esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Test Results and Discussion
### Failure Modes  
The analysis of various beam reinforcements unveiled a common pattern in the failure modes, providing valuable insights into their structural behavior under a load. Initially, cracks emerged in the flexural zone and midspan, accompanied by the development of additional vertical cracks within the shear span as the load intensified. Notably, steel-reinforced concrete beams exhibited midspan bending failure (Figure 7a,b), characterized by a premature failure.  
In stark contrast, both the GFRP- and BFRP-reinforced concrete beams showcased ductile behavior, characterized by a gradual expansion of the cracks. Diagonal cracks were consistently observed in Figure 7c-f and were particularly notable in the GFRP- and BFRP-reinforced concrete beams, where they manifested as long diagonal cracks. The GFRP-reinforced concrete beams displayed significant deformations and wider crack widths compared to the steel-reinforced concrete beams. The BFRP-reinforced concrete beams exhibited a combination of diagonal tension and flexural failure modes.  
Figure 6: Deformation in different sections of beam specimens: (**a**) deformation in compressive zone; (**b**) deformation in mid-section; (**c**) deformation in tension zone.  
The failure modes exhibited distinct characteristics: the steel-reinforced concrete beams primarily failed due to flexural tension, while the GFRP-reinforced concrete beams demonstrated diagonal tension failure, subsequently leading to shear tension failure. The initiation of shear failure was marked by the emergence of a significant diagonal crack within the shear span, originating from the applied load. This crack propagated towards the point of GFRP reinforcement, eventually extending horizontally at the level of the GFRP bars towards the beam's support. Consequently, bond failure occurred between the longitudinal GFRP bars and the concrete, as illustrated in Figure 7. These detailed observations shed light on the intricate failure mechanisms that are inherent to different types of reinforced concrete beams.  
In terms of failure modes, this study revealed a notable increase in crack formations within the FRP-reinforced concrete beams compared to their steel-reinforced counterparts. Specifically, beams reinforced with GFRP experienced shear failure, leading to longitudinal failure, with cracks propagating towards the support structures. The simulation images in Figure 7 provide a visual representation of the observed cracks in all the beam specimens before ultimate failure.
# Non-Contact High Voltage Measurement in the Online Partial Discharge Monitoring System
## 6 Linear Relationship between the Voltage \(U_{1}\) and \(U_{2}\)
The power transformer was started at 12:25, while the non-contact voltage measurement system module started recording at 13:35 (Figure 16). The maximum difference between the voltage measured with the capacitive probe and the voltage measured with the substation voltage transformer was 4.11 kV (1.02%), while the average difference was 1.34 kV (0.33%). These slight differences in voltage measurement were probably due to the imperfect filtration of external electromagnetic (EM) disturbances, the main source of which was corona discharge from HV transmission lines. Figure 15: The tested 250 MVA power transformer: (**a**) The result of PD localization using a standard auscultatory technique (SAT), (**b**) arrangement of AE sensors on the transformer tank selected based on the obtained PD location results using the SAT method, and (**c**) photo of the portable PD monitoring system installed on the tested transformer and the place of mounting the capacitive probe for non-contact high voltage measurement. for 105.7% of the transformer's rated voltage. The monitoring system stopped recording the AE pulses when the voltage across the transformer dropped to 408.5 kV. This suggests that the phenomenon of partial discharges was the source of the recorded pulses of acoustic emission. Summarizing the results of the conducted field test, it can be concluded that the proposed method based on a cheap and easy-to-build capacitive probe may be an alternative to typical, large-size devices for high voltage measurement. In addition, such a non-contact voltage measurement technique fits very well with the concept of short-term PD monitoring in a power transformer, using, e.g., the AE or UHF method because it does not require shutting down the tested unit for the time of installation of the module for HV measurement. In addition, it allows us to increase the diagnostic reliability of the monitoring system thanks to the possibility of correlating the intensity of PD with the voltage level.
