esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Research Area and Data Sources
### Dataset Acquisition and Pre-Processing
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Data**} & **Acquisition Time** & \multirow{2}{*}{**Bands**} & \multirow{2}{*}{**Resolution (m)**} \\  & **(YearMonthDay)** & & \\ \hline \multirow{4}{*}{**Landsat 8**} & Eastern Pamir & \multirow{4}{*}{30} \\  & 20171020 & OLI (B1, B2, B3, \\  & Nyaingentanglha & B4, B5, B6, and B7) & \\  & 20171023 & & \\ \hline \multirow{4}{*}{**Landsat 8**} & Eastern Pamir & \multirow{4}{*}{TIRS (B10)} & \multirow{4}{*}{100} \\  & 20171020 & & \\ \cline{1-1} \cline{2-3}  & Nyaingentanglha & & \\ \cline{1-1} \cline{2-3}  & 20171023 & & \\ \hline \multicolumn{2}{c}{**ASTER GDEM V2**} & 2009 & Estimation of terrain elevation & 30 \\ \hline \multicolumn{2}{c}{**The Randolph Glacier Inventory:**} & \multirow{2}{*}{2017} & \multirow{2}{*}{Estimation of glacier area change} & \multirow{2}{*}{} \\  & **Version 6.0 (RGI 6.0)** & & \\ \hline \multicolumn{2}{c}{**The second glacier inventory dataset of**} & \multirow{2}{*}{2006–2011} & \multirow{2}{*}{Estimation of glacier area change} & \multirow{2}{*}{} \\  & **China (CGI2)** & & \\ \hline \multicolumn{2}{c}{**A dataset of glacier inventory of West**} & \multirow{2}{*}{2018} & \multirow{2}{*}{Estimation of glacier area change} & \multirow{2}{*}{} \\  & **China in 2018 (WCGI)** & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data used for glacier mapping. the extent of glaciers through ArcGIS software. The processed glacier vector file is binarized to finally obtain a label file of the remote sensing image glacier distribution. 4. Utilize the OpenCV-Python library function in Python to read the 23 processed grayscale images and corresponding label files, and use the sliding window to crop the images; use the 'imgaug' library function to obtain the expanded dataset of all the cropped images according to the data enhancement method by operations such as panning and flipping; finally, through linear mapping of each dimensional feature into the target range [\(-\)1, 1], the normalized dataset is obtained, and all the normalized images are divided to obtain a training set, a validation set, and a test set.
## 3 Model Results for the Tested Area
### Vertical Velocity Map of Alexandria  
Figure 8 shows the vertical displacement map decomposed from ascending and descending LOS geometries, Negative values indicate subsidence, and positive values indicate a stable to slight uplift motion. The average of the vertical velocity in the study area is \(-2.3\) mm/yr. The surface of Alexandria is stable in the main city center, but with two small spots of subsidence in the historical sandy tombolo. The highest estimated subsidence value is \(-31.7\) mm/yr; high subsidence areas are distributed mainly in the former Abu Qir Lagoon, the former Mareotis Lake's dried and reclaimed area, and parts of the northeast Alagami area. Subsidence in Alexandria occurs mostly in the new urbanized areas. Hence, the evolution of Alexandria City was mapped as presented in the sub-data frame of Figure 8 valuate the correlation between urbanization processes and urban subsidence in the city.  
Figure 7: Time series of selected permanent scatterers (PS) points: (**a–c**) are presenting displacement in the ascending LOS, (**d–f**) are showing displacement in the descending orbit LOS).  
The insert map in Figure 8 presents the study area of Alexandria city, showing the evolution of the area's extent from 332 B.C to 2018. The city extent from the era of [PERSON] until 1917 was mapped according to [30]. The city extents in 1972 and 2018 were extracted from Landsat MSS data captured on 19 September 1972, and an S-2 image acquired on 24 October 2018. City areas that were constructed before 1917 were found to be relatively stable, relative to areas of urban expansion, starting at the beginning of the twentieth century as shown in Figure 9.  
The historical part of the city, built between 332-30 BC, has vertical velocity average of 0.56 mm/yr, as compared to \(-\)0.37 mm/yr and \(-\)0.21 mm/yr in built-up areas constructed between 30 BC-1766, and 1766-1917. Areas built between 1917 and 1972 have a relatively unstable surface with average subsidence of \(-\)2.44 mm/yr. The subsidence average increased in newly urbanized areas after 1972 to \(-\)3.07 mm/yr.  
