Abstract: The Brazilian Pantanal biome, known for its rich biodiversity and wetlands, is experiencing frequent and destructive fires. Detecting and monitoring burnt areas is vital for comprehending their present ecological condition, a key indicator for climate change and protective measures. Optical remote sensing methods, traditionally used in fire mapping, have limitations due to atmospheric conditions. Microwave Synthetic Aperture Radar (SAR) is a promising alternative, excelling in challenging environments and demonstrating sensitivity to surface properties. This study aimed to assesses the potential of SAR images for detecting burnt areas in a conservation unit inserted in the Brazilian Pantanal after intense fires in 2020. For this, the Normalized Burn Ratio (NBR) index was calculated from Sentinel-2 images before and after fire, and then the difference between these images (dNBR). Differences in backscatter coefficients of pre- and post-fire SAR/Sentinel-1 images in the two polarizations (dVH and dVV) were also calculated. To detect burnt areas, the three differences were classified using the Random Forest algorithm. The results showed adequate coincidence of burned areas between dVH and dVV compared to dNBR and high accuracy values of the algorithm model, indicating consistency between SAR and optical data in identifying burnt areas. Abstract biome wetlands condition measures methods mapping conditions (SAR alternative properties 2020 this NBR (NBR Sentinel2 Sentinel 2 Sentinel- dNBR. . (dNBR) pre postfire post SAR/Sentinel1 SARSentinel1 SARSentinel SAR/Sentinel 1 SAR/Sentinel- model 202 (dNBR Sentinel1 20

ABSTRACT Hybrid materials formed between polyaniline and layered double hydroxides of Zn and Al (Pani/ZnAlHDL) were obtained by adding previously prepared polyaniline (Pani) to the reaction medium during the synthesis of the HDLs, including HDLs modified with the anionic surfactants sodium dodecyl sulfate and sodium dodecylbenzenesulfonate. The composites exhibited a blue color characteristic of the emeraldine base phase of Pani. X-ray diffraction (XRD) patterns of these compounds showed a shift of the peaks (003) and (006) of ZnAlHDL to lower 2θ values, indicating the presence of both phases in the composites. Additionally, it was observed that the anionic surfactants also influence the structural characteristics of the composites. Fourier-transform infrared (FTIR) spectra support the XRD results. Composites obtained with surfactants were subjected to colorimetric sensor tests for HCl. After only 5 minutes of exposure to HCl vapors, the materials changed their color from blue to green, indicating the formation of the Pani emeraldine salt phase. This indication was confirmed by UV-Vis spectroscopy, as spectra after acid interaction showed new bands in the 400 nm and 820 nm regions, characteristic of the emeraldine salt, the green-colored structural form of Pani. These results demonstrate the feasibility of using these materials in acid sensing, providing a rapid and effective response. Pani/ZnAlHDL PaniZnAlHDL (Pani/ZnAlHDL (Pani dodecylbenzenesulfonate Xray X ray (XRD 003 (003 006 (006 θ values Additionally Fouriertransform Fourier transform FTIR (FTIR vapors green UVVis UV Vis spectroscopy 40 82 regions greencolored colored sensing response 00 (00 4 8 0 (0 (

RESUMO Materiais híbridos formados entre polianilina e hidróxidos duplos lamelares de Zn e Al (Pani/ZnAlHDL) foram obtidos adicionando a Polianilina (Pani) previamente preparada ao meio reacional da síntese dos HDLs e também HDLs modificado com os surfactantes aniônicos dodecilsulfato de sódio e dodecilbenzenosulfato de sódio. Os compósitos apresentaram cor azul que é característica da fase base esmeraldina da Pani. Os difratogramas de raios X destes compostos apresentaram deslocamento dos picos (003) e (006) do ZnAlHDL para menores valores de 2θ, indicando a presença das duas fases nos compósitos. Além disso, foi constatado também que os surfactantes aniônicos também exercem influência nas características estruturais dos compósitos. Os espectros de FTIR corroboram os resultados do DRX. Os compósitos obtidos com os surfactantes foram submetidos à testes de sensor colorimétrico para HCl. Após 5 minutos apenas de exposição aos vapores do HCl, os materiais apresentaram mudança na sua coloração de azul para verde, indicando a formação da fase da Pani, sal esmeraldina. Esse indício foi confirmado por espectroscopia na região do UV-Vis, uma vez que os espectros após interação com ácido, apresentaram novas bandas, nas regiões de 400 nm e 820 nm, que são características do sal esmeraldina, forma estrutural da Pani de coloração verde. Esses resultados mostram a viabilidade da utilização destes materiais no sensoriamento de ambientes ácidos, apresentando resposta rápida e eficaz. Pani/ZnAlHDL PaniZnAlHDL (Pani/ZnAlHDL (Pani 003 (003 006 (006 2θ θ disso DRX HCl verde UVVis, UVVis UV Vis, Vis UV-Vis ácido bandas 40 82 ácidos eficaz 00 (00 4 8 0 (0 (

