esa-sceva/satcom-chunk-collection huggingface dataset:
# Radial Gradient Seismic Metamaterials with Ultra-Low Frequency and Ultra-Wide Band Gap
## 2 Materials and Methods  
The propagation of seismic waves can be described by the propagation of elastic waves in inhomogeneous media. Assuming that the seismic wave propagates along the radial mid-plane of the structure, the governing equations can be written as:  
\[\rho(r)\ddot{u}(r)=\
abla\left\{[\lambda(r)+2\mu(r)](\
abla\cdot u(r))\right\} -\
abla\times[\mu(r)\times u(r)] \tag{1}\]where \(u(r)\) represents the displacement vector; \(\rho(r)\) denotes the mass density; \(\lambda(u)\), \(\mu(r)\) are the Lamb wave constants of the material. Due to the particularity of the structure, Equation (1) is extended to cylindrical coordinates. At this time, the wave equation can be rewritten as:  
\[\left\{\begin{array}{l}\rho\frac{\partial^{2}u}{\partial t^{2}}=(\lambda+2 \mu)\frac{\partial\theta_{t}}{\partial r}-\frac{2\mu}{r}\frac{\partial w^{ \prime}_{t}}{\partial\theta}+2\mu\frac{\partial w^{\prime}_{\theta}}{\partial \theta}\\ \rho\frac{\partial^{2}v}{\partial t^{2}}=(\lambda+2\mu)\frac{\partial\theta _{t}}{\partial r}-2\mu\frac{\partial w^{\prime}_{t}}{\partial r}+2\mu\frac{ \partial w^{\prime}_{t}}{\partial r}\\ \rho\frac{\partial^{2}v}{\partial t^{2}}=(\lambda+2\mu)\frac{\partial\theta _{t}}{\partial z}-\frac{2\mu}{r}\frac{\partial}{\partial r}(rw^{\prime}_{ \theta})+\frac{2\mu}{r}\frac{\partial w^{\prime}_{t}}{\partial t}\end{array}\right. \tag{2}\]  
where \(u\), \(v\), and \(w\) are the displacement components of the rectangular coordinate system; \(\rho\) is the density; \(t\) is the time; \(\lambda\) and \(\mu\) are the elastic wave constants of the material; and \(r\), \(\theta\), and z are the coordinates in the cylindrical coordinate system.  
In addition, the volume strain \(\theta_{t}\) and the rotational component (\(w^{\prime}_{r}\),\(w^{\prime}_{\theta}\),\(w^{\prime}_{z}\)) in the cylindrical coordinate system are defined as follows:  
\[\left\{\begin{array}{l}\theta_{t}=\frac{1}{r}\frac{\partial(r_{t})}{\partial r }+\frac{1}{r}\frac{\partial w}{\partial\theta}\frac{\partial w}{\partial z}\\ w^{\prime}_{r}=\frac{1}{2}\left(\frac{1}{r}\frac{\partial w}{\partial\theta}+ \frac{1}{r}\frac{\partial w}{\partial z}\right)\\ w^{\prime}_{\theta}=\frac{1}{2}\left(\frac{1}{r}\frac{\partial w}{\partial \theta}+\frac{\partial w}{\partial r}\right)\\ w^{\prime}_{z}=\frac{1}{2}\left(\frac{1}{r}\frac{\partial(r)}{\partial \theta}+\frac{\partial u}{\partial\theta}\right)\end{array}\right. \tag{3}\]  
According to [PERSON]'s theorem, the infinite periodic radial lattice unit can be simplified to a single lattice unit, and the boundary conditions of the lattice can be expressed as follows:  
\[u(r+r_{a},z)=u(r,z)e^{ik_{p}r_{a}} \tag{4}\]  
where \(a\) is the lattice constant, \(r\) is the radial position vector, and \(Kr\) represents the Bloch radial wave vector. Here, the Floquet periodic boundary condition is applied to the boundary connection of the unit cell. The top and bottom surfaces of the unit cell are free boundary conditions, that is, there is no reflection component on the surface.
