Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey of Machine Learning Algorithms and Techniques for Air Mobility Under Emergency Situations

Emergency situations in aviation pose serious risks to life and result in huge negative impacts on air mobility, causing a significant economic and reputation loss to airlines and airports. However, the decisions to deal with emergencies are usually made by flight dispatchers according to their experience, and they merely consider local-view optimization. Therefore, there is an urgent need to design a decision-making assistant system to alleviate the negative impact of perturbations on aviation air mobility from a global-view perspective. In this paper, we focus on using machine learning techniques and algorithms to solve emergency situations and improve air mobility. Through various techniques, researchers and computer scientists have studied how different reasons and factors affect air mobility. This paper will review previous research and provides a better view of using machine learning techniques and algorithms to improve air mobility in emergency situations.
Automatic seeds segmentation and classification using a novel method based on pixel intensity thresholds and convolutional neural networks

Due to the computational power and memory of modern computers, computer vision techniques and neural networks can be used to develop a visual inspection system of agricultural products to satisfy product quality requirements. In this paper, artificial vision techniques are employed to classify seeds in RGB images. As a first step, an algorithm based on pixel intensity threshold is developed to detect and classify a set of different seed types, such as rice, beans, and lentils. Afterward, the information inferred by this algorithm is exploited to develop a neural network model, which successfully achieves learning classification and detection tasks through a semantic-segmentation scheme. The applicability and good performance of the proposed algorithms (effectiveness of 98.26% and a cross-entropy error of 0.0423) are illustrated by testing with real images. The experimental results verify that both algorithms can directly detect and classify the proposed set of seeds in input RGB images.
Performance Evaluation of UAV-Assisted Millimeter Wave NOMA Networks

Mobile users may experience frequent service outages or poor quality of service in rural areas due to the scarcity of base stations in the proximity. Therefore, a cost effective and flexible solution is introduced in this paper with unmanned aerial vehicles (UAVs) and terrestrial base stations (TBSs), coexisting under a stochastic network with the combination of millimeter waves and non-orthogonal access (NOMA) techniques. This is achieved by deploying both the UAVs (millimeter wave frequencies) and TBSs (sub-6 GHz frequencies) in the same system, and selecting the transmitter based on a user association scheme. The maximum signal-to-interference-plus-noise ratio (SINR) and the closest transmitter user association schemes are compared using extensive simulations to identify the most suitable scheme in terms of the outage probability and the achievable downlink rate. The results show that the closest transmitter user association policy results in lower outage probability and higher downlink rates for UAV-assisted millimeter wave NOMA Networks.
All-Higher-Stages-In Adaptive Context Aggregation for Semantic Edge Detection

Convolutional Neural Networks (CNNs) can reveal local variation details and multi-scale spatial context in images via low-to-high stages of feature expression; effective fusion of these raw features is key to Semantic Edge Detection (SED). The methods available in the field generally fuse features across stages in a position-aligned mode, which cannot satisfy the requirements of diverse semantic context in categorizing different pixels. In this paper, we propose a deep framework for SED, the core of which is a new multi-stage feature fusion structure, called All-HiS-In ACA (All-Higher-Stages-In Adaptive Context Aggregation). All-HiS-In ACA can adaptively select semantic context from all higher-stages for detailed features via a cross-stage self-attention paradigm, and thus can obtain fused features with high-resolution details for edge localization and rich semantics for edge categorization. In addition, we develop a non-parametric Inter-layer Complementary Enhancement (ICE) module to supplement clues at each stage with their counterparts in adjacent stages. The ICE-enhanced multi-stage features are then fed into the All-HiS-In ACA module. We also construct an Object-level Semantic Integration (OSI) module to further refine the fused features by enforcing the consistency of the features within the same object. Extensive experiments demonstrate the superior performance of the proposed method over state-of-the-art works.
Novel Prediction in Storm Surge Using Ensemble Machine Learning Algorithms

