Miracle-dz/UniFAR-FTUs huggingface dataset:
Labile Trace Elements in Some Antarctic Carbonaceous Chondrites: Antarctic and Non-Antarctic Meteorite Comparisons

Abstract We report data for Ag, Au, Bi, Cd, Co, Cs, Ga, In, Rb, Sb, Se, Te, TI, U and Zn determined by radiochemical neutron activation analysis in consortium samples of Belgica (B) 7904, Yamato (Y) 82042, Yamato 82162 and Yamato 86720 carbonaceous chondrites. These trace elements cover a wide volatility/mobility range and give unique information on thermal histories of meteorites. The results indicate the unique nature of these carbonaceous chondrites. Y-82042 proves to have the volatile element pattern of a C2 ( ≡CM) chondrite and the petrologic characteristics of a CI (n≡CI) chondrite. These must be primary nebular condensation/accretion features, unaffected by post-accretionary processes. The other three meteorites were thermally metamorphosed in ≧2 parent regions over the 600-700 °C range, at relative temperatures B-7904 < Y-82162< Y-86720. Before heating, B-7904 and Y-86720 had C2-levels of volatile elements: Y-82162 had uniquely high volatile element concentrations, at about CI-levels. The data require a new classification scheme for such chondrites. Belgica 7904 and Y-82162 and -86720 seem to be derived from one or more thermally altered carbonaceous asteroids, and their spectral characteristics should be compared with those of B-, F-, G- or T-asteroids. These results indicate substantial differences in the thermal histories of Antarctic and non-Antarctic CI and C2 chondrite populations. In reviewing all that is known about the Antarctic and non-Antarctic meteorite populations, the overwhelming weight of evidence supports the view that these populations sample different extraterrestrial source materials, differing in thermal histories. It may be that over the extended collecting period of the Antarctic ice sheet, it has sampled a considerably greater proportion of near-Earth asteroids than do current falls.
Enhanced Dye-Sensitized Solar Cell Efficiency of Titanium Oxide (TiO2) -Doped Reduced Graphene Oxide (rGO)

The Titanium Dioxide (TiO2) doped reduced Graphene Oxide (rGO) was introduced as a semiconducting layer of Dye-Sensitized Solar Cell (DSSC). The rGO was prepared by using Hummer method while TiO2 doped rGO was prepared by combining Titanium (IV) Oxide nanopowder as precursor of TiO2 and chemically reduced oxide rGO. The molarity of rGO was first studied to determine the optimal molarity of rGO for doping process of TiO2 from its structural and optical properties. The morphological study shows that the 0.010M of rGO solution is the optimum molarity and has a higher absorbance compared to 0.005M and 0.015M. To determine the optimal organic dye for DSSC application, three different organic dyes with rGO thin film such as turmeric (curcumin), spinach (chlorophyll) and beetroot (anthocyanin) have been studied and the UV-Vis result shows that the chlorophyll pigment from spinach dye has several absorption peaks compared to others. Before fabricating DSSC, the X-ray Photoelectron Spectroscopy (XPS) measurement was conducted to determine the chemical bonding structure of TiO2 doped rGO. The results show that the presence of the Ti–O–C structure that indicates some of Ti atoms in TiO2 have been substituted by C atom during doping process of TiO2. The results show that rGO doping significantly improves the efficiency of DSSC.
Subdomain-Based Generality-Aware Debloating

