Miracle-dz/UniFAR-FTUs huggingface dataset:
Stratification produced by surface cooling in lakes with significant shallow regions

A reservoir with distinct shallow and deep regions can produce stratification in response to uniform surface heat loss. The shallow region cools more rapidly, and a cold dense gravity current forms that results in stratification at the base of the deep region and an upwelling of cold water. The surface mixed layer deepens by convective entrainment, and a steady mixed‐layer depth can result when the cold upwelling balances the rate at which the mixed layer deepens. The steady depth of the mixed layer depends on the ratio of the area of the shallow region to the area of the deep region. Significant stratification only results when the reservoir has shallow regions that account for more than 50% of the surface area. The depth of the surface mixed layer also depends on the ratio of the depths of the shallow and deep regions, and no significant stratification can form if this ratio is greater than 0.5. For a wedge‐shaped geometry, these observations can be generalized by considering the ratio of the average depth to the maximum depth in a reservoir; the gravity current can produce stratification in more than 50% of the depth when this ratio is less than 0.5. Results from a laboratory study and field data from Chaffey Reservoir, Australia, are presented on the surface mixed‐layer depth, along with estimates of the time scales needed for atmospheric forcing to lead to the development of stratification.
Consent-based Privacy-preserving Decision Tree Evaluation

Decision trees are prevalent machine learning models used for data classification. Cloud servers can build their decision tree models and provide users with many classification services, such as remote medical diagnosis. Moreover, users would also like to share the classification results with third-party applications for customized services. For example, the medical diagnosis results can be further utilized by a nutrition application to provide users with dietary recommendations. However, as stringent privacy regulations of personal data, such as GDPR, takes effect, the decision tree evaluation must comply with the following requirements. First, the model parameters and user data (input and output) should be protected from public disclosure. Second, different applications should obtain the classification results with users’ consent in the context of user-customised services. In this paper, we propose a consent-based privacy-preserving decision tree evaluation scheme, named CPDE. Specifically, to achieve model parameter privacy and user data privacy, the original decision tree evaluation is conducted in a private manner in CPDE. As a result, all operations can be performed in the encrypted domain using an additively homomorphic encryption primitive and a secure comparison protocol. In addition, by integrating a proxy re-encryption technique, CDPE enables user-authorized applications to obtain the user’s classification results even if the user is offline. The security analysis shows that CPDE achieves the desirable security properties and performance evaluation demonstrates CPDE is efficient and is suitable for real-world implementations.
“Pachamanta”, a multi-thematic platform for teaching friendly living with the environment.

The “Pachamanta” platform is an educational soft- ware aimed at elementary school students between 8 and 12 years of age in the schools of Cuenca (Ecuador). This software focuses on improving the teaching-learning process of values and responsible coexistence towards nature through games and recreational activities. The latter are based on educational content on responsible water management, recycling, importance of trees and water sources for the environment, among others. “Pachamanta” is based on the use of Artificial Intelligence through the structuring of two educational modules. The first of these modules is composed of questionnaires on educational content generated through decision trees and allows selecting the best choice of answers through probabilistic models. The second educational module implements a convolutional neural network, capable of recognizing and detecting objects of interest in photographs, the latter as part of the educational activities in the field of interest indicated above.
FGF20‐FGFR1 signaling through MAPK and PI3K controls sensory progenitor differentiation in the organ of Corti

Fibroblast Growth Factor 20 (FGF20)‐FGF receptor 1 (FGFR1) signaling is essential for cochlear hair cell (HC) and supporting cell (SC) differentiation. In other organ systems, FGFR1 signals through several intracellular pathways including MAPK (ERK), PI3K, phospholipase C ɣ (PLCɣ), and p38. Previous studies implicated MAPK and PI3K pathways in HC and SC development. We hypothesized that one or both would be important downstream mediators of FGF20‐FGFR1 signaling for HC differentiation.
Study, design and fabrication of a two-dimension beam scanning antenna in package

