Miracle-dz/UniFAR-FTUs huggingface dataset:
Terrain Orientation Theory for a Visual Approach to Landing

Terrain Orientation Theory for the visual guidance of an approach and landing is based on glide path perception dependent upon the orientation of the landing surface due to global and local features. Numerous studies have attempted to determine the primary cues used to visually guide an aircraft to landing during rich viewing conditions and during black hole or featureless terrain conditions. Research has focused on the runway and the horizon. No one model or theory however has yet to capture all aspects of the perceptual approach to landing process. Previous studies have implied the role of surface orientation and terrain but failed to manipulate conditions throughout the entire approach and landing to account for robust conclusions.
Control Allocation based Compensation for Faulty Blade Actuator of Wind Turbine

Abstract A blade and pitch system plays the key role in the control of optimal power production with a wind turbine. Any fault with a blade actuator can cause a significant error in pitch position of the corresponding blade. This problem may result in the insufficient torque so that the power reference cannot be achieved. In this paper, a control allocation technique is employed for compensating the effect of faulty blade actuator. Control allocation is one of the most commonly used fault-tolerant control techniques especially for over-actuated systems. This paper presents a control allocation based method to achieve the power reference even in the event of blade actuator fault by redistributing the lost torque due to erroneous pitch position into non-faulty blade actuators. The effectiveness of the proposed method is demonstrated through simulations with a benchmark model of the wind turbine.
High Resolution Range Profile-Jet Engine Modulation Analysis of Aircraft Models

This paper presents an analysis of high resolution range profile-jet engine modulation (HRRP-JEM) signatures of aircraft models. Two types of aircraft models are designed, and radar cross section (RCS) data is collected using a shooting and bouncing rays (SBR) technique. Then HRRP-JEM signatures are acquired using a two-stage Fourier transform-based signal process. For the radar target identification, the location information is estimated by an engine location extraction algorithm. The analysis results show that the estimation of the location information of the engine could be useful for radar target identification.
Scattered moonlight observations with X-shooter-Implications for the aerosol properties at Cerro Paranal and the ESO sky background model

Estimating the sky background is critical for ground-based astronomical research. In the optical, scattered moonlight dominates the sky background, when the moon is above the horizon. The most uncertain component of a scattered moonlight model is the aerosol scattering. The current, official sky background model for Cerro Paranal uses an extrapolated aerosol extinction curve. With a set of X-shooter sky observations, we have tested the current sky background model as well as determined the aerosol extinction from the ultra-violet (UV) to near-infrared (NIR). To our knowledge, this is the first time that a scattered moonlight model has been used for this purpose. These observations were taken of blank sky, during three different lunar phases, and at six different angular distances from the moon for each lunar phase. Overall, the current model does reproduce the observations for average conditions quite well. Using a set of sky background models with varying aerosol distributions to compare with the observations, we found the most likely aerosol extinction curves, phase functions, and volume densities for the three nights of observations and compare them with the current model. While there are some degeneracies in the aerosol scattering properties, the extinction curves tend to flatten towards redder wavelengths and are overall less steep compared to the extrapolated curve used in the current model. Also, the current model had significantly less coarse particles compared to the favored volume densities from the X-shooter data. Having more coarse particles affects the phase function by being more peaked at small angular distances. For the three nights of sky observations, the aerosol size distributions differed, most likely reflecting the changes in atmospheric conditions and aerosol content, which is expected. In short, the current sky background model is in fair agreement with the observations, and we have determined better aerosol extinction curves and phase functions for Cerro Paranal. Using nighttime sky observations of scattered moonlight and a set of sky background models is a new method to probe the aerosol content of the atmosphere.
The Effect of Strategic Innovation on Company Performance: A Case Study of the Industrial Estate of Thailand

The purpose of this study to examine the effect of strategic innovation on organization development, organizational effectiveness, and firm performance of companies in the Industrial Estate of Thailand. The sample of this study was 360 companies and data was collected by distributing questionnaires through mail and Google form. Out of the 360 questionnaires, 192 responses were received and usable. The study period was November 2020 to February 2021. Structural equation modeling (SEM) was used to test hypotheses regarding the influence of strategic innovation on organization development, organizational effectiveness, and firm performance. The results of this study show that strategic innovation has a positive direct effect on organization development, organizational effectiveness, and firm performance. Organizational development has no significant relationship with organizational effectiveness and firm performance, and organizational effectiveness has no significant relationship with firm performance. Strategic innovation has a strong direct positive effect on the company’s performance. It indicated that strategic innovation is essential for organizations to drive business growth, generate value for the company and its customers, and create a competitive advantage. This type of innovation is essential for organizations to adapt to the speed of technology change. In addition, theoretical contributions, managerial contributions, limitations, and future research recommendations were presented in this study, including conclusions were shown.
Prediction of Academic Achievement and Happiness in Middle School Students: The Role of Social-Emotional Learning Skills