Programs are becoming increasingly complex and typically contain an abundance of unneeded features, which can degrade the performance and security of the software. Recently, we have witnessed a surge of debloating techniques that aim to create a reduced version of a program by eliminating the unneeded features therein. To de-bloat a program, most existing techniques require a usage profile of the program, typically provided as a set of inputs $I$. Unfortunately, these techniques tend to generate a reduced program that is overfitted to $I$ and thus fails to behave correctly for other inputs. To address this limitation, we propose Domgad, which has two main advantages over existing debloating approaches. First, it produces a reduced program that is guaranteed to work for subdomains, rather than for specific inputs. Second, it uses stochastic optimization to generate reduced programs that achieve a close-to-optimal tradeoff between reduction and generality (i.e., the extent to which the reduced program is able to correctly handle inputs in its whole domain). To assess the effectiveness of Domgad, we applied our approach to a benchmark of ten Unix utility programs. Our results are promising, as they show that Domgad could produce debloated programs that achieve, on average, 50% code reduction and 95% generality. Our results also show that Domgad performs well when compared with two state-of-the-art debloating approaches.
Derivation and Comparison of Efficiency and Power in Non-resonant and Resonant Circuit of Capacitive Power Transfer

Capacitive Power Transfer (CPT) is safer than Inductive Power Transfer (IPT), because eddy current is not generated in CPT. In this study, conditions for transfer with high efficiency and high power were presented by deriving equation of the resonance conditions, efficiency, power, and optimal load considering internal resistance in non-resonance (N-N), primary resonance (S-N), secondary resonance (N-S), series-series (S-S) and series-parallel (S-P) without approximation. In addition, the value of efficiency and power are derived and compared in the five topologies. As a result, it is said that S-S can transfer 95W output power with 95% efficiency and S-S can transfer 98W output power with 95% efficiency when input voltage is 100V. And, S-S and S-P are superior to other three topologies. Furthermore, the validity of the results was shown by conducting experiments.
Improved Monte Carlo Integration Models for Ultraviolet Communications

Monte Carlo methods are widely used in ultraviolet communications. However, it needs many simulation photons due to its large path loss. In this paper, we use three methods to improve computation efficiency: antithetic variates method, quasi-random sequence, and parallel computing. Numerical simulation results show that the convergence rate is O(N-0.7499) for the antithetic variates quasi-Monte Carlo integration method while O(N-0.5083) for the Monte Carlo integration method. Moreover, numerical simulation results show that the parallel quasi-random Monte Carlo integration method is about 883.4 times the Monte Carlo integration method when the standard deviation is 10-15.
Direct Determination of a Peptide Torsional Angle ψ by Double-Quantum Solid-State NMR

Solid-state NMR is established as a useful method for estimating molecular structural details in systems which do not crystallize easily and which are unsuitable for liquid-state NMR. Several recent methods directly access angular information, circumventing the measurement of accurate internuclear distances. 1-6 In this paper, we report a new method of this type, which directly estimates the torsional angle ψ of a 15N13C-13C-15N molecular fragment in an isotopically-labeled peptide. The estimation is robust and highly accurate (in the region 140° e |ψ| e 180°) and does not require knowledge of chemical shift tensor orientations. The method is demonstrated on an isotopically-labeled tripeptide hydrochloride GGG ‚HCl. The new technique is a variety of double-quantum heteronuclear local field spectroscopy (2Q-HLF) 1,3 and is referred to as NCCN-2Q-HLF, where the prefix indicates the four relevant nuclei. The principle of the method is similar to the previously described HCCH-2Q-HLF experiment, 3 in which the torsional angle of a 1H-13C-13C-1H fragment is measured. Both techniques exploit a correlated transverse spin state (doublequantum coherence) between the neighboring 13C spins. The double-quantum evolution is sensitive to the correlations of the local fields produced by the directly-bonded heteronuclei. The local field correlations depend on the geometrical relationship of the internuclear vectors and, hence, on the molecular torsional angle. The15N-13C-13C-15N case requires a new experimental strategy, because the 15N-13C couplings are typically quite small and are readily averaged out by the magic-angle sample rotation. The radio frequency (rf) pulse scheme for NCCN-2Q-HLF spectroscopy is shown in Figure 1. Radio frequency irradiation is applied at the Larmor frequencies of the three isotopes 1H, 13C, and 15N, requiring a triply-tuned magic-angle-spinning NMR probe. The sample is rotated at the angular frequency ωr about an axis subtending the magic angle tan -1x2 with the main magnetic field. The rf sequence starts by ramped crosspolarization, 7 generating enhanced 13C transverse magnetization. This is converted intoC2 double-quantum coherence by a π/2 pulse followed by a C7 rf sequence, as described elsewhere. 8
Effects of polyethylene glycol on protein interactions

