Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review on Deep Learning Techniques for Railway Infrastructure Monitoring

In the last decade, thanks to a widespread diffusion of powerful computing machines, artificial intelligence has been attracting the attention of the academic and industrial worlds. This review aims to understand how the scientific community is approaching the use of deep-learning techniques in a particular industrial sector, the railway. This work is an in-depth analysis related to the last years of the way this new technology can try to provide answers even in a field where the primary requirement is to improve the already very high levels of safety. A strategic and constantly evolving field such as the railway sector could not remain extraneous to the use of this new and promising technology. Deep learning algorithms devoted to the classification, segmentation, and detection of the faults that affect the railway area and the overhead contact system are discussed. The railway sector offers many aspects that can be investigated with these techniques. This work aims to expose the possible applications of deep learning in the railway sector established on the type of recovered information and the type of algorithms to be used accordingly.
Wideband Dual Circularly Polarized Open-Ended Waveguide Antenna

A wideband dual circularly polarized antenna element based on open-ended waveguides is proposed in this paper. It adopts a cross-shaped structure with two orthogonal staggered arms, and each arm corresponds to a rectangular open-ended waveguide. Feed waveguides are respectively arranged on the side of the wide walls of the two open-ended waveguide arms, and the left-handed circularly polarization (LHCP), as well as the right-handed circularly polarization (RHCP) are excited. This structure removes the high axial ratio from the working band, thus realizing a wider axial ratio bandwidth. It is shown from the simulated results that the proposed dual circularly polarization open-ended waveguide antenna has an LHCP axial ratio band-width of 27.3 % and an RHCP axial ratio bandwidth of 28.5 %. This design lays the foundation for the subsequent construction of large-size arrays.
Dew contribution to the water balance in a semiarid coastal steppe ecosystem (Cabo de Gata, SE Spain)

Dewfall deposition can be a significant source of moisture in arid and semiarid ecosystems, thus contributing to improve daily and annual water balances. Occurrence, frequency and amount of dewfall were measured in the Balsa Blanca site (Cabo de Gata, Almeria, Spain) from January 2007 to May 2008. This area has a sparse vegetation cover dominated by Stipa tenaccisima combined with bare soil and biological soil crusts. Wetness sensors were used to detect occurrence and frequency of dew events. The single-source Penman—Monteith equation simplified for water condensation was used to calculate dew amount. Micro-meteorological conditions during dew formation were also measured. Dew condensation was recorded over 98% of nights during the study period. Dewfall length varied from 1 to 16 hours per day with an average of 6.3 ±0.5. hours per day. Average dew amount was 15.6±. 0.34 g H2O m-2.h-1 showing a relatively small variability throughout the year. Therefore dew amount was mostly dependent on dew duration. Dew episodes were longer in late autumn and winter and decreases along spring. Annual dewfall was 30.5 mm in 2007, which represents 15% of total rainfall. 2007 was a relatively wet year with 280 mm rainfall. Comparing contribution of dew to the local water balance during a wet (January-May 2007, 120 mm rainfall) and dry period (January-May 2008, 15 mm rainfall), we have found a dew contribution of 15% in 2007 and 128% in 2008. These results highlight the relevance of dew as a relatively small but constant source of water in arid ecosystems, as well as its significant contribution to the local water balance mainly during dry periods.
Issues of Interference-Resistant Processing for Electrocardiodiagnostics

The article is devoted to the features of interference-resistant processing of electrocardiosignals and the construction of an electrocardiodiagnostic system. Improving the interference immunity in the processing of electrocardiosignals makes it possible to improve the reliability of automatic conclusions and, in the end, improves the quality of cardiovascular disease diagnosis and treatment. The basic sources, types and characteristics of interference that distort electrocardiosignals are considered. The main issues with electrocardiosignals processing are outlined, promising paths to their resolution are supported, and a redundancy-based strategy for interference-resistant electrocardiosignals processing is proposed. Within the framework of the Hilbert-Huang transform, we propose an approach, that is based on the processing of the energy density surface, formed by the empirical electrocardiosignal modes. The methods and algorithms described in the article are implemented in the system of non-invasive electrocardiographic diagnostics for critical conditions. The developed system allows to reduce the burden on highly qualified cardiologists and to increase the effectiveness of emergency care for cardiovascular system conditions that threaten life.
SPACEBORNE SAR DATA FOR LAND-COVER CLASSIFICATION AND CHANGE DETECTION.