This study was conducted to test whether social-emotional learning skills are a significant predictor of academic achievement and happiness in middle school students. A total of three different data collection tools were used in the study: A Personal Information Form, The Social-Emotional Learning Skills Scale, and The Adolescent Happiness Scale. The study was carried out with 337 middle school students from six different middle schools in Erzurum province. Of these students, 203 (60.2%) were female, and 134 (39.8%) were male. The ages of these students varied between 10 and 14, and the mean age was 12.07 (Sd = 0.93). Descriptive statistics, correlation analysis, and hierarchical regression analysis were employed in the analysis of the data. The findings obtained as a result of the study indicated that there were positive and significant correlations between social-emotional learning skills and academic achievement and happiness. In addition, social-emotional learning skills were a significant predictor of academic achievement and happiness in middle school students. Accordingly, social-emotional learning skills explained 18% of the total variance in academic achievement and 44% of the variance in happiness in middle school students. When all these results are evaluated together, it can be said that promoting the social-emotional learning skills of middle school students will increase academic achievement and happiness.
Algorithmic stability and generalization of an unsupervised feature selection algorithm

Feature selection, as a vital dimension reduction technique, reduces data dimension by identifying an essential subset of input features, which can facilitate interpretable insights into learning and inference processes. Algorithmic stability is a key characteristic of an algorithm regarding its sensitivity to perturbations of input samples. In this paper, we propose an innovative unsupervised feature selection algorithm attaining this stability with provable guarantees. The architecture of our algorithm consists of a feature scorer and a feature selector. The scorer trains a neural network (NN) to globally score all the features, and the selector adopts a dependent sub-NN to locally evaluate the representation abilities for selecting features. Further, we present algorithmic stability analysis and show that our algorithm has a performance guarantee via a generalization error bound. Extensive experimental results on real-world datasets demonstrate superior generalization performance of our proposed algorithm to strong baseline methods. Also, the properties revealed by our theoretical analysis and the stability of our algorithm-selected features are empirically confirmed.
Force Appropriation for Nonlinear Systems (FANS) Using Optimisation Methods

Normal mode force appropriation is a method of physically exciting and measuring the undamped natural frequencies and normal mode shapes of a structure. Traditionally used in the aerospace industry for ground vibration testing, it is capable of accurate normal mode estimates. The method attempts to determine multi-point force vectors that will induce single mode behaviour, thus allowing each mode to be viewed in isolation. However it fails to tackle changing dynamic response with forcing level of excitation in nonlinear systems. The method of Force Appropriation for Nonlinear Systems or FANS, produces a special appropriated force vector resulting in nonlinear response. The structure responds dominantly in the target linear mode shape permitting the direct nonlinear characteristics of that mode to be identified in the absence of cross coupling effects.
A Hybrid Deep Neural Network for Human Activity Recognition based on IoT Sensors

Internet of things (IOT) sensors, has received a lot of interest in recent years due to the rise of application demands in domains like ubiquitous and context-aware computing, activity surveillance, ambient assistive living and more specifically in Human activity recognition. The recent development in deep learning allows to extract high-level features automatically, and eliminates the reliance on traditional machine learning techniques, which depended heavily on hand crafted features. In this paper, we introduce a network that can identify a variety of everyday human actions that can be carried out in a smart home environment, by using raw signals generated from Internet of Thing’s motion sensors. We design our architecture basing on a combination of convolutional neural network (CNN) and Gated recurrent unit (GRU) layers. The CNN is first deployed to extract local and scale-invariance features, then the GRU layers are used to extract sequential temporal dependencies. We tested our model called (CNGRU) on three public datasets. It achieves an accuracy better or comparable to existing state of the art models. Keywords—IoT; deep learning; CNN; GRU; CNGRU; human activity recognition
Fluid mechanics of free subduction on a sphere , 1 : The axisymmetric case

