Oriserve

oriserve

Authors:
(1) Antonio Riotto, Département de Physique Theorique, Universite de Geneve, 24 quai Ansermet, CH-1211 Geneve 4, Switzerland and Gravitational Wave Science Center (GWSC), Universite de Geneve, CH-1211 Geneva, Switzerland;
(2) Joe Silk, Institut d’Astrophysique, UMR 7095 CNRS, Sorbonne Universite, 98bis Bd Arago, 75014 Paris, France, Department of Physics and Astronomy, The Johns Hopkins University, Baltimore MD 21218, USA, and Beecroft Institute of Particle Astrophysics and Cosmology, Department of Physics, University of Oxford, Oxford OX1 3RH, UK. Table of Links Abstract and 1 Introduction 2 Some open questions 2.1 What is the abundance of PBHs? 2.2 What is the effect of PBH clustering? 2.3 What fraction of the currently observed GW events can be ascribed to PBHs? 2.4 Are PBHs the Dark Matter? 3 The PBH Roadmap 3.1 High redshift mergers 3.2 Sub-solar PBHs 3.3 Plugging the pair instability gap with PBH? 3.4 PBH eccentricity, 3.5 PBH spin and 3.6 Future gamma-ray telescopes 4 Conclusions and References 2.3 What fraction of the currently observed GW events can be ascribed to PBHs? Thanks to the many binary BH events detected so far with many more expected in the next few years, gravitational-wave astronomy is about to become a population study. Currently hierarchical Bayesian analysis combining astrophysical formation models and PBHs may only constrain the fraction of a putative subpopulation of PBHs in the data (see for instance Ref. [13]). The exciting possibility that PBHs are contributing to current observations will be verified only by further reducing uncertainties in astrophysical and primordial formation models. This is another fundamental open issue. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Antonio Riotto, Département de Physique Theorique, Universite de Geneve, 24 quai Ansermet, CH-1211 Geneve 4, Switzerland and Gravitational Wave Science Center (GWSC), Universite de Geneve, CH-1211 Geneva, Switzerland; (2) Joe Silk, Institut d’Astrophysique, UMR 7095 CNRS, Sorbonne Universite, 98bis Bd Arago, 75014 Paris, France, Department of Physics and Astronomy, The Johns Hopkins University, Baltimore MD 21218, USA, and Beecroft Institute of Particle Astrophysics and Cosmology, Department of Physics, University of Oxford, Oxford OX1 3RH, UK. Authors: Authors: (1) Antonio Riotto, Département de Physique Theorique, Universite de Geneve, 24 quai Ansermet, CH-1211 Geneve 4, Switzerland and Gravitational Wave Science Center (GWSC), Universite de Geneve, CH-1211 Geneva, Switzerland; (2) Joe Silk, Institut d’Astrophysique, UMR 7095 CNRS, Sorbonne Universite, 98bis Bd Arago, 75014 Paris, France, Department of Physics and Astronomy, The Johns Hopkins University, Baltimore MD 21218, USA, and Beecroft Institute of Particle Astrophysics and Cosmology, Department of Physics, University of Oxford, Oxford OX1 3RH, UK. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Some open questions 2 Some open questions 2.1 What is the abundance of PBHs? 2.1 What is the abundance of PBHs? 2.2 What is the effect of PBH clustering? 2.2 What is the effect of PBH clustering? 2.3 What fraction of the currently observed GW events can be ascribed to PBHs? 2.3 What fraction of the currently observed GW events can be ascribed to PBHs? 2.4 Are PBHs the Dark Matter? 2.4 Are PBHs the Dark Matter? 3 The PBH Roadmap 3 The PBH Roadmap 3.1 High redshift mergers 3.1 High redshift mergers 3.2 Sub-solar PBHs 3.2 Sub-solar PBHs 3.3 Plugging the pair instability gap with PBH? 3.3 Plugging the pair instability gap with PBH? 3.4 PBH eccentricity, 3.5 PBH spin and 3.6 Future gamma-ray telescopes 3.4 PBH eccentricity, 3.5 PBH spin and 3.6 Future gamma-ray telescopes 4 Conclusions and References 4 Conclusions and References 2.3 What fraction of the currently observed GW events can be ascribed to PBHs? Thanks to the many binary BH events detected so far with many more expected in the next few years, gravitational-wave astronomy is about to become a population study. Currently hierarchical Bayesian analysis combining astrophysical formation models and PBHs may only constrain the fraction of a putative subpopulation of PBHs in the data (see for instance Ref. [13]). The exciting possibility that PBHs are contributing to current observations will be verified only by further reducing uncertainties in astrophysical and primordial formation models. This is another fundamental open issue. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

What Fraction of the Currently Observed GW Events Can Be Ascribed to PBHs?

