esa-sceva/satcom-chunk-collection huggingface dataset:
# Appearance-Based Gaze Estimation as a Benchmark for Eye Image Data Generation Methods
## 2 Prior Work  
### Image Rendering Tools  
A number of general-purpose 3D rendering tools have been adopted for the ET domain, the most notable examples being Unity and Blender. A Blender head model with a highly detailed eye region was presented in [23]. The public availability of the source code allows for fine-grain control over most parameters of the rendered scene through the Python API. The most significant downside of the model is its use of only one artificial subject, which makes it impossible to introduce subject variability in the rendered images. Moreover, the visual appearance of the rendered images is substantially different from what can be seen in real eye image data sets, such as MPIIGaze [14]. This is problematic, as a lack of realism in synthetic images has been shown to reduce gaze estimation performance [26].  
Improving the realism of eye image rendering is an active research area. A Unity-based solution was introduced in [27]. It offered substantial variability in subject appearance and ambient conditions with extremely fast rendering speed. [PERSON] et al. [28] presented RIT-Eyes. Implemented in Blender, this rendering pipeline generates synthetic images and 2D/3D segmentation masks using eye attributes, gaze position, and environmental/sensor parameters (camera pose, etc.) as input. One key advancement of this pipeline versus prior work is the ability to generate temporally coherent eye videos that mimic the blink, head, and eye movements found in publicly available data sets [29]. [PERSON] et al. in [22] introduced novel large-scale data for near-eye gaze estimation. They presented a synthetic data set based on anatomically informed eye and face models with variations in face shape, gaze direction, pupil and iris, skin tone, and external conditions (2 M images with the \(1280\times 960\) resolution), and a real-world data set collected from 35 subjects (2.5 M images with the \(640\times 480\) resolution). Unfortunately, there is a common drawback in all these methods. The authors did not make the source code publicly available, at best only released either the already generated data (NvGaze) or the compiled executable with very little control over the generated sample domain (UnityEyes). These complex 3D environments are also challenging to reproduce from scratch, which greatly reduces the applicability of their solutions to novel settings.
## 2 VRS concept  
The VRS concept, [10-13] is a derivative of the multiple reference station approach, [14,15]. The main difference is that the surrounding reference GPS stations are used to determine \"synthetic\" dual-frequency code and carrier-phase GPS at a virtual base station that is located close to the user's receiver. These GPS reference stations are strategically located in an extensive geographical area andare connected to a network control centre. This centre receives information from the GPS stations in real-time and, using an NTRIP protocol, creates the differential corrections to be applied anywhere inside the geographical area where the GPS network is located.  
A user connected to this network will receive the differential corrections and a virtual reference station is created near the rover position. This virtual reference station acts as the master station in RTK positioning.  
The main benefit of the VRS approach is that it does not require a physical reference station close to the user (usually with known coordinates in the reference frame in which the survey will take place). However, it does require a reference GPS station infrastructure and a control centre.  
The VRS approach is also more flexible in terms of permitting users to use their current receivers and software without involving any special software or communications equipment (if used in post-processed mode) and the number of instruments needed for field operations is greatly reduced.  
Finally, with VRS, users within the network can operate at greater distances than with the conventional RTK GPS model without degrading accuracy. Under ideal conditions, the VRS approach can deliver single-point coordinate accuracies of a 3-5 centimetres for a rover separated 50-70 km from the reference stations.