# Gait Recognition Based on Gait Optical Flow Network with Inherent Feature Pyramid
## 3 Proposed Approach  
### Gait Optical Flow Image Extraction  
To distinguish people based on their gait sequences, the GOFI representation describes the instantaneous motion using the optical flow descriptor. The GOFI retains both temporal and spatial condition features while preserving the invariant gait features.  
For a three-channel RGB gait image model, first the image is subtracted from the background image and transformed into a binary image. Then, the outlier points and the holes caused by the subtraction are eliminated using morphological operations. The center of the subject is detected using the intensity distribution. For some samples, the person is too close to the camera, and the image is filled with foreground, so these samples, in which the sum of pixel intensities is large, are eliminated. Then, the method introduced by [26] is used to calculate the two-channel optical flow matrix \([u,v]\). The optical flow of each pixel draws the direction and the intensity of body movement at the next step. Assuming the brightness is constant and the movement is tiny, the optical flow matrix is calculated as follows:  
\[I_{x}u+I_{y}v+I_{t}=0 \tag{1}\]  
where \(I_{x}\) and \(I_{y}\) are, respectively, image gradients in the vertical and horizontal direction, which can be obtained using the Sobel filter, and \(I_{t}\) represents the temporal gradient, which can be obtained using the frame differential method. Then, the optical flow is transformed into an HSV image formulated as  
\[H_{i,j}=\tan^{-1}\frac{u_{i,j}}{v_{i,j}} \tag{2}\]  
where, for a location \((i,j)\), \(H_{i,j}\) is the hue of the pixel and \(u_{i,j}\) and \(v_{i,j}\) are the optical flow component in the vertical and horizontal directions of the pixel, respectively.  
\[S_{i,j}=\sqrt{{u_{i,j}}^{2}+{v_{i,j}}^{2}} \tag{3}\]  
where \(S=\left\{{S_{i,j}}\right\}\) means the magnitude map and \(S_{i,j}\) is the magnitude at the pixel.  
\[V_{i,j}=\frac{S_{i,j}}{max(S)-min(S)} \tag{4}\]  
where \(V_{i,j}\) means the value of the pixel.  
As shown in Figure 1, the HSV image \(I_{1}\) is merged with the silhouette edge image \(I_{2}\). To obtain \(I_{2}\), the origin image is transformed into a grayscale image and then the Canny operator is applied to detect the border of the character image. Only the edge is used instead of the whole silhouette because we are more concerned about the instantaneous motion, which is more obvious at the edges but is confusing inside the silhouette. The gait optical flow image model \(I_{3}\) is formulated as  
\[I_{3}=\sigma I_{1}+(1-\sigma)I_{2} \tag{5}\]where \(\sigma\) represents the intensity factor. Finally, the original silhouette is cropped into subject-centered images whose size is 64 \(\times\) 64 to obtain the GOFI.