Storm surge elevates the water level, resulting in floods that cause significant harm to our lives and property. Predicting possible occurrences of storm surge using machine learning technology has long piqued the interest of the different communities. However, it is observed that the accuracy rate of predictions is insufficient. This paper aims to develop a framework for evaluating the performance of various machine learning models. Various algorithms are being used to forecast storm surge incidents in Hong Kong and Kaohsiung. The inclusion of trend changes in atmosphere pressure and wind components throughout the implementation of the algorithms to the dataset gathered over the past is one of the innovative aspects of this work. These inclination characteristics are very vital and crucial to capture important correlations and features for the potential occurrence of storm surges. This study yields two major findings. First, even the basic method of an ensemble algorithm produces better results than any single algorithm. Second, determining the best performing ensemble models for those storm surge events is achievable based on the technique findings for the data analyzed.
Mid- and far-infrared properties of Spitzer Galactic bubbles revealed by the AKARI all-sky surveys

We have carried out a statistical study on the mid- and far-infrared (IR) properties of Galactic IR bubbles observed by Spitzer. Using the Spitzer 8 ${\mu}{\rm m}$ images, we estimated the radii and covering fractions of their shells, and categorized them into closed, broken and unclassified bubbles with our data analysis method. Then, using the AKARI all-sky images at wavelengths of 9, 18, 65, 90, 140 and 160 ${\mu}{\rm m}$, we obtained the spatial distributions and the luminosities of polycyclic aromatic hydrocarbon (PAH), warm and cold dust components by decomposing 6-band spectral energy distributions with model fitting. As a result, 180 sample bubbles show a wide range of the total IR luminosities corresponding to the bolometric luminosities of a single B-type star to many O-type stars. For all the bubbles, we investigated relationships between the radius, luminosities and luminosity ratios, and found that there are overall similarities in the IR properties among the bubbles regardless of their morphological types. In particular, they follow a power-law relation with an index of $\sim$3 between the total IR luminosity and radius, as expected from the conventional picture of the Str$\rm{\ddot{o}}$mgren sphere. The exceptions are large broken bubbles; they indicate higher total IR luminosities, lower fractional luminosities of the PAH emission, and dust heating sources located nearer to the shells. We discuss the implications of those differences for a massive star-formation scenario.
Lookup tables to compute high energy cosmic ray induced atmospheric ionization and changes in atmos

A variety of events such as gamma-ray bursts and supernovae may expose the Earth to an increased flux of high-energy cosmic rays, with potentially important effects on the biosphere. Existing atmospheric chemistry software does not have the capability of incorporating the effects of substantial cosmic ray flux above 10 GeV . An atmospheric code, the NASA-Goddard Space Flight Center two-dimensional (latitude, altitude) time-dependent atmospheric model (NGSFC), is used to study atmospheric chemistry changes. Using CORSIKA, we have created tables that can be used to compute high energy cosmic ray (10 GeV - 1 PeV) induced atmospheric ionization and also, with the use of the NGSFC code, can be used to simulate the resulting atmospheric chemistry changes. We discuss the tables, their uses, weaknesses, and strengths.
Carbon Trading Based on Quadratic Modal Decomposition and Recurrent Neural Network Price Prediction Model

The lack of experience in managing the national carbon market makes it difficult to determine the factors affecting carbon market prices, while the highly non-linear and noisy nature of carbon trading prices makes them difficult to predict accurately. Therefore, this paper proposes a hybrid decomposition forecasting model CEEMDAN-VMD-GRU that combines two types of data noise reduction for carbon price forecasting. The experimental principle uses a decomposition algorithm to decompose the original data and then decompose the subsequence with the highest sample entropy value again, which are used as inputs to the model. Finally, the prediction results are integrated to obtain the final prediction results. The empirical results confirm that the secondary decomposition prediction hybrid model has a higher accuracy rate.
Construct Digital Twin Models in Cyber Space for Physical Objects of Intelligent Optical Network

