Miracle-dz/UniFAR-FTUs huggingface dataset:
TSCMDL: Multimodal Deep Learning Framework for Classifying Tree Species Using Fusion of 2-D and 3-D Features

Accurate tree species information is a prerequisite for forest resource management. Combining light detection and ranging (LiDAR) and image data is one main method of tree species classification. Traditional machine learning methods rely on expert knowledge to calculate a large number of feature parameters. Deep learning technology can directly use the original image and point cloud data to classify tree species. However, data with different patterns require the use of different types of deep learning methods. In this study, a tree species classification multimodal deep learning (TSCMDL) that fuses 2-D and 3-D features was constructed and then used to combine data from multiple sources for tree species classification. This framework uses an improved version of the PointMLP model as its backbone network and uses ResNet50 and PointMLP networks to extract the image features and point cloud features, respectively. The proposed framework was tested using unmanned aerial vehicle LiDAR (UAV LiDAR) data and red, green, blue (RGB) orthophotos. The results showed that the accuracy of the tree species classification using the TSCMDL framework was 98.52%, which was 4.02% higher than that based on point cloud features only. In addition, when the same hyperparameters were used for training the model, the efficiency of the model training was not significantly lower than for models based on point cloud features only. The proposed multimodal deep learning framework extracts features directly from the original data and integrates them effectively, thus avoiding manual feature screening and achieving more accurate classification. The feature extraction network used in the TSCMDL framework can be replaced by other suitable frameworks and has strong application potential.
The Central Molecular Zone in H13CO+ and thermal SiO emission lines

We present results from a high-resolution wide-field imaging observation of the Central Molecular Zone (CMZ) in H13CO+ J = 1–0 and SiO v = 0, J = 2–1 emission lines using the 45-m telescope at the Nobeyama Radio Observatory in order to depict the high-density molecular gas mass distribution and explore molecular gas affected by interstellar shocks. By using the clumpfind algorithm we identify 114 molecular clumps in the H13CO+ data cube. The clump mass function (CMF) can be described by a power law (dN/dM ∝ M−γ , with γ 2) in the range of 5 × 103 < Mclump < 2 × 104 M . The brightness temperature ratio RT = T(SiO)/T(H13CO+) indicates the strength of the shock. The Sgr B2 complex has several expanding shell-like structures with a large velocitywidth ( V > 50 km s−1). They correspond to the high brightness temperature ratio area (RT 8). A Large Velocity Gradient model analysis with the RADEX program confirms the enhancement of SiO molecules in the shells. On the other hand, there is no such large shell with a high ratio in the Sgr A complex. A hollow hemispherical structure is found in the l–b–v data cube of the Sgr B2 complex. The central part of the structure has a high ratio (RT 8). The ratio decreases with proximity to the outer area. The properties are consistent with those expected from simulations of cloud–cloud collisions. The structure is probably a site of ongoing cloud–cloud collision.
EM fields in a human body due to a Manpack transceiver for different soil conditions

In this work, electromagnetic (EM) energy radiating into a model of a human body carrying a Manpack transceiver is calculated numerically using the finite-difference time-domain (FDTD) method. The specific absorption rate (SAR) values are calculated for the cases of a human body standing on dry and 13.77% wet loamy soil conditions at the frequencies of 80 MHz and 150 MHz. Initially the near- and far-field radiation field patterns of the Manpack transceiver in free space are validated by comparing computations from FDTD and the method of moments. This study shows that more EM radiated energy is coupled to the lower part of the body than to the upper region at 80 MHz, while more energy is coupled to the upper body region at 150 MHz. The wet loamy soil case resulted in higher SAR values in the foot region as compared to the dry loamy soil case.
Frequency Switchable Global RFID Tag Antennae with Metal Compatibility for Worldwide Vehicle Transportation

