Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Reinforcement Learning-Based UAV Data Collection and Offloading in NOMA-Enabled Marine IoT Systems

The rapid growth of maritime wireless communication demand and the complex offshore wireless communication environment have brought challenges to ensure the real-time and reliability of data transmission in the marine Internet of Things (MIoT). Unmanned aerial vehicles (UAVs) have great advantages in enhancing coverage and channel quality. Hence, we investigate a UAV-assisted data collection and data offloading system based on nonorthogonal multiple access (NOMA) technology in this paper. We jointly optimize the buoy-UAV association relationship, transmit powers, and the UAV trajectory to minimize the total mission completion time while ensuring data transmission requirements. We first propose a UAV trajectory optimization algorithm based on deep reinforcement learning (DRL). Then, we design a heuristic algorithm to effectively solve the subproblem of power control and the association relationship. Finally, we propose a joint optimization scheme to solve the minimization problem. Simulation results show the effectiveness of the proposed scheme.
The Effect of Task Performance and Partnership on Interpersonal Brain Synchrony during Cooperation

Interpersonal brain synchrony (IBS) during cooperation has not been systematically investigated. To address this research gap, this study assessed neural synchrony during a cooperative jigsaw puzzle solving task using functional near-infrared spectroscopy (fNIRS)-based hyperscanning. IBS was measured for successful and failed tasks in 31 dyads in which the partners were familiar or unknown to each other. No significant difference in IBS was observed between the different types of cooperative partnership; however, stronger IBS within regions of the pars triangularis Broca’s area, right frontopolar cortex, and right temporoparietal junction was observed during task success. These results highlight the effect of better task performance on cooperative IBS for the first time and further extend understanding of the neural basis of cooperation.
Hyperreactivity to uncertainty is a 1 key feature of subjective cognitive 2 impairment 3

10 With an increasingly ageing global population, more people are presenting with con- 11 cerns about their cognitive function, but not all have an underlying neurodegenerative 12 diagnosis. Subjective cognitive impairment (SCI) is a common condition describing self- 13 reported deficits in cognition without objective evidence of cognitive impairment. Many 14 individuals with SCI suffer from depression and anxiety, which have been hypothesised 15 to account for their cognitive complaints. Despite this association between SCI and affec- 16 tive features, the cognitive and brain mechanisms underlying SCI are poorly understood. 17 Here, we show that people with SCI are hyperreactive to uncertainty and that this might 18 be a key mechanism accounting for their affective burden. Twenty-seven individuals with 19 SCI performed an information sampling task, where they could actively gather informa- 20 tion prior to decisions. Across different conditions, SCI participants sampled faster and 21 obtained more information than matched controls to resolve uncertainty. Remarkably, 22 despite their ‘urgent’ sampling behaviour, SCI participants were able to maintain their effi- 23 ciency. Hyperreactivity to uncertainty indexed by this sampling behaviour correlated with 24 the severity of affective burden including depression and anxiety. Analysis of MRI resting 25 functional connectivity revealed that SCI participants had stronger insular-hippocampal 26 connectivity compared to controls, which also correlated with faster sampling. These 27 results suggest that altered uncertainty processing is a key mechanism underlying the 28 psycho-cognitive manifestations in SCI and implicate a specific brain network target for 29 future treatment.
Interaction between Actors in China’s Energy Diplomacy in Indonesia Period 2002-2012

: This study aims to analyze the interaction between actors in China’s energy diplomacy in Indonesia by using state actor and non-state actors based on their national energy policy. In this paper, the author analyzes the involvement of the state actors and non-state actors, their role, as well as the interactions that are formed in the diplomatic activities, especially Chinese energy diplomacy in Indonesia in an effort to obtain energy resources. State actors are represented by government officials, while non-state actors are represented by Chinese and Indonesian state-owned energy companies. The author describes and explains the interactions of actors based on three forms of interaction in economic diplomacy by Susan Strange’s theory of diplomacy. First is diplomacy between countries or state-state diplomacy, second is diplomacy between the firm and state or state-firm diplomacy, and third is diplomacy between companies or firm-firm diplomacy. Qualitative methods are applied in this research, by taking data through literature studies. The study found that the three forms of interaction in diplomacy between state actors and non-state actors are related to each other. The connection of interactions in each diplomacy shows the political and economic relations that affect the effectiveness of energy’s agreements between countries.
Microsoft Word-40326921-file00