# An Efficient Routing Scheme Based on Node Attributes for Opportunistic Networks in Oceans
## 3 The Proposed Scheme: RSNA
### Routing Decision Scheme  
In OppNets, an efficient routing scheme not only improves the success delivery ratio of message forwarding, but also degrades the overhead of the network. To promote our expression below, we utilize \(F_{N_{n}}\) to denote the neighbor sets of node \(N_{n}\), and \(F_{N_{n}}^{m}\) indicates the message sets carried by node \(N_{n}\). In Algorithm 1, if the link between sender node \(N_{l}\) and its neighbor node \(N_{r}\) is available and node \(N_{r}\) (\(N_{r}\)\(e\)\(N_{n}\)) is destination node \(N_{d}\), the messages are directly transferred from node \(N_{r}\) to node \(N_{d}\) (Algorithm 1: line 4). Otherwise, if the link availability of relay node \(N_{r}\) and its neighbor \(N_{rf}\) is greater than 0 (Algorithm 1: line 6), it is implied that node \(N_{r}\) has a next hop, which identifies the unavailability of node \(N_{1}\) without further relay nodes to the destination node in the example of Figure 2. Moreover, the forwarding willingness mechanism is further introduced to qualify whether the nodes can participate in the packet delivery process. When the forwarding willingness between node \(N_{l}\) and node \(N_{r}\) meets the communication demands, the value of \(FW(N_{r},N_{rf})\) is further predicted (Algorithm 1: line 8). In \(FW(N_{r},N_{rf})\), the prediction of \(DC(N_{r},N_{rf})\) can abort some seemingly qualified nodes due to the insufficient of device capacity, and combining the movement behavior of \(BN(N_{r},N_{rf})\) can jointly detect the unreachability of nodes \(N_{5}\) and \(N_{8}\) to the destination node in the example of Figure 2. This is because the nodes periodically exchange the metrics with each other during message routing so that nodes are available when they have an opportunistic contact. Then the Equation (9) is used to make decisions on whether the messages forward to the relay node \(N_{r}\) (Algorithm 1: line 9). Finally, the messages are delivered hop-by-hop until the destination node is in proximity, and eventually to the destination node itself.  
```
1:for each message m \(\epsilon\)\(F_{N_{t}}^{m}\)do
2:for each node \(N_{l}\)\(\epsilon\)\(F_{N_{t}}^{m}\)do
3:if the link between sender \(N_{l}\) and neighbor node \(N_{r}\) is available and \(N_{r}=N_{d}\)then
4:\(N_{r}\) transmits message \(m\) to \(N_{d}\)
5:else
6:while\(p_{la}(N_{r},N_{rf})>0\)do
7:if the value of \(FW(N_{l},N_{r})\) meets the communication demands then
8: predict the value of \(FW(N_{r},N_{rf})\)
9:if\(U(N_{r},m)\geq U(N_{l},m)+\delta\)then
10: node \(N_{l}\) forwards message \(m\) to node \(N_{r}\)
11:endif
12:endif
13:endwhile
14:endif
15:endfor
16:endfor
17:End
```  
**Algorithm 1** RSNA routing algorithm
## 3 Results
### Model Performance Comparison
#### 3.2.2 Evaluation Indicators  
In this study, we used precision, \(F1\) score, accuracy, and recall as evaluation metrics to assess the effectiveness of different network models in the classification and recognition tasks targeting tomato leaf disease images, where Equations (5)-(8) are the formulas for \(F1\), _Accuracy_, _Precision_, and _Recall_, respectively.  
\[F1=2\times\frac{Precision\times Recall}{(Precision+Recall)} \tag{5}\]  
\[Accuracy=\frac{\text{Identify the correct total number of disease and pest images}}{Total\text{ number of disease and pest images}} \tag{6}\]  
\[Precision=\frac{TP}{TP+FP} \tag{7}\]  
\[Recall=\frac{TP}{TP+FN} \tag{8}\]  
where \(TP\) is the number of positive samples predicted by the classifier with a positive true result, i.e., the number of positive samples correctly identified; \(FP\) is the number of negative samples predicted by the classifier with a positive result but a negative true result, i.e., the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Module** & **Accuracy** & **Loss** & **Parameters** & **Train Time/s** \\ \hline LBFB & 0.6267 & 1.0625 & 689,034 & 4633 \\ LBFB + cascade & 0.9567 & 0.1513 & 955,722 & 2158 \\ LBFB + three-channel & 0.9688 & 0.1034 & 798,276 & 1347 \\ LBFB + cascade + three-channel & & & & \\ attention mechanism & 0.9906 & 0.0408 & 897,188 & 966 \\ LBFB + SE & 0.5578 & 1.2754 & 691,098 & 5194 \\ LBFB + cascade + SE & 0.9465 & 0.1703 & 957,786 & 2879 \\ LBFB + CA & 0.8922 & 0.3146 & 776,914 & 1552 \\ LBFB + CA + cascade & 0.9683 & 0.1220 & 962,386 & 2312 \\ LBFB + ECA & 0.8745 & 0.3650 & 773,584 & 1432 \\ LBFB + CCA + cascade & 0.9615 & 0.1405 & 955,728 & 1786 \\ LBFB + DUAL & 0.8853 & 0.3411 & 794,060 & 2434 \\ LBFB + DUAL + cascade & 0.9588 & 0.1261 & 976,204 & 2755 \\ LBFB + CBAM & 0.9089 & 0.3053 & 794,940 & 1537 \\ LBFB + cascade + CBAM & 0.9790 & 0.0815 & 777,468 & 1172 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Effect of different key modules on model performance.  
