Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Metric Learning-Based Feature Embedding for Hyperspectral Image Classification

Learning from a limited number of labeled samples (pixels) remains a key challenge in the hyperspectral image (HSI) classification. To address this issue, we propose a deep metric learning-based feature embedding model, which can meet the tasks both for same- and cross-scene HSI classifications. In the first task, when only a few labeled samples are available, we employ ideas from metric learning based on deep embedding features and make a similarity learning between pairs of samples. In this case, the proposed model can learn well to compare whether two samples belong to the same class. In another task, when an HSI image (target scene) that needs to be classified is not labeled at all, the embedding model can learn from another similar HSI image (source scene) with sufficient labeled samples and then transfer to the target model by using an unsupervised domain adaptation technique, which not only employs the adversarial approach to make the embedding features from the source and target samples indistinguishable but also encourages the target scene’s embeddings to form similar clusters with the source scene one. After the domain adaptation between the HSIs of the two scenes is finished, any traditional HSI classifier can be used. In a simple manner, the nearest neighbor (NN) algorithm is selected as the classifier for the classification tasks throughout this article. The experimental results from a series of popular HSIs demonstrate the advantages of the proposed model both in the same- and cross-scene classification tasks.
Evaluating Salient Object Detection in Natural Images with Multiple Objects having Multi-level Saliency

Salient object detection is evaluated using binary ground truth with the labels being salient object class and background. In this paper, we corroborate based on three subjective experiments on a novel image dataset that objects in natural images are inherently perceived to have varying levels of importance. Our dataset, named SalMoN (saliency in multi-object natural images), has 588 images containing multiple objects. The subjective experiments performed record spontaneous attention and perception through eye fixation duration, point clicking and rectangle drawing. As object saliency in a multi-object image is inherently multi-level, we propose that salient object detection must be evaluated for the capability to detect all multi-level salient objects apart from the salient object class detection capability. For this purpose, we generate multi-level maps as ground truth corresponding to all the dataset images using the results of the subjective experiments, with the labels being multi-level salient objects and background. We then propose the use of mean absolute error, Kendall's rank correlation and average area under precision-recall curve to evaluate existing salient object detection methods on our multi-level saliency ground truth dataset. Approaches that represent saliency detection on images as local-global hierarchical processing of a graph perform well in our dataset.
A 10 MHz thin-film piezoelectric-on-silicon MEMS resonator with T-shaped tethers for Q enhancement

Thin-film piezoelectric-on-silicon (TPoS) MEMS (i.e. micro-electro-mechanical system) resonators have attracted much attention due to its unique features, such as CMOS-compatible fabrication process, however, its applications require, in particular, the enhancement of quality factor (Q). Herein, a 10 MHz TPoS MEMS resonator with T-shaped tethers coupled with reflecting blocks was proposed. Finite-element analysis was employed for modeling the proposed TPoS resonators to investigate the underlying mechanism of Q enhancement by the introduction of T-shaped tethers and reflecting blocks. It was figured out that the combination of T-shaped tethers and reflecting blocks can significantly reduce the energy leakage through the anchor substrate by reflecting mechanical waves and trapping the energy. The measured results of fabricated TPoS resonators further verified the effectiveness of Q enhancement by the as-presented new design, and the insertion loss and the Q were improved to 3.27 dB and 1903, respectively.
Data reduction and calibration of the FMG onboard ASO-S

The Full-disk vectorMagnetoGraph (FMG) instrument will carry out polarization observations at one wavelength position of the Fe I 5324.179 °A spectral line. This paper describes how to choose this single wavelength position, the relevant data-processing and the magnetic field calibrations based on the measured polarization signals at one single wavelength position. It is found that solar radial Doppler velocity, which can cause the spectral line to shift, is a disadvantageous factor for the linear calibration at one wavelength position. Observations at two symmetric wavelength positionsmay significantly reduce the wavelength shift effect (∼ 75%), but simulations show that such polarization signals located at the solar limbs (e.g., beyond the longitude range of ±30°) are not free from the effect completely. In future work, we plan to apply machine learning techniques to calibrate vector magnetic fields, or employ full Stokes parameter profile inversion techniques to obtain accurate vector magnetic fields, in order to complement the linear calibration at the single wavelength position.
Decomposition of inframonogenic functions with applications in elasticity theory

