esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model
### Topology Control Model  
The topology control model is utilized for achieving two purposes in this work. First, the data transmission latency from source node to destination will be reduced or minimized by adjusting the transmission range of sensors flexibly. There is one more point, we should touch on: the energy utilization efficiency will be improved or maximized by neighbors' selection strategies, rather than by saving power of the sensor node. Considering the limited energy storage ability, a sensor cannot always be able to conserve energy. Therefore, since the energy can be replenished continually, a sensor will consume the energy as soon as possible only if its energy can support its operation.  
To further illustrate the operation of the topology control model, an example is provided, which is shown in Figure 5. The sensor \(i\) senses the surroundings and generates packets, which will be forwarded to the destination sink \(S\) through multiple-hop data transmission. In traditional WSNs, by the Equation (2), we know that a sensor always select a closer neighbor as its next-hop node for data receiving in order to save energy. Therefore, data packets will be transmitted to sink \(S\) along the routing path \(\{i,a,c,e,S\}\). While, in R-WSNs, considering the energy of sensor \(i\) can be replenished continually, it may select sensor \(c\) as its net-hop node only if it has enough energy to support this operation. The new routing path \(\{i,c,S\}\) can be established for data transmission. A packet is forwarded to sink \(S\) through two hops in the new path, while it is four hops in the original path. Therefore, the End to End delay for data transmission is reduced dramatically depending on the topology control model, which is significantly different to traditional WSNs.
# Hyperspectral Image Super-Resolution Based on Spatial Group Sparsity Regularization Unmixing
## 5 Results and Analysis
### Computational Cost  
To evaluate the computational cost of the proposed work, we report the runtime for all methods in Tables 3 and 4, which were obtained by reconstructing the HSIs from the corresponding data sets. BSR was the most time-consuming method, as it needs to learn a dictionary for sparse coding. FUSE is a simple fusion strategy, making it the fastest method; however, it had poor super-resolution performance. Compared with the rest of the methods, the time consumed by our method was moderate and acceptable. For the \"Harvard\" data set, our method did not have a superior performance in run-time. This was due to the super-pixel procedure in our method, the run-time of which is mainly influenced by image size. For all remote sensing data sets, the run-time of our method was superior to that of LTTR but worse than that of CSU. Considering both HSI super-resolution and computational cost, the method proposed in this paper can be considered effective.
## 4 Dispersing the Light
### Echelle Gratings
#### 4.2.2 Fabrication  
Although the potential advantages of PEGs are attractive at face value, their fabrication of PEGs is a complex, multi-step process due to the requirement of reflective surfaces. In addition, the scale of the gratings requires the use of precision lithography, such as electron-beam lithography. A promising new  
Figure 5: Schematic of a photonic echelle grating spectrograph. A ray representation is shown for simplicity although the dimensions of the waveguides and reflecting surfaces require a wave treatment. Green shows the light emanating from the input waveguide and yellow shows the reflected light. The brown colored region shows a number of output waveguides sampling the dispersed light from different spectral orders (for e.g., orders 49, 50, and 51).  
technique uses electron-beam lithography to achieve a low-loss (insertion loss \(\sim\)1.4 dB) PEG using silver coating on the reflecting facet with a \(Si_{3}N_{4}/SiO_{2}\) platform [76]. Recently, low-loss PEGs have also been demonstrated in the J-band using a SiNOI platform [80]. Another method for on-chip reflection, called DBR, is simpler for fabrication since there is no extra step to deposit reflective material. It has shown promise for astrophotonic echelle gratings [79], albeit with a higher insertion loss (\(\sim\)1.5 dB).  
**Challenges and future:** Echelle gratings with reflective facets have potential to be broadband, since the wavelength dependence of the design is minimal. A key challenge with echelle gratings is polarization independence. Both metal facet and DBR have directionality due to the fabrication process and inherent design, respectively. New designs are being proposed to tackle this problem (e.g., by introducing a polarization compensation area in the free propagation region [81]) and will be further developed in the near future.
