Miracle-dz/UniFAR-FTUs huggingface dataset:
Atmospheric Slant Delay from SAR Interferometry, GNSS and Numerical Weather Prediction Model: A Comparison Study in View of a Geosynchronous SAR Mission

This work investigates the capability of Interferometric SAR (InSAR) technique to monitor the atmosphere. Changes in the Atmospheric Phase Screen (APS) derived by InSAR are strictly related to changes in the atmosphere stratification and to the 2-D distribution of the water vapor columnar content. High spatial/temporal resolution APS maps provide by a SAR geosynchronous satellite (GEO-SAR) can be assimilated into limited area models (LAMs) for a better characterization of the local scale phenomena. As the observation of GEO-SAR are taken with high incidence angles, the path delay is measured along the slant direction (STD) and thus both the observation operator and the Global Navigation Satellite System (GNSS) products used for calibrating the APS must be considered along that direction. In this work, we compare the STD predicted by the Weather Research and Forecasting Model (WRF), and retrieved by a network of GNSS stations with the APS derived from Sentinel-1.
Pr-Based Sar Reconstruction Autofocus Algorithm for Persistent Surveillance Change Detection

Random phase noises arising from frequency jitter of transmit signal and atmospheric turbulence result in corrupted synthetic aperture radar (SAR) imagery, which in turn degrades change detection (CD) performance. In this paper, a phase retrieval (PR) based SAR reconstruction autofocus framework by exploiting the hidden convexity is proposed with the goal of achieving reliable persistent surveillance CD. Firstly the original non-convex quartic SAR reconstruction is reformulated as a convex quadratic program. Under the minimum phase assumption, the auto-correlation retrieval- Kolmogorov factorization (CoRK) algorithm is then utilized to optimally and efficiently retrieve the underlying SAR reflectivity. The devised scheme possesses effective capabilities of phase noise mitigation, thus has a superior CD performance. Experimental results are provided to verify the effectiveness of the proposed method.
Member-Owned Alternatives

Cooperatives are member-owned organisations, run for the common benefit of their members. While cooperatives are a longstanding way of organising, they have received little attention in CSCW. In this paper, through interviews with 26 individuals from 24 different cooperatives, our focus is an exploratory inquiry on how cooperatives could expand thinking into what future economies can look like and the part technologies may play in them. We discuss (1) the work to make the co-op work, that is, the special effort involved in managing an enterprise in a democratic and inclusive way, (2) the multiple purposes that cooperatives can serve for their members, well beyond financial benefit, and (3) ICT usage within cooperatives as a site of tension and dialogue. We conclude by discussing the meaning and measures of success in alternative economies, and lessons learned for CSCW scholarship on civic and societal organisations.
Context-based spectrum sharing in 5G wireless networks based on Radio Environment Maps

Dynamic spectrum sharing can provide many benefits to wireless networks operators. However, its efficiency requires sophisticated control mechanisms. The more context information is used by it, the higher performance of networks is expected. A facility for collecting this information, processing it, and controlling base stations managed by various network operators is a so-called Radio Environment Map (REM) subsystem. This paper proposes REM-based schemes for the allocation of base stations power levels in 4G/5G networks, while considering interference generated to a licensed network. It is assumed that both networks have different profiles of served users, e.g., area of their positions and movement, which opens opportunities for spectrum sharing. The proposed schemes have been evaluated by means of extensive system-level simulations and compared with two widely adopted policy-based spectrum sharing reference schemes. Simulation results show that dynamic schemes utilizing rich context information outperforms static, policy-based spectrum sharing schemes.
A Semi-Empirical Model to Estimate Biophysical Parameters in Southern Mexico