Supervised maximum-likelihood classifications of Seasat, SIR-A, and Landsat pixel data demonstrated that SIR-A data provided the most accurate discrimination (72 percent) between five land-cover categories. Spatial averaging of the SAR data improved classification accuracy significantly due to a reduction in both fading and within-field variability. The best multichannel classification accuracy (97.5 percent) was achieved by combining the SIR-A data with two Seasat images (ascending and descending orbits). In addition, semiquantitative analysis of Seasat-A digital data shows that orbital SAR imagery can be successfully used for multitemporal detection of change related to hydrologic and agronomic conditions by using simple machine processing techniques.
Outlier detection and correction for GRACE data to improve the continental water balance

GRACE ( Gravity Recovery and Climate Experiment) gravity measurements provide a direct measure of monthly changes in mass over the Earth’s continents. As such changes in mass mainly correspond to water storage changes, these measurements allow to close the continental water balance on large spatial scales and on a monthly time scale within the respective error bounds. When quantifying uncertainties, positive and negative peaks are detected in GRACE aggregated time series that do not correspond to hydrological or atmospheric signals. These peaks must be interpreted as outliers, which carry the danger of signal degradation. In this study an algorithm is developed to identify outliers and replace them with appropriate values. The procedure of outlier detection is verified by evaluating catchment based aggregated GRACE signals with ground truth from hydrology and atmospheric signals. The results show a significant improvement in the correlation of GRACE versus atmospheric and hydrological signals over 255 catchments. Also, the noise level is significantly reduced. 1. Problem Statement In order to obtain meaningful error estimates for remote sensing missions an evaluation with ground truth measurements is needed. However, ground truth measurements for an evaluation of GRACE mass estimates seem to be cumbersome [5]. However on landmasses, storage changes derived from hydrological data or fram atmospheric models can be used as ground truth for GRACE temporal derivatives aggregated over certain area (e.g. catchment) which is formulated as: dM dt ↔ P −R− ETa (1a) dM dt ↔ −MFD −R (1b) where dM/dt is derivative of aggregated mass deviation over individual catchments (hereafter mass derivative), P is precipitation, R is runoff, MFD is moisture flux divergence and ETa is actual evapotranspiration. In other words, hydrological (P − R − ETa) and atmospheric (−MFD − R) water storage change rates over any specific area are compared with mass changes rates. During comparison, positive and negative peaks are detected in GRACE aggregated time series that do not correspond to ground truth (figure 1). Jan 2003 Jan 2004 Jan 2005 Jan 2006 Jan 2007 Jan 2008 −100 0 100 200 Time E T a (m m /m on th ) −200 −100 0 100 200
weBurst can be Harmless: Achieving Line-rate Software Traffic Shaping by Inter-flow Batching

Traffic shaping is a common function at end hosts. Compared with hardware ones, software shapers are more flexible to be developed and deployed, and thus are very attractive. Nevertheless, software approaches are still unsatisfactory as they struggle to saturate 40Gbps and higher speed.While much effort has been made to reduce the intrinsic overhead of software traffic shaping, we find that it is the extrinsic overhead, such as PCIe communications and interrupts, that hinders shaping from achieving 40Gbps - 100Gbps speed. Batching is an effective way to amortize these overheads. However, blindly batching can degrade the network performance, as it introduces bursts into the network. Diving into the dilemma, we find that intra-flow burst is to blame for harming the network performance, while inter-flow burst, consisting of packets from different flows, can be naturally demultiplexed in the network.Based on the insight, we present FlowBundler, which can achieve efficient traffic shaping by inter-flow batching. Testbed experiments show that FlowBundler can achieve an accurate shaping of 98Gbps with a single CPU core, which is 2.6× better than state-of-the-art approaches. Large-scale simulations show that FlowBundler can batch packet transmissions without harming the network performance.
Vat Polymerization by Three-Dimensional Printing and Curing of Antibacterial Zinc Oxide Nanoparticles Embedded in Poly(ethylene glycol) Diacrylate for Biomedical Applications

Digital light processing (DLP) is a vat photopolymerization 3D printing technique with increasingly broad application prospects, particularly in personalized medicine, such as the creation of medical devices. Different resins and printing parameters affect the functionality of these devices. One of the many problems that biomedical implants encounter is inflammation and bacteria growth. For this reason, many studies turn to the addition of antibacterial agents to either the bulk material or as a coating. Zinc oxide nanoparticles (ZnO NPs) have shown desirable properties, including antibacterial activity with negligible toxicity to the human body, allowing their use in a wide range of applications. In this project, we developed a resin of poly(ethylene glycol) diacrylate (PEGDA), a cross-linker known for its excellent mechanical properties and high biocompatibility in a 4:1 weight ratio of monomers to water. The material’s mechanical properties (Young’s modulus, maximum elongation, and ultimate tensile strength) were found similar to those of human cartilage. Furthermore, the ZnO NPs embedding matrix showed strong antibacterial activity against Escherichia coli (E. coli) and Staphylococcus aureus (S.A.). As the ZnO NPs ratio was changed, only a minor effect on the mechanical properties of the material was observed, whereas strong antibacterial properties against both bacteria were achieved in the case of 1.5 wt. % NPs.
Analysis of influence of mechanical vibration on ghost imaging

