Miracle-dz/UniFAR-FTUs huggingface dataset:
Reliably Analyzing the Chemical Composition of Plumes during Flybys at Velocities Exceeding 5 km/s

Spacecraft flybys of planetary bodies provide an opportunity to analyze the chemical composition of their outer atmosphere by mass spectrometry. The chemical composition of the atmosphere provides key insights into the origin and evolution of an object. Additionally, sampling a plume, if present, during the flyby will provide detailed information on the complex chemistry present below the surface. At present, composition information available for many of these objects is sparse, as on one hand, the ground-based observations lack both sensitivity and spatial resolution for a detailed investigation and on the other hand recently performed flythroughs were conducted using outdated instrumentation resulting in a moderate sensitivity within a limited mass range. In contrast to the tenuous exosphere of celestial objects, plumes feature significantly increased number density of species in a spatially confined geometry. Flybys are quick events given the relative encounter velocity are easily exceeding 5 km/s, up to about 20 km/s, resulting in major design drivers for instrumentation. The tens of seconds spent in the region of interest requires instant recording of detailed data. The high flyby velocity requires sampling that prevents species from hypervelocity impact induced bond-dissociation. Such a fragmentation complicates the reliable identification of observed signals to species especially in complex mixtures, if not making it impossible. Here we present the development of a sensitive time-of-flight mass spectrometer that can analyze both the tenuous exosphere of celestial objects and plumes, if present, during hypervelocity flybys exceeding 5 km/s enabled by its novel direct open ion source. It uniquely prevents hypervelocity impact induced bond-dissociation as it directly analyzes species without contact with a wall of, for example, an antechamber or other structures in a relative encounter velocity range of 0 to 20 km/s. Using electron ionization, it is capable of analyzing even heavy species in complex mixtures. Such a capability allows for reliably analyzing plumes in a mass range of $m/z\ 1$ to 1000 for a relative encounter velocity of 10 km/s and a mass range of $m/z\ 1$ to 800 for 20 km/s, enabled by a mass resolution exceeding $m/\Delta m\approx 1000$ (full width at half maximum) for higher masses. Thanks to its sensitivity, it is able to analyze even traces of species in both the exosphere and the plumes. This novel instrument allows for detailed and rapid mass spectrometric investigations in all mission scenarios where high flyby speeds at planetary objects are foreseen, for example, Io and Enceladus. This offers the opportunity to measure these objects' exospheres at large distances and measure the plumes when in proximity to the closest approach.
Detection of Educational Influencers and Communities on TikTok

TikTok is a video-sharing social networking service that has rapidly grown in recent years. TikTok reached 1.2 billion monthly active users in Q4 2021 and is expected to reach 1.8 billion by the end of 2022. TikTok, compared to other social networks, has a higher audience engagement, allowing information to be broadcasted quickly and effectively. In order to facilitate communities' exploration and topics identification, we present a study that includes the social networks analysis techniques in conjunction with the statistical and machine learning approaches applied to TikTok #learnontiktok campaign data to explore and provide insightful and valuable information about influencers, groups and educational areas and thus develop a data-driven culture for decision making.
High Performance CsPbI3 NCs Decorated Few-layer WS2 Hybrid Photodetectors

A promising device architecture that can achieve high response in photodetector devices can be consists of an effective semiconductor layer with high carrier mobility and highly photoabsorbing material. Here, we report a superior broadband visible photodetector with a few-layer tungsten disulfide (WS2) decorated with cesium lead iodide (CsPbI3) nano-crystals (NCs) in hybrid heterojunctions. An enhancement in photocurrent is demonstrated in the CsPbI3 NCs decorated WS2 0D/2D hybrid nanostructure compared to pristine WS2, revealing the potential of hybrid systems in next generation optoelectronic devices.
Automated extraction of seed morphological traits from images

The description of biological objects, such as seeds, mainly relies on manual measurements of few characteristics, and on visual classification of structures, both of which can be subjective, error prone and time‐consuming. Image analysis tools offer means to address these shortcomings, but we currently lack a method capable of automatically handling seeds from different taxa with varying morphological attributes and obtaining interpretable results. Here, we provide a simple image acquisition and processing protocol and introduce Traitor, an open‐source software available as a command‐line interface (CLI), which automates the extraction of seed morphological traits from images. The workflow for trait extraction consists of scanning seeds against a high‐contrast background, correcting image colours, and analysing images with the software. Traitor is capable of processing hundreds of images of varied taxa simultaneously with just three commands, and without a need for training, manual fine‐tuning or thresholding. The software automatically detects each object in the image and extracts size measurements, traditional morphometric descriptors widely used by scientists and practitioners, standardised shape coordinates, and colorimetric measurements. The method was tested on a dataset comprising of 91,667 images of seeds from 1228 taxa. Traitor's extracted average length and width values closely matched the average manual measurements obtained from the same collection (concordance correlation coefficient of 0.98). Further, we used a large image dataset to demonstrate how Traitor's output can be used to obtain representative seed colours for taxa, determine the phylogenetic signal of seed colour, and build objective classification categories for shape with high levels of visual interpretability. Our approach increases productivity and allows for large‐scale analyses that would otherwise be unfeasible. Traitor enables the acquisition of data that are readily comparable across different taxa, opening new avenues to explore functional relevance of morphological traits and to advance on new tools for seed identification.
Near Real-time Orbit Determination of Navigation Satellite Using Regional Tracking Network

