Miracle-dz/UniFAR-FTUs huggingface dataset:
Cost Optimized Switching of Routing Protocol Scheme for IoT Applications

In this work, we propose a context-aware switching of routing protocol scheme for specific application requirements of IoT in real-time using a softwaredefined networking controller in wireless sensor networks. The work planned has two stages i) Selection of suitable routing protocol (RP) for given IoT applications using higher cognitive process and ii) Deployment of the corresponding routing protocol. We use the supervised learning-regression method for classification of the routing protocol while considering the network parameters like stability, path delay, energy utilization, and throughput. The chosen routing protocol will be set in the sensor network using a software-defined networking controller in an exceedingly flexible manner during the second stage. Extensive simulation has been done and results are evaluated to point out the strength of the proposed work, while dynamically varying the specific requirements of IoT applications. We observe that the work proposed is path-breaking the prevailing methods, where a specific routing protocol is employed throughout the period of time. It’s clearly shown that the proposed, Low-cost Context-Aware Protocol Switching (LCAPS) scheme is efficient in improving the performance of the sensor network and also meets the specific application requirements of IoT by using Software-Defined Wireless Sensor Networks SDWSNs.
Within-Field Rice Yield Estimation Based on Sentinel-2 Satellite Data

Rice is considered one of the most important crops in the world. According to the Food and Agriculture Organization of the United Nations (FAO), rice production has increased significantly (156%) during the last 50 years, with a limited increase in cultivated area (24%). With the recent advances in remote sensing technologies, it is now possible to monitor rice crop production for a better understanding of its management at field scale to ultimately improve rice yields. In this work, we monitor within-field rice production of the two main rice varieties grown in Valencia (Spain) JSendra and Bomba during the 2020 season. The sowing date of both varieties was May 22–25, while the harvesting date was September 15–17 for Bomba and October 5–8 for JSendra. Rice yield data was collected over 66.03 ha (52 fields) by harvesting machines equipped with onboard sensors that determine the dry grain yield within irregular polygons of 3–7 m width. This dataset was split in two, selecting 70% of fields for training and 30% for validation purposes. Sentinel-2 surface reflectance spectral data acquired from May until September 2020 was considered over the test area at the two different spatial resolutions of 10 and 20 m. These two datasets were combined assessing the best combination of spectral reflectance bands (SR) or vegetation indices (VIs) as well as the best timing to infer final within-field yields. The results show that SR improves the performance of models with VIs. Furthermore, the correlation of each spectral band and VIs with the final yield changes with the dates and varieties. Considering the training data, the best correlation with the yields is obtained on July 4, with R2 for JSendra of 0.72 at 10 m and 0.76 at 20 m resolution, while the R2 for Bomba is 0.87 at 10 m and 0.92 at 20 m resolution. Based on the validation dataset, the proposed models provide within-field yield modelling Mean Absolute Error (MAE) of 0.254 t×ha−1 (Mean Absolute Percentage Error, MAPE, of 3.73%) for JSendra at 10 m (0.240 t×ha−1; 3.48% at 20 m) and 0.218 t×ha−1 (MAPE 5.82%) for Bomba (0.223 t×ha−1; 5.78% at 20 m) on July 4, that is three months before harvest. At parcel level the model’s MAE is 0.176 t×ha−1 (MAPE 2.61%) for JSendra and 0.142 t×ha−1 (MAPE 4.51%) for Bomba. These results confirm the close correlation between the rice yield and the spectral information from satellite imagery. Additionally, these models provide a timeliness overview of underperforming areas within the field three months before the harvest where farmers can improve their management practices. Furthermore, it highlights the importance of optimum agronomic management of rice plants during the first weeks of rice cultivation (40–50 days after sowing) to achieve high yields.
A Novel Big Data Storage Reduction Model for Drill Down Search

Multi-level searching is called Drill down search. Right now, no drill down search feature is available in the existing search engines like Google, Yahoo, Bing and Baidu. Drill down search is very much useful for the end user to find the exact search results among the huge paginated search results. Higher level of drill down search with category based search feature leads to get the most accurate search results but it increases the number and size of the file system. The purpose of this manuscript is to implement a big data storage reduction binary file system model for category based drill down search engine that offers fast multilevel filtering capability. The basic methodology of the proposed model stores the search engine data in the binary file system model. To verify the effectiveness of the proposed file system model, 5 million unique keyword data are stored into a binary file, thereby analysing the proposed file system with efficiency. Some experimental results are also provided based on real data that show our storage model speed and superiority. Experiments demonstrated that our file system expansion ratio is constant and it reduces the disk storage space up to 30% with conventional database/file system and it also increases the search performance for any levels of search. To discuss deeply, the paper starts with the short introduction of drill down search followed by the discussion of important technologies used to implement big data storage reduction system in detail.
Evaluating the Territorial Impact of Built-Up Area Expansion in the Surroundings of Bucharest (Romania) through a Multilevel Approach Based on Landsat Satellite Imagery