This paper presents an effective way to design an RFID tag antenna to operate at three different frequencies by incorporating a switching technique. PIN diode has been used to switch the RF frequency because of its good efficiency and simplicity. The conventional dipole-based RFID tag has been improvised with added co-planar ground and PIN diode. The layout of the antenna is designed with a size of 0.083 λ0 × 0.094 λ0 at UHF (80–960) MHz, where λ0 is the free-space wavelength corresponding to the mid-point of the targeted UHF range. The RFID microchip is connected to the modified ground and dipole structures. Bending and meandering techniques on the dipole length help to match the complex chip impedance with the dipole impedance. Additionally, it scales down the total structure of the antenna. Two PIN diodes are placed along the dipole length at appropriate distances with proper biasing. The ON-OFF switching states of the PIN diodes enable the RFID tag antenna to switch over the frequency ranges (840–845) MHz (India), 902–928 MHz (North America), and 950–955 MHz (Japan).
Characteristics of Australia's alien flora vary with invasion stage

Aim: Directly or indirectly, humans select the plants that they transport and introduce outside of species native ranges. Plants that have become invasive may therefore reflect which species had the chance to invade, rather than which species would become invasive given the chance. We examine characteristics of failed introductions, along with invasion successes, by investigating (a) variation in plant characteristics across invasion stages, and (b) how observed characteristics predict the likelihood of species moving through invasion stages
Severe Geostationary Environments: Numerical Estimation of Spacecraft Surface Charging from Flight Data

This paper defines a series of severe plasma environments in terms of spacecraft surface charging in geostationary orbit. In-flight plasma measurements are used to extract environments associated with large absolute spacecraft potentials and fluxes. An exhaustive list of events is selected over 15 years of Los Alamos National Laboratory data between the 1990s and 2000s. Electron spectra are compared to the literature and guidelines, especially the strong event measured by the Spacecraft Charging at High Altitude satellite in 1979. The impact on a telecommunications spacecraft of such charging environments is simulated numerically with the Spacecraft Plasma Interaction Software. The correlation between the model results and the instrument data indicates that the numerical approach is reliable for estimating charging risks in operational conditions. Measured environments are ranked on their susceptibility to generate significant charging and electrostatic discharge risks.
All-fiber 770 nm Laser Based on Second Harmonic Generation

We present an all-fiber 770 nm laser module based on second harmonic generation (SHG) in a periodically poled lithium niobate (PPLN) waveguide. The laser module provides an output power of 192 mW with an SHG conversion efficiency of 22.9 %. The side mode suppression ratio (SMSR) of 38.6 dB, and the relative intensity noise (RIN) below -120 dBc/Hz between 100 Hz and 10 MHz Fourier frequency is achieved. The laser linewidth of 15 kHz is acquired. The high optical power and low noise of the 770 nm all-fiber laser module are suitable for precise quantum measurements, such as atomic magnetometers and optical atomic clocks.
Heterogeneous Statistical QoS Provisioning for Scalable Software-Defined 6G Mobile Networks

Due to the explosively increasing number of mobile users and the new types of data demands in the fifth generation (5G) mobile wireless network, research in wireless networks has shifted toward the development of the sixth-generation (6G) wireless network. Although the research for software-defined network (SDN) architectures in 5G mainly focuses on the dynamic programming for the internet backbone, these software programming techniques can be also applied at the network edge to support the exponentially increasing demands from mobile users under constrained wireless resources. In order to study the interference problem resulted by massive mobile users, the scaling law is a powerful tool to show how fast the levels of network imperfections can be tolerated as the number of mobile users increases. In this paper, we investigate the scaling behavior of software-defined architectures over 6G wireless networks. We consider the wireless channel in three scenarios: single-input-single-output (SISO), multiple-input-single-output (MISO), and multiple-input-multiple-output (MIMO), where we derive the corresponding scaling law for each scenario, respectively. Our derived scaling law shows how the network performance scales with the number of mobile users in a wireless network. Then, we propose a software-defined network slicing scheme to select the optimal mobile users and derive their optimal resource allocations, according to our derived scaling law, under SISO, MISO, and MIMO wireless channel, respectively. Finally, we validate and evaluate the derived scaling behavior of the software-defined architecture over 6G wireless networks through numerical analyses.
A laser printing based approach for printed electronics