In order to make the system design meet the requirements of practical ghost imaging, the impact of mechanical vibration on the ghost imaging is analyzed. In ghost imaging system, the light field modulated by a digital micromirror device (DMD) is used to illuminate the target and the transmission or scattering light is detected by a single pixel detector. The target is reconstructed by combining the results of the detector and the intensity distribution of light field, so the modulation matrices of light field play a vital role in ghost imaging. By considering the form of imaging system to vibration and taking the modulation transfer function as an evaluation function, this paper quantitatively analyzes the impacts of various forms of mechanical vibration on the intensity distribution of light field. Combining engineering practice, several solutions are proposed to reduce the impact of vibration on the imaging quality. The results of simulation and experiment indicated that the analysis is correct and usable.
A Data Set and Reference Experiments for Multipath Wireless Emulation on Public Testbeds

As mobile devices with dual WiFi and cellular interfaces become widespread, network protocols have been developed that utilize the availability of multiple paths. To evaluate the performance of these protocols on a testbed, researchers often use link emulation with either synthetic traces or real traces from live WiFi and cellular networks. However, in a multipath scenario, the two links are not necessarily independent. The available bandwidth on the WiFi and cellular links may be negatively correlated (e.g. when moving from indoors to outdoors or vice versa) or positively correlated (e.g. when the local density of other users increases, so that both networks become more congested at the same time), and the result of an experimental evaluation of a multipath protocol will depend heavily on this relationship. Link traces of WiFi and cellular links measured at the same time, in the same place are necessary for the reproducible evaluation of multi path protocols in a testbed environment. The contributions of this work, therefore are: first ever data set of wireless link traces measured simultaneously on WiFi and cellular interfaces of a mobile device, and reference experiments for replaying these traces on emulated links on the CloudLab and FABRIC testbeds.
Cognitive Behavioral Therapy for Obsessive Compulsive Disorder

Obsessive Compulsive Disorder (OCD) is a neuropsychiatric condition characterized by obsessions (defined as recurrent and persistent thoughts, impulses or images that are intrusive and inappropriate and cause marked distress) and compulsions (repetitive behaviors or mental acts driven by obsessions to reduce distress or prevent dreaded events [1]. OCD is the fourth most prevalent psychiatric disorder and was considered as one of the most debilitating disorders with a prevalence rate of 2.5 % in an individuals’ lifetime [2, 3]. OCD is widespread among both adults and children [4]. OCD is considered a relatively common occurrence in childhood and adolescence where prevalence rates are between 1 to 3.6% [5-7]. As it is the case with other mood and anxiety disorders, females tend to be at a higher risk of developing OCD compared to males [8]. About one third to one half of adult OCD cases has its origins in childhood or adolescence [9, 10]. However, a recent meta-analysis found that pregnant women and post partum women have higher prevalence rates of OCD compared to the general population, with postpartum women at a much greater risk compared to the general female population [11]. Research has found that early onset is associated with males while late onset is associated with females [10].
Push the Limit of LPWANs with Concurrent Transmissions

Low Power Wide Area Networks (LPWANs) have been shown promising in connecting large-scale low-cost devices with low-power long-distance communication. However, existing LPWANs cannot work well for real deployments due to severe packet collisions. We propose OrthoRa, a new technology which significantly improves the concurrency for low-power long-distance LPWAN transmission. The key of OrthoRa is a novel design, Orthogonal Scatter Chirp Spreading Spectrum (OSCSS), which enables orthogonal packet transmissions while providing low SNR communication in LPWANs. Different nodes can send packets encoded with different orthogonal scatter chirps, and the receiver can decode collided packets from different nodes. We theoretically prove that OrthoRa provides very high concurrency for low SNR communication under different scenarios. For real networks, we address practical challenges of multiple-packet detection for collided packets, scatter chirp identification for decoding each packet and accurate packet synchronization with Carrier Frequency Offset. We implement OrthoRa on HackRF One and extensively evaluate its performance. The evaluation results show that OrthoRa improves the network throughput and concurrency by 50× compared with LoRa.
On the chaotic dynamics of a modified Nose-Hoover oscillator

This article reports on a study of the two-dimensional parameter space of a generalized time-reversible Nos´e-Hoover oscillator. Instead of considering its original form, whose study of the one-dimensional parameter space has recently received new insights, we examine the Nos´e-Hoover model slightly modiﬁed by using a new parameter (cid:15) to introduce a dissipative term to its energizing-damping variable. We use the maximum Lyapunov exponent to numerically characterize the chaotic dynamics of the model, at representative points of the two-dimensional parameter space, treating separately two dynamical regimes resulting from: (i) the eﬀect of lower values of (cid:15) and (ii) the eﬀect of higher values of (cid:15) . It is shown that, for higher values of the new dissipative parameter (cid:15) , the existence of chaotic behavior only prevails for the backwards dynamics.
Study on Weighted K-Nearest Neighbors Indoor Location Algorithm Based on Random Forest Access Point Selection

