esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methods
### Ancillary Data
#### 2.2.3 Sea Ice Freeboard (Fb)  
The CryoSat-2 mission, which was launched by ESA in 2010, carries the Synthetic Aperture Radar (SAR) Interferometric Radar Altimeter (SIRAL) operating at Ku-band (\(\sim\)13.6 GHz) to detect and monitor topographical fluctuations and trends over land and sea ice [6; 16]. A combination of elevation data with ancillary data, including sea ice type and snow depth and density, enables the estimation of sea ice Fb, i.e., the height of sea ice above sea level [47]. Cryosat-2 altimetry data have shown to be sensitive to SIT above 1 m, with increasing uncertainty for thinner ice [6]. Because Fb data contain information on the sea ice variability, predominantly thicker ice, it was considered to be a relevant input parameter to complement the L-band observations. Time series of the CryoSat-2 Level-2 SIRAL Geophysical Data Record 2 (GDR) full-orbit segments were projected onto a 12.5 km EASE-Grid 2.0 to generate daily Fb maps. The data are available for both hemispheres and they can be downloaded from [[https://science-pds.cryosat.esa.int/](https://science-pds.cryosat.esa.int/)]([https://science-pds.cryosat.esa.int/](https://science-pds.cryosat.esa.int/)) (accessed on 7 February 2021) [48].
## 2 Materials and Methods
### Monte Carlo Simulations  
XRF spectra simulations were performed with a modified version of the XRMC package, version 6.4.1., a Monte Carlo algorithm based on the xraylib database [21]. In this custom version, it is possible to simulate rough surfaces as well [22]. This package is capable of simulating interactions up to any order. For the present work, a threshold was set at the third order, since, as the order increases, errors in the atomic parameters used in the interaction's simulation are accumulated. Therefore, for higher  
Figure 1: Gilded sample. Regions represent the gilding glue on substrate (**A**), gold leaf applied on gilding glue (**B**), gold leaf applied on substrate (**C**) and substrate (**D**). Black rectangle represents the Macro-X-ray fluorescence (MA-XRF) scanned area.  
interaction orders, the simulated value loses its significance. This code can simulate several types of fluorescence lines, including M-lines, also allowing the simulation of more complex sample geometries, surface roughness, and the characterization of both structure and elemental composition within a multilayered structure [19; 22; 23].  
Regions A, B, C, and D depicted in Figure 1 were simulated. For the present simulations, all irradiated volumes were simulated as a stack of parallel planes. For the A region, a bilayered volume was considered, with a 0.5 cm thick TiO\({}_{2}\) substrate and a 50 \(\upmu\)m thick resin layer (11 wt% H, 11 wt% O and 78 wt% C). The substrate thickness is enough to assure this layer behaves as an infinitely thick layer. It is well-known that above the infinitely thick threshold, the exceeding thickness can be disregarded as it does not affect the outputs.  
Region B was simulated analogously to region A with the addition of an uppermost 0.20 \(\upmu\)m thick 98.5% gold 1.5% Ag layer. Region C was simulated as a bilayered structure with a 0.5 cm thick TiO\({}_{2}\) substrate and a 0.20 \(\upmu\)m thick 98.5% gold 1.5% Ag layer. Lastly, region D was simulated as a monolayered structure composed solely by a 0.5 cm thick TiO\({}_{2}\) substrate. The simulated resin thickness was exaggerated (50 \(\upmu\)m) to clearly highlight any influence caused by it. The gold layer thickness was chosen as an average of the leaves used in the real sample. Both measured and simulated data were analyzed in an analogous way.  
The excitation tube profile used in the simulations was of an Ag-anode tube, while the system geometry was the same as the physical analytical system, 27\({}^{\circ}\) between tube and the sample normal. The tube and detector were positioned symmetrically.