## 3 Proposed Sensing Waveform
### Proposed Waveform Design Strategy
Therefore, the main problem of the sensing waveform design is how to design the proper parameter \(U\) to achieve acceptable velocity estimation performance. Two metrics can be used to characterize the velocity ambiguity function. They are the peak-to-sidelobe ratio (PSLR) and the integrated sidelobe ratio (ISLR), i.e., the ratio of the peak of the sidelobeto the peak of the main lobe and the ratio of the energy of the sidelobe to the main lobe, respectively. They are defined as  
\[\mathrm{PSLR}_{\mathbf{U}}=\frac{\max[\chi u(f_{di})]^{2}_{[f_{d}\
otin[f_{a},f_{b}]]} }{\max[\chi u(f_{di})]^{2}_{f_{d}\in[f_{a},f_{b}]}}, \tag{7}\]  
\[\mathrm{ISLR}_{\mathbf{U}}=\frac{\sum_{f_{d}\
eq}\chi u(f_{di})]^{2}_{f_{d}\
otin[f_{a },f_{b}]}}{\sum_{f_{d}\
eq}\chi u(f_{di})]^{2}_{f_{d}\in[f_{a},f_{b}]}}, \tag{8}\]  
where \([f_{a},f_{b}]\) stands for the range of the main lobe. Thus, under the constraints of AS1 and AS2, the proposed waveform design strategy employing inner-frame time division multiplexing can be described as the following optimization problem:  
\[ \begin{split}\min_{\mathbf{U}}~{}F(\mathbf{U})\\ s.t.\begin{cases}\mathrm{mod}(\lfloor n_{l}/14\rfloor,5)\in\varepsilon _{1}\\ \mathrm{mod}(n_{l},28)\
otin\varepsilon_{2},\forall\mathrm{mod}(n_{l},(N/2))= 0\\ \lfloor n_{i}/14\rfloor\
eq\lfloor n_{j}/14\rfloor,\forall i\
eq j\\ L\leq K_{\mathrm{s}}N\end{cases}\end{split}  \tag{9}\]  
where \(F(\mathbf{U})\) is the cost function, defined as  
\[F(\mathbf{U})=\eta\mathrm{PSLR}_{\mathbf{U}}+(1-\eta)\mathrm{ISLR}_{\mathbf{U}}. \tag{10}\]  
Here, \(\eta\) is the scaling factor that determines the trade-off between the PSLR and ISLR. It should be noted that in (9), the first constraint is the downlink constraint in AS1, the second constraint is the common channel constraint in AS2, the third constraint consists of (at most) one OFDM sensing symbol in each slot, and the fourth constraint is the sparsity constraint. \(K_{\mathrm{s}}\) denotes the sparsity of the sensing waveform, defined as  
\[K_{\mathrm{s}}=\frac{L_{\mathrm{s}}}{N}\times 100\%, \tag{11}\]  
where \(L_{\mathrm{s}}\) denotes the number of symbols used when the sparsity is \(K_{\mathrm{s}}\).
## 3 Results  
The following section presents the main findings of the reviewed articles on the potential of Earth observation to assess the impacts of climate change and EWEs in temperate forests:  
* First, the distribution of publications in different journal categories is shown in Section 3.1.
* In Section 3.2., the publications are subdivided spatially, both with regard to the affiliation of the first author and with regard to the study area.
* The analysis of the sensor name and sensor type, as well as their carrier system, is presented in Section 3.3.
* In Sections 3.4 and 3.5, the spatial and temporal resolutions, as well as the different study periods, are analyzed in detail.
* This is followed by an in-depth examination of the thematic foci in Section 3.6, including the differentiation of various EWEs and trend analyses.
* Subsequently, in Section 3.7, an in-depth analysis of the detailed forest differentiation of the respective studies is presented in order to identify conclusive research gaps.
## 2 Materials and Methods
### Samples Characterization  
XRD investigations of the powder samples were carried out with a Bruker D8 Advance diffractometer using copper K\(\alpha\) radiation (\(\lambda\) = 1.54056 A) as the X-ray source, under an ambient condition (i.e., temperature 25 \({}^{\circ}\)C, humidity 25%). The measurements were performed in the Bragg-Brentano configuration, with a resolution of 0.02\({}^{\circ}\) and an integration time of 4 s.  
A TESCAN Mira 3 high-resolution scanning electron microscope was used to perform the morphological characterization of the samples. SEM images were acquired at a working distance in the range 8.0-8.5 mm, an electron acceleration voltage of 20.00 kV, and 5.00-10.00 k\(\times\) magnification.  
Total absorption spectra were recorded with a PerkinElmer LAMBDA 1050 High Performance Series spectrophotometer including an integrating sphere for the collection of all the light scattered by the sample.  