To understand how spherical geometry influences the dynamics of gravity-driven subduction of oceanic lithosphere on Earth, we study a simple model of a thin and dense axisymmetric shell of thickness h and viscosity η1 sinking in a spherical body of fluid with radius R0 and a lower viscosity η0. Using scaling analysis based on thin viscous shell theory, we identify a fundamental length scale, the ‘bending length’ lb, and two key dimensionless parameters that control the dynamics: the ‘flexural stiffness’ St = (η1/η0)(h/lb) 3 and the ‘sphericity number’ Σ = (lb/R0) cot θt, where θt is the angular radius of the subduction trench. To validate the scaling analysis, we obtain a suite of instantaneous numerical solutions using a boundary-element method based on new analytical point-force Green functions that satisfy free-slip boundary conditions on the sphere’s surface. To isolate the effect of sphericity, we calculate the radial sinking speed V and the hoop stress resultant T2 at the leading end of the subducted part of the shell, both normalised by their ‘flat-Earth’ values (i.e., for Σ = 0). For reasonable terrestrial values of η1/η0 (≈ several hundred), sphericity has a modest effect on V , which is reduced by < 7% for large plates such as the Pacific plate and by up to 34% for smaller plates such as the Cocos and Philippine Sea plates. However, sphericity has a much greater effect on T2, increasing it by up to 64% for large plates and 240% for small plates. This result has important implications for the growth of longitudinal buckling instabilities in subducting spherical shells.
Proximity effect correction for reticle fabrication

Proximity effect correction is a key technology for fabricating reticles by electron beam writing systems. To write patterns of 1 Gbit or higher-capacity DRAMs, the dimensional accuracy required for the correction is better than about 10 nm. Conventional methods do not have sufficient accuracy at the position where pattern density changes sharply. We propose a new correction method with higher accuracy for various patterns and show that we can achieve corrections accurate to about 5 nm.
Automatic decision support with a new visualization concept for power systems

The requirements for operators in today's power system control centers increase due to the liberalization of electricity markets. Devices are utilized more efficiently, load flows change frequently according to a growing number of wheeling contracts, and the possibilities for control actions are reduced. Therefore the energy management system must support the control engineer with high-quality state information and proposals for effective actions in a user friendly way. The paper presents a new visualization concept which solves this task in a human focused way. The user interface is adapted to the structure of the human decision process and provides interactive access to different detail levels. Intelligent tools reduce the huge amount of single measurements to a few state indicators, which characterize the operating point and it's distance to the technical limits. Furthermore the automatic provision of control actions to the operator is described in the paper.
Meaning and Uniqueness of Ethics and Ethical Teacher Behaviors in the Teaching Profession

This research aimed to determine ethical teacher behaviors according to the opinions of prospective teachers and teachers. In this qualitatively designed study, the phenomenology design was used, and 30 participants were interviewed. Research results showed that the participants understood the responsibilities of teachers, the respect and importance of the teaching profession, effective teaching, developing healthy relationships with school members, and benefiting students and society from teacher's professional ethics. Almost all of the participants stated that the teaching profession should have unique ethical codes for different reasons. Ethical teacher behavior has emerged within the scope of rights and justice, interest and importance to people and the profession, not to harm or benefit, and the boundary of public and private space. Finally, suggestions for improving professional teaching ethics, which can be evaluated in professional, individual, social, organizational, and political contexts, were put forward in the research. It is believed that the research will make a significant contribution to the literature, as it addresses the issue of ethics by taking the views of teachers both preparing for the profession and within the profession, reveals the specific context in Turkey, and reflects descriptions of real-life experiences.
AGAINST MARINE RISK: MARGINS DETERMINATION OF OCEAN MARINE INSURANCE

As the origin of modern insurance, ocean marine insurance exhibits numerous distinctive characteristics compared with the rest of the property/casualty insurances. This paper originally investigates the determination of the underwriting margin of ocean marine insurance, which is the crucial component of the ocean marine insurance price. Focused on industry-wide level, earlier studies ignored the time series characteristics of underwriting margin to utilize conventional regression, or employed time series methods but were limited with the stationary property of variables. Consistent with prior empirical studies despite of whether considerable variables have unit roots, an ARDL approach for underwriting margins in U.S. ocean marine insurance market during the sample period: 1951-2001 demonstrates that the financial pricing hypothesis [3] is the most suitable model. Such results confirm several unique features of ocean marine insurance market and give reasonable explanation of underwriting cycle, which is a familiar fluctuations progress in marine insurance
Estimation of heterogeneous instantaneous reproduction numbers with application to characterize SARS-CoV-2 transmission in Massachusetts counties