In this paper, we consider functions satisfying the sandwich equation ∂x_f∂x_=0 , where ∂x_ stands for the Dirac operator in Rm . Such functions are referred as inframonogenic and represent an extension of the monogenic functions, ie, null solutions of ∂x_ .
Adaptive multi-fidelity sampling for CFD-based optimisation via radial basis function metamodels

The paper presents a study on four adaptive sampling methods of a multi-fidelity (MF) metamodel, based on stochastic radial basis functions (RBF), for global design optimisation based on expensive CFD computer simulations and adaptive grid refinement. The MF metamodel is built as the sum of a low-fidelity-trained metamodel and an error metamodel, based on the difference between high- and low-fidelity simulations. The MF metamodel is adaptively refined using dynamic sampling criteria, based on the prediction uncertainty in combination with the objective optimum and the computational cost of high- and low-fidelity evaluations. The adaptive sampling methods are demonstrated by four analytical benchmark and two design optimisation problems, pertaining to the resistance reduction of a NACA hydrofoil and a destroyer-type vessel. The performance of the adaptive sampling methods is assessed via objective function convergence.
A piezoelectric energy harvester with light-activated shape memory polymers

Abstract In this research, a host thin-plate laminated with LASMP and piezoelectric patch was presented to investigate its natural frequencies and further developed into a hybrid energy harvester to study its energy harvesting responses. The dynamic stiffness and noncontact mechanism which are special to LASMP are employed to adjust the natural frequencies of the hybrid energy harvester so as to achieve broadband resonance energy harvesting responses. Furthermore, effect of piezoelectric patch thickness on power output is discussed in detail. Numerical discussions indicate that the power output of the self-tuned energy harvester increases 40% for each mode.
Analisa Service Brake Malfunction pada Unit Dump Truck (HD) 1500 – 7

Dump Truck adalah sebuah alat pengangkut matrial dari jarak sedang hingga jauh, dimana matrial yang dibawa oleh dump truck dapat diisikan oleh excavator, wheel loeder, maupun shovel. Dump Truck HD 1500-7 sudah menggunakan Full Hydraulic pada system brake nya,Pada unit Dump Truck tersebut terdapat error pada monitor panel lampu indicator brake tidak berfungsi. error tersebut mengidikasi bahwa brake relay valve malfunction, hal ini membuat service brake abnormal. Tujuan dari Analisa error pada monitor panel adalah untuk mengetahui dan mengatasi akar masalah dalam rangka meningkatkan performa unit. Peneliti melakukan observasi langsung dilapangan dan studi pustaka. Analisa dan masalah dilakukan dengan cara mengguanakan 8 step troubleshooting. Hasil dari penelitian menunjukkan bahwa penyebab service brake malfunction adalah pressure switch yang tidak mengirim signal ke retard control monitor, dikarenakan micro switch yang rusak disebabkan tombol micro switch yang tidak berfungsi karena benturan piston pressure switch secara terus menurus oliakan masuk ke dalam pressure switch karena lama penggunaannya. Untuk mengatasi agar tidak terjadi service brake malfunction maka dilakukan penggantian pressure switch assy (pc1677). Penggantian pressure switch assy diharapkan tidak terjadi trouble pada service brake malfunction pada unit dump truck HD 1500-7
Improvement of third-order finite difference WENO scheme at critical points

