nils-herrmann/clean_scirepeval huggingface dataset:
A Review of Multitemporal Synthetic Aperture Radar (SAR) for Crop Monitoring

Synthetic Aperture Radars (SARs) transmit and receive energy at microwave frequencies. The response recorded by these sensors is largely a function of the structure and dielectric properties of the target. The structure of a canopy is different among crops, and changes as crops grow. SARs respond very well to these structural differences and thus these sensors are able to accurately identify crop type and have proven sensitive to several crop biophysical parameters (Leaf Area Index, biomass, canopy height) Although optical sensors have traditionally been used for crop monitoring, advances in SAR applications research coupled with availability of SAR data at different frequencies and polarizations has raised the profile of these sensors for agricultural monitoring. And the "all weather" capability of SARs makes their use in operational activities of particular interest. Advancements in SAR applications development, continued improved access to data, and a push to transfer SAR research methods to monitoring agencies will lead to an increased role of SAR in monitoring agricultural production. This chapter reviews SAR research as it relates to crop type and acreage estimation, as well as determination of crop condition and crop bio physical properties.
Multitemporal and multiresolution leaf area index retrieval for operational local rice crop monitoring

This paper presents an operational chain for high resolution leaf area index (LAI) retrieval from multiresolution satellite data specifically developed for Mediterranean rice areas. The proposed methodology is based on the inversion of the PROSAIL radiative transfer model through the state of the art nonlinear Gaussian process regression (GPR) method. Landsat and SPOT5 data were used for multitemporal LAI retrievals at high resolution. LAI estimates were validated using time series of in situ LAI measurements collected during the rice season in Spain and Italy. Ground LAI data were collected with smartphones using PocketLAI, a specific phone application for LAI estimation. Temporal evolution of the LAI estimates using Landsat and SPOT5 data followed consistently the temporal evolution of the in situ LAI measurements acquired on several Mediterranean rice varieties. The estimates had a root mean square error (RMSE) of 0.39 and 0.51 m 2 /m 2 in Spain and 0.38 and 0.47 m 2 /m 2 in Italy for Landsat and SPOT5 respectively, with a strong correlation (R 2 0.92) for both cases. Spatial temporal assessment of the estimated LAI from Landsat and SPOT5 data confirmed the robustness and consistency of the retrieval chain. This paper demonstrates the importance of an adequate characterization of the underlying rice background in order to address changes in background condition related to water management. Results highlight the potential of the proposed chain for deriving multitemporal near real time decametric LAI maps fundamental for operational rice crop monitoring, and demonstrate the readiness of the proposed method for the processing of data such as the recently launched Sentinel 2.
Crop Monitoring Based on SPOT 5 Take 5 and Sentinel 1A Data for the Estimation of Crop Water Requirements
Venue: Remote. Sens.

Optical and microwave images have been combined for land cover monitoring in different agriculture scenarios, providing useful information on qualitative and quantitative land cover changes. This study aims to assess the complementarity and interoperability of optical (SPOT 5 Take 5) and synthetic aperture radar (SAR) (Sentinel 1A) data for crop parameter (basal crop coefficient (Kcb) values and the length of the crop's development stages) retrieval and crop type classification, with a focus on crop water requirements, for an irrigation perimeter in Angola. SPOT 5 Take 5 images are used as a proxy of Sentinel 2 data to evaluate the potential of their enhanced temporal resolution for agricultural applications. In situ data are also used to complement the Earth Observation (EO) data. The Normalized Difference Vegetation Index (NDVI) and dual (VV VH) polarization backscattering time series are used to compute the Kcb curve for four crop types (maize, soybean, bean and pasture) and to estimate the length of each phenological growth stage. The Kcb values are then used to compute the crop's evapotranspiration and to subsequently estimate the crop irrigation requirements based on a soil water balance model. A significant R2 correlation between NDVI and backscatter time series was observed for all crops, demonstrating that optical data can be replaced by microwave data in the presence of cloud cover. However, it was not possible to properly identify each stage of the crop cycle due to the lack of EO data for the complete growing season.
Crop Monitoring Using Vegetation and Thermal Indices for Yield Estimates: Case Study of a Rainfed Cereal in Semi Arid West Africa
Venue: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