On the basis of adding normal equation method,the one day-long delayed global station's data and real time regional station's data have been synchronously dealed for navigation satellite orbit determination.Compared with the IGS precision orbit,the average of 3D RMS of the computed near real-time orbit is 14 cm,and the average of 3D RMS of the near real-time prediction orbit is better than 30 cm.
Transcriptome Analysis of Rice Embryo and Endosperm during Seed Germination

Seed germination is a complex, multistage developmental process that is an important step in plant development. In this study, RNA-Seq was conducted in the embryo and endosperm of unshelled germinating rice seeds. A total of 14,391 differentially expressed genes (DEGs) were identified between the dry seeds and the germinating seeds. Of these DEGs, 7109 were identified in both the embryo and endosperm, 3953 were embryo specific, and 3329 were endosperm specific. The embryo-specific DEGs were enriched in the plant-hormone signal-transduction pathway, while the endosperm-specific DEGs were enriched in phenylalanine, tyrosine, and tryptophan biosynthesis. We categorized these DEGs into early-, intermediate-, and late-stage genes, as well as consistently responsive genes, which can be enriched in various pathways related to seed germination. Transcription-factor (TF) analysis showed that 643 TFs from 48 families were differentially expressed during seed germination. Moreover, 12 unfolded protein response (UPR) pathway genes were induced by seed germination, and the knockout of OsBiP2 resulted in reduced germination rates compared to the wild type. This study enhances our understanding of gene responses in the embryo and endosperm during seed germination and provides insight into the effects of UPR on seed germination in rice.
Kalman Filtering in the Air Quality Monitoring

Data assimilation is a process where an improved prediction is obtained from a weighted combination between experimental measurements and mathematical model data. In the present work this procedure is applied to pollutant atmospheric dispersion by using a Kalman filter (KF). This is interesting approach, because the KF gives an output in which the balance between the data from the diffusion model and the experimental data is done automaticaly, through the Kalman gain. In addition, the Kalman filter computes the propagation of the error.
Genetic Ablation of the MET Oncogene Defines a Crucial Role of the HGF/MET Axis in Cell-Autonomous Functions Driving Tumor Dissemination

Simple Summary The MET receptor and its ligand Hepatocyte Growth Factor (HGF) sustain cell proliferation, survival, motility, and invasion. Several pieces of evidence suggest that these biological activities support the metastatic potential of transformed cells. Cancer cell dissemination is sustained by cell-autonomous (executed by cancer cells) and non-cell-autonomous (executed by cells composing the tumor microenvironment) functions. To define the role of the HGF/MET axis in this complex process, we genetically knocked out the MET gene in cancer cells that were not dependent on MET signaling for their growth. In this way, we exclusively evaluated the relevance of the HGF/MET axis in prompting cell-autonomous activities (MET signaling in tumor microenvironment cells is maintained intact) independently of their intrinsic ability to proliferate (that is sustained by different oncogenic drivers). Our results proved that HGF/MET axis plays a crucial role in directing cell-autonomous functions regulating cancer cell dissemination, supporting a possible use of MET inhibitors in metastatic cancers. Abstract Cancer cell dissemination is sustained by cell-autonomous and non-cell-autonomous functions. To disentangle the role of HGF (Hepatocyte Growth Factor) and MET ligand/receptor axis in this complex process, we genetically knocked out the MET gene in cancer cells in which MET is not the oncogenic driver. In this way, we evaluated the contribution of the HGF/MET axis to cancer cell dissemination independently of its direct activities in cells of the tumor microenvironment. The lack of MET expression in MET−/− cells has been proved by molecular characterization. From a functional point of view, HGF stimulation of MET−/− cancer cells was ineffective in eliciting intracellular signaling and in sustaining biological functions predictive of malignancy in vitro (i.e., anchorage-independent growth, invasion, and survival in the absence of matrix adhesion). Cancer cell dissemination was assessed in vivo, evaluating: (i) the ability of MET−/− lung carcinoma cells to colonize the lungs following intravenous injection and (ii) the spontaneous dissemination to distant organs of MET−/− pancreatic carcinoma cells upon orthotopic injection. In both experimental models, MET ablation affects the time of onset, the number, and the size of metastatic lesions. These results define a crucial contribution of the HGF/MET axis to cell-autonomous functions driving the metastatic process.
A Systematic Literature Review of Offensive and Defensive Security Solutions With Software Defined Network

