Optical Cable Research Articles

Digital Networks of Democratic Mobilization: Examining Performative, Material, and Rooted Approaches

In a world increasingly and unevenly connected through fiber-optic cables, satellite waves, and smartphones, spatial approaches attentive to questions of power and scale are key to grounding analysis of digital networks and illuminating the possibilities and instabilities created through network linkages. This article examines digital networks of democratic mobilization through the analytical lenses of poststructuralist, new materialist, and feminist and decolonial approaches. By developing a heuristic of performative, material, and rooted approaches to network analysis, this article exposes the underlying theoretical presuppositions and analytical possibilities enabled by different ways of thinking with and through networks in geography. Performative approaches advance an understanding of networks as dynamic, dispersed assemblages, providing a theoretical lens that accounts for fluidity and multicausality. Material approaches call attention to the concrete backbones of digital infrastructures, highlighting the pathways and chokepoints that enable and constrain multiscalar flows of information. Rooted approaches foreground the spatial dimensions of social embeddedness, illuminating how digital networks emerge from and are constrained by longer struggles over territory and place making. Through empirical engagement with the networked mobilizations of the Arab Spring and the Milk Tea Alliance, we bring the insights of geographic scholarship to bear on emerging work on digital social networks and show how historically rooted spatial relations condition new vectors of risk and crisis. By tracing the ontological and epistemological orientations of particular strands of performative, new materialist, and rooted approaches, we recenter scale, territory, and place making, while emphasizing the political nature of network analysis across today’s digital landscapes.

Remote condition monitoring of rail tracks using distributed acoustic sensing (DAS): A deep CNN-LSTM-SW based model

Railroad condition monitoring is paramount due to frequent passage through densely populated regions. This significance arises from the potential consequences of accidents such as train derailments, hazardous materials leaks, or collisions which may have far-reaching impacts on communities and the surrounding areas. As a solution to this issue, the use of distributed acoustic sensing (DAS)-fiber optic cables along railroads provides a feasible tool for monitoring the health of these extended infrastructures. Nevertheless, analyzing DAS data to assess railroad health or detect potential damage is a challenging task. Due to the large amount of data generated by DAS, as well as the unstructured patterns and substantial noise present, traditional analysis methods are ineffective in interpreting this data. This paper introduces a novel approach that harnesses the power of deep learning through a combination of CNNs and LSTMs, augmented by sliding window techniques (CNN-LSTM-SW), to advance the state-of-the-art in the railroad condition monitoring system. As well as it presents the potential for DAS and fiber optic sensing technologies to revolutionize the proposed CNN-LSTM-SW model to detect conditions along the rail track networks. Extracting insights from the data of High tonnage load (HTL)- a 4.16 ​km fiber optic and DAS setup, we were able to distinguish train position, normal condition, and abnormal conditions along the railroad. Notably, our investigation demonstrated that the proposed approaches could serve as efficient techniques for processing DAS signals and detecting the condition of railroad infrastructures at any remote distance with DAS-Fiber optic cable setup. Moreover, in terms of pinpointing the train's position, the CNN-LSTM architecture showcased an impressive 97% detection rate. Applying a sliding window, the CNN-LSTM labeled data, the remaining 3% of misclassified labels have been improved dramatically by predicting the exact locations of each type of condition. Altogether, these proposed models exhibit promising potential for accurately identifying various railroad conditions, including anomalies and discrepancies that warrant thorough exploration.

Heated fibre-optic cable installed in compacted bentonite layers: numerical evaluation of influences of the compaction-induced variability on thermal conductivity estimation

Active heating of fibre-optic (FO) cable combined with distributed temperature sensing has been applied as a quality control tool for emplacing granulated bentonite buffer. For calibrating the tool, the FO cable is installed in specimens with known dry densities. Layer-by-layer compaction is often employed to prepare the specimens. Although the average dry density is guaranteed, some studies report possible variations in the dry density within a layer. In this study, heat transfer from a heated FO cable installed in granulated bentonite with different compaction-induced variabilities was numerically simulated. The reference case used homogeneous bentonite with an average dry density of 1.5 g cm −3 . For three other cases the granulated bentonite was filled in two, three and four layers, respectively, each of which had a distinct dry density gradient ranging from 1.3 to 1.7 g cm −3 . For each case, the FO cable placed at the centre of the container was numerically heated, and the thermal conductivity was calculated using temperature changes. In the cases where the FO cable was placed on the layer interface at which the dry density was discontinuous, the calculated apparent thermal conductivity was found to have been underestimated with an error of as much as 4%. In the other two cases, the thermal conductivities obtained were nearly identical to those corresponding to the average dry density. The influence of the compaction-induced variability in the dry density was found to be insignificant and the estimated thermal conductivity was essentially that of the bentonite represented by its average dry density. Thematic collection: This article is part of the Sustainable geological disposal and containment of radioactive waste collection available at: https://www.lyellcollection.org/topic/collections/radioactive

Non-invasive low-cost deep tissue blood flow measurement with integrated Diffuse Speckle Contrast Spectroscopy.

