photovoltaic system, a standalone diesel generator, and a battery energy storage system, along with flexible and non-flexible loads tied to the local grid. Battery energy storage acts as a primary backup system, while diesel generators act as a standalone secondary backup system. The performance of the proposed optimization technique is validated using Matlab/Simulink, substantiating its performance and robustness, thus affirming its pragmatic suitability for real-world implementation. A comparison has been made with other optimization techniques and found that the proposed technique gives enhanced efficiency, improved resource allocation, load scheduling, and greater adaptability to varying demand and supply dynamics. Moreover, the proposed system exhibits a superior ability to achieve optimal energy utilization and realize noteworthy cost savings in comparison to the alternatives that underwent evaluation.
Stochastic Geometric Performance Analysis for NR Sidelink Multi-Hop Relay

Sidelink relay enhancement, as a promising item in 3rd Generation Partnership Project (3GPP) R18, can support further vertical domain expansion of New Radio (NR). However, existing research on performance analysis of NR sidelink relay, either considered simple cases of single lane model or partially (if not totally) ignored the important factors of NR sidelink, such as resource allocation, power control, and coexistence with the cellular network. In this paper, a general stochastic geometry theoretical framework for performance analysis is developed to evaluate the success probability, time cost, and transmission distance of NR sidelink multi-hop relay in a typical unicast scenario, closely following the 3GPP specifications in both physical layer characteristics and parameters. As verified by extensive experiments, the considered performance metrics under different parameter settings can be accurately described through this theoretical framework, which holds great prospects for NR sidelink network design and deployment.
A wideband, thin profile and enhanced gain microstrip patch antenna modified by novel mushroom-like EBG and periodic defected ground structures

This paper presents a wideband, thin-profile and enhanced-gain microstrip patch antenna improved by using a novel mushroom-like Electromagnetic Band Gap (EBG) structure and forming periodic Defected Ground Structures (DGS) on the ground plane. The proposed antenna operates between 9.56 and 14 GHz and has 4.44 GHz–10 dB impedance bandwidth. With A4, the gain of the reference antenna is increased by 64.4%, while a 38% bandwidth is also achieved. The parametric analyses carried out on the metallic part area of the novel EBG patch indicated that when the area of the used EBG patches are approximately three quarters or less of the conventional ones, greater gain values for the designed antenna are obtained. This relation between the EBG patches and the antenna gain can be pointed out as the novelty of the study. The results were analysed by the simulations carried out with CST Microwave Studio and were verified by measurements from manufactured prototypes.
Metasurface-Based Index Modulation for Multi-User MIMO

The programmable metasurface (MTS), which can enhance the signal quality by flexibly manipulating the electromagnetic (EM) responses of reflected waves, has emerged as a promising technology for multiple-input multiple-output (MIMO) transmission due to its superior energy efficiency and cost-effective hardware implementations. In this paper, we consider a multi-user MIMO (MU-MIMO) system equipped with a MTS-based multi-feed transmitter, where an array of metallic elements is split into several subarrays (SAs), each irradiated by a unique radio-frequency (RF) feed. Additionally, each user is allocated with one unique RF-feed-SA pair for data services. Then we develop a novel index modulation (IM) scheme based on this array-of-SA (AoSA) structure of the MTS, named as MTS-SA -IM. More specifically, the allocation strategy of the SAs to different users, termed as SA allocation pattern (SAP), is flexibly controlled by the information bits. In other words, aside from classical amplitude/phase modulation bits, additional binary bits, referred to as index bits, can be embedded into the indices of the allocated SAs without extra power consumption, leading to enhancement of both spectrum and energy efficiencies. Furthermore, we propose a distributed mapping rule design between the index bits and SAPs, which guarantees that the index bits at each user are exclusively dependent on the index of its own allocated SA. By constructing a binary-tree structure, a general form of the proposed distributed mapping rule is generated recursively, which can be extended to the cases of arbitrary number of users. Simulation results demonstrate that the proposed MTS is capable of achieving desirable performance gain over its classical counterpart.
Defensive Randomization Against Adversarial Attacks in Image-Based Android Malware Detection

