The role of land use in climate change – from CO2 source to cost-efficient mitigation

The present studies focus on the land use contributions to industrial-age carbon emissions and future abatement potentials. A practicable estimation scheme is presented to transparently identify the driving terms behind past emissions and future mitigation possibilities. Regarding the major emissions sources, 10 % of total present CO2 emissions are possible in tail of primary forest clearing outside of wood consumption; 3 % are attributed to desertification and peat cultivation; on the opposite, 5 % are counteracted by sequestration from forest gain. Regarding mitigation, prudent land use has the potential to reduce more than 50 % of all present anthropogenic emissions at approximate zero costs. Prerequisite is that biomass be considered a scarce resource and therefore, carefully supported and solely used in high-efficiency applications.

An effective evapotranspiration estimation scheme based on statistical indicators for sustainable environments in humid and semi-arid area of Khyber Pakhtunkhwa, Pakistan

Reference evapotranspiration (ETo) is critical for irrigation design and water management in rainfed and irrigated agriculture. The Penman-Monteith (FAO-56(PM)) equation was demonstrated to be the most reliable and adaptive to a wide range of humid to semi-arid climates. However, it requires several environmental parameters (e.g., wind speed, solar radiation), rarely available in developing countries. Therefore, numerous temperature-based formulas have been designed to address this issue for various environments. Their calibration and validation against the local climate frequently lead to increases in performance. We revised the Hargreaves exponent (EH) and substituted a value of (0.16) for the original value (0.5). The modified Hargreaves formula enhances the ETo predictions with a mean absolute error ranging from (0.791) mm per day for Balakot to (2.36) mm per day in Risalpur, averaging (3.797) mm per day, as compared to the Hargreaves-Samani (16.827) mm per day. In general, all the selected models showed high accuracy. However, the modified Hargreaves equation appeared to be the most promising results. It ranked first in (50%) of the whole area based on the standard error of estimate for estimating ETo in Khyber Pakhtunkhwa. Additional research must be conducted to determine the study's relevance to other regions.

Satellite-based monitoring and prediction of tropical cyclone intensity and movement

ABSTRACT. Capabilities of meteorological satellites have gone a long way in meeting requirements of synoptic analysis and forecasting of tropical cyclones. This paper shows the impact made by the satellite data in the intensity estimation and track prediction of tropical cyclones in the Indian Seas and also reviews the universally applied Dvorak algorithm for performing tropical cyclone intensity analysis. Extensive use of Dvorak's intensity estimation scheme has revealed many of its limitations and elements of subjectivity in the analysis of tropical cyclones over the Arabian Sea and the Bay of Bengal, which, like cyclones in other ocean basins, also exhibit wide structural variability as seen in the satellite imagery. Satellite-based cyclone tracking techniques include: (i) use of satellite-derived mean wind flow, (ii) animation of sequence of satellite images and extrapolation of the apparent motion of the cloud system and (iii) monitoring changes in the upper level moisture patterns in the water vapour absorption channel imagery. Satellite-based techniques on tropical cyclone intensity estimation and track prediction have led to very significant improvement in disaster warning and consequent saving of life and property.

Channel Estimation for Large Intelligent Surface-Based Transceiver Using a Parametric Channel Model

<div>Large intelligent surface-based transceivers (LISBTs), in which a spatially continuous surface is being used for signal transmission and reception, have emerged as a promising solution for improving the coverage and data rate of wireless communication systems. To realize these objectives, the acquisition of accurate channel state information (CSI) in LISBT-assisted wireless communication systems is crucial. In this paper, we propose a channel estimation scheme based on a parametric physical channel model for line-of-sight dominated communication in millimeter and terahertz wave bands. The proposed estimation scheme requires only five pilot signals to perfectly estimate the channel parameters assuming there is no noise at the receiver. In the presence of noise, we propose an iterative estimation algorithm that decreases the channel estimation error due to noise. The training overhead and computational cost of the proposed scheme do not scale with the number of antennas. The simulation results demonstrate that the proposed estimation scheme significantly outperforms other benchmark schemes.</div>

Channel Estimation for Large Intelligent Surface-Based Transceiver Using a Parametric Channel Model

<div>Large intelligent surface-based transceivers (LISBTs), in which a spatially continuous surface is being used for signal transmission and reception, have emerged as a promising solution for improving the coverage and data rate of wireless communication systems. To realize these objectives, the acquisition of accurate channel state information (CSI) in LISBT-assisted wireless communication systems is crucial. In this paper, we propose a channel estimation scheme based on a parametric physical channel model for line-of-sight dominated communication in millimeter and terahertz wave bands. The proposed estimation scheme requires only five pilot signals to perfectly estimate the channel parameters assuming there is no noise at the receiver. In the presence of noise, we propose an iterative estimation algorithm that decreases the channel estimation error due to noise. The training overhead and computational cost of the proposed scheme do not scale with the number of antennas. The simulation results demonstrate that the proposed estimation scheme significantly outperforms other benchmark schemes.</div>

A Decentralized Sensor Fusion Scheme for Multi Sensorial Fault Resilient Pose Estimation

This article proposes a novel decentralized two-layered and multi-sensorial based fusion architecture for establishing a novel resilient pose estimation scheme. As it will be presented, the first layer of the fusion architecture considers a set of distributed nodes. All the possible combinations of pose information, appearing from different sensors, are integrated to acquire various possibilities of estimated pose obtained by involving multiple extended Kalman filters. Based on the estimated poses, obtained from the first layer, a Fault Resilient Optimal Information Fusion (FR-OIF) paradigm is introduced in the second layer to provide a trusted pose estimation. The second layer incorporates the output of each node (constructed in the first layer) in a weighted linear combination form, while explicitly accounting for the maximum likelihood fusion criterion. Moreover, in the case of inaccurate measurements, the proposed FR-OIF formulation enables a self resiliency by embedding a built-in fault isolation mechanism. Additionally, the FR-OIF scheme is also able to address accurate localization in the presence of sensor failures or erroneous measurements. To demonstrate the effectiveness of the proposed fusion architecture, extensive experimental studies have been conducted with a micro aerial vehicle, equipped with various onboard pose sensors, such as a 3D lidar, a real-sense camera, an ultra wide band node, and an IMU. The efficiency of the proposed novel framework is extensively evaluated through multiple experimental results, while its superiority is also demonstrated through a comparison with the classical multi-sensorial centralized fusion approach.

Sliding Mode Fault Tolerant Control for a Quadrotor with Varying Load and Actuator Fault

In this paper, an adaptive sliding mode fault-tolerant control scheme based on prescribed performance control and neural networks is developed for an Unmanned Aerial Vehicle (UAV) quadrotor carrying a load to deal with actuator faults. First, a nonsingular fast terminal sliding mode (NFTSM) control strategy is presented. In virtue of the proposed strategy, fast convergence and high robustness can be guaranteed without stimulating chattering. Secondly, to obtain correct fault magnitudes and compensate the failures actively, a radial basis function neural network-based fault estimation scheme is proposed. By combining the proposed fault estimation strategy and the NFTSM controller, an active fault-tolerant control algorithm is established. Then, the uncertainties caused by load variation are explicitly considered and compensated by the presented adaptive laws. Moreover, by synthesizing the proposed sliding mode control and prescribed performance control (PPC), an output error transformation is defined to deal with state constraints and provide better tracking performance. From the Lyapunov stability analysis, the overall system is proven to be uniformly asymptotically stable. Finally, numerical simulation based on a quadrotor helicopter is carried out to validate the effectiveness and superiority of the proposed algorithm.