Authors:
(1) Scott Conn, California Institute of Technology, Pasadena, California;
(2) Joseph Fitzgerald, California Institute of Technology, Pasadena, California;
(3) Jorn Callies, California Institute of Technology, Pasadena, California. Table of Links Abstract and Intro Observations Models Results Discussion Conclusion APPENDIX A APPENDIX B APPENDIX C References 3. Models a. The PM Model For each event, we use a constant mixed layer depth that is an average of the time-varying mixed layer depth over duration of the event. Since the events are relatively short, the error in this approximation is minimal for most events. We make this choice of a constant mixed layer depth to avoid complications that arise otherwise, especially in the YBJ model discussed below. b. The YBJ Model The YBJ equation is a three-dimensional partial differential equation, making it substantially more computationally expensive to integrate than the PM model. We solve the YBJ equation using the pseudospectral solver Dedalus (Burns et al. 2020) with a mixed explicit and implicit diagonal RK2 scheme. As discussed above, we start the simulations with no waves. We use a domain that is 400 km×400 km in the horizontal (centered on the mooring array) and 4 km deep. Each dimension is discretized with 128 modes. The vertical dimension is finite and represented using Chebyshev polynomials. The horizontal dimensions are made periodic and represented using Fourier modes. The stratification and the wind forcing are taken to be horizontally uniform, capturing the forcing at a scale much larger than the mesoscale. Smaller-scale structure in the wind stress can generate smaller-scale NIWs, but the energy input tends to be strongly dominated by the large-scale winds (Rama et al. 2022). To construct the mesoscale streamfunction used in the simulations, we take 𝜓 from observations and calculate the vorticity by taking the finite-difference Laplacian of 𝜓 on the sphere. We then interpolate this vorticity onto the Cartesian simulation grid centered on the location of the central mooring. We apply a taper to the vorticity, such that it goes to zero on the domain boundaries. We invert this tapered vorticity field for the streamfunction on the periodic domain, making the resulting streamfunction periodic as well. Our conclusions are neither sensitive to the form nor width of the taper. For the results shown below, we use the following form of the taper: When the winds blow on the ocean surface, they generate turbulence that mixes momentum downwards. If a mixed layer already exists and is not too deep, the momentum input from the wind will be rapidly homogenized within the mixed layer (Pollard and Millard 1970; Kato and Phillips 1969). For some events at the OSMOSIS site, the mixed layer was up to a few hundred meters deep. These deep mixed layers are likely the result of convection driven by buoyancy forcing rather than the mechanical wind forcing (Thompson et al. 2016). In these cases, it is unlikely that the momentum is uniform across the mixed layer, especially if the buoyancy forcing has ceased. The depth structure of NIWs obtained from the OSMOSIS mooring confirms this picture. The waves are initially forced over a layer that is thinner than the mixed layer before they propagate to depth. To avoid forcing over an unrealistically large depth, we cap the forcing layer at 80 m. This value is guided by the observations and represents an average depth over which waves are forced when the mixed layer is deep. We discuss possible ways to improve this representation below. In order to solve the YBJ equation, we must further specify vertical boundary conditions. The requirement that the vertical velocity is zero on the top and bottom of the domain translates to the requirement that 𝑀 be horizontally uniform at the top and bottom boundaries (Young and Ben Jelloul 1997). Since 𝑀 is determined by 𝜕𝑧𝑀 up to an arbitrary horizontal function, without loss of generality we specify 𝑀(𝑥, 𝑦,−𝐻,𝑡) = 0. The top boundary condition can be found by vertically integrating the YBJ equation twice and requiring that 𝑀 be horizontally uniform at 𝑧 = 0 (Appendix B). The result is which we integrate a priori and then use as a Dirichlet boundary condition on 𝑀. This paper is available on arxiv under CC 4.0 license. Authors: (1) Scott Conn, California Institute of Technology, Pasadena, California; (2) Joseph Fitzgerald, California Institute of Technology, Pasadena, California; (3) Jorn Callies, California Institute of Technology, Pasadena, California. Authors: Authors: (1) Scott Conn, California Institute of Technology, Pasadena, California; (2) Joseph Fitzgerald, California Institute of Technology, Pasadena, California; (3) Jorn Callies, California Institute of Technology, Pasadena, California. Table of Links Abstract and Intro Abstract and Intro Observations Observations Models Models Results Results Discussion Discussion Conclusion Conclusion APPENDIX A APPENDIX A APPENDIX B APPENDIX B APPENDIX C APPENDIX C References References 3. Models a. The PM Model a. The PM Model For each event, we use a constant mixed layer depth that is an average of the time-varying mixed layer depth over duration of the event. Since the events are relatively short, the error in this approximation is minimal for most events. We make this choice of a constant mixed layer depth to avoid complications that arise otherwise, especially in the YBJ model discussed below. b. The YBJ Model b. The YBJ Model The YBJ equation is a three-dimensional partial differential equation, making it substantially more computationally expensive to integrate than the PM model. We solve the YBJ equation using the pseudospectral solver Dedalus (Burns et al. 2020) with a mixed explicit and implicit diagonal RK2 scheme. As discussed above, we start the simulations with no waves. We use a domain that is 400 km×400 km in the horizontal (centered on the mooring array) and 4 km deep. Each dimension is discretized with 128 modes. The vertical dimension is finite and represented using Chebyshev polynomials. The horizontal dimensions are made periodic and represented using Fourier modes. The stratification and the wind forcing are taken to be horizontally uniform, capturing the forcing at a scale much larger than the mesoscale. Smaller-scale structure in the wind stress can generate smaller-scale NIWs, but the energy input tends to be strongly dominated by the large-scale winds (Rama et al. 2022). To construct the mesoscale streamfunction used in the simulations, we take 𝜓 from observations and calculate the vorticity by taking the finite-difference Laplacian of 𝜓 on the sphere. We then interpolate this vorticity onto the Cartesian simulation grid centered on the location of the central mooring. We apply a taper to the vorticity, such that it goes to zero on the domain boundaries. We invert this tapered vorticity field for the streamfunction on the periodic domain, making the resulting streamfunction periodic as well. Our conclusions are neither sensitive to the form nor width of the taper. For the results shown below, we use the following form of the taper: When the winds blow on the ocean surface, they generate turbulence that mixes momentum downwards. If a mixed layer already exists and is not too deep, the momentum input from the wind will be rapidly homogenized within the mixed layer (Pollard and Millard 1970; Kato and Phillips 1969). For some events at the OSMOSIS site, the mixed layer was up to a few hundred meters deep. These deep mixed layers are likely the result of convection driven by buoyancy forcing rather than the mechanical wind forcing (Thompson et al. 2016). In these cases, it is unlikely that the momentum is uniform across the mixed layer, especially if the buoyancy forcing has ceased. The depth structure of NIWs obtained from the OSMOSIS mooring confirms this picture. The waves are initially forced over a layer that is thinner than the mixed layer before they propagate to depth. To avoid forcing over an unrealistically large depth, we cap the forcing layer at 80 m. This value is guided by the observations and represents an average depth over which waves are forced when the mixed layer is deep. We discuss possible ways to improve this representation below. In order to solve the YBJ equation, we must further specify vertical boundary conditions. The requirement that the vertical velocity is zero on the top and bottom of the domain translates to the requirement that 𝑀 be horizontally uniform at the top and bottom boundaries (Young and Ben Jelloul 1997). Since 𝑀 is determined by 𝜕𝑧𝑀 up to an arbitrary horizontal function, without loss of generality we specify 𝑀(𝑥, 𝑦,−𝐻,𝑡) = 0. The top boundary condition can be found by vertically integrating the YBJ equation twice and requiring that 𝑀 be horizontally uniform at 𝑧 = 0 (Appendix B). The result is which we integrate a priori and then use as a Dirichlet boundary condition on 𝑀. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

The Models We Used to Observed Interactions Between Near-Inertial Waves and Mesoscale Eddies

