Miracle-dz/UniFAR-FTUs huggingface dataset:
Single/Multi-Band Coupled-Multi-Line Filtering Section and Its Application to RF Diplexers, Bandpass/Bandstop Filters, and Filtering Couplers

A type of compact single/multi-band coupled-multi-line filtering section and its application to the design of RF filtering devices are presented. This one-port filtering cell is made up of <inline-formula> <tex-math notation="LaTeX">${N+1}$ </tex-math></inline-formula> intercoupled quarter-wavelength transmission-line segments for the generation of a transfer function with a total of <inline-formula> <tex-math notation="LaTeX">${N+1}$ </tex-math></inline-formula> transmission zeros (TZs) distributed at both sides of all its <inline-formula> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula> first-order passbands when arranged in the transmission mode. Design formulas for the produced poles and TZs of its corresponding normalized coupling-routing diagram and illustrative theoretical responses are provided. Furthermore, for experimental demonstration purposes, microstrip prototypes of the following RF filtering components that exploit the conceived filtering section are manufactured and measured: 1) a quasi-elliptic-type diplexer with a dual-band bandpass filter (BPF) junction; 2) two input-reflectionless triple-band BPF and bandstop filter (BSF) circuits; and 3) an out-of-phase equal-power-division bandpass filtering coupler with input-quasi-reflectionless behavior.
Three Essays on Shrinkage Estimation and Model Selection of Linear and Nonlinear Time Series Models

The primary objective in time series analysis is forecasting. Raw data often exhibits nonstationary behavior: trends, seasonal cycles, and heteroskedasticity. After data is transformed to a weakly stationary process, autoregressive moving average (ARMA) models may capture the remaining temporal dynamics to improve forecasting. Estimation of ARMA can be performed through regressing current values on previous realizations and proxy innovations. The classic paradigm fails when dynamics are nonlinear; in this case, parametric, regime-switching specifications model changes in level, ARMA dynamics, and volatility, using a finite number of latent states. If the states can be identified using past endogenous or exogenous information, a threshold autoregressive (TAR) or logistic smooth transition autoregressive (LSTAR) model may simplify complex nonlinear associations to conditional weakly stationary processes. For ARMA, TAR, and STAR, order parameters quantify the extent past information is associated with the future. Unfortunately, even if model orders are known a priori, the possibility of over-fitting can lead to sub-optimal forecasting performance. By intentionally overestimating these orders, a linear representation of the full model is exploited and Bayesian regularization can be used to achieve sparsity. Global-local shrinkage priors for AR, MA, and exogenous coefficients are adopted to pull posterior means toward 0 without over-shrinking relevant effects. This dissertation introduces, evaluates, and compares Bayesian techniques that automatically perform model selection and coefficient estimation of ARMA, TAR, and STAR models. Multiple Monte Carlo experiments illustrate the accuracy of these methods in finding the ”true” data generating process. Practical applications demonstrate their efficacy in forecasting.
Extended Dissipativity Performance of High-Speed Train Including Actuator Faults and Probabilistic Time-Delays Under Resilient Reliable Control

The problem of extended dissipativity performance of a high-speed train (HST) with probabilistic delays and actuator faults under resilient reliable control has been exploited in this article. An extended dissipativity index is selected to perform a unified control design procedure for the HST dynamics, which is more general than $(\mathbb {Q},\mathbb {S},\mathbb {R})$ -dissipativity index. Specifically, a sequence of random variables responding the Bernoulli distribution is exploited to govern the probabilistic time-delays. By utilizing the tighter integral inequality (TII) and reciprocally convex inequality (RCI) technique, some criteria are launched in terms of the linear matrix inequalities (LMIs). Finally, simulations show the effectiveness and applicability of the offered control law by inspecting the Japan Shinkansen HST with its experimental values, along with a comparison study also been exploited to showing the merits and generalization of the proposed control design technique.
PENGARUH KEPUTUSAN INVESTASI, KEPUTUSAN PENDANAAN, KEBIJAKAN DIVIDEN DAN TINGKAT INFLASI TERHADAP NILAI PERUSAHAAN

