Miracle-dz/UniFAR-FTUs huggingface dataset:
Optical Time Domain Reflectometry for Simultaneously Characterizing Forward and Backward Crosstalk Along Multi-Core Fibers

Uncoupled multi-core fibers (MCFs) are promising transmission media for realizing long-haul and large-capacity transmission using space division multiplexing. When the assignment of the signal transmission directions in adjacent cores is unidirectional or bidirectional, the forward and backward crosstalk respectively limits the capacity and distance of the transmission system using uncoupled MCFs. Understanding both the forward and backward crosstalk is crucial for determining an optimal transmission scheme according to traffic demands. In this paper, we propose a novel method based on optical time domain reflectometry for simultaneously characterizing the forward and backward crosstalk along uncoupled MCFs. We measured the forward and backward crosstalk along a four-core fiber by the proposed method. The forward and backward crosstalk obtained with the proposed method agreed well with those measured with unidirectional and bidirectional transmission methods. Since the proposed method enables us to simultaneously evaluate the forward and backward crosstalk along uncoupled MCFs from one end, it would be useful for testing those characteristics during the construction of MCF transmission lines.
Streptococcus intermedius Bacteremia and Pyogenic Liver Abscess in a Patient With No Risk Factors

This case report depicts a 39-year-old male with no significant past medical history who was admitted for fever of unknown origin and sepsis. He was then found to have Streptococcus intermedius bacteremia and pyogenic liver abscess. The treatment course was complicated by pleural empyema leading to readmission. The case presented here adds to the medical literature, where a clear underrepresentation has been noted, and outlines a unique case of S. intermedius liver abscess complicated by pleural empyema in a patient without significant medical history, risk factors, or typical physical exam findings.
Hardening Substations against Supply Chain Attacks Under Operational Constraints

Supply chain attacks, which are attacks that exploit vulnerabilities injected into devices before their shipment or during firmware updates, represent an increasingly important security threat to the smart grid. One obvious way to prevent supply chain attacks is to replace distrusted suppliers. However, this is not always feasible in practice due to operational constraints, such as one operator being bound to a limited number of suppliers. Although other hardening options, such as adding firewalls or relocating services, might be available, their effectiveness against supply chain vulnerabilities is unclear. Finally, relying on administrators’ experiences to manually fix supply chain vulnerabilities is prone to human errors. In this paper, we develop an automated hardening framework to improve the security posture of smart grid substations against supply chain attacks. The key idea is to unify a variety of hardening options (such as adding firewalls, patching known vulnerabilities, and diversifying components) under the same framework, such that it can improve the supply chain security even when suppliers cannot be easily replaced. Specifically, we first define models for supply chain attacks, hardening options, and the costs; we then instantiate the hardening framework through several use cases; finally, we evaluate our solution through simulations.
Evaluation of Ventilation Methods and Cardiorespiratory Outcomes in Children with Foreign Body Aspiration Undergoing Rigid Bronchoscopy Within 2008 - 2019

Background: Foreign body aspiration is a common accident with life-threatening outcomes. Rigid bronchoscopy is considered an effective tool in the diagnosis and treatment of this complication; however, there is still no agreement on the preferred ventilation method for the treatment of patients with foreign body aspiration. Objectives: This study aimed to compare the cardiovascular and respiratory outcomes followed by ventilation methods in patients with foreign body aspiration undergoing rigid bronchoscopy. Methods: In this cross-sectional study, the information was recorded from 90 patients’ files, including age, gender, aspirated foreign body, interval from aspiration to referral, signs, and symptoms, comorbidity, bronchoscopic results, ventilation method during the procedure, occurrence of respiratory complications, heart rate, blood pressure, arterial oxygen saturation, length of hospital stay, and mortality of patients. The data were analyzed by SPSS software (version 22). A p-value less than 0.05 was considered statistically significant. Results: Out of 90 reviewed cases, 57.67% were male, and the mean age was 2.06 ± 1.25 years. The most common aspirated foreign body was peanuts (38.89%). The most common clinical signs were cough (81.11%) and wheezing (51.11%). The most common symptoms were wheezing (54.44%) and decreasing respiratory sounds (46.67%). Bronchoscopy was associated with foreign body extraction in 65.6% of the patients. The relationship between the ventilation method with blood pressure changes, length of hospital stay, cardiopulmonary complications, and oxygen saturation was not significant; however, the cases with spontaneous breathing referred at shorter intervals from aspiration and experienced fewer heart rate changes. Conclusions: The results of the present study showed no preference between spontaneous and controlled ventilation during rigid bronchoscopy in patients with foreign body aspiration.
Missing Values for Classification of Machine Learning in Medical data