Steady state and time-resolved PL was measured by an Edinburgh FLS980 spectrometer equipped with a Peltier-cooled Hamamatsu R928 photomultiplier tube (185-850 nm). An Edinburgh Xe900 450 W Xenon arc lamp was used as the exciting light source. Corrected spectra were obtained via a calibration curve supplied with the instrument (lamp power in the steady state PL experiments ~0.6 mW/cm\({}^{-2}\), spot area 0.5 cm\({}^{2}\)). Emission lifetimes were determined with the single photon counting technique by means of the same Edinburgh FLS980 spectrometer using a laser diode as the excitation source (1 MHz, \(\lambda_{\text{exc}}\) = 635 nm, 67 ps pulse width and about 30 ps time resolution after deconvolution) and a Hamamatsu MCP R3809U-50 (time resolution 20 ps) as detector (laser power in the TRPL experiment ~1.6 mW/cm\({}^{-2}\), spot area 0.3 mm\({}^{2}\)).
## 4 Results
### Statistical Characterization of Discontinuities  
The orientation of each discontinuity was calculated by use of the method described in Section 2.4. Statistical analysis was performed on the 1401 discontinuities identified in the slope. The 1401 discontinuities were input into the DIPS 7.0 software, which generated a pole density map of the slope's discontinuities. Three different groups could be identified in the pole density map (Figure 6a). Further classification was carried out and the discontinuity information was visualized through Origin 2024 software, as shown in Figure 6.  
Figure 5: Discontinuity identification results and representative window examples: (**a**) overview of the identification results; (**b**) identification results of window a; (**c**) identification results of window b; (**d**) identification results of window c.  
The first group (272 discontinuities) corresponds to a high-density cluster in the southern sector of the pole density plot, exhibiting a near E-W strike with a mean dip direction of N095\({}^{\circ}\) E and steep dip angle of 80\({}^{\circ}\). These steeply inclined discontinuities display concentrated strike orientations, as evidenced by a pronounced E-W trend in the rose diagram. Probability density function (PDF) analysis indicates their trace lengths follow a log-normal distribution, peaking in the 2-5 m range and dominated by short traces.  
The second group (258 discontinuities) forms a low-angle cluster in the southwestern sector, characterized by a dip direction of N195\({}^{\circ}\) E and gentle dip angle of 15\({}^{\circ}\), representing subhorizontal features. Their near N-S strike distribution contrasts sharply with Group 1. Although trace lengths similarly adhere to a log-normal distribution, the frequency peak is slightly lower, with short traces (<5 m) predominating and rare occurrences exceeding 20 m.  
Group 3 (871 discontinuities), the most prevalent cluster, occupies the highest-density southern zone of the pole density plot. These near-vertical discontinuities (dip direction: N185\({}^{\circ}\) E; dip angle: 85\({}^{\circ}\)) exhibit trace lengths conforming to a log-normal distribution, with maximum probability density in the 3-6 m range. An extended distribution tail suggests a significant population of long traces (>10 m).
# The Intra-Class and Inter-Class Relationships in Style Transfer
## 3 Method
### Analysis for Gram-Matrix Method  
For a given content image \(x_{c}\) and a style image \(x_{s}\), the goal of style transfer is to make the generated image \(x^{*}\) both have content image's content and style image's style. When \(x_{c}\), \(x_{s}\), \(x^{*}\) pass the layer \(l\) of CNN, their feature maps are denoted by \(P^{l}\in R^{N_{l}\times M_{l}}\), \(S^{l}\in R^{N_{l}\times M_{l}}\), and \(F^{l}\in R^{N_{l}\times M_{l}}\)where \(N_{l}\) is the number of the feature maps in the layer \(l\), and \(M_{l}\) is the width times the height of the feature map. _Gram-Matrix_ method generates an image \(x^{*}\) which depicts the content of image \(x_{c}\) in the style of image \(x_{s}\) by minimizing the following loss function:  
\[\mathcal{L}_{total}=\alpha\mathcal{L}_{content}+\beta\mathcal{L}_{style} \tag{1}\]  
where \(\alpha\) and \(\beta\) are the weights for content and style losses. The content loss \(\mathcal{L}_{content}\) is the mean-squared distance between the feature maps of \(x_{c}\) and \(x^{*}\) at a specific layer \(l\), while the style loss \(\mathcal{L}_{style}\) measures the distributional difference of the feature maps of \(x_{c}\) and \(x^{*}\) at several specified style layers. The \(\mathcal{L}_{content}\) and \(\mathcal{L}_{style}\) can be formalized as:  
