Miracle-dz/UniFAR-FTUs huggingface dataset:
Trajectory tracking of a Quadcopter using Supertwisting Sliding Mode Control

The main aim of this paper is to present a robust trajectory tracking of a quadcopter. A quadcopter is a complex, highly non-linear and under-actuated system. Hence, a non-linear control strategy which expands the operating region and improves the stability is best suited. For this purpose a super-twisting sliding mode control (STSMC) strategy is applied which is robust against modelling uncertainties and matched external disturbances. The effectiveness of the presented approach is verified through numerical simulations. A comparative analysis of the applied STSMC technique with a robust backstepping procedure based SMC is done. Results indicate that the applied control algorithm, guarantees enhanced performance in terms of improved trajectory tracking errors along with chattering attenuation.
Dual Mode Host-Based and Cloud-Based Smartphone Intrusion Detection System

Smartphones are one of the most important devices in our daily life. It produces transfers and stores a significant amount of data about individuals such as banking transactions, authentication credentials, location details, call logs, etc. If the security of smartphones is compromised, it can severely affect the individual’s life. As a result, the security system must be both efficient and dependable. We present a novel dual-mode Intrusion Detection System (IDS) that can operate both offline (without internet) and online (cloud mode). It collects application run-time data and analyses it locally on a smartphone or in the cloud, based on the user’s configuration, to predict the app’s behaviors and detect any intrusion.
Feature Selection of EEG Signals using Cat Coyote optimization algorithm (CCOA) with Classification

An EEG signal-based communication system that uses brain activity to command computers or other external devices. The purpose of BCI research is to enable communication for persons with neurological neuromuscular conditions such amyotrophic lateral sclerosis, brain stem stroke, or spinal cord damage or who are completely paralyzed. A BCI system captures brain signals, classifies them using machine learning methods, and then executes a computer-controlled action. The impacts of feature selection for Electroencephalograph (EEG) signals are examined in this study. Additionally researched are feature extractions using the Walsh Hadamard Transform (WHT) and feature selection using the Principal Component Analysis (PCA). The Cat Coyote optimization algorithm (CCOA) is suggested for feature selection. Bagging and decision tree classifiers are used to categorize the features.
Dynamics based Observer for Differential-Drive Vehicles

Localization, i.e., pose estimation of a ground vehicle with respect to some inertial frame, is among key issues in mobile robotics. Majority of approaches employed to solve this problem depend primarily on fusion of information from multiple sensors. Frequently, these approaches rely on kinematics model of vehicle for predicting forward in time during fusion. The kinematic models are simple and fast, but do not account for dynamics. The use of dynamics based fusion framework can lead to relatively accurate prediction by exploiting information from the cause of the motion, which kinematic model based frameworks fail to do. Additionally, when depending mainly on exteroceptive sensors such as GPS, Vision, Lidar, etc, for localizing, there may be cases where the sensors may fail temporarily due to changes in environment which may cause localization accuracy to suffer degradation or even loss of localization. The information from dynamics can come in handy for localizing in such scenarios. In this work, we derive a continuous-time dynamic model, which also takes friction into account. Additionally, we propose a Continuous-Discrete Extended Kalman Filter(CD-EKF) based observer, which uses the derived dynamic model as process model for prediction, and measurements from position sensor as measurement updates. Moreover, we perform experiments to show that proposed observer can auto-calibrate the dynamic model in real time, which is more versatile than methods which calibrate dynamic models beforehand, as it enables adaptation to changing conditions like a terrain transition, payload or tyre air-pressure etc.
DGER: Delta Gamma Energy Ratio as a Biomarker for the Identification of Schizophrenia

Schizophrenia (SCZ) characterized as chronic psychotic mental disorder which severely affects the social life of an individuals. The present study analysed the delta and gamma band using resting EEG signals in SCZ and healthy controls (HC). Total of 10 subjects were selected for each group and 19 channel rest EEG data was recorded. The relative band energy for delta and gamma band was extracted and the ratio of delta and gamma energy (DGER) was calculated as a main feature. The results showed significant difference in the DGER value at frontal and prefrontal electrode locations. The DGER value observed to be significantly low in SCZ group compared to HC group. The classification output showed maximum classification accuracy of 89% using linear discriminant classifier when classifying the SCZ and HC group using DGER. In conclusion, the present study suggests the use of DGER as useful neurophysiological marker for identification of SCZ.
Self-Feedback-Based Resilient Consensus Network

