esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Image Quality Metrics  
PSF images were used to calculate the lateral full-width at half-maximum (FWHM) to give an indication of the resolution, and the peak side-to-main lobe ratio (PSMR) and side-to-main lobe energy ratio (SMER) to assess the artifacts [29]. The PSMR was determined by the ratio between the amplitude of the maximum side peak to the amplitude of the main lobe. The SMER was defined as the sum of the intensity of the sidelobes, divided by the sum of the intensity of the main lobe, where the cutoff points for the main lobes and side lobes were \(-6\) dB, and between \(-40\) and \(-6\) dB respectively. Thus, the SMER is given by,  
\[\text{SMER}=20\text{log}_{10}\left(\frac{\int_{-40\text{dB}}^{-6\text{dB}}\left( \frac{\overrightarrow{r}}{\text{r}}\right)\dd\overrightarrow{r}}{\int_{-6 \text{dB}}^{0\text{dB}}\left(\overrightarrow{r}\right)\dd\overrightarrow{r }}\right) \tag{1}\]  
where I\(\left(\overrightarrow{r}\right)\) is the intensity of a pixel located at position \(\overrightarrow{r}\).  
The maximum imaging depth affected by the cross-talk artifacts generated by direct transmissions was estimated as half of the distance between the extreme elements of both arrays (first element for probe 1 and last element for probe 2 (gray horizontal line in Figure 1)). The energy of the direct transmissions was quantified by summing the squared amplitudes of the trans-receive RF data acquired in the water tank and converting to decibel units.  
To evaluate the image quality of the tissue-mimicking phantom, four further imaging metrics were used: the speckle resolution, calculated from the autocorrelation function of a speckle region as the FWHM of the Gaussian-fitted curve; the contrast ratio (CR); contrast-to-noise ratio (CNR); and the generalized contrast-to-noise ratio (gCNR) [30],  
\[CR=20\text{log}_{10}\left(\frac{\mu_{i}}{\mu_{o}}\right) \tag{2}\]  
\[CNR=\frac{|\mu_{i}-\mu_{o}|}{\sqrt{\sigma_{i}^{2}+\sigma_{o}^{2}}} \tag{3}\]  
\[gCNR=1-OVL \tag{4}\]where \(\mu_{i}\) and \(\mu_{o}\) are the means of the signal in a region of interest (ROI) inside and outside of the anechoic cyst, respectively, \(\sigma_{i}\) and \(\sigma_{o}\) are the corresponding standard deviations of the signals in the ROIs, and OVL is the overlap area between the probability density functions of both ROI signals [30].
## 4 Case Studies
### Viceregal Duwellings (Mexico)  
Figure 5 depicts a typical viceregal dwelling constructed during the 17 th and 18 th centuries in various cities across Mexico during the Spanish colonial period. The building comprises two floors with interstory heights ranging from 4.95 to 5.10 m. The architectural layout features rooms surrounding two patio, with doors and windows aligned. The wall thicknesses are 0.90 m for the facade, 0.80 m for the inner walls on the ground floor, and 0.60 m for the inner walls on the upper floor [73; 74].  
Rubble masonry was employed for constructing the foundations and walls, whereas columns and arches were built with dressed stones. The building features flexible flat slabs for both floor and roof, consisting of a large array of timber joists with a cross-section of 0.15 \(\times\) 0.20 m. These joists are arranged perpendicular to the facade, with a spacing of 0.30 m, providing support for a layer of brick, compacted earth, and pavement. There is no mechanical connection between the joists and walls; their stability relies only on frictional resistance to prevent any sliding of the joists [75].  
Figure 4: Gaoleiro building: (a) ground floor; (b) upper floors; (c) main facade; (d) cross-section.  
The numerical model used in this study focuses on a specific section of the building, as highlighted in Figure 5. To simulate the OOP and IP responses, the model comprises a portion of the facade and a transversal wall with two doors (denominated as WWO) (Figure 6a). Additionally, a variant of the model was generated to study the response of a fully solid transversal wall without any openings (denominated as SW) (Figure 6b). To ensure consistency with the models of the panels and apply the same criteria, a 2D simplification was employed. Both numerical models consist of quadrilateral plane stress elements, where the width of the elements corresponds to the real dimensions of the walls. The facade section is discretized into seven elements throughout the thickness to enhance result accuracy. Beam elements with equivalent cross-sections represent the floors and roof. Equivalent masses in the beams account for the weight of all floor/roof components. To simulate more realistic interactions, the beams are connected to the walls using nonlinear connectors. These connectors permit sliding under tension to enable the free outward motion of the walls while ensuring zero relative displacements under compression to prevent any penetration of the beams in the inward direction. The timber used for beams and lintels (at the doors) exhibits elastic properties, with a density of 610 kg/m\({}^{3}\), [PERSON]'s modulus of 8800 MPa, and Poisson's ratio of 0.3. For masonry, the mechanical properties are as described in Section 4.
