Abstract
The manufacturing process of thermoregulation products with polyester (PES) fabric and conductive polymers such as poly(3,4-ethylenedioxythiophene) doped with poly(styrene sulfonate) (PEDOT:PSS) with proper wearability, comfort, and high performance is still a challenge due to low adhesion, environment instability and nonuniform coatings. This study presents a simple and effective method for producing thermoregulatory PES fabrics using the Joule heating effect. Textiles treated with dielectric barrier discharge (DBD) plasma were functionalized with PEDOT:PSS incorporating secondary dopants, such as dimethyl sulfoxide (DMSO) and glycerol (GLY). PEDOT:PSS was used because it does not compromise the mechanical properties of base materials. DBD plasma treatment was applied to PES to improve the substrate's functional groups and consequently increase adhesion and homogeneity of the PEDOT:PSS on the substrate. The polymer were applied to the textiles by dip-pad-drycure method ensuring uniform distribution and homogeneous heating of the materials. The samples' conductivity, impedance, potential and Joule effect, and their morphological, chemical and thermal properties were studied. Control samples without plasma treatment and secondary dopants were also prepared. The results showed that the DBD-treated samples, coated with 5 layers of PEDOT:PSS, doped with DMSO 7 % (w/v), displayed the best conductivity and Joule effect performance reaching 44.3 degrees C after 1 h.

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Abstract
This paper presents a novel approach to improve the conversion of interferometric synthetic aperture radar (InSAR) ascending and descending orbit measurements into horizontal and vertical deformation components, explicitly considering SAR product characteristics (acquisition geometry, resolution, and positional accuracy). Conventional decomposition methods use square grids, inadequately addressing directional biases associated with satellite images characteristics, reducing measurement accuracy. It is proposed optimized alternative geometries - rectangle, hexagon, and double inverted isosceles trapezoid (diIT) - derived from theoretical analysis of scatterer influence areas for Sentinel-1 imagery and calibrated data from the European ground motion service (EGMS). Validation was conducted comparing results against global navigation satellite system (GNSS) ground-truth data. Accuracy was quantitatively evaluated using deformation velocity (DV) and average Euclidean distance (ED) metrics. Results demonstrated an average 25% improvement in DV detection over traditional square grids, with only minor trade-offs, such as lower scatterer density and sub-millimetric increases in error for hexagon and diIT geometries.

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Despite their extreme sensitivity, speckle-based fiber optical sensors are typically limited by the camera frame rate and dynamic range. In this context, recent developments in event-based sensors make them a promising and affordable tool for high-speed interrogation for such class of sensors, offering a low-latency approach to detecting dynamic changes in illumination patterns, well-suited for fast interrogation with frequency response up to the MHz range. In this manuscript, we investigate the potential of using an event-based vision sensor (EVS) as an interrogator for a speckle-based optical fiber sensor operating at 532nm to detect vibrations induced by an off-the-shelf sound speaker. In contact with the fiber, these vibrations induce dynamic changes in the speckle pattern, which are tracked by the EVS and processed to construct temporal frames with timestamps below 100 mu s. Approximating the differential operator of the deformation in the linear regime, we show a successful reconstruction of the acoustic signal for two illustrative case studies: i)a single-frequency signal at 1.2 KHz and ii)a linear ramp between 300 Hz to 2.5 kHz. The results demonstrate the ability to accurately identify not only the fundamental frequencies but also their harmonics generated by the speaker up to 5 KHz, paving an innovative path to harness the potential of speckle-based sensors in multiple scenarios of optical metrology and dynamic sensing applications.

Abstract
When directional data fall in the positive orthant of the unit hypersphere, a folded directional distribution is preferred over a simple directional distribution for modeling the data. Since directional data, especially axial data, can be modeled using a Watson distribution, this paper considers a folded Watson distribution for such cases. We first address the parameter estimation of this distribution using maximum likelihood, which requires a numerical algorithm to solve the likelihood equations. We use the Expectation-Maximization (EM) algorithm to obtain these estimates and to analyze the properties of the concentration estimator through simulation. Next, we propose the Bayes rule for a folded Watson distribution and evaluate its performance through simulation in various scenarios, comparing it with the Bayes rule for the Watson distribution. Finally, we present examples using both simulated and real data available in the literature.

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