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Dataset related to article: Heavy-oxide glasses with superior mechanical assets for nonlinear fiber applications in the mid-infrared. Cl��ment Strutynski, Florian Calzavara, Th��o Guerineau, Laura Loi, Romain Laberdesque, Jean-Michel Rampnoux, Steeve Morency, Yannick Ledemi, Yannick Petit, Marc Dussauze, Fr��d��ric D��s��v��davy, Fr��d��ric Smektala, Sylvain Danto, Lionel Canioni, Younes Messaddeq, Evelyne Fargin, Thierry Cardinal. Optical Materials Express. 2021, volume 11, issue 5, pp. 1420-1430 https://doi.org/10.1364/ome.417699 The data were measured internally with instrumentations belonging to the tem in Bordeaux. UV-Visible-NIR transmission spectra of the glass were recorded in the 0.2 ��m - 3.3 ��m range using an Agilent Cary 5000 UV-Vis-NIR spectrometer. Transmission in the Mid-IR was collected using a Bruker FTIR Optical losses measurements of single-index fibers were carried out using the cutback method on several meters-long samples. The Raman gain cross-section was calculated from spontaneous scattering spectra measured in in VV (vertical polarization for the excitation and the analysis) configuration using a micro-Raman setup. A spectroscopic phase modulated ellipsometric measurement had thus been performed with an UVISEL apparatus (HORIBA Jobin-Yvon) to determine the refractive index n of the GGBLaY glass, after a fine calibration of the ellipsometre with the reference Silica glass over the spectral range between 0.260 ��m to 2 ��m thanks to the comparison to literature.

This dataset contains sensor values from Force Myography (FMG) sensors and Inertial Measurement Units (IMUs) and corresponding Ankle Joint Power and Ground Reaction Force. The FMG strap was positioned about two inches above the ankle of the left leg. Four IMUs were positioned on foot, distal shank, distal thigh, and proximal thigh of the left leg. There are sensors values and corresponding reference values from nine participants and five different walking speeds (0.4 m/s, 0.7 m/s, 1.0 m/s, 1.3 m/s, and 1.6 m/s). This dataset portrays how values from different sensors vary with respect to Ankle Joint Power and Ground Reaction Force. The dataset can be utilized to train machine learning models to predict Ankle Joint Power and Ground Reaction Force using FMG and IMUs sensor values, which in turn can eliminate the requirement of a complex biomechanical setup to measure Ankle Joint Power and Ground Reaction Force.