Miracle-dz/UniFAR-FTUs huggingface dataset:
MSPPF-Nets: A Deep Learning Architecture for Remote Sensing Image Classification

Nowadays, deep learning has got a major success in computer vision, especially in image recognition. In this paper, a new architecture based on DenseNets which is referred to as Multi-Scale Input Spatial Pyramid Pooling Fusion Networks (MSPPF-nets) is proposed for the work of classification of local climate zones (LCZs). Multi-scale remote sensing images can be inputs of the networks by the benefit of Spatial Pyramid Pooling (SPP) layer, multi-scale features from different channels were extracted and fused by our multi-branch-input framework. The final classification results have illustrated the feasibility of this presented classification method.
High Performance AlGaN/GaN Schottky Barrier Diodes with Floating Gate

In this paper, we report a high performance AlGaN/GaN Schottky barrier diodes (SBDs) with floating gate. We design a GaN SBDs with floating gate on Si substrate, fabricated in a 6-inch wafer process line. Compared with normal SBDs, the SBDs with floating gate presented almost 13% smaller turn-on voltage (VON); also, the floating gate cut down reverse current by 25%. This device structure can be widely used for GaN power devices on a large-size silicon substrate.
Capacitive Silicon Modulator Design With V-Shaped SiO$_{2}$ Gate Waveguide to Optimize $V_{\pi }\times L$ and Bandwidth Trade-Off

This work presents a capacitive modulator design and modeling of a new waveguide architecture using silicon and poly-si technologies. We use a methodology involving the entire lumped-type modulator design process, from the optical design to the small-signal RF analysis. By means of geometric and physical parameters adjustments, the trade-offs among several figures of merit are analyzed in this paper. Due to the waveguide configuration, it is shown that the modulator can improve the trade-off between <inline-formula><tex-math notation="LaTeX">$V_{\pi }$</tex-math></inline-formula> and bandwidth compared to the state-of-the-art for this kind of device. For instance, the best combinations are <inline-formula><tex-math notation="LaTeX">$V_{\pi }\simeq$</tex-math></inline-formula> 1 V @ 20.4 GHz and <inline-formula><tex-math notation="LaTeX">$V_{\pi }$</tex-math></inline-formula> = 5.7 V @ <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>31 GHz, with equivalent optical losses of 3 dB and 1.1 dB, respectively.
Deep Learning Method Based on Gated Recurrent Unit and Variational Mode Decomposition for Short-Term Wind Power Interval Prediction

Wind power interval prediction (WPIP) plays an increasingly important role in evaluations of the uncertainty of wind power and becomes necessary for managing and planning power systems. However, the intermittent and fluctuating characteristics of wind power mean that high-quality prediction intervals (PIs) production is a challenging problem. In this article, we propose a novel hybrid model for the WPIP based on the gated recurrent unit (GRU) neural networks and variational mode decomposition (VMD). In the hybrid model, VMD is employed to decompose complex wind power data into simplified modes. Basic GRU prediction models, comprising a GRU input layer, multiple fully connected layers, and a rank-ordered terminal layer, are then trained for each mode to produce PIs, which are combined to obtain final PIs. In addition, an adaptive optimization method based on constructed intervals (CIs) is proposed to build high-quality training labels for supervised learning with the hybrid model. Several numerical experiments were implemented to validate the effectiveness of the proposed method. The results indicate that the proposed method performs better than the traditional interval prediction models with much higher quality PIs, and it requires less training time.
Getting into sync: Data‐driven analyses reveal patterns of neural coupling that distinguish among different social exchanges