The monitoring of tropical ecosystems is complex due to the type of terrain and adverse weather conditions present during most of the year. This paper proposes the development and implementation of a methodology based on RADARSAT-2 images and a semi-empirical model to obtain surface parameters in a tropical forest. The model takes into consideration the sensor configuration, and vegetation and soil parameters to represent the behavior of the wave in the scene. To retrieve the surface parameters, the model is implemented in an optimization framework using all polarizations (HH, HV, VH and VV). Finally, as outputs of the optimization process, we obtain the trunk diameter (Dt), the crow height (hc), and the optical penetration depth $(\boldsymbol{\tau})$. By using $\tau$, the vegetation water content (VWC) is estimated. The accuracy of this methodology is about 83% when compared to ground data.
SAR Patch Categorization Using Stacked Sparse Coding

This paper presents Synthetic Aperture Radar (SAR) patch categorization using unsupervised feature learning framework. It is based on layer based sparse coding, which extends a sparse coding to a multi layer architecture. A contribution of this paper is a framework which consists of 3 layers of sparse coding, local spatial pooling layer, normalization layer, map reduction layer and a classification layer. The new method is able to learn several levels of sparse representation of the image which capture features at a variety of abstraction levels and simultaneously preserve the spatial smoothness between the neighboring image patches. The proposed method achieved promising results in SAR patch categorization.
Complementary and alternative medicine (CAM) and atopic eczema

Abstract. There is a substantial and growing interest in complementary and alternative medicine (CAM) in the general population. This paper aims to answer in how far patients with atopic eczema use CAM and which techniques. Furthermore the evidence basis on the efficacy of CAM in the use for atopic eczema should be reviewed. For that purpose randomized controlled trials (RCT) were searched systematically. In Germany about 46% of the general population and up to 51% of inpatients with eczema use CAM. Acupuncture, homeopathy, diets and supplements comprise the most popular techniques. Better educated, middle-aged women use CAM more frequently. In general the evidence basis concerning studies on the efficacy (and safety) of CAM for atopic eczema with appropriate size and quality is limited. Most studies were found on essential fatty acids and Chinese herbs, whereby the results remain conflicting. There was not enough evidence to assess the efficacy of acupuncture, homeopathy and salt baths. A single study on bioresonance did not show superiority compared to a sham procedure. Single studies indicated beneficial effects for topical hypericum, autologous blood injection, massage therapy, Vitamin E and D, and topical Viatmin B12. These results must be confirmed by future studies. CAM are frequently used in atopic eczema, the evidence basis for that, however, is limited.
Enhanced Interactive Remote Sensing Image Retrieval with Scene Classification Convolutional Neural Networks Model

In this paper, we address the semantic gap problem in high-spatial resolution remote sensing images retrieval. We propose a useful semantic image representation that improves the understanding of the machine with respect to the human perception. We use a remote sensing scene classification Convolutional Neural Network (CNN) model to detect the semantic concepts. The similarity distance is calculated to retrieve the most similar images to the given query image. Then, to improve the performance of the retrieval results, a relevance feedback phase has been proposed, which ensures that the final result corresponds to the user need. Our proposal shows promising results and improves the retrieved quality with respect to state-of–the-art approaches.
Inshore Ship Detection in Sar Images Based on Deep Neural Networks

Inshore ship detection in SAR image faces difficulties on correctly identifying near-shore ships and onshore objects. This article proposes a multi-scale full convolutional network (MS-FCN) based sea-land segmentation method and applies a rotatable bounding box based object detection method (DR-Box) to solve the inshore ship detection problem. The sea region and land region are separated by MS-FCN then DR-Box is applied on sea region. The proposed method combines global information and local information of SAR image to achieve high accuracy. The networks are trained with Chinese Gaofen-3 satellite images. Experiments on the testing image show most inshore ships are successfully located by the proposed method.
Smart and Functional Conducting Polymers: Application to Electrorheological Fluids

