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We study the long-term behavior of the bright gamma-ray blazar PKS 0402-362. We collected approximately 13 years of Fermi-LAT data between Aug 2008 to Jan 2021 and identified three bright $\gamma$-ray activity epochs. The second was found to be the brightest epoch ever seen in this source. We observed most of the $\gamma$-ray flare peaks to be asymmetric in profile suggesting a slow cooling time of particles or the varying Doppler factor as the main cause of these flares. The $\gamma$-ray spectrum is fitted with PL and LP spectral models, and in both cases, the spectral index is very steep. The $\gamma$-ray spectrum does not extend beyond 10 GeV energy suggesting the emission is produced within the BLR. The absence of time lags between optical-IR and $\gamma$-ray suggest one zone emission model. Using the above information, we have modeled the broadband SED with a simple one-zone emission model using the publicly available code `GAMERA'. The particle distribution index is found to be the same as expected in diffusive shock acceleration suggesting it as the main mechanism of particle acceleration to very high energy up to 4 - 6 GeV. Throughout the various flux phases, we noticed that the optical emission is dominated by the thermal disk, suggesting it to be a good source to examine the disk-jet coupling. We postulate that the observed broadband flares could be linked with perturbation produced in the disk, which propagates to the jet and interacts with the standing shock. However, a more detailed examination is required., Comment: Accepted for publication in The MNRAS

In this study, we carry out a comprehensive variability analysis of the BL Lac object 4FGL J2202.7+4216 detected by the Fermi-LAT, over a period of more than three years, from April 27, 2019 to August 09, 2022. We detect the presence of quasi-periodic fluctuations with a period of ∼100 days with a confidence level exceeding 4σ. We also tentatively detect two other quasi-periodic fluctuations with a period of ∼75 days and ∼50 days, which persist over the entire duration of observation. This is the first time such a variability feature pertaining to this source is being reported. We propose that the observed QPO may be related to the precession of the blazar jet with a high Lorentz factor or to the motion of a plasma blob through the helical structure of the jet. The harmonicity among the detected periodicities suggest the global p-mode oscillations of the thick disk to be a plausible mechanism as well. For a decisive conclusion on the physical origin of such fluctuation, further multi-wavelength complementary observations, especially Very Long Baseline Interferometric observations, would be required.

Tomographic reconstruction is an important step toward visualization, identification and quantification of local damage through of structural elements. We have developed mathematical guiding principles for passive wave tomography. We have then utilized these guiding principles to develop a novel technique: Fast Tomography for computational and information efficiency in tomographic reconstruction with passive stress waves in a distance decaying (sensing) environment. In fast tomography, (i) a node-independent travel path is developed for computational efficiency and (ii) Apriori ranking of AE events using power spectral entropy (PSE) of the AE waveform to distinguish waveforms with high information content for tomographic reconstruction for information efficiency are proposed. Fast Tomography was studied theoretically and experimentally to benchmark the proposed method in terms of computational and information efficiency. Our algorithm provides a significant (>100x) improvement of computational efficiency over an existing approach. And a PSE-based ranking system for AE events enhances information efficiency by 50% as compared to a non-ranked system. Finally, we have validated the application of our method with intractably generated AE events in an accelerated damage test of a steel fiber–reinforced concrete beam.

We present the first report on reflectometric optical fiber sensing based on mechanically induced cascaded long-period grating (LPG) structure. This method utilizes in-house designed mechanical casings and a bare fiber. When a fiber is sandwiched between the casings and pressed by tightening screws, an LPG is induced due to a photo-elastic effect. By exploiting Fresnel refection at a fiber end, a cascaded LPG structure can be implemented using a single LPG, enabling reflectometric configuration. When this sensor is subjected to external physical processes, the attenuation bands change clearly, and their shift can be used for non-destructive evaluations. We show experimentally that this sensor can measure loads of up to 10 N without producing any permanent change in the fiber properties, indicating its pressure sensing capability. We also show that this sensor can potentially measure strain and temperature. In our experiments, the strain and temperature sensitivities are 9.4 nm/% and 0.045 nm/°C, respectively.

