Miracle-dz/UniFAR-FTUs huggingface dataset:
Spectra of Atoms and Molecules

1. Introduction 1.1-Waves, Particles, and Units 1.2-The Electromagnetic Spectrum 1.3-Interaction of Radiation with Matter 2. Molecular Symmetry 2.1-Symmetry Operations 2.2-Groups 2.3-Notation for Point Groups 3. Matrix Representation of Groups 3.1-Vectors and Matrices 3.2-Symmetry Operations and Position Vectors 3.3-Symmetry Operators and Basis Vectors 3.4-Symmetry Operators and Basis Fuctions 3.5-Equivalent, Reducible, and Irreducible Matrix Representations 3.6-Great Orthogonality Theorem 3.7-Character Tablesity Theorem 4. Quantum Mechanics and Group Theory 4.1-Matrix Representation of the Schrodinger Equation 4.2-Born-Oppenheimer Approximationchrodinger Equation 4.3-Symmetry of the Hamiltonianionchrodinger Equation 4.4-Projection Operatorsltonianion 4.5-Direct Product Representations 4.6-Integrals and Selection Ruless 5. Atomic Spectroscopyction Ruless 5.1-Introductionoscopyction Rules 5.2-Angular Momentumpy 5.3-The Hydrogen Atom and One-Electron Spectra 5.4-Many-Electron Atomsnd One-Electron Spectra 5.5-Selection Rulestomsnd One-Electron Spectra 5.6-Atomic Spectrastoms 5.7-Intensity of Atomic Lines 5.8-Zeeman EffectAtomic Lines 5.9-Stark EffecttAtomic Lines 6. Rotational Spectroscopy 6.1-Rotation of Rigid Bodies 6.2-Pure Rotational Spectroscopy of Diatomic and Linear Molecules 6.3-Intensity of Pure Rotational Transitions of Diatomic and Linear Molecules 6.4-Symmetric Tops 6.5-Asymmetric Topsre Rotational Transitions of Diatomic and 6.6-Structure Determination 7. Vibrational Spectroscopy 7.1-Diatomic MoleculesRotational Transitions of Diatomic and 7.2-Vibrational Motion of Polyatomic Molecules 7.3-Vibrational Spectra of Symmetric Tops 7.4-Infrared Transitions of Spherical Tops 7.5-Vibrational Spectra of Asymmetric Topsules 7.6-Vibration-Rotation Line Intensitiesps 7.7-Fermi and Coriolis Perturbationsl Tops 7.8-Inversion Doubling and Fluxional Behaviors 8. The Raman Effection Line Intensitiesps 8.1-BackgroundCoriolis Perturbationsl Tops 8.2-Rotational Raman EffectFluxional Behavior 8.3-Vibration-Rotation Raman Spectroscopy 8.4-Rayleigh and Raman Intensitiesns 8.5-Conclusionsaman Effect 9. Electronic Spectroscopy of Diatomic Molecules 9.1-Orbitals and StatesIntensities 9.2-Vibrational Structure 9.3-Rotational Structure of Electronic Transitions of Diatomic Molecules 9.4-The Symmetry of Diatomic Energy Levels: Parity 9.5-Rotational Line Intensities 9.6-Dissociation, Photodissociation, and Predissociation 10. Electronic Spectroscopy of Polyatomic Molecules 10.1-Orbitals and Statesomic Energy Levels: Parity 10.2-Vibrational Structure of Electronic Transitions 10.3-Vibronic Coupling: The Herzberg-Teller Effect 10.4-Jahn-Teller Effect 10.5-Renner-Teller Effect 10.6-Nonradiative Transitions: Jablonski Diagram 10.7-Photoelectron Spectroscopy 10.8-Rotational Structure: H2CO and HCN 10.9-Intensity of Transitions 11. Appendix A: Units, Conversions, and Physical Constants 12. Appendix B: Character Tables 13. Appendix C: Direct Product Tables 14. Appendix D: Introductory Textbooks Covering All of Spectroscopy 9.6-Dissociation, Photodissociation, and Predissociation
Insar Phase Reduction Using the Remove-Compute-Restore Method