Various emerging high data-rate applications such as ultra-high definition video streaming and wireless personal area networks have made the transition to the next generation communication systems at mmWave frequencies inevitable. Moreover, the antenna in package has been proved to be a suitable solution for a compact and efficient antenna system platform at this frequency range. Due to smaller wavelengths and aperture illumination at this frequency range, these antennas tend to have a limited radiation gain which consequently results into a limited connection range. To compensate, a high gain array antenna structure with beam steering capability is inevitable. Moreover, current developed mmWave frequency scanning antennas suffer from some shortcomings such as using active phase shifters, narrow impedance bandwidth, limited scanning range, lack of circular polarization, one dimensional beam steering, neglected electromagnetic interference, and non-efficient inter-layer interconnects. In view of these issues, the initial objective of this thesis is to study different antenna packaging methods as well as the recent developments in mmWave frequency and scanning antenna structures in order to design and produce a performant system. Consequently, a new design for a wideband circularly polarized frequency scanning periodic leaky-wave antenna has been proposed and tested which features a wide seamless scanning range of about 95° including the broadside and a fractional -10 dB impedance bandwidth of about 47%. Moreover, an accurate empirical model is developed for this antenna, as well as optimization methods to minimize the side lobe level and the axial ratio. In order to integrate band-pass filtering capability, a modified version of this antenna is also developed and tested in the scope of this research. With the goal of integrating band-pass filtering capability into the inter-layer transitions, new types of broadside coupled multi-layer filters have been designed and tested. With a fractional bandwidth of about 40% at the center frequency of 25 GHz, these filters are compact, tunable, and have a simple structure that makes them suitable candidates for replacing via interconnects in multi-layer antenna in package configurations. Finally, the antenna is combined as a linear phased array structure with a Rotman lens beamformer in a multi-layer structure to perform beam scanning in two dimensions. This system is designed and fabricated at 25 GHz and can steer the radiation beam in both E (90o) and H (60o) planes without any active phase shifter. The beamforming performance is preserved in a wideband frequency range from 20 to 30 GH and the undesired signals out of this band are attenuated using the integrated band pass filters.
A Maximin Optimal Online Power Control Policy for Energy Harvesting Communications

A new online power control policy is constructed for a discrete-time energy harvesting communication system equipped with a battery of finite capacity. This policy only requires the knowledge of the (effective) mean of the energy arrival process and is optimal in the sense that it maximizes the minimum asymptotic expected average reward for i.i. d. energy arrival processes with the same (effective) mean. We show that the fixed fraction policy by Shaviv and O¨ zgu¨ r can be interpreted as a linear approximation of this new policy in the large batterycapacity limit and propose a locally fixed fraction policy as its more accurate approximation. A systematic comparison of these policies is conducted via analytical and numerical performance analysis.
Fusion Strategy of Multi-sensor Based Object Detection for Self-driving Vehicles

Lidar and optical camera are common sensors in the sensor layer of autopilot system. Lidar can use depth data to obtain accurate relative distance and contour information of obstacles, which is not easily affected by external light conditions. Optical camera can obtain rich object/environment semantic information through high-resolution image, which is relatively mature in technology. The two different sensors are highly complementary, and previous studies show that the fusion of laser point cloud and image data can greatly improve the efficiency of object detection in out door environment. In this paper, a deep convolutional neural network detection model based on Lidar and image information features layered fusion is studied. We try different fusion depth at the CNN model to seek the best solution according to the detection performance. The experimental results on the KITTI dataset show that the detection accuracy of the fusion based on YOLOv3 is 1.08% higher than original model. Another small scale experiment with our own self-driving platform on local area also show the final fusion model can achieve better detection accuracy in real road condition.
Back Analysis of Concrete Temperature Field and Simulation of Temperature Control Measures Based on Simulated Annealing Algorithm

At present, with the rapid development of society, concrete has been widely used in various construction. For example, building construction, bridge construction and so on, almost all construction can not be separated from concrete. Therefore, the use of concrete is also profoundly related to the quality and safety of the building. In the process of using concrete, the temperature field of concrete has a great influence on concrete. In the process of concrete construction, cracks are likely to appear due to excessive temperature difference between inside and outside, which has a serious impact on building quality and safety. Therefore, it is particularly important to study the temperature field of concrete and effectively control its temperature. Thermodynamic parameters and boundary conditions have the greatest influence on the accuracy of concrete temperature control experimental calculation. However, obtaining these relevant parameters by means of traditional experiments requires high experimental cost, and is often inconsistent with the actual situation of concrete. Therefore, on the basis of simulated annealing algorithm, this paper makes relevant improvements. Through implementation, it can be found that the improved simulated annealing algorithm can be applied in the back analysis of concrete temperature field, and relevant Suggestions and measures can be put forward for the temperature control of concrete.
A Norm-Regulation-Based Limit Cycle Control of Vertical Hoppers

