Miracle-dz/UniFAR-FTUs huggingface dataset:
An Overview of Packet Error Rate Models for Wireless Communications

We present models for the average packet error rate (PER) for various transmission schemes available in literature. We define criteria for evaluating these models. We find that most of the presented models are verified only for some modulation and coding schemes and that some of the models need an additional evaluation of approximation parameters to be used for a specific transmission scheme. We compare existing PER models and highlight the cases that need additional evaluation.
A Diversified Supervised based U-shape Colorectal Lesion Segmentor with Meaningful Feature Supplement and Multi-Level Residual Attention Mechanism

Colorectal cancer is a commonly diagnosed cancer of digestive system. Automatic and accurate segmentation of colorectal tumors from medical images (e.g., CT) has great significance for diagnosis, staging and treatment planning. However, the blurred boundary of tumors, as well as variability of their location and shape, make most traditional methods ineffectual. In this paper, we propose a diversified supervised U-shape CNN colorectal lesion segmentor (DSUCLS) to overcome this challenge. Our model mainly contains three key components: 1) the weakly supervised transfer learning module for supplementing generic features, where the irrelevant ones are filtered out by extra convolutional layers and image-level label, 2) an encoder-decoder structure based on U-shape architecture for learning specific pathological representation from medical images, 3) the multilevel supervised attention module incorporated into decoder path for producing coarse-to-fine guidance and guaranteeing finer attention map. 4), the pre-processing and post-processing strategies are applied to further improve segmentation performance. The experimental results illustrate that the proposed model outperforms other state-of-the-art techniques for colorectal lesion segmentation on CT images, achieving Dice scores of 0.733 and dramatically decreasing Hausdorff distance to 17.62.
APLICACIÓN DE LODOS DE PLANTA DE TRATAMIENTO DE CELULOSA: EFECTO EN ALGUNAS PROPIEDADES FÍSICAS Y QUÍMICAS DE SUELOS VOLCÁNICOS

The sludge from cellulose treatment plant is rich in organic matter (MO) and can be used to improve the chemical and physical properties of the soil. The aim of this study was to evaluate the effect of the application of sludge from cellulose wastewater treatment plant on the physical and chemical properties of volcanic soils (series Gorbea and Collipulli). There was added sludge (0, 10, 20, 30 and 50 Mg ha-1) to the soils and were incubated for 75 days at 22 ± 2oC. At 15 and 75 days the content of organic matter (MO), available nitrogen (N), pH, extractable Al, field capacity, bulk density and aggregate stability was determinated. The application of sludge improved significantly (p ≤ 0.05) the levels of nitrogen and organic matter in both soils, as well as the pH, with the consistent decrease of Al level. On the other hand, the different doses of sludge did not improve significantly the physical properties of the soils, with the exception of the stability of the aggregates in the soil series Gorbea. The minor effect in the physical properties could happen due to the fact that the period of incubation (75 days) was not sufficient to provoke significant changes in the physical properties of the soils.
Mechanics of the feldspar framework : crystal structure of Li-feldspar