The extensive popularity of Android operating system hones the increased malware attacks and threatens the Android ecosystem. Machine learning is one of the versatile tools to detect legacy and new malware with high accuracy. However, these Machine Learning (ML) models are vulnerable to adversarial attacks, which severely threaten their cybersecurity deployment. To combat the deterrence of ML models against adversarial attacks, we propose a novel randomization method as a defense for image-based detection systems. In addition to defensive randomization, the paper also introduces a novel method, called AutoE, for transforming an APK to an image by leveraging API calls only. To evaluate the effectiveness of randomization as a defense against adversarial settings, we compare our AutoE with two state-of-the-art image-based Android malware detection systems. The experimental results reveal that the randomization is a strong defensive hood for image-based Android malware detection systems against adversarial attacks. Moreover, our novel AutoE detects malware with 96% accuracy and the randomization approach makes it harder against adversarial attacks.
eBPFlow: A Hardware/Software Platform to Seamlessly Offload Network Functions Leveraging eBPF

—NFV and SDN enable flexibility and programmability at the data plane. In addition, offloading packet processing to a hardware saves processing resources to compute other workloads. However, fulfilling requirements such as high throughput and low latency with a flexible and programmable data plane is challenging. This paper introduces eBPFlow, a platform for seamlessly accelerating network computation. It builds upon eBPF. eBPFlow combines flexibility and programmability in software with high performance using an FPGA. We implemented our system on the NetFPGA SUME, performing tests on a physical testbed. We built a range of NFs. Our results show that the eBPFlow supports offloading of NFs with throughput at the line rate, latency between 20 µ s and 40 µ s, communication with host, and consumption of 22 W. Moreover, eBPFlow processes 12.05 Mpps more than the kernel. eBPFlow has a throughput of 2.59 Gbps higher than the hXDP, a system similar to eBPFlow.
Burnout and mental health problems among health care workers during second wave of Covid-19 pandemic: A hospital based cross sectional study

Background: Large number of healthcare workers exposed to COVID-19 might be at increased risk of developing mental and psychological disorders. This study aimed to find out the prevalence of burnout, depression, anxiety and stress and its correlates. Methods: This was hospital based cross-sectional study using printed Questionnaire conducted during second wave of COVID-19. Total 96 healthcare workers participated in this study. Depression Anxiety Stress Scale (DASS-21) and Copenhagen Burnout Inventory (CBI) were used to measure study variables. Multivariable logistic regression analysis was done to determine the associate factors. Results: The symptoms of depression, anxiety and stress were found to be 31.3%, 40.6 % and 14.6 % respectively. Doctors were 5.2 and 6 fold more likely to experience anxiety and depression respectively. The prevalence of personal, work, and client-related burnout were 43.7%, 31.3% and 14.6% respectively. Younger healthcare workers (HCWs) less than 27 years were 3.5 fold more likely to develop personal burnout. Healthcare workers who had worked for more than 100 hours/week were 5 times more likely to experience personal burnout. Doctors and nurses were 7.9 and 3.5 times more likely to develop work related burnout. Personal, work and client related burnout were significantly correlated with anxiety. Conclusion: This study finds considerate proportion of burnout, depression, anxiety and stress among HCWs. Doctors and younger HCWs with increased working hours had high level of these symptoms. Early identification of mental health disorders and awareness intervention could increase the efficiency and quality of care.
Graph-Based Semantic Embedding Refinement for Zero-Shot Remote Sensing Image Scene Classification