In this paper, we present a continuous norm-regulation-based limit cycle control for vertical hoppers, inspired by the phased-locked controller for an anchored spring-mass-damper system. Our approach provides continuous real-time norm regulation during the stance phase, leading to faster convergent rate and larger disturbance rejection capability as compared to the conventional impulsive or continuous stance phase control approaches. We analytically proved that the proposed controller can asymptotically stabilize the vertical hopper to a desired limit cycle with an intermittent transition between the stance and flight phases. In addition, we also proved that the convergence of the controller can be preserved even with model parameter uncertainty. Simulations and experiments were conducted to demonstrate the effectiveness of the proposed controller.
Channel Access Mechanism for IEEE 802.11ah-Based Relay Networks

In this paper, we propose a channel access scheme for relay-based IEEE 802.11ah network and analyze the feasibility and efficiency of it for multi-hop and large-scale Internet of Things (IoT). A new Restricted Access Window (RAW) scheme is designed to consider the currently active number of stations in a group for channel access. The proposed protocol facilitates multi-hop communication efficiently by utilizing the available channels. Accordingly, the relay node can maintain the best possible frame size for multi-hop communication. Simulation and analysis results show that the proposed protocol improves throughput performance up to 30.7% and the average frame delay up to 41.6% over the traditional scheme.
A Q-learning strategy for federation of 5G services

5G networks aim to provide orchestration of services across multiple administrative domains through the concept of federation. In this paper, we are exploring the federation feature of a platform for 5G transport network of vertical services. Then we formulate the decision problem that directly impacts the revenue of 5G administrative domains, and we propose as solution a Q-learning algorithm. The simulation results show near optimum profit maximization and a well-trained Q-learning algorithm can outperform the intuitive “greedy” approach in a realistic scenario.
Co-deployment of UAV and Ground Base Station for Data Collection in Wireless Sensor Networks

In wireless sensor networks (WSNs) enabled by fixed ground infrastructure, sink nodes located at the margin of the coverage area often suffer from bad communication channels. In this paper, we use UAV-carried aerial base station (ABS) to assist ground base station (GBS) for data collection from sink nodes in WSN, and focus on the co-deployment of ABS and GBS from aspects of coverage partition and bandwidth resource allocation, aiming at minimizing the total energy consumption for uplink transmission of all the sensor nodes in WSN. In particular, we first derive the estimated energy consumption with the sink nodes’ density instead of their exact positions. Then, to tackle the non-convexity of the original problem, we decomposing it into two subproblems, which are alternately solved to iteratively improve the sum energy consumption. Numerical results show that the proposed design significantly reduces the energy consumption of all sink nodes as compared to benchmark schemes.
Limited Feedback Channel Estimation for Multi-User Massive MIMO Visible Light Communications

Building on the existing illumination infrastructure, visible light communication (VLC) utilizes light emitting diode (LED) luminaries as wireless access points. The presence of large number of ceiling lights in conference halls, classrooms and similar public places makes possible the implementation of massive multiple-input multiple-output (MIMO) VLC systems in a distributed manner. In order to obtain the full benefits of massive MIMO communications, channel state information must be available at transmitter side. Estimation of downlink channel through pilot signals, if not judiciously designed, requires huge pilot overhead and makes the implementation of massive MIMO system infeasible. In our study, we address the design of massive MIMO VLC systems with a limited number of pilot signals. We explore different pilot arrangements in spatial, frequency, and time domains where interpolation is performed to obtain global channel matrix taking advantage of the indoor environment geometry and layout of luminaries. We consider direct current biased orthogonal frequency division multiplexing as the basis of physical layer and present simulation results to demonstrate the achievable spectral efficiencies for pilot signal arrangements under consideration.
Efficient Energy Planning With Decomposition-Based Evolutionary Neural Networks

