Miracle-dz/UniFAR-FTUs huggingface dataset:
Pattern-of-Life Modeling using Data Leakage in Smart Homes

In recent years, smart home devices have become one of the most popular categories in the Internet of things (IoT). Smart devices are relatively inexpensive, readily available, and easily integrated into homes and offices. As smart technologies become more prevalent, consumers must make informed purchasing decisions as retailers provide IoT devices from manufacturers with little scrutiny in regards to device security or known vulnerabilities. In response to the growth and commonplace of IoT, the United States Government Accountability Office released a report in July 2017 to congressional committees stressing that further assessments and guidance are needed to address security risks of IoT in the Department of Defense (DoD) [1]. Specifically, there must be investigation into the security ramifications IoT devices have on operations security, intelligence collection, and leadership safety. Wi-Fi and Bluetooth Low Energy (BLE) are two protocols increasingly used in a range of IoT devices such as security cameras, locks, and motion sensors. There are two characteristics that cause these devices to inadvertently leak privacy information: (i) they continuously broadcast unencrypted information, such as Wi-Fi Media Access Control (MAC) addresses or BLE device names, which anyone with a properly-tuned receiver can observe, and (ii) IoT devices send unique and predictable wireless traffic in the clear during communications. This research shows how data leakage from these protocols, combined with device vulnerabilities, enable an eavesdropper to collect wireless traffic from IoT devices in a smart home or office to identify devices, track user movements, identify events, and ultimately gain physical access to the home. To demonstrate these capabilities, a Smart Home Automation Architecture (SHAA) was designed by integrating a variety of commercial off-the-shelf (COTS) Wi-Fi and
Simulated microphysical properties of winter storms from bulk-type microphysics schemes and their evaluation in the Weather Research and Forecasting (v4.1.3) model during the ICE-POP 2018 field campaign

Abstract. This study evaluates the performance of four bulk-type microphysics schemes, Weather Research and Forecasting (WRF) double-moment 6-class (WDM6), WRF double-moment 7-class (WDM7), Thompson, and Morrison, focusing on hydrometeors and microphysics budgets in the WRF model version 4.1. 3. Eight snowstorm cases, which can be sub-categorized as cold-low, warm-low, and air–sea interaction cases are selected, depending on the synoptic environment during the International Collaborative Experiment for Pyeongchang Olympics and Paralympics (ICE-POP 2018) field campaign. All simulations present a positive bias in the simulated surface precipitation for cold-low and warm-low cases. Furthermore, the simulations for the warm-low cases show a higher probability of detection score than simulations for the cold-low and air–sea interaction cases even though the simulations fail to capture the accurate transition layer for wind direction. WDM6 and WDM7 simulate abundant cloud ice for the cold-low and warm-low cases, and thus snow is mainly generated by aggregation. Meanwhile, Thompson and Morrison schemes simulate insignificant cloud ice amounts, especially over the lower atmosphere, where cloud water is simulated instead. Snow in the Thompson and Morrison schemes is mainly formed by the accretion between snow and cloud water and deposition. The melting process is analyzed as a key process to generate rain in all schemes. The discovered positive precipitation bias for the warm-low and cold-low cases can be mitigated by reducing the melting efficiency in all schemes. The contribution of melting to rain production is reduced for the air–sea interaction case with decreased solid-phase hydrometeors and increased cloud water in all simulations.
Unified Kinematics of Prismatically Actuated Parallel Delta Robots

Summary This paper presents a unified formulation for the kinematics, singularity and workspace analyses of parallel delta robots with prismatic actuation. Unlike the existing studies, the derivations presented in this paper are made by assuming variable angles and variable link lengths. Thus, the presented scheme can be used for all of the possible linear delta robot configurations including the ones with asymmetric kinematic chains. Referring to a geometry-based derivation, the paper first formulates the position and the velocity kinematics of linear delta robots with non-iterative exact solutions. Then, all of the singular configurations are identified assuming a parametric content for the Jacobian matrix derived in the velocity kinematics section. Furthermore, a benchmark study is carried out to determine the linear delta robot configuration with the maximum cubic workspace among symmetric and semi-symmetric kinematic chains. In order to show the validity of the proposed approach, two sets of experiments are made, respectively, on the horizontal and the Keops type of linear delta robots. The experiment results for the confirmation of the presented kinematic analysis and the simulation results for the determination of the maximum cubic workspace illustrate the efficacy and the flexible applicability of the proposed framework.
Selective Properties of Mid-Infrared Tamm Phonon-Polaritons Emitter with Silicon Carbide-Based Structures