Software Defined Networking (SDN) is one of the most significant innovations in telecommunication systems in the past two decades. From the very beginning, the scientific community understood the importance of investigating the possible usages of SDN as a means to increase network security, but also their potential to be exploited as an attack device. For this reason, there has been a massive production of research works, which, however, do not form a well-defined corpus. The literature is spread over many venues and composed of contributions with very different flavors. Though some review works already exist, in this work we conduct a systematic literature review of the field, gathering 466 relevant publications— the largest curated dataset on the topic to the best of our knowledge. In our work, the dataset undergoes a twofold analysis: (a) quantitative, through publication metadata, which allows us to chart publication outlets, approaches, and tackled issues; (b) qualitative, through 14 research questions that provide an aggregated overview of the literature contributions to the key issues, also to spot gaps left open. From these analyses, we derive a call for action to address the main open challenges.
Effect of retardation and capillarity on organic contaminant diffusion through an unsaturated composite liner: An analytical approach

In unsaturated composite liners, the heterogeneous distribution of water content and capillarity are common phenomena with remarkable influence on the transport parameters. This study presents an analytical solution to assess the effect of unsaturation‐dependent retardation and capillarity on the organic contaminant diffusion through an unsaturated composite liner. The solution is obtained by the generalized integral transform technique and is verified against other analytical and numerical solutions. Particular attention is paid to the potential errors in predicting organic contaminant transport caused by unsaturation‐independent retardation factor assumption. The results show that neglecting saturation‐dependent retardation may cause considerable errors. The considerations of capillary fringe and degradation also have significant effects on the errors. In addition, a simplified solution based on a new averaged retardation factor is proposed and its performance is evaluated.
Distributed micro-radar system for detection and tracking of low-profile, low-altitude targets

Proposed airborne surveillance radar system can detect, locate, track, and classify low-profile, low-altitude targets: from traditional fixed and rotary wing aircraft to non-traditional targets like unmanned aircraft systems (drones) and even small projectiles. Distributed micro-radar system is the next step in the development of passive monopulse direction finder proposed by Stephen E. Lipsky in the 80s. To extend high frequency limit and provide high sensitivity over the broadband of frequencies, multiple angularly spaced directional antennas are coupled with front end circuits and separately connected to a direction finder processor by a digital interface. Integration of antennas with front end circuits allows to exclude waveguide lines which limits system bandwidth and creates frequency dependent phase errors. Digitizing of received signals proximate to antennas allows loose distribution of antennas and dramatically decrease phase errors connected with waveguides. Accuracy of direction finding in proposed micro-radar in this case will be determined by time accuracy of digital processor and sampling frequency. Multi-band, multi-functional antennas can be distributed around the perimeter of a Unmanned Aircraft System (UAS) and connected to the processor by digital interface or can be distributed between swarm/formation of mini/micro UAS and connected wirelessly. Expendable micro-radars can be distributed by perimeter of defense object and create multi-static radar network. Low-profile, lowaltitude, high speed targets, like small projectiles, create a Doppler shift in a narrow frequency band. This signal can be effectively filtrated and detected with high probability. Proposed micro-radar can work in passive, monostatic or bistatic regime.
Role of Bisection Method

: The bisection method is the basic method of finding a root. As iterations are conducted, the interval gets halved. So method is guaranteed to converge to a root of “f” if “f” is a continuous function at an interval [a,b] and f(a) and f(b) should have opposite sign. In this paper we have explained the role of bisection method in computer science research. we also introduced a new method which is a combination of bisection and other methods to prove that with the help of bisection method we can also develop new methods. It is observed that scientists and engineers are often faced with the task of finding out the roots of equations and the basic method is bisection method but it is comparatively slow. We can use this new method to solve these problems and to improve the speed.
Nonparametric versus Parametric Goodness of Fit

We consider two tests for testing the hypothesis that a density f lies in a parametric class of densities. The first lest is based on the integrated squared distance of the kernel density estimator from its hypothetical expectation, the second test is based on the maximal deviation of the kernel estimate on a grid. The unknown parameter is estimated by the maximum likelihood estimator. The main result is the derivation of the asymptotic behavior of the power of both tests under Pitman and “sharp peak” type alternatives. The connection of the rate of convergence of these local alternatives, the bandwidth of the kernel estimator, the parameter estimator and the power of both tests are studied and are compared. It turns out that under Pitman alternatives the L 2-test is always not worse than the L α-test, but there exist sharp peak alternatives such that the L α-test is better.
Eddy Current Testing Methods and Design for Pipeline Inspection System: A Review

