Miracle-dz/UniFAR-FTUs huggingface dataset:
ASSESSMENT OF CANOPY AND GROUND HEIGHT ACCURACY FROM GEDI LIDAR OVER STEEP MOUNTAIN AREAS

Abstract. Active remote sensing systems orbiting the Earth are only a small portion of the current constellation of satellites and will increase in number and advance in technology in the future. The launch of the GEDI sensor in December 2018, for an expected life-span period of about 2 years, is a fundamental step of this revolution, as it is the first spaceborne full-waveform lidar specifically designed for measuring the structure of ecosystems, providing information of the vertical profile of forests.Accuracy assessment of GEDI height metrics in the context of an Alpine forest environment in steep terrain scenarios has been conducted in this study. We used discrete return lidar from a recent aerial laser scanner survey as reference to analyse differences of heights of terrain elevation and maximum canopy height of the vegetation detected in each GEDI footprint. The height metrics differences between the discrete lidar and the GEDI data were then analysed to verify any correlation with the following factors: morphology (terrain slope), land cover (land cover type, fraction of canopy cover, vegetation density), GEDI laser beam characteristics (day/night-time acquisition, full power vs coverage laser beam, beam ID, laser sensitivity). Further analysis involved shifting the footprints’ location in 8 different direction and 4 distances to assess the impact of geolocation errors on accuracy and precision.Results show that what most influences accuracy in this study is the terrain slope, very likely linked to the uncertainty of geolocation of the GEDI footprints, suggesting caution in using single GEDI footprints if located in steep environments. Other than slope, terrain height accuracy varies mostly with forest type (conifer vs broadleaves), but not significantly with other factors. Canopy height instead is affected by most factors; high vegetation canopy is overestimated by ∼3 m in GEDI, and underestimated by 3 m over heath and bushes (median difference). Higher sensitivity pulses and night-time pulses provide better accuracy. Laser beams with full power also have better accuracy; beams with id 1000 and 1011 provide the most accurate canopy heights. Shifting the footprint position decreased accuracy except at 15 m and 270° with respect to orbit direction (left-looking).
Influenza vaccination uptake during COVID-19 pandemic: A trusted messenger approach

Objective: Seasonal influenza is a preventable disease that may cause high morbidity and mortality. In the USA, the 2020 influenza season overlapped with the first wave of coronavirus disease 2019 (COVID-19) pandemic. There is a dearth of research on understanding influenza vaccination uptake and attitudes towards the vaccine among marginalised communities in the USA. The aim of this study was to examine general knowledge, misconceptions about, and barriers to influenza vaccination and determine if flu education delivered via trusted messengers improved knowledge among Asian Americans. Design: Quantitative prospective pre- and post-test design. Setting: Asian American communities in Metro Detroit area, Hamtramck, and Kent County in Michigan. Method: The study started with a training event in which 26 community leaders were trained to become trusted messengers to disseminate flu education. A total of 260 Asian American participants subsequently completed pre- and post-education surveys. Results: Asian Americans in the study reported low flu vaccination rate (48.5%). Reasons varied but included access-related issues (e.g. no access and no insurance) and health literacy (did not know they needed it). After education by trusted messengers, the paired t tests measuring improvement between pre- and post-survey scores showed a significant improvement (p < .05) for all of the knowledge items. Conclusion: Educational campaigns using targeted messages to address specific barriers and acknowledge individuals’ concerns regarding misconceptions while providing equitable vaccine access can be key for the successful implementation of vaccination programmes.
The Influence of Non-Sinusoidal Inductance on Saliency-Based Position Estimation for Permanent Magnet Machine

This paper analyzes the influence of non-sinusoidal inductance distortion on the saliency-based position estimation for the permanent magnet (PM) machines. Because of the high-power-density design of PM machines, the flux saturation usually causes the inductance self-saturation, inductance cross-saturation, and most importantly, secondary inductance harmonics. This paper fully investigates the saturation effect on saliency-based drives. Analytical model for the position estimation with saturated inductance is developed to understand the stability of saliency-based drive. It is confirmed that the saturation effect deviates the estimated flux position from actual rotor position. These estimation errors are critical for the saliency-based drive especially under load. This paper defines the feasible estimation region for PM machines with saturated and non-sinusoidal inductances. Considering the position estimation errors due to the saturated and non-sinusoidal inductance, the typical maximum torque per amp (MTPA) current trajectory based on the encoder-based control system should be modified especially for the IPM machines at full load. For the machines where the saturation condition is unknown, q-axis current without negative d-axis current is suggested to maintain control stability while the torque reduction is resultant. By contrast, the modified current trajectory could also be designed once the saturation reflected position estimation errors could be obtained. Finite element analysis (FEA) with inverter co-simulation is used to investigate the inductance distortion at various loads. A 6kW IPM machine prototype with highly non-sinusoidal inductance is tested for the experimental verification.
A Laboratory Open-Set Martian Rock Classification Method Based on Spectral Signatures