Figure 8: Vertical displacement in Alexandria presented on the Sentinel-2 gray image of 24 October 2018 blue band, with the city area evolution along the history presented in the sub-data frame.  
Figure 9: Average of recent vertical motion based on the city area over history.
# Shadow Estimation for Ultrasound Images Using Auto-Encoding Structures and Synthetic Shadows
## 3 Materials and Methods
### Datasets  
We evaluate the performance of our method on US images of fetal heart diagnosis. Data for the experiments were acquired in Showa University Hospital, Showa University Toyosu Hospital, Showa University Fujigaoka Hospital, and Showa University Northern Yokohama Hospital. All the experiments were conducted in accordance with the ethical committee of each hospital. We collected 157 videos of 157 women who are 18-34 weeks pregnant. All the data are taken by convex probes with fetal cardiac preset on Voluson E8 or E10 (GE Healthcare, Chicago, IL, USA).  
We converted 107 of the videos into 37,378 images and used them as an unlabeled dataset. From the remaining 50 videos, experts extracted 445 images with shadows and annotated them at a pixel-level. Annotated 445 images were split into a training dataset with 259 images, a validation dataset with 91 images, and a testing dataset with 95 images (corresponding to 30, 12, and 8 videos, respectively).
# Impedance-Based Stability Analysis of Grid-Connected Inverters under the Unbalanced Grid Condition
## 3 The Simplified Stability Analysis Method Based on the GNC and Verification
### Stability Analysis Method of Inverters under the Unbalanced Grid Condition
Therefore, the relationship between matrices is  
\[\text{enc}(\mathbf{Y})=\text{enc}(\mathbf{D})+\text{enc}\Bigl{(}\mathbf{A}-\mathbf{B}\mathbf{D}^{ -1}\mathbf{C}\Bigr{)} \tag{24}\]  
where enc(Matrix) represents the number of eigenvalues of the matrix encircling the origin anticlockwise. The number of eigenvalues of the matrix encircling the origin anticlockwise takes a negative value [38]. Based on matrix theory, matrix \(\mathbf{D}\) can be expressed as the sum of a unit matrix and matrix \(\mathbf{D}_{\text{sub}}\) as  
\[\mathbf{D}=\mathbf{I}+\mathbf{D}_{\text{sub}}=\mathbf{I}+ \begin{bmatrix}Y_{33}Z_{\text{g}33}& 0\\ 0&Y_{44}Z_{\text{g}44}\end{bmatrix}  \tag{25}\]  
Then, the number of eigenvalues of the matrix \(\mathbf{D}\) encircling the origin anticlockwise is equal to the number of eigenvalues of the matrix \(\mathbf{D}_{\text{sub}}\) encircling the \((-1,\,\mathrm{j}0)\) point anticlockwise. Similarly, another submatrix \(\mathbf{A}-\mathbf{B}\mathbf{D}^{-1}\mathbf{C}\) can be expressed as  
\[\mathbf{A}-\mathbf{B}\mathbf{D}^{-1}\mathbf{C}=\mathbf{I}+\mathbf{A}_{\text{sub}} \tag{26}\]  
where \(\mathbf{A}_{\text{sub}}\) represents the equivalent partial matrix. In summary, based on the Schur Complement and matrix theory, the process of analyzing the stability of higher-order systems is simplified. The stability analysis of the DSOGI-PLL-based inverter under the unbalanced grid condition is transformed into the analysis of two second-order matrix eigenvalues. If the eigenvalues of matrix \(\mathbf{D}_{\text{sub}}\) and matrix \(\mathbf{A}_{\text{sub}}\) neither pass through nor encircle anticlockwise the \((-1,\,\mathrm{j}0)\) point, the grid-connected inverter system is stable. And matrix \(\mathbf{D}_{\text{sub}}\) is equivalent to the traditional model that does not consider the three-phase unbalance and the frequency-coupling effect. This paper aims to investigate the inverter instability phenomenon that cannot be predicted based on the analysis of the traditional model. Therefore, the Nyquist curves based on matrix \(\mathbf{D}_{\text{sub}}\) will not be shown below to save space. The simplified stability analysis method provided in this section can be applied to similar MIMO systems while ensuring the accuracy of the analysis.