As the cracking mode (tensile or shear) of a crack is related to the underlying physical mechanisms, crack mode classification is a very useful method to identify the damage state of a structure for proper maintenance to enhance structural safety and durability. Acoustic Emission (AE) is a passive structural health monitoring technique based on the stress wave generated due to cracking in a structure. A framework has been designed in this study for automated probabilistic classification of the cracks in cementitious components based on the AE signals. With this approach, unlabeled hand designed waveform parameters, i.e. RA values (RA) and Average frequency (AF) are clustered using density dictated unsupervised clustering algorithm. Intersecting clusters in the data were then separated through a hyperplane created using Support Vector Machine (SVM) algorithm. Based on physical insight obtained from labeled data, unlabeled data was classified into events corresponding to different cracking modes. The framework was applied to the analysis of AE data from Steel Fiber Reinforced Concrete (SFRC) beam under bending and Strain Hardening Cementitious Composite (SHCC) samples under direct tension. The cracking modes obtained from the proposed machine learning approach are found to be in good agreement with expectations based on composite theory. With good qualitative prediction, the proposed approach shows promise for the prediction of damage state in structures based on unlabeled data obtained in the field.

4C+28.07 is a $\gamma$-ray Flat Spectrum Radio Quasar (FSRQ) type source. It is often monitored at different frequencies, though long-term multi-wavelength data of this source have not been modelled in detail before. We have analyzed $\sim 12$ years (Aug, 2008 - May, 2020) of Fermi-LAT data with a binning of 10 days time scale and observed three distinctive flaring states. Each flaring state consists of different phases of activity, namely, pre-flare, flare \& post-flare regions. $\gamma$-ray spectral analysis of these different activity phases has been performed and the best fit model for its spectra is found to be a Log-parabola model. We have also studied the correlation of simultaneous $\gamma$-ray light curves with the optical \& radio counterparts in these flaring states and report the DCF with a 95\% significance level. A large time delay is found between radio and gamma-ray data for two flares, indicating two zones of emission. We have fitted the multi-wavelength data with a two-zone leptonic model. In our two-zone leptonic model, the maximum required power in the jet is 9.64 $\times$ 10$^{46}$ erg sec$^{-1}$, which is lower than its Eddington luminosity $2.29\times 10^{47}$ erg sec$^{-1}$., Comment: 23 pages, 16 figures, Accepted for publication in ApJ

A detailed study of the BL Lacertae PKS 0903-57 has been done for the first time with 12 years of Fermi Large Area Telescope data. We have identified two bright gamma-ray flares in 2018 and 2020. Many sub-structures were observed during multiple time binning of these flares. We have performed detailed temporal and spectral study on all the sub-structures separately. A single-zone emission model is used for time-dependent leptonic modeling of the multi-wavelength spectral energy distributions. Our estimated values of variability time scale, magnetic field in the emission region, jet power obtained from leptonic modeling of PKS 0903-57 are presented in this work. Currently, we have a minimal number of observations in X-rays and other bands. Hence, more simultaneous multi-wavelength monitoring of this source is required to have a better understanding of the physical processes happening in the jet of the blazar PKS 0903-57., 29 pages, 22 figures, 14 tables, Accepted for Publication in ApJ

Vis-NIR hyperspectral imaging (HSI) system combined with artificial neural networks was investigated for the first time to monitor color changes of large yellow croaker (Larimichthys crocea) fillets during low-temperature storage. Feed-forward neural networks (FNN) empowered with the leaky rectified linear unit (Leaky-Relu) have been developed as a non-linear quantitative analysis model. It presented accurate predictive power for color changes based on optimal spectra (with R

