Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Guidance Trajectories for a Nanosat Docking with a Non-Cooperative Resident Space Object

There has been an increasing interest in on-orbit autonomous servicing and repair of satellites as well as controlled active debris removal (ADR) in the space industry recently. One of the most challenging tasks for servicing/repair as well as for ADR is the rendezvous and docking with a non-cooperative tumbling resident space object (RSO). This paper presents a propellant optimal maneuver profile for a servicing spacecraft to perform proximity operations and eventually dock with a non-cooperative target. The strategy is to find an optimal trajectory which will guide the servicing spacecraft to approach the tumbling satellite such that the two vehicles will eventually have no relative motion. Therefore, a subsequent docking or capture operation can be safely performed. The research described here elaborates on the previous work that studied the minimum-control-effort for a 3-D rendezvous to a tumbling object considering a full six-degree-of-freedom model of both chaser and target. The current work expands the scope by adding a new set of linearized equations of motion that capture not only the effect of the J2 geopotential disturbance force but also quadratic drag force. Typically, Hill's linearized equations of relative motion have been used for this analysis, but they fail to capture the effect of J2 disturbance force and drag on the chaser satellite. It appears that there is a need for a set of linearized equations that are as easy and useful as Hills equations, but at the same time capture the effect of the J2 disturbance and drag force acting on the spacecrafts. The current paper presents a set of linearized, constant coefficient differential equations that capture the J2 and quadratic drag perturbation. The importance of these perturbations is studied by doing trajectory propagation. Finally, it is shown for a control problem, the effect on the control-solution due to the formulated high fidelity dynamical constraints with respect to the simplified Hill's equations.
GeST: A Grid Embedding based Spatio-Temporal Correlation Model for Crime Prediction

Crime prediction greatly contributes to improving public safety in urban cities. Recent studies have achieved effectiveness by considering spatio-temporal crime distribution correlations among regions. However, with developments of advanced telecommunications and intelligent transportation in urban cities, urban regions tend to be more interacted and integrated, which makes existing methods difficult to capture in-depth geographical and contextual inter-area spatial correlations. To solve the problem, we propose the Grid-embedding based Spatio-Temporal correlation (GeST) model, which consists of grid-embedding module and crime graph prediction module. In the grid-embedding module, the convolutional AutoEncoder can explore distance-based inter-area spatial correlations and decompose crime distributions into hidden crime spatial bases. The bases are regarded as the representation of decomposed crime distribution. The Graph Convolutional Network (GCN) in grid-embedding module can capture contextual spatial correlations among feature-similar regions. After combining two types of grid-embedding vectors, the crime graph prediction module utilizes Long Short-Term Memory (LSTM) neural network to learn temporal correlations of crime distribution. Experiments conducted on two real-world datasets show that the proposed model achieves better prediction performance than other methods.
Critical Analysis of Botnet Detection Techniques for Web Applications

A botnet is a collection of computers in co-ordinated way to perform certain malicious activity. Recently, Botnet have become one of the most critical challenges. Every day botnet developed new evasion techniques to make their presence undetectable. So there should be a comparison of such botnet detection methodologies which needs more examination to show their efficiency and appropriateness for detecting malicious attacks. Although there are many different detection techniques have been developed still there are some challenges in detection technique to find out parameters in botnet attack. So there must be focus on developing more secure authenticated and reliable detection methods for botnet attack. The main aim of this paper is to provide an overview of botnet, its categories, detection techniques and analysis. By using this study researchers will able to work with efficient detection technique for further research in this particular area.
Knowledge Fusion and Spatiotemporal Data Cleaning: A Review

Knowledge fusion is aimed to establish the relationship between heterogeneous ontology or heterogeneous instances. Data cleaning is one of the key technologies for solving knowledge fusion problems. In this paper, we provide a brief survey of knowledge fusion and data cleaning. We first briefly introduce the importance and background of knowledge fusion and data cleaning. Then we discuss some recent methods for knowledge fusion and spatiotemporal data cleaning. Finally, we outline some future directions of knowledge fusion and data cleaning.
Submarine Target Sonar Image Segmentation Based on FCM Clustering with Three-Dimensional Gray Features