Electro-responsive smart electrorheological (ER) fluids consist of electrically polarizing organic or inorganic particles and insulating oils in general. In this study, we focus on various conducting polymers of polyaniline and its derivatives and copolymers, along with polypyrrole and poly(ionic liquid), which are adopted as smart and functional materials in ER fluids. Their ER characteristics, including viscoelastic behaviors of shear stress, yield stress, and dynamic moduli, and dielectric properties are expounded and appraised using polarizability measurement, flow curve testing, inductance-capacitance-resistance meter testing, and several rheological equations of state. Furthermore, their potential industrial applications are also covered.
Exogenous imaging contrast and therapeutic agents for intravascular photoacoustic imaging and image-guided therapy

Intravascular photoacoustic (IVPA) imaging has been developed in recent years as a viable imaging modality for the assessment of atherosclerotic plaques. Exogenous imaging contrast and therapeutic agents further enhance this imaging modality and provide significant benefits. Imaging contrast agents can significantly increase photoacoustic signal, resulting in enhanced plaque detection and characterization. The ability to use these particles to molecularly target markers of disease progression makes it possible to determine patient-specific levels of risk and plan treatments accordingly. With improved diagnosis, clinicians will be able to use therapeutic agents that are synergistic with IVPA imaging to treat atherosclerotic patients. Pre-clinical and clinical studies with relevance to IVPA imaging have shown promise in the area of diagnosis and therapeutics. In this review, we present a variety of imaging contrast agents that are either designed for or are compatible with IVPA imaging, cover uses of therapeutic agents that compliment this imaging modality, and discuss future directions of research in the field.
Land Use Analysis Using a Compact Parametrization of Multi-Temporal SAR Data

The present work presents a novel and robust method that exploits multi-temporal Sentinel-1 SAR data for land use classification. The method is built on an interferometric stack of multi-temporal Sentinel-1 SAR data and combines the intensity of backscattering, the short time interferometric coherence and the decorrelation time of scatterers. Promising preliminary results of land use classification and river corridor coverage are presented. The test case is located in the flat area of the Padana plains in west northern part of Italy. The proposed method is a novel, robust and all-conditions tool for improving unsupervised land use classification at large scale. In the future, this technique will be integrated with optical information for testing the potentiality offered to the classification of land-uses and river features.
Herpes Zoster DNA Vaccines with IL-7 and IL-33 Molecular Adjuvants Elicit Protective T Cell Immunity

Herpes zoster (HZ), or shingles, is caused by the reactivation of latent varicella-zoster virus (VZV) from the sensory ganglia when VZV-specific T-cell immunity is decreased because of aging or immunosuppression. In the present study, we developed HZ DNA vaccine candidates encoding VZV proteins and cytokine adjuvants, such as IL-7 and IL-33. We immunized C57BL/6 mice with DNA plasmids encoding VZV glycoprotein E (gE), immediate early (IE) 63, or IE62 proteins and found that robust VZV protein-specific T-cell responses were elicited by HZ DNA vaccination. Co-administration of DNA plasmids encoding IL-7 or IL-33 in HZ DNA vaccination significantly enhanced the magnitude of VZV protein-specific T-cell responses. Protective immunity elicited by HZ DNA vaccination was proven by challenge experiments with a surrogate virus, vaccinia virus expressing gE (VV-gE). A single dose of HZ DNA vaccine strongly boosted gE-specific T-cell responses in mice with a history of previous infection by VV-gE. Thus, HZ DNA vaccines with IL-7 and IL-33 adjuvants strongly elicit protective immunity.
Expanding Expressive Range: Evaluation Methodologies for Procedural Content Generation

Procedural Content Generation (PCG) has been a part of video games for the majority of their existence and have been an area of active research over the past decade. How- ever, despite the interest in PCG there is no commonly ac- cepted methodology for assessing and analyzing a generator. Furthermore, the recent trend towards machine learned PCG techniques commonly state the goal of learning the design within the original content, but there has been little assess- ment of whether these techniques actually achieve this goal. This paper presents a number of techniques for the assess- ment and analysis of PCG systems, allowing practitioners and researchers better insight into the strengths and weaknesses of these systems, allowing for better comparison of systems, and reducing the reliance on ad-hoc, cherry-picking-prone tech- niques.
Identification of Winter Land Use in Temperate Agricultural Landscapes based on Sentinel-1 and 2 Times-Series