Missing values are an unavoidable problem for classification tasks of machine learning in medical data. With the rapid development of the medical system, large scale medical data is increasing. Missing values increase the difficulty of mining hidden but useful information in these medical datasets. Deletion and imputation methods are the most popular methods for dealing with missing values. Existing studies ignored to compare and discuss the deletion and imputation methods of missing values under the row missing rate and the total missing rate. Meanwhile, they rarely used experiment data sets that are mixed-type and large scale. In this work, medical data sets of various sizes and mixed-type are used. At the same time, performance differences of deletion and imputation methods are compared under the MCAR (Missing Completely At Random) mechanism in the baseline task using LR (Linear Regression) and SVM (Support Vector Machine) classifiers for classification with the same row and total missing rates. Experimental results show that under the MCAR missing mechanism, the performance of two types of processing methods is related to the size of datasets and missing rates. As the increasing of missing rate, the performance of two types for processing missing values decreases, but the deletion method decreases faster, and the imputation methods based on machine learning have more stable and better classification performance on average. In addition, small data sets are easily affected by processing methods of missing values.
Kinematic Transfer Learning of Sampling Distributions for Manipulator Motion Planning

Recent research has shown that guiding sampling-based planners with sampling distributions, learned from previous experiences via density estimation, can significantly decrease computation times for motion planning. We propose an algorithm that can estimate the density from the experiences of a robot with different kinematic structure, on the same task. The method allows to generalize collected data from one source manipulator to similarly designed target manipulators, significantly reducing the computation time for new queries for the target manipulator. We evaluate the algorithm in two experiments, including a constrained manipulation task with five different collaborative robots, and show that transferring information can significantly decrease planning time.
Intrusion Distance and Reaction Time Estimation for Safe and Efficient Industrial Robots

Circular economy and agile manufacturing require a safe and efficient industrial robot system working in close human proximity. Although, close proximity local sensing enables safe collaboration with small cobots. However, they cannot ensure safety at high velocities with a heavy-duty industrial robot. Stereo-camera and 3D LiDAR-based touch-less global sensing methods exist, but they do not address the safety standards. This work proposes a novel method for estimating the safety parameters in speed and separation monitoring mode for 3D vision sensors. Accurate estimation of these parameters ensures compact sensing zones. Thus, enabling efficient human-robot collaboration for permanent operator presence. The method requires less effort in setup. The developed software with a graphical user interface enables workers from wide technical expertise to perform safety measurements at a precision of ±15ms in reaction time estimation. The experiments are repeatable and capture the statistical data for low error in estimation. The estimated parameters for two exemplary 3D sensors enable a human to work in close proximity of 20 cm while enabling safety from a collision.
Prevalence of Mycoplasma bovis Infection in Calves and Dairy Cows in Western Australia

Simple Summary Mycoplasma bovis is an emerging pathogen of economic and welfare concern for both adult and young cattle. A study was conducted to determine the prevalence of M. bovis in adult cows and calves in the southwest region of Western Australia. Nasal swabs and blood samples were collected from the animals and bulk tank milk samples were assessed for both seroprevalence and active infections of M. bovis infections in adult cows and calves. The study recorded a high seroprevalence of M. bovis in 699 apparently healthy adult lactating cows and 495 young calves on 29 dairy farms. The herd-level seroprevalence was also detected as being higher in both adult lactating cows and calves. No current active infections were recorded on the farms. The female calves and pure Holstein–Friesian animals were found to be twice as likely to be seropositive for M. bovis compared to male calves and the Holstein–Friesian crossbred calves. The high seroprevalence of M. bovis in both adult and young cattle in the southwest dairy farms of Western Australia warrants more effective farm biosecurity measures and further evaluation of the current prevention and management measures practiced on the farms. Abstract Mycoplasma bovis (M. bovis) can cause a multitude of diseases in cattle, with detrimental effects on the farm economy and the welfare of both adult and young cattle. The objective of this study was to determine the prevalence of M. bovis in adult cows and calves in the south-west region of Western Australia. A cross-sectional study was conducted on 29 dairy farms with 699 apparently healthy adult lactating cows and 495 young calves during 2019–2020. Nasal swabs and blood samples collected from the animals and bulk tank milk (BTM) samples were assessed for M. bovis-specific proteins and antibodies by using polymerase chain reaction (PCR) and Mycoplasma immunogenic lipase A- Enzyme-Linked Immune Sorbent Assay (MilA ELISA). A seroprevalence of 42.5% (95% CI: 38.9–46.2) and 61% (95% CI: 56.6–65.2) was found in adult lactating cows and calves, respectively. The herd-level seroprevalence of M. bovis ranged from 4% (95% CI: 07–19.5) to 92% (95% CI: 75.0–97.8) in adult lactating cows and 25% (95% CI: 10.2–49.5) to 87% (95% CI: 67.9–95.5) for calves in these farms. None of the BTM and nasal swab samples were positive for M. bovis, indicating an absence of any current active infections on the farms. The female calves and pure Holstein–Friesian animals are twice as likely to be seropositive for M. bovis compared to male calves (OR 2.4; 95% CI: 1.7–3.5) and Holstein–Friesian crossbred calves (OR 2.4; 95% CI: 1.7–3.5). The high seroprevalence in both adult and young cattle in the southwest dairy farms of Western Australia warrants more effective farm biosecurity measures and further evaluation of the current prevention and management measures practiced on the farms.
Yield forecast of forage oat due to climate change in the northwest of Chihuahua, Mexico