Here we report a study of printing of electronics using an office use laser printer. The proposed method eliminates those critical disadvantages of solvent-based printing techniques by taking the advantages of electroless deposition and laser printing. The synthesized toner acts as a catalyst for the electroless copper deposition as well as an adhesion-promoting buffer layer between the substrate and deposited copper. The easy metallization of printed patterns and strong metal-substrate adhesion make it an especially effective method for massive production of flexible printed circuits. The proposed process is a high throughput, low cost, efficient, and environmentally benign method for flexible electronics manufacturing.
Lightweight Approximate Multiplier with Improved Accuracy in FPGA for Error Resilient Application

Approximate computing finds its application in image processing, machine learning, data mining and multimedia data processing. The paper proposes a scalable and lightweight $4\times 4$ approximate multiplier, which is designed to efficiently utilize the FPGA resources. We have divided the partial product matrix and selectively chosen the input variables for the effective usage of the LUTs and carry-chain. The bit error position due to the approximation is reduced by assigning the possible lower bits to ‘1’ which reduces the maximum error magnitude from 16 to 4. The proposed $4\times 4$ approximate multiplier achieves a reduction in area, power and latency of 26.7%, 57.6% and 60.1% respectively compared to the Xilinx multiplier IP. The proposed multiplier is used as a building block to implement $8\times 8$ approximate multipliers for image sharpening applications. We achieve a high PSNR value of 69.4 dB using the $8\times 8$ approximate multiplier.
IoT Anomaly Detection Using a Multitude of Machine Learning Algorithms

The security of IoT (Internet of Things) systems is crucial yet challenging. Anomaly detection can help assess and improve the security of these devices and systems. The detection of anomalous traffic can be performed with the use of machine learning algorithms. Gradient Boosting is a Machine Learning (ML) technique that handles both regression and classification problems and uses decision tree algorithms to produce a prediction model. eXtreme Gradient Boosting (XGBoost) is a unique implementation of Gradient Boosting that has shown very good performance and outcomes in various problems. In this paper, XGBoost’s classification abilities are examined when applied to the adopted IoT-23 dataset to see how well anomalies can be identified and what type of anomaly exists in IoT systems. Moreover, the results obtained from XGBoost are compared to other ML methods including Support Vector Machines (SVM) and Deep Convolutional Neural Networks (DCNN). The classification results were assessed based on accuracy, precision, recall, and various other performance metrics. Our experimental results showed that XGBoost outperformed both SVM and DCNN achieving accuracies up to 99.98%. In addition, XGBoost proved to be the most efficient method with respect to execution time.
A General-Purpose Distributed Analytic Platform Based on Edge Computing and Computational Intelligence Applied on Smart Grids

Currently, in many data landscapes, the information is distributed across various sources and presented in diverse formats. This fragmentation can pose a significant challenge to the efficient application of analytical methods. In this sense, distributed data mining is mainly based on clustering or classification techniques, which are easier to implement in distributed environments. However, the solution to some problems is based on the usage of mathematical equations or stochastic models, which are more difficult to implement in distributed environments. Usually, these types of problems need to centralize the required information, and then a modelling technique is applied. In some environments, this centralization may cause an overloading of the communication channels due to massive data transmission and may also cause privacy issues when sending sensitive data. To mitigate this problem, this paper describes a general-purpose distributed analytic platform based on edge computing for distributed networks. Through the distributed analytical engine (DAE), the calculation process of the expressions (that requires data from diverse sources) is decomposed and distributed between the existing nodes, and this allows sending partial results without exchanging the original information. In this way, the master node ultimately obtains the result of the expressions. The proposed solution is examined using three different computational intelligence algorithms, i.e., genetic algorithm, genetic algorithm with evolution control, and particle swarm optimization, to decompose the expression to be calculated and to distribute the calculation tasks between the existing nodes. This engine has been successfully applied in a case study focused on the calculation of key performance indicators of a smart grid, achieving a reduction in the number of communication messages by more than 91% compared to the traditional approach.
Memristor-based High Speed and Area Efficient Comparators in IMPLY Logic