number of negative samples incorrectly predicted; _FN_ is the number of positive samples for which the classifier predicts a negative outcome but the true outcome is a positive sample, i.e., the number of missed positive samples. Accuracy is the ratio of the total number of correctly identified disease images to the total number of disease images.
## 2 Methods
### Row-Column Addressed Arrays and Velocity Estimation  
The principles of a 2D RC addressed arrays are explained already in previous papers [18; 20], and a brief introduction is given here.  
An RC addressed array can be viewed as two orthogonal 1-D arrays with tall elements, mounted on top of each other. Compared with an \(N\times N\) matrix array, the total number of interconnections in an \(N+N\) RC array is reduced by a factor of \(N/2\), which simplifies the interconnect and opens up for transducers with both a large footprint and a small pitch. Figure 2 illustrates this principle.  
Figure 1: Example of a tensor velocity image. Arrows show the 3D velocity vector in each sample in the volume. The volume is sampled in 11 planes, each plane providing a cross-sectional view of a vessel with laminar flow.  
The long elements generate cylindrical waves, and the delay-and-sum beamformer must be adjusted accordingly [20]. Using the convention that row-elements are parallel to the \(x\)-axis, and column-elements are parallel to the \(y\)-axis, [PERSON] et al. [22] showed that a cylindrical wave emitted by row elements allows the \(x\) and \(z\) velocity components to be estimated in a \(XZ\)-plane. Similarly, an emission with column elements allows the \(y\) and \(z\) velocity components to be estimated in a \(YZ\)-plane. The full 3D velocity vector is available in the intersection of these planes. The \(x\) and \(z\) velocity components are obtained in a volume by steering the beam in different directions using emissions by the rows, and the \(y\) and \(z\) components were measured similarly by steered column emissions [23]. The base emission sequence employed used 9 steered column emissions followed by 11 steered row emissions that were then repeated a number of times to provide continuous data. The \(n\)th emission in the \(i\)th sequence was then correlated with the \(n\)th emission in the \(i+1\)st sequence, i.e., the two signals given to the velocity estimator were separated in time by \((9+11)T_{prf}\), where \(T_{prf}\) is the pulse repetition period. For an auto-correlation estimator, the aliasing limit \(v_{z,max}\) is  
\[v_{z,max}=\frac{\lambda}{4T_{prf,eff}}, \tag{1}\]  
where \(\lambda\) is the wavelength of the ultrasound pulse, and \(T_{prf,eff}\) is the effective pulse repetition period, which in this case was \(20T_{prf}\).  
This work extends on these principles and introduces modifications to overcome the aliasing limit.
## 4 Fuzzy Logic Model
### Fuzzification  
In fuzzification, the input variables should be converted to linguistic values to determine the membership degree. Thus, fuzzification is the process of making a crisp quantity fuzzy. The crisp quantity is converted to fuzzy values by recognizing the quantities that are not deterministic, as well as carrying considerable uncertainty. If such uncertainty happens by imprecision, ambiguity or vagueness,then the variable can be represented by a membership function. The outputs of this stage are fuzzy values that can be processed by the inference engine to define the output value. In the proposed scheme, the input crisp values are _Active time_, _Energy_ and _Velocity_, as shown in Figure 1.  
