Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of the GHSSmooth and the Rayleigh-Rice surface scatter theories

The scalar-based GHSSmooth surface scatter theory results in an expression for the BRDF in terms of the surface PSD that is very similar to that provided by the rigorous Rayleigh-Rice (RR) vector perturbation theory. However it contains correction factors for two extreme situations not shared by the RR theory: (i) large incident or scattered angles that result in some portion of the scattered radiance distribution falling outside of the unit circle in direction cosine space, and (ii) the situation where the relevant rms surface roughness, σrel, is less than the total intrinsic rms roughness of the scattering surface. Also, the RR obliquity factor has been discovered to be an approximation of the more general GHSSmooth obliquity factor due to a little-known (or long-forgotten) implicit assumption in the RR theory that the surface autocovariance length is longer than the wavelength of the scattered radiation. This assumption allowed retaining only quadratic terms and lower in the series expansion for the cosine function, and results in reducing the validity of RR predictions for scattering angles greater than 60°. This inaccurate obliquity factor in the RR theory is also the cause of a complementary unrealistic “hook” at the high spatial frequency end of the predicted surface PSD when performing the inverse scattering problem. Furthermore, if we empirically substitute the polarization reflectance, Q, from the RR expression for the scalar reflectance, R, in the GHSSmooth expression, it inherits all of the polarization capabilities of the rigorous RR vector perturbation theory.
Energy-Efficient Integrated Motion Planning and Control for Unmanned Surface Vessels

This brief studies the online simultaneous motion planning and control of unmanned surface vessels (USVs) with multiple practical constraints. An online economic model predictive control (EMPC)-based integrated planning and control framework is developed to greatly reduce energy consumption. In particular, a novel heuristic terminal cost guarantees both the planning control performance and facilitates the online optimization, and an improved cross-entropy (CE)-based optimization algorithm speeds up the solving of the nonconvex economic optimization problem. Experimental results show that the proposed integrated planning and control approach can be implemented in real-time with the online optimization frequency of 100 Hz, and comparative studies indicate that it can save energy up to almost 18%.
Analysis of Pre-post Covid-19 Influence in Bangka Belitung Islands Province: Socio-Economic Aspects in 7 Regencies/Cities

This study will analyze the variables of economic growth, poverty rate, unemployment rate, and the Human Development Index using situational trend analysis of the 4 factors before - after Covid-19 and spatial autocorrelation to see the inter-regional linkages as well as the distribution pattern of the observed data. The purpose of the study was: 1) to find out the description of the situation of the 4 factors before – after Covid-19; and 2) knowing the spatial autocorrelation based on Moran’s index with the spatial weighting matrix (WIJ). Based on the results of the analysis, the rate of economic growth in each district/city of Prov. Bangka Belitung tends to have a downward trend in the period 2018–2020 except Kab. Bangka and Pangkalpinang City. The variable rate of economic growth also did not have a spatial autocorrelation during the 2018-2021 period, which was indicated by the distribution pattern of the 2018-2019 data spreading (the Moran index was negative) and 2020-2021 was clustered (the Moran index was positive). Unemployment variable in all districts/cities of Prov. Bangka Belitung had a significant upward trend in the 2018-2021 period. The variable percentage of unemployment also did not have a spatial autocorrelation, which was indicated by a data distribution pattern that spreads over the 2018-2021 time period. The poverty variable in all districts/cities of Prov. Bangka Belitung had a downward trend in the 2018-2021 period. The variable percentage of poverty also does not have a spatial autocorrelation, which was indicated by a data distribution pattern that spreads over the 2018-2021 observation period (the Moran index was negative). Variable Human Development Index in all regencies/cities prov. Bangka Belitung has an upward trend during the 2018-2021 timeframe. The HDI variable also does not have a spatial autocorrelation, but the data distribution pattern tends to collect during the 2018-2021 time period (positive Moran index). Keywords: economic growth, poverty, unemployment, human development, spatial autocorrelation, Covid 19
Laminar–turbulent transition and wave–turbulence interaction in stratified horizontal two-phase pipe flow