Satellite InSAR time series are used to estimate the displacements of radar scatterers. This estimation problem includes the estimation of integer phase ambiguities, which is an ill-posed problem. Consequently, InSAR displacement estimation cannot yield unique solutions and may therefore be significantly biased. Here we show that phase reduction, using a priori information and the remove-compute-restore (RCR) methodology, is a viable way to solve this problem, as it reduces the likelihood of ambiguity errors. We found that application of this methodology to pastures on peat soils leads to a significant improvement in the estimated displacements. We assert that InSAR displacement estimation should always include an explicit statement on the first-order approximations and included assumptions on expected signal smoothness. We anticipate that a more systematic inclusion of the RCR method in standard processing algorithms will lead to more reliable and repeatable results of InSAR analyses.
FESD: An Approach for Biometric Human Footprint Matching Using Fuzzy Ensemble Learning

Biometric traits such as fingerprint, retina scan, and palm-prints are used to identify a person at attendance monitoring, banking, passport, travel, and many other applications. Biometric-based person identification is the only method that never changes according to time, and no one can copy it without knowledge. Footprint-based biometric is one way to recognize a person based on different features associated with human footprints. For example, some places, such as airports, nanotechnology laboratories, silicon industries, temples, and public areas, require high security. It is necessary to add a footprint-based biometric trait for such high alert areas. The number of subjects taken by existing footprint-based methods is limited to very few subjects. The above research gaps motivate to add more subjects for this study. The proposed algorithm utilizes the fuzzy logic-based method for personal identification. Considerably 220 subjects with temporal aspects are taken into account to fill the existing methods gap. Three approaches, Fine Gaussian SVM (FSVM), Fine KNN (FKNN), and Fuzzy Ensemble Subspace Discriminant (FESD), have been utilized to create the enhanced human footprint matcher. The Fine Gaussian SVM approach exhibits an accuracy of 84.7%, the FKNN approach results in an accuracy of 92.3%, and the FESD approach gives an accuracy of 98.89%. FESD approach rectifies the recognition rate(to reach the required accuracy of 98.88%) False Match Rate (FMR, the rate of falsely as genuine classified imposters) at 0.01, False Non-Match Rate at 0.093 which is the rate of falsely as imposter classified genuine users) to a set of different matchers for the identification task. It improves the speed of recognition with 220 subjects by implementing the prototype schemes for footprint biometric to evaluate system properties, including accuracy and performance.
Hybrid Context-aware Message Flooding for Dead Spot Mitigation in V2I Communication

An often overlooked practical problem in Intelligent Transportation Systems (ITS) is the presence of areas without cellular network connectivity, so-called dead spots, which aggravates the communication between vehicles and the external infrastructure. In our previous work, we suggested to mitigate this problem by using a hybrid data dissemination protocol that combines cellular network communication with ad-hoc networks between vehicles. If the vehicles in such ad-hoc networks are in a dead spot but have a good estimation about the time they will leave it again, messages can be forwarded to the vehicle that is supposed to regain cellular network coverage first. Since this vehicle may transmit the stored messages immediately after having left the dead spot, the delivery time is improved. In this paper, we first analyze the behavior of the aforementioned data dissemination protocol in larger dead spots in which a message may be carried by several vehicles before being delivered via the cellular network. The analysis reveals that messages are not always delivered in the fastest possible time. To address this concern, a new protocol variant named context-aware message flooding protocol is introduced. This protocol, indeed, guarantees the fastest possible forwarding of messages to their recipients. This is achieved at the cost of delivering duplicates that, however, are only produced when the delivery of a message is sped up.
Oil Spill Detection from SAR Images by Deep Learning

Oil spills, caused by accidents or by ships cleaning their tanks, represent big threats for maritime and coastal ecosystems health. A very effective detection of oil spills can be performed using satellite synthetic aperture radar (SAR) systems, operating regardless of cloud coverage and sunlight and capable of discriminating oil from regular sea surface. However, discriminating between real oil spills and lookalikes (such as natural oils and seepages, often occurring in upwelling sea areas), although well performed by expert SAR image interpreters, poses a great challenge for automatic processes. In addition, a visual check performed by human operators on a great number of images would be too expensive. Therefore, many solutions for automatic detection have been tried in the last few years, using probabilistic models and, more recently, machine learning. This work presents an innovative solution based on image-to-image translation using convolutional neural networks (CNNs) trained with an adversarial loss function. The proposed approach has been tested, with very promising results, using Radarsat-2 and Sentinel-1 SAR data over the Mediterranean Sea and some areas of the Atlantic Ocean and the North Sea.
Towards Safe Automated Driving: Design of Software-Defined Dynamic MmWave V2X Networks and PoC Implementation