The accuracy of sonar image segmentation results directly affects the subsequent processing steps and the final understanding of sonar images, so the sonar image segmentation plays an important role in sonar application. Fuzzy c-Means (referred to as FCM) clustering can be used for image segmentation. This paper investigates the applications of FCM clustering with 10 kinds of three-dimensional gray feature vectors to sonar image segmentation. The experimental results show that sonar image segmentation using FCM clustering can obtain better segmentation results if three-dimensional feature selection is appropriate.
Quantum Computing for Enhancing Grid Security

This paper introduces quantum computing as a necessary and viable tool in addressing the needs of a modernized power grid. The application of quantum computing in enhancing physical security of the grid – an increasingly difficult problem to solve– is investigated. A comparative study based on mathematically proven computing performance measures shows the merits of the proposed method and further unveils the potential benefits of quantum computing in improving grid performance.
A Correlative Study of Film Lifetime, Hydrogen Content, and Surface Passivation Quality of Amorphous Silicon Films on Silicon Wafers

We examine correlations between the recombination lifetime and hydrogen content of hydrogenated amorphous silicon films (a-Si:H) and the surface passivation afforded by such films when deposited on crystalline silicon wafers, during annealing at 350–500 °C. Our results show that, as the annealing duration increases, both the a-Si:H recombination lifetime and the surface recombination velocity evolve at a similar rate to the hydrogen concentration. This suggests that the loss of hydrogen during annealing is the direct cause of the reduction in the a-Si:H film lifetime, and that the loss of hydrogen occurs both at the a-Si:H/c-Si interface as well as in the bulk of a-Si:H film. We calculated the activation energy of the surface depassivation reaction during annealing to be 0.62 ± 0.1 eV, which suggests that the depassivation reaction is limited by the migration of hydrogen within the film, without significant hydrogen trapping. Secondary-ion mass spectrometry further demonstrates the loss of hydrogen across the film thickness during annealing.
Feature Redundancy Based on Interaction Information for Multi-Label Feature Selection

Recent years, multi-label feature selection has gradually attracted significant attentions from machine learning, statistical computing and related communities and has been widely applied to diverse problems from music recognition to text mining, image annotation, etc. However, traditional multi-label feature selection methods employ cumulative summation strategy to design methods, which suffers from the problem of overestimation the redundancy of some candidate features. Additionally, the cumulative summation strategy will lead to the high value of the goal function when one candidate feature is completed related with one or few already-selected features, but it is almost independent from the majority of already-selected features. To address these issues, we propose a new multi-label feature selection method named Feature Redundancy Maximization (FRM), which combines the cumulative summation of conditional mutual information with the ‘maximum of the minimum’ criterion. Additionally, FRM can be rewritten as another form of multi-label feature selection method that employs interaction information as a measure of feature redundancy that obtains an accurate score of feature redundancy as the number of already-selected features increases. Finally, extensive experiments are implemented on fourteen benchmark multi-label data sets in comparison to six state-of-the-art methods. The experimental results demonstrate the superiority of the proposed method.
Metaheuristic for Solving Multi-Objective Job Shop Scheduling Problem in a Robotic Cell

This paper deals with the multi-objective job shop scheduling problem in a robotic cell (MOJRCSP). All the jobs are processed according to their operations order on workstations. Different from classical job shop scheduling problem, the studied problem considers that jobs’ transportation is handled by a robot. Also, the jobs are expected to be finished in a time window, instead of a constant due date. A mixed Integer Programming (MIP) model is proposed to formulate this problem. Due to the special characteristics of the studied problem and its NP-hard computational complexity, a metaheuristic based on Teaching Learning Based Optimization (TLBO) algorithm has been proposed. The proposed algorithm determines simultaneously the operations’ assignments on workstations, the robot assignments for transportation operations, and the robot moving sequence. The objective is to minimize the makespan and the total earliness and tardiness. Computational results further validated the effectiveness and robustness of our proposed algorithm.
Autoantibodies Blocking M3 Muscarinic Receptors Cause Postganglionic Cholinergic Dysautonomia