The interactions between globular proteins in the presence of poly (ethylene glycol) (PEG) are probed through the measurement of the protein solution second virial coefficient (B2). The solution properties of PEG are characterized for four molecular weights (400, 1000, 6000, and 12 000), providing an opportunity for quantitative comparison of measurements and theoretical predictions of B2. PEG displays a buffer and molecular weight-dependent lower critical solution temperature. As the polymer solution approaches phase separation, the consequences of depletion attractions increase significantly. For lysozyme and bovine serum albumin in sulfate buffers with PEG, B2 is not well described by standard depletion models. This failure is accentuated in acetate buffers where B2 is a nonmonotonic function of polymer concentration. The attractive minima in B2 are closely associated with the proximity of the heating-induced phase separation of aqueous PEG solutions. The experimental data for both proteins in the pres.
Automated Third-Party Library Detection for Android Applications: Are We There Yet?

Third-party libraries (TPLs) have become a significant part of the Android ecosystem. Developers can employ various TPLs with different functionalities to facilitate their app development. Unfortunately, the popularity of TPLs also brings new challenges and even threats. TPLs may carry malicious or vulnerable code, which can infect popular apps to pose threats to mobile users. Besides, the code of third-party libraries could constitute noises in some downstream tasks (e.g., malware and repackaged app detection). Thus, researchers have developed various tools to identify TPLs. However, no existing work has studied these TPL detection tools in detail; different tools focus on different applications with performance differences, but little is known about them. To better understand existing TPL detection tools and dissect TPL detection techniques, we conduct a comprehensive empirical study to fill the gap by evaluating and comparing all publicly available TPL detection tools based on four criteria: effectiveness, efficiency, code obfuscation-resilience capability, and ease of use. We reveal their advantages and disadvantages based on a systematic and thorough empirical study. Furthermore, we also conduct a user study to evaluate the usability of each tool. The results showthat LibScout outperforms others regarding effectiveness, LibRadar takes less time than others and is also regarded as the most easy-to-use one, and LibPecker performs the best in defending against code obfuscation techniques. We further summarize the lessons learned from different perspectives, including users, tool implementation, and researchers. Besides, we enhance these open-sourced tools by fixing their limitations to improve their detection ability. We also build an extensible framework that integrates all existing available TPL detection tools, providing online service for the research community. We make publicly available the evaluation dataset and enhanced tools. We believe our work provides a clear picture of existing TPL detection techniques and also give a road-map for future directions.
High-Speed Collision Avoidance using Deep Reinforcement Learning and Domain Randomization for Autonomous Vehicles

Recently, deep neural networks trained with Imitation-Learning techniques have managed to successfully control autonomous cars in a variety of urban and highway environments. One of the main limitations of policies trained with imitation learning that has become apparent, however, is that they show poor performance when having to deal with extreme situations at test time- like high-speed collision avoidance - since there is not enough data available from such rare cases during training. In our work, we take the stance that training complex active safety systems for vehicles should be performed in simulation and the transfer of the learned driving policy to the real vehicle should be performed utilizing simulation to-reality transfer techniques. To communicate this idea, we setup a high-speed collision avoidance scenario in simulation and train the safety system with Reinforcement Learning. We utilize Domain Randomization to enable simulation-to-reality transfer. Here, the policy is not trained on a single version of the setup but on several variations of the problem, each with different parameters. Our experiments show that the resulting policy is able to generalize much better to different values for the vehicle speed and distance from the obstacle compared to policies trained in the non-randomized version of the setup.
Hirshfeld surface analysis