# Kolmogorov-Arnold Networks for Reduced-Order Modeling in Unsteady Aerodynamics and Aeroelasticity
## 3 Numerical Method
### Method Validation  
The AGARD CT5 benchmark [36] is used to verify the accuracy of the CFD solver. This benchmark involves an NACA0012 airfoil subjected to single-degree-of-freedom pitching oscillation. The pitching motion can be described by the following equation:  
\[\alpha(t)=\alpha_{m}+\alpha_{0}\sin(\omega t) \tag{11}\]  
where \(\alpha(t)\) represents the angle of attack, \(\alpha_{m}\) is the mean angle of attack, \(\alpha_{0}\) denotes the amplitude of the pitch oscillations, \(\omega\) is the angular frequency of the pitch oscillations, and \(k\) is the reduced frequency, defined as \(k=\frac{\omega c}{2U_{\infty}}\). In this context, \(c\) is the chord length, and \(U_{\infty}\) denotes the free-stream velocity. The pitching motion occurs around the quarter-chord point. The computational parameters are \(\alpha_{m}=0.016^{\circ}\), \(\alpha_{0}=2.51^{\circ}\), \(Ma=0.755\), \(Re=5.5\times 10^{6}\), and \(k=0.0814\).  
The computational grid utilizes a C-type structured mesh with 14,818 cells, as shown in Figure 4.  
The numerically computed hysteresis loops of the lift and moment coefficients are compared with the experimental values provided by AGARD CT5 [36], as shown in Figure 5. The results indicate a good agreement between the CFD-calculated values and the experimental data, which confirms the accurate predictive capabilities of the unsteady flow solver used in this study.  
Figure 4: Computational grid of the NACA0012 airfoil.
## 4 Variable Optical Coupler
### Controlling the Variable Optical Coupler  
The control of the actuator is accomplished by applying a voltage between 0 and 150 V. The voltage is provided by controller 914-2 made by the company [52] Evanescent Optics Inc. The coupler is connected to the controller at the output of piezoelectric (output port 916). In order to control the piezo-controller, a voltage between 0 and 5 V is required. This is done by the data acquisition device LabJack U3-HV, provided by the same company LabJack Corporation [51]. The analog output DAC0 by LabJack U3-HV is used. The maximum output voltage is 5 V with a resolution of 10 bits. This corresponds to a minimum possible resolution of about 4.9 mV. The output voltage of the LabJack is controlled by a dynamic library (DLL) specified by the manufacturer. Through the LabVIEW graphical user interface, the library is included in the programming environment and run on a Windows computer. A simplified diagram of the structure for control and characterization of the variable coupler is shown in Figure 7. The three outputs of the optical coupler (TP = Transmission port CP = coupling port, Back = feedback port) can be connected alternately to an optical spectrum analyzer (OSA). Yokogawa 6370C is used as a spectrum analyzer [47]. The spectral wavelength range is from 600 nm to 1700 nm, with a resolution of 0.02 nm and a dynamic range of 78 dB. The spectrum analyzer is connected via the General Purpose Interface Bus (GPIB) and Agilent USB/GPIB adapter to a Windows computer. For a complete characterization of the optical coupler, measurements are carried out on all three connecting ports with variation of both degrees of freedom. The scale of the micrometer screw is divided into 10 sub-blocks, each of 25 units. This results in 250 units of the micrometer screw. Since an exact conversion of these units is not known, they are referred to as _arbitrary unit_, abbreviated as [a.u.]. The starting point for the characterization is when the micrometer screw is turned to the extent that almost all division lines under the screw are covered. When the coupler is now used, about 10 to 15 units of the first sub-block are still visible. The characterized area of the coupler was chosen between 15 a.u. and 165 a.u. The increment of the micrometer screw depends on the required resolution such as ten, five or two a.u. With each selected micrometer setting, the output voltage of the LabJack U3 is tuned such that the control voltage of the piezo-controller is between zero and four volts. For this purpose, the voltage is varied linearly between zero and four volts, increasing from four to zero volts and decreasing from four to zero volts, maintaining a step size of 0.1 V either way. Measuring the output power at the terminals TP, CP and Back (see Figure 7) is carried out separately and one after the other with the help of spectrum analyzer. The measurement can be interpreted as a type of procedure, shown as a pseudo-code in Algorithm 1.
# Automated 3D Image Processing System for Inspection of Residential Wall Spalls
## 3 Results
### Quantification of Spall  
To further improve the analysis of wall damage, the length and width of detected wall spall defects were calculated using the density-based spatial clustering (DBSCAN) [40] and Minimum Bounding Box (MBB) algorithms. Considering the impact of temperature on spalling development, we incorporated temperature and humidity variation data into the analysis of periodic inspections. As shown in Figure 17, by conducting a quantitative analysis of the collected data, we compared the development of spalling over different time periods, considering factors such as temperature and humidity. Table 9 lists the geometric information and development status of spall samples under different time and temperature conditions.  
