esa-sceva/satcom-chunk-collection huggingface dataset:
# Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives
## 6 Research Challenges
### Accuracy Assessment  
Assessment of the accuracy of the satellite-derived LST and SUHI intensity is challenging and rare. Generally, LST can be verified by the temperature-based (T-based), radiance-based (R-based), and inter-comparison methods, but they all have limitations [39; 202; 238]. The T-based method, depending on in-situ LST measurements, is usually restricted to homogeneous surfaces (i.e., rural areas) in order to increase the representativeness of the observations at the satellite pixel scale. The R-based method, which relies on the simulated radiances at the top of the atmosphere, is largely limited by the accuracy of the model inputs, including land surface emissivity and in-situ atmospheric profiles. The inter-comparison method, which is based on other well-validated LST data, is strongly influenced by varying spatial scales, acquisition times, and viewing angles between sensors. Further details on LST validation can be found in a recent review by [PERSON] et al. [39]. As a result, most of the SUHI studies did not report the accuracy of the retrieved LST, especially those using Landsat and ASTER data. Validation of SUHI estimates is more limited because of the lack of the urban-rural paired in-situ observations synchronized with image acquisition. Fortunately, a few validation studies suggested a relatively high accuracy of satellite-derived LST (relative error \(<\) 5%) and its capability in characterizing an urban thermal environment [224; 239; 240; 202]. In addition, all the studies used the standard satellite products and the commonly-used LST retrieval algorithms. These together solidify the reliability of previous research findings on SUHI.
# Thermal Fatigue Life Prediction of Thermal Barrier Coat on Nozzle Guide Vane via Master-Slave Model
## 1 Introduction
[15] studied the heat transfer coefficients and stresses on blade surfaces using the finite volume (FV) and FE methods and obtained the maximum material temperature and thermal stress at the trailing edge near mid-span. Meanwhile, he also discussed the life prediction methods of turbine components by coupling aero-thermal simulation with a nonlinear thermal-structural FE model and a slip-based constitutive model [16]. [PERSON] et al. [17] predicted the cracks on the vane of a power generation gas turbine by the FTS method and [PERSON] et al. [18] carried out a simulation investigation of temperature variation, and obtained the stress and vibration characteristics of a NGV by the FTS method. Although the FTS technology can reveal the physical characteristics of a NGV in the thermal cycle under macroscopic scales, the thermal fatigue problems of TBC gradually emerge from micro-cracks [19]. Up to now, it is possible to establish a mathematical grid model with respect to both the macro and micro scales. However, the solution of the grid model requires huge computing resources and time consumption, which seriously restricts the development of new thermal barrier coated NGVs [20]. Figure 1: Thermal barrier coating and its four components. TC: Top coat; TGO: Thermally grown oxide. Therefore, to provide an effective TFL model of the TBC, it is necessary to integrate flow, heat transfer, and structure with the micro- and macro-scale to perform an integrity design. The concept of high-integrity was proposed by the United States Air Force in 2012 for the numerical simulation of a NGV with a TBC [21]. High-integrity requires the consistency of the numerical simulation with a real structure. Due to the costly computation, however, we have not found related works to highly-integrated TBC simulation so far. Along with the heuristic thought, it is unsatisfactory for a highly-integrated analysis of the life prediction of a NGV with a TBC to only consider heat shock performance in the macro- or micro-scale. This paper attempts to develop a high-integrity approach, in other words, the master-slave model involving the FTS coupling technology, phenomenological life prediction method, and volume mesh mapping algorithm for TBC thermal fatigue life prediction and the improvement in prediction precision and efficiency. Here, the master-slave model was used to establish the relationship between the macro-scale and micro-scale in the thermal fatigue life prediction of a TBC. The remainder of this paper is organized as follows. Section 2 introduces the modeling and simulation approaches including the physical model, material parameters, boundary conditions, meshing, and simulation procedure. The verification strategy and establishment of thermal fatigue life are discussed in Section 3. In Section 4, the results and discussion involving the heat transfer analysis, temperature filed analysis, thermo-structural analysis, and thermal fatigue life prediction of the TBC on a NGV are investigated. Section 5 gives the conclusions and findings of this paper.
