Initial Transmission Research Articles

Anti-Jamming Beam Alignment in Millimeter-Wave MIMO Systems

In millimeter-wave (MMW) multiple-input multiple-output (MIMO) communications, users and their corresponding base station (BS) have to align their beam during both initial access and data transmissions to compensate for the high propagation loss. The beam alignment (BA) procedure specified for 5th Generation (5G) New Radio (NR) has been designed to be fast and precise in the presence of non-malicious interference and noise. A smart jammer might exploit this weakness and may launch an attack during the BA phase in order to degrade the accuracy of beam selection and, thus, adversely impacting the end-to-end performance and quality-of-service experienced by the users. In this paper, we study the effects of a jamming attack at MMW frequencies during the BA procedure used to perform initial access for idle users and adaptation/recovery for connected users. We show that the BA procedure adopted in 5G NR is extremely vulnerable to a smart jamming attack and, consequently, we propose a countermeasure based on the idea of randomized probing, which consists of randomly corrupting the probing sequence transmitted by the BS in order to reject the jamming signal at the UE via a subspace-based technique based on orthogonal projections and jamming cancellation. Numerical results corroborate our theoretical findings and show the very satisfactory accuracy of the proposed anti-jamming approach.

Few-ns, kW peak power Q-switched pulses from orange and red Pr:YLF lasers

We report on the generation of few-ns pulses with kW peak power in the red and orange spectral range obtained from a passively Q-switched Pr3+:YLF laser. A compact resonator and thorough investigation of the Co:MgAl2O4 saturable absorber’s initial transmission enables peak powers in excess of 1.1 kW at pulse durations of 2.4 ns and 5.3 ns at 640 nm and 607 nm, respectively. These are the shortest pulse durations among any Q-switched Pr3+ laser and make this system a suitable front-end for efficient frequency doubling into the ultraviolet range.

Continuous-wave and passively Q-switched pulse Er:Yb:Ca3NbGa3Si2O14 microlasers at 1.5–1.6 μm

Compact continuous-wave and passively Q-switched pulse microlasers at 1.5–1.6 μm were developed by using a 5-mm-thick Er:Yb:Ca3NbGa3Si2O14 crystal as a gain medium. End-pumped by a 975.6 nm continuous-wave laser diode, a 1555 nm continuous-wave laser with a maximum output power of 1.05 W and a slope efficiency of 8.8% was obtained at an absorbed pump power of 14.2 W. End-pumped by a 975.6 nm pulsed laser diode with a pulse width of 0.3 ms and a pulse period of 1.0 ms, a stable 1534 nm passively Q-switched pulse laser operating at a repetition frequency of 1.0 kHz was realized by using a Co2+:MgAl2O4 crystal with an initial transmission of 95% as a saturable absorber. At a peak absorbed pump power of 23.6 W, the obtained single-pulse energy, pulse width and peak output power were 8.06 μJ, 7.9 ns, and 1.02 kW, respectively.

Pelacakan Perahu Masyarakat Yang Mengalami Kondisi Darurat Di Perairan Sipora Utara Kab. Kepulauan Mentawai Berbasis Internet Of Things

Technology in search and rescue has been growing at this time, especially in early detection systems. Basarnas as the leading sector in search and rescue, has used an early detection communication tool called Radio Beacon to quick response search and rescue for aircraft and ship accidents, and conditions that endanger humans. However, in its application in the field, this early detection system has not been touched by the local community, especially because the price is still relatively expensive and the socialization of the importance of safety itself has not been maximized in the community. From these problems, this paper designs a tracking device that works like a Radio Beacon where the switch will activate the Arduino device, GPS module and SIM800l module. The GPS module receives the location coordinates to be further processed by Arduino and sent using the SIM800l module to Firebase and notifications is received by the smartphone and the location can be monitored through an android application created using the MIT App Inventor platform. From the test results, it is found that the initial data transmission process starts from the switch is activated until the notification is received by the smartphone, a delay of 70 seconds occurs while in the process of repeating sending data there is a delay between 10-35 seconds from what has been set in the program looping every 50 seconds.