Rocks are one of the major surface features of Mars. The accurate characterization of the chemical and mineralogical composition of Martian rocks would yield significant evolutionary information about relevant geological processes and exobiological exploration. Many existing rock recognition systems generally assume that all testing classes are known during training. Over real planetary surfaces, the autonomous recognition system is likely to encounter an unknown category of rock that is crucial to the performance of the rock classification task. Therefore, we develop an open-set Martian rock-type classification framework based on their spectral signatures, with the subgoal of new/unknown rock-type recognition and category-incremental learning for expanding the recognition model. First, the spectral signatures of rock samples are captured to characterize their mineralogical compositions and physical properties, which serves as the input of the developed framework. To further produce the highly discriminative feature representation from the original spectral signatures, a transformer architecture integrated with contrastive learning is constructed and trained in an end-to-end manner to force instances of the same class to remain close-by while pushing those of a dissimilar class farther apart. Following this, according to the extreme value theorem (EVT), category-specific distance distribution analysis is conducted to detect and identify new/unknown types of rock samples due to the isolated characteristics of new/unknown rock samples in the latent feature space. Finally, the recognition model is incrementally updated to learn these identified “unknown” samples without forgetting previously known categories when the associated labels are progressively obtained. The multispectral camera, a duplicated payload of the counterpart onboard the Zhurong rover, is used as the multispectral sensor for capturing the spectral information of the laboratory rock dataset shared by the National Mineral Rock and Fossil Specimens Resource Center for both qualitative and quantitative evaluations. Experimental results indicate the effectiveness and robustness of the developed in situ analysis framework.
“Yo era la única rara:” School Belonging and English-Learner Students at a Southeastern Virginia High School

ABSTRACT This study draws on interviews and classroom observations with Latinx, English Learners (ELs) at a high school in Southeastern Virginia to analyze how this group of students builds social connections and constructs “belonging” at school. Findings indicate that these EL students’ school experiences are framed by underdeveloped and ad hoc educational and support infrastructures aimed at serving their needs. Students turned to those with whom they shared language, culture, and ethnic affiliations to build networks of support; however, often these strategies to belong did not facilitate ongoing connections with students“ English-speaking counterparts, hampering relationship-building within the broader school community. We argue that school programming and institutional practices that holistically addressed these students’ language, social, and educational needs, while incorporating their strengths as multicultural agents, would foster increased school belonging and alleviate a situation in which these students must choose between supportive relationships and acceptance and greater progress toward meeting academic goals. To deliver a truly equitable education to these students, schools must adopt reforms that address racial inequities and recognize these students” strengths and resilience as possessors of essential skills for navigating life in today’s multicultural society.
Updating FRCS, the Fuel Reduction Cost Simulator, for national biomass assessments

In 2005 the USDA and DOE jointly published a report (Perlack et al. 2005) concluding that it would be technically feasible to supply a billion dry tons of biomass annually from farms and forests throughout the United States in support of an emerging bioenergy and bioproducts industry. The report was criticized because it defined “supply” largely in terms of physical availability without a formal evaluation of economic feasibility. As a result, during 2008 and early 2009 the two agencies conducted a follow-up study in an effort to assess the potential biomass supply economically by deriving county-level supply curves for farm and forest biomass. This paper describes the forest harvesting model that was used to estimate the costs associated with collecting and preparing forest biomass for transport to processing facilities. The model was updated from an earlier version that was limited to forest conditions in the Interior West. The revised model has been designed for use in all regions of the contiguous United States. Actual results from the study are not provided in this paper because they have not yet been cleared for release by the USDA and DOE.
Teknik Penyembunyian Data yang Reversible pada Citra JPEG Terenkripsi