Using the methods described above, spalls are quantified, and the extent of spalling on the wall surface, along with its development over time and due to factors like temperature, is measured and evaluated to provide a scientific basis for assessing the structure's health. This process aids in the early detection of potential safety hazards and the prevention of major accidents while also extending the service life of structures.
## 3 Proposed Method
### Information Diffusion Model
\(I_{i}(t)\) represents the influence of information on node \(v_{i}\) at time \(t\); \(a_{i}\) is the characteristic of node \(v_{i}\) and represents the degree of aversion to the information. Figure 3: The process of information diffusion consists of three parts: information perception, information evaluation, and information transmission. The positive pressure action time \(T^{+}\) represents the length of time that the individual is affected. The individual's attention to information is related to the individual's forget speed, so we define it as follows, \(f_{i}\) represents the feature of node \(v_{i}\):  
\[T^{+}_{i}=100*f_{i} \tag{6}\]  
In the information perception part of our proposed model, individuals will trust people who are closely related to them, considering the effects of social communication; the higher the degree of trust, the larger the probability of acceptance. The community is a way to distinguish the degree of closeness, and the internal nodes of the community are close. Previous studies [35] have proved that the more common neighbors between two nodes, the higher the probability that they will exist in the same community. Therefore, the probability that the node \(v_{i}\) accepts the information sent by the neighbor node \(v_{j}\) is defined as:  
\[p^{a}_{ij}=\frac{1+N_{ij}}{d_{i}} \tag{7}\]  
where \(p^{a}_{ij}\) indicates the accept probability, \(d_{i}\) represents the degree of node \(v_{i}\), and \(N_{ij}\) represents the number of common neighbors about node \(v_{i}\) and node \(v_{j}\). In the information evaluation part, individuals evaluate the information and update their status based on the evaluation. Due to the different background knowledge and the time of receiving, the evaluations of information will be different. In addition, affected by the herd mentality, individuals tend to maintain the same state with their neighbors, especially those living in the same community. In this work, we define the information evaluation function as follows:  
\[M^{t}_{i}=I_{i}(t)=I^{+}_{i}(1-\frac{t}{T^{+}})e^{-\alpha_{i}t} \tag{8}\]  
where \(M^{t}_{i}\) represents the evaluation value of the received information by node \(v_{i}\) at time \(t\) and \(N_{i}\) represents the neighbors of node \(v_{i}\). According to the behavior of the users on OSNs, the result of information evaluation can also indicate the state of the node. The higher the evaluation result, the more active the node will be. \[s^{t}_{i}=1-e^{-M^{t}_{i}} \tag{9}\]  
Since personal interest in information declines over time and individuals tend to share fresh and higher value information, in the last stage of the process we define the probability of individuals sending information to neighbors as:  
\[p^{s}_{ij}=\frac{I^{t}_{i}}{I^{+}_{i}}=(1-\frac{t}{T^{+}})e^{-\alpha_{i}t} \tag{10}\]  
where \(p^{s}_{ij}\) specifies the probability of node \(v_{i}\) sending information to node \(v_{j}\).
## 3 Results
### Robustness of DStr and DSize for Low Edge Gradient Images  
The robustness of DStr and DSize for contour maps is assessed against grid size and Gaussian blur radius. The grid size is used due to the fact that it is one of the factors which substantially influences the shape of map contours [23]. The Gaussian blur is applied due to its ability to reduce the many negligible details in a grayscale image (Figure 5).  
The experiment was organized as follows. The image brightness was normalized and presented using the relative scale [0, 1]. Wide ranges for grid size and Gaussian blur radius (Table 1) were used to access the robustness of DStr and DSize for Boeing engine condensation trails, a melanoma, and a cloud. The identical program settings were used to calculate DStr and DSize for all experiments: the minimal number of transects is equal to three, the minimal number of pixels between transects is equal to three, and the differences in the number of transects for two sets of transects, S\({}_{\text{j}}\) and S\({}_{\text{j+1}}\), is equal to six (ReadMe file, ZENODO).  