## 4 Experiment and Results
### Ablation Studies  
We conduct ablation studies to evaluate the impact of key components, including the ResNet34 backbone, DEFP, EFAM, and CBAM modules, as well as the BCE and Dice loss functions, using evaluation metrics of MAE, SM, \(E_{\beta}\), and \(F_{\beta}\).  
Table 2 (Rows #1-#3) shows the impact of key components, including the ResNet34 backbone, DEFP, EFAM, and CBAM modules, where Row #1 is used as the baseline. As evidenced in Table 2 (Rows #1-#3), the model exhibits a progressive and substantial performance enhancement with sequential incorporation of each component. Specifically, the model combining DEFP and EFAM (Row #2) achieves improved performance compared to Row #1, demonstrating the complementary effectiveness of the edge-aware mechanism in enhancing feature attention. Finally, Row #3, which integrates all modules (DEFP, EFAM, and CBAM), achieves the best performance with the lowest MAE (0.0132) and highest SM (0.9373), \(E_{max}\) (0.9758), and \(F_{max}\) (0.9396), conclusively proving the synergistic effectiveness of these components in enhancing the overall model performance.  
Table 2 (Rows #4-#6) shows the impact of the BCE and Dice loss functions on our DEPANet. From Table 2 (row #6), our DEPANet trained with hybrid loss, i.e., BCE and Dice, achieves improvements of 0.22% (0.17%), 3.58% (2.32%), 1.6% (0.84%), and 2.4% (1.76%) over the model trained with BCE (Dice) in terms of MAE, SM, \(E_{\beta}\), and \(F_{\beta}\), respectively.
# Sensitivity Analysis for Gaussian-Associated Features
## 5 Sensitivity Matrix Associated with a Least-Squares Sphere Fit to Data
### Equatorial Band of a Sphere  
The calculations in Section 5.1 can be used to estimate sensitivities associated with measurements distributed along an equatorial band of a sphere defined by \(-\pi\leq\theta\leq\pi\) and \(-\beta\leq\phi\leq\beta\leq\pi/2\). Here \(\beta\) is the angle between top and bottom of the band and the equatorial plane. These calculations are also relevant to measurements using ball bar or machine checking gauge that rotates about a fixed point and defines points on a virtual sphere. The area over which the integration is performed is \(|A|=4\pi\sin\beta\). For points approximately uniformly distributed in an equatorial band, if the point-cloud data are associated with variance matrix \(\sigma_{R}^{2}I\), then the variance matrix \(V_{A}\) associated with the fitted sphere parameters \(\mathbf{a}\) is approximated by  
\[V_{A}\approx\frac{\sigma_{R}^{2}}{m}V_{\beta}\]  
where  
\[V_{\beta}=\left[\begin{array}{cccc}2/(1-\sin^{2}\beta/3)&0&0&0\\ 0&2/(1-\sin^{2}\beta/3)&0&0\\ 0&0&3/\sin^{2}\beta&0\\ 0&0&0&1\end{array}\right]. \tag{19}\]  
Table 3 gives the square roots \(s(\mathbf{a})\) of the diagonal elements of \(V_{\beta}\) in (19) for selected values of the parameter \(\beta\). For points \(\mathbf{x}_{1:m}\) approximately uniformly distributed on an equatorial band of the sphere with elevation angle satisfying \(-\beta\leq\phi_{i}\leq\beta\leq\pi/2\) and for point-cloud variance matrix \(\sigma_{R}^{2}I\), the uncertainties \(u(\mathbf{a})=\sigma_{R}s(\mathbf{a})/\sqrt{m}\). The parameters \(x_{0}\), \(y_{0}\) and \(r_{0}\) remain well-defined as \(\beta\) approaches zero while the uncertainty associated with \(z_{0}\) scales with \(1/\beta\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \(\gamma\)/\(\mathbf{\mathrm{deg}}\) & \(s(\mathbf{x_{0}})\) & \(s(\mathbf{y_{0}})\) & \(s(\mathbf{z_{0}})\) & \(s(\mathbf{r_{0}})\) \\ \hline
180 & 1.73 & 1.73 & 1.73 & 1.00 \\ \hline
135 & 1.65 & 1.65 & 2.03 & 1.04 \\ \hline
90 & 1.73 & 1.73 & 3.46 & 2.00 \\ \hline
80 & 1.83 & 1.83 & 4.19 & 2.66 \\ \hline
70 & 1.97 & 1.97 & 5.26 & 3.67 \\ \hline
60 & 2.19 & 2.19 & 6.93 & 5.29 \\ \hline
50 & 2.52 & 2.52 & 9.70 & 8.03 \\ \hline
40 & 3.04 & 3.04 & 14.81 & 13.11 \\ \hline
30 & 3.95 & 3.95 & 25.86 & 24.15 \\ \hline