Abstract The crystal structure of LiAlSi3O8was determined by single crystal X-ray diffraction, with 813 observed structure factors, refined toR1 = 0.079. Li-feldspar has the smallest unit cell volume of any feldspar with known crystal structure. Comparison of LiAlSi3O8with various alkali feldspars, including the most open structure of RbAlSi3O8, illustrates the noncollapsible nature of the feldspar-type aluminosilicate framework. Noncollapsible frameworks in which theT–O–Tangles (whereTis a tetrahedrally coordinated atom; here Si and/or Al) antirotate when a tetrahedron rotates upon compression or expansion of the framework are mechanically stable and can maintain open pores without propping by inserted cations. TheT–O–Tangles serve as hinges between the rigid SiO4and AlO4tetrahedra. In monoclinic alkali feldspars, the antirotation of theT–O(A2)–TandT–O(C)–Tangles balances the framework. Oxygen atom O(A2) is located on a mirror and connects the aluminosilicate double crankshafts of TO4tetrahedra into slabs parallel to (001), while atom O(C) is in a general position. In triclinic alkali feldspars, the pairs of angles around atoms O(Bo) and O(Bm), O(Co) and O(Cm), and finally O(Do) and O(Dm) (all in general positions) are in an equilibrium transmitted through oxygen atom O(A2). Despite its stability, the noncollapsible feldspar framework is extremely flexible. IndividualT–O–Tangles can vary in the feldspar framework by up to 27° without having a strong effect on the unit cell volume and without any significant effect on the overall meanT–O–Tangle. The cell constantarelates to the short interatomicM–O(A2) distance (whereMis an alkali atom) and theT–O(A2)–Tangle. The unit cell volume (V) depends primarily ona, whilebandcchange little with variation ofV. Thus theT–O(A2)–Tangle is the linchpin of the framework mechanics. A further consequence of the noncollapsibility of the framework is that hydrogen feldspar does not collapse upon itself, even though it lacks an alkali atom.
Current understanding of radiation-induced degradation in light water reactor structural materials

Current phenomenological knowledge and understanding of mechanisms are reviewed for radiation embrittlement of reactor pressure vessel low alloy steels and irradiation assisted stress corrosion cracking of core internals of stainless steels. Accumulated test data of irradiated materials in light water reactors and microscopic analyses by using state-of-the-art techniques such as a three-dimensional atom probe and electron backscatter diffraction have significantly increased knowledge about microstructural features. Characteristics of solute clusters and deformation microstructures and their contributions to macroscopic material property changes have been clarified to a large extent, which provide keys to understand in the degradation mechanisms. However, there are still fundamental research issues that merit study for long-term operation of reactors that requires reliable quantitative prediction of radiation-induced degradation of component materials in low-dose rate high-dose conditions.
Optimization of Microwave Hydrothermal Carbonization Conditions of Hydrochar for Ammonium Adsorption

This study aimed to optimize microwave hydrothermal carbonization (MHTC) conditions of rice husk hydrochar for maximum ammonium adsorption capacity. Processing parameters including hydrothermal temperature (150 - 200°C), residence time (20 - 60 min), and water to biomass ratio (5 - 15 mL g−1) were optimized based on the Box-Behnken design. The ANOVA results indicated that only hydrothermal carbonization temperature showed a significant effect on the ammonium adsorption capacity of rice husk hydrochar. The optimum MHTC conditions were the water to biomass ratio of 10 mL g−1under 200°C for 60 min. Hydrochar produced under the optimum condition had the ammonium adsorption capacity of $\mathbf{0.89}\pm \mathbf{0.4\ mg}$ NH4+ -N g−1. Findings from this study suggested that hydrochar derived from rice husk through MHTC has the potential to be an alternative adsorbent for preventing ammonium run off from the agricultural area.
Model Development of Information Technology Value for Downstream Petroleum Industry

Until today, industries know about their Information Technology (IT) spending year by year. Those industries calculate and understand that the spending always grows. The problem is they only know that their IT spending is just costs and no effect to performance in term of revenue. This study aim is to create model that can explain relation between IT spending and industry's performance. The research process employs IT value analysis as a basis. This approach can generate IT value through capabilities and core competencies. Hence it will impact on performance. The study will employ Downstream Petroleum (DP) industry. We take DP because the case study is non IT industry. As non IT industry, DP industry only spends low cost on IT. The industry has a need to know the IT value. The research result shows that certain IT resources may have positive impacts to IT value by deploying IT value model.
J2*: A New Method for Alignment-free Sequence Similarity Measurement