Abstract: Mapping the submerged bottom is a hampered task when traditional vessels are inserted in shallow places that pose a danger to navigation. In this sense, current research has sought optical remote sensing to obtain bathymetry estimates in larger locations in less time. However, most studies employ a large sample of bathymetric points to estimate depth with orbital images. The need for a large amount of random bathymetric points can make these procedures less viable and unattractive. Thus, the present work proposes a methodology to estimate bathymetric depths from orbital images using sections of bathymetric points previously spaced in the study area, avoiding the need for a high amount of points collected through traditional bathymetric surveys. This work also compares this methodology using the NDWI index and the ANNs. Furthermore, the study showed that the points contained in the sections are quite efficient for extracting bathymetry with orbital images, especially through the implementation of neural networks, achieving a volume estimation accuracy within 4% of the actual volume of the reservoir in question. Abstract navigation sense time However unattractive Thus area surveys ANNs Furthermore networks 4 question

ABSTRACT For the classification and zoning study of crops at Jianshan Farm, data were acquired by integrating Sentinel-2 optical satellite imagery with Sentinel-1 radar satellite imagery. Different crop spectral features were extracted, and four vegetation indices (NDVI, SAVI, EVI, and NDWI) were computed to analyze the spectral differences among soybean, corn, potato, and sorghum. The UNet algorithm was employed to conduct crop zoning on the remote sensing images of Jianshan Farm from September 2020 to September 2023 for four consecutive years. An improved UNet model incorporating a channelwise attention and spatial attention module (CBAM) achieved a prediction accuracy of 100% for the 2023 image data. This represents a 12.5% improvement over the baseline UNet model. These results provide data support for research on crop classification methods. Sentinel2 Sentinel 2 Sentinel- Sentinel1 1 extracted NDVI, NDVI (NDVI SAVI EVI NDWI soybean corn potato sorghum 202 years CBAM (CBAM 100 125 12 5 12.5 methods 20 10 12.

Abstract This work presents the development of a refractive index sensing methodology for measuring carbon dioxide concentrations, considering the effect of temperature environment. We propose and demonstrate, using simulations, the use of tapered and coated Long Period Gratings as a way to enhance the refractive index sensitivity. In addition, artificial neural networks were used to improve the estimation of carbon dioxide concentration, considering the crosssensitivity effect with temperature. concentrations environment demonstrate simulations sensitivity addition concentration

Abstract This work proposes a new tactile sensing system for biometric authentication. The system operates based on the response of multiplexed optical fiber macro-bend sensors to plantar pressure. The setup contains a set of 6 in-series sensors installed in holes drilled in a foot-shaped slab of Polymethyl Methacrylate. Each sensor is a loop of standard single-mode telecommunication fiber with 2.5 mm radius, encapsulated with silicone elastomer. The instrumentation required for interrogation includes only a broadband visible light source and a ultraviolet-visible spectrometer. Personal authentication is based on recognizing the unique pattern of pressures applied by the person’s foot in the metatarsal, outside of the foot arch, and heel areas monitored by the 6 sensors. The light spectrum transmitted by the set of multiplexed sensors in the spectral range from 400 nm to 800 nm carries information about the magnitude of the pressure applied on each sensor by the foot. The distribution of pressures exerted by different individuals alters the geometry of the macro-bends, resulting in spectral changes in transmitted light that allow personal authentication. Principal component analysis and support vector machine method are responsible for foot recognition. Data analysis returned hit rates greater than 85% for 6 different feet. macrobend macro bend inseries series footshaped shaped Methacrylate singlemode single mode 25 2 5 2. radius elastomer ultravioletvisible ultraviolet spectrometer persons person s metatarsal arch 40 80 macrobends, macrobends bends, bends macro-bends recognition 85 feet 4 8