## 1 Introduction
The influence of the inherent inhomogeneity of earth material on the mechanical properties of adobe is emphasized, and finally the constitutive equation describing the strength and deformation characteristics of adobe under uniaxial compression is established. [PERSON] et al. [1] carried out uniaxial compression tests on earth blocks under different working conditions, put forward the recommended loading rate and standard specimen size for uniaxial compression tests of earth materials, and established the constitutive equation of earth materials. [PERSON] et al. [21] carried out uniaxial compression tests on brick, cubic, prismatic, and cylindrical specimens. They believed that the uniaxial compressive strength of earth specimens should not be obtained from the existing shape and size correction coefficients of other materials. At the same time, the constitutive equations of specimens with different sizes under uniaxial compression were established according to their own test results. From the above literature, it can be seen that the shape, size, curing age, and loading rate of the specimen are the factors affecting the uniaxial compression performance of earth materials that have been widely studied by scholars because there is no complete consensus on the uniaxial compression test method. Most of the uniaxial compression constitutive equations of earth materials proposed by scholars are based on fitting the test data obtained by themselves [1; 18; 19; 20; 21]. Whether it has wide applicability and statistical significance-based methods for calculating parameters related to constitutive equations has not received attention by scholars. In this research work, uniaxial compression tests are carried out on earth specimens with different shapes (cylinders and cubes), different sizes (100 mm, 70.7 mm, and 150 mm cubes), different curing ages (14 d, 21 d, and 28 d), and different loading rates (0.2 mm, 1.0 mm, and 3.0 mm). The effects of the above factors on the failure shape, strength, and coefficient of variation of earth specimens are investigated. The proposed method for uniaxial compression testing of earth materials is put forward. On the basis of the existing typical constitutive equation of earth, the Illampas constitutive equation, which can better reflect the mechanical characteristics of earth, is selected and modified. The applicability of the modified constitutive equation is verified by fitting the stress-straincurve of the uniaxial compression test in this study and the related representative literature. The shape parameters of the constitutive equation curve and peak compressive strain calculation equations based on standard specimens characterized by compressive strength are established. The established constitutive equation is widely applicable, and the relevant parameters can be determined only by obtaining the measured compressive strength of an earth specimen. This is a good reference value to promote the development of computational analysis methods for earth structures and to facilitate the engineering design applications of earth structures.
## 3 Results
### Correlation between Predicted Ages and Disease Severity  
Correlations among severity scores, predicted and actual chronological age, BMI, and sex were calculated.  
In Figure 2, the resulting correlation matrix is reported, considering as input both the binned spectra and the metabolite/lipoprotein data acquired from the cohorts of advPD patients. As we can see from the plot, the correlation with severity scores becomes much higher when the predicted age is considered with respect to the chronological age.  
As we can see from Figure 2, the correlation of severity scores with predicted (biological) age is much higher than its correlation with actual chronological age. We therefore attempted the same correlation plot (Figure 3), considering the dn\({}^{2}\)PD patients and their respective severity scores. In this case, correlations are not as strong as in the case of  
Figure 2: [PERSON]’s correlation coefficients among predicted age (estimated using PLS), severity scores, and other demographic and physiological variables. Data are obtained using the advPD cohort. Asterisks represent \(p\)-values \(<\) 0.05.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \multicolumn{4}{c}{**Overestimated**} & \multicolumn{4}{c}{**Underestimated**} \\ \cline{2-9}  & **\%Subj/Group** & **RA(m \(\pm\) SD)** & **PA(m \(\pm\) SD)** & **PA-RA** & **\%Subj/Group** & **RA(m \(\pm\) SD)** & **PA(m \(\pm\) SD)** & **RA-PA** \\ \hline advPD & 31.8 & 64.29 \(\pm\) 7.54 & 79.74 \(\pm\) 15.49 & 15.45 \(\pm\) 10.51 & 40.9 & 72.33 \(\pm\) 6.58 & 60.94 \(\pm\) 5.09 & 11.39 \(\pm\) 4.87 \\ dn\({}^{2}\)PD & 40.8 & 55.84 \(\pm\) 10.81 & 70.54 \(\pm\) 9.74 & 14.7 \(\pm\) 6.40 & 16.7 & 74.23 \(\pm\) 5.59 & 64.2 \(\pm\) 6.30 & 10.01 \(\pm\) 3.24 \\ Ctr & 13.8 & 55.81 \(\pm\) 15.78 & 66.36 \(\pm\) 16.17 & 10.55 \(\pm\) 4.84 & 13.3 & 84.86 \(\pm\) 16.45 & 74.41 \(\pm\) 16.2 & 10.44 \(\pm\) 3.88 \\ \hline \end{tabular}  
* RA: real age in years; m: mean; SD: standard deviation; PA: predicted age in years.  