The technical exploration of safe automated driving has evolved from single vehicle intelligence to vehicular networking. Therefore, to a certain extent, the safety requirements have been translated into the requirements for network performance. Although Vehicle-to-Everything (V2X) communication technologies have developed decades of years, from the Dedicated Short-Range Communication (DSRC) to the Long Term Evolution V2X (LTE-V2X), the introduction of millimeter wave (mmWave) in the 5G era will achieve an unprecedented leap in V2X performance by empowering multi-gigabit data transmission with ultra-low latency. Meanwhile, the emergence of Software Defined Networking (SDN) enables centralized network management and allows flexible scheduling of communication resources to match V2X capacities. Their benefits for vehicular networks have been revealed by plenty of studies respectively. Nevertheless, no researchers investigate their combination to improve the safety of automated driving, especially lack of scalable prototype systems for proof-of-concepts (PoCs), not to mention the field test with real traffic. In this paper, we firstly propose a novel architecture, i.e. software-defined dynamic mmWave V2X network, to support cooperative perception. And then, necessary hardware and software platforms are introduced to compose prototype systems. Step by step, we conduct PoCs from the indoors to the outdoors, which demonstrate the network functions, the prototype scalability and most importantly, the significant enhancement to the vehicle perception for safe automated driving.
Uncertainty in Identification Systems

High-dimensional identification systems consisting of two groups of users in the presence of statistical uncertainties are considered in this work. The task is to design enrollment mappings to compress users’ information and an identification mapping that combines the stored information in the database and an observation to estimate the underlying user index. The compression-identification trade-off regions are established for the compound, extended compound, general and mixture settings. It is shown that several settings admit the same compression-identification trade-offs. We then study a connection between the Wyner-Ahlswede-Körner network and the identification setting. It indicates that a strong converse for the WAK network is equivalent to a strong converse for the identification setting. Finally, we present strong converse arguments for the discrete identification setting that are extensible to the Gaussian scenario.
Effects of Crystal Form on Solubility and Pharmacokinetics: A Crystal Engineering Case Study of Lamotrigine

In this contribution, we describe how the supramolecular synthon approach can be used for discovery of novel crystal forms and for enhancing the relevant preclinical properties of a low solubility antiepileptic drug, lamotrigine (6-(2,3-dichlorophenyl)-1,2,4-triazine-3,5-diamine). Ten novel crystal forms are reported: lamotrigine methylparaben cocrystal form I (1:1) (1), lamotrigine methylparaben cocrystal form II (1:1) (2), lamotrigine nicotinamide cocrystal (1:1) (3), lamotrigine nicotinamide cocrystal monohydrate (1:1:1) (4), lamotrigine saccharin salt (1:1) (5), lamotrigine adipate salt (2:1) (6), lamotrigine malate salt (2:1) (7), lamotrigine nicotinate dimethanol solvate (1:1:2) (8), lamotrigine dimethanol solvate (1:2) (9), and lamotrigine ethanol monohydrate (1:1:1) (10). A selected set of the reported crystal forms were studied to determine their dissolution rate, solubility, and pharmacokinetic behavior. The solubilities were measured in aqueous media and in acidified aqueous media (pH = 1). It .
An FFT-Accelerated and Tucker-Enhanced Inductance Extraction for Voxelized Superconducting Structures

An integral equation simulator is proposed to compute the inductance of the superconducting structures discretized by voxels. The proposed simulator extends the VoxHenry inductance extractor by taking into account the superconductivity in the computations. In addition to possessing all distinctive features of the VoxHenry simulator, the proposed simulator makes use of Tucker decompositions to compress its large data structures involving the block circulant tensors. By doing so, it reduces the memory requirement of these tensors from gigabytes to megabytes and CPU time required to obtain these tensors from hours/minutes to seconds for the large-scale problems.
Seizure Detection Using Time Delay Neural Networks and LSTMs