\[\mathcal{L}_{content}=\frac{1}{2}\sum_{i=1}^{N_{l}}\sum_{j=1}^{M_{l}}(F_{ij}^ {l}-P_{ij}^{l})^{2} \tag{2}\]  
\[\mathcal{L}_{style}=\sum_{l}w_{l}\mathcal{L}_{style}^{l} \tag{3}\]  
where \(w_{l}\) is the weight of layer \(l\), and \(\mathcal{L}_{style}^{l}\) is the mean-squared distance between the features correlations expressed by Gram matrices of \(x^{*}\) and \(x_{s}\):  
\[\mathcal{L}_{style}^{l}=\frac{1}{4N_{l}^{2}M_{l}^{2}}\sum_{l=1}^{N_{l}}\sum_{j =1}^{N_{l}}(G_{ij}^{l}-A_{ij}^{l})^{2} \tag{4}\]  
Here, the _Gram matrix_\(G_{l}\in R^{N_{l}\times N_{l}}\) is the inner product between the vectorized feature maps of \(x^{*}\) in layer \(l\):  
\[G_{ij}^{l}=\sum_{k=1}^{M_{l}}F_{ik}^{l}F_{jk}^{l} \tag{5}\]  
Similarly, \(A_{l}\) is the _Gram matrix_ corresponding to \(S_{l}\).  
According to our definitions for inter-class and intra-class operations, we observed that, for a particular feature map \(x\), the _Gram matrix_ contains both kinds of operations; therefore, the statistical distributions of feature maps in this method belong to both intra-class and inter-class relationships.  
Furthermore, we found that all the intra-class relationships appear on the position of main diagonal elements of the matrix, while all the inter-class relationships appear on the off-diagonal positions. In other words, the main diagonal elements of the _Gram matrix_ contains the intra-class relationships of the statistical distributions of feature maps. If we only keep the main diagonal elements of the _Gram matrix_ and delete other elements, a method called _Gram-MDE-Matrix_ can be generated. In Section 4, we conduct experiments to compare this method with others.
## 2 Materials and Methods
### Determination of Water-Soluble Protein Content  
The concentration of water-soluble proteins was quantified using the Bradford assay, a colorimetric method that involves binding proteins to Coomassieie Brilliant Blue G-250 dye [25]. Bradford reagent was prepared by dissolving 0.1 g Coomassieie Brilliant Blue G-250 in 50 mL of 95% ethanol and mixing it with 100 mL of 85% phosphoric acid. The obtained solution was then diluted with distilled water to a final volume of 1 L. A 0.16 g portion of the BCP was weighed into a 50 mL polypropylene centrifuge tube and extracted with 8 mL of distilled water. The mixture was homogenized for 1 min at 1000 rpm, followed by centrifugation at 6000 rpm for 15 min. The resulting supernatant was filtered through a membrane filter to remove particulates. An aliquot of 0.1 mL of the filtered supernatant was mixed with 3.0 mL of Bradford reagent and incubated at room temperature for 10 min. Absorbance was measured with a UV-Vis spectrophotometer at 595 nm. Protein concentration was determined from a standard calibration curve generated with known concentrations of bovine serum albumin. The final protein content in the sample was calculated using Equation (8):  
\[\mathrm{Soluble\ proteins\ (mg/g)=m_{1}/m_{2}} \tag{8}\]  
where m\({}_{1}\)--protein concentration determined from the calibration curve, mg/mL; m\({}_{2}\)--mass of the sample in the extract, g/mL.
# RIS-Assisted High-Speed Communications with Time-Varying Distance-Dependent Rician Channels
## 3 Received Power Maximization and Doppler Shift Alignment
### Received Power Maximization  
Using (22), the average received signal power can be calculated as \(\mathbb{E}\{|y(t)-w(t)|^{2}\}\). In order to maximize it, the phase parts of the LoS components for the direct and cascaded links should be the same. Thus, we have \(\phi_{mn}(t)=2\pi f_{c}\big{(}\tau_{0}(t)-\tau_{mn}(t)\big{)}\). However, since \(\tau_{0}(t)\geq\tau_{mn}(t)\), then \(\phi_{mn}(t)\leq 0\), which cannot be achieved based on our model. Thus, \(\phi_{mn}(t)\) should be added by an additional full carrier signal period delay \(2\pi\zeta_{mn}(t)\) to ensure \(\phi_{mn}(t)\in(0,2\pi]\). Therefore, the continuous optimal phase shift set for maximizing the received signal power is  
\[\phi_{mn}^{\text{opt}}(t)=2\pi f_{c}\bigg{(}\tau_{0}(t)-\tau_{mn}(t)\bigg{)}+2 \pi\zeta_{mn}(t), \tag{23}\]  
where \(\zeta_{mn}(t)=\big{\lceil}f_{c}\big{(}\tau_{mn}(t)-\tau_{0}(t)\big{)}\big{\rceil}\). It is noteworthy that (23) can not only maximize \(\mathbb{E}\{|y(t)-w(t)|^{2}\}\) but also minimize the delay spread of the whole system [11; 14].  