Land cover and land use monitoring, particularly during winter season, is still a major environmental and scientific issue in agricultural areas. From an environmental point of view, the presence and type of vegetation cover in winter have an impact on pollutant transport to water bodies. From a methodological point of view, characterizing spatio-temporal dynamics of winter land cover and land use at a field scale remains a challenge due to the diversity of farming strategies and practices. The objective of this study was to evaluate the potential of optical and SAR time-series to improve the monitoring of winter land use in an area of 130 km2. For that purpose, Sentinel-1 and 2 time-series were classified using SVM and RF algorithms. Winter land use was identified with an overall accuracy of 81% and a kappa index of 0.77 from a combination of Sentinel-1 and 2 images.
Comparison of temporomandibular disorders between menopausal and non-menopausal women

Objectives Hormonal changes during menopause alter a woman's susceptibility to some disorders. Information regarding the prevalence of temporomandibular disorder (TMD) in menopausal women is limited in the literature. In this study, the prevalence and severity of TMDs were compared between menopausal and non-menopausal women. Materials and Methods The study included 140 women (69 premenopausal and 71 postmenopausal) 45 to 55 years of age that were examined in Shiraz Dental School, Shiraz in Iran. The Helkimo clinical dysfunction index (Di) was used to evaluate temporomandibular joint (TMJ) dysfunction. The data were analyzed using chi-square and Fisher's exact tests. Results Occurrence of TMD was significantly higher in menopausal than non-menopausal women (P<0.001). All the TMD criteria based on Helkimo Di except range of mandibular movement were significantly more common in menopausal women. The range of mandibular movement was not significantly different between menopausal and non-menopausal women (P=0.178). Conclusion The results from this study show that TMD can be considered more common and severe in menopausal than non-menopausal women. This finding indicates that, similar to other conditions in menopausal women such as arthritis and osteoporosis, TMD should be taken into consideration by dental and medical professionals.
Crocin Ameliorates Atopic Dermatitis Symptoms by down Regulation of Th2 Response via Blocking of NF-κB/STAT6 Signaling Pathways in Mice

Crocin, a major constituent of Gardenia jasminoides, is a natural colorant carotenoid compound that has been reported to have anti-inflammatory effects. This study investigated the therapeutic effects of crocin on mice with atopic dermatitis induced by Dermatophagoides farinae crude extract, which is a common environmental allergen in house dust that causes atopic dermatitis in humans. Crocin application ameliorated Dermatophagoides farinae crude extract-induced atopic dermatitis symptoms by inhibiting the dermatitis severity score, ear thickness, and serum immunoglobulin E levels in NC/Nga mice. The increases in epidermal thickness and dermal inflammatory cells (eosinophil and mast cells) infiltrations observed on the dorsal back skin of atopic dermatitis control mice were inhibited in a dose-dependent manner by topical application of crocin in atopic dermatitis treatment mice. Crocin inhibited the Dermatophagoides farinae crude extract-induced increase of thymus and activation-regulated chemokines, interleukin (IL)-4, and IL-13 on the dorsal skin of mice. Crocin also inhibited Dermatophagoides farinae crude extract-induced activation of nuclear factor-κB (NF-κB) and signal transducer and activator of transcription (STAT) 6. These results show that crocin ameliorates atopic dermatitis symptoms by down regulation of the Th2 cells-mediated immune response via blocking of NF-κB/STAT6 signaling pathways.
The Impact of Different Channel of Online Review on Consumers' Online Trust