Penelitian ini bertujuan untuk mengetahu pengaruh dari keputusan investasi, keputusan pendanaan, kebijakan dividen, dan tingkat inflasi terhadap nilai perusahaan. Penelitian ini menggunakan data dari 27 perusahaan manufaktur yang terdaftar di Bursa Efek Indonesia (BEI) tahun 2007 hingga 2011. Sampel penelitian ini meliputi perusahaan manufaktur yang terdaftar di Bursa Efek Indonesia (BEI). Sampel diambil secara purposive sampling. Alat analisis yang digunakan adalah analisis regresi berganda.
Imaging propagative exciton polaritons in atomically thin 
WSe2
 waveguides

Exciton polariton (EP) is a half-light and half-matter quasiparticle promising for exploring both fundamental quantum phenomena as well as photonic applications. Van der Waals materials, such as transition-metal dichalcogenide (TMD), emerge as a new nanophotonics platform due to its support of long propagative EPs even at room temperature. However, real-space studies have been limited to bulk crystal waveguides with a thickness no less than 60 nm. Here we report the nano-optical imaging of the transverse-electric EPs in WSe 2 nanoflakes down to a few atomic layers, which can be turned on and off by tuning excitation laser polarization. Unlike previously-studied transverse-magnetic modes that exist only in bulk TMD waveguides, we found that the transverse-electric EPs could reside in ultrathin WSe 2 samples, owing to the alignment of the electric field with the in-plane dipole orientation of 2D excitons. Furthermore, we show that the EP wavelength and propagation length can be largely controlled by varying laser energy and sample thickness. These findings open opportunities to realize near-infrared polaritonic devices and circuits truly at the atomically-thin limit.
CIM: Principles of Computer-Integrated Manufacturing

Part 1 Integrated production and the CIM method: introduction computer-integrated manufacturing - staking all on technology from cottage industries to the integrated plant what is involved a continually growing market. Part 2 The technology available for a CIM approach: from isolated data-processing systems to a CIM model control of the firm - the master schedule product planning, design and manufacture production control manufacturing plant and production systems communication between applications. Part 3 Integrated production control - the backbone of CIM: on the importance of an integrated system of production control formal structure of the technical data in an industrial company MRP and MRP2 the truth about production costs order tracking as support for quality control. Part 4 "Just-In-Time" production systems: Make-to-order production reducing wastage and simplifying continuous flow manufacturing the Kanban system from the Kanban system to the synchronous system. Part 5 Structure of data and databases: CIM and the structure of management information systems databases and database management systems. Part 6 Methods for the analysis and design of an information system: importance of a method for designing an information system MERISE - the guiding principles of the model the SADT method in the functional analysis of manufacturing plant and manufacturing systems towards second generation information technology - the KADS method and artificial intelligence. Part 7 The financial return from a CIM project: are computer-aided systems worthwhile? investment decisions evaluation of investment plans costs and benefits of the MRP2 system. Appendices: CIM and standardization glossary of terms and abbreviations case study of a CIM unit (BULL) case study of just-in-time production (Peugeot).
Deep Learning-Based Short-Term Load Forecasting for Supporting Demand Response Program in Hybrid Energy System

A novel method for short-term load forecasting (STLF) is proposed in this paper. The method utilizes both long and short data sequences which are fed to a wavenet based model that employs dilated causal residual convolutional neural network (CNN) and long short-term memory (LSTM) layer respectively to hourly forecast future load demand. This model is aimed to support the demand response program in hybrid energy systems, especially systems using renewable and fossil sources. In order to prove the generality of our model, two different datasets are used which are the ENTSO-E (European Network of Transmission System Operators for Electricity) dataset and ISO-NE (Independent System Operator New England) dataset. Moreover, two different ways of model testing are conducted. The first is testing with the dataset having identical distribution with validation data, while the second is testing with data having unknown distribution. The result shows that our proposed model outperforms other deep learning-based model in terms of root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). In detail, our model achieves RMSE, MAE, and MAPE equal to 203.23, 142.23, and 2.02 for the ENTSO-E testing dataset 1 and 292.07, 196.95 and 3.1 for ENTSO-E dataset 2. Meanwhile, in the ISO-NE dataset, the RMSE, MAE, and MAPE equal to 85.12, 58.96, and 0.4 for ISO-NE testing dataset 1 and 85.31, 62.23, and 0.46 for ISO-NE dataset 2.
Fixed Point Theorems for Monotone Mappings in Ordered Banach Spaces Under Weak Topology Features