Climate change can affect crop productivity in a particular region. The objective was to identify the variability in the production of dryland forage oat ( Avena sativa L.) using a stochastic function and analyze their relationship with temperature and precipitation as affected by sceneries of climate change in the northwestern region of Chihuahua, Mexico. It was obtained 13 years of statistical information (2001-2013) at meteorological stations in the municipalities of Bachiniva and Namiquipa, using information on July, August and September months, where it is concentrated about 80% of annual precipitation. Information was taken from HADCM3 (2001) model that describes the behavior of climatic variables under pollutant emission scenarios A2 and B2 on the years 2050 and 2080. The changes in climate variables in the short term (2050) was taken and it is expected to increase both in temperature and precipitation, allowing in the case of Bachiniva improve the production of oats from 3.57 t/ha to 8.11 t/ha under pollutant emission scenario A2. In the long term (2080) a permanent increase in temperature is expected, and no matter how much precipitation improve, this will cause in the current oat producing regions fail to develop crops for harvest.
Design and Trajectory Tracking Control of a New Bi-Copter UAV

A pair of vectored thrusts brings versatility and power efficiency to bi-copter unmanned aerial vehicles (UAVs). One common difficulty confronted by state-of-the-art bi-copter UAVs is the non-minimum phase nature, which puts fundamental limitations in the system and challenges the control. This letter introduces a new minimum phase bi-copter UAV that outperforms the conventional bi-copter UAV on attitude control with faster angular response and halved settling time. The comparison is made based on the optimal performance found by a particle swarm optimization (PSO)-based attitude controller on SO(3). The differential flatness property of generic bi-copter UAVs considering servo dynamics is analyzed. Flatness-based trajectory generation and tracking control of the new bi-copter UAV are described in detail together with flight experiments demonstrating the performance of the bi-copter flying through a drone race gate following a four-dimensional trajectory.
Optimal ventilation strategies for balancing indoor air quality, comfort and energy use in educational buildings. the IAQ4EDU project.

Currently, there is great concern about adequate indoor ventilation to prevent adverse effects on occupants’ health, and, within educational buildings, to ensure students and professors’ well-being and improve learning processes. This paper aims to present an innovative framework, developed within the IAQ4EDU project, to improve the effectiveness of the ventilation strategies in educational centres considering the indoor air quality, thermal comfort, energy consumption and global costs. This consists on characterise indoor air quality, simulate building ventilation strategies using reduced order models and develop multi criteria decision making method. 40 classrooms located in 20 Spanish educational buildings are used as case study.
Visualization of noise current distribution in an SiC power module using near-field magnetic scanning

: This report investigates noise current distribution in the switching operation of a silicon carbide (SiC) power module for optimization of the layout and packaging design. The noise current distribution is measured via the intensity of the near- ﬁ eld magnetic ﬁ eld, and the measurement methodology visualizes the noise current distribution on a wiring plane in the power module. The effect of a direct-current-link (DC-link) snubber capacitor on suppressing the voltage overshoot and ringing oscillation is evaluated using the proposed noise current identi ﬁ cation method.
Assessment of CALIOP-Derived CCN Concentrations by In Situ Surface Measurements

The satellite-based cloud condensation nuclei (CCN) proxies used to quantify the aerosol-cloud interactions (ACIs) are column integrated and do not guarantee the vertical co-location of aerosols and clouds. This has encouraged the use of height-resolved measurements of spaceborne lidars for ACI studies and led to advancements in lidar-based CCN retrieval algorithms. In this study, we present a comparison between the number concentration of CCN (nCCN) derived from ground-based in situ and spaceborne lidar cloud-aerosol lidar with orthogonal polarization (CALIOP) measurements. On analysing their monthly time series, we found that about 88% of CALIOP nCCN estimates remained within a factor of 1.5 of the in situ measurements. Overall, the CALIOP estimates of monthly nCCN were in good agreement with the in situ measurements with a normalized mean error of 71%, normalized mean bias of 39% and correlation coefficient of 0.68. Based on our comparison results, we point out the necessary measures that should be considered for global nCCN retrieval. Our results show the competence of CALIOP in compiling a global height- and type-resolved nCCN dataset for use in ACI studies.
Switched 4-to-1 Transimpedance Combining Amplifier for Receiver Front-End Circuit of Static Unitary Detector-Based LADAR System