In this paper, we use the linguistic variables, such as _Very Low_, _Low_, _Medium_, _High_ and _Very High_, represented as _VL_, \(L\), \(M\), \(H\) and _VH_, respectively, for fuzzy sets. The fuzzy sets \(H\) and _VH_ contain active sensors \(S\) and _ETR_ relay nodes having \(\mu_{N}\)_(_S_) = \(1\) and \(\mu_{S}\)_(_ETR_) = \(1\), where \(\mu\) represents the degree of membership function and \(N\) represents the number of sensors in the network. The \(M\) includes those active sensors \(S\) having \(\mu_{N}(S)>0\). Similarly, fuzzy set \(M\) for _ETR_ relay nodes contains active sensor nodes having \(\mu_{S}(ETR)>0\). The fuzzy sets \(L\) and _VL_ contain active sensors \(S\) having \(0<\mu_{N}(S)<1\) and _ETR_ relay nodes having \(0<\mu_{S}(ETR)<1\).  
Fuzzy logic deals with the analysis of information by using fuzzy sets. Figure 2 shows the detail of a fuzzy dataset that considers the active/sleep scheduling and _ETR_ relay node selection as a crisp and fuzzy selection, respectively. In addition, Figure 2 further shows the comparison between a crisp active/sleep scheduling and an _ETR_ selection with a fuzzy set represented as _Low_. Moreover, the figure shows that the crisp selection intersects the fuzzy set at a membership value of 0.2, which is considered as \(L\). Similarly, the intersection of the fuzzy set \(M\), the fuzzified active set and _ETR_ selection occurred at a membership value of 0.5, as shown in Figure 2. Furthermore, it is noticeable that the intersection of the two fuzzy sets is a small triangle whose largest membership value is equal to 0.5, measured as \(M\).  
In addition to the energy problem, the proposed scheme dynamically discovers the eligible forwarding choices through a fuzzy controller and manages the routing toward the destination. It is important to note that the deadline of a packet is achieved if the required velocity of a packet is met at each relaying node [5]. Hence, the problem of meeting an end-to-end packet deadline is mapped to the local problem of meeting the required velocity of a packet at each relaying node. The proposed fuzzy controller considers the current network conditions to adapt the required velocity of the packet. This implies that the fuzzy controller output can be satisfactory if the required end-to-end velocity of a packet is met at the next hop relaying node.  
Figure 1: Fuzzy logic controller structure.
## 2 Experimental Details
### Organic Extraction from Sediment Samples  
A weighted amount of 15 g from each sediment sample, including clean sediment, was transferred in conical flasks of 100 mL. Then, 50 mL and 5 mL of CH\({}_{2}\)Cl\({}_{2}\) and CH\({}_{3}\)OH were respectively added to each sediment sample. Additionally, 20 g Na\({}_{2}\)SO\({}_{4}\) and 0.5 mL of surrogate solution were added to each sediment sample. Sodium sulfate was used in order to absorb the content moisture of sediments. Ultrasonic extraction was then applied in all sediment samples at room temperature and at 50-60 Hz frequency. The whole procedure occurred in an ultrasonic bath, and the extraction process was repeated twice for 30 min each run. After extraction, all sediments samples were filtered by common filtration, and the procedure was repeated three times by adding solvent mixture at the precipitate. The collected organic filtrates were finally carried to the top of a sodium sulfate chromatographic column for clean-up. The column was preconditioned with dichloromethane. The total collected organic phase was first condensed to a volume of 2-3 mL by rotary evaporation and then condensed under gentle injection of nitrogen steam to a final volume of 1 mL [41].