CYP2G2 , pseudogenized in human, is expressed in nasal mucosa of cynomolgus monkey and encodes a functional drug-metabolizing enzyme Abstract Cytochrome P450 ( P450 or CYP ) 2G2P is pseudogenized in human due to two nonsense mutations (c.76C>T in exon 1 and c.382C>T in exon 3) in the putative coding region of the gene sequence, whereas mouse, rat, and rabbit CYP2Gs are expressed and functional in nasal mucosa. In this study, we assessed the intactness of CYP2G in cynomolgus monkey, a macaque species important for drug metabolism studies due to its evolutionary closeness to human. Based on a gene sequence (highly identical to human CYP2G2P ) found in the macaque genome, CYP2G2 cDNA was successfully isolated from cynomolgus monkey nasal mucosa. an open reading frame of 494 amino acids, was shown to share high sequence identity (nearly 95%) with the putative coding region of human CYP2G2P sequence P450s.
Interference cancellation for higher harmonics of supply-modulated efficient RF power amplifier systems

In building highly efficient transmitters of today, one is forced to sacrifice linearity for efficiency. Some of the highest power amplifier efficiency figures are reported by envelope tracking (ET) amplifiers. These amplifiers can generate strong higher-order harmonics, which can lead to interference with receivers operating at the harmonic frequencies. Using non-linear interference cancellation, we can help to remove the interference being caused in those receivers. This paper looks at the problem of modeling the third and second-order harmonic emission from an ET amplifier. It derives the non-linear kernel for estimating such interference. This kernel has been rigorously expanded to show its correlation with the various harmonics and its effectiveness in predicting the harmonic content. We then set up an envelope amplifier test-bench to capture harmonic signal content and cancel it using the derived kernel model. The experiment yields excellent agreement with theory and provides a validation of the system and concept.
Tunable Two-Color Ultrafast Yb:Fiber Chirped Pulse Amplifier: Modeling, Experiment, and Application in Tunable Short-Pulse Mid-Infrared Generation

In this thesis, I have developed a tunable two-color two-stage ultrafast Yb:fiber chirped pulse amplifier for the generation of short-pulse mid-infrared (MIR) radiation in the longwavelength side of the “molecular fingerprint” (2.5-25 μm) using difference frequency generation (DFG) technique. The two colors called blue and red are in the wavelengths 1.03-1.11 μm and are amplified simultaneously in the same Yb-doped fiber amplifier (YDFA) stages in order to reduce the induced environmental noise on the phase difference of the pulses and to minimize the complexity and system cost. I will present numerical simulations on the two-stage YDFA system to amplify a twocolor spectrum in the wavelengths 1.03-1.11 μm. The first and second YDFA called preamplifier and main amplifier are single-clad, single-mode and double-clad, single-mode YDFA respectively. From numerical simulations, the optimal length of the preamplifier to have equal power at two colors centered at 1043 nm and 1105 nm are in agreement with experimental results. It is well known that the power of MIR radiation generated by difference frequency mixing of two wavelengths scales up with the product of mixing powers in a fixed-field approximation. Furthermore, for the gain narrowing effect on the short-wavelength side of the YDFA gain profile, the spectral bandwidth of the blue color decreases resulting in pulse broadening. In addition, for the two colors separated largely, the amplified spontaneous emission is intensified. Considering the cited factors, I will present the modeling results on the two-color, two-stage YDFA system that the product of the power of the two colors is maximized for a maximized wavelength separation between the two mixing colors and a minimized gain narrowing on the blue color in order to build an as broadly tunable and powerful as possible ultrafast mid-infrared source by difference frequency mixing of the two colors. In this research, I achieved a wavelength separation as broad as 71 nm between pulses centered at 1038 nm and 1109 nm from the two-color ultrafast YDFA system. I achieved combined average powers of 2.7 W just after the main amplifier and 1.5 W after compressing the two-color pulses centered at 1041 nm and 1103 nm to nearly Fourier transform limited pulses. From autocorrelation measurements, the full width at half maximum (FWHM) of the compressed two-color pulses with the peak wavelengths of 1041 nm and 1103 nm was ∼500 fs. By mixing the tunable two-color pulses in a 1-mm-thick GaSe crystal using DFG technique, I achieved tunable short-pulse MIR radiation. In this research, I achieved short-pulse MIR radiation tunable in the wavelengths 16-20 μm. The MIR tuning range from the lower side was limited to the 16 μm because of the
Slip-based terrain estimation with a skid-steer vehicle

ABSTRACT In this paper, a novel approach for online terrain characterisation is presented using a skid-steer vehicle. In the context of this research, terrain characterisation refers to the estimation of physical parameters that affects the terrain ability to support vehicular motion. These parameters are inferred from the modelling of the kinematic and dynamic behaviour of a skid-steer vehicle that reveals the underlying relationships governing the vehicle-terrain interaction. The concept of slip track is introduced as a measure of the slippage experienced by the vehicle during turning motion. The proposed terrain estimation system includes common onboard sensors, that is, wheel encoders, electrical current sensors and yaw rate gyroscope. Using these components, the system can characterise terrain online during normal vehicle operations. Experimental results obtained from different surfaces are presented to validate the system in the field showing its effectiveness and potential benefits to implement adaptive driving assistance systems or to automatically update the parameters of onboard control and planning algorithms.
A Novel Closed-Loop Clustering Method for Hierarchical Load Forecasting