ABSTRACT In the paper, an improved third-order finite difference weighted essentially non-oscillatory scheme is presented for achieving the optimal order near critical points. A new global smoothness indicator is obtained with the way of Taylor expansion for the local smoothness indicator. In the framework of the conventional WENO-Z scheme, we present an improved third-order finite difference WENO scheme. The proposed scheme is verified to achieve third-order accuracy by several benchmarks and the behaviour of the present scheme is proved on a variety of one- and two- dimensional standard numerical examples. Numerical results demonstrate that the proposed scheme gives better performance in comparison with the other third-order WENO schemes.
Regge-Like Mass Relation of Singly Heavy Hadrons

We propose a general Regge-like mass relation to describe the spectra of singly heavy hadrons with the slope ratio π : 2 between radial and orbital Regge trajectories. The hadron mass shift M → M −MQ + ksμlQ with the heavy quark mass MQ and the reduced mass of heavy-light systems μlQ are taken into account. The relation is successfully tested against the observed spin-averaged masses of singly heavy mesons and baryons in their radially and orbitally excited states. Some new predictions are made for more excited states.
Leaf feature extraction using glcm, moment invariant and shape morphology for indonesian medicinal plants recognition

This study aims to determine the extraction of GLCM texture features, shape morphology and moment invariant features on the leaf image of medicinal plants and determine the accuracy of plant recognition based on these three features by using Artificial Neural Network Classifiers. The procedure performed to classify medicinal plants based on their leaf image is image acquisition, image pre-processing, feature extraction, image classification and calculating the accuracy of test results. The introduction had tested for ten Indonesian medicinal plant samples, namely: Bangun-Bangun, Binahong, Jarak, Kemuning, Mangkokan, Mengkudu, Pegagan, Sambiloto, Sambung Nyawa, and Sirih. Based on the test results, obtained 97% accuracy with GLCM features, 69% with Shape Morphological features, 86% with GLCM and Shape Morphological features and 79% with moment invariant features.
Passive Microwave Precipitation Retrieval Algorithm With $A~Priori$ Databases of Various Cloud Microphysics Schemes: Tropical Cyclone Applications

The accuracy of a physically based passive microwave precipitation retrieval algorithm is affected by the quality of the a priori knowledge it employs, which indicates the relationship between the precipitation information obtained from cloud-resolving models (CRMs) and the simulated brightness temperatures (TBs) from radiative transfer models. As various microphysical assumptions reflecting a wide variety of sophisticated microphysical properties are applied to the CRMs, the TBs simulated based on the model-driven 3-D precipitation fields are determined by the selected microphysical assumption. In this article, we developed a prototype precipitation retrieval algorithm that incorporates various cloud microphysics schemes in its a priori knowledge (i.e., databases). In the retrieval process, a specific a priori database is selected for every target precipitation scene by comparing the similarities of the simulated and observed microwave emission and scattering signatures. The prototype algorithm was tested through application to precipitation retrieval for tropical cyclones at various intensity stages, which occurred over the northwestern Pacific region in 2015. The a priori databases constructed using the weather research and forecasting double-moment (WDM6) and Thompson Aerosol Aware schemes are superior when used for weak-to-moderate rainfall systems, whereas the databases constructed with the other schemes are superior within strong rain rate regions. The retrieval results obtained using the best-performing database are generally superior for all rain rate regions. Furthermore, we confirm that the database quality is more important than the number of databases. In comparison with the data from the dual-precipitation radar, the retrieval’s correlations, bias, and root mean square are 0.75, 0.14, and 5.62, respectively.
Simulação e Optimização de Sistemas Modelados por Equações Algébrico-Diferenciais

