Miracle-dz/UniFAR-FTUs huggingface dataset:
Biomass L2 Prototype Processor: Current Status

The ESA BIOMASS mission will be the 7th Earth Explorer measuring the above-ground biomass (AGB) in the world’s forests. The current ESA Level-2 (L2) implementation study focuses on defining and implementing the main algorithms for forest parameter retrieval from BIOMASS data. After the first year of L2 study innovative results were achieved: the development of ground cancellation, in particular, has proved to be huge value, since it removes from the data the effects of environmental variability and contributions unrelated to the forest carried in the ground scattering. In this paper the current processor implementation and validation activities of the L2 team will be described.
Service Resource Management in Edge Computing Based on Microservices

5G networks make the IoT devices and mobile devices more widespread. In order to offload the traffic from core network to edge network, the service providers deploy its computing resources and services to the edge network. However, edge network resources are less than cloud computing resources, and the need to use low coupling and autonomous features in microservices needs to be considered. Therefore, based on the characteristics of microservices, we propose a microservices platform and provide service deployment. We also propose a FSODM algorithm which provides a fast and efficient auto-scaling resource management solution.
Automatic Tuning Procedure at Standstill for Extended Kalman Filter in Sensorless Control of Permanent Magnet Synchronous Motors

In modern ac drives the reliability issue is pushing towards sensorless control. Extended Kalman filter is a valid candidate for the estimation of rotor position in permanent magnet synchronous motor drives. The traditional weak point is the tuning of its covariance matrices, which is still a loose thread to tie up after many years. This work proposes an innovative automatic procedure for the covariance matrices tuning based on both the rated values of the motor and the result of signal injection tests. The output of the automatic procedure was tested on an experimental motor prototype.
Redox Potential of the Oxygen-Evolving Complex in the Electron Transfer Cascade of Photosystem II.

In photosystem II (PSII), water oxidation occurs in the Mn4CaO5 cluster with the release of electrons via the redox-active tyrosine (TyrZ) to the reaction-center chlorophylls (PD1/PD2). Using a quantum mechanical/molecular mechanical approach, we report the redox potentials (Em) of these cofactors in the PSII protein environment. The E values suggest that the Mn4CaO5 cluster, TyrZ, and PD1/PD2 form a downhill electron transfer pathway. E for the first oxidation step, Em(S0/S1), is uniquely low (730 mV) and is ~100 mV lower than that for the second oxidation step, Em(S1/S2) (830 mV) only when the O4 site of the Mn4CaO5 cluster is protonated in S0. The O4-water chain, which directly forms a low-barrier H-bond with the Mn4CaO5 cluster and mediates proton-coupled electron transfer in the S0 to S1 transition, explains why the second lowest oxidation state, S1, is the most stable and S0 is converted to S1 even in the dark.
A Modified RMoG Model for Forest Height Inversion Using L-Band Repeat-Pass Pol-InSAR Data

This paper addresses the forest height inversion based on the modified RMoG model using repeat-pass Pol-InSAR data. The linear variance of the Gaussian motion distribution in the RMoG model is replaced by a linear standard deviation to describe the volumetric motion heterogeneity and related coherence function is deduced based on that. Furthermore, a forest parameter inversion algorithm is proposed based on this modified model and its performance is investigated with L-band repeat-pass ALOS-1 quad-polarization data acquired over German forest site with temporal baselines of 46 days. Inversion results indicates that in comparison with the traditional RVoG and RMoG methods, the modified method can reduce 27.73% and 8.57% of the overestimation errors caused by the temporal decorrelation.
Cloud Detection and Classification for S-NPP FSR CRIS Data Using Supervised Machine Learning

Cloud detection and classification are important and challenging in satellite infrared remote sensing. This study proposes an end-to-end cloud detection and classification method for the Cross-track Infrared Sounder (CrIS) based on big data analysis and relevant machine learning schemes. Using the full spectral resolution (FSR) CrIS data, a set of FSR cloud detection indexes (FCDIs) is derived from the brightness temperatures of the selected CrIS LWIR-SWIR channel pairs. Linear and cubic regressions are compared and discussed when deriving FCDI. It’s shown that FCDIs can capture clouds and structures well by comparing with the Visible Infrared Imaging Radiometer Suite (VIIRS) cloud cover/layer product. Selected from several supervised machine learning schemes, the extreme learning machine (ELM) is applied to train FCDI for cloud detection. Overall the ELM classification accuracy is about 80%. Results and discussion are provided.
Global Stabilization of Nonholonomic Mobile Robots via a Smooth Output Feedback Time-Varying Controller