\end{table}
Table 5: Difference between the chronological age and the predicted age (years) for each study group. Overestimated are the individuals that are predicted at least 6 years older than their actual chronological age; underestimated are individuals that are predicted at least 6 years younger than their actual chronological age. Percentage reported refers to the total of misclassified individuals per group.  
the correlations in advPD patients. This can be attributed to the fact that dn\({}^{2}\)PD patients are at the beginning of the disease and, as can be seen from Table 2, they are characterized by very low severity scores compared to advPD patients.
# Exploiting Outage and Error Probability
## 3 System Performance Analysis and Our Proposed Cooperative Routing Protocols  
This section presents outage and error probability performance analysis of incremental relaying cooperative diversity with retransmissions. Based on the analysis, we then propose two cooperative routing protocols; ACE and E-ACE.
# Harnessing the Potential of Emerging Technologies to Break Down Barriers in Tactical Communications
## 2 Picture of Tactical Communications: A Panoramic View
### Legacy Tactical Waveforms  
Given the expanding battlespace and the presence of formidable adversaries, the presence of robust military communication systems has become exceptionally imperative. Tactical waveforms, being pivotal components, play a crucial role in ensuring the dependable and secure exchange of information to and from combatants on the battlefield. The most frequently employed waveforms in tactical scenarios are outlined in Figure 1.  
* **HAVE-QUICK:** It is a waveform designed for an electromagnetic-resistant frequency-hopping system that protects the military. It is used in the UHF band for aeronautical mobile traffic.
* **SINCGARS:** Employing a non-IP, low-bandwidth voice coding format called CNR, this waveform has been integrated into numerous legacy tactical radios. Additionally, it facilitates low-rate data communications and finds application in vehicle-mounted, backpack, airborne, and handheld devices.  
* **ECCM:** It is a waveform used especially in SDN-type radio. It is mainly designed to improve information security and resist interference. This waveform has the frequency hopping technique, which allows for quick change of the frequency of the energy that is being transmitted and received at only that frequency. In this way, it is difficult for enemies to detect frequency changes or predict the next frequency jump.
* **SRW:** This open-standard voice and data waveform, designed for mobile ad hoc usage, serves the purpose of extending wideband battlefield networks to the tactical periphery. SRW operates as a node or router within a wireless network, enabling the transmission of crucial information across significant distances. Additionally, it is employed by soldiers, small units, and even very compact sensors, like ground or air vehicles.
* **WNW:** This narrowband waveform is specifically crafted to establish network connectivity between aircraft and ground vehicles beyond line of sight (BLOS). It also serves the purpose of software-defined radio (SDR), offering connectivity to command posts at various levels, including company, platoon, and battalion.
* **MUOS:** It is a narrowband tactical satellite communication system that provides enhanced and secure communication capabilities, including simultaneous voice, video, and data for mobile and remote users. It is mainly used by terminal and manpack radios in software-defined radios (SDR) to provide narrowband communication in the UHF band.
# Contribution of Etalon Observation to Earth Rotation Parameters under a New Observation Scenario
## 4 Discussion  
A 3-month Etalon tracking campaign was conducted by ILRS in 2019. On average, the numbers of weekly SLR observations to Etalon-1/2 satellites in the new observation scenario are 1.4 and 1.7 times larger than in the noncampaign period. This paper analyzes the contribution of Etalon observations in a new scenario to the ILRS standard products, such as station coordinates, ERPs and satellite orbit. The standard deviations of Xp, Yp and LOD for LAGEOS+Etalon solutions in the new scenario are 167.64 \(\upmu\)as, 231.90 \(\upmu\)as and 45.13 \(\upmu\)as/day, respectively. Compared with the statistical information of the LAGEOS-only solutions, the precisions of Xp, Yp and LOD are improved by 6.9%, 14.3% and 5.1%, respectively. The results are similar to those shown in [32], in which the quality of Xp and Yp can be improved by 10% when the Etalon observations are increased by a factor of three in simulations. The work of [PERSON] in [2] reveals that the RMS of Xp and Yp can be improved by 7% and 10%, respectively, by combining LAGEOS with Starlette, Stella and AJISAI, which are LEO satellites. In addition, the research conducted by [PERSON] in [23,24] reports that combining the SLR data of LAGEOS and GNSS satellites has little effect on the estimation of pole motion. However, the quantity of weekly observations of Etalon satellites in the new scenario is approximately a factor of eight or more lower than those of LEO and GNSS satellites, which implies that more time is spent to achieve the same level of improvement. The results obtained with real data in this paper confirm that Etalon satellite observations are beneficial for ERP estimation, particularly pole motion.  