The optical network has been evolving into intelligent infrastructure in which the physical network can be automatically and predictably managed by the deployed software functions in the cyber space. This paper proposes novel construction approaches of digital twin models for intelligent optical network. A tensor based three layers, three slices, and four features framework was presented to promote the traditional optical network to intelligent information infrastructure. In order to proof and realize the framework, this paper firstly introduces four units, namely device unit, management unit, intelligent unit, and service unit, then illustrates how to construct a cyber physical social system to implement the corresponding functions of these four units. In the cyber space, three types of technologies including digital twin, artificial intelligent, special algorithm are used to analyze date and generate controlling instructions for the optical network devices in the physical space. A cyber physical system prototype for the intelligent optical network was developed and demonstrated. We present two application scenarios of the mentioned prototype, namely quality evaluation of the optical network and prediction of transmission performance. Finally, the paper describes future work and evolution trend of the cyber physical system for intelligent optical network.
An empirical approach to the extragalactic background light from AEGIS galaxy SED-type fractions

Abstract The extragalactic background light (EBL) is of fundamental importance both for understanding the entire process of galaxy evolution and for γ-ray astronomy. However, the overall spectrum of the EBL between 0.1 and 1000 μm has never been determined directly, neither from observed luminosity functions (LFs), over a wide redshift range, nor from any multiwavelength observation of galaxy spectral energy distributions (SEDs). The evolving overall spectrum of the EBL is derived here utilizing a novel method based on observations only. It is emphasized that the local EBL seems already well constrained from the UV up to the mid-IR. Different independent methodologies such as direct measurement, galaxy counts, γ-ray attenuation and realistic EBL modelings point towards the same EBL intensity level. Therefore, a relevant contribution from Pop III stars to the local EBL seems unlikely.
Analysis of Changes in Induction Motor Efficiency due to Voltage Changes and Defects in Cageball Using the Segregated Loss Method

This study discusses the efficiency of a three-phase induction motor based on the segregated loss method, which is then simulated by experiencing changes in voltage conditions and defects in the cageball with the software application. The segregated loss method is used to find any losses that exist in a three-phase induction motor. The conditions experienced by a three-phase induction motor are voltage changes, namely 365 V, 371 V and 377 for undervoltage, 380 V for nominal voltage, and 383 V, 389 V and 395 V for overvoltage. As for the defects in the cageball in the form of a moment of inertia of 5 kg.m2. The test results obtained will be compared and analyzed what happens to the induction motor if it experiences these conditions.
Spin-up/spin-down of neutron star in Be-X-ray binary system GX 304-1

ABSTRACT We analyze spin-up/spin-down of the neutron star in Be X-ray binary system GX304-1 ob-served by Swift/XRT and Fermi/GBM instruments in the period of the source activity fromApril 2010 to January 2013 and discuss possible mechanisms of angular momentum transferto/from the neutron star. We argue that the neutron star spin-down at quiescent states of thesource with an X-ray luminosity ofL x ∼ 10 34 erg s −1 between a series of Type I outbursts andspin-up during the outbursts can be explained by quasi-spherical settling accretion onto theneutron star. The outbursts occur near the neutron star periastron passages where the densityis enhanced due to the presence of an equatorial Be-disc tilted to the orbital plane. We alsopropose an explanation to the counterintuitive smaller spin-up rate observed at higher lumi-nosity in a double-peak Type I outburst due to lower value of the speciﬁc angular momentumof matter captured from the quasi-spherical wind from the Be-star by the neutron star movingin an elliptical orbit with eccentricity e &0.5. Key words: X-ray:binaries – (stars:)pulsars:individual – GX304-1
Coulomb-Gas Approach For Percolation Theory

The aim of this work is to present a non trivial confirmation of the powerful of the Coulomb gas-techniques for Boundary Conformal Field Theory (BCFT). We show that we can re-derive the known Cardy result of percolation problem via the technics developed by S. Kawai in the Coulomb-Gas formalism.
Bit-wise Detection for OTFS Modulation via Message Passing Algorithm