In this paper we report a novel output feedback controller for a class of mobile robots that exhibit nonholonomic restrictions. The control objective is to regulate the robot position and orientation at a given desired point. The controller is a (nonautonomous) smooth certainty equivalent PD scheme and the velocity observer is designed using the Immersion & Invariance principle. Simulations are presented to corroborate our theoretical findings.
Infranet: A Geospatial Data-Driven Neuro-Evolutionary Artwork

“Infranet” is a generative artwork interweaving data visualization and sonification, artificial intelligence, and evolutionary algorithms in a population of artificial life creatures, thriving upon geospatial data of the infrastructure of a city as its sustenance and canvas. Each exhibit of Infranet utilizes public data available on the host city; including Gwangju, South Korea (2018), New York, USA (2019), and Vancouver, Canada (2019). This paper documents the motivations behind the work, its design and subsequent implementation in details. At its heart is the speculative proposition of the data of a city as a habitat for new forms of life. Our design in response utilizes neural networks at individual, as well as population-wide scales, along with horizontal gene transfer and contagion/entrainment as means for the living beings to open-endedly discover the variety in the data habitat.
Performance of patient acuity rating by rapid response team nurses for predicting short-term prognosis

Background Although scoring and machine learning methods have been developed to predict patient deterioration, bedside assessment by nurses should not be overlooked. This study aimed to evaluate the performance of subjective bedside assessment of the patient by the rapid response team (RRT) nurses in predicting short-term patient deterioration. Methods Patients noticed by RRT nurses based on the vital sign instability, abnormal laboratory results, and direct contact via phone between November 1, 2016, and December 12, 2017, were included. Five RRT nurses visited the patients according to their shifts and assessed the possibility of patient deterioration. Patient acuity rating (PAR), a scale of 1–7, was used as the tool of bedside assessment. Other scores, including the modified early warning score, VitalPAC early warning score, standardised early warning score, and cardiac arrest risk triage, were calculated afterwards. The performance of these scores in predicting mortality and/or intensive care unit admission within 1 day was compared by calculating the area under the receiver operating curve. Results A total of 1,426 patients were included in the study, of which 258 (18.1%) died or were admitted to the intensive care unit within 1 day. The area under the receiver operating curve of PAR was 0.87 (95% confidence interval [CI] 0.84–0.89), which was higher than those of modified early warning score (0.66, 95% CI 0.62–0.70), VitalPAC early warning score (0.69, 95% CI 0.66–0.73), standardised early warning score (0.67, 95% CI 0.63–0.70) and cardiac arrest risk triage (0.63, 95% CI 0.59–0.66) (P<0.001). Conclusions PAR assessed by RRT nurses can be a useful tool for assessing short-term patient prognosis in the RRT setting.
Fridericia platyphylla (Cham.) L.G. Lohmann root extract exerts cytotoxic and antiproliferative effects on gastric tumor cells and downregulates BCL-XL, BIRC5, and MET genes

Fridericia platyphylla (Cham.) L.G. Lohmann (FP) has cytotoxic, anti-inflammatory, and analgesic properties. We aimed to characterize the cytotoxic and antiproliferative effects of FP extract on normal (GAS) and tumor-derived (ACP02 and HepG2) cell lines. The effective concentrations (EC50s) by tetrazolium bromide assay (MTT) were 56.16, 43.68, and 42.57 µg mL−1 and 69.38, 41.73, and 52.39 µg mL−1 by neutral red assay for GAS, ACP02, and HepG2 cells, respectively. The extract decreased nuclear division indices, which was not reflected in cell proliferation curves. Flow cytometric analyses showed that even 30 µg mL−1 extract (shown to be noncytotoxic by MTT assay) increased the sub-G1 population, indicating cell death due to apoptosis and necrosis. A cytokinesis-block micronucleus cytome assay showed that 30 µg mL−1 of the extract increased the frequency of nuclear buds in tumor cells. Real-time quantitative polymerase chain reaction showed CCND1 upregulation in doxorubicin-treated GAS cells and BCL-XL, BIRC5, and MET downregulation in 5 or 30 µg mL−1 in FP extract-treated ACP02 cells. In conclusion, FP extract modulated apoptosis- and cell cycle-related genes and presented selective cytotoxicity toward tumor cells that deserves further investigation by testing other cell types. Our results demonstrated that even medicinal plants exert adverse effects depending on the extract concentrations used and tissues investigated.
Rolling Guidance Recursive Filtering-based Multiple Kernel Learning for Hyperspectral Image Classification