Abstrak—Dengan semakin banyaknya fasilitas penyimpanan publik data di cloud storage dengan menggunakan internet sebagai sarana pertukaran informasi data. Dengan internet setiap orang bisa mendapatkan informasi apapun baik itu tidak rahasia maupun rahasia, informasi yang rahasia ini memerlukan aspek keamanan sebagai pencegahan dari pihak yang tidak berwenang. Apabila informasi rahasia didapatkan oleh pihak yang tidak berwenang, informasi yang rahasia dapat diubah maupun dimanipulasi sehingga informasi rahasia tersebut tidak tersampaikan dengan baik kepada pihak yang ditujukan. Salah satu cara untuk meningkatkan keamanan pada data atau informasi seperti citra yaitu dengan melakukan teknik penyembunyian data yang reversible. Kebanyakan teknik penyembunyian data dilakukan pada citra yang belum terkompres. Steganografi merupakan salah satu teknik penyembunyian data yang dilakukan secara reversible. Dalam penelitian ini telah dilakukan simulasi penyembunyian data terhadap citra data sebagai informasi rahasia yang disisipkan pada citra host sebagai media penampung kemudian dilakukan pemulihan agar mendapatkan kembali informasi citra data yang telah disisipkan. Keberhasilan pemulihan citra data bisa dilakukan dengan membandingkan terhadap citra data asli dengan menggunakan pengukuran parameter korelasi koefisien (CC). Semakin baik kualitas citra data pemulihan akan memiliki nilai CC yang mendekati nilai 1. Dalam penelitian ini telah dilakukan pengujian dengan berbagai skenario terhadap 4 citra host dan 4 citra data yang berbeda. Dari pengujian terhadap 4 citra data berbeda yang berbentuk teks, dan diperoleh hasil CC rata-rata sebesar 0.959375, dengan nilai CC terbaik sebesar 0.97268 dan terendah sebesar 0.95015. Nilai CC tertinggi diperoleh dari citra data asli dengan kondisi teks yang paling jelas dan bold, sementara CC terburuk diperoleh dari kondisi citra data berisi teks dengan ukuran karakter yang kecil dan tipis.
Design of Reverse Flow Blockage Device for Reactor Coolant Pumps

Future Sodium cooled Fast Reactors (SFR) in India, are being designed with 3 Reactor Coolant Pumps (RCP). Reactor Coolant Pumps (RCP) are used in pumping the coolant fluid through the reactor core. As the RCPs are operating in parallel, failure of one pump will result in a significant portion of the pumped coolant to bypass the core via tripped RCP. Thus, the reactor has to be shut down and the tripped RCP has to be replaced, which results in down time. Operation of reactor with only two RCP (2/3 mode) will supplement the power generation. Henceforth, if the flow path through the tripped RCP is made highly resistant as compared to the core, then eventually, flow from the two operating RCPs will go through reactor core. Such a flow blocking/resisting arrangement can also provide the flexibility during reactor startup. A cylindrical shell is designed for closing the suction passage of the pump and the shell is raised or lowered from above the roof slab with the help of tie-rods. The cylindrical shell (sleeve shell) is designed to withstand water hammer shock due to inadvertent sudden closure of the suction passage. To determine the shell thickness, pump performance curves are developed based on reactor operating conditions. The sleeve shell will not be able to perfectly close the suction passage as space is required for movement of sleeve shell over the pump shell, thus a study is performed on incorporating a labyrinth to minimize the leakage.
Techno-Economic Performance Evaluation and Enhancement for a PV – Diesel Hybrid System

In this research paper, an illustration for system size optimization for a stand-alone PV – diesel hybrid system is obtained. The requirement is to obtain an optimal size that can meet energy demand at an optimized cost for a given lifetime period of the project, this will be achieved using HOMER software to further improve the system parameters like performance ratio, renewable energy fraction, MATLAB will be used. This research study will be done basing on a system currently installed at the School of Renewable Energy, Naresuan University (SERT), this system has a capacity of 120 kW, and it is a hybrid system with PV array, Diesel generator and battery storage system. The cost parameters that will be addressed are; - Net present cost (NPC), Cost of Energy (COE), Capital cost (CC). The initial size of the hybrid system is PV-120kW, Diesel generator -100kW and battery storage of 200kWh after modelling and simulation with HOMER software using special models to show the predicted performance of the final outcome, the optimal size created has a PV size of 100kW, diesel generator with a size of 100kW and battery storage of 100kWh and compared to the initial system COE od 1.01$/kWh, the optimal size has a COE of 0.934$/kWh.
AN EXPOSURE TOWARDS CONSIDERATION OF IMPORTANT FEATURES OF ONLINE TESTS BY DATA VISUALIZATION

Previously years, database community has committed particular efforts towards extraction of understanding from data. Probably the most important methods for understanding extraction is data mining, which apply automatic calculations to differentiate designs within assortment of huge data. Within our work we concentrate on discovery of behavioural designs of learners in addition to conceptual associations between test products. We describe a technique for discover understanding linked to student activities throughout online tests, that are utilized by tutors to create novel test methods. A manuscript symbolic approach of information visualization was introduced, which is often used inside a Understanding discovery tactic to graphically highlight behavioural designs along with other earlier unknown features connected towards the learners’ activity within online tests.
A Robust Comparative Analysis of Graph Neural Networks on Dynamic Link Prediction