The orthogonal time frequency space (OTFS) modulation as a promising signal representation attracts growing attention for integrated sensing and communication. This paper devotes to signal detection for OTFS system. Notice the characteristics of high dimension and sparsity of OTFS channel, we propose a bit-wise message passing (MP) algorithm to support Soft-Input Soft-Output detection. We leverage the channel sparsity to simplify the factor graph, and approximate the discrete messages with continuous Gaussian distribution to facilitate message updating. Simulations in various cases validate the outperformance of the proposed algorithm contrast to existed symbol-wise MP and linear detectors.
Fuzzy Logic Based Satellite Image Classification Using Automatically Generated Fuzzy Membership Functions and Rules from Training Set

An automated method for generating fuzzy rules and fuzzy membership functions for pattern classification from training sets of examples is presented. Also, the performance of our method has been quantitatively assessed on a multispectral SPOT image and compared to those of 1-nearest neighbor classifier and quadratic Gaussian classifier. The method is based on adaptive multi-scale feature space partitioning. Initially, fuzzy subspaces are created from the partitions formed by the minimum and maximum of individual feature values of each class. The initial membership functions are determined according to the generated fuzzy partitions. The fuzzy subspaces are further iteratively partitioned if the prespecified classification performance has not been archived on the training set. The advantages of our method are: 1) the tracking of highly non-linear decision hypersurfaces, 2) resolving the conflicts in patterns due to the inheritage of the fuzzy rule-based method. As a result, our classifier can achieve high performances in highly non-linear pattern classification problem. Our classifier was trained and tested on patterns consisting of the DN of each band, (XS1, XS2, XS3), extracted from SPOT multispectral scene. The training and testing was prepared by experts on the analysis of satellite images. Our method archived 95.84 % classification accuracy, while 1-nearest neighborhood classifier 92.09 %, quadratic Gaussian classifier 91.31%. The result represents that our method has higher generalization power.
Precise Nanoruler for Retrieving Asymmetry of Fano-Resonant Metasurfaces by Second-Harmonic Generation

Achieving geometric parameters at the nanoscale is highly desired in the applications including sensing and measurement. We demonstrate that second-harmonic generation (SHG) can be a very useful tool to quantitatively reflect the structural asymmetry of Fano metasurfaces with high sensitivity. A horizontal shift of the nanobar as small as 10 nm leads to a 13% change in our proposed SHG intensity ratio, which is clearly observable.
Measuring the Evolution of the M/L Ratio from the Fundamental Plane

The Fundamental plane provides a sensitive tool to measure the change in the M/L ratio of early type galaxies with redshift. The evolution of the M/L ratio is a function of the star formation history. It depends on the IMF, the formation redshift, and cosmology. Some model examples are shown, and a first result on the cluster Abell 665 at z=0.18 is given. The measurements confirm the cosmological surface brightness dimming, and imply an evolution of the (red) L/M ratio ∝ (1 + z)1.8±0.7. More data are needed to extend this result to higher redshifts, and to test the underlying assumptions.
A UNIFIED MODEL FOR GRB PROMPT EMISSION FROM OPTICAL TO γ-RAYS; EXPLORING GRBs AS STANDARD CANDLES

The origin of prompt emission from gamma-ray bursts (GRBs) remains to be an open question. Correlated prompt optical and γ-ray emission observed in a handful of GRBs strongly suggests a common emission region, but failure to adequately fit the broadband GRB spectrum prompted the hypothesis of different emission mechanisms for the low- and high-energy radiations. We demonstrate that our multi-component model for GRB γ-ray prompt emission provides an excellent fit to GRB 110205A from optical to γ-ray energies. Our results show that the optical and highest γ-ray emissions have the same spatial and spectral origin, which is different from the bulk of the X- and softest γ-ray radiation. Finally, our accurate redshift estimate for GRB 110205A demonstrates promise for using GRBs as cosmological standard candles.
Quality evaluation of the JPEG Pleno Holography Call for Proposals response