Chilean Electric Transmission Regulation: From a Merchant Approach to Central Planning

From the 1980s onwards, many jurisdictions reformed their electricity sectors to create energy markets. To this end, they unbundled transmission from generation, raising the issue of whether and how to regulate transmission. This paper contributes to this literature by analyzing the 40-year Chilean experience. To do so, it describes the initial transmission regulation, the changes introduced, the causes that triggered them, and their results regarding grid access and expansion and their impact on the energy market. The Chilean Electricity Act, issued in 1982, established open access to transmission facilities but left access and expansion conditions unregulated. This situation caused generators’ grid access problems, harming energy market competition. In 2004, lawmakers amended the Act to require the regulator to develop annual expansion plans and regulate transmission remuneration and financing. Following these changes, transmission expanded adequately for about a decade. However, since 2016, renewable energy curtailments have been significant due to congestion on some transmission lines due to construction delays. The increasing empowerment of civil society in the decision-making processes of new investments is relevant in explaining the delays. The main lesson is that citizen consensus-building is crucial for grid expansion.

Neural Networks Based Beam Codebooks: Learning mmWave Massive MIMO Beams That Adapt to Deployment and Hardware

Millimeter wave (mmWave) and massive MIMO systems are intrinsic components of 5G and beyond. These systems rely on using beamforming codebooks for both initial access and data transmission. Current beam codebooks, however, generally consist of a large number of narrow beams that scan all possible directions, leading to large training overhead. Further, these codebooks do not normally account for hardware impairments or possible non-uniform array geometries, and their calibration process is expensive. To overcome these limitations, this paper develops an efficient online machine learning framework that learns how to adapt the codebook beam patterns to the specific deployment, surrounding environment, user distribution, and hardware characteristics. This is done by designing a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">complex-valued neural network</i> architecture in which the neuron weights directly model the beamforming weights of the analog phase shifters, accounting for the key hardware constraints. This model learns the codebook beams through online and self-supervised training avoiding the need for explicit channel state information. This respects the practical situations where the channel is imperfect or hard to obtain. Simulation results highlight the capability of the proposed solution in learning environment and hardware aware beam codebooks, which reduce the training overhead and improve the robustness against possible hardware impairments.

Analysis of Transmission Depth and Photon Number in Monte Carlo Simulation for Underwater Laser Transmission

The modeling of laser transmission characteristics in complex seawater is fundamental for bathymetric and oceanographic laser detection systems. Because the factors affecting laser transmission in seawater are independent of one another, firstly, in this paper, a Monte Carlo model of laser propagation in seawaters with suspended matter was established to study the influence of suspended matter with specific radius on the underwater laser transmission. Secondly, the influence of transmission depth and the number of photons on the laser extinction coefficients of seawater containing different concentrations of suspended matter were analyzed, respectively. Thirdly, the relationships between maximum transmission depth, the number of initial photons, and the concentrations were built and verified by simulations. Lastly, an experimental platform was set up and experiments were carried out to verify the Monte Carlo model and the relationships. Results show that (1) both the minimum initial photon number and maximum transmission depth depend exponentially on the concentrations of the suspended matter; (2) the extinction coefficients obtained by the Monte Carlo model and those obtained by experiments are consistent. The absolute values of the differences are less than 0.028 m−1, implying that (1) the proposed Monte Carlo model is effective for simulating laser propagation in seawaters with suspended matter; (2) the established relationships between maximum transmission depth, the minimum initial photon number, and the concentrations of suspended matter have better accuracies, which are valuable for the simulations on attenuation of laser transmission in seawater. The method of this paper can also be extended to the study of suspended solids with other radii and improve the simulation accuracy and decrease simulation time consumption.