There has been a wide interest in understanding the vulnerabilities of consensus network that allows an attacker to cause harm. At the same time, it can be utilized to enhance resilience against such attackers. In this paper, we investigate vulnerabilities of a single integrator consensus network that can be exploited to launch an attack. We show that root nodes are more vulnerable as compared to non-root nodes. If an attack is injected on root nodes the network dynamics are destabilized, while, an attack on non-root nodes prevents agents from reaching consensus. Resilience against such attackers can be improved by adding self-feedback at each node of the network. We show that a self-feedback-based consensus network remains stable in the presence of a destabilizing attack. Moreover, we investigate the use of self-feedback at the root nodes and at the non-root nodes as well. We found that the placement of self-feedback only at root nodes is sufficient to ensure resilience against attack. Simulation examples are provided to validate the results developed in the paper.
Training a Reinforcement Learning Agent with AutoRL for Traffic Signal Control

Traffic signal control system affects the efficiency of transportation. Adaptive traffic signal control has attracted attention for automatically adjusting the phase according to different traffic conditions. Researchers have been trying to apply deep reinforcement learning to the design of adaptive traffic signal control. The meta-parameter like $\gamma$, the discount rate of future reward, is crucial in reinforcement learning. Researchers, especially across disciplines, need to conduct extensive experiments to find appropriate values for the meta-parameters. Automated Reinforcement Learning (AutoRL) can automate the design choices of meta-parameters instead of manual tuning. The gradient-based meta-learning algorithm belongs to AutoRL. To save time in exploring meta-parameters, we integrate the gradient-based meta-learning algorithm into DQN (GBML-DQN). We conduct experiments on the traffic simulator SUMO. Our results show that GBML-DQN promotes convergence of the Q-value function and avoids overestimation to some extent, especially in the case of inappropriate training settings, whereas DQN fails in training.
Robust Linear Controller Design for Tilt Quadrotor based on Euler Angles

In the existing literature, the designs of the robust linear controllers are done for the linearized model obtained about the quadrotor's hover flight. As a result, these controllers are not able to satisfactorily track a wide range of attitude changes. This work proposes a robust linear controller design for a tilt quadrotor that guarantees to track a wide range of attitude changes. This design is based on traditional two-loop control schemes where the outer loop controls the vehicle's attitude (orientation) based on Euler angles while the inner loop controls the angular velocity. The robust stability analysis of the proposed controllers has been examined against the model and actuator parameter uncertainties. Hardware results are presented to validate the proposed controller. Video of the experimental results can be found at https://youtube/fA7IcpnkfRk
Nanoscale Two Channel Plasmonic Demultiplexer