We report the results from our study of the blazar S5 1803+784 carried out using the quasi-simultaneous $B$, $V$, $R$, and $I$ observations from May 2020 to July 2021 on 122 nights. Our observing campaign detected the historically bright optical flare during MJD 59063.5$-$MJD 59120.5. We also found the source in its brightest ($R_{mag}$= 13.617) and faintest ($R_{mag}$= 15.888) states till date. On 13 nights, covering both flaring and non-flaring periods, we searched for the intraday variability using the power-enhanced $F-$test and the nested ANOVA test. We found significant variability in 2 out of these 13 nights. However, no such variability was detected during the flaring period. From the correlation analysis, we observed that the emission in all optical bands were strongly correlated with a time lag of $\sim$ 0 days. To get insights into its dominant emission mechanisms, we generated the optical spectral energy distributions of the source on 79 nights and estimated the spectral indices by fitting the simple power law. Spectral index varied from 1.392 to 1.911 and showed significant variations with time and $R-$band magnitude. We have detected a mild bluer-when-brighter trend (BWB) during the whole monitoring period while a much stronger BWB trend during the flare. We also carried out a periodicity search using four different methods and found no significant periodicity during our observation duration. Based on the analysis during the flaring state of the source one can say that the emissions most likely originate from the jet rather than the accretion disk., Comment: 17 pages, 8 figures, 7 tables, accepted for publication in The Astrophysical Journal (ApJ)

Underwater acoustic (UWA) channels are highly challenging in nature and from the past few decades, many researchers are investigating the efficient way of UWA channel estimation. Different approaches have been taken for UWA channel estimation like OFDM, Compressed Sensing, etc. In this paper, a survey is presented on various estimation techniques involved in the UWA channel. Work is presented in three different parts. The first part presents different challenges in the UWA channel. In the second part, works on MIMO-OFDM-based UWA channels are presented. In the last part, a survey on compressed sensing based channel estimation of the UWA-MIMO-OFDM system is presented. A performance comparison is given on the different channel estimation algorithms.

Acoustic emission is a powerful experimental structural health monitoring technique for determining the location of cracks formed in a member. Pinpointing wave arrival time is essential for accurate source location. Conventional arrival detection technique’s accuracy deteriorates rapidly in low signal to noise ratio (5–40 dB) region, thus unsuitable for source location due to this inaccuracy. A new technique to pinpoint the arrival time based on the power of the wave is proposed. We have designed an adaptive filter based on the power characteristics of acoustic emission wave. After filtration of the acoustic emission wave, sliding window is employed to accurately identify the region of wave arrival based on the change in transmitted power. The results from various experimental and numerical arrival time detection experiments consistently show that the proposed methodology is stable and accurate for a wide range of signal to noise ratio values (5–100 dB). Particularly, in signal to noise ratio region (5–40 dB), the method is significantly more accurate as compared to the other methods described in the literature. The method was then employed to study the localized damage progression in a steel fiber–reinforced beam under four-point bending. The results suggest that calculated source location using the new method is consistent with that from visual inspection of the member at failure and more accurate than the localization results from existing method.

Source localization is the process of assigning spatial metadata to a set of unlabeled time of arrival for acoustic signals that may be interlaced with extreme outliers caused by detection and material-induced errors. Exploiting the fact that location error is attributed to high residuals, a new framework for non-linear source localization is proposed in this article. The detected time of arrival was employed to calculate the intermediate source and event time. The framework then quarantines sensors with high residual based on the intermediate event time and relative distance from the intermediate source. Then, the time of arrivals for quarantined sensors were reviewed and adjusted. Finally, with new arrival time, the framework iteratively optimizes the spatial location and event time, along with the updating of velocity field through a feedback loop. The new framework was used to localize acoustic emission sources with extreme outliers in an anisotropic medium. The results indicate that the proposed framework can accurately quarantine the bad sensor and localize sources with an accuracy better than existing source localization methods. Finally, the framework was employed to study the progressive damage in a steel fiber–reinforced beam under four-point bending. The source localization result is consistent with that from visual inspection of the member at failure.

Characterization of surface cracks is one of the key steps towards condititional assessment and understanding the durability of strain hardening cementitious composites (SHCCs). Under laboratory conditions, surface crack statistics can be obtained from images of specimen surfaces through manual inspection. This is subjective, time consuming and laborious. In order to automate this process a framework encompassing a deep learning model for rapid identification and computation of crack parameters of thin SHCC cracks in presented in this work. A tailored deep convolutiontional neural network (TDCNN) was trained to detect thin cracks and then crack parameters were computed using image processing technique. The proposed technique does not require optimal lighting conditions, proper surface treatment, and prior (manual) selection of the correct region for proper inference.. The results from the controlled study suggest that the inference ability of TDCNN is reasonably good, resilient against epistemic uncertainty and tunable for completely independent but adverse observations. From the crack pattern computed using TDCCN, crack parameters-average crack width (ACW) and crack density (CD) can be calculated to facilitate conditional and durability assessment in a practical environment.