Authors:
(1) Scott Conn, California Institute of Technology, Pasadena, California;
(2) Joseph Fitzgerald, California Institute of Technology, Pasadena, California;
(3) Jorn Callies, California Institute of Technology, Pasadena, California. Table of Links Abstract and Intro Observations Models Results Discussion Conclusion APPENDIX A APPENDIX B APPENDIX C References 2. Observations a. NIW Data We extract observations of NIWs from data collected for the Ocean Surface Mixing, Ocean Submesoscale Interaction Study (OSMOSIS; Buckingham et al. 2016). As part of the study, measurements were taken from nine moorings anchored over the Porcupine Abyssal Plain in the northeastern part of the Atlantic Ocean from September 2012 to September 2013. The moorings were all distributed less than 10 km from a central mooring located at (48.6875◦N, −16.1875◦E) (Fig. 1a,b). In this study, we focus on observations from the central mooring and the four outermost moorings. Each mooring took measurements of the horizontal velocity once every ten minutes using an acoustic current meter (ACM). On the central mooring, there were 13 ACMs spaced nominally between 50 m and 500 m depth. On the outer moorings there were five ACMs nominally spaced over the same depth range. The deepest ACM failed on the outer northeast (ONE) mooring, and so measurements are only available to a depth of 361 m. The actual depth of the instruments varied in time due to drag exerted by the flow on the moorings. Over the course of the year, there were several times where the moorings experienced knockdown by up to 200 m (Callies et al. 2020). We flag these events if the knockdown on the central mooring is more than 10 m when averaged with a 1-day running mean. Outside of these knockdown events the variations in mooring depth about the nominal depth is ∼2 m. The interpretation of the observed NIW signal during these knockdown events is complicated because the filter we use to extract the NIW signal does not commute with evaluating the signal along the trajectory of the ACMs. We do not attempt to explain the observations during the knockdown periods. b. Stratification and mixed layer Depth c. Wind Data d. Altimetry To characterize the mesoscale eddy field, we use observations of the sea surface height (SSH) from the Data Unification and Altimeter Combination System’s (DUACS) delayed-time (DT) 2018 release (Taburet et al. 2019). The DUACS DT2018 SSH maps are provided at a nominal (1/4) ◦ and daily resolution. We convert these measurements to a geostrophic streamfunction using 𝜓 = 𝑔ℎ/ 𝑓 , where ℎ is the SSH and 𝑓 is the latitude-dependent Coriolis parameter. This paper is available on arxiv under CC 4.0 license. Authors: (1) Scott Conn, California Institute of Technology, Pasadena, California; (2) Joseph Fitzgerald, California Institute of Technology, Pasadena, California; (3) Jorn Callies, California Institute of Technology, Pasadena, California. Authors: Authors: (1) Scott Conn, California Institute of Technology, Pasadena, California; (2) Joseph Fitzgerald, California Institute of Technology, Pasadena, California; (3) Jorn Callies, California Institute of Technology, Pasadena, California. Table of Links Abstract and Intro Abstract and Intro Observations Observations Models Models Results Results Discussion Discussion Conclusion Conclusion APPENDIX A APPENDIX A APPENDIX B APPENDIX B APPENDIX C APPENDIX C References References 2. Observations a. NIW Data a. NIW Data We extract observations of NIWs from data collected for the Ocean Surface Mixing, Ocean Submesoscale Interaction Study (OSMOSIS; Buckingham et al. 2016). As part of the study, measurements were taken from nine moorings anchored over the Porcupine Abyssal Plain in the northeastern part of the Atlantic Ocean from September 2012 to September 2013. The moorings were all distributed less than 10 km from a central mooring located at (48.6875◦N, −16.1875◦E) (Fig. 1a,b). In this study, we focus on observations from the central mooring and the four outermost moorings. Each mooring took measurements of the horizontal velocity once every ten minutes using an acoustic current meter (ACM). On the central mooring, there were 13 ACMs spaced nominally between 50 m and 500 m depth. On the outer moorings there were five ACMs nominally spaced over the same depth range. The deepest ACM failed on the outer northeast (ONE) mooring, and so measurements are only available to a depth of 361 m. The actual depth of the instruments varied in time due to drag exerted by the flow on the moorings. Over the course of the year, there were several times where the moorings experienced knockdown by up to 200 m (Callies et al. 2020). We flag these events if the knockdown on the central mooring is more than 10 m when averaged with a 1-day running mean. Outside of these knockdown events the variations in mooring depth about the nominal depth is ∼2 m. The interpretation of the observed NIW signal during these knockdown events is complicated because the filter we use to extract the NIW signal does not commute with evaluating the signal along the trajectory of the ACMs. We do not attempt to explain the observations during the knockdown periods. b. Stratification and mixed layer Depth b. Stratification and mixed layer Depth c. Wind Data c. Wind Data d. Altimetry d. Altimetry To characterize the mesoscale eddy field, we use observations of the sea surface height (SSH) from the Data Unification and Altimeter Combination System’s (DUACS) delayed-time (DT) 2018 release (Taburet et al. 2019). The DUACS DT2018 SSH maps are provided at a nominal (1/4) ◦ and daily resolution. We convert these measurements to a geostrophic streamfunction using 𝜓 = 𝑔ℎ/ 𝑓 , where ℎ is the SSH and 𝑓 is the latitude-dependent Coriolis parameter. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

How We Observed Interactions Between Near-Inertial Waves and Mesoscale Eddies and What We Found