# A Novel Breast-Volume Self-Measurement Method with Improved Convenient and Accuracy
## 1 Introduction  
A well-fitting bra is essential for both physiological and psychological well-being, as it provides necessary support for the breasts [1]. However, research shows that over 85% of women wear the wrong bra size, largely due to inaccuracies in measuring breast volume [2]. Proper bra fit requires accurate breast-volume measurements to ensure that the cup size fully accommodates the breast shape [3; 4; 5]. This is not only important for women's health management, but also critical for manufacturers to design bras that cater to diverse body types.  
Accurately measuring breast volume has always been challenging due to the complexity of breast anatomy [6]. Current methods, including advanced imaging technologies, low-cost techniques, and formula-based approaches, show significant variability in results and often lack practicality for everyday use [7; 8; 9; 10; 11; 12]. Techniques such as X-rays, MRI, and 3D scanning, while accurate, are costly, complex, and often involve radiation exposure, making them unsuitable for routine health assessments [13; 14; 15; 16]. Low-cost methods, suchas thermoplastic molding and water displacement, though simple, require assistance and lack precision, limiting their applicability for self-measurement [9; 17; 18; 19; 20]. Formula-based methods are more accessible but often oversimplify the breast's complex geometry, leading to significant measurement errors [21; 22; 23].  
To address these challenges, this study aims to develop a novel method for breast-volume self-measurement, allowing women to accurately assess their breast volume without specialized equipment.
## 4 Result and Discussion
### Prediction of Mixing Temperature  
It was confirmed through experiments that the mixing temperature error occurs due to the use of the existing mixing temperature sensor of the AHU. Therefore, the average temperature values for the return airflow, outdoor airflow, outdoor air, and CFD simulation data of each case were used to develop the mixing temperature prediction model. Multiple regression analysis was performed, and the independent variables were return airflow, outdoor airflow and outdoor air temperature, while the dependent variable was the mixing temperature. The prediction model for mixing temperature is represented by Equation (10).  
\[\mathrm{T_{mix,\,predict}}=203.827\mathrm{Q_{RA}}-475.603\mathrm{Q_{OA}}+0.42 \mathrm{T_{OA}}+15.366 \tag{10}\]  
Here, \(\mathrm{T_{mix,\,predict}}\) is the prediction model's mixing temperature (\({}^{\circ}\)C), \(\mathrm{Q_{RA}}\) is the return airflow (CMH), \(\mathrm{Q_{OA}}\) is the outdoor airflow (CMH) and \(\mathrm{T_{OA}}\) is the outdoor air temperature (\({}^{\circ}\)C).
# Nanofiber-Based Materials for Persistent Organic Pollutants in Water Remediation by Adsorption
## 2 Moderately Hydrophobic Pesticides
### Nanofibers for Pesticide Adsorption
#### 2.2.1 Impact of Adsorbent Specific and Contact Surface Area
Indeed, both Alachlor and Diazinon are planar molecules, while Atrazine is non-planar. Molecule planar geometry may favour the alignment of an adsorbate molecule onto an adsorptive site, while allowing other pesticide molecules to diffuse; a non-planar molecule when adsorbed might in a certain configuration form a blockage to other pesticide molecules. In such a scenario, a lower density of functional sites and consequently a lower specific surface area can result in improved adsorptive performance. Pesticide molecule polarity could also influence the localized adsorption on the adsorbent surface, limiting adsorption performance via hydrophobic interactions. Figure 2 shows the variation in adsorption capacity related to the adsorbent diameter or sheet thickness, in the adsorption of Atrazine. An exponential-type decreasing trend of the adsorption capacity for increasing material size can be observed. Indeed, carbon nanotubes of 40 nm diameter demonstrated a capacity of 105 mg/g (Table 4, \(N7\)). On the other hand, granular activated carbon of 37 \(\upmu\)m size showed a capacity below 10 mg/g (Table 4, \(N12\)). Carbon nanofibers and polyacrylamide nanofibers, both with a mean diameter of 160 nm, showed a capacity of 2.2 mg/g and 1.6 mg/g, respectively (Table 4, \(N1\), \(N5\)). All adsorbents represented here are carbonaceous, and diameters below 200 nm were reported for nanomaterials adsorbents. A higher contact surface area, and hence a lower diameter, would thus increase the proportion of electron vacancies on the nano-adsorbent surface by decreasing the volume of bulk material. As carbon-based adsorbents are prone to adsorb by electron interaction, such as \(\pi\)-\(\pi\) stacking between adsorbent and adsorbate respective unsaturated bonds, more electron vacancies and hence a lower adsorbent diameter would result in improved performance. The choice of adsorbent structure does not seem to impact on the performance, suggesting that the surface chemistry and charge distribution play a major role in the adsorptive performance. In this study, adsorbent diameters below 100 nm on average led to adsorption capacities over 20 mg/g for the removal of Atrazine.