Because of its In-Memory-Computation (IMC) capacity and low area footprint, memristive technology is a rapidly growing alternative to traditional computer architectures. Using IMPLY logic, this research proposes an efficient multi-bit comparator architecture in memristor technology. This article proposes three one-bit comparator designs, two of which conduct serial computations and the other performs parallel computations. An area efficient comparator design takes only four memristors to make the comparison in thirteen computational steps by reusing the input memristors. When the number of memristors that are reused decreases, the design speed increases. This is seen in the second serial comparator design, which computes the result in only eight steps using six memristors. A comparator design with seven memristors that performs the parallel computations takes only six steps to compute the output. The proposed method can be extended to multi-bit designs. A two-bit high speed comparator is also proposed, which takes twenty-two steps to perform the magnitude comparison. The proposed comparator designs were simulated in the Cadence Virtuoso using the VTEAM model.
Identifying nano-Schottky diode currents in silicon diodes with 2D interfacial layers

In silicon technology, Schottky diodes mainly exhibit high current levels, and attempts are regularly made to reduce these by introducing 2D layers between the metal contact and the silicon. Defects in such interfacial layers, from weakly bonded structures to actual pinholes, can lead to high, localized metal-semiconductor Schottky currents. Using the example of diodes with an interfacial layer of pure boron (PureB) between an aluminum metallization layer and the Si, a signature for such ‘‘nano-Schottky’s’’ is determined by evaluating the results of several different test-structure arrays and measurement techniques. An adapted bipolar-type measurement is introduced as an additional method to determine whether any high current characteristics originate from a low Schottky barrier height over the entire diode surface or from a localized nano-Schottky structure.
Light absorption and scattering properties of indole secondary organic aerosol prepared under various oxidant and relative humidity conditions

Abstract Secondary organic aerosol (SOA) from indole was produced in a smog chamber under different relative humidity (RH = <2%, 25% and 50%) and with different oxidants (O3, OH, OH + NOx, and NO3). The mass absorption coefficients were measured by extracting SOA in methanol and taking absorption spectra in the 200-700 nm range. Chromophoric compounds were tentatively identified by liquid chromatography – spectrophotometry – mass spectrometry. One of the strongest chromophores was nitroindole, produced in high abundance by both NO3 and OH + NOx oxidation. Indole SOA was also prepared in an oxidation flow reactor (OFR) using the same set of oxidants under variable RH conditions. Densities of the OFR-generated SOA particles ranged from 1.05 g/cm3 for indole + O3 SOA to 1.33 g/cm3 for indole + OH SOA. The real (n) and imaginary (k) refractive indices were quantified using a broadband cavity-enhanced spectrometer (BBCES) over the 360–425 nm spectral range. The values of k ranged from 10−2 to 10−1 at 405 nm, which is comparable to those for humic-like substances and at least an order of magnitude larger than k values of SOA produced from other biogenic and anthropogenic volatile organic compounds. The k values for the indole + NO3 SOA had a strong and non-monotonic dependence on RH, suggesting two competing reaction mechanisms for the chromophores, highlighting the complex effect of water on the absorption coefficient of SOA from heterocyclic aromatic precursors. Strong absorption of indole SOA may contribute to the degradation of visibility near its emission sources, which include animal husbandry facilities and stressed plants. Copyright © 2023 American Association for Aerosol Research
The Mid-Infrared luminosity function of galaxies using the AKARI mid-infrared All-Sky Survey Catalogue

