In this article, we have studied the electron acoustic waves in a magnetized plasma containing Kappa distributed ions which are streaming with high velocity within the plasma and under the action of external force. We employed the homotopy perturbation method (HPM) to obtain the graphical plots of solitary structures and their evolution into an envelope soliton. Analytically, we obtained the linear dispersion relation and studied its characteristics. Further, we derived the Korteweg–De Vries (KdV) equation using the reductive perturbation technique and studied the parametric dependence on the KdV solitary profile. These findings augment the homotopy results. The analytical and simulation results thus obtained will be helpful in interpretating and identifying electron acoustic wave (EAW) modes in such a plasma system.

Autonomous underwater vehicles (AUVs) typically rely on underwater sound to communicate with sound channel physics defining communication performance. Current acoustic propagation models incorporate channel information and physics but are computationally expensive to run onboard an AUV. This work explores reduced-order proxies for these models through a machine-learning approach that can use in-situ measurements of observed sound-speed profile (SSP) to quickly provide transmission loss (TL) propagation information for vehicles to optimize communication performance through repositioning behaviors. The vertical structure from a year-long time series of mooring observations from offshore San Diego is decomposed with empirical orthogonal functions to reduce the SSPs to three unique amplitudes that explain 95.6% of the total variance. The acoustic propagation model Bellhop is used to estimate TL for a given SSP and different thresholds of TL are used to represent communications performance. Machine-learning models are trained with binarized Bellhop outputs to indicate locations of improved underwater acoustic communications. Amplitude values, desired TL, vehicle depth, and range are used as inputs into machine-learning models for optimizing TL. The best trained model was a random forest (ensemble) network with a 93.3% dice score. The decision tree had a faster prediction speed, relative to Bellhop, with similar prediction accuracy as the ensemble. This work aims to improve sound awareness of AUVs by generating SSP-informed communication performance maps.

The theoretical investigation of shocks and solitary structures in a dense quantum plasma containing electrons at finite temperature, nondegenerate cold electrons, and stationary ions has been carried out. A linear dispersion relation is derived for the corresponding electron acoustic waves. The solitary structures of small nonlinearity have been studied by using the standard reductive perturbation method. We have considered collisions to be absent, and the shocks arise out of viscous force. Furthermore, with the help of a standard reductive perturbation technique, a KdV–Burger equation has been derived and analyzed numerically. Under limiting cases, we have also obtained the KdV solitary profiles and studied the parametric dependence. The results are important in explaining the many phenomena of the laser–plasma interaction of dense plasma showing quantum effects.

Using reductive perturbation technique, small-amplitude ion-acoustic solitary wave has been investigated in multi-component dense plasma, in which an electron beam propagates along the general streaming motion. The electrons in plasma have the q-exponential distribution. The positive and negative ions follow a regular Maxwellian distribution. It has been found that the positive and negative ion densities as well as the beam concentration have significant effect on the formation and properties of solitary structures. The streaming velocities of corresponding particles also have pronounced effect on the features of the solitons.

In this paper, we discuss the time evolution of a forced KdV equation by using a standard reductive perturbation technique. Using conservation laws, we der