20 & 5.82 & 5.82 & 57.44 & 55.72 \\ \hline
10 & 11.50 & 11.50 & 228.02 & 226.29 \\ \hline \(\gamma\leq 5\) deg & & & & \\ \hline \(\gamma/\mathrm{rad}\) & \(\approx\)2/\(\gamma\) & \(\approx\)2/\(\gamma\) & \(\approx\)\(\sqrt{48}/\gamma^{2}\) & \(\approx\)\(\sqrt{48}/\gamma^{2}\) \\ \hline \end{tabular}
\end{table}
Table 2: Cap of a sphere. Square roots \(s(\mathbf{a})\) of the diagonal elements of \(V_{\gamma}\) in (18). For points \(\mathbf{x}_{1:m}\) approximately uniformly distributed on the sphere with elevation angle satisfying \(\pi/2-\gamma\leq\phi_{i}\leq\pi/2\) and for point-cloud variance matrix \(\sigma_{R}^{2}I\), the uncertainties \(u(\mathbf{a})=\sigma_{R}s(\mathbf{a})/\sqrt{m}\).
# Numerical Modeling of Heat and Mass Transfer during Cryopreservation Using Interval Analysis
###### Abstract  
In the paper, the numerical analysis of heat and mass transfer proceeding in an axially symmetrical articular cartilage sample subjected to the cryopreservation process is presented. In particular, a two-dimensional (axially symmetrical) model with imprecisely defined parameters is considered. The base of the heat transfer model is given by the interval Fourier equation and supplemented by initial boundary conditions. The phenomenon of cryoprotectant transport (Me\({}_{2}\)SO) through the extracellular matrix is described by the interval mass transfer equation. The liquidustracking (LT) method is used to control the temperature, which avoids the formation of ice regardless of the cooling and warming rates. In the LT process, the temperature decreases/increases gradually during addition/removal of the cryoprotectant, and the articular cartilage remains on or above the liquidus line so that no ice forms, independent of the cooling/warming rate. The discussed problem is solved using the interval finite difference method with the rules of directed interval arithmetic. Examples of numerical computations are presented in the final part of the paper. The obtained results of the numerical simulation are compared with the experimental results, realized for deterministically defined parameters.  
Keywords: cryopreservation; heat and mass transfer; interval finite difference method; directed interval arithmetic +
Footnote †: journal: Applied Sciences
## 3 Results  
### Evaluation of S2-Derived Products  
The results of the comparison of the spectral signatures (first seven bands: 442-780 nm) as produced by the 6 SV for the three invariant surfaces attested to the quality of the atmospheric correction: for all the S2 images the coefficient of variation is in fact low: 0.26 for sandy beach, 0.23 for evergreen vegetation, and 0.14 for rooftops. In particular, the bands that present the highest coefficients of variation are the b6 and b7 bands, i.e., those that are closest to the near-infrared. As a result, only the first five bands of Rrs product (442-705 nm) were used as input for the BOMBER code. Furthermore, by removing the last two bands, the coefficients of variation of the invariant surfaces become 0.25 for sandy beach, 0.15 for evergreen vegetation, and 0.1 for rooftops. By adding the previous validating studies with S2 over Lake Garda [54,55] we might hence conclude that the atmospheric correction carried out by 6 SV is robust and coherent in order to obtain temporal Rrs that are comparable.  
The mean value of the error maps of the BOMBER products estimation for the two ROIs (bottom and macrophytes) was calculated for each year and for each season (Figure S2) and were considered sufficient for the purposes of this study. In particular, on an annual basis, the average value of error is lower than 20%, while on a seasonal basis it has always been lower than 15%. More generally, considering all S2 images available, the mean error value is 7% and 10% for bottom ROI and macrophyte ROI, respectively. In addition, the mean error value of the bottom ROI is always lower than the macrophyte one.