ABSTRACT Remote sensing data used in this study included MSI (Multispectral Instrument) Sentinel-2, SAR (Synthetic Aperture Radar) Sentinel-1, GLCM (Grey Level Co-Occurrence Matrix) texture data derived from Sentinel-1, and geomorphometric data derived from SRTM (Shuttle Radar Topography Mission) images. The input data was divided into separate groups for machine learning algorithms, including Support Vector Machine (SVM), Classification and Regression Tree (CART), and Random Forest (RF), which were implemented on the Google Earth Engine platform. RF showed the highest overall accuracies (93 to 97%), regardless of the dataset used as input, with the Kappa index ranging from 0.89 (optical and SAR data) to 0.95 (optical, SAR, and geomorphometric data). CART showed identical overall accuracy values (92.5%) except for the dataset supplemented with SAR texture data, which showed slightly lower accuracy (91.7%), with the Kappa index ranging from 0.89 to 0.91. The worst performance was classifying optical data by SVM, resulting in 59% accuracy and a Kappa index of 0.37. However, the synergy of optical, SAR, and geomorphometric data classified by SVM achieved 75% accuracy. Multispectral Instrument Sentinel2, Sentinel2 Sentinel 2, 2 Sentinel-2 Synthetic Sentinel1, Sentinel1 1, 1 Sentinel-1 Grey CoOccurrence Co Occurrence Matrix Shuttle Mission images algorithms , (SVM) CART, (CART) RF, (RF) platform 93 (9 97%, 97 97% 97%) 089 0 89 0.8 095 95 0.9 data. . 92.5% 925 92 5 (92.5% 91.7%, 917 91.7% 91 7 (91.7%) 091 0.91 59 037 37 0.37 However 75 Sentinel- (SVM (CART (RF 9 ( 08 8 0. 09 92.5 (92.5 91.7 (91.7% 03 3 0.3 92. (92. 91. (91.7 (92 (91. (91

RESUMO Foram utilizados dados de sensoriamento remoto adquiridos pelos sensores MSI (Multispetral Instrument) do satélite Sentinel-2 e SAR (Synthetic Aperture Radar) Sentinel-1, dados de textura GLCM (Grey Level Co-Ocurrence Matrix) derivados das imagens Sentinel-1 e dados geomorfométricos derivados de imagens SRTM (Shuttle Radar Topography Mission). Os dados compuseram diferentes grupos de entrada para os classificadores de aprendizagem de máquina Support Vector Machine (SVM), Classification and Regression Tree (CART) e Random Forest (RF), implementados na plataforma Google Earth Engine. O RF apresentou as maiores exatidões globais (93 a 97%), independente do conjunto de dados utilizados como entrada, com o índice Kappa variando de 0,89 (dados ópticos e SAR) a 0,95 (dados ópticos, SAR e geomorfométricos). O CART apresentou valores idênticos de exatidão global (92,5%) exceto para o conjunto de dados acrescido dos dados de textura SAR, que apresentou exatidão ligeiramente mais baixa (91,7%), com índice Kappa variando de 0,89 a 0,91. O pior desempenho foi o da classificação de dados ópticos por SVM, resultando em 59% de exatidão e 0,37 de índice Kappa. Todavia, a sinergia de dados ópticos, SAR e geomorfométricos classificados por SVM atingiu 75% de exatidão. Multispetral Instrument Sentinel2 Sentinel 2 Sentinel- Synthetic Sentinel1, Sentinel1 1, 1 Grey CoOcurrence Co Ocurrence Matrix Shuttle Mission. Mission . Mission) , (SVM) (CART RF, (RF) Engine 93 (9 97%, 97 97% 97%) 089 0 89 0,8 095 95 0,9 geomorfométricos. geomorfométricos) 92,5% 925 92 5 (92,5% 91,7%, 917 91,7% 91 7 (91,7%) 091 0,91 59 037 37 0,3 Todavia 75 (SVM (RF 9 ( 08 8 0, 09 92,5 (92,5 91,7 (91,7% 03 3 92, (92, 91, (91,7 (92 (91, (91