Diffuse Correlation Spectroscopy (DCS) is a widely used non-invasive measurement technique to quantitatively measure deep tissue blood flow. Conventional implementations of DCS use expensive single photon counters as detecting elements and optical probes with bulky fiber optic cables. In recent years, newer approaches to blood flow measurement such as Diffuse Speckle Contrast Analysis (DSCA) and Speckle Contrast Optical Spectroscopy (SCOS), have adapted speckle contrast analysis methods to simplify deep tissue blood flow measurements using cameras and single photon counting avalanche detector arrays as detectors. Here, we introduce and demonstrate integrated Diffuse Speckle Contrast Spectroscopy (iDSCS), a novel optical sensor setup which leverages diffuse speckle contrast analysis for probe-level quantitative measurement of tissue blood flow. iDSCS uses a standard photodiode configured in photovoltaic mode to integrate photon intensity fluctuations over multiple integration durations using a custom electronic circuit, as opposed to the high frequency sampling of photon counts with DCS. We show that the iDSCS device is sensitive to deep-tissue blood flow measurements with experiments on a human forearm and compare the sensitivity and dynamic range of the device to a conventional DCS instrument. The iDSCS device features a low-cost, low-power, small form factor instrument design that will enable wireless probe-level measurements of deep tissue blood flow.

Experimental study of seepage in levee with transverse structure and water barrier

A quasi-real-scale experiment was conducted to understand seepage in levees with internal transverse structures. The experiment involved fabricating levees with transverse structures in the same location and using the same method as that in sand levee experiments previously conducted to examine the feasibility of the optical-fiber-distributed temperature system. Levees with internal structures are susceptible to internal erosion and piping risks, which necessitates the installation of water barriers to mitigate these issues. However, conventional concrete water barriers require curing time, leading to potential secondary damage from flooding during construction. As a countermeasure, a compounded polypropylene (PP) water barrier was developed, which is easy to construct and cost-effective. The performances of both concrete and compounded PP water barriers were verified and compared. Water pressure changes inside the levees were tracked, and to monitor temperature changes inside the levees and gain a deeper understanding of seepage, distributed optical fiber cables were employed. The experimental results revealed that levees with transverse structures had a higher risk of internal erosion and piping due to seepage compared to those without such structures, and such levees require continuous monitoring. Furthermore, internal temperature measurements demonstrated the effectiveness of water barriers in mitigating seepage and backward erosion piping.

Fiber-Optic Sensor for Identifying Liquids and Determining Solutions Concentration

Fiber-optic sensors for identifying liquids and determining the concentration of solutions have been studied with the possibility of using various types of single-mode optical fibers produced by industry and widely used in optical cables and telecommunications to create sensors for identifying liquids and determining the concentration of solutions. To identify liquids with different refractive indices and determine the concentration of substances dissolved in water, the peak value of the reflectograms of the optical fiber located at the interface between the optical fiber core and the environment can be used as an information parameter. The value of the information parameter depends on the refractive index of the liquid in which one end of the optical fiber is located. The parameters of fiber-optic sensors for identifying liquids and determining the concentration of solutions were studied by optical reflectometry in different wavelength ranges of optical radiation with a duration of reflectometer probe pulses from 25 to 300 ns. It has been established that the fiber-optic sensor can operate at any wavelength of optical radiation corresponding to the transparency windows of the optical loss spectrum of the optical fiber. The influence of the length of the optical fiber between the recording device and the place where the concentration of a liquid solution is determined using a fiber-optic sensor was studied. The possibility of creating a fiber-optic sensor for determining the concentration of the liquid solutions based on optical fibers has been demonstrated.