Zero-shot remote sensing image scene classification (ZS-RSISC) aims to identify remote sensing (RS) image scenes of unseen classes whose samples are unavailable in the training stage. To transfer knowledge from seen RS classes to unseen RS classes, existing methods either rely on laborious manual labeling to learn semantic features or directly use the word embeddings learned based on the general corpus and independently of zero-shot models. They ignore the complex interclass correlation information, which plays a vital role in communicating seen with unseen classes. Besides, current studies in ZS-RSISC impose the same penalty to equally constrain each class for the interclass separation and intraclass compactness, which results in unclear classification boundaries. In this article, we tackle ZS-RSISC via graph-based semantic embedding refinement (GSER) in an end-to-end manner. We propose semantic graph convolutional networks (S-GCNs) to explore the correlation structure among classes in a unified framework. The semantic graph embeddings are further refined by the learning of the semantic-guided class patterns and component patterns. Specifically, we propose adaptive additive separation (AAS) loss to adaptively adjust the appropriate penalty for each class and explicitly promote intraclass compactness and interclass separation. Further, instance-level alignment and class-level alignment are proposed to enhance the discriminative ability of the semantic-guided class patterns. To alleviate model bias toward seen classes, semantic-guided component patterns shared by seen and unseen classes are exploited via feature reconstruction. Extensive experiments of both the zero-shot and generalized zero-shot settings demonstrate the effectiveness of our proposed GSER.
How to Spatial Audio with the WebXR API: a comparison of the tools and techniques for creating immersive sonic experiences on the browser

The WebXR Device API provides a powerful set of functionalities that can be used for creating immersive experiences that can be accessed directly from a web browser. However, while creating a compelling visual experience is easy to implement using the WebXR API and related tools, creating the audio part of the experience is less clear and with more open questions and possibilities. This paper presents a comparison of six tools oriented to practitioners and used for implementing and delivering interactive spatial audio experiences in XR applications running on a browser. These tools are selected and evaluated based on their compatibility with the WebXR Device API, ease of use, provided support and documentation, and specific features like reverberation, head-related transfer functions (HRTFs), sound scene rotation, and real-time microphone input. The paper then discusses the integration of these spatial audio tools into WebXR applications, highlighting their potential and limitations. It also addresses the absence of standardized spatial audio frameworks for the web. The challenges of choosing the right tool regarding resource allocation, learning curve, and compatibility with XR systems are acknowledged. This overview aims to provide insights into the current state of spatial audio tools for the web, making them more accessible to developers, musicians, and researchers seeking guidance on selecting suitable 3D spatial audio tools for experiences based on WebXR.
Empirical Examination of the Relationship Between Venture Capital Financing and the Profitability of the Portfolio Companies in Uganda

PurposeThis paper empirically examined the relationship between Venture capital Financing and the profitability of portfolio companies in Uganda. Several empirical works paint an intriguing picture that VC financing is a reality in fostering economic growth of the early-stage enterprisesDesign/methodology/approachThe paper embraced a mixed-method wherein quantitative data were collected from 68 key VC stakeholders and statistical data was analysed using a multiple regression model by generating the results from the SPSS. This was supplemented by 16 semi-structured face-to-face interviews. Qualitative data were transcribed and analysed using Altas-ti.Findings The key findings from this study suggest superior performance of VC-financed enterprises following VC financing when compared to non-VC financed enterprises. The VC-financed enterprises realised tremendous growth in terms of: Return on Equity - 42.3%; profitability - 30% - 50%; Return on Assets - 44.5%. In addition, 63% of the respondents confirmed a positive impact of government regulatory policy in enhancing the development of the early-stage firms. The higher the percentage of growth the better for the growth of the company. Research limitations/implicationsThis study may assist the policymakers and business fraternity to develop policy frameworks tailored to enhancing a vibrant venture capital market in Uganda. Contribution/value-addThis study makes a vital contribution to knowledge by offering a diversified framework for enterprise success, to benefit the VC stakeholders in customizing funding programs that can propel the early-stage enterprise success in Uganda and similar emerging economies.
A Wideband Colinearly Polarized Composite Antenna With High Isolation

In this paper, a high-isolation two-port co-linearly polarized (co-LP) composite antenna is proposed. Dipole and W8JK array modes with co-LP omnidirectional patterns can be simultaneously supported by a plate with two etched back-to-back tapered slots. A microstrip differential and a T-divider coupling feeding networks are developed to selectively excite the dipole and W8JK modes, which results in high isolation and low correlation between two co-LP ports without using any decoupling structures. Reflecting ground is applied to realize the directional radiation. Measurement results show that the overlapped operating band of the antenna can cover 3.4–4.1 GHz with low measured envelope correlation coefficients (< 2.5 × 10−3) and high isolation (> 20 dB). A dual-polarized four-port element with good performances can be easily obtained based on the proposed co-LP antenna. The simulated results of the four-port prototype indicate that the proposed antenna is a promising candidate for the future compact massive MIMO array antenna.
A Deep Learning-Based Moving Target Detection Method by Combining Spatiotemporal Information for ViSAR