# A Novel Metasurface Lens Design for Synthesizing Plane Waves in Millimeter-Wave Bands
## 3 Metasurface Lens Design
### Simulated-Array Performance  
To illustrate the electric-field intensity coverage capability, the metasurface lens and feeding-antenna array were simulated by using a full-waveform FDTD method, which was modified and developed over the last decades and directly computes the impulse response of an electromagnetic system in a single simulation. In this simulation, the feeding-antenna array was located on the focal plane of the lens, which was parallel to the metasurface lens. The distance between the lens and the feeding-antenna array was 0.45 m, and the metasurface lens was 0.4 m \(\times\) 0.4 m, corresponding to a focal-diameter ratio of 1.1.  
Figure 7 clearly shows a uniform electric-field intensity distribution area forming a circular focusing region at a reduced distance of 1.2 m with respect to the boresight direction when the metasurface lens is illuminated by the feeding-antenna array at a normal incidence. The circular focusing region is the designed QZ, which is perpendicular to the electromagnetic-wave propagation. With the increase of the electromagnetic-wave propagation distance, the phase-shift element of the metasurface lens varies and leads to the circular-focusing region's enlargement. Meanwhile, an increase in the path loss for the metasurface lens results in a lower magnitude and eventually leads to a limited dynamic range in the QZ area.
# Automatic Detection of Whistler Waves in the Top-Side Ionosphere: The WhISPER Technique
## 3 Results
The equinoctial months exhibit a similar distribution pattern (panels a and c). To investigate the potential influence of solar activity on whistler occurrence in the top-side ionosphere, we analyzed the distribution of whistler events during quiet, disturbed, and stormy geomagnetic days, according to what is reported in Section 2.3. Figure 10 illustrates the Probability Distribution Function (PDF) of the number of detected whistlers per semiorbit for (a) all days, (b) quiet days, (c) disturbed days, and (d) stormy days. The PDFs exhibit similar decay patterns suggesting that whistler occurrence is not significantly affected by variations in geomagnetic activity at middle-low latitude (from \(-40^{\circ}\) to \(-40^{\circ}\)). Figure 8: Geographical distribution of whistler density. In addition, a comparative analysis of diurnal and nocturnal observations demonstrates a higher whistler detection rate during nighttime, as illustrated in Figure 11, which presents the average number of detected whistlers per semiorbit for diurnal and nocturnal observations. The WhISPER algorithm identifies an average of 12.86 whistlers per diurnal semiorbit and 34.98 whistlers per nocturnal semiorbit. Figure 10: PDF of detected whistlers per semiorbit during (**a**) all days, (**b**) quiet days, (**c**) disturbed days, (**d**) stormy days. Figure 9: Geographical distribution of whistler density during (**a**) March, April, and May; (**b**) June, July, and August; (**c**) September, October, and November; (**d**) December, January, and February.
# Machine Learning Based Energy-Efficient Design Approach for Interconnects in Circuits and Systems
## 2 Low Power Approach for Interconnect Design
### Off-Chip Interconnect Design Flow
#### 2.1.1 Frequency Domain Design  
According to circuit theory, the lumped model is employed to represent circuit components with neglecting propagation time. At a higher frequency, the propagation time in the interconnect is considerable in comparison to the signal period. Hence, the lumped model is insufficient to describe the structure. In this case, the interconnect is the transmission line modeled by the distributed model. In transmission lines, voltages and currents are expressed as wave propagation along the line. Moreover, the characteristic impedance (\(Z_{0}\)) of the transmission line is defined as the ratio of voltage and current wave propagation along the line [20]. Alternatively, the characteristic impedance is also a function of the R, L, G, C parameters of the transmission line, which are frequency-dependent. Thus, this characteristic impedance is also a frequency-dependent parameter. Theoretically, this impedance is constant along the length of the transmission line at a specific frequency. However, the system includes not only the transmission line but also other connectors such as vias, SMA, and so on. Hence, the mismatch impedance between these components results in reflection loss. As a consequence, the impedance is one of the most significant parameters in the interconnect design that typically is 50 \(\Omega\) for a single-ended signal and 100 \(\Omega\) for a differential signal.  