## 2 Theory and Formulation
#### Nonlinear Temperature Changes  
For a typical GPLRC panel, which is in the temperature environment, it is assumed that the temperature can be distributed across its thickness. The parameter of \(W\) is the work done by temperature changes and is declared as following [57]:  
\[ \begin{array}{l}W=\frac{1}{2}\iint\limits_{A}{\bigg{[}{\bigg{(}N_{2}^{C} \bigg{)}\bigg{(}\frac{\partial w_{0}}{\partial x}\bigg{)}^{2}+\bigg{(}N_{1}^{C }\bigg{)}\bigg{(}\frac{\partial w_{0}}{\partial y}\bigg{)}^{2}}\bigg{]}\,(1+ \frac{z}{R_{1}})(1+\frac{z}{R_{2}})dA}:\\ \delta W=\iint\limits_{A}{\bigg{[}{\bigg{(}N_{2}^{C}\bigg{)}\bigg{(}\frac{ \partial w_{0}}{\partial x}\frac{\partial\delta w_{0}}{\partial x}\bigg{)}+ \bigg{(}N_{1}^{C}\bigg{)}\bigg{(}\frac{\partial w_{0}}{\partial y}\frac{ \delta w_{0}}{\partial y}\bigg{)}\bigg{]}\,(1+\frac{z}{R_{1}})(1+\frac{z}{R_{2} })dA}}\end{array}  \tag{11}\]  
where the thermal stress resultants are defined with the following relations:  
\[ \begin{array}{l}N_{1}^{C}=\int_{-h/2}^{h/2}\,\,\,(T-T_{0})\times\,\,\overline {\alpha}\times(\overline{Q}_{12}+\overline{Q}_{11})dz,\\ \\ N_{2}^{C}=\int_{-h/2}^{h/2}\,\,(T-T_{0})\times\,\,\overline{\alpha}\times( \overline{Q}_{22}+\overline{Q}_{21})dz\end{array}  \tag{12a}\]  
Temperature distribution along thickness direction in the panel can be obtained by solving the following steady-state heat-transfer equation:  
\[-\frac{d}{dz}\bigg{(}k\frac{dT}{dz}\bigg{)}=0 \tag{12b}\]where \(k\) is the thermal conductivity coefficient of the structure. Boundary conditions for transfer heat are expressed as follows:  
\[ \begin{array}{l}T(z=-h/2)=T_{c},\\ T(z=h/2)=T_{m}\end{array}  \tag{12c}\]  
We assume that the thermal conductivity in all layers are the same. Now by substituting Equation (14) into Equation (13), the temperature distribution function for the GPLRC panel are expressed as follows:  
\[T=T_{c}-\sum_{n=1}^{\infty}\left(T_{c}-T_{m}\right)\times\left(z/h+1/2\right) ^{n} \tag{12d}\]  
where n denotes the non-negative power index of temperature variation function. For example, considering \(n\geq 2\), the temperature variation along the thickness becomes nonlinear [92]. It is assumed that the temperature changes nonlinearly through the thickness direction from the external surface (\(T_{m}\)) to the internal surface (\(T_{c}\)) of each layer. Eventually, by substituting Equations (8), (10), and (11) in Equation (7) the motion equations and associated boundary conditions can be achieved as follows:  
\[
# A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification
## 2 Methods
### Preprocessing  
Denoising: Multiple factors will interfere with the quality of the ECG signal. The origins of interference are usually various. For example, during the acquisition of ECG signals, power-line interference generates because of the frequency influence of the used power. Interference will display in the form of noise and distort the waveform of ECG signals, leading to the decrease of ECG identification performance. Real raw ECG signals contain three major noise, namely, baseline drift, power-line interference and Electromyogram (EMG) artifact. Generally, the frequency of baseline drift is less than 0.5 Hz [27] and that of power-line interference is 50 Hz (or 60 Hz) [28]. While EMG artifact is a random noise that spreads over the entire frequency range [29]. In this paper, wavelet transform (WT) is employed as the de-nosing method due to its property of sparsity, locality and multi-resolution [30]. The wavelet-based de-noising process is summarized as follows: Raw signals are decomposed to 9 levels by lifting wavelet transform with wavelet db4; Obtained detail coefficients of different levels are thresholded by shrinkage (soft) strategy; Reconstructing the original sequence from the thresholded wavelet detail coefficients leads to removal of noise. Here the shrinkage strategy uses the universal 'VisuShrink' threshold given by [31]:  