Non-destructive testing and complex systems integration are commonly used in transportation, aircraft, automotive, manufacturing, petrochemical, and defence. Eddy current testing (ECT) is a common non-destructive evaluation technique, especially in the coating, nuclear, and oil and gas industries. However, the eddy current testing method must continue to focus on the probe’s construction and application. It ascribed noisy data to inadequate detection and a severe false alarm problem. Nonetheless, only few academics have elaborated on the intelligent ECT crack detection method. This study provides an overview of the eddy current testing technique and the probe structure design elements that influence fracture detection accuracy. The first section describes the Eddy current testing by encircling coil system for a differential probe (ECDP) and an encircling coil for an absolute probe design (ECDP). The explanations focus mostly on the probe’s creation and detection in Rotating electromagnetic field eddy current (RoFEC). The advantages and disadvantages of eddy current testing surrounding probes are analysed. In addition, a summary of prior studies investigating testing samples of ferromagnetic pipe, steel rod, and wire rope is presented. The second segment focuses on Eddy current testing employing a Giant Magnetoresistance (GMR) sensor system, with a particular emphasis on sensor arrays, GMR, and conventional eddy current coils for crack identification. Finally, a comprehensive overview of eddy current testing methods and design for non-destructive pipeline inspection systems is presented.
A Small Sample Photovoltaic Hot Spot Identification Method Based on Deep Transfer Learning

A small-sample photovoltaic hot spot identification method based on deep transfer learning has been proposed as a solution to the problem that traditional deep learning models require a substantial amount of training data, whereas the number of hot spot effect samples is relatively low and difficult to collect. This issue can be resolved by the development of a solution that employs deep transfer learning. The Inception-v 4 model serves as the basis for building a deep transfer learning model. After finishing the training procedure using a small-sample hot spot dataset of negative sample multi-classification, a network model for hot spot detection is built. Experiments reveal that even when the number of accessible samples is restricted, the model trained using the deep transfer learning technique has great identification accuracy, a low false detection rate, and strong generalization capability.
Multi-angle face expression recognition based on integration of lightweight deep network and key point feature positioning

In the current advancement of science and technology, robot and machine vision technology have significant application value. Due to their mimetic structure, quadruped robots are better equipped to adapt to the requirements of walking over complex terrain. This paper introduce lightweight deep network and combining key point feature positioning for multi-angle face expression recognition. Using robot dog to recognize facial expressions will be affected by distance and angle. To solve this problem, this paper proposes a method for facial expression recognition at different distances and angles,which solved the larger distance and deflection angle of face expression recognition accuracy and real-time issues. The dataset used in this study was collected by adjusting the camera mounted in an AI robotic dog in a laboratory scenario. The result shows a common and fast and intuitive visual representation of human beings to convey emotions or mental states, facial expression is an effective way for robots to obtain information and interact with people through visual images.
Medical Image Analysis of Lung Tumor Diagnosis Based on Generalized Fuzzy C-Means Clustering Algorithm

In recent years, the rapid development of medical imaging technology has brought medical image analysis into the era of big data. CT imaging technology is one of the most common imaging methods for disease screening. This paper introduces medical image analysis of lung tumor diagnosis based on Generalized fuzzy c - means clustering algorithm was proposed. Brief introduce lung CT imaging to diagnose lung tumors requires a huge workload, which is often accompanied by long-term image reading and subjective evaluation. The experimental data comes from 15 patient samples which include two groups of samples are selected as test objects. The result shows the positive rate is high, resulting in misdiagnosis. In addition, great success of deep learning technology in the field of computer vision has made it possible to realize computer-aided diagnosis and screening of lung cancer.
Indoor location recognition device and method based on indoor robot

This paper describes an indoor location recognition device and method based on an indoor robot. The device includes a computer Raspberry Pi, a lidar, a CMOS camera, an ultrasonic sensor, a first microcontroller, and a servo motor; the lidar is connected to the computer Raspberry Pi. Pie, the lidar is driven by a servo motor, the ultrasonic sensor is connected to the computer Raspberry Pi through the first microcontroller, and the computer Raspberry Pi controls the lidar to scan the location to measure the width and length of the current location, and controls the ultrasonic sensor to the location. Perform up and down scanning to measure the distance between the ultrasonic sensor and the ground and the ceiling respectively, and obtain the height of the room; the CMOS camera is connected to the computer Raspberry Pi, and the computer Raspberry Pi sends an instruction to control the CMOS camera to take pictures of objects and places and use them Opencv and other related technologies achieve the purpose of identification. The device and method can continuously identify indoor locations under various complex conditions, and at the same time improve the accuracy and reliability of location recognition.