Stratified cocurrent flow of air and water was studied experimentally in a 5 cm diameter horizontal pipe. The velocity in the liquid phase was measured using planar particle image velocimetry, and the instantaneous interfacial profile was recorded using a separate camera. The resulting velocity fields extended from the pipe wall to the wavy interface. The principal aims of the study were to investigate the laminar–turbulent transition of the liquid phase in stratified gas–liquid flow, and to explore the interaction between the transition process and the interfacial waves. The boundaries of transition were determined in both the smooth and the wavy region. The occurrence of waves had the effect of increasing the Reynolds numbers at the end of transition. On the other hand, the transition to turbulence caused a change from the ‘2D small-amplitude’ to the ‘3D small-amplitude’ wave pattern, which were seen to correspond to the capillary–gravity and gravity–capillary solutions of the dispersion relationship respectively. In light of this, the flowmap of the wavy region was recast into Weber number–Froude number coordinates, which provided a physical interpretation of the interaction between the developing turbulence and the changing wave patterns.
HINNet + HeadSLAM: Robust Inertial Navigation With Machine Learning for Long-Term Stable Tracking

In recent years, human position tracking with wearable sensors has been rapidly developed and shown great potential for applications within healthcare, smart homes, sports, and emergency services. Unlike tracking researches with sensors on the foot, human positioning studies with head-mounted sensors are fewer and still remain problems that have not been solved. We have proposed two studies solve part of the problems separately: HINNet is able to track people with free head rotations; HeadSLAM allows long-term tracking with stable errors. In this letter, to allow free head rotations meanwhile support long-term tracking, HINNet is combined with HeadSLAM and tested. The result shows that the combination could effectively distinguish head rotations and keep a low and stable position error in long-term tracking, with an absolute trajectory error (ATE) of 2.69 m and relative trajectory error (RTE) of 3.52 m.
Capturing ultrafast spectral evolution with transient grating photoluminescence spectroscopy

We have developed a new method, transient grating photoluminescence spectroscopy (TGPLS), allowing the collection of broadband ultrafast photoluminescence spectroscopy with low photoluminescence background. In TGPLS, two ultrafast laser pulses generate a multiplexed transient grating (TG) by the optical Kerr effect. The gated signal is diffracted by the TG and spatially separated from background fluorescence. This high performance nonlinear optical gate delivers time resolution less than 200 fs, spectral bandwidth covering the entire visible region with extremely low fluorescence background. Here we present two applications of TGPLS that provide deeper insight into ultrafast energy transfer in multi-chromophore perylene arrays and ultrafast structural relaxation in oligothiophenes.
A Machine Learning Technique for Detection of Social Media Fake News

The emergence of the Internet and the growing development of online platforms (like Facebook and Instagram) opened the way for disseminating information that hasn't been experienced in the history of mankind earlier. Consumers generate and share more information and a massive amount of data than ever with the growing utilization of social media sites, many of which are deceptive with little relevance to reality. A daunting task is the automated classification of a text article as misleading or misinformation. To see the latest news alerts, individuals often utilize e-newspapers, Twitter, Instagram, Youtube, and many more. Fake news created on social media can lead to uncertainty amongst individuals and psychiatric illness. We may detect that news obtained based on machine learning techniques is either true or false. This study proposes a machine learning technique to detect fake news by carrying out filtration on social media data, classifying the preprocessed data using a machine learning algorithm, evaluating the developed system, and evaluating the results.
A One-Dimensional Model of Turbulent Flow Through ‘Urban’ Canopies: Updates Based on Large-Eddy Simulation