The current research represents a nanoscale and compact two channel plasmonic demultiplexer include cavity resonators considered as a candidate to be employed in optical telecommunication systems. The results have been achieved using simulation based on the well known finite difference time domain (FDTD) method. Keywords-nanoscale; plasmonic; demultiplexer; resonator; FDTD.
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8 This study investigated the effect of processed forms (defatted or hydrolyzed) of black soldier fly 9 larvae ( Hermetia illucens L., BSFL) as a protein substitute on broilers. Experiment 1 was a feeding 10 experiment, and Experiment 2 was a metabolism experiment. In Experiment 1, a total of 120 day-old 11 Arbor Acres broilers (initial body weight 39.52 ± 0.24 g) were used for 28 days. There were 8 replicate 12 pens, and 5 broilers were assigned to each pen. In Experiment 2, a total of 36 day-old broilers (initial 13 body weight 39.49 ± 0.21 g) were used for the metabolism trial. There were 2 broilers in a metabolism 14 cage and six replicate cages per treatment. The dietary treatments were as follows: a basal diet (CON), a 15 basal diet without fishmeal and substitute with defatted BSFL (T1), a basal diet without fishmeal and a 16 substitute with hydrolyzed BSFL (T2). In Experiment 1, during the entire experimental period, the T2 17 group significantly increased ( p < 0.05) body weight gain and feed intake compared to the CON and T1 18 groups. The feed conversion ratio showed a lower tendency ( p = 0.057) in the T2 group than in the CON 19 and T1 groups. At week 2, the CON and T2 groups were significantly higher ( p < 0.05) crude protein 20 (CP) digestibility than the T1 group. At week 4, the total protein level significantly increased ( p < 0.05) in 21 the CON and T2 groups compared to the T1 group. In Experiment 2, the CP digestibility significantly 22 increased ( p < 0.05) in the T2 group compared to the CON and T1 group at weeks 2 and 4. At week 4 23 amino acid digestibility, the T2 group significantly increased ( p < 0.05) lysine, methionine, tryptophan, 24 and glycine digestibility compared to the T1 group. There was no difference in fecal microbiota among 25 the treatment groups. In conclusion, feeding hydrolyzed BSFL as a fishmeal substitute in broiler diets 26 improved growth performance, CP digestibility, and specific amino acid digestibility. Therefore, it is
Extension of quasi-H ¨ o lder embeddings between unit spheres of p -normed spaces

In this paper, we introduce and study the quasi-H ¨ older mappings between unit spheres of p -normed spaces. The quasi-H ¨ older map is a natural generalization of the H ¨ older map, Lipschitz map, quasi-isometry and ε -isometry, etc. Concretely, we study the extension of a quasi-H ¨ older embedding between unit spheres of p -normed spaces (0 < p ≤ 1) under the quasi-H ¨ older or anti-H ¨ older type assumption. We generalize the main results in Xiao and Lu (Ann Funct Anal 13(2):Paper No. 25, 2022) from the r -isometric case to the wider case of quasi-H ¨ older mappings. Moreover, the method we adopted here is different; and the Hahn–Banach property, required as a condition in the main theorems of [30] has been removed in our results.
High BRCA1 expression is an independent prognostic biomarker in LUAD and correlates with immune infiltration

Lung adenocarcinoma (LUAD) patients with elevated breast cancer susceptibility gene 1 (BRCA1) expression had markedly worse overall survival and progression‐free survival compared to those with reduced BRCA1 levels. In contrast, BRCA1 expression did not correlate with survival outcomes in squamous cell carcinoma patients. The overexpression of BRCA1 was an independent risk factor for LUAD and was indicative of an immune‐suppressive tumor microenvironment.
Two-stage communication-efficient distributed sparse M-estimation with missing data

Distributed estimation based on different sources of observations has drawn attention in the modern statistical learning. When the distributed data are missing at random, we propose a two-stage -penalized communication-efficient surrogate likelihood (CSL) algorithm based on inverse probability weighting to eliminate the estimation bias caused by the missing data and construct sparse distributed M-estimator simultaneously. In the first stage, we consider a parametric propensity model and directly apply the -penalized CSL method to obtain an efficient and sparse distributed estimator of the propensity parameter. In the second stage, we construct an IPW-based -penalized CSL loss function to eliminate the bias and obtain the sparse M-estimation. The finite-sample performance of the estimators is studied through simulation, and an application to house sale prices data set is also presented.
Application of nature inspired optimization algorithms in bioimpedance spectroscopy: simulation and experiment

Accurate extraction of Cole parameters for applications in bioimpedance spectroscopy (BIS) is challenging. Precise estimation of Cole parameters from measured bioimpedance data is crucial, since the physiological state of any biological tissue or body is described in terms of Cole parameters. To extract Cole parameters from measured bioimpedance data, the conventional gradient-based non-linear least square (NLS) optimization algorithm is found to be significantly inaccurate. In this work, we have presented a robust methodology to establish an accurate process to estimate Cole parameters and relaxation time from measured BIS data. Six nature inspired algorithms, along with NLS are implemented and studied. Experiments are conducted to obtain BIS data and analysis of variation (ANOVA) is performed. The Cuckoo Search (CS) algorithm achieved a better fitment result and is also able to extract the Cole parameters most accurately among all the algorithms under consideration. The ANOVA result shows that CS algorithm achieved a higher confidence rate. In addition, the CS algorithm requires less sample size compared to other algorithms for distinguishing the change in physical properties of a biological body.
Vitamin D status of healthy childbearing age women in South Asia: A pooled analysis of community-based studies