Authors:
(1) Scott Conn, California Institute of Technology, Pasadena, California;
(2) Joseph Fitzgerald, California Institute of Technology, Pasadena, California;
(3) Jorn Callies, California Institute of Technology, Pasadena, California. Table of Links Abstract and Intro Observations Models Results Discussion Conclusion APPENDIX A APPENDIX B APPENDIX C References ABSTRACT The evolution of wind-generated near-inertial waves (NIWs) is known to be influenced by the mesoscale eddy field, yet it remains a challenge to disentangle the effects of this interaction in observations. Here, the model of Young and Ben Jelloul (YBJ), which describes NIW evolution in the presence of slowly evolving mesoscale eddies, is compared to observations from a mooring array in the Northeast Atlantic Ocean. The model captures the evolution of both the observed NIW amplitude and phase much more accurately than a slab mixed layer model. The YBJ model allows for the identification of specific physical processes that drive the observed evolution. It reveals that differences in the NIW amplitude across the mooring array are caused by the refractive concentration of NIWs into anticyclones. Advection and wave dispersion also make important contributions to the observed wave evolution. Stimulated generation, a process by which mesoscale kinetic energy acts as a source of NIW potential energy, is estimated to be 20 µW m−2 in the region of the mooring array, which is two orders of magnitude smaller than the global average input to mesoscale kinetic energy and likely not an important contribution to the mesoscale kinetic energy budget in this region. Overall, the results show that the YBJ model is a quantitatively useful tool to interpret observations of NIWs. 1. Introduction Near-inertial waves (NIWs), internal waves with a frequency close to the inertial frequency 𝑓 , are resonantly excited by atmospheric winds exerting a stress on the ocean’s surface. It has long been recognized that these waves can interact with mesoscale eddies and that this interaction may be important in the life cycle of wind-generated NIWs. Observational evidence of NIW–mesoscale interactions is accumulating, but interpreting the observed NIW evolution in the presence of mesoscale eddies remains challenging. Here, we employ the theoretical framework of Young and Ben Jelloul (1997, from hereon YBJ) to identify and interpret such interactions in mooring observations in the Northeast Atlantic Ocean. NIWs are associated with a large vertical shear, which can result in shear instabilities and vertical mixing (for a review, see Alford et al. 2016). These shear instabilities are a key mechanism by which atmospheric storms can cause the surface mixed layer to deepen. Jochum et al. (2013) showed that an improved representation of NIWs in a climate model led to a deepening of the mixed layer on average, which in turn resulted in significant changes in sea surface temperatures, winds, and precipitation. It has also been proposed that NIWs not only react to the presence of mesoscale eddies but feed back on the eddies and affect their evolution. Approximately 80% of the ocean’s kinetic energy exists as mesoscale motions (Ferrari and Wunsch 2009). The geostrophic constraint on mesoscale eddies traps energy at large scales, and it is not entirely clear how the energy input into mesoscale motion is balanced by dissipation (M¨uller et al. 2011). A number of mechanisms by which mesoscale eddies lose energy are known, including dissipation in bottom boundary layers (e.g., Arbic and Flierl 2004), the generation of dissipative lee waves (e.g., Nikurashin et al. 2013), energy loss near western boundaries (e.g., Zhai et al. 2010), and the top drag arising from the current dependence of the wind stress (e.g., Dewar and Flierl 1987; Renault et al. 2016). The extraction of energy from mesoscale eddies by NIWs presents another possibility (Xie and Vanneste 2015; Rocha et al. 2018). Given the great importance of mesoscale eddies to the transport of heat and carbon (e.g., Jayne and Marotzke 2002; Gnanadesikan et al. 2015), even small changes (see discussion in Asselin and Young 2020) to the mesoscale eddy field caused by NIWs may be significant to the impact of the ocean on climate. Despite its successes in capturing some aspects of the observed NIW signal, the PM model cannot explain the propagation of NIWs out of the mixed layer. If the NIW field is initially uniform, it will remain so. The model captures neither 𝛽-refraction nor the interaction with mesoscale eddies. This interpretation of the Ocean Storms Experiment was pursued further by Balmforth et al. (1998), who ran spin-down simulations of the YBJ equation. NIWs were initialized in the mixed layer and evolved in the presence of an idealized, barotropic mesoscale eddy field. Qualitative comparisons between the simulations and observations showed that YBJ dynamics were not inconsistent with the observed time for NIWs to escape the mixed layer. Balmforth and Young (1999) showed that including the 𝛽-effect improved the agreement with observations. Because an idealized eddy field was used, however, no quantitative conclusions could be drawn about the ability of YBJ to capture the observed evolution. In this study, we aim to bridge the gap between theory and observations by using the YBJ framework to interpret the observed evolution of NIWs in the Northeast Atlantic Ocean. We use an array of nine moorings to capture some of the mesoscale variations in the NIW field. The YBJ framework allows us to attribute the observed NIW evolution to a set of well-defined physical processes. We integrate the three-dimensional YBJ equation using observational inputs for the wind forcing, mesoscale streamfunction, and stratification, and we compare these simulations to simpler slab models. We show that the YBJ model offers significant improvements in modeling NIW evolution, without the need for any tuning. We use the YBJ energy budgets to provide a dynamical interpretation of spatial and temporal variations in the NIW field and quantify the relative importance of the various physical processes involved. We also provide an estimate for the importance of stimulated generation in this region. This paper is available on arxiv under CC 4.0 license. Authors: (1) Scott Conn, California Institute of Technology, Pasadena, California; (2) Joseph Fitzgerald, California Institute of Technology, Pasadena, California; (3) Jorn Callies, California Institute of Technology, Pasadena, California. Authors: Authors: (1) Scott Conn, California Institute of Technology, Pasadena, California; (2) Joseph Fitzgerald, California Institute of Technology, Pasadena, California; (3) Jorn Callies, California Institute of Technology, Pasadena, California. Table of Links Abstract and Intro Abstract and Intro Observations Observations Models Models Results Results Discussion Discussion Conclusion Conclusion APPENDIX A APPENDIX A APPENDIX B APPENDIX B APPENDIX C APPENDIX C References References ABSTRACT The evolution of wind-generated near-inertial waves (NIWs) is known to be influenced by the mesoscale eddy field, yet it remains a challenge to disentangle the effects of this interaction in observations. Here, the model of Young and Ben Jelloul (YBJ), which describes NIW evolution in the presence of slowly evolving mesoscale eddies, is compared to observations from a mooring array in the Northeast Atlantic Ocean. The model captures the evolution of both the observed NIW amplitude and phase much more accurately than a slab mixed layer model. The YBJ model allows for the identification of specific physical processes that drive the observed evolution. It reveals that differences in the NIW amplitude across the mooring array are caused by the refractive concentration of NIWs into anticyclones. Advection and wave dispersion also make important contributions to the observed wave evolution. Stimulated generation, a process by which mesoscale kinetic energy acts as a source of NIW potential energy, is estimated to be 20 µW m−2 in the region of the mooring array, which is two orders of magnitude smaller than the global average input to mesoscale kinetic energy and likely not an important contribution to the mesoscale kinetic energy budget in this region. Overall, the results show that the YBJ model is a quantitatively useful tool to interpret observations of NIWs. 1. Introduction Near-inertial waves (NIWs), internal waves with a frequency close to the inertial frequency 𝑓 , are resonantly excited by atmospheric winds exerting a stress on the ocean’s surface. It has long been recognized that these waves can interact with mesoscale eddies and that this interaction may be important in the life cycle of wind-generated NIWs. Observational evidence of NIW–mesoscale interactions is accumulating, but interpreting the observed NIW evolution in the presence of mesoscale eddies remains challenging. Here, we employ the theoretical framework of Young and Ben Jelloul (1997, from hereon YBJ) to identify and interpret such interactions in mooring observations in the Northeast Atlantic Ocean. NIWs are associated with a large vertical shear, which can result in shear instabilities and vertical mixing (for a review, see Alford et al. 2016). These shear instabilities are a key mechanism by which atmospheric storms can cause the surface mixed layer to deepen. Jochum et al. (2013) showed that an improved representation of NIWs in a climate model led to a deepening of the mixed layer on average, which in turn resulted in significant changes in sea surface temperatures, winds, and precipitation. It has also been proposed that NIWs not only react to the presence of mesoscale eddies but feed back on the eddies and affect their evolution. Approximately 80% of the ocean’s kinetic energy exists as mesoscale motions (Ferrari and Wunsch 2009). The geostrophic constraint on mesoscale eddies traps energy at large scales, and it is not entirely clear how the energy input into mesoscale motion is balanced by dissipation (M¨uller et al. 2011). A number of mechanisms by which mesoscale eddies lose energy are known, including dissipation in bottom boundary layers (e.g., Arbic and Flierl 2004), the generation of dissipative lee waves (e.g., Nikurashin et al. 2013), energy loss near western boundaries (e.g., Zhai et al. 2010), and the top drag arising from the current dependence of the wind stress (e.g., Dewar and Flierl 1987; Renault et al. 2016). The extraction of energy from mesoscale eddies by NIWs presents another possibility (Xie and Vanneste 2015; Rocha et al. 2018). Given the great importance of mesoscale eddies to the transport of heat and carbon (e.g., Jayne and Marotzke 2002; Gnanadesikan et al. 2015), even small changes (see discussion in Asselin and Young 2020) to the mesoscale eddy field caused by NIWs may be significant to the impact of the ocean on climate. Despite its successes in capturing some aspects of the observed NIW signal, the PM model cannot explain the propagation of NIWs out of the mixed layer. If the NIW field is initially uniform, it will remain so. The model captures neither 𝛽-refraction nor the interaction with mesoscale eddies. This interpretation of the Ocean Storms Experiment was pursued further by Balmforth et al. (1998), who ran spin-down simulations of the YBJ equation. NIWs were initialized in the mixed layer and evolved in the presence of an idealized, barotropic mesoscale eddy field. Qualitative comparisons between the simulations and observations showed that YBJ dynamics were not inconsistent with the observed time for NIWs to escape the mixed layer. Balmforth and Young (1999) showed that including the 𝛽-effect improved the agreement with observations. Because an idealized eddy field was used, however, no quantitative conclusions could be drawn about the ability of YBJ to capture the observed evolution. In this study, we aim to bridge the gap between theory and observations by using the YBJ framework to interpret the observed evolution of NIWs in the Northeast Atlantic Ocean. We use an array of nine moorings to capture some of the mesoscale variations in the NIW field. The YBJ framework allows us to attribute the observed NIW evolution to a set of well-defined physical processes. We integrate the three-dimensional YBJ equation using observational inputs for the wind forcing, mesoscale streamfunction, and stratification, and we compare these simulations to simpler slab models. We show that the YBJ model offers significant improvements in modeling NIW evolution, without the need for any tuning. We use the YBJ energy budgets to provide a dynamical interpretation of spatial and temporal variations in the NIW field and quantify the relative importance of the various physical processes involved. We also provide an estimate for the importance of stimulated generation in this region. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Interpreting Observed Interactions Between Near-Inertial Waves and Mesoscale Eddies