— Hierarchical load forecasting (HLF) is an approach to generate forecasts for hierarchical load time series. The performance of HLF can be improved by optimizing the forecasting model and the hierarchical structure . Previous studies mostly focus on the forecasting model while the hierarchical structure is usually formed by clustering of natural attributes like geography, customer type, or the similarities between load profiles. A major limitation of these natural hierarchical structures is the mismatched objectives between clustering and forecasting. Clustering aims to minimize the dissimilarity among customers of a group while forecasting aims to minimize their forecasting errors. The two independent optimizations could limit the overall forecasting performance. Hence, this paper attempts to integrate the hierarchical structure and the forecasting model by a novel closed-loop clustering (CLC) method. It links the objectives of forecasting and clustering by a feedback mechanism to return the goodness-of-fit as the criterion for the clustering. In this way, the hierarchical structure is enhanced by re-assigning the cluster membership and the parameters of the forecasting models are updated iteratively. The method is comparatively assessed with existing HLF methods. Using the same forecasting model, the proposed hierarchical structure outperforms the bottom-up structure by 52.20%, ensemble-based structure by 26.89%, load-profile structures by 19.90%, respectively.
Robust stabilization technique for a quadrotor slung-load system using sliding mode control

This paper addresses the problem of the robust stabilization control for a quadrotor slung-load system using sliding mode control. The hybrid model of the slung-load system is presented as two separate systems, one fully actuated, and another under actuated. To achieve the robust stabilization control with the unexpected external disturbance, a sliding mode controller is proposed to enforce the slung-load system tracking of the desired trajectory with a slight swing of the suspended load. The Lyapunov function is defined as the system energy function that is always dissipated over time until nullity, when the system reaches the point of equilibrium. The stability of the controller is analyzed by the Lyapunov function and the stability criteria are given. Finally, the simulations are performed to evaluate the effectiveness of the proposed control scheme.
A multi-level representation learning method for the classification with emerging new classes on power event monitoring data

For the classification problem of power event monitoring data, manual rules are mainly used to recognize the known classes of events, and new classes of events cannot be extracted by using existing manual rules. But new classes may emerge with the upgrading of power equipment and traditional representation of this time series data is not proper for the task. To solve this issue, we introduce an effective approach that includes three parts: i) embed the raw object of power event in semantic space ii) learn the representation of power event with multi-level. iii) detect new classes, classify old classes, and update models to classify both new classes and old classes. Experiments on real power event monitoring dataset demonstrate that the proposed method outperforms the state-of-the-art methods.
Modeling and Stability Analysis of Interconnected System in Weak Grid

Compared with the conventional grid, the interconnection system has the advantages of high power supply flexibility, high equipment utilization, small installed capacity, and more economical operation. However, unlike in the analysis of stability, the dynamic response of the converter of the interconnected system is faster, and to model its internal structure and control, a new model applicable to it is needed. The article firstly models three different control types of inverters, then starts from the IEEE 9-bus interconnected system composed of multiple types of inverters, models the system and analyzes its stability by root trajectory calculation, and obtains the influence of each parameter change on the stability of the system, based on this and optimizes the sensitive parameters of the system by sensitivity analysis, so as to enhance the stability of the system. Finally, the theory is verified by time domain simulation, and the correctness of the model is verified.
Diagnosis Technology of Substation Abnormal State Based on Cloud Edge Collaboration

The substation monitoring system based on cloud edge collaborative computing is an important application field of power Internet of things (IoT) technology, which can effectively improve the data processing capacity of the system. First of all, the problems of computing range, computing power, delay performance, task priority, information transmission and cross are analysed when cloud and edge computing are applied in the abnormal state monitoring of substation. The application functions of the two computing modes in the monitoring system are studied. Secondly, the system of abnormal state detection and the diagnosis process of substation based on cloud edge collaborative computing are designed. Then, the design of abnormal state diagnosis system for a substation in Laver Park of Lianyungang is proposed. Finally, it is verified that the design has the ability to reduce the data transmission and raise the level of fault processing through the abnormal state diagnosis of transformer oil chromatogram in substation.
A Universal Power-law Prescription for Variability from Synthetic Images of Black Hole Accretion Flows