Assessing the relentless expansion of built-up areas is one of the most important tasks for achieving sustainable planning and supporting decision-making on the regional and local level. In this context, techniques based on remote sensing can play a crucial role in monitoring the fast rhythm of urban growth, allowing the regular appraisal of territorial dynamics. The main aim of the study is to evaluate, in a multi-scalar perspective, the built-up area expansion and the spatio–temporal changes in Ilfov County, which overlaps the surroundings of Bucharest, capital of Romania. Our research focuses on processing multi-date Landsat satellite imagery from three selected time references (2000, 2008, 2018) through the supervised classification process. Further on, the types of built-up area dynamics are explored using LDTtool, a landscape metrics instrument. The results reveal massive territorial restructuring in the 18 years, as the new built-up developments occupy a larger area than the settlements’ surface in 2000. The rhythm of the transformations also changed over time, denoting a significant acceleration after 2008, when 75% of the new development occurred. At the regional level, the spatial pattern has become more and more complex, in a patchwork of spatial arrangements characterized by the proliferation of low density areas interspersed with clusters of high density developments and undeveloped land. At the local level, a comparative assessment of the administrative territorial units’ pathway was conducted based on the annual growth of built-up areas, highlighting the most attractive places and the main territorial directions of development. In terms of the specific dynamics of built-up areas, the main change patterns are “F—NP increment by gain”, followed by “G—Aggregation by gain”, both comprising around 80% of the total number of cells. The first type was prevalent in the first period (2000–2008), while the second is identified only after 2008, when it became the most represented, followed in the hierarchy by the previously dominant category. The spatial pattern differentiations were further explored in three complementary case studies investigated in correlation with socioeconomic data, revealing a heterogeneous landscape.
Remote Sensing-Based Urban Sprawl Modeling Using Multilayer Perceptron Neural Network Markov Chain in Baghdad, Iraq

The global and regional land use/cover changes (LUCCs) are experiencing widespread changes, particularly in Baghdad City, the oldest city of Iraq, where it lacks ecological restoration and environmental management actions at present. To date, multiple land uses are experiencing urban construction-related land expansion, population increase, and socioeconomic development. Comprehensive evaluation and understanding of the effect of urban sprawl and its rapid LUCC are of great importance to managing land surface resources for sustainable development. The present research applied remote sensing data, such as Landsat-5 Thematic Mapper and Landsat-8 Operation Land Imager, on selected images between July and August from 1985 to 2020 with the use of multiple types of software to explore, classify, and analyze the historical and future LUCCs in Baghdad City. Three historical LUCC maps from 1985, 2000, and 2020 were created and analyzed. The result shows that urban construction land expands quickly, and agricultural land and natural vegetation have had a large loss of coverage during the last 35 years. The change analysis derived from previous land use was used as a change direction for future simulation, where natural and anthropogenic factors were selected as the drivers’ variables in the process of multilayer perceptron neural network Markov chain model. The future land use/cover change (FLUCC) modeling results from 2030 to 2050 show that agriculture is the only land use type with a massive decreasing trend from 1985 to 2050 compared with other categories. The entire change in urban sprawl derived from historical and FLUCC in each period shows that urban construction land increases the fastest between 2020 and 2030. The rapid urbanization along with unplanned urban growth and rising population migration from rural to urban is the main driver of all transformation in land use. These findings facilitate sustainable ecological development in Baghdad City and theoretically support environmental decision making.
Green Measurements for Software Product Based on Sustainability Dimensions

Software is a central component in the modern world and vastly affects the environment’s sustainability. The demand for energy and resource requirements is rising when producing hardware and software units. Literature study reveals that many studies focused on green hardware; however, limited efforts were made in the greenness of software products. Green software products are necessary to solve the issues and problems related to the long-term use of software, especially from a sustainability perspective. Without a proper mechanism for measuring the greenness of a particular software product executed in a specific environment, the mentioned benefits will not be attained. Currently, there are not enough works to address this problem, and the green status of software products is uncertain and unsure. This paper aims to identify the green measurements based on sustainable dimensions in a software product. The second objective is to reveal the relationships between the elements and measurements through empirical study. The study is conducted in two phases. The first phase is the theoretical phase, where the main components, measurements and practices that influence the sustainability of a software product are identified. The second phase is the empirical study that involved 103 respondents in Malaysia investigating current practices of green software in the industrial environment and further identifying the main sustainability dimensions and measurements and their impact on achieving green software products. This study has revealed seven green measurements of software product: Productivity, Usability, Cost Reduction, Employee Support, Energy Efficiency, Resource Efficiency and Tool Support. The relationships are statistically significant, with a significance level of less than 0.01 (p = 0.000). Thus, the hypothesised relationships were all accepted. The contributions of this study revolve around the research perspectives of the measurements to attain a green software product.
Hyperspectral Image Mixed Noise Removal Using Subspace Representation and Deep CNN Image Prior