Abstract We present the first determination of the 18 μm luminosity function (LF) of galaxies at 0.006 < z < 0.7 (the average redshift is ~ 0.04) using the AKARI mid-infrared All-Sky Survey catalogue. We have selected a 18 μm flux-limited sample of 243 galaxies from the catalogue in the SDSS spectroscopic region. We then classified the sample into four types; Seyfert 1 galaxies (including QSOs), Seyfert 2 galaxies, LINERs and Star-Forming galaxies using mainly [OIII]/Hβ vs. [NII]/Hα line ratios obtained from the SDSS. As a result of constructing Seyfert 1 and Seyfert 2 LFs, we found the following results; (i) the number density ratio of Seyfert 2s to Seyfert 1s is 3.98 ± 0.41 obtained from Sy1 and Sy2 LFs; this value is larger than the results obtained from optical LFs. (ii) the fraction of Sy2s in the entire AGNs may be anti-correlated with 18 μm luminosity. These results suggest that the torus structure probably depends on the mid-infrared luminosity of AGNs and most of the AGNs in the local Universe are obscured by dust.
A New Class of Zero-Inﬂated Logarithmic Series Distribution

Through this paper we suggest an alternative form of the modified zero-inflated logarithmic series distribution of Kumar and Riyaz (Statistica, 2013) and study some of its important aspects. The method of maximum likelihood is employed for estimating the parameters of the distribution and certain test procedures are considered for testing the significance of the additional parameter of the model. Further, all the procedures are illustrated with the help of two real life data sets.
A Digital Control Strategy for Wind Turbine-Permanent Magnet Synchronous Generator

This paper presents a digital control technique for the control of wind turbine-permanent magnet synchronous generator (WT-PMSG) in the MATLAB-Simulink environment. It is digital in the sense that instead of the commonly employed strategy of using complex transformations and design of various controllers, this technique uses only two embedded programs to achieve the desired control. The control strategy is simple yet very strong and gives results according to the theoretical concepts. The focus here is on the electrical side control of the WT-PMSG-inverter system under variable wind and variable load conditions, with the aim that it should be implementable in hardware without needing changes in the strategy. The simulation results in various operating modes validate the effectiveness of the proposed control technique. The achievement of this paper is the spike free pure sinusoidal load voltage without needing to design any conventional controller.
Common Neurologic Features of Lyme Disease That May Present to a Rheumatologist

Lyme disease, caused by Borrelia burgdorferi (Bb) infection, has a broad spectrum of clinical manifestations and severity. Patients with possible Lyme disease may seek out or be referred to rheumatologists. Today, the most common reason to engage a rheumatologist is due to complaints of arthralgia. After skin, neurologic manifestations of Lyme disease are now among the most common. Therefore, it is important for rheumatologists to be aware of clues that suggest neurologic Lyme disease and prompt help from a neurologist experienced with Lyme disease.
Multimaterial 4D printing with a tunable bending model

Shape-memory polymer (SMP)-based functional structures may now be produced more efficiently via four-dimensional (4D) printing, benefiting from the recent advances in multi-material three-dimensional printing technologies. Composite material design using 4D printing has opened new possibilities for customizing the shape memory property of smart polymers. This work studies a design strategy to harness desirable morphing by 4D printing multimaterial composites with a focus on the detailed finite element (FE) procedure, experimental results, and soft robotic application. Composites with bilayer laminates consisting of a SMP and a flexible elastomer are constructed with variable thickness ratios to control the self-bending of the composite. FE simulations are used to understand the underlying processes of composite materials and to generate accurate predictions for the experimental results, which reduces cost and development time. The application of 4D printing and multi-material composite programming is demonstrated with a soft robotic gripper for manipulating fragile objects.
Manipulations of Optical Resonances from Visible to Near-IR Based on a-Si Elliptic Nanopillar Arrays

A polarization dependent amorphous silicon (a-Si) subwavelength elliptic nanopillars array is studied. By manipulating the parameters of units and polarization states of incident plane wave, the characteristics of optical resonances from visible to near-IR are investigated. Multipole decomposition method is used to formulate the multipoles’ contribution to the formation of resonance. After fabricating arrays with electron beam lithography (EBL), the transmission spectrum of arrays is measured and found to be consistent with the simulation results. Based on the properties of the multipoles to modulate the parameters of the array, resonance features can be predicted and fabricating-friendly nanodevices can be realized. The array is arising new interest in applications such as biosensing, optical filtering and magneto-optical enhancement.