Most of the time, when creating responsive applications in Angular, we just need to use CSS and media queries. Every once in a while though, we need to alter some interactivity or logic based on these same media queries. Meaning, we need to access them in some way from JavaScript. And this can get a little messy sometimes. Luckily for us, we can use the Breakpoint Observer utility provided by the Angular CDK. Let’s check it out! https://youtu.be/aKxFbZG_3go?embedable=true To start off, we really should avoid doing this type of thing if possible. If we can simply change some style with a media query in CSS, that’s going to provide the best experience. This technique should really only be used when style isn’t the only thing that needs to be changed. Like, when logic or functionality is involved. What is the Angular CDK Breakpoint Observer? Ok, so what is the Breakpoint Observer? Well, it’s a utility that is part of the Angular CDK or Component Development Kit. And, you can probably guess by its name, but it allows us to monitor media queries programmatically and then react to them as needed. It’s probably easiest to understand through an example, so, let’s look at one. Ok, here we have this layout with our navigation next to our main content. And, when we resize this smaller, the menu is hidden. We can toggle the sidebar display by clicking the "Show Menu" button in the upper right corner. So far, so good. But where the problem comes in, is when we toggle the menu open, then resize larger, and then resize smaller again, the menu is still open. We don’t want this. What we really want to do here is remove the class that makes this visible as soon as we resize to where the menu is always visible. That way, when we shrink back down, it will start off hidden again. So in our code, we are simply toggling the isVisible property when we click the button. nav.component.html <button (click)="isVisible = !isVisible">
  {% raw %}{{ isVisible ? 'Hide Menu' : 'Show Menu' }}{% endraw %}
</button> Then, we bind this visible class on our nav element when true. <nav [class.visible]="isVisible">
  ...
</nav> That class is what positions the menu and makes it visible. So, we’re going to want to set this property to false at the breakpoint where the menu becomes always visible again. Alright, to begin using the Breakpoint Observer, you need to have the Angular CDK installed. To do this, you just need to use your command line from within your angular app, and run the following command: npm i @angular/cdk That will properly install the CDK and all of its dependencies so that we can use them. Now, in this demo project, I’ve already installed it so we’re ready to use it. We start by injecting it within our constructor. nav.component.ts import { BreakpointObserver } from '@angular/cdk/layout';

@Component({
  selector: 'app-nav',
  standalone: true,
  imports: [CommonModule],
  templateUrl: './nav.component.html',
  styleUrls: ['./nav.component.scss']
})
export class NavComponent {
    constructor(private breakpointObserver: BreakpointObserver) {}
} Now, what we want to do is react when our browser is resized and the breakpoint that hides our menu no longer matches. This style change occurs at a of . max-width 585px Now, in our ngOnInit method, we’ll access our breakpoint observer. We'll use its use the observe method. This method takes either a single media query as a string, or an array of media query strings. In our case, we have a single media query, a of , so we’ll add it. max-width 585px This method returns an observable that will fire with a value of a object which contains a matches Boolean value. This value will be true when we are matching our breakpoint, so when we are narrower than . And, since it’s an observable we’ll need to subscribe. But, before we do, we need to make sure to properly remove the subscription on destroy. So we add our pipe, then our , and then we pass it our . Then we’ll name the state object returned, state. BreakpointState 585px takeUntilDestroyed DestroyRef When this state does not match, we’ll want to set our property to false. The observe method fires with a value every time the viewport is resized and it will simply set this property to true when it matches and false when it doesn’t. isVisible @Component({
  selector: 'app-nav',
  standalone: true,
  imports: [CommonModule],
  templateUrl: './nav.component.html',
  styleUrls: ['./nav.component.scss']
})
export class NavComponent {
    protected isVisible = false;