Alignment-free sequence comparison methods can compute the similarity of a huge number of sequences much faster than traditional sequence alignment methods. Here, a new non-parametric alignment-free sequence comparison algorithm, $J_{2}^{*}$ is proposed to measure sequence similarity based on the suffix tree data structure. Compared against other state-of-the-art alignment-free methods, namely, $D_{2}^{z}, D_{2}, D_{2}^{sh}, D_{2}^{*}, WFV, DV$, Shi, CPF, $DM_{k}, K_{2}$ and $K_{2}^{*}, J_{2}^{*}$ has three main advantages: (1) it has the fastest running time in theory and in practice. $J_{2}^{*}$ reduces the time for k-words search from $O(N^{2})$ to $O(N)$. Our experimental results confirm that it is the fastest among the 11 popular approaches. (2) $J_{2}^{*}$ is easy to use: unlike the other alignment-free methods that often need to choose a suitable parameter k, there is no parameter selection for $J_{2}^{*}$. (3) $J_{2}^{*}$ does not have any particular requirement for data distribution. Unlike the the parametric methods (such as the D2-family) that require certain distribution for the data, $J_{2}^{*}$ has no demand for a specific input distribution. The improved running time from $J_{2}^{*}$ will be very useful in this era of big data, especially, with the increasing data volume of genome sequences.
Time-frequency Analysis Based on Hilbert-Huang Transform for Depression Recognition in Speech

In recent years, automatic detection of depression from speech has attracted many researchers. One of the key points is finding discriminable patterns in voice between depressed patients and healthy people. For this goal, we employed the Hilbert-Huang transform (HHT) to implement time-frequency analysis. Speech signals were decomposed into different sub-band signals and further were transformed into energy-frequency features for analysis and detection of depression. In the experiment 124 participants’ (68 females and 56 males) speech were recorded in three patterns: interview, reading, and picture description for data collection. The results showed that the energy distribution of intrinsic mode functions (IMFs) between depressed patients and healthy people was significantly different, and this difference mainly was found in a relatively high-frequency range (1kHz). This finding fitted the clinical observation of depressed patients’ “energy loss”. Further, a speech-based depression classification model based on the above finding was built and validated on the dataset. The results showed classification accuracy was 75.5% and 71.2% for female and male, respectively and each specificity was 88.4% and 78.2% These results implied HHT-based energy-frequency feature is a promising indicator for automatic depression assessment.
Bayesian Iterative Channel Estimation and Turbo Equalization for Multiple-Input–Multiple-Output Underwater Acoustic Communications

This article investigates a robust receiver scheme for a single carrier, multiple-input–multiple-output (MIMO) underwater acoustic (UWA) communications, which uses the sparse Bayesian learning algorithm for iterative channel estimation embedded in Turbo equalization (TEQ). We derive a block-wise sparse Bayesian learning framework modeling the spatial correlation of the MIMO UWA channels, where a more robust expectation–maximization algorithm is proposed for updating the joint estimates of channel impulse response, residual noise, and channel covariance matrix. By exploiting the spatially correlated sparsity of MIMO UWA channels and the second-order a priori channel statistics from the training sequence, the proposed Bayesian channel estimator enjoys not only relatively low complexity but also more stable control of the hyperparameters that determine the channel sparsity and recovery accuracy. Moreover, this article proposes a low complexity space-time soft decision feedback equalizer (ST-SDFE) with successive soft interference cancellation. Evaluated by the undersea 2008 Surface Processes and Acoustic Communications Experiment, the improved sparse Bayesian learning channel estimation algorithm outperforms the conventional Bayesian algorithms in terms of the robustness and complexity, while enjoying better estimation accuracy than the orthogonal matching pursuit and the improved proportionate normalized least mean squares algorithms. We have also verified that the proposed ST-SDFE TEQ significantly outperforms the low-complexity minimum mean square error TEQ in terms of the bit error rate and error propagation.
101 Gb/s 2×2 End-to-End MIMO Fiber-Wireless System in W Band Using WDM Radio-over-Fiber