Electromagnetic (EM) absorbers and emitters have attracted much interest because of their versatile applications. A photonic heterostructure composed of silicon carbide (SiC) layer/germanium (Ge) cavity/distributed Bragg reflector (DBR) has been proposed. Selective emission properties have been investigated through rigorous coupled wave analysis (RCWA) method. The results illustrate that Tamm phonon-polaritons can be excited, and the magnetic field is partially centralized at the junction of Ge cavity and SiC film, aimed to improve the interactions of photon–phonon. The absorptivity/emissivity of the structure can be better optimized by controlling the coupling of surface modes with the incident wave. Near-unity absorption can be achieved through optimizing the SiC grating/Ge cavity/distributed Bragg reflector (DBR) multilayer structure with geometrical parameters of ds = 0.75 μm, dg = 0.7 μm, d1 = 1.25 μm and d2 = 0.75 μm, respectively. Physical mechanism of selective emission characteristics is deliberated. In addition, the simulation results demonstrate that the emitter desensitizes to the incidence angle and polarization state in the mid-infrared (MIR) range. This research ameliorates the function of the selective emitters, which provides more efficient design for SiC-based systems.
From Soils to Streams: Connecting Terrestrial Carbon Transformation, Chemical Weathering, and Solute Export Across Hydrological Regimes

Soil biota generates carbon that exports vertically to the atmosphere (CO2) and transports laterally to streams and rivers (dissolved organic and inorganic carbon, DOC and DIC). These processes, together with chemical weathering, vary with flow paths across hydrological regimes; yet an integrated understanding of these interactive processes is still lacking. Here we ask: How and to what extent do subsurface carbon transformation, chemical weathering, and solute export differ across hydrological and subsurface structure regimes? We address this question using a hillslope reactive transport model calibrated using soil CO2 and water chemistry data from Fitch, a temperate forest at the ecotone boundary of the Eastern temperate forest and mid‐continent grasslands in Kansas, USA. Model results show that droughts (discharge at 0.08 mm/day) promoted deeper flow paths, longer water transit time, carbonate precipitation, and mineralization of organic carbon (OC) into inorganic carbon (IC) (∼98% of OC). Of the IC produced, ∼86% was emitted upward as CO2 gas and ∼14% was exported laterally as DIC into the stream. Storms (8.0 mm/day) led to carbonate dissolution but reduced OC mineralization (∼88% of OC) and promoted DOC production (∼12% of OC) and lateral fluxes of IC (∼53% of produced IC). Differences in shallow‐versus‐deep permeability contrasts led to smaller difference (<10%) than discharge‐induced differences and were most pronounced under wet conditions. High permeability contrasts (low vertical connectivity) enhanced lateral fluxes. Model results generally delineate hillslopes as active CO2 producers and vertical carbon transporters under dry conditions, and as active DOC producers and lateral carbon transporter under wet conditions.
Electromyographic assessment of the shoulder girdle and arm muscles during exercises with axial and rotational loads

ABSTRACT The knowledge of the electromyographic activity produced dur-ing shoulder exercises can help in determining its clinical applica-bility. The purpose of this study was to assess the influence of theload direction and the extremity condition on the electrical activityof the shoulder girdle and upper limb muscles during exerciseswith fixed distal extremity and external axial load (FEAL) and mo-bile extremity with rotational external load (MERL). Twenty 23.2 ±0.9 years old female sedentary volunteers were selected. The tri-ceps brachii, biceps brachii, major pectoral, trapezium and deltoidmuscles were assessed. The surface electromyography was re-corded during two FEAL and two MERL exercises using 100% ofthe previously established maximal resistance. The RMS valuesnormalized by the maximal voluntary contraction were comparedby a mixed effect model with 5% significance level. In these ex-perimental conditions, the results found in the present study haveshown that similar exercises classified by extremity condition andload direction applied on the upper limbs promote similar levels ofthe electromyographic activity only in part of the assessed mus-cles. These findings discuss the ability of the classification sys-tem used in this study to predict the type of the muscular responseexpected during different tasks with the same classification.
Food insecurity and severe mental illness: understanding the hidden problem and how to ask about food access during routine healthcare

SUMMARY Food insecurity occurs when an individual lacks the financial resources to ensure reliable access to sufficient food to meet their dietary, nutritional and social needs. Adults living with mental ill health, particularly severe mental illness, are more likely to experience food insecurity than the general adult population. Despite this, most interventions and policy reforms in recent years have been aimed at children and families, with little regard for other vulnerable groups. Initiating a conversation about access to food can be tricky and assessing for food insecurity does not happen in mental health settings. This article provides an overview of food insecurity and how it relates to mental ill health. With reference to research evidence, the reader will gain an understanding of food insecurity, how it can be assessed and how food-insecure individuals with severe mental illness can be supported. Finally, we make policy recommendations to truly address this driver of health inequality.
CSNTSteg: Color Spacing Normalization Text Steganography Model to Improve Capacity and Invisibility of Hidden Data