Abstract. In mesoscale climate models, urban canopy flow is typically parameterized in terms of the horizontally-averaged (1-D) flow and scalar transport, and these parameterizations can be informed by Computational Fluid Dynamics (CFD) simulations of the urban climate at the microscale. Reynolds Averaged Navier-Stokes Simulation (RANS) models have been previously employed to derive vertical profiles of turbulent length scale and drag coefficient for such parameterization. However, there is substantial evidence that RANS models fall short in accurately representing turbulent flow fields in the urban roughness sublayer. When compared with more accurate flow modeling such as Large Eddy Simulations (LES), we observed that vertical profiles of turbulent kinetic energy and associated turbulent length scales obtained from RANS models are substantially smaller specifically in the urban canopy. Accordingly, using LES results, we revisited the urban canopy parameterizations employed in the one-dimensional model of turbulent flow through urban areas, and updated the parameterization of turbulent length scale and drag coefficient. Additionally, we included the parameterization of the dispersive stress, previously neglected in the 1-D column model. For this objective, the PArallelized Large-Eddy Simulation Model (PALM) is used and a series of simulations in an idealized urban configuration with aligned and staggered arrays are considered. The plan area density is varied from 0.0625 to 0.44 to span a wide range of urban density (from sparsely developed to compact midrise neighborhoods, respectively). To ensure the accuracy of the simulation results, we rigorously evaluated the PALM results by comparing the vertical profiles of turbulent kinetic energy and Reynolds stresses with wind tunnel measurements, as well as other available LES and DNS studies. After implementing the updated drag coefficients and turbulent length scales in the 1-D model of urban canopy flow, we evaluated the results by a) testing the 1-D model against the original LES results, and demonstrating the differences in predictions between new (derived from LES) and old (derived from RANS) versions of the 1-D model, and b) testing the 1-D model against LES results for a test-case with realistic geometries. Results suggest a more accurate prediction of vertical turbulent exchange in urban canopies, which can consequently lead to an improved prediction of urban heat and pollutant dispersion at the mesoscale.
Modulating Effects of Planetary Wave 3 on a Stratospheric Sudden Warming Event in 2005

AbstractThe Eliassen–Palm flux (EPF) and Plumb’s wave activity flux (WAF) were computed, using ERA-Interim data, to analyze the influence of planetary wave 3 on a stratospheric sudden warming event from 17 February to 15 March 2005 (SSW05). It was found that 1) SSW05 consisted of three stages: a prior minor warming (MnW05), a late final warming (FW05), and a warming stagnation between MnW05 and FW05; 2) the wave 3 first decreased total upward EPFs by more than 30% at 100 hPa, resulting in the warming stagnation, and then increased upward EPFs by greater than 50%, leading to FW05; and 3) the anomalies of wave-3 activity fluxes were associated with the pattern of Atlantic blocking high in the latter two stages. The interactions between the wave 3 and wave 1 partitioned the zonal upward channel of total wave activity fluxes from one longitudinal region into two longitudinal regions and affected SSW05.
Microscopic thermal characteristics of parallelly arranged nanowires in a liquid: the role of interface thermal resistance, solid-like liquid layer, and the restricted phonon transport

Nanofluids based on extended nanostructures, such as nanowires, have been demonstrated improved thermal conductivities (κ). However, the lack of a complete understanding at the microscopic level hinders the development of such nanofluids towards practical applications. We aim to provide it by investigating how the interface thermal resistance (R b ), ballistic phonon transport, and the solid-like liquid layer affect the heat conduction in nanowire-based nanofluids. By employing Non-Equilibrium Molecular Dynamics (NEMD), it is found that the heat conduction in the parallelly arranged liquid and the nanowires exhibit a coupled thermal behavior owing to the R b . This contradicts the predictions of the classical parallel heat conduction model, therefore, a novel model is proposed taking this coupled behavior into account. Using this model, it is shown that the high κ of the solid phase has a limited contribution to the effective κ of nanofluids having short nanowires due to the dominant R b effect. For the case of long nanowires, however, the individual nanowire κ becomes a vital parameter defining the effective κ. Further, NEMD calculations reveal that the κ of suspended nanowires in a liquid is markedly reduced, questioning the validity of classical effective medium theories which use the bulk parameters. This reduction is attributed to surface atoms’ restricted vibrational freedom and the nanowire’s phonon-boundary scattering. By substituting this reduced κ of the solid phase into the new mathematical model, the theoretical predictions align closely with the NEMD calculations, exhibiting deviations below 10%. The sole contribution from the solid-like liquid layer to the κ enhancement lies between 20%–30% in the nanofluids presently considered. Therefore, the findings of this study highlight the important roles play by the identified microscopic thermal characteristics in defining the effective κ of nanofluids based on nanowires.
A conveyor belt characterization information extraction method based on YOLOv5 and the skeleton method