Laser detection and ranging (LADAR) systems are commonly used to acquire real-time three-dimensional (3D) images using the time-of-flight of a short laser pulse. A static unitary detector (STUD)-based LADAR system is a simple method for obtaining real-time high-resolution 3D images. In this study, a switched 4-to-1 transimpedance combining amplifier (TCA) is implemented as a receiver front-end readout integrated circuit for the STUD-based LADAR system. The 4-to-1 TCA is fabricated using a standard 0.18 μm complementary metal-oxide-semiconductor (CMOS) technology, and it consists of four independent current buffers, a two-stage signal combiner, a balun, and an output buffer in one single integrated chip. In addition, there is a switch on each input current path to expand the region of interest with multiple photodetectors. The core of the TCA occupies an area of 92 μm × 68 μm, and the die size including I/O pads is 1000 μm × 840 μm. The power consumption of the fabricated chip is 17.8 mW for a supplied voltage of 1.8 V and a transimpedance gain of 67.5 dBΩ. The simulated bandwidth is 353 MHz in the presence of a 1 pF photodiode parasitic capacitance for each photosensitive cell.
Infiltration Behaviour of Polymer‐Modified Porous Concrete and Porous Asphalt Surfaces used in SuDS Techniques

Permeable pavements are one of the world's most widely applied techniques for source control in sustainable drainage systems. Porous concrete (PC) and porous asphalt (PA) are two of the most studied surfaces in terms of runoff reduction. Nevertheless, previous research has highlighted a lack of a comprehensive laboratory methodology for the analysis of the topographical variables, runoff surface length (RSL) and surface slope (SS), and their impact on the infiltration behaviour of these porous surfaces. This research paper analyses the infiltration performance of polymer-modified PC and PA-16 for 0, 3, 5, 7 and 10% slope on newly built and clogged surfaces, using an improved version of the Cantabrian fixed infiltrometer and LCS permeameter, enabling comparison of the infiltration behaviour. This laboratory methodology has proved to be well suited to the study of the infiltration behaviour of porous surfaces and also to the quantification of their infiltration capacity reduction due to clogging. As main results, this paper presents regression models with high R2 obtained with a confidence level of 95%, based on RSL and SS variables, corresponding to each porous surface and clogging level.
Average case analysis of graph-searching algorithms

We estimate the expected value of various search quantities for a variety of graph-searching methods, for example depth-first search and breadth-first search. Our analysis applies to both directed and undirected random graphs, and it covers the range of interesting graph densities, including densities at which a random graph is disconnected with a giant connected component. We estimate the number of edges examined during the search, since this number is proportional to the running time of the algorithm. We find that for hardly connected graphs, all of the edges might be examined, but for denser graphs many fewer edges are generally required. We prove that any searching algorithm examines $\Theta$($n$ log $n$) edges, if present, on all random graphs with $n$ nodes but not necessarily on the complete graphs. One property of some searching algorithms is the maximum depth of the search. In depth-first search, this depth can be used to estimate the space needed for the recursion stack. For random graphs of any density, even for disconnected graphs, we prove that this space is $\Theta$($n$). On the other hand, the depth of breadth-first search is $\Theta$(log $n$/log($pn$)), where $p$ is the probability of the existence of an edge. The size of the data structure needed by any searching algorithm is proved to be $\Theta$($n$). If the search terminates at a particular node, any searching algorithm needs a data structure of size $\Theta$($n$), and examines only $\Theta$($n$) edges. Finally, we derive similar results for variants of the above searching algorithms, more general classes of searching algorithms, and for random graphs with multiple edges. These results are verified through simulations. The techniques employed to permit simulations of big graphs (few millions of nodes) and the results obtained are of general interest, especially to those performing similar experiments.
Patient-tailored Adaptive Control for Robot-aided Orthopaedic Rehabilitation

Robot-aided rehabilitation is pushing forward novel robotic architectures to provide physical therapy. This paper presents a patient-tailored control architecture for upper-limb robot-aided orthopaedic rehabilitation capable of i) adapting the robot workspace on the basis of patient Range of Motion (RoM); ii) generating a tunnel, around the desired path to be followed by the patient, which guarantees spatial autonomy; iii) introducing a back-wall inside the tunnel sliding with variable speed on the basis of patient performance to ensure temporal autonomy; iv) rehabilitating to working gestures, thanks to a DMP-based trajectory planner, with the aim of favoring an effective translation of the patient's motor recovery results to the occupational sphere; v) ensuring a patient-tailored assistance also thanks to the evaluation of performance indicators. The designed system was validated demonstrating the adaptability of the system to orthopaedic patient motor imnrovements.