In addition to the characteristic impedance, the values of scattering parameters (S-parameters) need to be investigated carefully in the design procedure. Typically, an N-port network is employed to represent the network at high frequency. The concept of equivalent voltages and current and the related impedance and admittance matrices are developed to describe the network. However, these concepts become abstract when dealing with the high-frequency network. Thus, the scattering matrix is preferable to provide a complete description of an N-port network. While the characteristic impedance represents the ratio between voltage and current wave in the transmission line, the S-parameters express the relationship between the incident and reflected voltage in the N-port network [20]. In the N-port, a specific parameter \(S_{ij}\) is determined as below  
\[S_{ij}=\frac{V^{-}i}{V^{+}_{\ j}}\quad with\quad{V^{+}}_{k}=0\quad for\quad k\
eq j \tag{1}\]  
where:  
\(V^{-}i\): Reflected voltage from port \(i\)  
\(V^{+}j\): Incident voltage at port \(j\)The S-parameters of a four-port network show all the significant parameters of two coupled interconnects. \(S_{21}\) represents the insertion loss throughout the channel including material, conductor, and radiation loss. The reflection loss caused by the mismatch impedance is represented in \(S_{11}\). Additionally, the crosstalk comprised of near-end crosstalk (NEXT) and far-end crosstalk (FEXT) is represented by \(S_{31}\) and \(S_{41}\), respectively.
## 2 Background on Reinforcement Learning
### Reinforcement Learning  
Learning can be broadly categorized as supervised, unsupervised and reinforcement learning. Reinforcement learning (RL) is a model-free strategy learning process which aims at learning to act appropriately by interacting with the environment only through trial and error [18]. In general, the RL problem is typically formulated as a finite-state MDP which comprises a discrete state space \(S\) and a discrete action space \(A\). Time is represented by a sequence of time steps \(t=0,1,\ldots\). At each time step \(t\), the controller observes the system's current state \(s_{t}=s\in S\) and selects an action \(a_{t}=a\in A\) to execute. After the action is performed, the state of system transits to a new state \(s_{t+1}=s^{\prime}\in S\) with the state transition probability \(P_{ss^{\prime}}(a)\). As a result, the environment generates a reward \(r_{t}=r(s_{t},a)\in R\) passing to the agent. To understand it better, a graphic representation is shown in Figure 1.  
RL approach attempts to find a policy that maximizes the total expected discounted reward over infinite steps. The objective of the agent is then to learn an optimal policy \(\pi^{*}(s)\) for each state \(s\), in this case, there exits an optimal deterministic stationary policy [19].  
Figure 1: Learning-environment interaction.  
The value function of state \(s\) is defined as the expected infinite discounted sum of rewards that the agent gains if it stars in that state:  
\[V_{\pi}(s)=E\left[\sum_{t=0}^{\infty}\gamma\tau(s_{t},\pi(s_{t}))|s_{0}=s\right] \tag{1}\]  
where \(s_{0}\) is the initial state, \(\gamma\in[0,1)\) is the discount factor. According to the principle of [PERSON]'s expectation equation, we can rewrite (1) as:  
\[V_{\pi}(s)=E[r(s,\pi(s))]+\gamma\sum_{s^{\prime}\in S}P_{ss^{\prime}}(\pi(s))V_ {\pi}(s^{\prime})\]  
It has been proven that the optimal policy satisfies the [PERSON]'s optimality equation:  
\[V^{*}(s)=\max_{a\in A}\left\{E[r(s,a)]+\gamma\sum_{s^{\prime}\in S}P_{ss^{ \prime}}(a)V^{*}(s^{\prime})\right\} \tag{2}\]  
The optimal action-value function is defined as the right-hand side of (3):  
\[Q^{*}(s,a)=Q^{\pi^{*}}(s,a)=E[r(s,a)]+\gamma\sum_{s^{\prime}\in S}P_{ss^{ \prime}}(a)V^{*}(s^{\prime}) \tag{3}\]  
Given the optimal action-value function, we can specify the optimal policy as:  
\[\pi^{*}(s)=\underset{a}{\text{argmax}}\;Q^{*}(s,a) \tag{4}\]
# Focus Estimation Methods for Use in Industrial SFF Imaging Systems
## 4 Discussion  
This article discusses the application of digital filters on images for use in 3D imaging systems by using the SFF method. This study compares the selected operators' performance on images collected on a test stand. This study determines the impact of several factors crucial for using the SFF-based measurement system under industrial conditions. The aforementioned factors include the product texture, the contrast obtained during acquisition, the type of the illuminator, and its alignment with the camera. In addition, the research is extended to include algorithm parameters essential for implementing the SFF method in the measurement system: the operator window size and methods for comparing the  
Figure 17: AFE values as a function of the illuminator for each operator selected for comparison. Values obtained for the CoG focus plane determination method.  