## 2 Materials and Methods
### Experimentation  
#### 2.3.1 Parameters Setting  
Before applying the Hermite transform-motion magnification method to the datasets, the following parameters must be defined: the size of the Gaussian window, the order maximum of the spatial decomposition, the cutting frequencies in the temporal filtering, and the amplification factor used.  
In Appendix A.1, we present the suitable values to the Gaussian window and consequently, the maximum expansion order \(2\mathcal{N}\) of the Hermite transform; thus, to avoid the blur artifacts in the reconstruction step, we used a Gaussian window of \(5\times 5\) pixels (\(\mathcal{N}=4\)), which allows us a maximum order of the expansion of \(8\) (\(2\mathcal{N}=2\times 4\)) giving a perfect reconstruction of the image sequence. A Gaussian window of \(3\times 3\) pixels (\(\mathcal{N}=2\)) also would avoid the blurring effect in the edges but would limit the expansion order to \(4\), giving a less quality of the reconstructed images.  
On the other hand, in [37] was presented a multi-resolution version of the Hermite transform, which allows us to analyze the spatial structures in the image at different scales. This multi-resolution analysis is independent to the reconstruction process, whence we performed a spatial decomposition of  
Figure 4: Example of the distances computed (number of frames) for an inhalation–exhalation cycle to form the binary vector \(A\), each color represents a different cycle.  
8 levels of resolution with a sub-sampling factor of 2 without affecting of the quality of the reconstructed image sequence.  
For motion magnification, we applied a temporal band pass filter to the Hermite coefficients through the difference of two IIR (Infinite Impulse Response) low-pass filter as in [22], where the cutting frequencies for the band pass filter were fixed to 0.15-0.4 Hz corresponding to 9-24 breaths per minute. This range is sufficient to detect breathing rate in healthy subjects at rest.  
The amplification factor \(\alpha\) was set to 20, in such a way that allowed effortlessly seeing the respiratory movements in the thorax. In Appendix B, we describe the value limit of the amplification factor, the relation of it with the spatial wavelength of the image sequence, and how it is applied in each level of the spatial decomposition.  
For the approach including a ROI, the user selects a point in the thoracic region, and a window of size (48 \(\times\) 64 pixels ) centered in the selected point is created, the size of the ROI window must be equal or higher to 28 \(\times\) 28 pixels, since the CNN resizes the input images to correspond with 28 \(\times\) 28 pixels. The size of the original frame video, as mentioned above, is 480 \(\times\) 640 pixels. The size of the ROI was chosen experimentally but in relation to the distance of the camera and the spatial resolution. In this case the size of the ROI is \(\frac{1}{10}\) of the size of the image frame. This manual strategy allows choice of a zone including the thoracic area where its motion is clearly appreciated facilitating in this way the classification to the algorithm.
# Adaptive Constant False Alarm Detector Based on Composite Fuzzy Fusion Rules
## 5 Simulation Experiment Results and Analysis
### CFVI-CFAR Detection Performance
#### 5.2.3 Clutter Edge Environment  
In a clutter edge environment, the clutter power within the reference cells fluctuates between different levels. The clutter power levels at different positions are also different, meaning that the reference cells are not identically distributed. With \(P_{FA}\) set to \(10^{-4}\), the false alarm probabilities of the VIHCEMOS-CFAR and CFVI-CFAR detectors are shown in Figure 8.  
As shown in Figure 8, in a clutter edge environment, VIHCEMOS-CFAR uses the heterogeneous clutter estimate CFAR detection algorithm and exhibits significant false alarm spikes. In contrast, CFVI-CFAR prevents the occurrence of noticeable false alarm spikes by selecting GO-CFAR, thereby demonstrating better false alarm control ability compared with VIHCEMOS-CFAR. The average false alarm probability of both detectors in a clutter edge environment with an SNR of 10 dB is shown in Table 12.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Environment** & **VIHCEMOS** & **CFVI** & **CA** & **OS** & **KLQ** & **Ideal** \\ \hline One interference in the leading reference window & 52.29\% & 55.95\% & 36.37\% & 53.44\% & 54.08\% & 59.26\% \\ Three interferences in the leading reference window & 48.38\% & 53.17\% & 15.58\% & 50.25\% & 50.28\% & 59.26\% \\ One interference in both reference windows & 50.07\% & 56.01\% & 23.76\% & 52.63\% & 53.33\% & 59.26\% \\ \hline \hline \end{tabular}
\end{table}
Table 11: The average detection probability of each detector in multiple-target environment.  