The rapid growth of online communication has increased the demand for secure communication. Most government entities, healthcare providers, the legal sector, financial and banking, and other industries are vulnerable to information security issues. Text steganography is one way to protect secure communication by hiding secret messages in the cover text. Hiding a large amount of secret information without raising the attacker’s suspicion is the main challenge in steganography. This paper proposes the Color and Spacing Normalization stego (CSNTSteg) model to resolve the low capacity and invisibility problems in text steganography. CSNTSteg consists of two stages: the pre-embedding stage, which achieves high capacity by utilizing RGB coding and character spacing. It is designed to increase the number of bits per location and usable characters. Besides, it applies the Huffman coding technique to compress the secret message to add more capacity enhancement. The second stage is color and space normalization, which accomplishes high invisibility by normalizing the RGB coding and character spacing of the cover and stego text. CSNTSteg overcomes the color difference issue between the cover and stego texts regardless of the color of the cover text. To assess the quality of CSNTSteg, the experimental results are compared with existing works. CSNTSteg shows superior capacity over the existing studies with a percentage of 98.85%. CSNTSteg also achieves high invisibility by reducing the color difference with a percentage of 4.7% and 5.07% for black and colored cover text, respectively. Furthermore, CSNTSteg improves robustness by 94.22% by reducing the distortion in stego text. Overall, the CSNTSteg model embeds a high capacity of secret data while maintaining invisibility and security, offering a new perspective on text steganography to protect against visual and statistical attack issues.
Assessing the Synergistic Activity of Clarithromycin and Therapeutic Oils Encapsulated in Sodium Alginate Based Floating Microbeads

We developed alginate-based floating microbeads of clarithromycin with therapeutic oils for the possible eradication of Helicobacter pylori (H. pylori) infections by enhancing the residence time of the carrier at the site of infection. In pursuit of this endeavor, the alginate was blended with hydroxy propyl methyl cellulose (HPMC) as an interpenetrating polymer to develop beads by ionotropic gelation using calcium carbonate as a gas generating agent. The developed microbeads remained buoyant under gastric conditions for 24 h. These microbeads initially swelled and afterwards decreased in size, possibly due to the erosion of the polymer. Furthermore, swelling was also affected by the type of encapsulated oil, i.e., swelling decreased with increasing concentrations of eucalyptus oil and increased with increasing concentrations of oleic acid. Antibacterial assays of the formulations showed significant antibacterial activity against Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli); these assays also showed synergistic activity between clarithromycin and therapeutic oils as evident from the higher zone of inhibition of the microbeads as compared to the pure drug and oils. Scanning electron microscopy (SEM) images revealed a smoother surface for oleic acid containing the formulation as compared to eucalyptus oil containing the formulation. Differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) revealed the development of a stable formulation, while Fourier transform infrared spectrophotometry (FTIR) studies did not reveal any interaction between the polymers and the active ingredients. Optimized formulations (CLM3 and CLM6) were designed to release the drug in a controlled manner in gastric media by Fickian diffusion. Conclusively, the developed microbeads are a promising carrier to overcome the narrow therapeutic index and low bioavailability of clarithromycin, while the presence of therapeutic oils will produce synergistic effects with the drug to eradicate infection effectively.
3D Printing of Artificial Breast Mould Based on the Real Medical Images

Three-dimensional (3D) printing and reverse engineering are usually superior to traditional teachnologies due to patient-tailored approach, scalability, and lower costs. This paper aims at the presentation of the own concept of the use of 3D medical images and reverse engineering for breast reconstruction for education and everyday clinical practice purposes. Presented concept of 3D breast reconstruction constitutes a relatively novel solution, and its further development may lead to the novel family of of patient-tailored solutions and optimized 3D-based technology seful in everyday clinical practice and online e-learning system.
A Low Computational Cost Method for Mobile Malware Detection Using Transfer Learning and Familial Classification Using Topic Modelling