Automatic detection of seizures from EEG signals is an important problem of interest for clinical institutions. EEG is a temporal signal collected from multiple spatial sources around the scalp. Efficient modeling of both temporal and spatial information is important to identify the seizures using EEG. In this paper, we propose a neural network system using the time-delay neural network to model temporal information (TDNN) and long short term memory (LSTM) layer to model spatial information. On the development subset of Temple University seizure dataset, the proposed system achieved a sensitivity of 23.32 % with 11.13 false alarms in 24 hours.
SAR and Temperature Distributions in a Head Model Due to Electromagnetic Radiation with Frequencies up to 100 GHz

A numerical study is conducted to investigate the SAR, temperature distribution and penetration depth of an electromagnetic plane wave incident on a human head model, for frequencies up to 100 GHz. A multi-layered, planar human head model is utilized for the analysis using the dispersive finite-difference time-domain formulation based on the auxiliary differential equation method along with the bio-heat equation. Numerical results show that penetration depths decrease exponentially with frequency, maximum SAR and resulting temperature rise values increase with frequency. The results obtained in this investigation are useful to evaluate the effect of the EM plane wave incident on the human head model up to 100 GHz.
Switch-Based Hybrid Analog/Digital Channel Estimation for mmWave Massive MIMO

This paper addresses the problem of channel estimation using pilots in hybrid analog/digital massive multiple-input multiple-output (MIMO) systems for future millimetre wave (mmWave) communications. To further reduce system cost and implementation complexity, we consider an alternative architecture derived from RF switches as opposed to the phase shifters in the conventional literature. The channel estimation is formulated as a combinatorial optimization problem where the aim is to minimize the mean square error (MSE) between the real and estimated channels over a finite set of allowed values for the precoder switches. A genetic algorithm (GA) is developed for solving this problem and obtaining the MIMO channel estimates. Simulations show that the proposed scheme can estimate channels as accurately, if not more, as an existing solution using phase shifters.
Accurate Distance Estimation for RSS Localization With Statistical Path Loss Exponent Model

We study the ratio-of-distance estimation from the difference of received signal strength (DRSS) when the source transmission power is unknown. Due to fluctuations in the wireless medium, the path loss exponent is also considered unknown and is modelled statistically as a uniformly distributed random variable. When maximum-likelihood estimation is employed, we are required to solve a highly non-linear function which contains a summation of exponential and Q-function terms in the log-ratio-of-distances. In the state-of-the-art [8], the Q-function terms are ignored in order to derive an approximate but erroneous solution in closed-form. In this work, we firstly expose the adverse impact of ignoring the Q-function terms on the estimation performance. Next, we propose a uniform search root finder (USRF) method which solves the original non-linear function without ignoring any of the underlying terms. Numerical studies reveal that the proposed method not only provides much more accurate ratio-of-distance estimates than the state-of-the-art, but also provides significantly better localization accuracy.
A novel deep neural design and efficient Pipeline architecture for Person Re-Identification in high resolution Video

The primary objective of person re-identification (Re-ID) is to retrieve a person of interest across different nonintersecting cameras for managing in distributed surveillance systems. This has added to its increasing popularity on account of its widespread use, applications and research significance. In this study, we have proposed a novel pipelined deep learning architecture which acts as a robust feature extractor and also helps in reducing down the search space by generating feature embeddings followed by executing a distance metric measurement for finding the similar neighbourhood embeddings and subsequently sorting the cluster centroids of the matching embeddings for finding a set of the nearest match before passing down to a siamese network for similarity checking in the reidentification process. Our experiments reported to achieve a performance accuracy of $85 \sim 90$% with a model size of 288 MB executing at 30 fps in real-time.
Bit-Labeling and Page Capacities of TLC Non-Volatile Flash Memories

The reliability of flash memories suffers from various error causes. Program/erase cycles, read disturb, and cell to cell interference impact the threshold voltages and cause bit errors during the read process. Hence, error correction is required to ensure reliable data storage. In this work, we investigate the bit-labeling of triple level cell (TLC) memories. This labeling determines the page capacities and the latency of the read process. The page capacity defines the redundancy that is required for error correction coding. Typically, Gray codes are used to encode the cell state such that the codes of adjacent states differ in a single digit. These Gray codes minimize the latency for random access reads but cannot balance the page capacities. Based on measured voltage distributions, we investigate the page capacities and propose a labeling that provides a better rate balancing than Gray labeling.
Experimental Study of Microbial Pyrite Oxidation: A Suggestion for Geologically Useful Biosignatures