Figure 7: ROC in a multiple-target environment.  
As shown in Table 12, in a clutter edge environment, the average false alarm probability of the CFVI-CFAR detector is 1.80 \(\times\) 10\({}^{-4}\), while the average false alarm probability of VIHECMOS-CFAR is 1.09 \(\times\) 10\({}^{-2}\). CFVI-CFAR demonstrates superior performance in false alarm control ability.
## 4 Results and Analysis
### Dynamics: Acceleration, Spin, Oscillations
(**b**) Orientation of the gondola body axes in the accelerations analysis, the \(x\)- and \(y\)-axes correspond to the pitch and roll axis respectively, while the z-axis corresponds to the yaw axis. During ascent, the payload is subjected to a variety of dynamic actions due to air drag and to the pull of the balloon, resulting in oscillations of the gondola. With respect to the axes shown in Figure 20b, the gondola undergoes pitch and roll oscillations while it rotates around the yaw axis. Within the IMU, a magnetometer is used, along with the gyro and rate sensors, to determine the heading of the roll axis and the rotation around the yaw axis. As shown in Figure 21a, the ascent phase is characterized by small oscillations of amplitude decreasing with altitude. Figure 21b shows a detail of pitch oscillation close to burst altitude. At burst, the payload undergoes a series of upturns, and this was also confirmed by the recorded videos. Contrary to ascent, the descent phase, which is dominated by the parachute dynamics, is quite shaky. Pitch and roll oscillations have a similar behavior. Given the pitch, roll and yaw attitude angles measured by the IMU on the payload, it is possible to reconstruct the projected ground path as seen by the bottom side of the payload, where the antenna was mounted. To this end, a MATLAB script was written which first computes the deviation from the local vertical after pitch and roll, then projects it in the appropriate direction determined through the yaw measurement from the magnetometer, and finally outputs a.kml file that can be imported for visualization in Google Earth Pro. The end result, shown in Figure 22, is the ground track of the antenna boresight, which helps in determining the location of the source of the RF emission received. Figure 21: (**a**) Pitch oscillations vs. time registered by the IMU during flight ISM-HAB #3. (**b**) Low-pass filtered pitch oscillations signal at 27 km altitude. Figure 22: Pointing path (white) and Global Positioning System (GPS) positions (red). From a Fourier analysis of the pitch oscillations, it appears that there are no clearly detectable, dominant characteristic frequencies in angular motion during the first phases of the ascent (Figure 23). At low altitude, the air turbulence produces a pitch signal that is essentially random. On the contrary, in the stratosphere, a characteristic frequency is clearly visible. As expected, this frequency (0.178 Hz) is equal to the free oscillation frequency of a pendulum in empty space with a length equal to the distance between the payload and the balloon. An attitude stabilization system would enhance the operational capability of the platform and make it well-suited to a large variety of technology experiments of interest for future space missions. If such attitude stabilization is to be introduced, the most suitable phase of the mission for it would definitely be over 25 km altitude, where environmental torque disturbances are at a minimum and attitude stabilization could prove most effective.
# Genetic Programming to Remove Impulse Noise in Color Images
## 5 Experimental Results  
Experiments were conducted to evaluate the performance of the proposed adaptive filter. The filter was implemented using scikit-image 0.18.3, a Python module that includes a collection of algorithms designed for image processing. Additionally, the evolutionary process (described in Algorithm 1) and its variation operations were implemented using DEAP 1.2.2, an open source evolutionary computation framework. The experiments were carried out on a 3.6 GHz Intel Core i7 (Mac) with four cores, 8 GB of RAM, and OS X 11.5.2.