Graph neural networks (GNNs) are rapidly becoming the dominant way to learn on graphstructured data. Link prediction is a near-universal benchmark for new GNN models. Many advanced models such as Dynamic graph neural networks (DGNNs) specifically target dynamic graphs. However, these models, particularly DGNNs, are rarely compared to each other or existing heuristics. Different works evaluate their models in different ways, thus one cannot compare evaluation metrics and their results directly. Motivated by this, we perform a comprehensive comparison study. We compare link prediction heuristics, GNNs, discrete DGNNs, and continuous DGNNs on the dynamic link prediction task. In total we summarize the results of over 4500 experimental runs (≈ 2 years of computation time). We find that simple link prediction heuristics often perform better than GNNs and DGNNs, different sliding window sizes greatly affect performance, and of all examined graph neural networks, that DGNNs consistently outperform static GNNs. This work is a continuation of our previous work, a foundation of dynamic networks and theoretical review of DGNNs. In combination with our survey, we provide both a theoretical and empirical comparison of DGNNs.
Using Evolutionary Multiobjective Optimization Algorithms to Evolve Lacing Patterns for Bicycle Wheels

This thesis investigates the use of evolutionary algorithms (EAs) to evolve and optimize lacing patterns of spokes for a bicycle wheel. There are multiple objectives and tradeoffs to be considered when evaluating a lacing pattern, for instance, strength versus balance. To handle this, an evolutionary multiobjective optimization (EMO) method has been used. Various EMO algorithms and approaches are tested. Among these, the new NSGA-III algorithm is used. Different representations of the lacing patterns for the wheels are compared, and also two different representations of the problem for the EMO. A novel wheel simulator has been made to test the lacing patterns. As the number of needed wheel simulations take a considerable amount of time, the work is distributed among multiple computers. The EMO is successful in the search for good lacing patterns. One of the most common patterns, the 3x, is found, along with other comparable patterns with different tradeoffs. The results show no improvement in using the new NSGAIII algorithm compared to the older NSGA-II. The representation with a bias for uniformly laced spokes let the EA evolve better wheels than other representations, showing that carefully choosing the representation for what is being optimized is important. The results also show how important it is to represent a problem using multiple objectives when possible, as the wheels evolved using a higher number of objectives were found to be best.
Vitamin E (Alpha-Tocopherol) Metabolism and Nutrition in Chronic Kidney Disease

Vitamin E (alpha-tocopherol) is an essential micronutrient and fat-soluble antioxidant with proposed role in protecting tissues from uncontrolled lipid peroxidation. This vitamin has also important protein function and gene modulation effects. The metabolism of vitamin E depends on hepatic binding proteins that selectively retain food alpha-tocopherol for incorporation into nascent VLDL and tissue distribution together with esterified cholesterol and triglycerides. Chronic kidney disease (CKD) is a condition of oxidative stress and increased lipid peroxidation, that are associated with alterations of alpha-tocopherol metabolism and function. Specific changes have been reported for the levels of its enzymatic metabolites, including both short-chain and long-chain metabolites, the latter being endowed with regulatory functions on enzymatic and gene expression processes important for the metabolism of lipids and xenobiotics detoxification, as well as for the control of immune and inflammatory processes. Vitamin E therapy has been investigated in CKD using both oral vitamin E protocols and vitamin E-coated hemodialyzers, showing promising results in the secondary prevention of cardiovascular disease, as well as of immune and hematological complications. These therapeutic approaches are reviewed in the present article, together with a narrative excursus on the main findings indicating CKD as a condition of relative deficiency and impaired metabolism of vitamin E.
Using mobile laser scanner and imagery for urban management applications

Despite autonomous navigation is one of the most proliferate applications of three-dimensional (3D) point clouds and imagery both techniques can potentially have many other applications. This work explores urban digitization tools applied to 3D geometry to perform urban tasks. We focus exclusively on compiling scientific research that merges mobile laser scanning (MLS) and imagery from vision systems. The major contribution of this review is to show the evolution of MLS combined with imagery in urban applications. We review systems used by public and private organizations to handle urban tasks such as historic preservation, roadside assistance, road infrastructure inventory, and public space study. The work pinpoints the potential and accuracy of data acquisition systems to handled both 3D point clouds and imagery data. We highlight potential future work regarding the detection of urban environment elements and to solve urban problems. This article concludes by discussing the major constraints and struggles of current systems that use MLS combined with imagery to perform urban tasks and to solve urban tasks.
GaN Power Electronics and Applications

With GaN-based power devices for microwave applications commercially available on the market [1], the development of GaN-based transistors and diodes in power electronics for electric power conversion has gained primary focus[2][3]. The low on-resistance and high working frequency characteristics of GaN devices enable high conversion efficiency leading to energy savings. Here we report the new development of 600V class GaN power diodes and transistors for power conversion applications.