In social interactions, each individual's brain drives an action that, in turn, elicits systematic neural responses in their partner that drive a reaction. Consequently, the brain responses of both interactants become temporally contingent upon one another through the actions they generate, and different interaction dynamics will be underpinned by distinct forms of between‐brain coupling. In this study, we investigated this by “performing functional magnetic resonance imaging on two individuals simultaneously (dual‐fMRI) while they competed or cooperated with one another in a turn‐based or concurrent fashion. ” To assess whether distinct patterns of neural coupling were associated with these different interactions, we combined two data‐driven, model‐free analytical techniques: group‐independent component analysis and inter‐subject correlation. This revealed four distinct patterns of brain responses that were temporally aligned between interactants: one emerged during co‐operative exchanges and encompassed brain regions involved in social cognitive processing, such as the temporo‐parietal cortex. The other three were associated with competitive exchanges and comprised brain systems implicated in visuo‐motor processing and social decision‐making, including the cerebellum and anterior cingulate cortex. Interestingly, neural coupling was significantly stronger in concurrent relative to turn‐based exchanges. These results demonstrate the utility of data‐driven approaches applied to “dual‐fMRI” data in elucidating the interpersonal neural processes that give rise to the two‐in‐one dynamic characterizing social interaction.
Cognitive Infotainment Systems for Intelligent Vehicles

Lately, the automotive world is witnessing a trend related to the deployment and extensive use of Intelligent Transportation Systems (ITS) inside vehicles. Such systems generate large volumes of real-time data that need to be managed, communicated, interpreted, aggregated, and analyzed in order to support real-time decision-making capabilities. Furthermore, automotive industries have introduced In-Vehicle-Infotainment (IVI) systems in supporting drivers/passengers with a varying set of functions. The main purpose of this study is to present an IVI cognitive functionality that automatically and dynamically proposes the optimum music genre to the drivers /users when they want to travel with their vehicles. The proposed system utilizes (i) driver/user’s profile data and his/her current situation, (ii) driver/user’s personal preferences, (iii) external environment information obtained from sensor measurements, and (iv) previous knowledge, turned into experience, in an automated manner. Knowledge is obtained through the exploitation of a machine learning technique based on the Bayesian networking principles. Indicative simulation results showcase the behavior of the aforementioned IVI cognitive functionality in proactively identifying the optimal music genre and accordingly notifying the driver/user.
Draft Genome Sequence of the Halophilic Pararhodobacter-Like Strain CCB-MM2, Which Has Polyhydroxyalkanoate-Synthesizing Potential

Pararhodobacter-like strain CCB-MM2 is a halophilic alphaproteobacterium isolated from estuarine sediment collected from Matang Mangrove Forest in Malaysia. Here, we present the draft genome sequence of CCB-MM2 and provide insights into its physiological roles and metabolic potential. ABSTRACT Pararhodobacter-like strain CCB-MM2 is a halophilic alphaproteobacterium isolated from estuarine sediment collected from Matang Mangrove Forest in Malaysia. Here, we present the draft genome sequence of CCB-MM2 and provide insights into its physiological roles and metabolic potential.
Output Synchronization of Multi-Agent Systems with Time-Delay and Unknown Leader Dynamics

This paper studies the output synchronization problem for linear heterogeneous discrete-time multi-agent systems in a leader-following architecture. The major technical challenges lie in the mix of input time-delays and unknown leader dynamics. To handle unknown leader dynamics, an observer is required for each follower to estimate both a leader’s state and its system dynamics. It results in an adaptive distributed observer-based approach subject to input-delays.
Influence of Cirrus Clouds on the Estimate of Sea Surface Temperature

Because of the thin optical depth, the cirrus cloud is one the clouds that commonly overlooked as clear sky by the cloud mask products. To study the influence of cirrus clouds on the satellite thermal infrared (TIR) data estimated sea surface temperature (SST), the MODIS Terra SST products are used in this study. Six buoy stations measured sea temperatures are acquired from National Oceanic and Atmospheric Administration’s National Data Buoy Center (NDBC). In addition, the cirrus optical depth are calculated based on the cirrus reflectance of MODIS cloud mask products. The SST pixels with MODIS cloud products labeled as confident clear-sky and cirrus reflectance flag is cirrus or contrail pixel are taken as cirrus pixels. Taking buoy sea temperatures as the true values, the MODIS SST errors are between 1.2 K and -0.6 K. In addition, five stations’ MODIS SST are tend to underestimated as cirrus optical depth increase.
Jaxa Global Sar Mosaics – Assessing Compliance with Ceos Analysis Ready Data for Land (Card4l) Specifications