\[Thr=\sigma\sqrt{2\log(N)} \tag{1}\]  
Figure 1: Diagram of electrocardiogram (ECG) identification methodology proposed.  
where \(N\) is the number of data points and \(\sigma\) represents the estimated noise level, which is obtained according to [32]:  
\[\sigma_{i}=\frac{median(|\omega_{i}|)}{0.6745} \tag{2}\]  
where \(\sigma_{i}\) is the noise level of the \(i\)-th level and \(median(x)\) can output the median value of input sequence \(x\). The functions _lxt_, _wthresh_, and _lxt_ in MATLAB were employed as the method for wavelet decomposition, coefficient thresholding and signal reconstruction respectively. Figure 2 shows the comparison between the original signal and the denoised signal.  
Segmentation: After denoising, the R and T peak detection task is performed on denoised signals by using the ECGPUWAVE tool box [33]. Then the detected peak points at T are taken as the delimiters for segmentation. Figure 3 shows a beat extracted in the T-T way. It can be seen that the beat starts with its former T peak and ends with its own T peak. The duration is exactly a cardiac cycle.  
Figure 3: An extracted heart beat taking T as the delimiters.  
Figure 2: Comparison between the original signal and the de-noising signal.
# Post-Fire Vegetation Succession and Surface Energy Fluxes Derived from Remote Sensing
###### Abstract  
The increasing frequency of fires inhibits the estimation of carbon reserves in boreal forest ecosystems because fires release significant amounts of carbon into the atmosphere through combustion. However, less is known regarding the effects of vegetation succession processes on ecosystem C-flux that follow fires. This paper describes intra- and inter-annual vegetation restoration trajectories via MODIS time-series and Landsat data. The temporal and spatial characteristics of the natural succession were analyzed from 2000 to 2016. Finally, we regressed post-fire MODIS EVI, LST and LSWI values onto GPP and NPP values to identify the main limiting factors during post-fire carbon exchange. The results show immediate variations after the fire event, with EVI and LSWI decreasing by 0.21 and 0.31, respectively, and the LST increasing to 6.89 \({}^{\circ}\)C. After this initial variation, subsequent fire-induced variations were significantly smaller; instead, seasonality began governing the change characteristics. The greatest differences in EVI, LST and LSWI were observed in August and September compared to those in other months (0.29, 6.9 and 0.35, respectively), including July, which was the second month after the fire. We estimated the mean EVI recovery periods under different fire intensities (approximately 10, 12 and 16 years): the LST recovery time is one year earlier than that of the EVI, GPP and NPP decreased after the fire by 22-45 g C\(\cdot\)m\({}^{-2}\)-month\({}^{-1}\) (30-80%) and 0.13-0.35 kg C\(\cdot\)m\({}^{-2}\)-year\({}^{-1}\) (20-60%), respectively. Excluding the winter period, when no photosynthesis occurred, the correlation between the EVI and GPP was the strongest, and the correlation coefficient varied with the burn intensity. When changes in EVI, LST and LSWI after the fire in the boreal forest were more significant, the severity of the fire determined the magnitude of the changes, and the seasonality aggravated these changes. On the other hand, the seasonality is another important factor that affects vegetation restoration and land-surface energy fluxes in boreal forests. The strong correlations between EVI and GPP/NPP reveal that the C-flux can be simply and directly estimated on a per-pixel basis from EVI data, which can be used to accurately estimate land-surface energy fluxes during vegetation restoration and reduce uncertainties in the estimation of forests' carbon reserves.  