With the extensive use of Android applications, malware growth has been increasing drastically. The high popularity of Android devices has motivated malware developers to attack these devices. In recent times, most researchers and scholars have used deep learning approaches to detect Android malware. Although deep learning techniques provide good accuracy and efficiency, they require high computational cost to train huge and complex data sets. Hence, there is a need for an approach that can efficiently detect novel malware variants with a minimum computational cost. This paper proposes a novel framework for detecting and clustering Android malware using the transfer learning and the topic modelling approach. The transfer learning approach minimizes new training data by transferring well-known features from a qualified source model to a destination model, and hence, a high amount of computational power is not required. In addition, the proposed framework clusters the detected malware variants into their corresponding families with the help of Latent Dirichlet Allocation and hierarchical clustering techniques. For performance assessment, we performed several experiments with more than 50K Android application samples. In addition, we compared the performance of our framework with that of similar existing traditional machine learning and deep learning models. The proposed framework provides better accuracy of 98.3% during the classification stage by using the transfer learning approach as compared to other state-of-the-art Android malware detection techniques. The high precision value of 98.7% is obtained during the clustering stage while grouping the obtained malicious applications into their corresponding malware families.
Electrothermal Modeling of IGCT: Application to Hybrid DC Circuit Breaker

IGCT has broad application prospects in DC power grid because of its characteristics of low on-state voltage drop, high inrush current resistance and low manufacturing cost. However, few literatures have been published on the application of IGCT in DC grid. The research on the electrical and thermal characteristics of IGCT in such application scenarios is not sufficient, and the simulation is not accurate enough. Based on the internal structure and manufacturing process, the IGCT model is built in Sentaurus TCAD and its quasi-static characteristics are calibrated. Then the model is applied to the topology of DC circuit breaker, and the correctness of the model is verified by comparing with the experiment. The current density distribution and temperature rise characteristics of IGCT during on-off are studied.
Pseudo-Steady Diffusional Growth or Collapse of Bubbles Rising in Time Dependent Pressure Fields

Abstract : A pseudo-steady convective model for bubble diffusional growth or collapse while rising in time dependent pressure fields has been developed. The bubble model assumes the neglect of added mass, history, and interfacial momentum terms. Criteria are developed to show when these terms affect the bubble motion.The pressure fields investigated are a simple hydrostatic head, a decompression hydrostatic head, and a hydrostatic head with the oscillation of the reference pressure (atmosphere above the hydrostatic head). Keywords: Diffusion equations; Velocity profiles; Bubble motion; Bubble dynamics; Ship wakes; Fluid dynamics.
Frequency-Based Representation of Massive Alerts and Combination of Indicators by Heterogeneous Intrusion Detection Systems for Anomaly Detection

Although the application of a wide range of sensors has been generalized through the development of technology, the processing of massive alerts generated through data analysis and monitoring remains a challenge. This problem is also found in cyber security because the intrusion detection system (IDS) produces a tremendous number of alerts. Massive alerts not only significantly increase resources for analysis, but also make it difficult to analyze the overall situation of the system. In order to handle massive alerts, we propose using an indicator as a frequency-based representation. The proposed indicator is generated from categorical parameters of alerts that occur within a unit time utilizing frequency and is used for situational awareness with machine learning to detect whether there is a threat or not. The advantage of using indicators is that they can determine the situation for a period without analyzing individual alerts, which helps security experts to recognize the situation in the system and focus on targets that require in-depth analysis. In addition, the conversion from the categorical parameters which is highly related to analysis to numeric parameter allows for applying machine learning. For performance evaluation, we collect data from an HAI testbed similar to real critical infrastructure and conduct experiments using indicators and XGBoost, a classification machine learning algorithm against five famous vulnerability attacks. Consequently, we show that the proposed method can detect attacks with more than 90 percent accuracy, and the performance is enhanced using heterogeneous intrusion detection systems.
An Overview: Organophosphate Flame Retardants in the Atmosphere

Organophosphate flame retardants (OPFRs) have been adopted as safer alternatives to polybrominated biphenyl ether flame retardants. However, because of their ubiquitous presence in the environment and the growing evidence of health risks, there has been growing research interest in understanding the fate and effects of these compounds in the environment. This review focuses on the current knowledge of OPFRs in the atmosphere. There is no consensus on the target OPFR for analysis, making comparisons challenging. However, we can still conclude that OPFRs partition mainly in the particulate phase, and chlorinated-OPFRs are the main compounds in the atmosphere. The general concentration trends from the literature were: PM 2.5 > TSP > air. However, the gas-phase OPFRs in the published studies might be significantly underestimated because of sampling artifacts. OPFRs were also found in remote environments in Antarctica, the Arctic, and high mountains, suggesting long-range transport potential. For example, concentrations up to 92.3 ± 13.8 pg m –3 in the air have been measured in the Antarctic. Lastly, atmospheric transformations of some OPFRs have been reported as more toxic and persistent than parent compounds, indicating additional risk. However, they are currently not measured or included during risk assessment.