: We present several ﬁxed point theorems for monotone nonlinear operators in ordered Banach spaces. The main assumptions of our results are formulated in terms of the weak topology. As an application, we study the existence of solutions to a class of ﬁrst-order vector-valued ordinary diﬀerential equations. Our conclusions generalize many well-known results.
New insight on accretion shocks onto young stellar objects

Context. Material accreted onto classical T Tauri stars is expected to form a hot quasi-periodic plasma structure that radiates in X-rays. Simulations of this phenomenon only partly match observations. They all rely on a static model for the chromosphere and on the assumption that radiation and matter are decoupled. Aims. We explore the effects of a shock-heated chromosphere and of the coupling between radiation and hydrodynamics on the structure and dynamics of the accretion flow. Methods. We simulated accretion columns that fall onto a stellar chromosphere using the 1D ALE code AstroLabE. This code solves the hydrodynamics equations along with the first two moment equations for radiation transfer, with the help of a dedicated opacity table for the coupling between matter and radiation. We derive the total electron and ion densities from collisional-radiative model. Results. The chromospheric acoustic heating affects the duration of the cycle and the structure of the heated slab. In addition, the coupling between radiation and hydrodynamics leads to a heating of the accretion flow and of the chromosphere: the whole column is pushed up by the inflating chromosphere over several times the steady chromosphere thickness. These last two conclusions are in agreement with the computed monochromatic intensity. Acoustic heating and radiation coupling affect the amplitude and temporal variations of the net X-ray luminosity, which varies between 30 and 94% of the incoming mechanical energy flux, depending on which model is considered.
Experimental study about utilization of MWCNTs and graphene nanoplatelets water-based nanofluids in flat non-concentrating PVT systems

Although the increment the performance of PVT systems by using the nanofluids as working fluids have gained the attention of researchers during the last two decades, there is still, a lack in the literature study associated to this application. This study contributes to the investigations and researches of applying the nanofluids to increase the performance of PVT collectors. A flat non-concentrating PVT collector has been designed, constructed and outdoor tested in Karabuk University, Turkey. The considered working fluids in this study are MWCNTs (multiwall carbon nanotubes), and graphene nanoplatelets dispersed in water as a base fluid with a concentration of 0.5 wt%. The experiments were run with a volume flow rate of 0.5 L/min for the aforementioned nanofluids and distilled water (as a reference fluid). The study results have shown and revealed that the MWCNT-water nanofluid presented a better performance in terms of electrical energetic efficiency compared to graphene nanoplateletswater nanofluid and distilled water, while graphene nanoplateletswater nanofluid revealed the highest thermal energetic efficiency. Moreover adding thermal unit to PV module enhanced the total energetic efficiency by 53.4% for distilled water, 57.2% for MWCNTwater, and 63.1% for graphene-water.
Multi-GNSS time transfer based on the CGGTTS

This paper studies the use of multiple GNSS constellations for time transfer. The performances of each constellation are compared to one another. We show that, with its current constellation, Galileo already provides time transfer results with the same or better performances than GPS, for which some time links are affected by a diurnal variation. The paper also investigates different ways of combining the constellations into one global time transfer solution. We show that in a global AV solution, only one constellation should be calibrated, due to the inherent inter-system biases in the multi-GNSS clock products. Our results show that the best combination is to use the Galileo solution as a reference, and to combine the observations applying one bias (with respect to Galileo) per satellite of the other constellations. This allows reducing the impact of diurnal variations present in some GPS clock solutions. Combining Galileo and GPS satellites in this way improves the time transfer link with respect to the GPS-only or Galileo-only solutions. Adding moreover BeiDou-2 and GLONASS satellites gives, depending on the link, a solution similar or worse than the solution obtained from GPS and Galileo.
The forgotten ones: a case study on conceptualising and implementing a peer mentoring program for postgraduate coursework students