Online review is an important influencing factor for consumers’ online trust decision. Although previous studies have shown that the product reviews of other consumers can affect the purchase intention and trust of potential consumers, there are few studies on the influence of the review information available from different channels on the degree of consumer trust. In this paper, the channels for review information available to consumers are summarized into three types, namely Seller-based, Agent-based and Social Networking Services-based (SNS-based). In this research, by the experimental method, the authors investigate the influence of the review information from different channels on the consumer trust. The results are presented as follows. First, the data analysis result supports the definition and classification of channels of review information in this research. Second, the results demonstrate that the review information perceived by consumers from different channels has different degrees of influence on their trust. Moreover, this research also finds that the review information perceived by consumers from the social network has the greatest influence on their trust, while reviews in the seller’s own environment have the least influence on the consumer trust. Finally, this research points out that according to statistical results, there is no significant difference in the influence on consumer trust by Agent-based and SNS-based review information.
Interpolation and Gap Filling of Landsat Reflectance Time Series

Products derived from a single multispectral sensor are hampered by a limited spatial, spectral or temporal resolutions. Image fusion in general and downscaling/blending in particular allow to combine different multiresolution datasets. We present here an optimal interpolation approach to generate smoothed and gap-free time series of Landsat reflectance data. We fuse MODIS (moderate-resolution imaging spectroradiometer) and Landsat data globally using the Google Earth Engine (GEE) platform. The optimal interpolator exploits GEE ability to ingest large amounts of data (Landsat climatologies) and uses simple linear operations that scale easily in the cloud. The approach shows very good results in practice, as tested over five sites with different vegetation types and climatic characteristics in the contiguous US.
Genetic Algorithm with Novel Crossover, Selection and Health Check for Clustering

We propose a genetic algorithm for clustering records, where the algorithm contains new approaches for various genetic operations including crossover and selection. We also propose a health check operation that finds sick chromosomes of a population and probabilistically replaces them with healthy chromosomes found in the previous generations. The proposed approaches improve the chromosome quality within a population, which then contribute in achieving good clustering solution. We use fifteen datasets to compare our technique with five existing techniques in terms of two cluster evaluation criteria. The experimental results indicate a clear superiority of the proposed technique over the existing techniques.
Understanding Formal and Informal Learning Systems in Contemporary Learning Environments

Contemporary learning environments use formal learning management systems to facilitate learning. Students use these formal learning management systems as well as informal learning systems such as social network applications to support their learning. We seek to understand the use and effectiveness of these formal and informal systems in students learning. Supported from the information system success model, we developed and validated a research model to understand the role of the contemporary learning environment. Survey data has been collected through participants from a tertiary educational institution. Data analysis has been conducted using smart PLS and the findings highlight the characteristics that help in achieving learning performance.
Snow Cover Mapping with Poincare Sphere Parameters from Polsar Images Using an Auto-Encoder Network

This paper presents a novel framework for snow cover mapping with multi-basis Poincare sphere parameters obtained from full-polarimetric SAR images in conjunction with an Auto-Encoder neural network. The neural network comprises of two stages where an unsupervised stochastic sampling Auto-Encoder (AE) learns a summarized representation of multi-basis polarimetric SAR data, and a supervised Feed Forward (FF) network performs classification. The proposed algorithm is demonstrated for snow-cover mapping using the Radarsat-2 (FQ-28) C-band full-polarimetric SAR datasets acquired over the Manali-Dhundi region of Himachal Pradesh, India. The results are visualized along with the NDSI-based snow cover map derived from the LANDSAT-8 imagery for the region.
Computer Gaming for the Visually Impaired -- Status and Perspectives

Digital technology plays an important role in everyday activities and facilitates the lives of everyone. This raises the problem of designing technology accessible for everyone, including people with disabilities such as visual impairments. Assistive technology for visually impaired people requires specific approaches in its design due to the fact that the main focus is not on the visual aspects. The game industry has concentrated most of its resources on developing games for the regular community, leaving people with visual impairments only a few options. This paper presents the status of the applications and games for the blind, identifies the main challenges and the perspectives for development.