A 10‐year‐old girl presented with ileus, urinary retention, dry mouth, lack of tears, fixed dilated pupils, and diffuse anhidrosis 7 days after a febrile illness. We hypothesized that her syndrome was due to autoimmunity against muscarinic acetylcholine receptors, blocking their activation. Using an indirect enzyme‐linked immunosorbent assay for all 5 muscarinic receptors (M1–M5), we identified in the patient's serum antibodies that selectively bound to M3 receptors. In vitro functional studies confirmed that these autoantibodies selectively blocked M3 receptor activation. Thus, autoantibodies against M3 acetylcholine receptors cause acute postganglionic cholinergic dysautonomia. ANN NEUROL 2020;88:1237–1243
Commutability Assessment of Candidate External Quality Assessment Materials for Aminotransferase Activity Measurements Based on Different Approaches in China

Background Using commutable external quality assessment (EQA) materials is important for monitoring successful harmonization efforts. We assessed the commutability of four human serum pool (HSP) preparations to identify candidate EQA materials for alanine aminotransferase (ALT) and aspartate aminotransferase (AST) activity measurement. Methods One set each of 85 clinical samples (CSs) was collected for ALT and AST activity measurement. The 15 candidate EQA materials included four types of HSP preparations (A to D): materials A, C, and D contained human original recombinant (HOR) aminotransferases; materials B was mixed leftover samples. The CSs and 15 candidate EQA materials were analyzed using seven routine assays, and the ln-transformed results were analyzed in 21 assay pairs. Commutability was assessed using Deming regression, with a 95% prediction interval (CLSI approach) and the difference in bias with an error component model (International Federation of Clinical Chemistry and Laboratory Medicine [IFCC] approach). Results For ALT, all materials were commutable for 14–21 assay pairs according to the CLSI and IFCC approaches. For AST, B01–03 showed commutability for 14–21 assay pairs, and C01–03 and D01–03 showed commutability for no less than 10 assay pairs according to the two approaches. A01–06 were commutable for 9–16 assay pairs according to the CLSI approach, but for 6–9 assay pairs according to the IFCC approach. Conclusions Mixed leftover samples showed desirable commutability characteristics as candidate EQA materials for routine aminotransferase activity measurements. Human serum bases supplemented with HOR were commutable for most routine ALT activity measurements.
The risk factors for postoperative delirium in adult patients after hip fracture surgery: a systematic review and meta‐analysis

Postoperative delirium (POD) is the most common complication in patients after hip fracture surgery, and the incidence of POD is associated with mortality and disability in patients following hip fracture surgery. Therefore, this study aimed to identify the individual as well as anesthetic and operative risk factors associated with the POD in adult patients after hip fracture surgery.
Community engagement in outbreak response: lessons from the 2014–2016 Ebola outbreak in Sierra Leone