Exploiting the Potential of Urban DAS Grids: Ambient-Noise Subsurface Imaging Using Joint Rayleigh and Love Waves

Abstract Distributed acoustic sensing (DAS) data become important for seismic monitoring of subsurface structures in urban areas. Different from the previous studies that only focused on Rayleigh waves, we report successful observation and analysis of both Rayleigh and Love waves extracted from ambient-noise interferometry, using orthogonal segments of fiber-optic cables in San Jose, California. Theoretical angular responses of DAS ambient-noise cross correlation, together with numerical experiments, help identify DAS channel pairs expected to record stronger Love waves than Rayleigh waves. Based on these waveforms, we further obtain clear Rayleigh- and Love-wave dispersion maps, including both phase and group velocities, with various channel pair orientations. Finally, we perform a joint inversion of Rayleigh- and Love-wave dispersion curves to obtain depth-dependent subsurface velocity structures of the top 100 m. Our inversion result is consistent with the model from the previous study based on Rayleigh-wave dispersion and horizontal-to-vertical spectral ratio. In addition, the joint inversion of Love and Rayleigh is more robust than that of the independent inversion of either type of wave. Our new study demonstrates the potential of surface-wave analysis on fiber-optic cables with complex geometry, which can further advance the seismic monitoring of urban areas.

A semblance-based microseismic event detector for DAS data

SUMMARY Distributed acoustic sensing (DAS) is becoming increasingly popular in microseismic monitoring operations. This data acquisition technology converts fibre-optic cables into dense arrays of seismic sensors that can sample the seismic wavefield produced by active or passive sources with a high spatial density, over distances ranging from a few hundred metres to tens of kilometres. However, standard microseismic data analysis procedures have several limitations when dealing with the high spatial (intersensor spacing up to submetre scale) sampling rates of DAS systems. Here, we propose a semblance-based seismic event detection method that fully exploits the high spatial sampling of the DAS data. The detector identifies seismic events by computing waveform coherence of the seismic wavefield along geometrical hyperbolic trajectories for different curvatures and positions of the vertex, which are completely independent from external information (i.e. velocity models). The method detects a seismic event when the coherence values overcome a given threshold and satisfies our clustering criteria. We first validate our method on synthetic data and then apply it to real data from the FORGE geothermal experiment in Utah, USA. Our method detects about two times the number of events obtained with a standard method when applied to 24 hr of data.

Free space optical communication system in the presence of atmospheric losses

<span>Free space optical communication is gaining importance in the field of optical communication due to its high speed and high bandwidth applications. Free space optical communication system (FSOCS) provides many benefits as compared to traditional wireless communication system and fiber optic cables. This makes this technology the reasonable extension of metropolitan area network and also provides the quick recovery during natural disaster. This system performance is limited due to the atmospheric turbulence effect and various atmospheric losses such as rain, and fog. Gamma gamma atmospheric turbulent model is used to analyze the system performance in the presence of moderate to strong atmospheric turbulence. We have designed the FSO gamma <span>gamma turbulent model with non-return to zero (NRZ) modulation format employing wavelength division multiplexing (WDM), spatial diversity multiple input multiple output (MIMO) (8×8) at various atmospheric turbulence levels and attenuation loss of 10 dB/km at the distance of 2-4 km. Using the proposed model, the link distance is enhanced up to 4km in the presence of turbulence and atmospheric losses with minimum laser transmitted power.</span></span>

Sensing Optical Fibers for Earthquake Source Characterization Using Raw DAS Records

AbstractDistributed Acoustic Sensing (DAS) is becoming a powerful tool for earthquake monitoring, providing continuous strain‐rate records of seismic events along fiber optic cables. However, the use of standard seismological techniques for earthquake source characterization requires the conversion of data in ground motion quantities. In this study we provide a new formulation for far‐field strain radiation emitted by a seismic rupture, which allows to directly analyze DAS data in their native physical quantity. This formulation naturally accounts for the complex directional sensitivity of the fiber to body waves and to the shallow layering beneath the cable. In this domain, we show that the spectral amplitude of the strain integral is related to the Fourier transform of the source time function, and its modeling allows to determine the source parameters. We demonstrate the validity of the technique on two case‐studies, where source parameters are consistent with estimates from standard seismic instruments in magnitude range 2.0–4.3. When analyzing events from a 1‐month DAS survey in Chile, moment‐corner frequency distribution shows scale invariant stress drop estimates, with an average of Δσ = (0.8 ± 0.6) MPa. Analysis of DAS data acquired in the Southern Apennines shows a dominance of the local attenuation that masks the effective corner frequency of the events. After estimating the local attenuation coefficient, we were able to retrieve the corner frequencies for the largest magnitude events in the catalog. Overall, this approach shows the capability of DAS technology to depict the characteristic scales of seismic sources and the released moment.