Compared with ERPs, the effect of Etalon observations from the new observation scenario on station coordinates and orbit of LAGEOS satellites is marginal. For station coordinates, the range for the differences in station repeatability between LAGEOS and the combined solutions are -1 mm and 1 mm, which can be ignored. Notably, for station Komsomolsk (1868), the RMS values of coordinate residuals in the N, E and U directions using Helmert transformation are improved by 3.1 cm, 2.0 cm, and 2.3 cm when combining Etalon and LAGEOS satellite observations. This may result from the fact that station Komsomolsk (1868) is focused on the tracking of GNSS satellites and the low quantity of data on LAGEOS satellites.
## 3 Results and Discussion
### Performance of [DBDImI]I as a Solvent for Glucose Conversion
[23] reported that the chloride (Cl\({}^{-}\)) ion size has a small effect on the hydrolysis process. At the same time, [Bmim]Cl has the potential for cellulose degradation assisted by metal catalysts (sulfated zirconia), with a maximum HMF yield of 57.0% [23]. [PERSON] et al. [3] reported a maximum HMF yield of 69.9% in 2 h using [Bmim]Cl as solvent. The iodide anion's role is relatively significant in increasing glucose dehydration. The I\({}^{-}\) anion is larger and more polarized than Cl\({}^{-}\), which enhances the hydrolysis and pyranose-ring-opening of glucose.
## 5 Numerical Simulations
### Effect of Decay of Parameters  
The simulation results are summarised in Figures 6-8. In all cases, the perturbed domain \(\Omega=[-1,1]\times[0.8,f^{\omega}(x)]\). As expected, as the decay rate \(\sigma\rightarrow\infty\), the variance decreases. Since the overall variation is small, the expectations are not strongly affected by the decay rate.  
The quantity of interest is the \(L^{2}\)-norm of the transverse displacement. The results are computed using the true perturbed domain and the pulled-back field on the nominal domain. The smaller perturbations are reflected on the norms as well. The convergence graph of the modelling error (see Figure 7b) shows that the difference between the true and the mapped fields is not significant. This suggests that this admittedly crude approach can be used as the first approximation.  
Of particular interest is the deformation profile detail of Figure 7d. The shortest boundary layer proportional to thickness is clearly visible. This shortest length layer is often omitted from the considerations in the reduced models, but in this \"thick\" shell regime, it does play a role. Notice that the next axial layer proportional to square root of thickness extends over the whole domain, if both ends are considered.  
Variance results are in line with the a priori knowledge. Variance results are computed on the nominal domain only, and hence, numerical instability at the boundaries is observed. Interestingly, in Figure 8c, two things are evident: first, the increase in variance is logarithmic; second, the logarithmic scaling also applies to the distance from the boundary. Geometrically, this means that with smaller values of \(\sigma\), the variance in the boundary layer profiles is higher.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Simulation** & **Parameter** & **Range** \\ \hline Decay & \(\sigma\) & \(\sigma\in[2,4]\) \\ Thickness & \(d\) & (upper bound) \(d\leq[0.1,0.2]\) \\ Location & \(x_{0},R\) & \(x_{0}=0,\ R=2/3\) \\ Fourier Mode & \(K\) & \(K=4\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Outline of the simulation parametrisation.  
Figure 6: Effect of the decay rate. Statistical moments over the nominal domain. (**a**) Expectation of the transverse displacement. (**b**) Variance of the transverse displacement. Colouring: \(\sigma=2,2.5,3,3.5,4\) are orange, blue, green, red, and purple, respectively.