In Earth Observation, Analysis Ready Data (ARD) are commonly referred to as satellite data that have been processed to a minimum set of requirements and organised into a form that allows immediate analysis with a minimum of additional user effort and that facilitates interoperability through both time and space with other datasets. The Committee on Earth Observation Satellites (CEOS) is currently developing a set of ARD specifications for both optical and Synthetic Aperture Radar (SAR) satellite data under the so called CARD4L (CEOS Analysis Ready Data for Land) framework [1], [2]. The Japan Aerospace Exploration Agency (JAXA) generates global SAR mosaic on an annual basis from data acquired by the JERS-1 SAR, ALOS PALSAR and ALOS-2 PALSAR-2 missions. The mosaic products have been subject to full geometric and radiometric corrections and organised into a format that can be considered ARD. In order for the mosaic products to obtain CARD4L certification, JAXA has undertaken a product alignment assessment of what additional processing steps or ancillary information that would be required for the mosaic products also to be compliant with the CARD4L specifications for Normalised Radar Backscatter. The data products, alignment process and outcome of the assessment are described below.
Teachers’ Experiences with Emerging Learning Technologies

No scholars, at the study site, have conducted research to examine teachers’ experiences with emerging learning technologies. A sample of 20 teachers was purposefully selected and interviewed. The findings revealed that teachers need to master emerging learning technologies through in-house professional development opportunities on Web 2.0. All participants emphasized that Web 2.0 technologies would enhance their knowledge in the education field. Institutes of higher education, professional development providers, administrators’ associations, and school districts and leaders may benefit from having an awareness of these findings. Effectiveness of teachers can be increased through professional development opportunities on Web 2.0 that are ongoing and both systemic and systematic.
Non-Reference Quality Evaluation for Indoor 3d Point Clouds

This paper proposes a novel approach for indoor point clouds quality evaluation, which works well without reference point clouds. In this paper, we mainly evaluate indoor point clouds quality in two aspects: the smoothness of the walls and the degree of occlusion of the walls and the floor. Our approach involves three steps. Firstly, with the S3DIS dataset, we use a deep learning method to train a detector to label walls and floor from indoor scenes. Next, we calculate the normal vector of the wall and the normal vector of each point on the wall. The degree of smoothness of the wall is judged according to the angle between the normal vector of the wall and the normal vectors of the points. Finally, according to the cause of occlusion (objects on the floor or in front of the wall), the occlusion degree of the wall and the floor is obtained. The experimental results demonstrate that the proposed method is suitable for non-reference indoor point cloud quality evaluation.
Altered allostery of the left flipper domain underlies the weak ATP response of rat P2X5 receptors

Although the extracellular ATP-gated cation channel purinergic receptor P2X5 is widely expressed in heart, skeletal muscle, and immune and nervous systems in mammals, little is known about its functions and channel-gating activities. This lack of knowledge is due to P2X5's weak ATP responses in several mammalian species, such as humans, rats, and mice. WT human P2X5 (hP2X5Δ328–349) does not respond to ATP, whereas a full-length variant, hP2X5 (hP2X5-FL), containing exon 10 encoding the second hP2X5 transmembrane domain (TM2), does. However, although rat P2X5 (rP2X5) has a full-length TM2, ATP induces only weak currents in rP2X5, which prompted us to investigate the mechanism underlying this small ATP response. Here, we show that single replacements of specific rP2X5 residues with the corresponding residues in hP2X5 (S191F or F195H) significantly enhance the current amplitude of rP2X5. Using a combination of engineered disulfide cross-linking, single-channel recording, and molecular modeling, we interrogated the effects of S191F and F195H substitutions on the allostery of the left flipper (LF) domain. On the basis of our findings, we propose that the bound ATP-induced distinct allostery of the LF domain with that of other functional subtypes has caused the weak ATP response of rP2X5 receptors. The findings of our study provide the prerequisite for future transgenic studies on the physiological and pathological functions of P2X5 receptors.
Development of Operational Applications of Terrasar-X PAZ Constellation