Figure 18: RMSE values depending on the illuminator used for the operators selected for comparison.  
focus of a scene point between stack images. The research scope was limited to imaging flat surfaces due to the intention to reduce the contribution of errors arising from the 3D image reproduction stage.  
The results allow for the initial selection of the operators based on the standard conditions. The standard conditions have been defined as uniform illumination, strong contrast, and no supplementary interference. After rejecting some operators that already gave unsatisfactory results for standard conditions, additional tests have been carried out. The tests involve the application of interference, addressing scenarios encountered in the industry.  
Investigations into the influence of mask size on representation quality reveal a diminishing error with increasing window size. However, this increase in window size also escalates the computational power required for the computations. Minor changes in the error value above a 7 \(\times\) 7 mask size suggest that the computational cost associated with enlarging the window size beyond 7 \(\times\) 7 outweighs the potential benefits.  
Examination of the impact of contrast reveals a decreasing error value with increasing contrast in the image. Additionally, saturation's negative effect on the scene's representation quality has been observed. The DPTT, DSLT, and SVAR filters consistently yield the smallest error values.  
Investigations into the influence of the illumination method on reproduction quality demonstrate the broad applicability of the SFF method. Increased error values are observed when scenes are illuminated with collimated light.
## 2 Technically Challenging Areas
A search and rescue system usually consists of heterogeneous nodes, while a military system is a collection of mission-oriented sub-systems with resources and capabilities that provide complex functionalities that are far more than the sum of constituent systems. A search and rescue system is usually deployed over a smaller space, while a military system emphasises value-added functionalities that usually leave more time for decision-making and resource deployment, while an emergency system must offer an urgent life-critical response. Search and rescue systems may typically also use more limited communications and sensing than the global assets that military systems typically have, such as satellite communications and wide-band radar sensing.
## 3 Experimental Results  
### Cracking Pattern  
Figure 6 shows the final cracking patterns of the test walls. The cracks caused by the force in the push and pull directions were represented by the red lines and the blue lines, respectively. It can be observed that all the test walls showed predominantly flexural behavior in the positive (right/push) and negative (left/pull) loading directions, which was characterized by the horizontal cracks in the bed joints, in the bottom courses of the wall, at a low displacement. The outmost vertical rebar yielded due to the increasing tension load, which was consistent with the measurement of the strain gauge. The block, grouted concrete, mortar, and vertical rebar cooperated well with each other to carry the lateral loads. With increasing lateral load, the test wall reached its ultimate load (maximum load), with some vertical cracking at the end blocks under compression. As the lateral displacement increased, horizontal tensile cracks clearly appeared at the bottom of the specimen, and a repeated crack opening and closing process appeared at the first bed joint of the shear wall. At the post-peak stage, progressive deterioration of the wall toes was observed, consisting of face shell spalling, grout cracking, and rebar buckling. During this period, the lateral load of the specimens decreased gradually to 85% of the ultimate load, which was defined as the failure load. At the final loading stage, grout column crumbling and vertical rebar rupturing were observed at the wall toes. The tests were not terminated until the load capacity of the specimens exhibited distinct degradation and decreased to at least 65% of the ultimate load.  
Figure 6: Masonry wall cracking patterns: (**a**) PMW-1; (**b**) PMW-2; (**c**) PMW-3A; (**d**) PMW-3B; and (**e**) PMW-4.
# A Survey on Node Clustering in Cognitive Radio Wireless Sensor Networks
## 3 CR-WSN Characteristics and Architecture
### Types of CR-WSNs
#### 3.2.2 Few Nodes with CR Ability (CR-WSN-few)  
In this type of network, only few nodes have a CR module, and other nodes have only limited CR or no CR module. If the performance of a CR-WSN-all and a CR-WSN-few is similar, embedding a CR module in each sensor with higher hardware and energy costs is unnecessary. CR-WSNs in which only CHs are equipped with CR for local spectrum sensing and channel assignments could be a better approach.  
In CR-WSN-few, nodes with a CR module work as a coordinator node (a cluster head after cluster formation). They sense the local spectrum and report to the BS. That node broadcasts the channel selection decision to other neighbor nodes. This type of network is advantageous in terms of cost and energy consumption. However, coordinator node deployment is difficult, and may create a single point of failure.