We demonstrate an end-to-end multiple-input multiple-output (MIMO) seamless fiber–wireless system in the W band using an optical self-heterodyne wavelength-division multiplexing radio-over-fiber technique. Satisfactory performance is experimentally confirmed for 2×2 MIMO FBMC/OQAM signal transmission with a total capacity of approximately 93.5 Gb/s and 101 Gb/s using antenna in the same and different polarization, respectively. The system is scalable for large-scale MIMO signal transmission and can be a promising solution for future mobile networks.
Augmented Reality and Virtual Reality for Learning: An Examination Using an Extended Technology Acceptance Model

As the educational possibilities of AR (Augmented Reality) and VR (Virtual Reality) are getting more attention, understanding teachers’ readiness to integrate new technologies for instruction would help researchers and practitioners to plan how to support them. In this regard, the present study explores teachers’ willingness to integrate AR and VR technologies for teaching and learning practices. Employing an extended Technology Acceptance Model (eTAM), this study investigated whether technological pedagogical and content knowledge (TPACK), social norm (SN), and motivational support (MS) for teachers influence teachers’ intention to use the technologies. Analysis from 292 in-service teacher responses supported all of the eight hypotheses formulated in the study. TPACK was found to have a significant influence on perceived usefulness (PU) and perceived ease of use (PEU) while SN influenced PU. In addition, MS was found to have an influence on PEU, which ultimately affects attitudes toward technology use (ATU) and then behavioral intention (BI). The results imply the importance of providing technology professional development (PD) and support for teachers to promote the use of AR and VR in classrooms.
Investigation of the Stewart Platform Workspace Using MATLAB-Simulink and Simscape Multibody Library

The paper deals with the determination of the Stewart platform workspace using the simulation model created in the software MATLAB-Simulink and the Simscape Multibody library. The workspace investigation is performed with the use of the created simulation model that is used to find points in the space that meet certain criteria. Obtained set of points that meet defined criteria is considered as the workspace approximation for the constant orientation of the moving platform. The presented workspace approximation method can be used for the optimization of the mechanical structure of the Stewart platform and for the path planning of its moving platform.
Cumulated charging mechanisms at gate processing in high-κ first planar NMOS devices

In this work, different charging mechanisms occurring during processing of the high$-\kappa$ (HK) first gate stack of planar NMOS devices are disentangled by comparing various shapes of gate-on-field antennae. Based on different electrical measurements, the distinct electrical signatures of the charging damage related to these mechanisms are shown. Finally, a qualitative explanation is proposed in terms of oxide traps/charges and plasma bias polarity to account for this difference.
Enhancing Automated COVID-19 Chest X-ray Diagnosis by Image-to-Image GAN Translation

The severe pneumonia induced by the infection of the SARS-CoV-2 virus causes massive death in the ongoing COVID-19 pandemic. The early detection of the SARS-CoV-2 induced pneumonia relies on the unique patterns of the chest XRay images. Deep learning is a data-greedy algorithm to achieve high performance when adequately trained. A common challenge for machine learning in the medical domain is the accessibility to properly annotated data. In this study, we apply a conditional adversarial network (cGAN) to perform image to image (Pix2Pix) translation from the non-COVID-19 chest X-Ray domain to the COVID-19 chest X-Ray domain. The objective is to learn a mapping from the normal chest X-Ray visual patterns to the COVID-19 pneumonia chest X-ray patterns. The original dataset has a typical imbalanced issue because it contains only 219 COVID-19 positive images but has 1,341 images for normal chest X-Ray and 1,345 images for viral pneumonia. A U-Net based architecture is applied for the image-to-image translation to generate synthesized COVID-19 X-Ray chest images from the normal chest X-ray images. A 50-convolutional-layer residual net (ResNet) architecture is applied for the final classification task. After training the GAN model for 100 epochs, we use the GAN generator to translate 1,100 COVID-19 images from the normal X-Ray to form a balanced training dataset (3,762 images) for the classification task. The ResNet based classifier trained by the enhanced dataset achieves the classification accuracy of 97.8% compared to 96.1% in the transfer learning mode. When trained with the original imbalanced dataset, the model achieves an accuracy of 96.1% compared to 95.6% in the training from trainby-scratch model. In addition, the classifier trained by the enhanced dataset has more stable measures in precision, recall, and F1 scores across different image classes. We conclude that the GAN-based data enhancement strategy is applicable to most medical image pattern recognition tasks, and it provides an effective way to solve the common expertise dependence issue in the medical domain.
Affecting Factors of Consumers’ Purchase Decision on Sustainable Fashion Clothing Products