The pursuit of a cost-effective and green synthetic approach to chemical sensors and their application in the sensing of toxic and harmful substances is a never-ending exercise for scientists and researchers. Preparation of fluorescent carbon dots (C-dots) from biomass using water as a solvent and a hydrothermal autoclave to provide the required synthesis temperature offers a cheap and environmentally friendly synthetic approach. Herein, we report a faster, less costly and ecofriendly hydrothermal synthetic approach of carbon dots from Citrullus vulgaris peels as a precursor. The as-prepared carbon dots exhibited hydroxyl, carbonyl and amide functional groups on the surface and an amorphous structure with a particle size distribution of 1.7-3.0 nm. Moreover, the carbon dots displayed intense blue emission fluorescence at 470 nm after excitation at 400 nm. The as-prepared carbon dots demonstrated effective application without further modification towards the selective and sensitive optical recognition of 2-nitrophenol used in the manufacture of explosives. A limit of detection of 2.28x10-7 M was achieved, and no fluorescence quenching was observed in the presence of other nitroaromatic and benzene derivatives indicating excellent selectivity towards 2-nitrophenol. Finally, further studies are required to investigate the potential for the as-prepared carbon dots to monitor nitroaromatic pollutants in real environmental systems. SIGNIFICANCE: • Terrorism is an ever-increasing problem, and law enforcement agencies are continuously searching for and detecting explosives hidden in travel luggage, mail packages, vehicles and aircrafts using sophisticated equipment which are not available in developing countries such as Botswana. • This work unveils a facile and environmentally friendly approach towards the detection of 2-nitrophenol used in the manufacture of explosives by employing highly luminescent C-dots obtained from locally available agricultural waste. • The utilisation of agricultural waste can help advance a sustainable waste management programme and promote a circular economy.

Abstract Delimitation of areas subject to flooding is crucial to understand water dynamics and fluvial changes. This study analyzed the potential of C-band Synthetic Aperture Radar (SAR) images acquired by the Sentinel-1 satellite in 2017, 2018, and 2019 to delineate flooded areas in the Central Amazon. The images were processed by the Artificial Neural Network Multi-Layer Perceptron (ANN-MLP) and two K-Nearest Neighbor (KNN-7 and KNN-11) machine learning (ML) classifiers. Pre-processing of Single Look Complex (SLC) SAR images involved the following methodological steps: orbit-file application; radiometric calibration (σ0); Range-Doppler terrain correction; speckle noise filtering; and conversion of linear data to backscattering coefficients (units in dB). We applied the Lee filter, with a window size of 3x3, for speckle filtering. A set of 6000 randomly distributed samples for training (70%), validation (20%), and test (10%) was obtained based on visual interpretation of Sentinel-2 optical satellite image acquired in the same years of SAR images. We found the largest flooded areas in 2019 in the study area (municipality of Parintins and Urucará, Amazonas River, Brazil): 6244km2 by the ANN-MLP classifier; 6268km2 by KNN-7; and 6290km2 by KNN-11, while the smallest flooded areas were found in 2018: 5364km2 by ANN-MLP; 5412km2 by KNN-7; and 5535km2 by KNN-11. The three classifiers presented Kappa coefficients between 0.77 and 0.91. ANN-MLP showed the best accuracy. The presence of shadow effects in the SAR images increased the commission errors.