In recent years there has been a growth in computing capacity, which has opened up the possibility for solving new problems and more complex ones. A good example is the System of Differential Algebraic Equations(DAEs). Since the main objective of this work is the simulation and optimization of problems using DAEs, a research was developed on the tools with this capacity, reaching tools like Pyomo or JuMP, both being supported by open-source languages, Python and Julia, respectively. It was decided to model a type system (Stirred-Tank Reactor (RPA)) using the Pyomo, understanding the behavior of this tool in solving problems of the sort. In the simulation the degrees of freedom of the system were zero. The results obtained from Pyomo were compared with the results obtained from Wolfram Mathematicar , a simulation tool with sophisticated algorithms to solve this type of problems. For the analysis of the type model, several dynamic tests were performed on the system, and a Proportional, Integral (PI) controller was programmed and incorporated into the system. For the latter model, a perturbation was caused in an input variable and a change was made in the set-point. Afterwards, with the comparison of results between Pyomo and Wolfram Mathematicar , it was concluded that Pyomo, normally used as an optimization program, managed to obtain results just as good as Wolfram Mathematicar ’s regarding simulation. The next step was the optimization of the system and further transition to more complex and real systems such as the NOx absorption column.
Fault diagnosis based on dial-test data in datacenter networks

: The fast growth of datacenter networks, in terms of both scale and structural complexity, has led to an increase of network failure and hence brings new challenges to network management systems. As network failure such as node failure is inevitable, how to ﬁ nd fault detection and diagnosis approaches that can effectively restore the network communication function and reduce the loss due to failure has been recognized as an important research problem in both academia and industry. This research focuses on exploring issues of node failure, and presents a proactive fault diagnosis algorithm called heuristic breadth-ﬁ rst detection (HBFD), through dynamically searching the spanning tree, analyzing the dial-test data and choosing a reasonable threshold to locate fault nodes. Both theoretical analysis and simulation results demonstrate that HBFD can diagnose node failures effectively, and take a smaller number of detection and a lower false rate without sacri-ﬁ cing accuracy.
Optimal Catheter Selection for Anomalous Right Coronary Arteries (RCA)

Coronary artery angiography is a procedure in which a catheter is used to inject a contrast dye into the coronary arteries. However, in different humans a common catheter cannot be used due to the anatomical variation of the aorta and coronary arteries. Therefore, a cardiologist has to test different catheters on a patient and selects the one that best suits the patient. The whole process is time consuming and there is minor possibility of cancer due to the extreme exploitation to rays. To overcome the mentioned problems, we propose a computer-aided catheter selection procedure. In this paper, we present novel approach for patient specific optimal catheter selection for the angiography of the coronary arteries with high take off (anomalous RCA). It involves segmentation of the aorta and coronary arteries, nding the centerline and computing some important geometric parameters. These parameters include computing coronary arteries curve angle (CACA), distance of the Ostium from the aortic valves (DOAV) and the aorta diameter (AD) near the Ostium. Afterwards tip angle of the catheter (CTA), catheter curve two depth (CC2D) and catheter curve two width (CC2W) are computed. Finally, we compare CACA with CTA, DOAV with CC2D, AD with CC2W and suggest a catheter that is the closest to the patients arteries geometry. This solution avoids testing of many catheters during catheterization.
Probing the primordial Universe using the SKA in combination with other cosmological surveys

Faculty of Natural Science Department of Physics and Astronomy Master of Science by William Matthewson Next-generation surveys of the large-scale structure of the Universe will be of great importance in allowing us to extract invaluable information about the nature of the Universe and the physical laws that govern it, at a higher precision than previously possible. In particular, they will allow us to more closely study primordial non-Gaussianity, a feature which leaves an imprint on the power spectrum of galaxies on the ultra-large scales and which acts as a powerful probe of the physics of the early Universe. To investigate the extent to which upcoming surveys will be able to improve our knowledge of primordial non-Gaussianity, we perform a forecast to predict the observational constraints on local-type primordial non-Gaussianity, as well as an extension that includes a scale dependence. We study the constraining power of a multi-tracer approach, where information from different surveys is combined to help suppress cosmic variance and break parameter degeneracies. More specifically, we consider the combination of a 21cm intensity mapping survey with each of two different photometric galaxy surveys, and also examine the effect of including CMB lensing as an additional probe. The forecast constraint from a combination of SKA1, a Euclid-like (LSST-like) survey and a CMB Stage 4 lensing experiment is σ(fNL) ' 0.9 (1.4) which displays a factor of 2 improvement over the case without CMB lensing, indicating that the surveys considered are indeed complementary. The constraints on the running index of the scale-dependent model are forecast as σ(nNL) ' 0.12 (0.22) from the same combination of surveys. http://etd.uwc. ac.za/
A Robust Test for Checking the Homogeneity of Variability Measures and Its Application to the Analysis of Implicit Attitudes