A serious problem in the classification of hyperspectral images is that labeled samples are scarce. A single classifier cannot satisfy a variety of situations; therefore, a multiple kernel learning algorithm based on rolling guidance recursive filtering is proposed in this paper. It extracts feature images by the rolling guidance recursive filtering method, and the algorithm can obtain different types of feature images by changing the number of iterations. Additionally, a strong classifier is obtained by combining many of basic classifiers with different feature images based on the multiple kernel learning framework, which significantly improves the classification performance. The combination of basic classifiers is determined by evaluating the classification performance without solving complex optimization problems. Three real hyperspectral images are used to evaluate the proposed method, and the experimental results show that the proposed algorithm exhibits better classification performance than state-of-the-art methods.
Multipulse AC-DC Conversion Fed 3rd Harmonic Injection Based SPWM Controlled Cascaded MLI Driven VCIMD

This paper present a 36-pulse AC-DC converter fed 5-level cascaded multilevel inverter (MLI) driven vector controlled induction motor drive (VCIMD). A modular transformer is utilized to make the grid current nearly sinusoidal and the transformer is connected to achieve a 36pulse AC-DC converter. A symmetrical 5-level cascaded MLI is utilized on the motor side, which offers the good performances of the proposed VCIMD. A 3$^{rd}$ harmonics injection based sinusoidal pulse width modulation (SPWM) is proposed in this paper, for variable speed induction motor drive to achieve the performances similar to space vector modulation (SVM). A 5level cascaded MLI has less switching loss. It is because, it is operated at low switching frequency. The Simulink/MATLAB base simulations results are verified by the experimental results and both the simulation and experimental results are given in this paper.
Domestication of High-Copy Transposons Underlays the Wheat Small RNA Response to an Obligate Pathogen

Abstract Plant genomes have evolved several evolutionary mechanisms to tolerate and make use of transposable elements (TEs). Of these, transposon domestication into cis-regulatory and microRNA (miRNA) sequences is proposed to contribute to abiotic/biotic stress adaptation in plants. The wheat genome is derived at 85% from TEs, and contains thousands of miniature inverted-repeat transposable elements (MITEs), whose sequences are particularly prone for domestication into miRNA precursors. In this study, we investigate the contribution of TEs to the wheat small RNA immune response to the lineage-specific, obligate powdery mildew pathogen. We show that MITEs of the Mariner superfamily contribute the largest diversity of miRNAs to the wheat immune response. In particular, MITE precursors of miRNAs are wide-spread over the wheat genome, and highly conserved copies are found in the Lr34 and QPm.tut-4A mildew resistance loci. Our work suggests that transposon domestication is an important evolutionary force driving miRNA functional innovation in wheat immunity.
Differences in Gluteal and Quadriceps Muscle Activation During Weight-Bearing Exercises Between Female Subjects With and Without Patellofemoral Pain