Resumo A delimitação de áreas sujeitas a inundações é crucial para entender a dinâmica hídrica e as mudanças fluviais. Este estudo analisou o potencial de imagens de radar de abertura sintética (SAR) adquiridas na banda-C pelo satélite Sentinel-1 em 2017, 2018 e 2019 para delinear áreas inundadas na Amazónia Central. As imagens foram processadas pela Rede Neural Artificial Multi-Layer Perceptron (RNA-MLP) e dois classificadores de aprendizagem de máquina (ML) K-Nearest Neighbor (KNN-7 e KNN-11). O pré-processamento de imagens SAR Single Look Complex (SLC) envolveu as seguintes etapas metodológicas: aplicação do orbit-file; calibração radiométrica (σ0); correção de terreno Range-Doppler; filtragem de ruído speckle; e conversão de dados lineares para coeficientes de retroespalhamento (unidades em dB). O filtro de Lee com tamanho de janela de 3×3 foi aplicado para filtragem do ruído speckle. Um conjunto de 6000 amostras distribuídas aleatoriamente para treino (70%), validação (20%) e teste (10%) foi obtido com base na interpretação visual da imagem do satélite óptico Sentinel-2 adquiridas no mesmo ano das imagens de radar. As maiores áreas alagadas foram encontradas em 2019 na área de estudo (municípios de Parintins e Urucará, Rio Amazonas, Brasil): 6244km2 pelo classificador RNA-MLP; 6268km2 pelo KNN-7; e 6290km2 pelo KNN-11, enquanto as menores áreas alagadas foram encontradas em 2018: 5364km2 pelo classificador RNA -MLP; 5412km2 pelo KNN-7; e 5535km2 pelo KNN-11. Os três classificadores apresentaram coeficientes Kappa entre 0,77 e 0,91. A RNA-MLP apresentou a melhor precisão. A presença de efeitos de sombra nas imagens SAR aumentou os erros de comissão.

Résumé La délimitation de zones sujettes aux inondations est cruciale pour comprendre la dynamique hydrique et les changements fluviaux. Cette étude a analysé le potentiel des images radar à ouverture de synthétique (SAR) acquises en bande-C par le satellite Sentinel-1 en 2017, 2018 et 2019 pour délimiter les zones inondées en Amazonie Centrale. Les images ont été traitées par le Réseau de Neurones Artificiels Multicouches Perceptron (ARN-MLP) et deux classificateurs d'apprentissage automatique (ML) K-Nearest Neighbor (KNN-7 et KNN-11). Le prétraitement des images SAR images complexes à visée simple (SLC) a impliqué les étapes méthodologiques suivantes : application du fichier d'orbite; calibration radiométrique (σ0); correction de terrain Range-Doppler; filtrage du bruit de chatoiement; et conversion des données linéaires en coefficients de rétrodiffusion (unités en dB). Un filtre de Lee avec une taille de fenêtre de 3×3 a été appliqué pour filtrer le bruit de chatoiement. Un ensemble de 6000 échantillons assignés au hasard pour la formation (70%), la validation (20%) et les tests (10%) a été obtenu à partir de l'interprétation visuelle de l'image satellite optique Sentinel-2 acquise pour la même année que les images radar. Les plus grandes zones inondées ont été trouvées en 2019 dans la zone d'étude (municipalité de Parintins et Urucará, la Riviére Amazonas, Brésil) : 6244km2 selon le classificateur RNA -MLP; 6268km2 pour KNN-7; et 6290km2 pour KNN-11, alors que les plus petites zones inondées ont été trouvées en 2018: 5364km2 pour le classificateur RNA -MLP; 5412km2 pour KNN-7; et 5535km2 pour KNN-11. Les trois classifieurs présentaient des coefficients Kappa compris entre 0,77 et 0,91. L'ARN-MLP a montré une meilleure précision. La présence d'effets d'ombre dans les images SAR a augmenté les erreurs de commission.