    constructor(private breakpointObserver: BreakpointObserver,
                private destroyRef: DestroyRef) {}

    ngOnInit() {
      this.breakpointObserver.observe(`(max-width: 585px)`)
        .pipe(takeUntilDestroyed(this.destroyRef))
        .subscribe(state => {
          if (!state.matches) {
            this.isVisible = false;
          }
        });
    }
} Now, when we resize down, the menu is hidden. Then, when we open it, and resize wider, and then back down, we see that the menu is no longer open. Just like we wanted. Now, one thing I don’t like about our current set up is that we have our media query breakpoint in two locations. Once in our CSS, and then again in our breakpoint observer observe method. This could be pretty error prone. If we were to change the breakpoint where our menu becomes hidden and forget to update in the breakpoint observer, things would not work as expected. So, what I like to do is share this media query between the CSS and JavaScript maintaining it in a single location. To do this we can move the breakpoint to a sass variable that we can use in our media query. nav.component.scss $menuHidden: 585px; Then, on the host, I add a custom property with this same variable. For this, we need to use sass string interpolation for the variable because, if we don’t, the variable name with dollar sign and everything will be the rendered value for this custom property. We obviously don’t want that. What we do want is the actual value, so we use the hash and curly braces to get the string interpolated value of the variable. :host {
  --breakpointForMenu: #{$menuHidden};
} An important note here, unfortunately we can’t use custom properties in media queries, they just don’t work that way. Which is why I’m creating a sass variable and then using it in these two locations. This keeps it in a single location form maintainability while allowing to do what I need to do with both CSS and the breakpoint observer. @media (max-width: $menuHidden) {
  ...
} Ok, so now that we have this custom property on our host we can access its value from typescript instead of using the hard coded value from before. To do this, we need to inject in within our constructor. Then we can create a breakpoint variable. We’ll set it to the computed style of our ’s native element. Then, we’ll get the value of the custom property that we just added. Now, we can switch our code to use this breakpoint value instead. ElementRef ElementRef @Component({
  selector: 'app-nav',
  standalone: true,
  imports: [CommonModule],
  templateUrl: './nav.component.html',
  styleUrls: ['./nav.component.scss']
})
export class NavComponent {
    protected isVisible = false;

    constructor(private breakpointObserver: BreakpointObserver,
                private destroyRef: DestroyRef,
                private elementRef: ElementRef) {}

    ngOnInit() {
      const breakpoint = getComputedStyle(this.elementRef.nativeElement).getPropertyValue('--breakpointForMenu');
      this.breakpointObserver.observe(`(max-width: ${breakpoint})`)
        .pipe(takeUntilDestroyed(this.destroyRef))
        .subscribe(state => {
          if (!state.matches) {
            this.isVisible = false;
          }
        });
    }
} There, it should still all work as expected. This allows us to keep both the styles and functionality here in sync, and should prevent some mistakes in the future. CDK Predefined Breakpoints Something else that’s pretty cool about this layout module from the CDK is that they provide some standard breakpoints based on Google’s Material Design concepts that we can just use if we want. Here’s a list of the breakpoints and their values: So, there’s quite a few to choose from out of the box, but how do you use them? Well, let’s look at an example. I’ve added this variable for our current visible breakpoint range, and it will be either small, medium or large. @Component({
  selector: 'app-nav',
  standalone: true,
  imports: [CommonModule],
  templateUrl: './nav.component.html',
  styleUrls: ['./nav.component.scss']
})
export class NavComponent {
  ...
  protected currentRange?: 'Small' | 'Medium' | 'Large';
  ...
} This value is going to be rendered in a red bar at the bottom of our view once we set it properly. Now, let’s add an additional observe. This time, let’s use an array of breakpoints. We’ll need to import Breakpoints from the CDK layout module and then we'll pass the small breakpoint, medium, and large. Just like the previous example, we need to make sure to take until destroyed. Then we can subscribe. This time however, I’m not concerned with the state and you’ll see why in just a sec. Here we’re going to use another feature of the Breakpoint observer. We are going to check if our breakpoints are matched using the method. This returns a Boolean value based on the media query passed to it. From here we'll set the current range based on the matched media query. isMatched import { BreakpointObserver, Breakpoints } from '@angular/cdk/layout';

@Component({
  selector: 'app-nav',
  standalone: true,
  imports: [CommonModule],
  templateUrl: './nav.component.html',
  styleUrls: ['./nav.component.scss']
})
export class NavComponent {
    ...
    protected currentRange?: 'Small' | 'Medium' | 'Large';

    ...

    ngOnInit() {
      ...

      this.breakpointObserver.observe([Breakpoints.Small, Breakpoints.Medium, Breakpoints.Large])
      .pipe(takeUntilDestroyed(this.destroyRef))
      .subscribe(() => {
        if (this.breakpointObserver.isMatched(Breakpoints.Small)) {
          this.currentRange = 'Small';
        }
        if (this.breakpointObserver.isMatched(Breakpoints.Medium)) {
          this.currentRange = 'Medium';
        }
        if (this.breakpointObserver.isMatched(Breakpoints.Large)) {
          this.currentRange = 'Large';
        }
      });
    }
} Now we see our red bar at the bottom with the currently matched range. And, when we resize, it gets updated properly based on the media query that matches. Pretty cool. Ok, so I hope that helps for those of you out there who have a need for this type of thing in Angular. It’s definitely come in handy for me in the past. Be sure to keep an eye out for more videos in the future showcasing some of the power of the CDK. Want to See It in Action? Check out the demo code and examples of these techniques in the in the stackblitz example below. If you have any questions or thoughts, don’t hesitate to leave a comment. https://stackblitz.com/edit/stackblitz-starters-klqbbi?ctl=1&embed=1&file=src%2Fnav%2Fnav.component.ts&embedable=true