In this paper an overview of the TerraSAR-X/PAZ constellation applications, where two independent X-Band SAR missions work together to provide operational services is presented. Hisdesat and Airbus Defence and Space have teamed up to establish the first steps of the WorldSAR Constellation and to bring all benefits of a true constellation to users. The combined imaging and revisit characteristics of TerraSAR-X and PAZ will foster the development of operational applications satisfying important customer requirements related to data age, update frequency, and coverage. This paper is written from the perspective of a commercial data and service provider and focuses on three selected applications directly benefiting from frequent revisit capability of constellation. These three applications are wide area ship-detection, rapid multi temporal InSAR applications including Amplitude and Coherence Change detection and Stereo Radargrammetry Ground Control Points. The aim of this study is to validate and qualify the combined usage of TerraSAR/TandDEM and PAZ data sets for joint applications.
IoV based Real-Time Smart Traffic Monitoring System for Smart Cities using Augmented Reality

The transportation system has extended human movement system. Recent advancement of the embedded electronics system, communication, and control system have changed the transportation model to form a smart system. Further emergence of Industry 4.0, augments the Internet of Things(IoT) technologies to this model to form Internet of Vehicle(IoV). The intelligent vehicle system embedded with sensor infrastructure for sensing environment. This system applies for safe navigation, traffic management, and pollution control. The emergence of the Internet of Vehicle (IoV) has changed the paradigm of traditional transportation system augmenting intelligence, storage, communication, and learning capabilities. This paper presents a novel traffic management system using IoT and augmented reality. The enabling techniques have been compared with other methods in real-time. Further, the system is analyzed in the light of existing IoT infrastructure.
Collagen, Autoimmunity and Disease 1

The group of disorders first described as collagen diseases by Klemperer et al. (1942) was soon followed by the discovery of that autoimmune curiosity, the LE phenomenon (Hargreaves et al. 1948) and auto-antibodies to collagen itself (reviewed by Furthmayr & Timpl 1976). However, it was the advances in understanding of the structure and polymorphic varieties of collagen (reviewed by Dr V C Duance, Department of Animal Husbandry, Bristol) which has permitted the logical identification of collagen's antigenic determinants.
Micro/nanofabrication of brittle hydrogels using 3D printed soft ultrafine fiber molds for damage-free demolding

Hydrogels are very popular in biomedical areas for their extraordinary biocompatibility. However, most bio-hydrogels are too brittle to perform micro/nanofabrication. An effective method is cast molding; yet during this process, many defects occur as the excessive demolding stress damages the brittle hydrogels. Here, we propose a brand-new damage-free demolding method and a soft ultrafine fiber mold (SUFM) to replace the traditional mold. Both mechanical and finite element analysis (FEA) reveal that SUFMs have obvious advantages especially when the contact area between hydrogel and mold gets larger. By means of a high-resolution 3D printing called electrohydrodynamic (EHD) printing, SUFMs with various topological structures can be achieved with the fiber diameter ranging from 500 nm to 100 μm, at a low cost. Microfluidics and cell patterns are implemented as the demonstration for potential applications. Owing to the tiny scale of microstructures and the hydrophilicity of hydrogels, significant capillary effect occurs which can be utilized to deliver liquid and cells autonomously and to seed cells into those ultrafine channels evenly. The results open up a new avenue for a wider use of hydrogels in biomedical devices, tissue engineering, hydrogel-based microfluidics and wearable electronics; the proposed fabrication method also has the potential to become a universal technique for micro/nanofabrication of brittle materials.
Impact Of Lunar Terrain On Moon-Based Earth Observation

There is increasing concern about the new potential platform for Earth observation - the Moon-based Earth observation platform. The Moon-based platform has the advantages of large-scale, constant and long-term dynamic Earth observations, when compared with the space-borne and airborne platforms. Some scholars have presented ideas for the construction of Moon-based Earth observation, however, most of them ignored the impact caused by lunar terrain. In this paper, we propose a Moon-based Earth observation intervisibility algorithm, based on the Lunar Orbiter Laser Altimeter (LOLA) data. Then we apply the algorithm to the Sinus Iridum area and the Mare Nectaris area and analyze the inter-visibility conditions of the two areas. This intervisibility algorithm will give support to the site selection of future Moon-based Earth observation system or lunar exploration missions.
Evaluation of Extended Electro Motive Force Observer Dynamic Performances Using Complex Vectors