## 3 Results and Discussion  
In this section, the results regarding the simulation of the upper limb subjected to gravity during a simple kinematics are discussed. The input data are taken from the OpenSim model \"arm26\" [22]. As already mentioned, the analysed upper limb is composed of three bodies: ground, humerus and forearm; these are linked by two revolute joints, the shoulder and elbow, whose main data (inertial properties, relative locations and rotation axes) are listed in Tables 1-4. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Body**} & \multirow{2}{*}{**Mass \(m\) [kg]**} & \multicolumn{6}{c}{**Inertia Tensor Entries**} \\ \cline{2-9}  & & \(\overline{\mathbf{\Lambda}}_{xx}\) [g m\({}^{2}\)] & \(\overline{\mathbf{\Lambda}}_{yy}\) [g m\({}^{2}\)] & \(\overline{\mathbf{\Lambda}}_{zz}\) [g m\({}^{2}\)] & \(\overline{\mathbf{\Lambda}}_{xy}\) [g m\({}^{2}\)] & \(\overline{\mathbf{\Lambda}}_{yz}\) [g m\({}^{2}\)] & \(\overline{\mathbf{\Lambda}}_{xz}\) [g m\({}^{2}\)] \\ \hline Humerus & 1.86 & 14.81 & 4.55 & 13.19 & 0 & 0 & 0 \\ Forearm & 1.53 & 19.28 & 1.57 & 20.06 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Bodies’ inertial properties. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Body**} & \multicolumn{3}{c}{**Local Position Vector**} \\ \cline{2-5}  & \(\overline{\mathbf{u}}_{Gx}\) [m] & \(\overline{\mathbf{u}}_{Gy}\) [m] & \(\overline{\mathbf{u}}_{Gz}\) [m] \\ \hline Humerus & 0 & \(-\)0.180 & 0 \\ Forearm & 0 & \(-\)0.181 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Bodies’ mass center position with respect to the reference joints. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Joint**} & \multicolumn{3}{c}{**Local Position Vector**} \\ \cline{2-5}  & \(\overline{\mathbf{u}}_{Jx}\) [m] & \(\overline{\mathbf{u}}_{Jy}\) [m] & \(\overline{\mathbf{u}}_{Jz}\) [m] \\ \hline Shoulder & \(-\)0.0175 & \(-\)0.0070 & 0.1700 \\ Elbow & 0.0061 & \(-\)0.2904 & \(-\)0.0123 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Joints’ positions with respect to the reference joints. The musculoskeletal system is moved by six muscles (long triceps, lateral triceps, medial triceps, long biceps, short biceps and brachialis), characterised by parameters included in the Table 5.  
The wrapping surfaces included are a cylinder fixed to the ground interacting with the long triceps, a first ellipsoid interacting with the long biceps, a second ellipsoid interacting with the long triceps (both the ellipsoids are fixed to the humerus) and a cylinder fixed to the humerus interacting with all the triceps on the back and with the brachialis on the front. The wrapping surfaces have locations \(\overline{\mathbf{u}}_{w}\) and orientations \(\mathbf{\theta}_{w}\) with respect to the reference joints, these are reported in Table 6, along with the surface geometries. The degrees of freedom are the shoulder and the elbow rotations and the analysed kinematics, as reported in Figure 7, which shows the movement of the elbow from \(0^{\circ}\) to \(90^{\circ}\) during 1 s while the shoulder does not move.
## 2 Results and Discussion
### Characterization of C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\)
#### 2.1.3 XPS Analysis  
The chemical composition of C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) was investigated by XPS. As shown in Figure 5a, the wide scan XPS spectra showed photoelectron lines at a binding energy of about 284.18 eV and 532.12 eV, attributed to carbon (C1s) and oxygen (O1s). The C1s high-resolution scan could be fitted into four peaks (Figure 5b) with binding energies of 283.68 eV, 284.18 eV, 286.08 eV, and 288.08 eV, which were attributed to C-N, C-H, C-O, and O-C=O, respectively. The O1s high-resolution scan could be fitted into three peaks (Figure 5c) with binding energies of 529.42 eV, 531.62 eV, and 532.57 eV, which were attributed to O-H, O=C, and C-O, respectively. The successful functionalization was affirmed by the appearance of an N1s signal between 395 eV and 405 eV in a high-resolution XPS spectrum (Figure 5d). The N1s high-resolution scan could be fitted into two peaks with binding energies of 401.94 eV and 399.31 eV, which were attributed to C-N and C-N+, respectively. And no characteristic signal for iron (Fe 2p) between 710 eV and 740 eV in the high-resolution XPS spectrum was observed, confirming that there was no leakage of Fe\({}_{3}\)O\({}_{4}\) on the surface of C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\).