Potential of Earthquake Strong Motion Observation Utilizing a Linear Estimation Method for Phase Cycle Skipping in Distributed Acoustic Sensing

AbstractDistributed acoustic sensing (DAS) using existing optical fiber cables facilitates high‐density seismic observation. However, few studies have examined the reliability of the seismic waveform amplitude recorded by DAS. In this study, a DAS network was connected to optical fiber cables installed over a distance of 75 km along a high‐speed train (Shinkansen) railway in the Kumamoto prefecture, Japan. We successfully observed strong motions of the Mj6.6 earthquake (approximately 150 km from the fiber) on 22 January 2022, in Hyuga‐nada, in addition to several small local earthquakes. The observed strong motions from the Mj6.6 earthquake, using DAS, exhibited cycle skipping (clipping) issues due to dynamic range limitations at numerous channels. To address this, we estimated the shaking map, representing maximum strain distributions for Mj6.6, by replacing the clipped data with information from nearby unclipped channels and scaling their RMS amplitudes based on S‐coda (unclipped). Furthermore, we verified the reliability of the amplitude information obtained from DAS by estimating the distance attenuation of seismic waves while correcting for the differences in the structure type and coupling as much as possible. The distance attenuation property of local earthquakes was consistent with that of the peak ground velocities obtained from seismometers, indicating that DAS data acquired using fibers installed on infrastructure (various structures) can also be utilized to assess the spatial distribution of the relative amplitude values along the fiber. Obtaining high‐density seismic motion distributions is important for earthquake early warning and accurate damage estimation of strong motions.

Detection of microseismic events in continuous DAS data using convolutional neural networks

Detection and localization of microseismicity is an inevitable task in monitoring fluid injections into subsurface rocks during hydraulic stimulations. Traditionally, downhole geophones placed into boreholes or large surface geophone networks have been used for this, but in recent years, the use of distributed acoustic sensing (DAS) via fiber-optic cables placed into boreholes has become a common technique. However, DAS registrations still have lower signal-to-noise ratios than geophones; i.e., they cannot detect small-magnitude events. In this work, we develop and train a convolutional neural network capable of detecting microseismic events in continuous DAS recordings incorporating arrival-time information from geophones. The network is trained on DAS and geophone data from the Utah FORGE enhanced geothermal system project for which we are able to significantly shift the detection threshold toward smaller magnitude events. Although the number of microseismic events (approximately 150) used for training is small, the tested network performance is high and provides a complete event catalog down to magnitude MW = −1.6, a notable improvement over previous studies. Using a short recording period of several hours for training, such a network might be used for long-term, real-time monitoring of geothermal sites. Although the network is explicitly trained for the geometry of the data set used, the philosophy and network architecture can be adapted for similar case studies where long-term seismic monitoring is required (e.g., CO2 sequestration).

Measuring Pericellular Oxygen Tension for In Vitro Cell Culture.

Cell culture, the process of growing cells in conditions that mimic those in the body, is a key technique in biomedical research. Oxygen is not controlled in conventional cell culture, although chambers that control oxygen in the surrounding gas phase are commercially available. In both cases, it is valuable to understand the pericellular oxygen tension (i.e., the oxygen concentration that cells experience) in cultures. Herein we describe a procedure for using commercial optical sensor spots to measure pericellular oxygen for adherent and suspension cultures. Spots are placed on surfaces on which cells are grown, and optical cables are attached to the outside of the cell culture vessels and connected to a computer. Associated software allows for the real-time monitoring of pericellular oxygen during cell culture experiments. This procedure enhances the reproducibility and control of cell culture.

A Novel Method for Evaluating Micro-Bending Sensitivity to Ensure Applicability to High-Density Optical Fiber Cable

A Novel Method for Evaluating Micro-Bending Sensitivity to Ensure Applicability to High-Density Optical Fiber Cable

Designing a Distributed Sensing Network for Structural Health Monitoring of Concrete Tunnels: A Case Study

Structural health monitoring is essential for the lifecycle maintenance of tunnel infrastructure. Distributed fiber‐optic sensor (DFOS) technology, which is capable of distributed strain measurement and long‐range sensing, is an ideal nondestructive testing (NDT) approach for monitoring linear infrastructures. This research aims to develop a distributed sensing network utilizing DFOS for structural integrity assessment of concrete immersed tunnels. The primary innovations of this study lie in the development of a general flowchart for establishing a sensing network and obtaining reliable field data, as well as its subsequent validation through a detailed case study. Concentrated joint deformations in typical immersed tunnels, detectable by the DFOS, are key indicators of structural integrity. This study addresses crucial elements of field monitoring system design, including the selection of appropriate optical fibers or cables and the determination of vital interrogator system parameters. It also covers sensor parameter determination, installation techniques, field data collection, and postanalysis. Furthermore, this research is exemplified by a case study that illustrates the successful implementation of a distributed sensing network in an operational immersed tunnel, and monitoring data reveals cyclic structural deformations under impacts of daily tide and seasonal temperature variations. The data obtained from this network play a significant role in subsequent condition assessments of tunnel structures. The research findings contribute to the assessment of large‐scale infrastructure health conditions through the application of DFOS monitoring.