The paper presents a brief survey on the underwater acoustic communication channel estimation of orthogonal frequency division multiplexing using deep learning techniques. In recent times, deep learning has shown great potential in the field of communication. The underwater acoustic channel is a highly challenging system as it has a restricted bandwidth, enlarged multi-path, medium refractive characteristics, acute fading, rapid time variation, large Doppler shifts, etc. For the UWA-OFDM communication model, DL-based receivers can be elucidated as a deep neural network for a better estimation than the conventional techniques. Sufficient training is required for the recovery of the transmitted symbols. It does not require any explicit CE or equalization and balancing. The estimation is accomplished in two stages. The first one is training stage. For training, this deep neural network-transmitted data, the signal received in the unknown channel, etc., are used as labeled data. The second one is test stage, where the deep neural network receiver recovers transmitted symbols, given the received signal. The accuracy and the computational complexity, these two have been a great challenge for the underwater acoustic channel estimation with the existing traditional channel estimation methods. The deep learning method is useful to solve these problems. This survey is to find out a few optimum methods of deep learning to estimate the UWA channel in an efficient way.

3C 454.3 is frequently observed in flaring state. The long term light curve of this source has been analysed with 9 year (August 2008 - July 2017) data from Fermi LAT detector. We have identified five flares and one quiescent state. The flares have sub-structures with many peaks during flaring phase. We have estimated the rise and decay time of the flares and compared with flares of other similar sources. The modeling of gamma ray spectral energy distributions shows in most cases Log parabola function gives the best fit to the data. We have done time dependent leptonic modeling of two of the flares, for which simultaneous multi-wavelength data are available. These two long lasting flares Flare-2A and Flare-2D continued for 95 days and 133 days respectively. We have used the average values of Doppler factor, injected luminosity in electrons, size of the emission region and the magnetic field in the emission region in modeling these flares. The emission region is assumed to be in the broad line region in our single zone model. The energy losses (synchrotron, synchrotron self-Compton, external Compton) and escape of electrons from the emission region have been included while doing the modelling. Although, the total jet powers required to model these flares with leptonic model are higher compared to other sources, they are always found to be lower than the Eddington's luminosity of 3C 454.3. We also select some flaring peaks and show that time variation of the Doppler factor or the injected luminosity in electrons over short time scales can explain their light curves., Comment: 51 pages, 55 figures, 34 tables, Accepted for Publication in ApJS

Time of Arrival (TOA) detection is essential successful Acoustic Emission (AE) based structural health monitoring (SHM) system. During high burst rate results AE waves could overlap. AE waves shows similar narrowband, low frequency content thus overlapping of AE waves leads to overlap in both in time and frequency. As a result, the TOA information is lost in the coda of the previous wave and cannot be (reliably) detected by conventional techniques. In this paper, the problem was addressed through creation of a) Wave (Overlapping) Identification (WI) b) Wave Cleaning (WC) c) Arrival Detection (AD) approaches. In this paper, specifically the Wave Detection and Wave Cleaning approaches are introduced and described in detail. 3D Fingerprinting is meticulously designed to exploit wave arrival characteristics thus autogenously identifying the overlapping of waves for completing the WI system. Positive identification activates Wave Cleaning system which eradicates the influence of the Intersecting Wave using adaptive spectral subtraction (ASpS). Post removal of the TOA for the Intersected Wave was identified.

Accurate time of arrival (TOA) detection is essential for successful wave and vibration-based structural health monitoring (SHM) system including Acoustic Emission (AE). AE is the foremost among passive wave-based monitoring systems. During high burst rate results, AE waves could overlap. Overlapping of AE waves leads to overlap in both time and frequency. As a result, the TOA information is lost in the coda of the previous wave and cannot be (reliably) detected by conventional techniques. To this end, ICD is introduced. ICD involves 3 cascaded systems a) (Overlapping) Identification b) (Overlap) Cleaning c) (Arrival) Detection. A novel 3D Fingerprint is meticulously designed to autogenously and injectively identify the overlapping waves. Positive identification activates the cleaning system which eradicates the influence of the Intersecting Wave using newly proposed adaptive spectral subtraction (ASpS). Then, TOA for the Intersected Wave was identified. The approaches were verified in controlled as well as source localization tests. The results from controlled study validate robustness in low IRR values for a wide range of waveform parameters. Finally, source localization results from laboratory testing confidently display the scientific applicability of the proposed system. This system could enhance detectability, reliability, and accuracy of a conventional system.