Keywords:wildfire; boreal forest; vegetation response; C-flux; burn severity; forest succession +
Footnote †: journal: Remote Sensing, 10, 1000; doi:10.3390/rs10071000
Motivated by these considerations, this study aims to propose a new analytical solution to solve the inverse kinematics of shield thrust mechanism, in which all equations are explicit equations, and all the parameters are easy to determine. Based on the proposed model, the displacement, velocity, and acceleration of cylinders that drive the shield machine to the target motions can be solved exactly. These solutions provide the basic constrain relations for practical position control system. Moreover, a virtual prototyping model was developed to simulate the kinematic characteristics of the shield machine and validate the theoretical analysis. The inverse kinematic model established in the paper laysa crucial foundation of shield thrust mechanism design, workspace analysis, and trajectory tracking control. In particular, the analytical solutions of the inverse kinematics can be embedded into the hardware for real-time application to solve the motion state of the thrust cylinders when the shield machine is tracking a target trajectory, so as to achieve the purpose of real-time control of the shield pose. In this study, the innovation points are as follows:  
* Detailed mathematical modeling and analysis of the shield thrust mechanism are performed to elucidate the pose adjustment principle. * A new explicit analytical solution to solve the inverse kinematics of the shield thrust mechanism was proposed, to describe the relationship between the cylinders and TBM motions. * A virtual prototype model of the shield thrust mechanism was developed to simulate the kinematic behaviors of the shield machine and validate the accuracy of the proposed kinematic model.
## 3 Results
### Biomarker Concentrations
#### 3.3.4 IGF-BP2  
IGF-BP2 behaved similarly when comparing the sPAP groups over time. Ninety-six hours after TAVR, the plasma concentration of IGF-BP2 increased significantly in both groups compared to baseline (sPAP < 40 mmHg: baseline--125,163.32 \(\pm\) 128,179.49 pg/mL, 24 h--125,163.32 \(\pm\) 115,595.66 pg/mL, 96 h--186,473.04 \(\pm\) 104,689.81 pg/mL. sPAP \(\geq\) 40 mmHg: baseline--178,630.35 \(\pm\) 146,366.21 ng/mL, 24 h--178,630.35 \(\pm\) 143,275.97 ng/mL, 96 h--253,454.40 \(\pm\) 141,091.62 ng/mL). After 3 months (sPAP < 40 mmHg: 3 months--143,038.18 \(\pm\) 103,202.90 pg/mL. sPAP \(\geq\) 40 mmHg: 3 months--208,982.19 \(\pm\) 125,501.54 pg/mL) and 12 months (sPAP < 40 mmHg: 12 months--144,259.45 \(\pm\) 115,646.78 pg/mL. sPAP \(\geq\) 40 mmHg: 12 months--180,755.50 \(\pm\) 185,500.31 pg/mL), a successive decrease of IGF-BP2 in plasma was observed.  