We present a framework for characterizing the spatiotemporal power spectrum of the variability expected from the horizon-scale emission structure around supermassive black holes, and we apply this framework to a library of general relativistic magnetohydrodynamic (GRMHD) simulations and associated general relativistic ray-traced images relevant for Event Horizon Telescope (EHT) observations of Sgr A*. We find that the variability power spectrum is generically a red-noise process in both the temporal and spatial dimensions, with the peak in power occurring on the longest timescales and largest spatial scales. When both the time-averaged source structure and the spatially integrated light-curve variability are removed, the residual power spectrum exhibits a universal broken power-law behavior. On small spatial frequencies, the residual power spectrum rises as the square of the spatial frequency and is proportional to the variance in the centroid of emission. Beyond some peak in variability power, the residual power spectrum falls as that of the time-averaged source structure, which is similar across simulations; this behavior can be naturally explained if the variability arises from a multiplicative random field that has a steeper high-frequency power-law index than that of the time-averaged source structure. We briefly explore the ability of power spectral variability studies to constrain physical parameters relevant for the GRMHD simulations, which can be scaled to provide predictions for black holes in a range of systems in the optically thin regime. We present specific expectations for the behavior of the M87* and Sgr A* accretion flows as observed by the EHT.
Quick counting of probability distributions for the sum of multi-sided dice: a physical and didactic approach

There are many physical systems (with randomness) where the performance of the probability distribution is of great importance [for example the so-called radioactive dice (Murray and Hart 2012 Phys. Educ. 47 197–201)]. A relevant situation is when the value (positive integers) of the upper faces is added when rolling fair dice (unbiased), that is, balanced, regular or unshaven. It has been widely studied in the basic literature for one, two and a maximum of four dice. However, a generalization using these methods for many dice is too cumbersome to carry out. In this paper we explore a method that uses the moment generating function, already studied in Singh et al (2011 UNLV Gaming Res. Rev. J.), for five six-sided fair dice with positive integer values. We carry out the generalization of this method to study the performance of the probability distribution and make a quick counting without complications for the sum of n fair dice with s sides (s ⩾ 6), and real values. We also study the invariance of the distribution when we set the number of sides, that is, for s-fix. Therefore, we study the distributions for the outgoing total sum (random variable) of roll generalized dice, for combinations of n multi-sided dice.
Kano-FBS model: a data-driven innovative design approach for smart product-service system development

Smart product-service system (Smart PSS), as an emerging digital paradigm, should meet user expectations and optimize their experience with a higher degree of sustainability. As a complex solution bundle with digital technology, its innovative design method differs from the existing method for traditional products. For a reason that massive user-generated data can be collected in the usage stage, which can be utilized to obtain new user requirements and calculate the user satisfaction degree. According to the real-time user preference information, some modules can be upgraded or adjusted in order to extend the lifecycle of the sustainable Smart PSS. Nevertheless, few studies focus on conducting innovative design by concerning massive information collected in the usage stage in a sustainable Smart PSS environment, let alone a novel approach to support the information re-use in product development of other fields. Aiming to fill this gap, a new innovative design methodology is proposed by combining Function-Behavior-Structure (FBS) and Kano model to guide Smart PSS development. User requirements and behaviors are predicted and analysed by forecasting and collecting information from the perspective of information. As an illustrative case study, a self-service medicine vending system is described to explain the proposed approach. This research provides guidance for Smart PSS in the medical field.
Induction Motor Fault Diagnosis Based on Deep Neural Network of Sparse Auto-encoder

： To overcome the drawback of using supervised learning to extract fault features for classification in most of current induction motor fault diagnosis approaches, a deep neural network algorithm is presented, which is realized by the sparse auto-encoder combined with the denoising auto-encoder, to achieve unsupervised feature learning for fault diagnosis of induction motors. Sparse auto-encoder can learn the inherent features and extract the succinct expressions from complex data automatically. In addition, the method of denoising auto-encoder can increase the robustness of feature expression, thus improving the performance of the sparse auto-encoder. The extracted features can then be used to train a neural network classifier and complete the deep neural network construction. The back-propagation algorithm is used for fine-tuning the deep neural network with the purpose of improving the accuracy of fault classification. The “dropout” technique is also introduced into to the entire training process to reduce the prediction error caused by “overfitting”. Experimental results have shown that, compared with the traditional back propagation (BP) neural network, the presented deep neural network can realize induction motor fault diagnosis more effectively.
Synthesis of plans or policies for controlling dynamic systems

To be properly controlled, dynamic systems need plans or policies. Plans are sequences of actions to be performed, whereas policies associate an action to be performed with each possible system state. The model-based synthesis of plans or policies consists in producing them automatically starting from a model of the physical system to be controlled and from user requirements on the controlled system. This article is a survey of what exists and what has been done at ONERA for the automatic synthesis of plans or policies for the high-level control of dynamic systems.