Hypovitaminosis D (Vitamin D deficiency and insufficiency) is a major public health problem globally. A high prevalence of vitamin D deficiency (VDD) and vitamin D insufficiency (VDI) has been noted in South Asian (SA) women of childbearing age despite the abundant sunshine. Therefore, this study aimed to systematically review the existing data from population-based studies in this region to summate the magnitude of the problem. Articles published and reported prevalence of VDD/VDI in eight South Asian countries during period of 2010-2020 were searched and screened from PUBMED and Google Scholar using PRISMA guidelines. The study population was healthy women of childbearing age (18-45 years) and included pregnant and non-pregnant women. Fourteen population-based studies were found from SA countries India, Sri Lanka, Pakistan, Bangladesh and Nepal. No studies were available from Afghanistan, Bhutan, and Maldives pertaining to the searched period. Participant groups were from diverse socioeconomic and cultural backgrounds. The prevalence ranged from 61.5% from Pakistan to 100% from Bangladesh. This systematic review reported a significantly higher prevalence of hypo-vitaminosis D among SA women of childbearing age. Inadequate sun exposure, lack of consumption of good vitamin D food sources due to either illiteracy or economic constrains has been regarded as potential determinants of hypovitaminosis D among participants. The findings of this study would help pertinent stake holders to prepare suitable national level programs to enhance the Vitamin D status of women of childbearing age.
Genetic Algorithms Optimized Adaptive Wireless Network Deployment

Advancements in UAVs have enabled them to act as flying access points that can be positioned to create an interconnected wireless network in complex environments. The primary aim of such networks is to provide bandwidth coverage to users on the ground in case of an emergency or natural disaster when existing network infrastructure is unavailable. However, optimal UAV placement for creating an ad hoc wireless network is an NP-hard and challenging problem because of the UAV’s communication range, unknown users’ distribution, and differing user bandwidth requirements. Many techniques have been presented in the literature for wireless mesh network deployment, but they lack either generalizability (with different users’ distributions) or real-time adaptability as per users’ requirements. This paper addresses the UAV placement and control problem, where a set of genetic-algorithm-optimized potential fields guide UAVs for creating long-lived ad hoc wireless networks that find all users in a given area of interest (AOI) and serve their bandwidth requirements. The performance of networks deployed using the proposed algorithm was compared with the current state of the art on several experimental simulation scenarios with different levels of communication among UAVs, and the results show that, on average, the proposed algorithm outperforms the state of the art by 5.62% to 121.73%.
THE CLIMATE CHANGE OF YUNNAN OVER THE LAST 50 YEARS

Using the daily temperature precipitation observation data of 124 stations in Yunnan and 37 stations over the neighboring provinces(or municipalities) during the period 1961～2008,we objectively analyzed the fine-grid temperature data taking into account the vertical temperature lapse rate.On this basis,the climate changes of Yunnan over the last 50 years were studied.The results indicated that:(1) The temperature change trend of Yunnan over the last 50 years is consistent with that for the Northern Hemispheric and China,but the warming rate is relatively slow. (2) The climate zone of Yunnan also experiences significant changes.The area of the north tropical zone and the south subtropical zone have increased and the area of the middle subtropical zone,north subtropical zone and temperate zone have decreased. (3) The amplification of winter temperature is the biggest,followed by autumn,summer and spring temperature.Although for the last 50 years,the temperatures in most regions of Yunnan show an upward trend,they display a decreasing trend in a few regions.These cooling areas mainly concentrate in low-elevation valley areas. (4) The change in the mean annual precipitation of Yunnan over the last 50 years is not significant,but the seasonal and spatial change are very substantial.The precipitation of the rainy season(May—Oct.) and flood season(Jun.—Aug.) reduces and dry-season precipitation increases.In the spatial distribution,the precipitation in the eastern(middle) part of Yunnan reduces(increases) consistently.The precipitation in the west and south part of the province alternates between increasing and decreasing.
Operational Ocean Search and Rescue Using AVHRR: Cloud Limitations