Similar to H-FABP, a sustained, significantly higher plasma level of IGF-BP2 was seen in patients with severe AS and sPAP \(\geq\) 40 mmHg (baseline: \(p\) = 0.029; 24 h: \(p\) = 0.012; 96 h: \(p\) = 0.001; 3 months: \(p\) = 0.015; 12 months: \(p\) = 0.022).
# A Fast and Simple Method for Absolute Orientation Estimation Using a Single Vanishing Point
## 3 Experiments and Results
### Synthetic Data  
In this section, a virtual camera is synthesized, and the focal length is 20 mm, the pixel size is 14 \(\upmu\)m, the image resolution is 1280 \(\times\) 800, and the camera is located at the origin of the world frame. Then, 3000 sets of 3D parallel lines in the world frame are randomly generated and their 2D image projections are obtained using the virtual camera. Hence, the synthetic data consist of 3000 2D-3D line correspondences. For testing the performance compared with the other methods, 3000 3D points in the world frame are randomly generated in the same FOV (field of view) and their image 2D projections are obtained using the virtual camera. Hence, 3000 2D-3D point correspondences are obtained. Here, the virtual camera points to [0, 35, 20], and the roll angle is set to zero. The 3D points of the synthetic data are randomly generated in the box of [-15, 15] \(\times\) [20, 50] \(\times\) [5, 35] in the world frame.  
One 2D-3D line correspondence is selected randomly for our proposed method, three 2D-3D point correspondences are selected randomly for the P3P solver, and five are selected for the RPnP solver. Then, all the three methods are tested in terms of numerical stability, noise sensitivity, and computational speed.
# Efficient I/O Performance-Focused Scheduling in High-Performance Computing
## 2 Background
### Prioritizing High IOPS Jobs  
When scheduling jobs, it is crucial to correctly allocate the IOPS required by each job, as discussed in the previous section; in HPC systems, multiple jobs are requested simultaneously, making it inefficient to execute them one at a time [24]. Instead, as shown in Table 1, various strategies have been proposed to allocate these jobs for faster and more efficient scheduling. One such strategy is to prioritize those jobs that require the highest IOPS. By running these high-IOPS jobs first, the system can quickly complete tasks that need a lot of I/O resources, freeing up capacity for other jobs. High-IOPS jobs often need intense but short bursts of I/O, and when they are finished the remaining resources can be more easily allocated to jobs with lower IOPS needs. This approach ensures that high-IOPS tasks receive resources more quickly, thereby improving overall system efficiency by reducing I/O bottlenecks and enhancing resource management. By prioritizing high-IOPS tasks and delaying lower-priority jobs when I/O congestion is predicted, the system minimizes contention and reduces wait times for high-demand tasks [25]. Additionally, this prioritization helps to balance resource allocation, enabling smoother throughput and reducing delays from concurrent I/O-intensive workloads [26]. In contrast, if smaller jobs with lower IOPS requirements are scheduled first, they may occupy the I/O resources for a longer time, making it difficult to schedule high-IOPS jobs later. This can cause delays and lead to inefficient use of system resources, as high-IOPS jobs may have to wait in line for available I/O capacity. This inefficiency arises because high-IOPS jobs often rely on quickly accessing and processing large amounts of data to avoid bottlenecks in their execution. If these jobs are delayed, they can create a ripple effect that slows down the entire system. Moreover, high-IOPS jobs typically utilize a large portion of available I/O bandwidth for short periods [27; 28]. If this bandwidth is occupied by smaller jobs, it can lead to resource fragmentation, where small chunks of I/O capacity are left unused but are insufficient to handle larger and more demanding jobs. This situation not only increases the overall waiting time but also prevents the system from fully utilizing its resources. Thus, scheduling high-IOPS jobs first ensures a smoother flow of tasks and minimizes idle times, thereby maximizing overall system efficiency.