For the semiarid Sahelian region, climate variability is one of the most important risks of food insecurity. Field experimentations as well as crop modeling are helpful tools for the monitoring and the understanding of yields at local scale. However, extrapolation of these methods at a regional scale remains a demanding task. Remote sensing observations appear as a good alternative or addition to existing crop monitoring systems. In this study, a new approach based on the combination of vegetation and thermal indices for rainfed cereal yield assessment in the Sahelian region was investigated. Empirical statistical models were developed between MODIS NDVI and LST variables and the crop model SARRA H simulated aboveground biomass and harvest index in order to assess each component of the yield equation. The resulting model was successfully applied at the Niamey Square Degree (NSD) site scale with yield estimations close to the official agricultural statistics of Niger for a period of 11 years (2000 2011) $r 0.82,\text{p value} 0.05$ The combined NDVI and LST indices based model was found to clearly outperform the model based on NDVI alone $r 0.59,\text{p value} 0.10$ In areas where access to ground measurements is difficult, a simple, robust, and timely satellite based model combining vegetation and thermal indices from MODIS and calibrated using crop model outputs can be pertinent. In particular, such a model can provide an assessment of the year to year yield variability shortly after harvest for regions with agronomic and climate characteristics close to those of the NSD study area.
Cloud Cover Assessment for Operational Crop Monitoring Systems in Tropical Areas
Venue: Remote. Sens.

The potential of optical remote sensing data to identify, map and monitor croplands is well recognized. However, clouds strongly limit the usefulness of optical imagery for these applications. This paper aims at assessing cloud cover conditions over four states in the tropical and sub tropical Center South region of Brazil to guide the development of an appropriate agricultural monitoring system based on Landsat like imagery. Cloudiness was assessed during overlapping four months periods to match the typical length of crop cycles in the study area. The percentage of clear sky occurrence was computed from the 1 km resolution MODIS Cloud Mask product (MOD35) considering 14 years of data between July 2000 and June 2014. Results showed high seasonality of cloud occurrence within the crop year with strong variations across the study area. The maximum seasonality was observed for the two states in the northern part of the study area (i.e. the ones closer to the Equator line) which also presented the lowest averaged values (15% of clear sky occurrence during the main (summer) cropping period (November to February) In these locations, optical data faces severe constraints for mapping summer crops. On the other hand, relatively favorable conditions were found in the southern part of the study region. In the South, clear sky values of around 45% were found and no significant clear sky seasonality was observed. Results underpin the challenges to implement an operational crop monitoring system based solely on optical remote sensing imagery in tropical and sub tropical regions, in particular if short cycle crops have to be monitored during the cloudy summer months. To cope with cloudiness issues, we recommend the use of new systems with higher repetition rates such as Sentinel 2. For local studies, Unmanned Aircraft Vehicles (UAVs) might be used to augment the observing capability. Multi sensor approaches combining optical and microwave data can be another option. In cases where wall to wall maps are not mandatory, statistical sampling approaches might also be a suitable alternative for obtaining useful crop area information.
Evaluation of a LiDAR based 3D stereoscopic vision system for crop monitoring applications
Venue: Comput. Electron. Agric.

When dealing with unmanned agricultural vehicles (remotely controlled vehicles, robots) vision systems are a key factor for implementing field solutions having direct interactions with crops. Among all the possible information given by a vision system, the punctual estimation of the canopy volume is surely an interesting parameter: it is related to the crop vegetative status and, hence, it is fundamental for performing and setting up properly some important field operations (e.g. pruning/thinning, spraying) A system able to recognize the canopy volume can provide either the input signals for implementing a robotic real time site specific farming system or relevant information for a proper crop management. However, there are many practical difficulties in the field implementation of such a system: complex canopy shapes, different colours, textures and illumination conditions with projected shadows. Terrestrial/aerial vision systems working on visible light wavelengths and/or 2D images of crops, although capable of excellent performances, have a computationally heavy post processing; therefore, they are unsuitable for implementing low cost real time servo actuated cropping systems (e.g. robotised sprayers) Instead, a vision system composed by two LiDAR sensors aligned vertically, scanning the same targets, could give a sort of stereoscopic vision, here named " lateral linear stereoscopic vision " The aim of this study is assessing the opportunity to use such a system on an automatic or autonomous/robotised implement by performing some preliminary tests in a controlled environment. The resulting system is independent of the lighting conditions (it works also in the dark) is highly reliable (no projected shadows) and data processing is very fast. Although further studies are required to overcome the issues that could arise in a future field implementation, this system has all the premises to be successfully embedded in an automatized monitoring system.
SCNet: A simplified encoder decoder CNN for semantic segmentation
Venue: 2016 5th International Conference on Computer Science and Network Technology (ICCSNT)