The ever-increasing spectral resolution of hyperspectral images (HSIs) is often obtained at the cost of a decrease in the signal-to-noise ratio (SNR) of the measurements. The decreased SNR reduces the reliability of measured features or information extracted from HSIs, thus calling for effective denoising techniques. This work aims to estimate clean HSIs from observations corrupted by mixed noise (containing Gaussian noise, impulse noise, and dead-lines/stripes) by exploiting two main characteristics of hyperspectral data, namely low-rankness in the spectral domain and high correlation in the spatial domain. We take advantage of the spectral low-rankness of HSIs by representing spectral vectors in an orthogonal subspace, which is learned from observed images by a new method. Subspace representation coefficients of HSIs are learned by solving an optimization problem plugged with an image prior extracted from a neural denoising network. The proposed method is evaluated on simulated and real HSIs. An exhaustive array of experiments and comparisons with state-of-the-art denoisers were carried out.
Generalized Nash equilibrium problem based electric vehicle charging management in distribution networks

This paper presents a generalized Nash equilibrium problem (GNEP) approach for the management of plug‐in electric vehicle (PEV) charging activities in a distribution network. The PEV charging pricing signal is designed to reflect the distribution network operational cost. The charging strategy of individual PEVs is allowed to respond price tariff to minimize its charging cost, while must also consider the charging requirement and grid facility constraints, such as vehicle on board charger, distribution node, and substation power limits. With this approach, the selected charging strategies are near socially optimal solutions under physical constraints. The PEV charging GNEP is reformulated by Nikaido‐Isoda function, which converts a distributed decision‐making problem to a constrained optimization problem. Due to the non‐differentiability of individual objective functions in the GNEP, relaxation algorithm is employed for searching optimal solutions in an iteration process. To adapt to the uncertainty and randomness of PEV fleet and grid load dynamics, the PEV charging management algorithm updates the GNEP when there are newly connected/disconnected PEVs or the load variation in the distribution network exceeds a predefined value. To validate the presented method, two use cases are defined. Simulation results of these two use cases verify the effectiveness of this method in both regulating the PEV charging activities and achieving PEV charging customers' objectives. The effect of customer participation rate on the performance of the presented control scheme is also analyzed.
Early Labeled and Small Loss Selection Semi-Supervised Learning Method for Remote Sensing Image Scene Classification

The classification of aerial scenes has been extensively studied as the basic work of remote sensing image processing and interpretation. However, the performance of remote sensing image scene classification based on deep neural networks is limited by the number of labeled samples. In order to alleviate the demand for massive labeled samples, various methods have been proposed to apply semi-supervised learning to train the classifier using labeled and unlabeled samples. However, considering the complex contextual relationship and huge spatial differences, the existing semi-supervised learning methods bring different degrees of incorrectly labeled samples when pseudo-labeling unlabeled data. In particular, when the number of labeled samples is small, it affects the generalization performance of the model. In this article, we propose a novel semi-supervised learning method with early labeled and small loss selection. First, the model learns the characteristics of simple samples in the early stage and uses multiple early models to screen out a small number of unlabeled samples for pseudo-labeling based on this characteristic. Then, the model is trained in a semi-supervised manner by combining labeled samples, pseudo-labeled samples, and unlabeled samples. In the training process of the model, small loss selection is used to further eliminate some of the noisy labeled samples to improve the recognition accuracy of the model. Finally, in order to verify the effectiveness of the proposed method, it is compared with several state-of-the-art semi-supervised classification methods. The results show that when there are only a few labeled samples in remote sensing image scene classification, our method is always better than previous methods.
Upscaling Evapotranspiration from a Single-Site to Satellite Pixel Scale