HIV / AIDS as a model for emerging infectious disease: Origin, dating and circumstances of an emblematic epidemiological success

In June 1981, the Centers for Disease Control (CDC) "Morbidity and Mortality Weekly Report" described the first cases of what was to be known as the Acquired Immunodeficiency Syndrome (AIDS). Two years later, the agent responsible for the disease, the human immunodeficiency virus (HIV), was identified. Since then, according to the World Health Organization an estimated 40 million people have died from the disease. Where does this virus come from, and why such an emergence in the late 20th century? These are the questions that it is now possible to answer in large part thanks to the numerous studies published over a little more than three decades. As with other emerging infectious diseases, initial cross-species transmission from an animal reservoir and subsequent favorable sociological factors associated with the evolution of human societies have led to the spread of a dramatic disease, for which no vaccine is presently available.

Unraveling the hurdles of a large COVID-19 epidemiological investigation by viral genomics

COVID-19 local outbreak response relies on subjective information to reconstruct transmission chains. We assessed the concordance between epidemiologically linked cases and viral genetic profiles, in the Baixo Vouga Region (Portugal), from March to June 2020. A total of 1925 COVID-19 cases were identified, with 1143 being assigned to 154 epiclusters. Viral genomic data was available for 128 cases. Public health authorities identified two large epiclusters (280 and 101 cases each) with a central role on the spread of the disease. Still, the genomic data revealed that each epicluster included two distinct SARS-CoV-2 genetic profiles and thus more than one transmission network. We were able to show that the initial transmission dynamics reconstruction was most likely accurate, but the increasing dimension of the epiclusters and its extension to densely populated settings (healthcare and nursing home settings) triggered the misidentification of links. Genomics was also key to resolve some sporadic cases and misidentified direction of transmission. The epidemiological investigation showed a sensitivity of 70%–86% to detect transmission chains. This study contributes to the understanding of the hurdles and caveats associated with the epidemiological investigation of hundreds of community cases in the context of a massive outbreak caused by a highly transmissible and new respiratory virus.

Redundancy of Respiratory Humidification &amp; Dysfunction of Defence Mechanisms at Upper Airways of Large Number of People Due to Cumulative Adverse Effects of Climate Change -The Root Cause of the COVID-19 Outbreak

The COVID-19 has returned in the form of an outbreak of new variants. Viruses have evolved and accompanied mankind from times immemorial but seem to be increasingly threatening the survival of the human race especially in the last four decades. Various theories are available with respect to initial transmissions to humans yet none is endorsed in totality. At the same time climate change poses the greatest threat to human health and its effects are more pronounced in the last four decades than ever before. The purpose of this perspective is to establish scientifically a correlation between the outbreak of COVID-19 or its variants and climate change. This presentation is based on hitherto overlooked yet proven physics and engineering involved in the human respiratory humidification. Respiratory humidification is a uniquely evolved process of Natural Selection to withstand complex and dynamic environments. As COVID-19 is a respiratory viral disease, the gas exchange mechanism and human body’s normal temperature and alveolar air parameters are first explained to justify its uniqueness. A step by step review of physics and engineering involved in respiratory humidification is also done. It is then analyzed in context of Climate Change of a location and its impact on inspired air parameters. Based on Meteorological Data of Wuhan; the place of COVID-19 outbreak; it is established that the Climate Change resulted in redundancy of respiratory humidification and dysfunction of associated immune defence mechanisms of many inhabitants and facilitated viruses like SARS-CoV-2 an unchallenged access to the lungs. The outbreak of COVID-19 and subsequent pandemic was its outcome.

Genetic diversity of SARS-CoV-2 infections in Ghana from 2020-2021.