In the last few years the analysis of molecular crystal structures using tools based on Hirshfeld surfaces has rapidly gained in popularity. This approach represents an attempt to venture beyond the current paradigm—internuclear distances and angles, crystal packing diagrams with molecules represented via various models, and the identification of close contacts deemed to be important—and to view molecules as “organic wholes”, thereby fundamentally altering the discussion of intermolecular interactions through an unbiased identification of all close contacts.
Axial dispersion models for displacement washing of packed beds of wood pulp fibres

Axial dispersion models were developed for displacement washing of black liquor from packed beds of wood pulp fibres using pure wash water. For high initial bed liquor concentrations, the simple axially dispersed plug flow model was satisfactory for describing the experimental displacement washing breakthrough curves. For low initial bed liquor concentrations, the model was modified by the addition of a source term to account for the release of adsorbed and absorbed solutes which are transferred from the fibres to the wash fluid, and which become significant at low concentrations. Equilibrium between the fibres and the surrounding liquor was assumed. The highest Peclet numbers corresponding to the highest displacement washing efficiencies were obtained at high bed heights and low bed consistencies, for any superficial velocity. Des modeles de dispersion axiale ont ete mis au point pour le lavage par deplacement de la liqueur noire a partir de lits a garnissage de fibres de pulpe de bois utilisant de l'eau de lavage pure. Pour des concentrations initiales de liqueur de lit elevees, le modele simple d'ecoulement piston disperse axialement est satisfaisant pour decrire les courbes de rendement du lavage de deplacement experimental. Pour de faibles concentrations initiales de liqueur dc lit. le modele a ete modifie par l'addition d'un terme sourcc pour prendre en compte la liberation de solutes adsorbes et absorbes qui sont transferes des fibres au fluide de lavage, et qui devient important a de faibles concentrations. L'equilibre entre les fibres et la liqueur ambiante a ete suppose. Les nombres de Peclet correspondant aux effcacites de lavage de deplacement les plus grandes. ont ete obtenus a des hauteurs de lit elevees et a de faibles consistances. pour n'iniporte quellc vitesse superficielle.
Evaluation of Resilience Indicators for Public Transportation Networks by the Grey Relational Analysis

This article uses primarily the Grey Relational analysis method to analyze the effectiveness of 14 indicators related to transportation network resilience. In the process of analysis, we use the indicator data obtained from an unattacked network as the optimal reference sequence and a network attacked on the most connected node as the worst reference sequence. Besides the optimal and the worst scenarios, to study the network resilience, we define a network attacking strategy consisting in an assault on one node at a time, orderly for all nodes of the network. A relative Grey Correlation Degree is also proposed to evaluate the results. The analysis is made on 10 public transport networks. They show that the Global Efficiency is the indicator that has the greatest influence on the resilience of the public transportation network. We also categorized the resilience indicators into three different groups. We find that the most important category for network resilience is the Network Efficiency indicator, which includes the network structure plus the bus travel time.
Empirical analysis of generalization through augmentation for classifying images of vernacular handwritten texts

Handwritten image recognition [1] is a challenging problem that has seen tremendous research efforts in recent years from the computer vision community. Although many deep learning-based classifications algorithms are studied in the literature for handwritten image recognition [2], most of the works are concentrated on a few widely used benchmark datasets [3]. Hence, it is difficult to understand how well a certain model generalizes across different optical characters with vast complexity levels. The existing research works for classification from handwritten image datasets are primarily focused on developing better model architectures, better training procedures, better regularization methods such as Image Augmentation, Dropout, Dropblock, etc. [4–6], hard example mining techniques [7], better Loss Functions such as Focal loss [8], Weighted Cross Entropy loss, etc. In this paper, we focus on image augmentation techniques along with an example mining technique to establish a robust classification baseline for handwritten datasets using a novel combination of augmentation methods and online hard example mining. Through our experiments, we show the combination of advanced image augmentation techniques best suited for achieving the highest macro averaged recall score and highlight a few important trends therein.
A Comparison of YOLO and Mask R-CNN for Segmenting Head and Tail of Fish