Extended Electro Motive Force Observers in the estimated synchronous reference frame allow to estimate rotor position at medium and high speeds for Interior Permanent Magnet Synchronous Machines. This paper focuses on the analysis of the dynamic performances of these observers. Use of complex vector modeling allows to demonstrate that these performances are speed dependent. In order to overcome this problem, a Complex Vector Observer is proposed. It is proven that this structure achieves good robustness against load torque and speed variations over different operating conditions. An improved phase detector is also presented, to solve the common speed reversal problems of the back-EMF based methods.
A Novel Approach for The Retrieval Of Snow Water Equivalent Using SAR Data

Over the last decade, many snowpack parameter retrieval algorithms utilizing C- and X-band Synthetic Aperture Radar (SAR) data have been proposed. These algorithms are capable of retrieving snow density and snow wetness accurately, but not Snow Water Equivalent (SWE). In this paper, a novel approach of SWE estimation is proposed based on TerraSAR-X full-polarimetric data. A unique relationship between the extinction coefficient of the snowpack and snow wetness is built and verified with field data. This relationship enables the retrieval of Snow Depth (SD) and SWE using existing snow density algorithm and volume scattering power. The validation of SD and SWE is carried out with near-real-time ground truth measurements. The accuracy assessment for SD showed Mean Absolute (MAE) and Root Mean Squared (RMSE) Errors of 15 cm and 16 cm, respectively. On the other hand, SWE retrievals resulted in MAE and RMSE of 22.7 mm and 30 mm, respectively.
Image Compression at Very Low Bitrate Based on Deep Learned Super-Resolution

The problem of data storage and transmission on mobile devices is constantly growing up. Smartphones are nearly by default equipped with HD cameras that are taking high quality pictures, which can be instantly stored and easily uploaded on cloud platforms. Such a behavior favors the creation of a massive amount of data. In order to reduce the size of such data, it is mandatory to dispose of efficient compression techniques that can take into account the actual usage of such image data. For example, most of the pictures acquired by phone cameras are often displayed on a small screen and this, for a little amount of time. A solution to manage this kind of oversized data would be to store them in a lower resolution, additionally to a standard image compression. The downside of such an approach is that restoring an image to its original resolution is a challenging task, notably in the presence of complex compression artifacts, such as those introduced by sophisticated compression methods. In order to deal with such an issue, in this paper we propose a new model, specifically trained to perform super-resolution after compression with the BPG state-of-the-art codec. An advantage of the proposed approach comes from the fact that the underlying process can be interpreted as a postprocessing step, which can be easily added to any compression scheme without modifying the codec. Experimental results show that our model perceptually outperforms the state-of-the-art compression standards even for very low bitrates.
Multi-Scale Machine Learning for the Classification of Building Property Values

In this paper, we describe a multi-scale machine learning approach to estimate socio-economic attributes of citizens based on the analysis of aerial images. To analyse the effectiveness of the proposed approach we predict building property value classes. The classification of these building property values is a proxy for the socio-economic status of the residents. The approach is based on the fusion of deep Convolutional Neural Networks (CNNs). We compare the proposed approach with non-image and single-scale CNN approaches and demonstrate the effectiveness in a case study using statistical data collected in the city of Amsterdam, Netherlands. We show that the proposed multi-scale approach outperforms the baseline methods.
Design & Implementation of Solar Fed Intensity Controlled Streetlight

SPV based LED Streetlight has advantages over other conventional lighting systems as no power conversion is needed. LED work on DC and energy optimization is possible by controlling the duty cycle of the LED driver. The components of Solar Fed LED Street lighting system are SPV array, MPPT, dc-dc converter and battery unit. In this paper, the intensity of solar fed Street light is controlled from traffic hours to non-traffic hours which results in saving the electricity consumption. A hybrid street light model is also designed and developed. The simulation studies are performed in Matlab-Simulink environment.