Supplemental Digital Content is Available in the Text. Abstract Glaviano, NR and Saliba, S. Differences in gluteal and quadriceps muscle activation during weight-bearing exercises between female subjects with and without patellofemoral pain. J Strength Cond Res 36(1): 55–62, 2022—Therapeutic exercises targeting gluteal and quadriceps muscles have been previously investigated; however, these studies have only been conducted on healthy individuals. Female subjects with patellofemoral pain (PFP) present with weakness in their gluteal and quadriceps muscles and commonly perform exercises that target these muscles. Therefore, the purpose of this study was to compare lower extremity muscle activity during 5 weight-bearing exercises between female subjects with and without PFP. Twenty female subjects with PFP and 20 healthy female subjects completed 5 weight-bearing tasks: a single-leg squat (SLS), lateral step-down, step-up task, step-down task, and lunge. Surface electromyography of the gluteus medius (GMed), gluteus maximus (GMax), vastus lateralis (VL), and vastus medialis oblique (VMO) were collected. Electromyography activity were normalized to maximal voluntary isometric contractions to represent each gluteal and quadriceps muscle as a percentage. Female subjects with PFP completed a SLS and step-up tasks with less GMed and GMax activity (p < 0.05) compared with those without PFP. Female subjects with PFP also had greater VMO activity (p < 0.05) during the step-up, step down, and lunge and VL activity (p < 0.05) during the step-up and SLS compared with healthy female subjects. Practitioners should be aware that there are differences in gluteal and quadriceps muscle activation between female subjects with and without PFP while performing weight-bearing exercises. These findings should influence both the selection of specific exercises and exercise progression when developing a treatment program that has the intended focus to address gluteal or quadriceps strength deficits in female subjects with PFP.
Optical second harmonic generation in cobalt nanolayers influenced by nonmagnetic heavy metals

Magnetic planar nanostructures composed of ferromagnetic and heavy metals are a subject of intensive studies during the last years. Here we apply the technique of optical second harmonic generation (SHG) to the studies of planar nanostructures based on such materials, where the high sensitivity of the SHG probe to the spatial and temporal symmetry breaking is of principal importance. Based on the analysis of the SHG magnetic field dependencies, we show a clear difference of the magnetic behavior of the interfaces and of bulky materials, as well as additional features attributed to specific interfacial chiral magnetization distributions.
Amelioration of hippocampal dysfunction by adipose tissue‐targeted stem cell transplantation in a mouse model of type 2 diabetes

There is growing evidence that type 2 diabetes or insulin resistance is linked to cognitive impairment. We recently confirmed altered lipid composition, down‐regulation of insulin receptor expression and impaired basal synaptic transmission in the hippocampus of our transgenic murine model of adipocyte insulin resistance (AtENPP1‐Tg). Here we evaluated whether the correction of adipose tissue dysfunction [via the subcutaneous transplantation of mesenchymal stem cells (MSC)] can improve the hippocampal synaptic transmission in AtENPP1‐Tg mice versus their wildtype littermates. Animals were simply randomized to receive MSC, then weighed weekly for 12 weeks. At euthanasia, we assessed leptin in the collected serum and hippocampal synaptic high‐frequency stimulation long‐term potentiation (HFS‐LTP) using brain slices. MSC transplantation normalized AtENPP1‐Tg body and epididymal fat weights and was associated with increased leptin levels, a sign of adipocyte maturation. More importantly, transplantation restored the deficiency observed in AtENPP1‐Tg HFS‐LTP, the cellular readout of memory. Our results further corroborate the role of adipocyte maturation arrest in adipose tissue and highlight a role for the adipose tissue in modulating hippocampal cellular mechanisms. Further studies are warranted to explore the mechanisms for the MSC‐induced improvement of hippocampal HFS‐LTP.
Percutaneous paraspinal access to the azygos vein to create a neo‐vena cava

We present the case of a child with congenital heart disease repaired in infancy with diffuse central venous occlusions resulting in central venous insufficiency, superior vena cava (SVC) syndrome, and intracranial bleeds. He presented to the catheterization laboratory for multiple transcatheter interventions to recanalize central venous channels which were unsuccessful by conventional methods. Therefore, extravascular venous channels were created to decompress his upper body by creation of a neo‐SVC and his lower body with a neo‐azygos vein to the neo‐SVC. The latter procedure required direct percutaneous access from a paraspinal approach in order to obtain continuity with the azygos vein. At latest follow‐up the patient is clinically well and asymptomatic.
A Genetic Algorithm for Solving Flexible Flow Shop Scheduling Problem with Autonomous Guided Vehicles