To resolve the inaccurate localization of conveyor belt surface damage identification problem and to address the insufficiencies of the methods for extracting surface characterization information, this paper proposes a conveyor belt characterization information extraction method that integrates YOLOv5 deep learning and the skeleton method. By constructing a conveyor belt surface damage recognition model based on the YOLOv5 target detection algorithm, the identification, localization and cropping of the conveyor belt’s surface damage are implemented. After that, edge extraction and surface information extraction are also performed on the damaged parts. Finally, the collected data are analyzed and processed in real time by edge computing equipment to determine the degree of damage of the parts. Finally, intelligent operation of the belt conveyor is achieved with autonomous operations, unattended operations and decision alarms. The experimental results show that the recognition accuracy of YOLOv5 is approximately 93.11%, the speed is approximately 57 frames per second and the error of the data acquired by image processing is between 2% and 10%, which meets the real-time detection requirements of conveyor belt surface damage detection, and assists in the safety management supervision of the belt conveyer.
Burnout syndrome and work engagement in nursing staff: a systematic review and meta-analysis

Background A difficult and demanding work environment, such as that often experienced in healthcare, can provoke fatigue, anxiety, distress, and discomfort. This study considers factors that may influence levels of burnout and work engagement among nurses and seeks to determine the relationship between these conditions. Method A systematic scoping review was performed, in accordance with the PRISMA Extension for Scoping Reviews, based on data obtained from a search of the PubMed/MEDLINE and Scopus databases carried out in 2022 using the search equation: “work engagement AND nurs* AND burnout. ” This search identified nine quantitative primary studies suitable for inclusion in our analysis. Results Work overload, type of shift worked, and/or area of hospital service, among other elements, are all relevant to the development of burnout. This syndrome can be countered by social support and appropriate personal resources and values, which are all positively associated with work engagement. Our analysis revealed a significant correlation between work engagement and the different domains of burnout. The correlation effect size between burnout and work engagement was −0.46 (95% CI −0.58, −0.31), with p < 0.001. Conclusion Well-targeted interventions in the healthcare work environment can reduce burnout levels, strengthen work engagement, and enhance the quality of healthcare.
Deep Learning-based Pre-diagnosis and Analysis of Psychological Disorders

At present, due to the lack of sufficient treatment institutions as well as professional psychotherapists, numerous patients with mental illnesses do not receive help from professional psychiatrists, thus worsening their conditions. In order to determine the condition of mental illness patients at an early stage, this paper applies natural language processing technology to the psychological field and proposes a text-based and deep learning model for mental illness recognition. The BERT (Bidirectional Encoder Representations from Transformers) pre-trained language model is used to complete the sentence-level feature vector representation of mental health text data, and the obtained feature vectors are subsequently targeted and input to a classifier for classification, which can effectively identify depression, anxiety, post-traumatic stress, and unmet mental illness multiple classifications, breaking the previous common depression identification, improving from the original simple depression dichotomous classification to a mental illness multiple classification task, and focusing on a few focal points, which not only saves human and material resources, but also can achieve twice the result with half the effort. Finally, the algorithm is validated using mental health text dataset, and the experimental results show that the lowest F1 value of the trained model on the test set is 0.77, which can achieve fast screening of text content with the tendency of mental illness, reduce the expert labeling workload, improve the labeling efficiency, and provide a new idea for the recognition of mental illness.
An Internet-Wide Analysis of Diffie-Hellman Key Exchange and X.509 Certificates in TLS