**Remark 2**: _It is observed that the optimal phase shift set \(\phi_{mn}^{\text{opt}}(t)\) in (23) consists only of \(f_{c}\), \(\tau_{0}(t)\) and \(\tau_{mn}(t)\). In other words, the RIS works on the LoS part of the channel, instead of the NLoS one. Thus, the RIS would reflect more signals when the MR, the BS, and the RIS are close, i.e., the Rician factor becomes large and the LoS part dominates. Similarly, when \(d_{0}(t)\) and \(d_{mn}^{\text{MR}}(t)\) are very large, the system performance would be diminished not only by the decrease of the channel gain \(A_{0}(t)\) and \(A_{mn}^{\text{MR}}(t)\) in (6) and (13) but also the distance-dependent Rician factor, which causes the whole channel to be Rayleigh fading only._
# Fine Recognition of MEO SAR Ship Targets Based on a Multi-Level Focusing-Classification Strategy
## 2 Signal Modeling and Characteristic Analysis of MEO SAR Ships  
In this section, the signal models of MEO SAR ship targets are first established based on the geometry of MEO SAR system and a ship motion model. Combined with the SAR imaging principle, the defocusing characteristics of moving ships induced by ship sailing motions and rotational motions are then analyzed in detail.
## 5 Product Visualization and Access Mechanisms  
We worked with the ICIMOD to co-develop a web portal for the visualization, analysis, and downloading of HydroSAR data products. The web application is openly available to the general public and has been used by end users for inundation assessment in the HKH since 2022 ([[https://geoapps.icimod.org/Floodinundation/](https://geoapps.icimod.org/Floodinundation/)]([https://geoapps.icimod.org/Floodinundation/](https://geoapps.icimod.org/Floodinundation/)); accessed on 26 August 2024).  
In its role as an intergovernmental organization, the ICIMOD has been maintaining a number of science applications, which use Earth observation data together with ground-based observations and advanced geospatial information technology to help decision makers respond to environmental hazards in the HKH. To provide consistency across this science application portfolio ([[https://servir.icimod.org/science-applications/](https://servir.icimod.org/science-applications/)]([https://servir.icimod.org/science-applications/](https://servir.icimod.org/science-applications/)); accessed on 26 August 2024), ICIMOD developed unified service design principles which were also followed when designing the HydroSAR service (Figure 8):  
* Data products are exposed to the public via a web mapping service which allows visualizing water extent and depth information in a geographic context. This web map is supported by an ArcGIS image service on the backend, making it simple to distribute HydroSAR resources to desktop, mobile, and browser applications.  
Figure 8: The HydroSAR inundation mapping web application designed and maintained by ICIMOD. The application includes webmap-based visualization of HydroSAR data products, an option to analyze changes in flood parameters over time, and the capabilities to access and download individual HydroSAR products.  
* A time slider is included to support the assessment of changes in water extent and depth over time.
* A layer selector provides the capability to switch between different HydroSAR data products for cross-comparison, cross-validation, and joint hazard assessment.
* A product download feature allows users to access and download HydroSAR products over their area of interest.  
Access to and the downloading of HydroSAR data products are facilitated by the ICIMOD's Regional Database System (RDS) ([[https://rds.icimod.org/](https://rds.icimod.org/)]([https://rds.icimod.org/](https://rds.icimod.org/)); accessed 26 August 2024), a one-stop data portal for the HKH. The HydroSAR inundation data download tool shown in Figure 9 allows selecting HydroSAR data products by country, date range, or user-defined bounding boxes. Once a request is made, data are bundled into zip files, and a download link is sent to interested users via email.