The COVID-19 pandemic is one of the fastest evolving pandemics in recent history. As such, the SARS-CoV-2 viral evolution needs to be continuously tracked. This study sequenced 1123 SARS-CoV-2 genomes from patient isolates (121 from arriving travellers and 1002 from communities) to track the molecular evolution and spatio-temporal dynamics of the SARS-CoV-2 variants in Ghana. The data show that initial local transmission was dominated by B.1.1 lineage, but the second wave was overwhelmingly driven by the Alpha variant. Subsequently, an unheralded variant under monitoring, B.1.1.318, dominated transmission from April to June 2021 before being displaced by Delta variants, which were introduced into community transmission in May 2021. Mutational analysis indicated that variants that took hold in Ghana harboured transmission enhancing and immune escape spike substitutions. The observed rapid viral evolution demonstrates the potential for emergence of novel variants with greater mutational fitness as observed in other parts of the world.

A diffusive tuberculosis model with early and late latent infections: New Lyapunov function approach to global stability

This paper formulates a diffusive tuberculosis (TB) model with early and late latent infections, vaccination and treatment that may more properly describe the slow and fast dynamics of TB transmission. We develop a new concise approach to determine the combination coefficients in the Lyapunov function candidate for the model and its time derivative in the case that both are the linear combinations of several Volterra-type functions, which highly simplifies the computations in global dynamical analysis for the nonlinear high-dimensional model. Based on the TB case data reported in China, the parameter values of the model are estimated. We further predict the TB prevalence trend in China. Sensitivity analysis for the control reproduction number and endemic equilibrium is conducted to seek some effective interventions that can significantly reduce initial TB transmission and lower TB prevalence levels in China. In the end, numerical simulations show that the bigger diffusive rates pick up the speeds of convergence to the equilibria of the model.

How can One Health contribute to pandemic prevention? Looking at Ebola through a One Health lens

Abstract One Health has been gaining acceptance as an approach to addressing complex health challenges. Due to the multifactorial nature of disease emergence, pandemic prevention efforts benefit from a transdisciplinary approach to identifying risk factors, effective mitigation strategies and control points, hence making One Health attractive. When considering a zoonotic disease with pandemic potential, such as Ebola Virus Disease (EVD), gaining an understanding of the factors that can contribute to an increased risk of disease spillover occurring allows for early intervention, decreasing the risk of initial transmission from animal to human. However, despite the advantages that the One Health approach offers, there remain challenges to adopting and operationalising it effectively, including barriers of status, bias and power between professions and the effective integration of environmental health and social science expertise. One Health Impact Statement The SARS-CoV-2 pandemic has brought into sharp focus the interrelated nature of human, animal and environmental health. There is renewed focus on the importance of pandemic prevention efforts and an acknowledgement that human health cannot be viewed in isolation. By drawing on a wide variety of professional disciplines and embracing traditional and indigenous knowledge regarding diseases and social determinants of health, a much richer understanding of potential intervention points can be generated. Additionally, a deeper understanding can be gained concerning actions with the greatest potential to prevent the emergence and establishment of diseases with pandemic potential within a human population. This desk study highlights that there is value in harnessing the transdisciplinary One Health approach to increase knowledge regarding the currently poorly understood risk factors for Ebola Virus Disease (EBV) spilling over from animals to humans. It also highlights some of the challenges to effective implementation of the approach.

Compact Passively Q-Switched Tm:KY(WO&lt;sub&gt;4&lt;/sub&gt;)&lt;sub&gt;2&lt;/sub&gt; Laser