The conventional approach to determining fingerprint location is susceptible to errors induced by indoor small-scale fading, this shortcoming leads to a greater propensity for inaccuracy. In this study, we propose a novel Weighted K-Nearets Neighbors (WKNN) indoor location algorithm, which capitalizes on the benefits of random forest optimization (RF-WKNN). The primary advantage of this algorithm is its ability to leverage the random forest algorithm to classify the positioning base stations that have been pre-selected. This technique serves to mitigate the influence of received signal strength (RSS) in the presence of small-scale fading. In addition, we have conducted tests using 2.4GHz and 5GHz WIFI signals. The results of these tests reveal that the proposed algorithm outperforms the conventional WKNN algorithm by a margin of 6.5%.
The Development of Immunotherapy for the Treatment of Recurrent Glioblastoma

Simple Summary Glioblastoma (GBM) is the deadliest primary central nervous system (CNS) cancer in adults despite aggressive treatment. Once progressed, the prognosis is very poor and the effective traditional medicine treatment options are limited, so the management of recurrent glioblastoma (rGBM) remains challenging. Immunotherapy has revolutionized the prospects for many cancer types, but the intrinsic complexity of treating intracerebral tumors and the highly immunosuppressive environment have hampered the development of effective immunotherapies. The current focus of research in rGBM is on combination therapy, identifying predictive markers, and establishing synergy between immunotherapy and standard treatment. In this review, we discuss the current state of immunotherapy for rGBM, its future directions, and the challenges associated with each strategy. Abstract Recurrent glioblastoma (rGBM) is a highly aggressive form of brain cancer that poses a significant challenge for treatment in neuro-oncology, and the survival status of patients after relapse usually means rapid deterioration, thus becoming the leading cause of death among patients. In recent years, immunotherapy has emerged as a promising strategy for the treatment of recurrent glioblastoma by stimulating the body’s immune system to recognize and attack cancer cells, which could be used in combination with other treatments such as surgery, radiation, and chemotherapy to improve outcomes for patients with recurrent glioblastoma. This therapy combines several key methods such as the use of monoclonal antibodies, chimeric antigen receptor T cell (CAR-T) therapy, checkpoint inhibitors, oncolytic viral therapy cancer vaccines, and combination strategies. In this review, we mainly document the latest immunotherapies for the treatment of glioblastoma and especially focus on rGBM.
Fluid Resuscitation in Septic Patients

Sepsis is a life-threatening organ failure caused by a dysregulated response to infection. Fluid resuscitation and vasopressors are used to maintain systolic blood pressure and organ perfusion. Fluid resuscitation can be done with liberal or restricted fluids as well as colloids or crystalloid fluids. This review analyses the evidence for the use of liberal or restrictive fluids and colloids or crystalloids for the management of sepsis. A methodical search was conducted across PubMed, Cochrane Library, and ScienceDirect, and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines were followed for this study. Randomized controlled trials and retrospective observational studies were included in this study. Liberal and restrictive fluid strategies were found to be comparable in efficacy, but restrictive fluid regimens had the added benefit of a lower incidence of fluid overload. Balanced crystalloids were safer and more effective when compared to normal saline. Albumin replacement was found to be safe and showed efficacy in reducing mortality in patients with sepsis and septic shock.
Evaluation of Dual-Active Bridge Converter for DC Energy Buildings

Two-stage isolated active front-end (i-AFE) converters, based on an ac-dc and isolated dc-dc power stages, have gained significant research attention in the last few years due to several applications that need isolated power factor correction, including electric vehicles, and dc energy buildings. DC energy buildings have specific standards related to voltage levels, protection zones, and isolation. These requirements define the specific objectives for the power converters design. The dcdc stage is typically a challenge to design because the standard solution, the dual-active bridge converter, has limitations regarding soft-switching range, circulating current, and losses. To address the design of the dc-dc stage for i-AFE converters, this paper presents a guideline considering DC energy buildings as an application. The methodology and design requirements for DAB converters are described. In addition, a 5-kW prototype was assembled, and tested in the laboratory to validate the presented analysis.
Influence of selected factors on the parameters of a compact thermal model of an air transformer

The paper proposes a thermal model of an air transformer used in wireless power transfer (WPT) systems. This model takes into account self-heating in each of the windings and mutual thermal couplings between the windings. The description of the model takes into account the influence of the power dissipated in each of the windings and the distance between the windings on their self and transfer transient thermal impedances. The form of the proposed model, the method of determining its parameters, and the results of the model correctness verification on the example of a transformer with ring-shaped windings are presented. A good agreement was obtained between the results of measurements and simulations.