Documentation of structured community engagement initiatives and real-time monitoring of community engagement activities during large-scale epidemics is limited. To inform such initiatives, this paper analyses the Community Led Ebola Action (CLEA) approach implemented through the Social Mobilization Action Consortium (SMAC) during the 2014–2016 Ebola epidemic in Sierra Leone. The SMAC initiative consisted of a network of 2466 community mobilisers, >6000 religious leaders and 42 local radio stations across all 14 districts of Sierra Leone. Community mobilisers were active in nearly 70% of all communities across the country using the CLEA approach to facilitate community analysis, trigger collective action planning and maintain community action plans over time. CLEA was complemented by interactive radio programming and intensified religious leader engagement. Community mobilisers trained in the CLEA approach used participatory methods, comprised of an initial community ‘triggering’ event, action plan development and weekly follow-ups to monitor progress on identified action items. Mobilisers collected operational and behavioural data on a weekly basis as part of CLEA. We conducted a retrospective analysis of >50 000 weekly reports from approximately 12 000 communities from December 2014 to September 2015. The data showed that 100% of the communities that were engaged had one or more action plans in place. Out of the 63 110 cumulative action points monitored by community mobilisers, 92% were marked as ‘in-progress’ (85%) or ‘achieved’ (7%) within 9 months. A qualitative examination of action points revealed that the in-progress status was indicative of the long-term sustainability of most action points (eg, continuous monitoring of visitors into the community) versus one-off action items that were marked as achieved (eg, initial installation of handwashing station). Analysis of behavioural outcomes of the intervention indicate an increase over time in the fraction of reported safe burials and fraction of reported cases referred for medical care within 24 hours of symptom onset in the communities that were engaged. Through CLEA, we have demonstrated how large-scale, coordinated community engagement interventions can be achieved and monitored in real-time during future Ebola epidemics and other similar epidemics. The SMAC initiative provides a practical model for the design, implementation and monitoring of community engagement, integration and coordination of community engagement interventions with other health emergency response pillars, and adaptive strategies for large-scale community-based operational data collection.
A Distance-Threshold kNN Method for Imputing Medical Data Missing Values

— In medical filed, missing data often existed, which maybe result in the bias of research results. Therefore, this study proposes a new imputation method, that is a nearest neighborhood method based on distance threshold to impute missing value. The proposed imputation method has two merits: (1) utilize distance threshold to adjust the optimal nearest neighborhood for estimating missing values, (2) the proposed method compares with other imputation methods in medical data missing values. This study collected the stroke dataset from the International Stroke Trial (IST) to verify the proposed method, the result shows that the proposed method is better than other imputation methods, it means that the proposed method can be effectively utilized in practical medical dataset.
Clinical efficacy and safety of the PRO-glide device as a sUture-mediated ClosurE in Thoracic EndoVascular Aortic Repair in patients with previous groin intervention (from the PRODUCE-TEVAR Trial)

Background While the percutaneous approach is increasingly preferred, suture-mediated closure devices have been put into clinical practice to close the femoral artery during procedures requiring a large-sized introducer. However, scar in the groin is considered a contraindication or an exclusion criterion for percutaneous procedures. The aim of our study was to investigate the outcomes and safety of Pro-Glide device as suture-mediated closure device in patients who underwent thoracic endovascular aortic repair with percutaneous femoral access ≥22 F who had previous groin intervention. Methods A total of 73 patients who underwent endovascular repair with percutaneous femoral access were retrospectively included in the study. Previous groin intervention was defined as history of open surgical access or large sheath insertion (>18 F) to femoral artery because of endovascular or valvular intervention. Patients were divided into two groups as who had previous groin intervention PGI (+) and had not PGI (−). Results A total of 73 patients [60 male (82.2%)] were included in the study. Seventeen patients had PGI, and 56 did not. When groups were compared in terms of sheath sizes, a significantly higher sheath sizes were used in PGI (+) patients (24.5 ± 1.1 F vs. 23.8 ± 0.9 F, p = 0.005). The overall success rate in the femoral approach with pre-close technique was statistically insignificant between two groups (94.1% vs. 96.4%, p = 0.55). One patient in PGI (+) group and two patients in PGI (−) had technical failure for percutaneous femoral approach. One patient (5.9%) in PGI (+) group and one patient (1.8%) in PGI (−) group had femoral complications after the procedures; however, there was no significant difference between the groups in terms of complications (5.9% vs. 1.8%, p = 0.13). Conclusion Pro-Glide device may be a safe and less invasive method for femoral access in patients with PGI and might not be considered as a contraindication for patients with history of PGI.
High prevalence of low dairy calcium intake and association with insomnia, anxiety, depression and musculoskeletal pain in university students from Jordan