Abstract Sea surface currents can be derived from sequential Advanced Very High Resolution Radiometer (AVHRR) images. However, the operational feasibility of deriving ocean surface currents from AVHRR is severely limited by the inability of AVHRR to penetrate cloud cover. The application of AVHRR-derived currents is therefore limited to times and areas that are relatively cloud free. Three experiments were performed to evaluate the operational feasibility of AVHRR-derived sea surface currents: one offshore near Cape Race, St. John’s, Newfoundland, Canada, and two in the Delaware Coastal Current (DCC). Almost all of the Cape Race images, 60% of the 1993, and 40% of the 1994 DCC Experiment images had clouds covering the entire image. An additional 25%–30% of the images had clouds covering part of the region, further reducing the number of images suitable for velocity estimation. Possibilities for overcoming this limitation are discussed.
Heat and Moisture Budgets and Circulation Characteristics of a Frontal Squall Line

Abstract Heat and moisture budgets and mesoscale circulation features for the developing, mature, and dissipating stages of an intense frontal squall line that occurred in the central United States are investigated. The slow propagating behavior of the squall line made the dataset unique since observations covered a large fraction of the squall line life cycle. Budgets have been performed at six different times at intervals of 90 minutes using 1985 OK PRE-STORM rawinsonde data. The squall line was followed by a low-level cold front. The flow pattern normal to the squall line was generally similar to previous squall line studies except that a low-level rear inflow associated with the cold front was superimposed upon expected squall line FTR/RTF (front to rear/rear to front) flows. The midlevel RTF flow was quite weak well behind the squall line during the developing and mature stages and significantly strengthened during the dissipating stage as the stratiform region developed, suggesting that internal pro.
Moving Target Detection and Parameter Estimation Using BeiDou GEO Satellites-Based Passive Radar With Short-Time Integration

Owing to the global coverage and high visibility of global navigation satellite systems (GNSS), passive radar using GNSS satellites as transmitters of opportunity has shown great potential for moving target detection, and its possibility to detect short-range maritime targets has been verified. Nevertheless, the existing methods always require tens of seconds for echo integration, which is not ideal for the detection of targets with high maneuverability. To solve this problem, a bistatic configuration using BeiDou geostationary earth orbit (GEO) satellites as illuminators of opportunity, which is suited for short-time coherent integration of echo signals, is presented. In addition, the corresponding signal processing method is introduced. To achieve a more continuous trajectory of the target, a method used to fuse range-Doppler maps obtained individually from multiple BeiDou GEO satellites is also described. The effectiveness and functionality of our proposed algorithms are demonstrated with an experiment in which a ferry was used as a moving target and its real-time location data were adopted as a reference.
SEGUIMIENTO EN MODELOS DE REGRESIÓN LOGÍSTICA* MODEL FOLLOW-UP IN LOGISTIC REGRESSION MODELS

Most data mining projects in real life applications give as a result only static solutions which, in time, lose their inherent capacity to explain the phenomena they were originally built for. We introduce an theoretical-practical framework that allows to closely follow up logistic regression models to determine the moment when they must be updated, maintaining an strict control over their evolution, the variables in them and relevant changes that can occur in the population since they were originally designed. The statistical test presented include classical tests such as Kolmogorov-Smirnov and Chi-Squared statistic to measure changes in means of the variables present in the models, plus a novel test designed from the distribution of the models coefficients that allows to measure the moment when a population has changed more than the confidence intervals defined from the original parameters. The methodology was tested using the databases from two real world micro-entrepreneurs credit scoring projects developed between the years 2007 and 2008, with very good results.