The reproductive number is an important metric that has been widely used to quantify the infectiousness of communicable diseases. The time-varying instantaneous reproductive number is useful for monitoring the real time dynamics of a disease to inform policy making for disease control. Local estimation of this metric, for instance at a county or city level, allows for more targeted interventions to curb transmission. However, simultaneous estimation of local reproductive numbers must account for potential sources of heterogeneity in these time-varying quantities -- a key element of which is human mobility. We develop a statistical method that incorporates human mobility between multiple regions for estimating region-specific instantaneous reproductive numbers. The model also can account for exogenous cases imported from outside of the regions of interest. We propose two approaches to estimate the reproductive numbers, with mobility data used to adjust incidence in the first approach and to inform a formal priori distribution in the second (Bayesian) approach. Through a simulation study, we show that region-specific reproductive numbers can be well estimated if human mobility is reasonably well approximated by available data. We use this approach to estimate the instantaneous reproductive numbers of COVID-19 for 14 counties in Massachusetts using CDC case report data and the human mobility data collected by SafeGraph. We found that, accounting for mobility, our method produces estimates of reproductive numbers that are distinct across counties. In contrast, independent estimation of county-level reproductive numbers tends to produce similar values, as trends in county case-counts for the state are fairly concordant. These approaches can also be used to estimate any heterogeneity in transmission, for instance, age-dependent instantaneous reproductive number estimates. As people are more mobile and interact frequently in ways that permit transmission, it is important to account for this in the estimation of the reproductive number.
Impact of individual-level characteristics and transmission mitigation behaviors on SARS-CoV-2 infection and seroprevalence in a large Northern California Bay Area cohort

Comprehensive data on transmission mitigation behaviors and SARS-CoV-2 infection and serostatus are needed from large, community-based cohorts to identify SARS-CoV-2 risk factors and impact of public health measures. From July 2020 to March 2021, {approx}5,500 adults from the East Bay Area, California were followed over three data collection rounds. We estimated the prevalence of antibodies from SARS-CoV-2 infection and COVID-19 vaccination, and self-reported COVID-19 test positivity. Population-adjusted SARS-CoV-2 seroprevalence was low, increasing from 1.03% (95% CI: 0.50-1.96) in Round 1 (July-September 2020), to 1.37% (95% CI: 0.75-2.39) in Round 2 (October-December 2020), to 2.18% (95% CI: 1.48-3.17) in Round 3 (February-March 2021). Population-adjusted seroprevalence of COVID-19 vaccination was 21.64% (95% CI: 19.20-24.34) in Round 3. Despite >99% of participants reporting wearing masks, non-Whites, lower-income, and lower-educated individuals had the highest SARS-CoV-2 seroprevalence and lowest vaccination seroprevalence. Our results demonstrate that more effective policies are needed to address these disparities and inequities.
The Perspectives of Amasya University Students about Distance Education Application During the Coronavirus (Covid-19) Process

The Covid-19 virus, which affects the whole world, has affected human life in many areas, firstly including health and economy. Concordantly, first education was suspended for three weeks; then, compulsory distance education started in Turkey. With this qualitative research, it is aimed to deeply examine the thoughts of the students from Amasya University about the distance education. The study group of this current research consists of 18 students studying at Amasya University in the 2020-2021 academic year. Data has been collected through a semi-structured interview form. The analysis of the data was done by descriptive analysis method. While some of the participants think that distance education is inefficient and makes the student passive, some think that distance education is advantageous. Thanks to distance education, it is easy to reach and monitor the lessons. The most common problems of students can be listed as problems such as fear of academic failure and anxiety for the future, connection issues, lack of motivation, lack of documentation and material, and communication problems. In order to overcome these determined problems, the participant students have some recommendations such as students should be active, students should be motivated, the sound and camera in the system should be improved, internet packages should be provided to the students, and infrastructure should be improved. Based on the findings of this research, the researchers have concluded that the distance education process should be improved over time. When development is thought of as a process, today’s shortcomings will shed light on the ways of tomorrow.