How to Utilize Angular CDK Breakpoint Observer

Using Diagnostic Observers to Decouple Logging and Observability Code

By: , Senior Director of Product Management at Observe Jack Coates You’ve heard of security and observability, but maybe not security observability. Security observability uses logs, metrics, and traces to infer risk, monitor threats, and alert on breaches. The fundamentals of modern observability — cheap backend, low-friction ingest for any type of data, pay-as-you-go searching — are now crucial for security teams as well. Organizations have been using log data to identify known and unknown attacks since the beginning of the Internet, but each generational shift in volume and velocity has broken the old tools. Security observability starts as a way to bring your SecOps forward to a world with an architecture that separates storage from compute. To better understand the convergence of security and observability, Observe recently surveyed 500 full-time security decision-makers and practitioners— 40% of whom were either CISOs or CSOs — to compile the report. 2023 State of Security Observability Here are eight key takeaways from the survey: - Security observability is not just a buzzword. 99% of respondents say their organizations are prioritizing it, demonstrating the need for a new approach. Organizations of all sizes can use their observability platforms to help support security needs. SECURITY OBSERVABILITY IS A PRIORITY - The report found that 84% of organizations combine security and data operations into a single analytics tool. However, more than half of the security-relevant data that goes into observability systems needs to be transformed before it can be used. ORGANIZATIONS NEED HELP INTEGRATING OBSERVABILITY TOOLS - The vast majority (95%) of respondents use Security Information and Event Management (SIEM) tools in some way. SIEM has been positioned as a content and integration-rich entry point that gives access to dozens of rules and add-ons specific to the other products that your organization runs on. The reality is each integration has versioning and configuration requirements, each rule only works with properly abstracted data, and each alert expects that the customer can decide if it’s important or not. This requires continual maintenance from skilled users or costly professional services time. SIEM IS MORE COMPLEX THAN IT SEEMS - Smaller organizations struggle with limited resources in the security tools market, hindering effective adoption. They’re forced to wait for their SIEM vendor to make updates to the solution in order to stay current with new trends. SMALLER ORGANIZATIONS STRUGGLE WITH SECURITY TOOLS - Cloud infrastructure doesn’t provide sufficient operations or security observability on its own, and agents must be used. Host agents are used by 57% of organizations for observability and 51% for security, along with container agents (42% for observability and 44% for security) and sidecar agents (29% for observability and 28% for security). AGENTS ARE NECESSARY - Tool sprawl remains an issue with organizations — half of security incidents require escalation, and tool sprawl presents itself as an obstacle. Only 11% of respondents reported staying in a single pane of glass, with 18% using six or more tools to investigate issues. This indicates that organizations need an all-in-one platform to deal with incidents more efficiently. ORGANIZATIONS STRUGGLE WITH TOOL SPRAWL - The report shows that organizations value having skilled teams to find and respond to unknown threats. About 73% have in-house Incident Response (IR) teams and Security Operations Centers (SOC). Interestingly, newer tools like SOAR, UEBA, and EDR, expected to replace SIEM, haven't taken over as anticipated. Instead, organizations still heavily depend on SIEM for their security needs. VALUE IN HAVING SKILLED TEAMS - Cloud conversion has crossed the hallway mark, and 74% of organizations have built their current systems to be mostly or entirely cloud-native. As more organizations become cloud-native, they will need tools and infrastructure that can keep up with complex and ephemeral cloud-native environments. MORE ORGANIZATIONS BECOME CLOUD-NATIVE

8 Key Takeaways from Observe’s 2023 State of Security Observability Report

Authors:
(1) Guillaume Staerman, INRIA, CEA, Univ. Paris-Saclay, France;
(2) Marta Campi, CERIAH, Institut de l’Audition, Institut Pasteur, France;
(3) Gareth W. Peters, Department of Statistics & Applied Probability, University of California Santa Barbara, USA. Table of Links Abstract and 1. Introduction 2. Background & Preliminaries 2.1. Functional Isolation Forest 2.2. The Signature Method 3. Signature Isolation Forest Method 4. Numerical Experiments 4.1. Parameters Sensitivity Analysis 4.2. Advantages of (K-)SIF over FIF 4.3. Real-data Anomaly Detection Benchmark 5. Discussion & Conclusion, Impact Statements, and References Appendix A. Additional Information About the Signature B. K-SIF and SIF Algorithms C. Additional Numerical Experiments A. Additional Information About the Signature In this section, we provide additional information regarding the signature. Firstly, the formula for the coordinate signature is provided, followed by some further information about the truncated signature and the link of the signature with moments. We then introduce references and further discussion for the computation of the signature, the linear-closed form utilised and, lastly, a review of the inner product of tensors required for the signature method is presented. Computation of the Signature. The signature is usually computed on the linear paths reconstructed from observations (Lyons and McLeod, 2022; Fermanian, 2021) thanks to the two properties listed below. First, the signature benefits from a closed form when computed on linear paths. Second, a concatenation formula has been provided by Chen (1958), where the coordinate signature of two concatenate segments is given by a tensor product of the specific coordinate signatures on each segment. These two properties are described formally below. In practice, the signature is usually computed as follows. First, a linear path is reconstructed by interpolation from the observed data. In a second step, the signature of each segment of the linear path are calculated separately thanks to the closed-form (2). Then the signature of the entire path is computed recursively using the Chen’s identity property. Definition of Inner Product of Tensors for Signature Method. Following Chevyrev and Oberhauser (2022) and Lee and Oberhauser (2023), we introduce the inner product of the tensors required to compute the truncated signature and its kernel. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Guillaume Staerman, INRIA, CEA, Univ. Paris-Saclay, France; (2) Marta Campi, CERIAH, Institut de l’Audition, Institut Pasteur, France; (3) Gareth W. Peters, Department of Statistics & Applied Probability, University of California Santa Barbara, USA. Authors: Authors: (1) Guillaume Staerman, INRIA, CEA, Univ. Paris-Saclay, France; (2) Marta Campi, CERIAH, Institut de l’Audition, Institut Pasteur, France; (3) Gareth W. Peters, Department of Statistics & Applied Probability, University of California Santa Barbara, USA. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Background & Preliminaries 2. Background & Preliminaries 2.1. Functional Isolation Forest 2.1. Functional Isolation Forest 2.2. The Signature Method 2.2. The Signature Method 3. Signature Isolation Forest Method 3. Signature Isolation Forest Method 4. Numerical Experiments 4. Numerical Experiments 4.1. Parameters Sensitivity Analysis 4.1. Parameters Sensitivity Analysis 4.2. Advantages of (K-)SIF over FIF 4.2. Advantages of (K-)SIF over FIF 4.3. Real-data Anomaly Detection Benchmark 4.3. Real-data Anomaly Detection Benchmark 5. Discussion & Conclusion, Impact Statements, and References 5. Discussion & Conclusion, Impact Statements, and References Appendix A. Additional Information About the Signature A. Additional Information About the Signature B. K-SIF and SIF Algorithms B. K-SIF and SIF Algorithms C. Additional Numerical Experiments C. Additional Numerical Experiments A. Additional Information About the Signature In this section, we provide additional information regarding the signature. Firstly, the formula for the coordinate signature is provided, followed by some further information about the truncated signature and the link of the signature with moments. We then introduce references and further discussion for the computation of the signature, the linear-closed form utilised and, lastly, a review of the inner product of tensors required for the signature method is presented. Computation of the Signature. The signature is usually computed on the linear paths reconstructed from observations (Lyons and McLeod, 2022; Fermanian, 2021) thanks to the two properties listed below. First, the signature benefits from a closed form when computed on linear paths. Second, a concatenation formula has been provided by Chen (1958), where the coordinate signature of two concatenate segments is given by a tensor product of the specific coordinate signatures on each segment. These two properties are described formally below. Computation of the Signature. In practice, the signature is usually computed as follows. First, a linear path is reconstructed by interpolation from the observed data. In a second step, the signature of each segment of the linear path are calculated separately thanks to the closed-form (2). Then the signature of the entire path is computed recursively using the Chen’s identity property. Definition of Inner Product of Tensors for Signature Method. Following Chevyrev and Oberhauser (2022) and Lee and Oberhauser (2023), we introduce the inner product of the tensors required to compute the truncated signature and its kernel. Definition of Inner Product of Tensors for Signature Method. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Unlocking the Power of Signatures in Anomaly Detection