Strain Hardening cementitious composites (SHCCs) have been known to produce excellent crack width control and very good tensile strain capacity. As a result, strategically introducing SHCCs would lead to significant carbon saving through the creation of structures that are durable and could potentially undergo autogenous self-healing. However, for wider acceptability of any new material requires long term empirical data. This may be circumvented by continually ensuring a structure made with this new material is serviceable i.e. material has not failed. We present an approach to predict failure time without any prior information on operating conditions and material information for SHCCs. This method is based on the modeling of ‘finite time singularity’ based on mathematical laws. The method produces fairly accurate results when applied to estimate failure for SHCCs with a wide range of failure times and to both linear and non-linear loading regimes. The practical applicability of this method was thus demonstrated.

In strain-hardening cementitious composites, the bridging effect of fibers can control crack openings to very small values so the cracks can be self-healed in the presence of water. To rely on autogeneous self-healing to enhance the durability of structures, a method to assess the state of self-healing is required. A new strategy is presented in this paper for in-situ measurement of the healing effect without pre-knowledge of the environmental condition (such as moisture). Exploiting the fact that the electrical conductivity in the crack is lower than that of the undamaged section, the effective macroscopic cracking effect is first distinguished by frequency dependent impedance measurements both along (parallel to) and across the cracks. Dielectric spectroscopy along the cracks is found to correlate well with gravimetric measurements and can therefore, be used for inverse approximation of moisture content. The effect of moisture change on the equivalent resistance in the direction across the cracks can then be derived approximately from measurements under various moisture contents. The approximated resistance vs moisture curve is found to reflect the evolution of autogenous self-healing, and a curve indicating almost complete healing agrees well with the recovery in member stiffness measured from loading test.

For strain-hardening cementitious composites (SHCCs) the knowledge of operating regime is important for maintenance planning and monitoring of the element. This paper presents a methodology of regime discrimination for SHCC from acoustic emission (AE) signals. From an AE signal, various damage indicators (DIs) can be derived. A new DI called Power Spectral Entropy (PSE) is developed in this paper. New benchmarks are developed to quantify possible effect of external factors on the measurement accuracy. Theoretical results indicate that the PSE is Signal to Noise Ratio (SNR) invariant, insensitive to the choice of subjective parameters and can be performed in real time. PSE was then obtained from the test result of SHCC with elastic behavior followed by strain hardening and softening. The test results indicate that the PSE varies with strain in a very similar way to the applied load. An approach to distinguish between different operating regimes of a SHCC component based on PSE is then proposed and validated. The practical applicability of PSE is hence demonstrated.

The use of smart cementitious materials is becoming increasingly critical for the enhanced serviceability of structures. The addition of carbon fibers, carbon nanotubes, and various nano-powders such as nano-silica, carbon black, and graphite giving cementitious materials electrical properties that can be used for self-sensing has been known for almost two decades. Many sensing principles and techniques using smart materials have been successfully developed and applied mostly in laboratory testing over last few decades. The strong capacity of Fiber-Reinforced Cementitious Composites for autogenous healing in addition to crack control (especially in the case of Strain-Hardening Cementitious Composites) has been reported by many researchers. Similarly, the applications of different mineral and bio-additive materials to achieve the self-healing of cracks have been noted with great interest. Design for serviceability based on the durability of the materials used in concrete structures is often neglected. With durability performance testing becoming more sophisticated, detailed service life design is being demanded in the most important infrastructure projects. The present review is focused on identifying field applications and highlighting the Performance-Driven Design Approach for tailoring material solutions for the problems likely to be faced by civil infrastructures in the future. A real-life case study is presented to illustrate the minimal cost implications of adopting the latest smart material for an eco-friendly, durable, reliable, and resilient infrastructure. Identifying critical challenges faced by the industry and developing solutions for the same is going to help bridge the current gaps between research and adoption.