A new two-sample test for comparing variability measures is proposed. To make the test robust and powerful, a new modified structural zero removal method is applied to the Brown–Forsythe transformation. The t-test-based statistic allows results to be expressed as the ratio of mean absolute deviations from median. Extensive simulation study demonstrates that the proposed test is robust to small or unequal sample sizes across many distributions. Moreover, careful exploratory analysis provides a new method for calculating the implicit association test scores for reaction time data with multiplicative treatment effects. Using this, a possible difference between variability of men and women’s implicit attitudes toward gay men is analyzed.
Effect of fractional derivatives on natural convection in a complex‐wavy‐wall surrounded enclosure filled with porous media using nanofluids

In this paper, the heat transfer by the natural convection flow in wavy‐walls enclosures filled with porous media using nanofluids is represented by the fractional differential equations. Conformable fractional derivatives are applied on both the time and space of the governing equations. The geometry is considered as enclosures with horizontal wavy walls. The Buongiorno's model is used to simulate the nanofluids while the Darcy model is used for the porous medium. The governing equations are mapped to a rectangular domain using suitable grid transformations based on the chain rule in the fractional approaches then they are solved numerically using an implicit finite difference method. The obtained numerical results are presented in terms of the streamlines, the isotherms and the nanoparticles volume fraction contours as well as the local Nusselt number for wide ranges of the governing parameters. It is found that the decrease in order of the fractional derivatives enhances rate of the fluid flow and the local Nusselt number, particularly, at the middle part of the bottom wall. Also, an increase in the surface waviness parameter increases both of the nanofluid flow and rate of the heat transfer.
Existence and uniqueness of a weak solution of an integro-differential aggregation equation on a Riemannian manifold

A class of integro-differential aggregation equations with nonlinear parabolic term is considered on a compact Riemannian manifold . The divergence term in the equations can degenerate with loss of coercivity and may contain nonlinearities of variable order. The impermeability boundary condition on the boundary of the cylinder is satisfied if there are no external sources of ‘mass’ conservation, . In a cylinder for a sufficiently small , the mixed problem for the aggregation equation is shown to have a bounded solution. The existence of a bounded solution of the problem in the cylinder is proved under additional conditions. For equations of the form with the Laplace-Beltrami operator and an integral operator , the mixed problem is shown to have a unique bounded solution. Bibliography: 26 titles.
Millimeter-Wave Rectangular Dielectric Resonator Antenna Array With Enlarged DRA Dimensions, Wideband Capability, and High-Gain Performance

In this communication, a rectangular dielectric resonator antenna (DRA) is integrated with a backed cavity to enlarge the DRA side length by 1.8 times. The backed cavity also reduces the sensitivity of the DRA resonance to the DRA size, resulting in a relaxed fabrication tolerance. Based on the enlarged DRA design, a stacked DRA (sDRA) is proposed to improve the bandwidth from 8.7% to 15% and enhance the realized gain by an average of 1.5 dB. The proposed mechanisms are verified by fabricating a <inline-formula> <tex-math notation="LaTeX">$4\times4$ </tex-math></inline-formula> sDRA array with an enlarged DRA side length of 1.8 mm (<inline-formula> <tex-math notation="LaTeX">$0.40 {\lambda }$ </tex-math></inline-formula>), an improved bandwidth of 62.7–73.9 GHz (16.4%), and an enhanced measured gain of up to 17.2 dBi. The enlarged DRA dimension permits a higher DRA operating frequency without reducing the DRA size. The proposed millimeter-wave (mmW) sDRA array provides a wide bandwidth and high-gain solution for 67 GHz unlicensed band communications.