We employed an action research approach to develop a context specific peer mentoring program (Postgrad Assist) that aids first year postgraduate coursework (PGCW) students in their transition to postgraduate study. We explored the transition literature and best practice approaches, undertook comprehensive surveys of both our students and staff, conducted student focus groups, formed a diverse working party, with strong representation from students and adopted an on-going evaluation program to develop and refine Postgrad Assist. The program substantially alleviated transitioning students’ cultural and academic shock, and social isolation. The program makes a contribution to the transition and mentoring research domain in that it challenges the common misconception that PGCW students are similar to undergraduate students with no particular distinguishing features that would suggest a need for a different approach to their mentoring.
Sustainability Assessment of Alternative Strip Clear Cutting Operations for Wood Chip Production in Renaturalization Management of Pine Stands

In Mediterranean regions, afforested areas were planted to ensure the permanence of land cover, and to protect against erosion and to initiate the vegetation processes. For those purposes, pine species were mainly used; however, many of these stands, without silvicultural treatments for over fifty-sixty years, were in a poor state from physical and biological perspective, and therefore, clear-cutting on strips was conducted as silvicultural operation with the aim to eliminate 50% of the pine trees and to favor the affirmation of indigenous broadleaves seedlings. At the same time, the high and increasing demand of the forest based sector for wood biomass related to energy production, needs to be supplied. In a modern and multifunctional forestry, in which society is asking for sustainable forestry and naturalistic forest management, forestry operations should ideally be carried out in a sustainable manner, thus support the concept of sustainable forest management. All these aspects are also related to the innovation in forestry sector for an effective energetic sustainability. Three different forest wood chains were applied in pine plantations, all differing in the extraction system (animal, forestry-fitted farm tractor with winch, and double drum cable yarder). The method of the sustainability impact assessment was used in order to assess potential impacts of these alternative management options, and a set of 12 indicators covering economic, environmental, and social dimensions was analyzed. Further, to support decision makers in taking informed decisions, multi-criteria decision analysis was conducted. Decision makers gave weight towards the indicators natural tree regeneration and soil biological quality to support the achievement of the forest management goal. Results showed that first ranked alternative was case 2, in which extraction was conducted by a tractor with a winch. The main reason for that lies in the fact that this alternative had best performance for 80% of the analyzed criteria.
An analytical solution for nonlinear dynamic response and vibration of FG‐CNT reinforced nanocomposite elliptical cylindrical shells resting on elastic foundations

The present study deals with the nonlinear dynamic response and vibration of functionally graded carbon nanotubes (FG‐CNT) reinforced composite elliptical cylindrical shells resting on elastic foundations in thermal environments using an analytical solution. The nanocomposite elliptical cylindrical shells are reinforced by single‐walled carbon nanotubes. Based on the classical shell theory with the geometrical nonlinearity in von Kármán and the nonlinear dynamic response and vibration results are obtained by using the Airy stress function, Galerkin method, and Runge‐Kutta method. In numerical results, the effect of geometrical parameters, initial geometric imperfection, Winkler and Pasternak elastic foundations and volume fractions and types of distribution of CNTs on the nonlinear dynamic response and vibration of FG‐CNT reinforced composite elliptical cylindrical shells are investigated.
The Effect of Enzymatic Pre-Treatment in Agarwood Oil Extraction