Considering consumers’ sustainable purchase decisions, this paper analyzes how the sustainable fashion environment, sustainable fashion product features and sustainable consumption awareness impact on consumers’ purchase behavior. Sustainable fashion environment, sustainable fashion product features and sustainable consumption awareness have a significant influence on purchase behavior. Some suggestions are made to raise young people’s awareness of environmental protection and to help garment enterprises implement a sustainable strategy.
Scattering From Chiral Cylinders of Arbitrary Cross-Sections Above a Ground Plane

The main purpose of this work is to solve the problem of electromagnetic scattering from a chiral cylinder of arbitrary cross-section above an infinite perfect electric conducting (PEC) plane. Using image theory, this problem is reduced to two chiral cylinders in free-space. Surface equivalence principle is used to obtain three different equivalent problems for this two-cylinder problem. Then, the method of moments is used to solve these equivalent problems numerically. It is known that the image of a chiral body through a ground plane is another chiral body with the same permittivity and permeability but opposite chirality. Using this property, the two-body problem in the moment method may be reduced to a one-body problem with a complicated moment matrix. Computed numerical results include scattered fields and equivalent surface currents on the cylinder.
Performance Analysis of Index Modulation Based Multiple Access Under Imperfect Channel Estimation

Future generations of wireless communication systems are expected to serve a large number of users with different quality of service requirements. Given the limited spectrum resources, the current multiple access schemes will not be able to fulfill these requirements. The recently proposed Index Modulation based Multiple Access (IMMA) has shown a significant improvement in the performance of uplink transmission as compared to other conventional multiple access schemes in terms of the achievable bit error rate (BER) and the number of served users. As such, it holds great potential in replacing the current multiple access schemes. In this paper, the performance of the IMMA scheme is analyzed under the assumption of the imperfect channel estimation. The average BER is derived in a closed form expression where the impact of the channel estimation error is included. Analytical results are provided to validate the Monte Carlo simulations where a close match is shown over a pragmatic range of signal-to-noise-ratio.
Efficiency Improvement of Step-Up Series Resonant DC-DC Converter in Buck Operating Mode

The paper focuses on the galvanically isolated series-resonant dc-dc converter (SRC) operating at low values of the quality factor of the resonant tank. The SRC topology is considered as a step-up front-end dc-dc converter with buck-boost functionality for integration of renewable energy sources in dc microgrids. The input voltage range could be expanded significantly by implementing different buck control methods at the constant switching frequency. Moreover, an additional control degree of freedom allowing optimization of the converter operation is topology reconfiguration between the full-bridge mode and the half-bridge mode. The paper describes the transition between these two modes of the input-side full-bridge inverter by applying hybrid phase shift modulation and asymmetrical pulsewidth modulation, respectively. Experimental results show the operation of the converter in a wide input voltage range and transition transients between the two modes.
Deep Learning for Cooperative Spectrum Sensing

The performance of spectrum sensing methods available have the limitations under extreme channel conditions. With the advent of deep learning in various fields, this paper is an attempt to analyze the performance of cooperative spectrum sensing (CSS) wherein deep learning method is adopted for data fusion. We study the performance of convolutional neural network (CNN) based CSS under dynamic channel condition. The dataset is synthesized for the system model considering spatial-correlation among secondary users, path loss with log-normal shadowing and different fading conditions – Rayleigh and Nakagami-m. The performance is compared with the hard fusion rules - OR, AND, Majority rules, in terms of accuracy in classification.