The flexible flow shop (FFS) is defined as a multistage flow shop with multiple parallel machines. FFS scheduling problem is a complex combinatorial problem which has been intensively studied in many real world industries. In the FFS scheduling problem, each job has to be processed on any machine at each stage, but it is not considered that how the jobs are transported from one machine to another. In this study, materials are transported from one machine to another through autonomous guided vehicles (AGV) system. In this paper, we propose a genetic algorithm (GA) for solving FFS scheduling problem in which AGVs are used to transport materials. We design effective coding and decoding scheme and genetic operators including crossover and mutation. The effectiveness of the algorithm is verified by simulation experiments.
Proportional Navigation-Benchmarked Guidance Strategy using Artificial Neural Networks

Pursuit of an aerial target by interceptor has become a problem of paramount importance in the last several decades. Proportional Navigation (PN) guidance law is most widely used because of its simplicity, ease in implementation and robustness. Therefore, by benchmarking PN guidance, this paper obtains effective guidance strategies leveraging artificial neural networks (ANNs). Suitable input variables that can ensure good mathematical approximation, and optimal training strategy that can ensure sufficient training accuracy are obtained. Performances of ANNs with corresponding input variables are found to be equivalently effective in comparison with PN guidance in most of the engagement geometries.
Endmember Bundle Extraction Based on Pure Pixel Index and Superpixel Segmentation

Spectral unmixing is a fundamental issue that needs to be addressed in the application of hyperspectral images. Due to the complex imaging conditions in remote sensing, it is common for the same object to have different spectral signatures. In this paper, we present a novel endmember bundle extraction method based on pixel purity index and superpixel segmentation to deal with this problem, where each material is represented by a set of similar endmem-ber spectra. This method improves the accuracy of endmember extraction and focuses on the removal of redundant endmembers, leading to less spectral unmixing error than existing endmember bundle extraction algorithms. The experimental results on both synthetic dataset and real dataset demonstrate that the proposed method performs effectively in extracting variable endmember sets.
Dual-Channel Convolutional Neural Network for Polarimetric SAR Images Classification

This paper presents a new dual-channel convolutional neural network (Dc-CNN) for Polarimetric synthetic aperture radar (PolSAR) image classification when labeled samples are small. First, a neighborhood minimum spanning tree (MST) is used to enlarge the labeled sample set. Then, in order to obtain the abundant spatial information, a new dual-channel CNN is designed to PolSAR image to acquire different spatial features. This network model contains two parallel CNN structures, which can extract different features used two multiscale convolution structure. Experiments results show that compared with other methods, the proposed method shows a satisfactory classification result.
Polsar Image Classification Based on an Improved Bow Model with Mid-Level Semantic Features

In recent years, using mid-level features to interpret images has attracted more and more attention. Mid-level features can solve the semantic gap between the low-level features and the high-level features. This paper proposes a PolSAR image classification method using Support Vector Machines (SVM) classifier based on an improved bag-of-visual-words (BOW) model combined with contextual information to generate mid-level semantic features. In order to solve the synonym word or polysemic word problem caused by the uncertainty of the number of visual words in the traditional BOW model, the improved visual-vocabulary construction method is proposed by defining visual words using the prior knowledge to reduce the uncertainty and feature dimension effectively. Since the traditional BOW representation ignores the correlation between words, the scale context information between words will be integrated by down-sampling to construct a multi-scale visual-vocabulary. Moreover, comparative experiments are implemented to prove the feasibility of the proposed method.
Characterization of double-bounce scattering in RVoG scenarios using controlled HR-PolTomSAR experiments

This paper evaluates the potential of Polarimetric SAR Tomography (PolTomSAR) for analyzing a semi-opaque Random Volume lying over a rough Ground (RVoG), and to assess different characterization methods aiming to retrieve the ground characteristics. This study is based on the use of controlled experiments, during which a miniaturized RvoG-like scene is imaged with a laboratory 3-D SAR, operated along a 2-D aperture. Polarimetric and tomographic signals are acquired for various configurations, and different scattering mechanisms are naturally captured by removing, hiding or replacing different parts of the scene. This unique possibility to isolate specific scattering mechanisms is used to evaluate the performance and relevance of existing decomposition techniques generally applied to forest characterization.