# Envisat RA-2 Individual Echoes: A Unique Dataset for a Better Understanding of Inland Water
## 4 Selected Case Studies
### Rio Nahuapa
For range estimates within the main lobe, the improvement is from rmse 1.1 cm with Individual Echoes to 0.4 cm with burst integration. Here the rmse is intended as an estimator of the measurement noise, and is inferred from sample to sample difference. It is noteworthy that 0.4 cm is 1/100 of a range bin. More significant improvement is even made in the side lobes. The sub-cm accuracy can be useful for estimating the river slope. In Figure 14c is the range estimated on the three crossings over Rio Nahuapa, where the range increases with the river flowing down to meet Rio Tigre. Under the hypothesis of constant geoid slope over the three crossings, the level drop from the first to the last crossing would be 16.4 cm (about 0.35 range bins, corresponding to 16.4 cm with RA-2 parameters), over a run length of the river that is approximately measured in 11.3 km by satellite images. The dashed horizontal lines in Figure 14c clarify the level estimation in terms of fraction of range bin, after applying Equation (12). The average river slope is therefore 1.45 cm/km. The slope is in the right direction as it is known that Rio Nahuapa is flowing down to its confluence with Rio Tigre. Figure 12: (**a**) RCSs and model RCS for Crossing 2, 45-m wide; (**b**) RCSs and model RCS for Crossing 3, 65-m wide. Figure 13: (**a**) Range estimates with raw IEs; (**b**) Range estimates with coherently integrated bursts of IEs.
## 4 Simulation Analysis
### The Performance Analysis of the Proposed Method
Moreover, the proposed ACFHNR-SVD-TEO method is proved to be effective at extracting the incipient fault features of rolling bearings. Figure 8: The relationship between the singular value and (**a**) the kurtosis or (**b**) the ACFHNR index. Figure 7: The singular components. (**a**) The interference signal and mixed signal 1; (**b**) The impulse signal and mixed signal 2. Additionally, for comparison purposes, other well-established methods such as the median value of singular values based SVD method and the Difference spectrum based SVD method are employed to process the stimulated vibration signal, and the results are shown in Figure 10a,b, respectively. It can be seen that the median value of the singular values based on SVD cannot identify the fault features, as shown in Figure 10a. Although the Difference spectrum based SVD method can detect the fault frequency \(f_{b}\) and its second harmonic \(2f_{b}\), the fault features are not obvious, as shown in Figure 10b. The Kurtogram of the stimulated fault vibration signal is shown in Figure 11a, from which the optimal carrier frequency is determined as 2000 HZ, and the center frequency is 1000 HZ. Therefore, the optimal frequency band is 1000 HZ to 3000 HZ. As a result, the band-pass filter is designed to extract the potential effective fault features from the vibration signal. The envelop spectrum of the filtered signal is presented in Figure 11b. It can be found that the fault frequency \(3f_{b}\) and \(4f_{b}\) can be detected. However, the fault features are also not obvious. Therefore, traditional well-established SVD denoising and the fast Kurtogram can obtain little diagnostic information. Figure 10: The extracted feature signals and their envelop spectrum. (**a**) The median value of singular values based SVD method; (**b**) The Difference spectrum singular values based SVD method. Figure 9: The singular components, (**a**) their Teager energy operator spectrum; (**b**) The second spectrum; and (**c**) The third spectrum.
###### Abstract  
As urbanization progresses in China, the importance of preserving traditional settlements, particularly those located in mountainous areas, is increasingly recognized. To reveal the spatial morphology of mountain settlements influenced by topography, this study employs fractal geometry to analyze twelve mountain settlements within the Jiexiu City region. The correlation between the fractal dimensions of building structures in these settlements and those of suitable construction areas was examined, revealing a significant positive relationship. Moreover, an in-depth spatial distribution analysis of the representative village, Xingdi Village, was conducted to examine its sub-regional spatial morphology. Utilizing the Multiscale Geographically Weighted Regression (MGWR) model, this study explored the impact of slope, aspect, and elevation on the spatial form of mountainous settlements. The results indicate that the complexity of sub-spaces within Xingdi Village gradually decreases with village expansion, and there is a significant positive correlation between flat terrain and sub-spatial morphology. Based on this, a conservation framework rooted in the morphological characteristics of settlement typologies is proposed.  
Keywords:fractal dimension; fractal characteristics; mountainous settlements; spatial morphology; conservation framework +
Footnote †: journal: Applied sciences