Resumen La delimitación de áreas sujetas a inundaciones es crucial para comprender la dinámica del agua y los cambios fluviales. Este estudio analizó el potencial de las imágenes de radar de apertura sintética (SAR) adquiridas en la banda-C por el satélite Sentinel-1 en 2017, 2018 y 2019 para delinear áreas inundadas en la Amazonía Central. Las imágenes fueron procesadas por la Red Neuronal Artificial de Perceptrón Multicapa (RNA-MLP) y dos clasificadores de aprendizaje automático (ML) K-Nearest Neighbor (KNN-7 y KNN-11). El preprocesamiento de imágenes SAR complejas de una sola mirada (SLC) involucró los siguientes pasos metodológicos: aplicación del archivo de órbita; calibración radiométrica (σ0); corrección del terreno Range-Doppler; filtrado de ruido moteado; y conversión de datos lineales en coeficientes de retrodispersión (unidades en dB). Se aplicó El filtro Lee con un tamaño de ventana de 3×3 para filtrar el ruido moteado. Se obtuvo un conjunto de 6000 muestras asignadas aleatoriamente para entrenamiento (70%), validación (20%) y prueba (10%) en base a la interpretación visual de la imagen del satélite óptico Sentinel-2 adquirida el mismo año que las imágenes de radar. Los humedales más grandes se encontraron en 2019 en el área de estudio (municipio de Parintins y Urucará, Rió Amazonas, Brasil): 6244km2 por el clasificador RNA -MLP; 6268km2 por KNN-7; y, 6290km2 por KNN-11, mientras que, los humedales más pequeños se encontraron en 2018: 5364km2 por el clasificador RNA -MLP; 5412km2 por KNN-7; y 5535km2 por KNN-11. Los tres clasificadores presentaron coeficientes Kappa entre 0,77 y 0,91. RNA-MLP mostró la mejor precisión. La presencia de efectos de sombra en las imágenes SAR aumentó los errores de comisión.

Resumen La polarimetría con radar de apertura sintética (SAR) es una técnica conocida en teledetección por sus aplicaciones en el monitoreo de cambios sobre la superficie terrestre y en cuerpos de agua. El objetivo de la polarimetría radar es caracterizar la superficie terrestre analizando las propiedades de la señal que se dispersa cuando se utilizan diferentes combinaciones de polarización de las antenas transmisoras y receptoras, definidas como canales polarimétricos. La utilización de la polarización cruzada (vertical/horizontal) se mostró eficiente para estimar la extensión de las inundaciones producidas entre el 22 y el 25 de julio de 2021 en la cuenca del río Frío (Costa Rica). Para ello se utilizaron imágenes captadas por el sensor Sentinel 1A, de la Agencia Espacial Europea, mediante un flujo de procesamiento y una composición RGB posterior que permitió establecer una clara distinción entre las zonas inundadas de los cuerpos de agua permanentes. La utilización de imágenes ópticas para el mapeo de inundaciones está limitada por las condiciones climáticas, especialmente por la presencia de nubes. Las mediciones con SAR son independientes de la hora del día y de las condiciones climáticas, proporcionando valiosa información para el monitoreo de inundaciones (Horritt, 2006, p 187). Además, la extensión de las inundaciones es un factor crítico para la calibración y validación de modelos hidráulicos. La extensión de las inundaciones puede usarse también para evaluación y gestión del riesgo, y pueden beneficiarse de ello rescatistas durante las inundaciones (Corbley, 1999, p 1).

Resumo A polarimetria por radar de abertura sintética (SAR) é uma técnica conhecida em sensoriamento remoto por suas aplicações no monitoramento de mudanças na superfície terrestre e em corpos d'água. O objetivo da polarimetria de radar é caracterizar a superfície terrestre analisando as propriedades do sinal que é espalhado quando são utilizadas diferentes combinações de polarização das antenas transmissoras e receptoras, definidas como canais polarimétricos. O uso da polarização cruzada (vertical/horizontal) mostrou-se eficiente para estimar a extensão das inundações produzidas entre 22 e 25 de julho de 2021 na bacia do rio Frío (Costa Rica). Para isso, foram utilizadas imagens captadas pelo sensor Sentinel 1A da Agência Espacial Europeia, através de um fluxo de processamento e posterior composição RGB que permitiu distinguir claramente as áreas inundadas dos corpos d'água permanentes. O uso de imagens ópticas para mapeamento de inundações é limitado pelas condições climáticas, especialmente pela presença de nuvens. As medições de SAR são independentes da hora do dia e das condições meteorológicas, fornecendo informações valiosas para o monitoramento de enchentes (Horritt, 2006, p.187). Além disso, a extensão da inundação é um fator crítico para a calibração e validação de modelos hidráulicos. A extensão das inundações também pode ser usada para avaliação e gestão de risco e pode beneficiar os socorristas durante as inundações (Corbley, 1999, p. 1).