It is of great significance for the validation of remotely sensed evapotranspiration (ET) products to solve the spatial-scale mismatch between site observations and remote sensing estimations. To overcome this challenge, this paper proposes a comprehensive framework for obtaining the ground truth ET at the satellite pixel scale (1 × 1 km resolution in MODIS satellite imagery). The main idea of this framework is to first quantitatively evaluate the spatial heterogeneity of the land surface, then combine the eddy covariance (EC)-observed ET (ET_EC) to be able to compare and optimize the upscaling methods (among five data-driven and three mechanism-driven methods) through direct validation and cross-validation, and finally use the optimal method to obtain the ground truth ET at the satellite pixel scale. The results showed that the ET_EC was superior over homogeneous underlying surfaces with a root mean square error (RMSE) of 0.34 mm/d. Over moderately and highly heterogeneous underlying surfaces, the Gaussian process regression (GPR) method performed better (the RMSEs were 0.51 mm/d and 0.60 mm/d, respectively). Finally, an integrated method (namely, using the ET_EC for homogeneous surfaces and the GPR method for moderately and highly heterogeneous underlying surfaces) was proposed to obtain the ground truth ET over fifteen typical underlying surfaces in the Heihe River Basin. Furthermore, the uncertainty of ground truth ET was quantitatively evaluated. The results showed that the ground truth ET at the satellite pixel scale is relatively reliable with an uncertainty of 0.02–0.41 mm/d. The upscaling framework proposed in this paper can be used to obtain the ground truth ET at the satellite pixel scale and its uncertainty, and it has great potential to be applied in more regions around the globe for remotely sensed ET products’ validation.
Hyperspectral and Full-Waveform LiDAR Improve Mapping of Tropical Dry Forest's Successional Stages

Accurate estimation of the degree of regeneration in tropical dry forest (TDF) is critical for conservation policymaking and evaluation. Hyperspectral remote sensing and light detection and ranging (LiDAR) have been used to characterize the deterministic successional stages in a TDF. These successional stages, classified as early, intermediate, and late, are considered a proxy for mapping the age since the abandonment of a given forest area. Expanding on the need for more accurate successional forest mapping, our study considers the age attributes of a TDF study area as a continuous expression of relative attribute scores/levels that vary along the process of ecological succession. Specifically, two remote-sensing data sets: HyMap (hyperspectral) and LVIS (waveform LiDAR), were acquired at the Santa Rosa National Park Environmental Monitoring Super Site (SRNP-EMSS) in Costa Rica, were used to generate age-attribute metrics. These metrics were then used as entry-level variables on a randomized nonlinear archetypal analysis (RNAA) model to select the most informative metrics from both data sets. Next, a relative attribute learning (RAL) algorithm was adapted for both independent and fused metrics to comparatively learn the relative attribute levels of the forest ages of the study area. In this study, four HyMap indices and five LVIS metrics were found to have the potential to map the forest ages of the study area, and compared with these results, a significant improvement was found through the fusion of the metrics on the accuracy of the generated forest age maps. By linking the age group mapping and the relative attribute mapping results, a dynamic gradient of the age-attribute transition patterns emerged.
Learning-Accelerated ADMM for Distributed Optimal Power Flow

We propose a novel data-driven method to accelerate the convergence of Alternating Direction Method of Multipliers (ADMM) for solving distributed DC optimal power flow (DC-OPF) where lines are shared between independent network partitions. Using previous observations of ADMM trajectories for a given system under varying load, the method trains a recurrent neural network (RNN) to predict the converged values of dual and consensus variables. Given a new realization of system load, a small number of initial ADMM iterations is taken as input to infer the converged values and directly inject them into the iteration. We empirically demonstrate that the online injection of these values into the ADMM iteration accelerates convergence by a significant factor for partitioned 14-, 118- and 2848-bus test systems under differing load scenarios. The proposed method has several advantages: it maintains the security of private decision variables inherent in consensus ADMM; inference is fast and so may be used in online settings; RNN-generated predictions can dramatically improve time to convergence but, by construction, can never result in infeasible ADMM subproblems; it can be easily integrated into existing software implementations. While we focus on the ADMM formulation of distributed DC-OPF in this paper, the ideas presented are naturally extended to other distributed optimization problems.
Unsupervised calibration for noninvasive glucose-monitoring devices using mid-infrared spectroscopy

Noninvasive, glucose-monitoring technologies using infrared spectroscopy that have been studied typically require a calibration process that involves blood collection, which renders the methods somewhat invasive. We develop a truly noninvasive, glucose-monitoring technique using mid-infrared spectroscopy that does not require blood collection for calibration by applying domain adaptation (DA) using deep neural networks to train a model that associates blood glucose concentration with mid-infrared spectral data without requiring a training dataset labeled with invasive blood sample measurements. For realizing DA, the distribution of unlabeled spectral data for calibration is considered through adversarial update during training networks for regression to blood glucose concentration. This calibration improved the correlation coefficient between the true blood glucose concentrations and predicted blood glucose concentrations from 0.38 to 0.47. The result indicates that this calibration technique improves prediction accuracy for mid-infrared glucose measurements without any invasively acquired data.