# Lane-Level Traffic Flow Prediction with Heterogeneous Data and Dynamic Graphs
###### Abstract  
With the continuous development of smart cities, intelligent transportation systems (ITSs) have ushered in many breakthroughs and upgrades. As a solid foundation for an ITS, traffic flow prediction effectively helps the city to better manage intricate traffic flow. However, existing traffic flow prediction methods such as temporal graph convolutional networks(T-GCNs) ignore the dissimilarities between lanes. Thus, they cannot provide more specific information regarding predictions such as dynamic changes in traffic flow direction and deeper lane relationships. With the upgrading of intersection sensors, more and more intersection lanes are equipped with intersection sensors to detect vehicle information all day long. These spatio-temporal data help researchers refine the focus of traffic prediction research down to the lane level. More accurate and detailed data mean that it is more difficult to mine the spatio-temporal correlations between data, and modeling heterogeneous data becomes more challenging. In order to deal with these problems, we propose a heterogeneous graph convolution model based on dynamic graph generation. The model consists of three components. The internal graph convolution network captures the real-time spatial dependency between lanes in terms of generated dynamic graphs. The external heterogeneous data fusion network comprehensively considers other parameters such as lane speed, lane occupancy, and weather conditions. The codec neural network utilizes a temporal attention mechanism to capture the deep temporal dependency. We test the performance of this model based on two real-world datasets, and extensive comparative experiments indicate that the proposed heterogeneous graph convolution model can improve the prediction accuracy.  
late-level traffic flow prediction; temporal-spatial correlation; graph convolution neural network; attention mechanism Article
## 2 Materials and Methods
### Design  
Four different AAWs were employed during experimentation: (i) control, (ii) emotional voice, (iii) non-emotional voice, and (iv) synthesized emotional voice. At the beginning of the session, the participant wrote a gratitude letter in accordance with the Toepfer and Walker protocol [39] in a quiet, private room.  
The gratitude letter (see Appendix A) was then read out loud in front of a transductor to generate the emotional voice condition (ii). The non-emotional voice condition (see Appendix A) was a recording firstly of a text fragment from _Don Quixote de la Mancha_, under identical conditions as in case (ii), by the same subject. Non-emotional stimuli (iii) were recorded before emotional stimuli (ii) because the effects of writing a gratitude letter may be persistent over time [30]. The synthesized emotional voice condition (iv) was synthesized from the gratitude letter written by the participant, and it contained the same words and cadence of that condition (ii). During the readings (ii) and (iii), the participant's EEG recording was taken. The total time spent in the room (writing gratitude letter + placing EEG electrodes + recording emotional and non-emotional stimuli) was about 45 min. Subsequently, a word analysis algorithm using affective dictionaries was developed to quantify the emotional component of texts.  
Ultimately, an electro-acoustic radiation system based on a loudspeaker and passive acoustic treatment was installed in the CO\({}_{2}\) incubator. All AAWs were radiated over non-auditory cells for 72 h in independent experiments at a distance of 27 cm. In the control condition (i), the electroacoustic system remained off. Biological assays were carried out.
# Agile Stage-Gate Approach for Design, Integration, and Testing of a 1U CubeSat
## 3 Results
### Environmental Tests
shown in Figure 15. In the thermal test, the satellite was functional for the whole period, and the obtained data indicated that the nanosatellite was ready to fly. The results were proven using an actual satellite operating in orbit. All self-developed modules from the nanosatellite and all COTS components were reported to be in good condition after testing. The sensors' responses shown in Figure 15 illustrate the experimental cycles within the range of temperatures. Figure 16 illustrates the thermal vacuum test setup. At the bottom of Figure 15, it is possible to identify the color of each sensor by assigning a number related to the position of the thermocouples given in Table 4. Temperatures are considered accurate within +/ \(-\) 2 degree Celsius. It is critical for nanosatellite missions deployed from the Nanoracks CubeSat Deployer located on the International Space Station that the chosen materials do not pose a threat to the lives of astronauts because accumulated gases can exceed safety limits and create a poisonous environment. A thermal vacuum bakeout test can be performed to remove these contaminants and thereby ensure proper functioning of sensitive hardware before flight. Nanoracks recommends considering two basic properties of outgassing: Total Mass Loss (TML) and Collected Volatile Condensable Material (CVCM). According to data from the NASA/Goddard Space Flight Center (GSFC), which uses the ASTM E-1559 standard, a TML \(\leq\) 1% and CVCM \(\leq\) 0.1% were selected as materials for space flight use. Figure 14: Vibration test setup. Figure 15: Temperature profiles of AztechSat-1. The AztechSat-1 nanosatellite used compliant materials for outgassing and COTS components qualified to fly with a minimum verifiable flight history. The printed circuit boards (PCBs) were manufactured with layers of glass-reinforced epoxy laminate material (FR4) and copper coated with space-proof conformal coating. The solder used a 95 Sn-5 Ag silver alloy or a soft solder with a lead content of less than 36% (Sn62 Pb36 Ag2). The wire insulators and external electric harnesses used polytetrafluoroethylene (PTFE). A 2-mil polyimide tape with low-outgassing acrylic adhesive was also used. No outgassing measurements were performed during the TVAC tests. Notably, scalability in manufacturing was not considered when linking testing in our process development.