Abstract Objective: To assess dairy Ca intake and investigate its relationship with insomnia and other common co-morbidities including anxiety, depression and musculoskeletal pain (MSP) among university students. Design: Cross-sectional study. Setting: University, Irbid, Jordan. Participants: Male and female individuals (n 1000), aged 20·87 ± 2·69 years. Results: Low dairy Ca intake (<1000 mg/d) was reported by 96·5 % of participants, and moderate to severe insomnia reported by 15·6 % of participants. Abnormal anxiety and depression scores were reported by 26·2 and 18·0 % of participants, respectively. MSP was reported by 42·9 % of participants. Participants with moderate to severe insomnia had lower dairy Ca, higher anxiety and depression scores and higher measures of MSP compared to participants with no insomnia (P-values < 0·05). Dairy Ca was weakly inversely correlated with Insomnia Severity Index (ISI) score, depression score and measures of MSP (P-values < 0·05). Regression analysis indicated that insomnia was predicted by low dairy Ca, anxiety, depression, MSP and smoking (P-values < 0·05). Both anxiety and depression were predicted by increased ISI score (P-values < 0·05), while depression alone was predicted by low dairy Ca (P-value < 0·01). MSP was predicted by increased ISI and anxiety scores (P-values < 0·05). Conclusions: Low dairy Ca was highly prevalent and associated with insomnia and depression among university students. Individuals should be advised to increase dietary Ca intake to achieve the recommended daily amount. Further research is required to investigate a potential causal relationship between low Ca and both insomnia and its related co-morbidities.
Organic matter processing by larval black flies in a temperate woodland stream

We studied the role of a dense aggregation of filter-feeding black fly larvae in organic matter processing in a north temperate woodland stream. Black fly guts contained algae. individual bacterial cells, bacterial clusters, and detritus, but bacterial clusters and especially detritus were a much larger proportion of gut material. Particle size distribution and size variance within the largest size fraction of fine particulate organic matter (FPOM) studied (>100 μm) decreased downstream of black flies, due to the high proportion of uniform-sized fecal pellets in this size category. Ash free dry mass (AFDM) of FPOM was 28% higher downstream of the aggregation than upstream. even though black flies were actively feeding. As a control for factors other than black flies that might alter FPOM mass, we killed the black fly larvae with a highly specific microbial larvicide (Bacillus thuringiensis var. israelensis or Bti), then remeasured FPOM mass at the same stations. Following Bti treatment, FPOM mass was only about 5.6% higher downstream of the aggregation, an increase that was likely because FPOM flocculates from dissolved organic matter (DOM) due to turbulence. These data suggested that black flies were feeding from another pool of organic matter in addition to suspended FPOM, which we hypothesized to be DOM. Laboratory experiments using filtered (0.2 μm) stream water demonstrated that black flies ingested 14 C-labeled DOM. Our results indicate that the quantitative effects of black flies on FPOM in streams are linked to DOM ingestion. We suggest that a more complete examination of these processes is needed for a comprehensive understanding of organic matter processing in streams.
Exploiting a Single‐Crystal Environment to Minimize the Charge Noise on Qubits in Silicon

Electron spins in silicon offer a competitive, scalable quantum‐computing platform with excellent single‐qubit properties. However, the two‐qubit gate fidelities achieved so far have fallen short of the 99% threshold required for large‐scale error‐corrected quantum computing architectures. In the past few years, there has been a growing realization that the critical obstacle in meeting this threshold in semiconductor qubits is charge noise arising from the qubit environment. In this work, a notably low level of charge noise of S0 = 0.0088 ± 0.0004 μeV2 Hz−1 is demonstrated using atom qubits in crystalline silicon, achieved by separating the qubits from surfaces and interface states. The charge noise is measured using both a single electron transistor and an exchange‐coupled qubit pair that collectively provide a consistent charge noise spectrum over four frequency decades, with the noise level S0 being an order of magnitude lower than previously reported. Low‐frequency detuning noise, set by the total measurement time, is shown to be the dominant dephasing source of two‐qubit exchange oscillations. With recent advances in fast (≈μs) single‐shot readout, it is shown that by reducing the total measurement time to ≈1 s, 99.99% two‐qubit SWAP gate fidelities can be achieved in single‐crystal atom qubits in silicon.