Diode-pumped thulium lasers emitting in the spectral range near 2 μm are attractive for applications in different areas: surgery, rangefinding, and environmental atmosphere monitoring. In this article the latest results of Tm:KYW laser performance with a polycrystalline Cr:ZnSe as the most available saturable absorber for 2 μm spectral region are presented.A maximum continuous-wave output power of ≈ 0.65 W with a slope efficiency of 55 % was obtained at the wavelength of 1940 nm. Laser pulses with energy of 26 μJ and repetiotion rate of 6 kHz coresponding to 156 mW of average output power at 1910 nm were obtained at 2.2 W of incident puwp power for the Cr:ZnSe saturable absorber with initial transmission of 95 %. By using of saturable absorber with lower initial transmission of 90 % laser pulses with energy of 40 μJ and duration as short as 10 ns were realized. The maximal pulse repetition rate was 2.8 kHz at incident pump power of 2.2 W.Based on the obtained results, it can be concluded that Tm:KYW crystals are promising active media for the compact passively Q-switched lasers emitting in the spectral range near 2 μm for the usage in surgery and rangefinding. Also, described laser is planned to be used as a laser source in laser-induced damage threshold measurements setup for investigation of damage threshold of saturable absorbers as well as nonlinear crystals at the wavelength near 2 μm.

Monthly Trends in the Life Events Reported in the Prior Year and First Year of the COVID-19 Pandemic in New Zealand.

The current study examines changes in the economic, social, and well-being life events that women and men reported during the first 7 months of the COVID-19 pandemic. Analyses compared monthly averages in cross-sectional national probability data from two annual waves of the New Zealand Attitudes and Values Study collected between October 2018–September 2019 (N = 17,924), and October 2019–September 2020 (N = 41,653), which included the first 7 months of the pandemic (Mar–Sep 2020). Results indicated that people (particularly women) reported increased job loss in the months following an initial COVID-19 lockdown relative to the same months the year earlier. Women also experienced an increase in family troubles when restrictions eased and reported increased negative lifestyle changes that persisted throughout the first 7 months of the pandemic. The proportion of people experiencing many other life events (e.g., mental health, financial concerns) in New Zealand did not differ reliably from the pre-pandemic monthly baseline. These results highlight resilience to many potential negative life events within the first 7 months of the COVID-19 pandemic. However, the pandemic did not affect everyone equally, and the burden of increased negative events appears more heavily borne by women. As the pandemic continues more than 18 months from initial community transmission of COVID-19, our findings provide important insight into the impact of the pandemic on potential negative life events, especially among women, that may have critical consequences for mental health, gender equality, and social well-being over time.

Robust regularization for single image dehazing

Generally, conventional image dehazing methods attempt to improve the initial transmission map by using guidance information from the input image as a structural prior. However, these methods do not consider the structural differences between the guidance and the initial transmission map. Furthermore, these methods provide limited robustness against outliers. This paper tackles these problems by optimizing a nonconvex energy function that jointly leverages structural information from the transmission map and guidance. Specifically, a nonconvex regularizer, and correspondingly, an efficient energy function are proposed. The energy function is solved using a majorize-minimize algorithm. Consequently, an improved transmission map is obtained in which the edges are well-preserved. Ultimately, this improved transmission map provides a high-quality haze-free image that has vivid colors and fine details. Experiments were performed on various synthetic and real hazy image datasets. The performance of the proposed method is compared with the state-of-the-art methods by using the root mean square error (RMSE), structural similarity index (SSIM), fog aware density evaluator (FADE), and visibility level descriptor (VLD). Both the qualitative and quantitative results demonstrate the effectiveness of the proposed robust regularization.

Epidemic Data Analysis of Three Variants of COVID-19 Spread in Indonesia

Three variants of COVID-19 had been found in Indonesia. A control strategy may rely on the transmission rate of the variant. This study aims to investigate how the variants spread in Indonesia by computing a basic and effective reproduction number on the national and province scale. The basic reproduction number shows the indicator of initial transmission rate of alpha variant computed by an exponential growth rate model. The effective reproduction number describes the dynamic of the transmission rate estimated based on a Bayesian approach. This study revealed that each variant shows different characteristics. The alpha variant of COVID-19 in Indonesia was mainly initiated from big cities, then it spread to all provinces quickly because the control strategies were not established well at the beginning. A rapid increase of the effective reproduction number about July 2021 showed a novel delta variant, but it could be managed quite well by a large number of testing and stronger restrictions. Before the end of 2021, a novel variant omicron was also shown by the steeper change of the effective reproduction number. Thus, the variant spread rate can be estimated by how steep the effective reproduction number change is.