At its 93rd meeting, JPEG received the responses to the Call for Proposals on JPEG Pleno holography. This activity will lead to the first standardized coding solution for digital holograms. This paper describes the quality evaluation process, existing out of a subjective evaluation study and an objective evaluation based on preselected quality metrics. It also includes a critical evaluation of the challenges encountered when performing quality evaluation of holographic content. Based on the evaluation of the response, the JPEG committee was able to select a very reliable coding solution that depicted a performance well beyond that of the selected anchors.
Stochastic Modeling and Analysis of Automotive Wire Harness Based on Machine Learning and Polynomial Chaos Method

This paper proposes a method based on machine learning technique and polynomial chaos (PC) method to model and analyze the stochastic behavior of an automotive wire harness. In this research, we assume that the automotive wire harness is a bundle of wires above a conductor plane, and its behavior can be represented by stochastic transmission line equations. First, the proposed method constructs the regression models related to per-unit-length (p.u.l.) parameters by means of a machine learning technique. Then, the stochastic transmission line equations including the regression models are approximated using orthonormal polynomials through a PC formulation. Since the regression models correlate the geometric and shape parameters of the wires and the p.u. l. parameters, PC expansion coefficients can efficiently be calculated. We adopt three types of regression models and compare them to investigate the performance of the proposed method.
HIGH RESOLUTION ISAR IMAGING METHOD BASED ON SPECTRUM ESTIMATION THEORY

Based on the imaging model, this paper deduces the relationship between ISAR imaging and spectrum estimation, and puts forward a high resolution ISAR imaging method according to modern spectrum estimation theory, which can break through the limitation of data length, exceed resolution without increasing signal bandwidth and synthetic aperture, and effectively suppress side lobe of the imaging results. Compared with traditional RD and classical sparse imaging algorithms, the proposed method has the advantages of high resolution, low side lobe and high efficiency.
Study of Stability Variations in Reaction Torque Observer based Force Controllers

An effective method for determining the environmental impedance is to use a reaction torque observer (RTOB), which is a variant of the disturbance observer (DOB) concept. A robust yet accurate force controller can be developed by implementing both RTOB and DOB. This study examines the potential instability of force controllers based on RTOB. Traditionally, a RTOB-based force controller is examined under the presumption that the two cut-off frequencies for DOB and RTOB should always be equal to one another. But in some circumstances, it fails to maintain stability. The stability of the system is determined by the interactions between the mechanical, environmental, and controller dynamics, which also add to design constraints. There has not been any analysis of unstable scenarios in terms of system dynamics. In this paper, a novel stability constraint is derived as a result of the new stability assessment approach that is proposed. A new stability guideline is also suggested. The simulation results are used to confirm the validity of the proposals.
Smart Detection of Social Distance Violations using Gaussian Lens Model and Deep Learning

The COVID-19 pandemic has proven the presence of preventative measures such as social distancing to be essential to public life. Even with options for vaccination, the recommendation for a 6-foot guideline still carries much relevance -especially in public, crowded spaces. Thus, the aim of this study is to ultimately develop a social-distancing “detector” through the comparison of two models: a model implementing the Gaussian thin-lens formula and a machine-learning model. Both models were tested for accuracy in detecting distance from a first-person webcam. Three hundred images were taken of a human subject at different distances, 58 of which were used for testing the models and 242 to train the machine-learning model. Results show that the testing accuracy of the Gaussian Thin-lens Model was 0.3966, or 39.66%. The machine-learning model achieved a higher testing accuracy of 0.9483, or 94.83%. This is likely since the Gaussian Thin-lens Model requires strict conditions to be met in order to calculate distances, one being the ability to locate facial landmarks to obtain a reference point. In contrast, the machine-learning model is able to adapt, making it the optimal choice for detecting distance from a webcam. It is possible that the model could be used to enforce or detect social distancing.