###### Abstract  
UHS is one potential solution that could provide a steady source of clean energy to the globe. Given their infrastructure, depleted hydrocarbon reservoirs may be a suitable storage option. However, ensuring wellbore integrity is a significant challenge when storing hydrogen in such reservoirs. In this study, 3.81 \(\times\) 7.62 cm cement samples were cured for 12 and 18 months and were cyclically exposed to hydrogen for three 28-day cycles at 10.34 MPa and 50 \({}^{\circ}\)C. The pressure increment was achieved at the rate of 2.06 MPa/hr. The cement's porosity, permeability, and ultrasonic velocity were tested before and after each cycle. To investigate the changes in the surface structure and elemental composition, scanning electron microscopy (SEM) was conducted. The results illustrate increased porosity and permeability, but the ultrasonic velocity changes were insignificant. The SEM images do not exhibit any change in the microstructure. However, energy dispersive spectroscopy (EDS) mapping exhibited mineral dissolution. This study demonstrates how cyclic exposure to hydrogen will affect the integrity and the sealing ability of aged cement, which will be an essential factor to consider while repurposing existing oil and gas wells to hydrogen injectors or producers for UHS applications in depleted hydrocarbon reservoirs.  
underground hydrogen storage; well integrity; cementing; non-destructive testing; sealing ability; hydrogen leakage +
Footnote †: journal: Applied Sciences
## 1 Introduction
### Honeycomb Core Geometry Model and Material Model  
The honeycomb-core material is structurally different from the conventional metal-processing material. It is a porous thin-walled structure with a hexagonal shape, the hole has a side length of 3-6 mm and a single-layer-wall thickness of 0.05-0.15 mm. Honeycomb core geometry modeling is performed in ABAQUS/CAE, where a single core is drawn, then the entire sketch is completed by mirroring and arraying, and finally stretched vertically along the aperture according to the height of the desired honeycomb core. When the wall thickness of the shell unit is set, the thickness of the single layer is set to 0.1 mm, and the thickness of the double layer joint is 0.2 mm. The geometric model of the Nomex honeycomb-core composite material established in the finite element analysis is shown in Figure 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Features of the Model** & **Parameter** \\ \hline Cutter diameter & 50.8 mm \\ Angle of wedge & 14\({}^{\circ}\) \\ Height of tool & 5 mm \\ inner diameter of tool & 12 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometric parameters of disc-cutter.  
Figure 1: Disc-cutter geometry model.  
Figure 2: Nomex honeycomb-core composite geometry model.  
The main structure of Nomex honeycomb core is aramid paper. The molding of aramid paper material is made by arranging aramid fibers randomly distributed in XYZ direction in any ratio and combining them. Due to the flexibility and randomness of fiber arrangement, the mechanical properties of Nomex honeycomb-core materials cannot be directly measured by researchers using conventional measurement methods. To date, the more commonly used is derived from the empirical formula of the fiber modulus at random. The research shows that the mechanical properties of the aramid fiber in the vertical direction are basically consistent with the main direction of the material. In this paper, the honeycomb material and tool material used in cutting simulation processing The attributes are shown in Table 2 below.
# Project Robust Scheduling Based on the Scattered Buffer Technology
## 5 Experimental Analysis
### Analysis of Experimental Results
#### 5.2.1 Impact of Scheduled Due Date on the Robust Schedule
This is because, when the scale is bigger and the duration is tight, the size of buffer that can be set is under control and the number of activities that can be given a buffer will be reduced, which will lead to the decline in the solution robustness. When the duration is relatively loose, although the solution robustness presented by the value of \(SC\) is still declining, the decreasing degree gradually reduces with the extension of the available duration. At the same time, the value of \(SP\) increases instead, which is because, after the extension of duration, the restriction on the size of buffer and the number of activities will decrease to reduce the difference between the realized start time of project and the predicted start time, and the number of delayed activities as well. Thus, the decreasing degree of the value of \(SC\) will reduce, the value of \(SP\) will reduce at a slower rate, and even will be improved. 2 In terms of quality robustness, when the duration is relatively tight, UAS, RFDFF and STC are the same. The timely project completion probability (\(TPCP\)) is gradually decreased, which is the result of the scale of project becoming larger and the duration being relatively tight. When the duration is relatively loose, the timely project completion probability (\(TPCP\)) of three algorithms are higher than that in the shorter duration, and they are not affected by the size of scale, so they basically remain constant.