Transport Layer Security (TLS) is a mature cryptographic protocol, but has flexibility during implementation which can introduce exploitable flaws. New vulnerabilities are routinely discovered that affect the security of TLS implementations. We discovered that discrete logarithm implementations have poor parameter validation, and we mathematically constructed a deniable backdoor to exploit this flaw in the finite field Diffie-Hellman key exchange. We described attack vectors an attacker could use to position this backdoor, and outlined a man-in-the-middle attack that exploits the backdoor to force Diffie-Hellman use during the TLS connection. We conducted an Internet-wide survey of ephemeral finite field Diffie-Hellman (DHE) across TLS and STARTTLS, finding hundreds of potentially backdoored DHE parameters and partially recovering the private DHE key in some cases. Disclosures were made to companies using these parameters, resulting in a public security advisory and discussions with the CTO of a billion-dollar company. We conducted a second Internet-wide survey investigating X.509 certificate name mismatch errors, finding approximately 70 million websites invalidated by these errors and additionally discovering over 1000 websites made inaccessible due to a combination of forced HTTPS and mismatch errors. We determined that name mismatch errors occur largely due to certificate mismanagement by web hosting and content delivery network companies. Further research into TLS implementations is necessary to encourage the use of more secure parameters.
A Novel Energy-Efficient Scheme for RPL Attacker Identification in IoT Networks Using Discrete Event Modeling

The Internet of Things (IoT) paradigm facilitates communication for a multitude of connected smart objects and provisions essential and mission-critical services across diverse sectors. To route packets, IoT networks use Routing Protocol for Low-Power and Lossy Networks (RPL) by default. However, RPL lacks security features by design, making IoT-RPL prone to low-overhead internal attacks such as the rank and version attacks. The attack and normal traffic are found to be identical, making detection challenging for signature-based and anomaly-based Intrusion Detection Systems (IDS). Moreover, a formal proof of correctness of IDS schemes is lacking. In this paper, we propose a novel rank and version attack detection and rank attacker location identification mechanism that utilizes active probing and Discrete Event System (DES) based IDS. Our proposed IDS scheme is centralized with inputs from sensing at the leaf levels. IDS uses as an intelligent probing technique that helps distinguish normal and attack behaviour. Further, DES is used to model the normal and attack specifications. A DES diagnoser, constructed from the DES models, generates an alert when a malicious node is identified. We also prove the correctness and completeness of our scheme. The DES framework is implemented only at root node, therefore using our IDS does not require any heavy deployment, protocol modifications, or training. Proposed method is implemented in simulation and testbed, with a sufficiently large number of IoT devices. We compare our scheme to state-of-the-art approaches. Our performance is found to be energy-efficient, having minimal false positives and achieving more than 99% accuracy in detecting intrusions and identifying the malicious nodes.
Reducing the Number of Sensors in a Linear Antenna Array by ℓp Norm Minimization Algorithm

The excitations and locations of sensors in the non-uniformly spaced array affect the array performance such as sidelobe level and spatial resolution. Consequently, finding the optimal excitation coefficients and sensor positions of the array to produce a desired beam pattern with the smallest number of sensors is of great importance in practice. With the aim of reducing the number of the sensors in a linear antenna array, a novel method based on lp (0<p<1) norm minimization for optimizing both excitation coefficients and sensor locations of the array is proposed. Compared with the reweighted l1 norm minimization (IRWL1) method, the proposed method can reduce more array sensors by optimizing the objective functions that include the measurements of peak sidelobe level (PSL) and array sparsity denoted by the lp norm of excitations. Numerical experiments have proved the effectiveness and advantages of the proposed method in the reduction of the number of the sensors of the linear antenna array.