ASM-VoFDehaze: a real-time defogging method of zinc froth image

When the ambient temperature is low, a large amount of water mist and dust will inevitably appear around the zinc flotation cell, forming haze, which seriously affects the extraction of flotation froth image features. General defogging methods of natural image are difficult to obtain satisfactory results for such industrial haze image. Therefore, we propose a real-time defogging method based on ASM-VoFD (Atmospheric Scattering Model and the Variable-order Fractional Differential). First, the dark pixel ratio is used to detect fog in froth image, which solves the redundant calculation caused by unnecessary defogging operations. Second, the linear transformation of the atmospheric scattering model is used to calculate the initial transmission map, and the gaussian filter is used to optimize the initial transmittance, and the haze-free image is restored with atmospheric light estimation. Finally, a variable order fractional differential operator is used to enhance the edges and texture details of the restored froth image, which solves the problems of blurred edges and low contrast. The experiments show that the algorithm has a good defogging effect on the industrial images, enhance the edges of the image, and can be effectively implemented in O(N) time to meet the application requirements of real-time flotation monitoring.

Reinforcement Learning of Beam Codebooks in Millimeter Wave and Terahertz MIMO Systems

Millimeter wave (mmWave) and terahertz MIMO systems rely on pre-defined beamforming codebooks for both initial access and data transmission. These pre-defined codebooks, however, are commonly not optimized for specific environments, user distributions, and/or possible hardware impairments. This leads to large codebook sizes with high beam training overhead which makes it hard for these systems to support highly mobile applications. To overcome these limitations, this paper develops a deep reinforcement learning framework that learns how to optimize the codebook beam patterns relying only on the receive power measurements. The developed model learns how to adapt the beam patterns based on the surrounding environment, user distribution, hardware impairments, and array geometry. Further, this approach does not require any knowledge about the channel, RF hardware, or user positions. To reduce the learning time, the proposed model designs a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Wolpertinger</i> -variant architecture that is capable of efficiently searching the large discrete action space. The proposed learning framework respects the RF hardware constraints such as the constant-modulus and quantized phase shifter constraints. Simulation results confirm the ability of the developed framework to learn near-optimal beam patterns for line-of-sight (LOS), non-LOS (NLOS), mixed LOS/NLOS scenarios and for arrays with hardware impairments without requiring any channel knowledge.

Impact analysis of environmental and social factors on early-stage COVID-19 transmission in China by machine learning

As a highly contagious disease, COVID-19 caused a worldwide pandemic and it is still ongoing. However, the infection in China has been successfully controlled although its initial transmission was also nationwide and has caused a serious public health crisis. The analysis on the early-stage COVID-19 transmission in China is worth investigating for its guiding significance on prevention to other countries and regions.In this study, we conducted the experiments from the perspectives of COVID-19 occurrence and intensity. We eliminated unimportant factors from 113 variables and applied four machine learning-based classification and regression models to predict COVID-19 occurrence and intensity, respectively. The influence of each important factor was analysed when applicable.Our optimal model on COVID-19 occurrence prediction presented an accuracy of 91.91% and the best R2 of intensity prediction reached 0.778. Linear regression-based model was identified as unable to fit and predict the intensity, and thus only the variable influence on COVID-19 occurrence can be explained.We found that (1) CO VID-19 was more likely to occur in prosperous cities closer to the epicentre and located on higher altitudes, (2) and the occurrence was higher under extreme weather and high minimum relative humidity. (3) Most air pollutants increased the risk of COVID-19 occurrence except NO2 and O3, and there existed a lag effect of 6–7 days. (4) NPIs (non-pharmaceutical interventions) did not show apparent effect until two weeks after.