Numerical evaluation of strain transfer model for steel-reinforced optical fiber cable embedded in a cylindrical concrete beam with two void inclusions

In distributed optical fiber sensors, strain transfer is usually described through a simplified one-dimensional equation, derived from continuum mechanics. This equation, in which a parameter known as the strain-lag parameter takes into account the cable’s geometric and mechanical characteristics, establishes a relationship between the measured longitudinal strain profile within the optical fiber and the real strain profile occurring in the host material. In the case of steel-reinforced optical fiber cables, appreciated for their resistance to breakage during on-site instrumentation, a notable discrepancy between the measured and actual strain profiles is revealed especially in the presence of a strain gradient, indicating that the ability to transfer strain from the host material to the optical fiber is restrained using this type of cable. This paper assesses numerically the strain transfer model for steel-reinforced optical fiber sensors in the presence of a strain gradient generated by two void inclusions in a concrete beam. The good accuracy of the strain transfer model is observed by the comparison with a 3D finite element simulation. However, the result points out the critical necessity of precisely determining the strain-lag parameter.

Delta Tecnic introduces masterbatch for miniaturization of fiber optic cables

On January 15, Delta Tecnic announced that, facing increased demand for smaller cables, the company has formulated a masterbatch that is claimed to provide precise opacity and coloring functionalities, which should ensure that the tubes of fiber optic cables maintain their original visual identification.

Advanced portable micro-SORS prototype coupled with SERDS for heritage science.

We investigate the subsurface composition of turbid materials at the micro scale by means of a portable non-invasive technique, micro-spatially offset Raman spectroscopy (micro-SORS), combined with shifted excitation Raman difference spectroscopy (SERDS). This combination enables the microscale layer analysis and allows to deal effectively with highly fluorescing samples as well as ambient light, all in a form of an in-house portable prototype device optimised for applications in heritage science. The instrument comprises ability to simultaneously collect multiple spectra by means of an optical fibre bundle, thus reducing the dead time and simplifying the ease of deployment of the technique. The performance of the synergy between micro-SORS and 785 nm SERDS dual-wavelength diode laser is demonstrated on a stratified mock-up painting samples including highly fluorescing painted layers. This instrumental approach could be ground-breaking in heritage science, due to the largely unmet need of analysing the molecular composition of subsurface of artworks non-invasively and in situ, and in the presence of fluorescent background and ambient light. Moreover, many other fields are expected to benefit from this technological advancement such as solar energy, forensic and food analytical areas.

Minimally-intrusive, dual-band, fiber-optic sensing system for high-enthalpy exhaust plumes

<abstract> <p>The propulsion research lab at Utah State University has developed a minimally-intrusive optical sensing system for high-temperature/high-velocity gas-generator exhaust plumes. For this application glass fiber-optic cables, acting as radiation conduits, are inserted through the combustion chamber or nozzle wall and look directly into the flow core. The cable transmits data from the flame zone to externally-mounted spectrometers. In order to capture the full-optical spectrum, a blended dual-spectrum system was employed, with one spectrometer system tuned for best-response across the visible-light and near-infrared spectrum, and one spectrometer tuned for best-response in the near- and mid-infrared spectrum. The dual-band sensors are radiometrically-calibrated and the sensed-spectra are spliced together using an optimal Wiener filtering algorithm to perform the deconvolution. The merged spectrum is subsequently curve-fit to Planck's black-body radiation law, and flame temperature is calculated from associated curve maxima (Wien's law). The presented fiber-optic sensing systems performs a function that is analogous to Raman spectroscopy. The system non-contact, high-temperature measurement, and does not interfere with the heat transfer processes. In this report data collected from a lab-scale (200 N) hybrid rocket system are analyzed using the described method. Optically-sensed flame-temperatures are correlated to analytical predictions, and shown to generally agree within a few degrees. Additionally, local maxima in the optical spectra are shown to correspond to emission frequencies of atomic and molecular oxygen, water vapor, and molecular nitrogen; all species known to exist in the hybrid combustion plume. Presented data demonstrate that selected fiber-optics can survive temperature greater than 3000 ℃, for durations of up to 25 seconds.</p> </abstract>

Identification of hidden defects in optical cables based on BOTDR distributed detection technology

Identification of hidden defects in optical cables based on BOTDR distributed detection technology