Abstract Synthetic Aperture Radar (SAR) polarimetry is a remote sensing technique known for being applied to monitoring of land surface and water bodies. Polarimetry aims at charactering the land surface through the analysis of its response under different combinations of transmitting and receiving antennas polarization, called polarimetric channels. The use of cross-polarization (vertical/horizontal) proved to be efficient for estimating the extent of the floods between July 22nd and July 30th in the Frío river basin (Costa Rica). To that end, Sentinel-1 images from the European Space Agency have been used, with a workflow process and RGB composition that allowed to draw a clear distinction between flooded and permanent water bodies. The use of optical imagery for flood mapping is limited by weather conditions, specially by the presence of clouds. SAR measurements are independent of daytime and weather conditions, providing valuable information for monitoring flood events. Furthermore, flood extent is a critical factor for the calibration and validation of hydraulic models (Horritt, 2006). Flood extent can be also used for damage assessment and risk management and can be of benefit to rescuers during flooding (Corbley, 1999).

Abstract In this contribution, we propose a new plasmonic configuration that can be functionalized in two wavelength regimes to generate a single interface mode or multiple interface modes. The structure comprises a negative metamaterial or Left-Handed Material (LHM) coated on 2S2G-glass prism and adjacent with a sensing medium. According to the results, the negative metamaterial thickness affects significantly the potential of the structure to operate as conventional plasmonic structured formed by Fabry-Perot cavities. Additionally, we, also show that the structure can be used as a plasmonic refractive index sensor defined in the range of 1 to 1.53 refractive index unit (RIU) where the full width at half maximum (FWHM) of the SPR curve on the characteristic’s manipulation such that FWHM of p-reflectivity decreases for thick LHM layer. To understand the obtained results, the optical response from the proposed waveguide was numerically predicted by the use of the transfer matrix method (TMM) and Fresnel’s theory. In addition, the potentials of the designed waveguide as an optical modulator and Fabry-Perot interferometer are also presented.

Abstract In this work, measuring capabilities of a Sagnac Interferometer-based Distributed Optical Fiber Sensor (DOFS) system are demonstrated by comparing its results to a conventional amplitude-based Distributed Acoustic Sensing (DAS) system. Through a set of three different experiments, it was demonstrated that both sensing systems can detect and locate dynamical deformation along the fiber, in addition to determining its frequency components. The coherent amplitude-based DAS presented a lower background noise. In contrast, even though the Sagnac interferometry-based system presented low-frequency noise in the measurements, it is less susceptible to harmonic distortion for higher amplitude deformations since its sensing principles rely on the measurement of the accumulated phase rather than the amplitude of the backscattered signal purely. Accordingly, the Sagnac interferometry-based system arises as a promising and rather accessible topology for a DOFS implementation. work Interferometerbased Interferometer based (DOFS amplitudebased (DAS experiments fiber components contrast interferometrybased interferometry lowfrequency low measurements purely Accordingly implementation

Abstract The growing demand for multiparameter sensors includes compact devices accompanied by simple calibration processes to distinguish the outputs from each other. This paper evaluates a scheme to determine multiple parameters of a medium using localized surface plasmon resonances (SPR) excited on a Dshaped photonic crystal fiber (PCF) partially covered by two gold layers of different thicknesses. We demonstrate that the proposed sensing platform, once customized to characterize the possible dispersive profiles of the refractive index of the analyte, also allows interrogating the temperature of a sample from a linear relationship. Since the plasmonic resonances are excited at separated and low crosstalk spectral channels, different sensing responses can be obtained simultaneously in the same location of the D-shaped PCF. These features turn out the SPR sensor a suitable tool for simultaneous monitoring of optical dispersion and temperature. other (SPR PCF (PCF thicknesses platform analyte relationship channels D shaped