Agarwood oil was extracted from a pre-treated agarwood chip soaked in a specific enzyme concentration of 1 %, 3 % and 5 % respectively. The effect of soaking time was also investigated by applying soaking period in the range of 3 to 14 days. Hydrodistillation was carried out using the Clevenger-type apparatus to obtain the essential oil of agarwood chips. Subsequently the quality of the oil and the identification of its oil compounds were analyzed using GC-MS. The morphology of agarwood chips prior to hydrodistillation was characterized using scanning electron microscope (SEM). The SEM results of enzymatic pre-treated sample showed an obvious change in the cell morphology as compared to the sample with standard soaking of 6 days. The highest yield of oil with the amount of 0.125 % is produced from the sample pre-treated with 3 % enzyme concentration as compared to 0.065 % of oil yield from the non-pre-treated sample employing similar soaking days. The GC-MS analysis revealed a total of 335 compounds from all samples of agarwood oil and similar compounds were found in the standard soaking and enzymatic pre-treated samples. Caryophyllene, Gurjunene, and Alloaromadendrene that contributes to the unique odour of agarwood oil are found mostly in all samples. Thus, the enzymatic pre-treatment indicates a comparable yield and oil quality and it is achievable even with a reduced soaking time of 6 days as compared to standard 14 days.
Microwave Imaging by Means of Lebesgue-Space Inversion: An Overview

An overview of the recent advancements in the development of microwave imaging procedures based on the exploitation of the regularization theory in Lebesgue spaces is reported in this paper. Such inversion schemes have been found to provide accurate results in several microwave imaging scenarios, thanks to the different geometrical properties that Lebesgue spaces can exhibit with respect to the more classical Hilbert ones. Moreover, the recent extension to the more general case of variable-exponent Lebesgue spaces is also addressed. Experimental results involving reference data are shown for supporting the theoretical description of the approaches.
Feedback passivity‐based control of discrete nonlinear systems with time‐delay for variable geometry truss manipulator

In this paper, the feedback passivity‐based control of nonlinear discrete time‐delay systems for variable geometry truss manipulators is investigated. To determine an appropriate communication channel in the sense of feedback passivation, we first model the dynamics of the variable geometry truss manipulator as a generalized discrete nonlinear system with time‐delay. Then we further prove that when the infinite norm of estimated error is bounded, as long as there is a controller enables the closed‐loop system to be input‐strictly passive, there must be a deterministic equivalent controller to ensure that the system is stochastically quasi passive. After that, on the basis of the conclusion obtained, a more conclusive corollary is addressed for linear plants. Though passivity is a stricter condition than stability, feedback passivation does not impose more restrictions on estimate errors, and therefore does not require more communication channel information than mean square stability. Finally, we simplify the variable geometry truss dynamics to a linear plant to simulate to verify the validity of our method, and also compared the experimental results with the methods in the existing literature.
A High-Precision Positioning Approach for Catenary Support Components With Multiscale Difference

The catenary support components (CSCs) are the most important devices in high-speed railways to support contact lines for powering trains. To estimate the states of CSCs, it is very necessary to locate their positions in a monitoring system based on computer vision. Considering the application scenarios and characteristics of CSCs, an automatic and quick positioning system is designed in this paper to simultaneously position the multiscale CSCs with 12 categories. In the system, an effective framework called CSCs network (CSCNET) is presented, which cascades the coarse positioning network and the fine positioning network to reduce multiscale differences between different CSCs. In the coarse positioning network, a new unsupervised clustering algorithm based on the relative positioning information is proposed to classify the catenary images. Then, a convolutional neural network (CNN) classification network is trained to extract the structural features of catenary images and generate the proposal regions with labels. In the fine positioning network, a modified CNN positioning framework is applied to obtain the accurate positions of CSCs based on the coarse positioning results. Due to the special lightweight structure with a classification network, the relative position information is applied and makes the CSCNET sensitive to small-scale components. The experimental results from some high-speed railway lines in China show that the proposed system has obvious advantages in the CSCs positioning. The mean average precision and frames per second of CSCNET reach 0.837 and 2.17, respectively. Compared with some popular convolutional networks [faster region-based CNN (Faster R-CNN), etc.] and a typical positioning method, the proposed system significantly improves the AP without increasing the computational time.