Video synthetic aperture radar (ViSAR) can produce continuous images with a high frame rate and contain the moving target’s shadow, which provides numerous advantages for detecting moving targets. In this letter, a novel moving target detection method based on convolutional neural network (CNN) is proposed. The proposed network has a 3-D convolutional encoding path, a 2-D convolutional decoding path, and a bridge path to efficiently capture the target’s shadow information and summarize the spatiotemporal features from raw continuous images to high-level semantics. Furthermore, based on coordinate attention (CA), a temporal tri-CA (TTCA) module is proposed to obtain key spatiotemporal features in ViSAR data. The validity of the proposed method has been confirmed through experiments with actual ViSAR data and shows superior performance in the suppression of false alarms and missing alarms.
Comparisons of ionospheric electron density distributions reconstructed by GPS computerized tomography, backscatter ionograms, and vertical ionograms

Global Positioning System (GPS) computerized ionosphere tomography (CIT) and ionospheric sky wave ground backscatter radar are both capable of measuring the large‐scale, two‐dimensional (2‐D) distributions of ionospheric electron density (IED). Here we report the spatial and temporal electron density results obtained by GPS CIT and backscatter ionogram (BSI) inversion for three individual experiments. Both the GPS CIT and BSI inversion techniques demonstrate the capability and the consistency of reconstructing large‐scale IED distributions. To validate the results, electron density profiles obtained from GPS CIT and BSI inversion are quantitatively compared to the vertical ionosonde data, which clearly manifests that both methods output accurate information of ionopsheric electron density and thereby provide reliable approaches to ionospheric soundings. Our study can improve current understanding of the capability and insufficiency of these two methods on the large‐scale IED reconstruction.
ÖĞRETMENLERİN COVİD-19 SONRASI OKULA DÖNÜŞ SÜRECİNİ ONLİNE SESLİFOTO (OSF) YÖNTEMİYLE ANLAMAK

Sosyal bilgiler öğretmenlerinin Covid-19 salgını sonrası başlayan eğitim-öğretim sürecine ilişkin algılarını Online Seslifoto (OSF) yöntemiyle ele alan bu çalışmada amaç; (1) Covid-19 salgınından sonra yapılan eğitim-öğretimi zorlaştıran faktörleri bulmak; (2) zorlaştırıcı faktörleri ortadan kaldırmak için aktif savunuculuk yapmak ve (3) katılımcıların zorlaştırıcı faktörleri Ekolojik Sistemler Teorisinin hangi seviyesine atfettiklerini belirlemektir. Verileri toplamak için OSF yöntemi ve verileri analiz etmek için Online Yorumlayıcı Fenomenolojik Analiz yaklaşımı kullanılmıştır. Çalışmaya katılıp rıza gösteren 32 (10 kadın, 22 erkek) katılımcı bulunmaktadır. Çalışmada 6 tema (örnek olarak, öğrencilerin öğrenme kayıpları/ %34, n=11; uyum problemi %22, n=7; kaygı %16, n=5) bulunmuştur. Katılımcılar, salgın sonrası başlayan eğitim-öğretimde zorlaştırıcı faktörleri sırasıyla Ekolojik Sistemler Teorisinin şu seviyelerine atfetti; makrosistem (%47) mikrosistem ve, bireysel/içsel (%41), hepsi beraber (%32) ve ekzosistem (%22). Öğretmenlerin salgın sonrası yüz yüze eğitim-öğretimde karşılaştıkları en büyük zorlaştırıcı unsurun öğrenme kayıpları olduğu dikkate alınarak okullarda telafi eğitimlerinin yanı sıra bireyselleştirilmiş eğitim modelleri önerilmektedir. İkinci önemli bir zorlaştırıcı olan uyum problemi için akademik başarıyla beraber eğitim-öğretimin bütünlüğü gereği diğer yetkinliklere de (sorumluluk, sosyal uyum, iletişim vb.) aynı ölçüde önem verecek uygulamaların yeniden düşünülmesi önerilmektedir.