How to consider the effects of time of day, beam strength, and snow cover in ICESat-2 based estimation of boreal forest biomass?

Spaceborne lidar sensors have potential to improve the accuracy of forest above-ground biomass (AGB) estimates by providing direct measurements of 3D structure of forests over large spatial scales. The ICESat-2 (Ice, Cloud and land Elevation Satellite 2), launched in 2018, provides a good coverage of the boreal forest zone and has been previously shown to provide good estimates of forest canopy height and AGB. However, spaceborne lidar data are affected by various conditions, such as presence of snow, solar noise, and in the case of ICESat-2, the power difference between the so-called strong and weak beams. The aim of this study was to explore the effects of these conditions on the performance of AGB modeling using ICESat-2 photon data in a boreal forest area. The framework of the study is multiphase modeling, where AGB field data and wall-to-wall airborne laser scanning (ALS) data are used to produce proxy ALS plots on ICESat-2 track positions. Models between the ALS-predicted AGB and the ICESat-2 photon data are then formulated and evaluated by subsets, such as only strong beam data captured in snowy conditions.Our results indicate that, if possible, strong beam night data from snowless conditions should be used in AGB estimation, because our models showed clearly smallest RMSE (27.0%) for this data subset. If more data are needed, we recommend using only strong beam data and constructing separate models for the different data subsets. In the order of increasing RMSE\%, the next best options were snow/night/strong (30.5%), snow/day/strong (33.6%), and snowless/day/strong (34.2%). Weak beam data from snowy night conditions could also be used if necessary (31.1%).

Brain Activity Reflects Subjective Response to Delayed Input When Using an Electromyography-Controlled Robot

In various experimental settings, electromyography (EMG) signals have been used to control robots. EMG-based robot control requires intrinsic parameters for control, which makes it difficult for users to understand the input protocol. When a proper input is not provided, the response time of the system varies; as such, the user’s subjective delay should be investigated regardless of the actual delay. In this study, we investigated the influence of the subjective perception of delay on brain activation. Brain recordings were taken while subjects used EMG signals to control a robot hand, which requires a basic processing delay. We used muscle synergy for the grip command of the robot hand. After controlling the robot by grasping their hand, one of four additional delay durations (0 ms, 50 ms, 125 ms, and 250 ms) was applied in every trial, and subjects were instructed to answer whether the delay was natural, additional, or whether they were not sure. We compared brain activity based on responses (“sure” and “not sure”). Our results revealed a significant power difference in the theta band of the parietal lobe, and this time range included the interval in which the subjects could not feel the delay. Our study provides important insights that should be considered when constructing an adaptive system and evaluating its usability.

Resilience for coupled oscillatory power network via phase difference

This paper investigates the resilience of oscillatory power networks with Kuramoto-type nodal dynamics via phase difference. Here, we propose a general framework to measure the resilience of power network in response to perturbations affecting nodes and transmission lines during operation. Moreover, the assessing resilience approaches provide an estimation of the phase difference and power difference. To identify the resilience of power network that makes stable operation possible, several small-size oscillatory networks are selected to validate the effectiveness and feasibility of the proposed schemes. Simulation results show that the resilience plays a crucial role in the synchronous performance.

Correlations of power output fluctuations in an offshore wind farm using high-resolution SCADA data

Space–time correlations of power output fluctuations of wind turbine pairs provide information on the flow conditions within a wind farm and the interactions of wind turbines. Such information can play an essential role in controlling wind turbines and short-term load or power forecasting. However, the challenges of analysing correlations of power output fluctuations in a wind farm are the highly varying flow conditions. Here, we present an approach to investigate space–time correlations of power output fluctuations of streamwise-aligned wind turbine pairs based on high-resolution supervisory control and data acquisition (SCADA) data. The proposed approach overcomes the challenge of spatially variable and temporally variable flow conditions within the wind farm. We analyse the influences of the different statistics of the power output of wind turbines on the correlations of power output fluctuations based on 8 months of measurements from an offshore wind farm with 80 wind turbines. First, we assess the effect of the wind direction on the correlations of power output fluctuations of wind turbine pairs. We show that the correlations are highest for the streamwise-aligned wind turbine pairs and decrease when the mean wind direction changes its angle to be more perpendicular to the pair. Further, we show that the correlations for streamwise-aligned wind turbine pairs depend on the location of the wind turbines within the wind farm and on their inflow conditions (free stream or wake). Our primary result is that the standard deviations of the power output fluctuations and the normalised power difference of the wind turbines in a pair can characterise the correlations of power output fluctuations of streamwise-aligned wind turbine pairs. Further, we show that clustering can be used to identify different correlation curves. For this, we employ the data-driven k-means clustering algorithm to cluster the standard deviations of the power output fluctuations of the wind turbines and the normalised power difference of the wind turbines in a pair. Thereby, wind turbine pairs with similar power output fluctuation correlations are clustered independently from their location. With this, we account for the highly variable flow conditions inside a wind farm, which unpredictably influence the correlations.

Power Allocation for Reliable SIC Detection of Rectangular QAM-based NOMA Systems

This paper considers the power allocation for non-orthogonal multiple access (NOMA) users to enable using the successive interference cancellation (SIC) while providing reliable error performance. The derived closed-form expressions are applicable for arbitrary number of NOMA users each of which has a square or rectangular quadrature amplitude modulation (QAM) constellation with arbitrary order. The obtained numerical results show that power assignment process at the transmitter for the superposition process and at the receiver for the SIC process should be performed meticulously because the power difference between the weakest and strongest users can be tremendous when the number of users or the modulation orders increase. Moreover, the derived expressions can be used to reduce the computational complexity that is required to obtain the optimal power coefficients using brute force methods by significantly reducing the search space.

Power Allocation for Reliable SIC Detection of Rectangular QAM-based NOMA Systems

This paper considers the power allocation for non-orthogonal multiple access (NOMA) users to enable using the successive interference cancellation (SIC) while providing reliable error performance. The derived closed-form expressions are applicable for arbitrary number of NOMA users each of which has a square or rectangular quadrature amplitude modulation (QAM) constellation with arbitrary order. The obtained numerical results show that power assignment process at the transmitter for the superposition process and at the receiver for the SIC process should be performed meticulously because the power difference between the weakest and strongest users can be tremendous when the number of users or the modulation orders increase. Moreover, the derived expressions can be used to reduce the computational complexity that is required to obtain the optimal power coefficients using brute force methods by significantly reducing the search space.