Transak has announced its integration with Zerion Wallet, becoming the exclusive on-ramp provider for the platform's new ZERϴ Network. The partnership aims to facilitate cryptocurrency purchases through local payment methods for Zerion's 550,000 users. The integration enables users to purchase cryptocurrencies using credit cards, debit cards, and bank transfers directly within the Zerion Wallet interface. Transak's infrastructure operates under FinCEN registration as a Money Services Business, maintaining ISO/IEC 27001 certification and SOC II compliance. ZERϴ Network, Zerion's Layer 2 rollup solution, implements zero-knowledge proofs for Ethereum scalability. The network utilizes a sponsorship model to eliminate gas fees for users. The system operates through Zerion Wallet's verification of user eligibility via Onchain DNA, a reputation system that determines transaction sponsorship based on user activity. "The elimination of gas fees represents a significant change in transaction accessibility," states Evgeny Yurtaev, CEO of Zerion. The integration positions Transak to support ZERϴ Network from its initial launch. To mark the partnership, both companies have initiated a promotion where Zerion Loyalty Program users outside the UK can purchase ETH on ZERϴ Network without fees through Transak. However, standard card processing and bank fees may still apply depending on the payment method. Sami Start, CEO of Transak, commented on the partnership's focus on reducing barriers to Web3 participation through the elimination of gas fees. Technical details of the integration include: Support for 170+ cryptocurrencies


Coverage across 160 countries


Integration with 75+ blockchain networks


EVM compatibility for the rollup solution The partnership reflects ongoing developments in blockchain infrastructure: Expansion of zero-fee transaction capabilities


Integration of traditional payment systems


Implementation of reputation-based transaction validation


Development of scalable Layer 2 solutions Transak's expansion continues with offices in: Miami (Headquarters)


Bengaluru (Tech Hub)


London


Milan


Dubai


Hong Kong Market analysts note that such integrations potentially influence broader cryptocurrency adoption patterns. The implementation of zero-fee structures and traditional payment methods may affect user behavior and transaction volumes across blockchain networks. Zerion Wallet currently provides access to over 60 DeFi protocols through its non-custodial platform. The wallet's integration with Transak expands payment options for users while maintaining security protocols and regulatory compliance. Industry observers suggest that partnerships between infrastructure providers and wallet services could impact the development of Layer 2 solutions and user onboarding processes in the cryptocurrency sector. The success of such integrations may influence future developments in blockchain payment systems and network scalability solutions. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Transak has announced its integration with Zerion Wallet, becoming the exclusive on-ramp provider for the platform's new ZERϴ Network. The partnership aims to facilitate cryptocurrency purchases through local payment methods for Zerion's 550,000 users. The integration enables users to purchase cryptocurrencies using credit cards, debit cards, and bank transfers directly within the Zerion Wallet interface. Transak's infrastructure operates under FinCEN registration as a Money Services Business, maintaining ISO/IEC 27001 certification and SOC II compliance. Transak ZERϴ Network, Zerion's Layer 2 rollup solution, implements zero-knowledge proofs for Ethereum scalability. The network utilizes a sponsorship model to eliminate gas fees for users. The system operates through Zerion Wallet's verification of user eligibility via Onchain DNA, a reputation system that determines transaction sponsorship based on user activity. "The elimination of gas fees represents a significant change in transaction accessibility," states Evgeny Yurtaev, CEO of Zerion. The integration positions Transak to support ZERϴ Network from its initial launch. To mark the partnership, both companies have initiated a promotion where Zerion Loyalty Program users outside the UK can purchase ETH on ZERϴ Network without fees through Transak. However, standard card processing and bank fees may still apply depending on the payment method. Sami Start, CEO of Transak, commented on the partnership's focus on reducing barriers to Web3 participation through the elimination of gas fees. Technical details of the integration include: Support for 170+ cryptocurrencies Coverage across 160 countries Integration with 75+ blockchain networks EVM compatibility for the rollup solution Support for 170+ cryptocurrencies Support for 170+ cryptocurrencies Coverage across 160 countries Coverage across 160 countries Integration with 75+ blockchain networks Integration with 75+ blockchain networks EVM compatibility for the rollup solution EVM compatibility for the rollup solution The partnership reflects ongoing developments in blockchain infrastructure: Expansion of zero-fee transaction capabilities Integration of traditional payment systems Implementation of reputation-based transaction validation Development of scalable Layer 2 solutions Expansion of zero-fee transaction capabilities Expansion of zero-fee transaction capabilities Integration of traditional payment systems Integration of traditional payment systems Implementation of reputation-based transaction validation Implementation of reputation-based transaction validation Development of scalable Layer 2 solutions Development of scalable Layer 2 solutions Transak's expansion continues with offices in: Miami (Headquarters) Bengaluru (Tech Hub) London Milan Dubai Hong Kong Miami (Headquarters) Miami (Headquarters) Bengaluru (Tech Hub) Bengaluru (Tech Hub) London London Milan Milan Dubai Dubai Hong Kong Hong Kong Market analysts note that such integrations potentially influence broader cryptocurrency adoption patterns. The implementation of zero-fee structures and traditional payment methods may affect user behavior and transaction volumes across blockchain networks. Zerion Wallet currently provides access to over 60 DeFi protocols through its non-custodial platform. The wallet's integration with Transak expands payment options for users while maintaining security protocols and regulatory compliance. Industry observers suggest that partnerships between infrastructure providers and wallet services could impact the development of Layer 2 solutions and user onboarding processes in the cryptocurrency sector. The success of such integrations may influence future developments in blockchain payment systems and network scalability solutions. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: business blogging program business blogging program

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