Abstract We propose and experimentally investigate two distinct power-over-Fiber (PoF) approaches, aiming to remotely power Internet-of-Things (IoT) sensing systems for Industry 4.0 environments. The first proof-of-concept is focused on demonstrating a 1-W optical power transmission through a 50-m-fiber-optic link for powering an Arduino Uno, a temperature sensor (DS1820B), and a 433-MHz transceiver (FS1000A). The designed PoF link is able to provide up to 280 mW with power transmission efficiency (PTE) of 28.9%. The second implementation is based on a 100-m PoF link capable of transmitting over 0.6-W optical power and delivering 140-mW electrical power with PTE of 23%. In this scheme, an Arduino Pro Mini, another temperature sensor (LM35), and a 2.4-GHz transceiver (nRF24L01+) are employed. A voltage stability analysis enables to demonstrate that our PoF system is capable of delivering stable output voltage at 8.5 V and 5 V, with only 0.6% and 0.2% voltage fluctuations. In addition, an industrial oven is employed to evaluate the sensor performance considering temperature measurements from both sensing systems. The obtained results demonstrate that PoF might be considered as a potential technology to optically-power IoT wireless sensing systems for Industry 4.0 scenarios. poweroverFiber Fiber (PoF approaches InternetofThings Internet Things (IoT 40 4 0 4. environments proofofconcept proof concept 1W W 1 50mfiberoptic mfiberoptic 50 m fiber optic Uno DS1820B, DS1820B DSB , DS B (DS1820B) 433MHz MHz 433 FS1000A. FS1000A FSA . FS (FS1000A) 28 (PTE 289 9 28.9% 100m 100 0.6W 06W 0.6 6 140mW 140 23 23% scheme Mini LM35, LM35 LM (LM35) 2.4GHz 24GHz GHz 2.4 2 nRF24L01+ nRF24L01 nRFL nRF L (nRF24L01+ 85 8 8. 06 02 0.2 fluctuations addition opticallypower optically scenarios (DS1820B 43 (FS1000A 28.9 10 6W 0. 14 LM3 (LM35 4GHz 24 2. nRF24L0 (nRF24L01 28. (LM3 nRF24L (nRF24L0 (LM (nRF24L

Abstract: Among grain-producing species, soybean is one of the most important commodities, with increasing demand for production in coming years. Evaluation of soybean seed quality is fundamental for ensuring maximum germination and yield potential. Therefore, effective methods are necessary for examining different properties associated with physical-chemical, physiological, and seed-health changes that affect seed quality. This review focuses on the fundamental principles and on the application of techniques of radiographic imaging, magnetic resonance imaging, multispectral imagining, chlorophyll fluorescence imaging, and infrared thermography to evaluate changes related to loss of soybean seed quality, such as mechanical injury, injury caused by insects, embryonic malformation, and incomplete maturation. Computerized seedling image analysis is also presented for evaluation of seed lot vigor. The examples presented here show the potential of these image analysis techniques for identifying different types of injuries and increasing the efficiency of in-house quality control programs in soybean seed production companies. Abstract grainproducing grain producing species commodities years Therefore physicalchemical, physicalchemical physical chemical, chemical physical-chemical physiological seedhealth health imaging imagining insects malformation maturation vigor inhouse house companies

Resumo: Entre as espécies produtoras de grãos, a soja é uma das commodities mais importantes, com demanda crescente por produção nos próximos anos. A avaliação da qualidade da semente de soja é fundamental para garantir o máximo potencial de germinação e produtividade. Portanto, métodos eficazes são necessários para examinar diferentes propriedades associadas a alterações físico-químicas, fisiológicas e na sanidade das sementes que afetam a qualidade das sementes. Esta revisão enfoca os princípios fundamentais e a aplicação de técnicas de imagem radiográfica, ressonância magnética, imagem multiespectral, imagem de fluorescência de clorofila e termografia infravermelha para avaliar alterações relacionadas à perda de qualidade da semente de soja, como injúrias mecânicas, injúrias causadas por insetos, malformação embrionária e maturação incompleta. A análise computadorizada de imagens de plântulas também é apresentada para avaliação do vigor do lote de sementes. Os exemplos aqui apresentados mostram o potencial dessas técnicas de análise de imagens para identificar diferentes tipos de injúrias e aumentar a eficiência dos programas internos de controle de qualidade nas empresas produtoras de sementes de soja. Resumo grãos importantes anos produtividade Portanto físicoquímicas, físicoquímicas físico químicas, químicas físico-químicas radiográfica magnética multiespectral mecânicas insetos incompleta