## 2 Materials and Methods
### Materials Characterization  
The scanning electron microscopy (SEM) images were acquired using a JSM-6700F (JEOL, Akishima, Tokyo, Japan) field-emission system at an accelerating voltage of 10 kV. Transmission electron microscopy (TEM) characterization was performed on an HF-3300 microscope (Hitachi High-Technologies Corp., Chiyoda, Tokyo, Japan) equipped with an energy-dispersive X-ray spectroscopy (EDS) system operating at 200 kV. The crystallographic properties of the catalysts were examined using a Bruker D8 Advanced X-ray  
Figure 1: Schematic illustration of Ni-MOF@ZIF-L heterojunction.  
powder diffractometer (Bruker, MA, USA and Karlsruhe, Germany,) with Cu K\(\alpha\) radiation (\(\lambda\) = 0.15418 nm) at 40 kV and 40 mA. The surface chemical states were analyzed by X-ray photoelectron spectroscopy (XPS) measurements conducted on a Thermo Scientific ESCALAB 250 Xi (Thermo Fisher Scientific, East Grinstead, West Sussex, UK) system with Al K\(\alpha\) radiation (hv = 1486.6 eV). The X-ray absorption spectroscopy measurements at the Ni/Co K-edges were performed on beamline BL14W1 at the Shanghai Synchrotron Radiation Facility (SSRF, 3.5 GeV storage ring), Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, China, where both ex situ X-ray absorption near-edge structure (XANES) spectra and extended X-ray absorption fine structure (EXAFS) spectra were collected for the Ni-MOF/ZIF-L material and its corresponding heterojunction.  
All calculations were performed using spin-polarized density functional theory (DFT) within the projector augmented wave (PAW) method, as implemented in the Vienna Ab Initio Simulation Package (VASP). The wave functions of valence electrons (Ni: 3d84s2, Co: 3d74s2, O: 2s22p4, S: 3s23p4) were expanded using a plane-wave basis set with a kinetic energy cutoff of 450 eV. The exchange-correlation interactions were treated by the generalized gradient approximation (GGA) with the Perdew-Burke-Ernzerhof (PBE) functional. Structural relaxations were performed until reaching convergence criteria of 10\({}^{-4}\) eV for total energy and 0.02 eV/A for Hellmann-Feynman forces. A 3 \(\times\) 3 \(\times\) 1 Gamma-centered Monkhorst-Pack k-point mesh was used for Brillouin-zone integration during geometry optimizations. Atomic charges were analyzed using the Bader charge partitioning scheme [39].
# Comparison of Different Algorithms to Orthorectify WorldView-2 Satellite Imagery
## Materials and Methods  
### WorldView2 Imagery  
Launched in October 2009 by DigitalGlobe, the WV-2 satellite is positioned at an altitude of 770 km, with an orbit inclination of 97.2\({}^{\circ}\) and a period of 100 min. The revisited frequency is 1.1 days at nadir and 3.7 days at 20\({}^{\circ}\) off-nadir. The swath width, at nadir, is 16.4 km [29]. This satellite is part of DigitalGlobe's constellation, which now also includes WorldView-3 [30; 31; 32]. The WV-2 satellite has two push-broom scanners which acquire panchromatic and multispectral images, respectively. The panchromatic images have a spatial resolution of 0.46 m, resampled to 0.50 m for commercial purposes, and the spectral interval is 0.450-0.800 \(\upmu\)m. The multispectral images have a spatial resolution of 1.85 m, resampled to 2 m for commercial purposes; they are acquired in eight bands: Coastal (0.400-0.450 \(\upmu\)m); Blue (0.450-0.510 \(\upmu\)m); Green (0.510-0.580 \(\upmu\)m); Yellow (0.585-0.625 \(\upmu\)m); Red (0.630-0.690 \(\upmu\)m); Red Edge (0.705-0.745 \(\upmu\)m); Near-IR1 (0.770-0.895 \(\upmu\)m); Near-IR2 (0.860-1.040 \(\upmu\)m). The radiometric resolution for both panchromatic and multispectral images is 11 bits [29].