We present a simplified and novel fully convolutional neural network (CNN) architecture for semantic pixel wise segmentation named as SCNet. Different from current CNN pipelines, proposed network uses only convolution layers with no pooling layer. The key objective of this model is to offer a more simplified CNN model with equal benchmark performance and results. It is an encoder decoder based fully convolution network model. Encoder network is based on VGG 16 layer while decoder networks use upsampling and deconvolution units followed by a pixel wise classification layer. The proposed network is simple and offers reduced search space for segmentation by using low resolution encoder feature maps. It also offers a great deal of reduction in trainable parameters due to reusing encoder layer's sparse features maps. The proposed model offers outstanding performance and enhanced results in terms of architectural simplicity, number of trainable parameters and computational time.
Helicobacter pylori infection in children: should it be carefully assessed?
Venue: European review for medical and pharmacological sciences

The prevalence of H. pylori infection, mainly acquired during childhood and may be persisting throughout life, has been found high in developing countries; this high prevalence is related to low socioeconomic status. The persistence of bacterium exposure is related to gastritis and other severe complications including peptic ulcer, lymphoma MALT and gastric cancer, which are rarely present in the pediatric age due to a lower inflammatory and immunological response. Virulence factors, host gastric mucosal factors, and the natural environment of patients are associated with the clinical outcome of H. pylori infection. The main bacterial virulence factors include adhesins (BabA, SabA) vacuolating cytotoxin VacA, and the products of the cag pathogenicity island (cag PAI) There are geographic differences between cagA, vacA status and H. pylori related diseases. The main criteria to evaluate H. pylori infection in children are gastrointestinal and extra gastrointestinal manifestations related to H. pylori infection, familial history of gastric cancer, peptic ulcer, lymphoma MALT, symptomatic children living in high prevalence regions, and immigrant or adopted children in developed countries. Early detection of H. pylori and its virulence factors, in addition to effective methods of eradication associated with prevention programs, may lead to the decrease of H. pylori incidence and gastritis, especially in endemic high risk regions. The early assessment in children may prevent further severe complications in adulthood.
[Arsenic Poison or medicine?
Venue: Medycyna pracy

Arsenic (As) is commonly known as a poison. Only a few people know that As has also been widely used in medicine. In the past years As and its compounds were used as a medicine for the treatment of such diseases as diabetes, psoriasis, syphilis, skin ulcers and joint diseases. Nowadays As is also used especially in the treatment of patients with acute promyelocytic leukemia. The International Agency for Research on Cancer (IARC) has recognized arsenic as an element with carcinogenic effect evidenced by epidemiological studies, but as previously mentioned it is also used in the treatment of neoplastic diseases. This underlines the specificity of the arsenic effects. Arsenic occurs widely in the natural environment, for example, it is present in soil and water, which contributes to its migration to food products. Long exposure to this element may lead to liver damages and also to changes in myocardium. Bearing in mind that such serious health problems can occur, monitoring of the As presence in the environmental media plays a very important role. In addition, the occupational risk of As exposure in the workplace should be identified and checked. Also the standards for As presence in food should be established. This paper presents a review of the 2015 publications based on the Medical database like PubMed and Polish Medical Bibliography. It includes the most important information about arsenic in both forms, poison and medicine.
Be aware of error measures. Further studies on validation of predictive QSAR models

Validation is the most crucial concept for development and application of quantitative structure activity relationship (QSAR) models. The validation process confirms the reliability of the developed QSAR models along with the acceptability of each step during model development such as assessing the quality of input data, dataset diversity, predictability on an external set, domain of applicability and mechanistic interpretability. External validation or validation using an independent test set is usually considered as the gold standard in evaluating the quality of predictions from a QSAR model. The external predictivity of QSAR models is commonly described by employing various validation metrics, which can be broadly categorized into two major classes, viz. R2 based metrics namely R2test, Q2(ext_F1) and Q2(ext_F2) and purely error based measures like predicted residual sum of squares (PRESS) root mean square error (RMSE) and mean absolute error (MAE) The problem associated with the error based measures is the absence of any well defined threshold for determining the quality of predictions making the R2 based metrics more suitable for use due to easy comprehension. However, in this paper, we show the problems associated with the R2 based validation metrics commonly used in QSAR studies, since their values are highly dependent on the range of the response values of the test set compounds and their distribution pattern around the training/test set mean. We also propose a guideline for determining the quality of predictions based on MAE and its standard deviation computed from the test set predictions after omitting 5% high residual data points in order to obviate the influence of any rarely occurring high prediction errors that may significantly obscure the quality of predictions for the whole test set. In this manner, we try to evaluate the prediction performance of a model on most (95% of the data points present in the external set. An online tool (XternalValidationPlus) for computing the suggested MAE based criteria (along with other conventional metrics) for external validation has been made available at http:/dtclab.webs.com/software tools and http:/teqip.jdvu.ac.in/QSAR_Tools/ The MAE based criteria suggested here along with other commonly used validation metrics may be applied to evaluate predictive performance of QSAR models with a greater degree of confidence.
A Historical Excursus on the Statistical Validation Parameters for QSAR Models: A Clarification Concerning Metrics and Terminology
Venue: J. Chem. Inf. Model.