The oxidation of pyrite in cultures of Acidithiobacillus ferrooxidans (A.f) was studied. The experiments were performed at an initial pH of 2.5 at 28°C. The concentrations of total dissolved iron in solution and the pH were monitored during the first 36 days. Pyrite surfaces were examined by scanning electron microscopy and energy-dispersive spectrometry (SEM-EDS) after 100 days. The concentrations of total dissolved iron and hydrogen ions increased significantly in the presence of bacteria. SEM examination indicated that the crystal surfaces were subjected to two types of dissolution phenomena. Cracks were observable on the of crystal surfaces under both biotic and abiotic conditions, whereas rounded and polygonal pits appeared additionally on the surfaces under biotic conditions. The co-occurrence of the rounded and polygonal pits on the crystal surfaces and the presence of A.f at the pyrite surface suggests that A.f promotes pyrite oxidation by a contact mechanism. We propose that the rounded and polygonal pits be considered to represent a practical biosignature for tracing the evolution of microbial iron oxidation in the remote past.
Neural Network Complementary Sliding Mode Current Control of Active Power Filter

This study proposed an adaptive Radical Basis Function (RBF) neural control strategy with a complementary sliding mode approach to compensate the harmonic current in an Active Power Filter (APF). A backstepping algorithm is incorporated to simplify the design procedure. Meanwhile, a complementary sliding surface is employed to replace the standard sliding surface to eliminate the chattering. A neural estimator is designed to approximate the upperbound of the lumped nonlinearities in the APF. A simulation and real-time prototype using TMS320F28335 was built to demonstrate the validity of the proposed controller.
Diffusion and adsorption of benzene in Regina clay

The diffusion and adsorption coefficients for benzene were measured for a glaciolacustrine clay using the single- and double-reservoir methods, batch tests, and organic carbon content correlations. Single- and double-reservoir diffusion test cells and experimental procedures were specifically developed for volatile organic chemicals. Analytical methods based on solvent extraction with gas chromatography - flame ionization detection and purge and trap gas chromatography - mass spectrometry were used for the analysis of aqueous benzene solutions in the 0.5 µg/L to 500 mg/L range. The measured effective diffusion coefficient was 0.01 m2/a (3.2 × 10-6 cm2/s), and adsorption coefficients ranged from 0.1 to 1.0 mL/g. The diffusion cell tests indicate relatively low levels of retardation for benzene as it moves in clay soils with low organic carbon content.Key words: adsorption, diffusion, benzene, Regina clay.
Application Layer Throughput Control for Video Streaming over HTTP2

Video streaming is one of the most popular applications on the Internet nowadays. With the increasing number of Internet-connected devices, it is common to have multiple streaming clients on the same network connection, such as in e-learning and video games. In this paper, we propose a novel application layer throughput management framework for multiple streaming clients sharing the same network bottleneck. To our best knowledge, this is the first framework that includes a throughput estimation scheme and an application layer based throughput control scheme which utilizes the HTTP2’s Flow Control mechanism. Experimental results show that the proposed framework can effectively control throughput of individual clients. Specifically, the bandwidth is no longer disputed by the clients and throughput of each client is allocated according to the assigned ratio.
Estimation of the molecular weight distribution in batch polymerization

A filtering technique is proposed for on-line estimation of the temperature, monomer conversion, initiator conversion, and the entire molecular weight distribution in a batch methyl methacrylate polymerization reactor. The technique uses a detailed polymerization model combined with on-line measurements of conversion, temperature, and the molecular weight distribution, taken at different discrete time intervals. The polymerization model includes a chain-length-dependent termination rate constant which allows the prediction of the molecular weight distribution for common free-radical polymerization conditions. Comparisons between modeling and experimental results show that the polymerization model gives good predictions of the monomer conversion and the molecular weight distribution in the polymerization system. The performance of the estimation scheme is tested for cases of strong gel effect conditions leading to a bimodal molecular weight distribution, and poor initial conditions. Finally, off-line experimental data are used to test the algorithm under actual reactor operating conditions.