The visual appearance of the fish’s head and tail can be used to identify its freshness. A segmentation method that can well isolate those certain parts from a fish body is required for further analysis in a system for detecting fish freshness automatically. In this research, we investigated the performance of two CNN-based segmentation methods, namely YOLO and Mask R-CNN, for separating the head and tail of fish. We retrained the YOLO and Mask R-CNN pre-trained models on the Fish-gres dataset consisting of images with high variability in the background, illumination, and overlapping objects. The experiment on 200 images containing 724 heads and 585 tails annotated manually indicated that both models work optimally. YOLO’s performance was slightly better than Mask R-CNN, shown by 98.96% and 96.73% precision, and 80.93% and 75.43% recall, respectively. The experimental result also revealed that YOLO outperforms Mask R-CNN with mAP of 80.12% and 73.39%, respectively.
Scheduling bus holding times in time horizons considering transfer synchronization and service regularity

Transfers between public transport modes hinder the traveling by public transport due to the unreliability and long waiting times. Nonetheless, public transport operators are mainly interested in maintaining the regularity of different lines without accounting for the transfer waiting times. This work focuses on the application of bus holding to reduce the transfer waiting times, in-vehicle waiting times, and the deviation from the target headways. The determination of optimal holding times is performed in time horizons (e.g., hourly time periods) where past events are fixed, while future events are considered in the optimization process. This study models the bus holding problem which considers transfer synchronization as a nonlinear convex program (NLP) that can be solved by off-the-shelf solvers. Our approach is tested against state-of-the-art bus holding methods in a case study on the bus network of Almere (Netherlands) demonstrating a significant reduction in the transferring waiting times.
Motion Modeling and Simulation of Maritime Autonomous Surface Ships in Realistic Environmental Disturbances

A novel modeling and simulation pipeline is proposed in this paper to predict the six-degrees-of-freedom (6-DOF) motion of MASS under realistic environmental disturbances. The MASS model consists of several physics-based differential equations that are built based on state-of-the-art studies. The onboard sensors are utilized to drive the MASS model to keep the consistent control input with the real ships. A build-in hydrodynamic database describes the motion characteristics of ships. Moreover, an actual sea database is developed based on the third-generation wave numerical model to reproduce the ocean characteristics in the simulation. The spatial-and time-varying environmental vector fields are obtained from the real sea database to ensure consistency with the real environment. Three real voyage cases are reproduced by the presented method. The simulation results are compared with the records from onboard sensors for validation. The RMSE of the simulated trajectories is 3.789’, 3.110’, and 1.318’ under the single influence of disturbances due to the inappropriate description of the real sea. Under the coupling disturbances, the RMSE of trajectories reduces to 0.1829’, 0.5249’, and 0.4030’. Consequently, the results indicate that the ocean database accurately reflected the ocean characteristics realistically, and the presented method can effectively predict the motions of the real ship under the actual sea environmental disturbances.
Towards Equilibrium-based Interaction Modeling for Pedestrian Path Prediction

Predicting the future paths of pedestrians is shown to be crucial in various research domains, including intelligent vehicles and surveillance. Although numerous studies are conducted, the task has remained challenging, requiring a fundamental understanding of the complex human-human interactions. While current interaction-based frameworks are developed to benefit from information sharing between pedestrians, such frameworks are often ill-suited for monitoring the decisions-making process of pedestrians. In this paper, the problem of human-human interaction has been tackled by combining tools from multi-agent decision theory. Employing the game theory solution concept of Nash equilibrium, a novel equilibrium-based modification is proposed. With the aim of generalization, the proposed modification is applied to two different interaction-based frameworks and verified on publicly available datasets. Through these experiments, the advantages of the proposed modification are demonstrated.