In the last years, external validation of QSAR models was the subject of intensive debate in the scientific literature. Different groups have proposed different metrics to find "the best" parameter to characterize the external predictivity of a QSAR model. This editorial summarizes the history of parameter development for the external QSAR model validation and suggests, once again, the concurrent use of several different metrics to assess the real predictive capability of QSAR models.
Dimensionality reduction in data mining: A Copula approach
Venue: Expert Syst. Appl.

Sampling based dimensionality reduction technique.Eliminating linearly redundant combined dimensions.Providing a convenient way to generate correlated multivariate random variables.Maintaining the integrity of the original information.Reducing the dimension of data space without losing important information. The recent trends in collecting huge and diverse datasets have created a great challenge in data analysis. One of the characteristics of these gigantic datasets is that they often have significant amounts of redundancies. The use of very large multi dimensional data will result in more noise, redundant data, and the possibility of unconnected data entities. To efficiently manipulate data represented in a high dimensional space and to address the impact of redundant dimensions on the final results, we propose a new technique for the dimensionality reduction using Copulas and the LU decomposition (Forward Substitution) method. The proposed method is compared favorably with existing approaches on real world datasets: Diabetes, Waveform, two versions of Human Activity Recognition based on Smartphone, and Thyroid Datasets taken from machine learning repository in terms of dimensionality reduction and efficiency of the method, which are performed on statistical and classification measures.
Rule extraction using Recursive Rule extraction algorithm with J48graft combined with sampling selection techniques for the diagnosis of type 2 diabetes mellitus in the Pima Indian dataset

Diabetes is a complex disease that is increasing in prevalence around the world. Type 2 diabetes mellitus (T2DM) accounts for about 90 95% of all diagnosed adult cases of diabetes. Most present diagnostic methods for T2DM are black box models, which are unable to provide the reasons underlying diagnosis to physicians; therefore, algorithms that can provide further insight are needed. Rule extraction can provide such explanations; however, in the medical setting, extracted rules must be not only highly accurate, but also simple and easy to understand. The Recursive Rule eXtraction (Re RX) algorithm is a "white box" model that provides highly accurate classification. However, due to its recursive nature, it tends to generate more rules than other algorithms. Therefore, in this study, we propose the use of a rule extraction algorithm, Re RX with J48graft, combined with sampling selection techniques (sampling Re RX with J48graft) to achieve highly accurate, concise, and interpretable classification rules for the Pima Indian Diabetes (PID) dataset, which comprises 768 samples with two classes (diabetes or non diabetes) and eight continuous attributes. The use of this algorithm resulted in an average accuracy of 83.83% after 10 runs of 10 fold cross validation. Sampling Re RX with J48 graft achieved substantially better accuracy and provided a considerably fewer average number of rules and antecedents than the original Re RX algorithm. These results suggest that sampling Re RX with J48graft provides more accurate, concise, and interpretable extracted rules than previous algorithms, and is therefore more suitable for medical decision making, including the diagnosis of T2DM.
Exploration of Security Threat Analysis in Wireless Mobile Adhoc Network

Mobile Adhoc Networks (MANETs) due to its principle characteristics of network infrastructure, limited resources and transmission range are more vulnerable to multitude of attacks. The objective is to classify the attacks and its counter measures to detect and eliminate malicious nodes.  Each attack has been analyzed briefly based on its own characteristics and behavior. Also, the defeating methodologies against network attacks have been described and evaluated as a part of the measurements. We have also proposed an Algorithm to study and analyze networks on affected conditions. We presented analytics and classification of the attacks on the different layers of the network.  On attacked situation in network there will be no data available as the characteristics of the network are unknown. We need to simulate such types of conditions in the network. The performance of the network gets degraded at the time of attack. It was observed that the impact of attack depends on the proximity of the attacker to the source node, it is severe when close and least when far from the source. Each malicious node uses network feature (Distributed Network, Non centralized, Hop by Hop communications, Open network boundary or Wireless media) to break the security. The goal is to violate security service (Availability, Data Confidentiality and Integrity) In our algorithm, we showcased the normalization of the data set such that we get the maximum and minimum values for the classification of the network. The primary groups of data types for classification are: Delay, bandwidth utilization, and drop rate and packet type) There are some secondary classification like conjunction, status of process, running services and utilization of processor. The condition of system was presented as a vector by storing the normalized values in an array. We arrived at simulating a network in attacked situations.  The work can be extended to find ways to calculate the threshold effectively. Group attacks can be studied and derive the relationship between the average detection delay and the mobility of the nodes.