Load forecasting/prediction is a challenging task in the energy markets that require adequate attention in generating stable and reliable load demand to deal with energy management and planning strategies. Accurate load prediction is critical for electrical power systems operations, but nonlinear loads involve high volatility. Forecasting these kinds of complex load characteristics requires highly accurate forecasting devices. It is generally accomplished by constructing models on relative details, including weather and previous data on load demand. This study proposes six decomposition-based evolutionary neural networks for city-scale and building energy forecasting. These evolutionary neural networks have also been trained using historical load data, and weather records are already considered to have a significant effect on energy usage (for example, wind speed, precipitation, dry-dew point temperature, relative humidity, clouds fraction and mean sea level perception). The network structure of the model is built and based on error estimation, trend map, and appropriate method of measuring evolutionary neural networks efficiency. The model’s hidden layers and neurons network structure is selected based on recent smart models to enhance its accuracy. Several measures were used to improve performance, such as seasonal smoothing changes, coarse-graining of the humidity ratio, load-oriented day-type classification monitoring, outlier removal, and complex climate information. Results show that used models have a very high fitting accuracy and low error rate for short, medium, and long-term forecasting/planning tasks. These models have the potential to reduce overfitting issues, thereby improving load forecasting efficiency. Further, proposed models may also use for forecasting solar and wind demand. The efficiency of the model compared to the existing two models. Forecast results show superiority and efficiency in short, medium and long-term energy forecasting and offer the ability to manage unbalanced utility loads.
Socio-aware Public Safety Framework Design: A Contract Theory based Approach

Given the substantial penetration of social networks in citizens’ everyday life activities, the success of a public safety system depends on the citizens’ incentivization by the Emergency Control Center (ECC), and their effective effort contribution in the overall disaster management operation. In this paper, we introduce a formal method based on the principles of Contract Theory, to identify the optimal rewards to the citizens from the ECC’s perspective, and the optimal invested effort from the citizens’ side, referred to as contract pairs. The identification of these contract pairs (i.e., rewards and respective efforts) between the ECC and each citizen, depend on each citizen’s social and communication characteristics that are used to define their specific type and profile, while they are properly reflected in the corresponding designed utility functions to be optimized. The problem under consideration is treated for both cases of complete (ideal) and incomplete (realistic) information availability, with respect to the level of knowledge of the ECC about the exact type of each citizen. The overall framework was evaluated via modeling and simulation, in terms of its efficiency and effectiveness, by studying multiple operation approaches and scenarios.
Characterization of the high α -amylase levels in grain of the wheat cultivar BD 159

TThe advanced wheat breeding line BD 159, from Victoria, exhibited a wide variation in falling number values at trial sites in 1990 when corresponding values for standard cultivars were uniformly high. The variable and unpredictable behaviour of BD 159 appears to be typical of a number of advanced lines and parental stocks from Australian breeding programs. The grain samples of BD 159 with low falling numbers had elevated levels of a-amylase which was distributed evenly in the proximal and distal halves of the grains. This distribution pattern, which was quite distinct from the steep gradient in a-amylase activity typical of germinated grains, and the absence of any evidence of sprouting indicated that the anomalous behaviour of BD 159 is a new and different form of the late maturity a-amylase syndrome previously described in wheat varieties such as Spica and Lerma 52. The high levels of a-amylase were reproduced at Narrabri in northern New South Wales when plants were transplanted from the field and allowed to ripen in a cool temperature glasshouse. Plants which were left to ripen in the field produced grain with a very low a-amylase activity.
Adaptive Slide Window-Based Feature Cognition for Deceptive Information Identification

Numerous false information come up to be the primary threat for regular communication and cooperation, which always have similar expressions and diffusion patterns with regular information. To recognize deceptive information and alleviate their threat against business and economy, a number of research approaches for deceptive information identification have been proposed. However, these approaches suffer from their rough feature extraction processes, and thus cannot distinguish deceptive information from others. We introduce an adaptive slide-window based feature extraction, which captures semantic features and eliminates the trivial parts from them with adaptive slide windows on sentence elements, in order to facilitate accurate semantic structure representation of various texts. In addition, we propose a deep deceptive information identification model based on the proposed feature extraction scheme. Experiments on three real-world datasets demonstrate that the proposed deep deceptive information identification model can distinguish deceptive information from regular information accurately by extracting the significant features instead of trivial common expressions.
Contextual-Bandit based MIMO Relay Selection Policy with Channel Uncertainty

In this work, we exploit the potential benefits of multi-arm bandit scheme in cooperative multiple-input multiple output (MIMO) wireless networks. In particular, we consider an online-policy for amplify-and-forward MIMO relay selection (RS), where relays are provided with uncertain channel state information (CSI). We design the RS policy as a sequential experience-driven learning algorithm with a contextual bandit (CB) approach, where the algorithm learns to select an optimal relay node using the imperfect CSI provided as a context vector and the past experience of rewards procured with current policy, with the aim of maximizing the cumulative mean reward over time. Further, with extensive simulation result, we demonstrate that proposed CB based RS policy achieves superior performance gains compared to conventional Gram-Schmidt method.