Equations (1) and (2) were used to calculate RB(DStr) and RB(DSize) (Figure 6). The absolute and relative robustness of DStr and DSize against a different grid size varies from 0.86 to 0.98 (Figure 6a) for all images. This was also true for different Gaussian blur factors, except for the relRB(DSize) of the melanoma and cloud (Figure 6b).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Grids Size (Relative Scale)** & **Gaussian Blur Radius** \\ \hline Aircraft & 1, 0.5, 0.25 & 0, 3, 6, 9, 12, 15 \\ \hline Melanoma & 1, 0.5, 0.25 & 0, 2, 4, 6, 8 \\ \hline Cloud & 1, 0.5, 0.25 & 0, 2, 4, 6, 8, 10, 12, 14 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Binary contour maps. Setting for assessing the robustness of DStr and DSize against grid size and Gaussian blur radius.  
Figure 5: Images of Boeing trace contour maps for different blur factors. Contour intervals on the relative scales [0, 1] are 0.6 and 0.8.
# Wind-Induced Response Control of High-Rise Buildings Using Inerter-Based Vibration Absorbers
## 4 Wind-Induced Response Mitigation Using Inerter-Based Vibration Absorbers
### Effects of the Inerter-Based Vibration Absorbers on Wind-Induced Displacements
For example, for wind direction of 90\({}^{\circ}\) the TID has shown the best vibration mitigation effect among the three vibration absorbers: the \(F_{uu}\) is equal to 38.02% for the TID, 34.01% for the TMD and 33.74% for the TMDI. The worst vibration-absorbing effect of TID takes place for 330\({}^{\circ}\) wind direction, where the \(F_{uu}\) with TID is only 11.94% and \(F_{uu}\) with TMD and TMDI are only 10.56% and 10.52%, respectively. The two inerter-based vibration absorbers share a similar variation (vary synchronously) against wind excitations at different directions as it can be observed from the curvilinear shapes of both TMDI and TID in Figure 15. This leads to the result that the 1 st and the 3 rd, the 2 nd and the 4 th rows are identical. Generally, structural engineers are mainly concerned about the maximum absolute value of extreme displacement. For example, in this study, the maximum positive extreme top-floor displacement of the original structure is 0.23 m corresponding to 90\({}^{\circ}\) wind direction. The positive extreme displacement decreases from 0.23 m to around 0.14 m when the TID is installed on the original structure. The negative extreme top-floor displacement occurs for 75\({}^{\circ}\) wind direction. The displacement drops from \(-\)0.21 m to \(-\)0.14 m when the TID is used to mitigate the wind-induced vibration of the primary structure. The corresponding factor of vibration-absorbing is 34.07%.
## 2 Method
### Optimization Framework
#### 2.3.1 Image Recovery and Regularization
Then the loss function for our reconstruction algorithm is constructed of the form  
\[\text{argmin}\ E(z)=\frac{1}{2m}\sum_{j=1}^{m}\left(\left|A^{j}z\right|-y^{j} \right)^{2}+\frac{\lambda}{2}z^{H}[z-D(z)] \tag{12}\]  
The first term following the form of Equation (11) is the data fidelity term, which encourages the estimated Fourier modulus \(\left|A^{j}z\right|\) to match the measured data \(y^{j}\). The second term \(\frac{\lambda}{2}z^{H}[z-D(z)]\) is the RED term defined in the research [31]. Here, \(z^{H}\) is the transposed conjugate matrix of the recovered object z, \(\lambda\) is the regularization parameter, and \(D(z)\) is the denoising engine that can incorporate an arbitrary filter to plug in the regular term. The RED regularizer not only penalizes residual difference between the reconstruction data and its denoised self but also serves to prevent \(D(z)\) from removing the structure from \(z\). It is clear that if the denoised engine removes structure from the recovered data, this structure will show up in the residual difference [35]. The gradient of the data fidelity term is easily computable, and the gradient of the RED engine is described in previous research [34]. Then, the gradient of the loss function is given in the form  
\[\
abla_{z}E(z)=\frac{1}{m}\sum_{j=1}^{m}\left(A^{j}z-y^{j}\frac{A^{j}z}{\left|A ^{j}z\right|}\right)\left(A^{j}\right)^{H}+\lambda[z-D(z)] \tag{13}\]  