## 5 Discussion and Future Work
These steps can be applied to use cases where a parameter of the test can change the outcome and can compromise the system's functionality. The statistical design of experiments can also be used when the test has a lot of parameters and the experimenter needs to define which of these parameters actually affect the interoperability of the system. Stress tests are an example of this category. In the use case described in this tutorial, stress tests are not performed, An example of a stress test could be the conditions under which one smart meter can operate correctly, like for instance, in case harmonics are inserted in the low voltage distribution grid. The scope of this tutorial is applying the methodology for interoperability testing and providing an example use case that shows how to apply its fundamental steps answering to the question if the equipment under test is or not interoperable with the system. Therefore, a smart grid use case without statistical analysis has been chosen. As future work, a use case including statistical design of experiments will be developed, which should explain the statistical analysis part of the interoperability methodology. In general, it is considered that the more use cases developed, the better the methodology will be comprehended. In addition, when a larger number of use cases are developed, will help in identifying possible fields that need further development. In the SGILab it is intended to use the methodology in order to perform future interoperability tests on smart grid use cases as future work. Moreover, a repository is intended to be created, which will host all use cases developed and respective BAPS, BAIOPs, test cases (TC) and Design of Experiments (DoE). The scope is that after a number of use cases is created, the user will be able to identify more easily the actors interacting for his/ her use case and the derivation of BAPs, BAIOPs, TCs and DoEs will be facilitated. As a further step, and with a large number of use cases, the procedure of defining the interoperability test can be even made automatically, provided that there are similar use cases in the repository. Test results will also be stored in the repository in order to increase visibility of the use case outcome. In general, the field of interoperability is expected to attract the scientific interest for smart grid tests. The presented methodology is anticipated to help in facilitating such smart grid tests and its future usage through more use cases will also facilitate its evolution.
# Improvement of Surface Roughness and Cracking Study of Nb\({}_{3}\)Sn SRF Films
## 4 Study on Strain Limits of Nb\({}_{3}\)Sn Film Coated by Tin Vapor Diffusion Method
### Nb\({}_{3}\)Sn Cavity Tuning Experiment  
The tuning experiment was carried out on a 1.3 GHz single-cell cavity, which was coated by typical coating method and tested at 4.2 K. The cavity was tuned about 158 kHz on the tuning platform, the initial frequency of the cavity was 1296.032 MHz, and the cavity was squeezed to 1295.874 MHz, as shown in Figure 20a. After tuning, the cavity was prepared for another RF test following the standard preparation procedures. After tuning, the quality factor of the cavity was 8.22 \(\times\) 10\({}^{9}\) at 2.6 MV/m at 4.2 K and decreased to about 10\({}^{9}\) at 4 MV/m, with a strong Q-slope, as shown in Figure 20b.  
Figure 19: Maximum strain and stress along cavity inner surface while tuning at room temperature.  
Figure 20: Tuning of NS04 cavity (**a**) and test result (**b**) at 4.2 K before and after cavity tuning.  
This Q-slope phenomenon is most likely caused by the cracks mentioned above during the squeezing of the cavity. At a low field, the decrease in Q is not significant, indicating that the size of the crack is small and has no significant effect on the increase in RF loss. When the acceleration gradient reaches 4 MV/m or even higher, Q rapidly drops to the order of \(10^{8}\).  
Different from the quench phenomenon, the cavity did not capture a large amount of magnetic flux after the rapid decrease in Q value, and can still recover the Q performance of \(10^{9}\) at low field. There are many reasons for the Q-Slope phenomenon, such as cracks in the film, uneven temperature drop, etc., which may produce different degrees of Q-Slope. We also hope to conduct further research on this.  
This kind of fine crack at the inner surface cannot be directly observed by SEM, but it has great influence on the RF performance of the superconducting cavity, especially in the high field region. Therefore, it is best to perform some pre-tuning work before coating to ensure the performance of Nb\({}_{3}\)Sn superconducting cavities at the operating frequency when carrying a beam.