where \(\frac{A^{j}z}{\left|A^{j}z\right|}\) is the sign function for nonzero arguments (\(\frac{A^{j}z}{\left|A^{j}z\right|}=0\), which is adopted if \(A^{j}z=0\)), and \(\left(A^{j}\right)^{H}\) is the transposed conjugate matrix of \(A^{j}\). The update formula for the loss function is described as  
\[z_{t+1}=z_{t}-\mu\
abla_{z}E(z)\Big{|}_{zt}=z_{t}-\mu\left\{\frac{1}{m}\sum_{j =1}m\left(A^{j}z-y^{j}\frac{A^{j}z}{\left|A^{j}z\right|}\right)\left(A^{j} \right)^{H}+\lambda[z-D(z)]\right\}\Bigg{|}_{zt} \tag{14}\]  
where \(t\) is the iterate count, and \(\mu\) is the step size. The imaging reconstruction framework is summarized in Algorithm 1. ```
Input: Estimated Fourier magnitudes \(y^{j}=\left\{\phi^{j}\right\}_{j=1}^{m}\); sampling matrix \(\left\{A^{j}\right\}_{j=1}^{m}\). Output: Recovered image \(z\). 1 Parameters: Maximum number of iterations \(\mathbb{T}\); step size \(\mu\); Regularization parameter \(\lambda\). 2 Initialization: (1)  Run HIO algorithm 50 times with random initialization to form 50 estimates of the signal: \(\hat{z}_{1},z_{2}\ldots z_{50}\). (2)  Use the estimates \(\left\{z_{1}\right\}_{i=1}^{50}\) with the lowest residual \(\frac{1}{2m}\sum_{j=1}^{m}\left(\left|A^{j}\hat{z}_{i}\right|-y^{j}\right)^{2}\) as an initialization for HIO. (3)  Run HIO 50 times again, and the result is \(z_{0}\). 3 Loop:for\(t=0\) to \(T-1\) \[z_{t+1}=z_{t}-\mu\left\{\frac{1}{m}\sum_{j=1}^{m}\left(A^{j}z-y^{j}\frac{A^{j} z}{\left|A^{j}z\right|}\right)\left(A^{j}\right)^{H}+\lambda[z-D(z)]\right\} \Bigg{|}_{z_{t}},\text{ all }1\leq j\leq m.t:=t+1;\] end
```  
**Algorithm 1**The reconstruction framework for ptychographic imaging correlography.
## 3 Transformer Tank Cover Disassembly Algorithm
### Disassembly of Screw-Fastened Cover Plate
#### 3.3.4 Initialization of Embedded Square-Nut Template Matching Query  
To efficiently detect nuts on transformer covers, particularly when encountering various nut types, the Query is initialized with an embedded square-edge nut template match, which ensures that Query accurately captures the geometric features of the nut and adapts to variations in the size and shape of the nut.  
1. Predefined Orthogonal Nut Templates: Templates for positive N-sides define the geometry and dimensions of the nut, allowing for the flexible adaptation to various nut types. The vertices of the template can be calculated using the following formula: \[(x_{i},y_{i})=\left(x_{c}+l\cdot\cos(\frac{2\pi i}{N}),y_{c}+l\cdot\sin(\frac{ 2\pi i}{N})\right),\ i=1,2,\dots,N\] (12) where \((x_{i},y_{i})\) is the center coordinates of the nut formwork, \(l\) is the side length of the template defining the size of the nut, and \(N\) is the number of sides of the square polygon, allowing the model to adapt to various nut designs [34].
2. Matching Templates and Candidate Boxes: After generating the candidate box, each Query is initialized using the embedded nut template. Given a detected nut region represented by the candidate box \(B_{i}=(x_{i},y_{i},w_{i},h_{i})\), the initialization process of the Query is as follows:Adjust the side length of the template by the size of the candidate box \(l_{\text{match}}\) that makes the template fit the actual size of the nut: \[l_{\text{match}}=\frac{w_{i}+h_{i}}{2}.\] (13) Generate matched nut template vertices: \[T_{\text{match}}^{N}=\bigg{\{}x_{i}+l_{\text{match}}\cdot\cos(\frac{2\pi j}{N} ),y_{i}+l_{\text{match}}\cdot\sin(\frac{2\pi j}{N})\bigg{\}}_{j=1}^{N}.\] (14) This formula matches the center coordinates of the candidate box to the vertices of the positive N-side template, thus providing a geometric basis for the subsequent Query [35; 36]. (3) Geometry initialization of Query: The matched positive N-sided nut templates are used as the initial Query for subsequent target detection tasks. The geometry of each Query after initialization is as follows: \[Q_{i}^{\text{init}}=T_{\text{match}}^{N}.\] (15) This Query contains the geometric information of the nut, serving as an effective input for the subsequent multi-head self-attention mechanism.