esa-sceva/satcom-chunk-collection huggingface dataset:
# An Energy-Efficient Two-Stage Cooperative Routing Scheme in Wireless Multi-Hop Networks
## Appendix A Cooperative Beamforming Model  
Our cooperative transmission model in the second stage assumes \(n\) transmitters \(\mathcal{T}=\{t_{1},\cdots,t_{n}\}\) and a single receiver \(j\). In this sense, the model is similar to the MISO case. The following analysis has been inspired by [19].  
The transmitters in \(\mathcal{T}\) beamform the packet to the receiver and \(\mathbf{w}=\{w_{1},w_{n},\cdots,w_{n}\}\) denotes the cooperative beamforming vector. The link cost of the cooperative transmission is designed as the total transmit power weighted by the normalized residual energy: \(\mathcal{C}_{\mathcal{T},j}=\sum\limits_{i=1}^{n}\left(\frac{E_{\text{ini}}}{ E_{i}}\right)^{x}\left|w_{i}\right|^{2}\). The problem of cooperative link cost minimization subject to that node \(j\) can successfully receive the packet can be expressed as  
\[\begin{array}{ll}\min&\mathcal{C}_{\mathcal{T},j}\\ s.t.&\frac{\left|\sum\
olimits_{i=1}^{n}h_{ij}w_{i}\right|^{2}}{\sigma^{2}} \geq\gamma_{\text{min}}.\end{array} \tag{1}\]  
Using the Lagrange multiplier method we have  
\[L\left(w_{1},\cdots,w_{n},\lambda\right)=\sum\limits_{i=1}^{n}\left(\frac{E_{ \text{ini}}}{E_{i}}\right)^{x}\left|w_{i}\right|^{2}-\lambda\left(\frac{ \left|\sum\
olimits_{i=1}^{n}h_{ij}w_{i}\right|^{2}}{\sigma^{2}}-\gamma_{ \text{min}}\right). \tag{2}\]  
Take the partial derivatives for \(w_{i},i=\{1,\cdots,n\}\) and \(\lambda\), respectively, the following \(n+1\) equations can be obtained.  
\[\frac{\partial L}{\partial w_{i}}{=}2\bigg{(}\frac{E_{\text{ini}}}{E_{i}} \bigg{)}^{x}w_{i}-2\frac{\lambda h_{ij}\left|\sum\
olimits_{i=1}^{n}h_{ij}w _{i}\right|}{\sigma^{2}}{=}0,\quad i=\{1,\cdots,n\}\,, \tag{3}\]  
and  
\[\frac{\partial L}{\partial\lambda}{=}\frac{\left|\sum\
olimits_{i=1}^{n}h_{ ij}w_{i}\right|^{2}}{\sigma^{2}}-\gamma_{\text{min}}=0. \tag{4}\]  
Combine Equations (3) and (4), the solutions of \(\lambda\) can be solved as  
\[\hat{\lambda}=\frac{\sigma^{2}}{\sum\
olimits_{i=1}^{n}\left|h_{ij}\right|^{ 2}\left(\frac{E_{i}}{E_{\text{ini}}}\right)^{x}}, \tag{5}\]  
Substitute \(\hat{\lambda}\) into Equation (4), \(w_{i}\) can be solved  
\[\hat{w}_{i}=\frac{(E_{i})^{x}h_{ij}^{*}\sqrt{\sigma^{2}\cdot\gamma_{\text{min }}}}{\sum\
olimits_{t=1}^{n}\left(E_{t}\right)^{x}\left|h_{t_{j}}\right|^{2}}, i=\{1,\cdots,n\}. \tag{6}\]  
where \(\left(\cdot\right)^{*}\) stands for the complex conjugate operation.
# Inhomogeneous Illumination Image Enhancement Under Extremely Low Visibility Condition
## 4 Conclusions  
This study introduces a novel algorithm designed to substantially enhance image contrast under conditions of extremely low visibility. The algorithm strategically eliminates redundant low-frequency information through SDIF before proceeding to image enhancement. The filter parameter is adjudicated based on the structural similarity between the post-filtered and original images, with images deemed devoid of valuable information upon a significant shift in similarity rates. Post-filtering, images may exhibit a grayscale  
Figure 6: Comparisons of different image enhancement algorithms in the outdoor experiment. (**a**–**f**) are in different scenes. From top to bottom are, respectively, the original images, CLAHE, SSR, AMSR, and ours.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **CLAHE** & **SSR** & **AMSR** & **Ours** \\ \hline Time (ms) & 75 & 50 & 335 & 333 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Qualitative comparisons of average computational time using CPU Intel Core i7 11700H in all experiments.  
banding effect, which is effectively mitigated through a deliberate disturbance introduction, thus ensuring an improved visual presentation. The algorithm concludes with a comprehensive global enhancement of the image, an amplification of the interested high-frequency components, and a recombination of low-frequency components. Through rigorous indoor and outdoor experimental validation, the algorithm demonstrates a pronounced capability to enhance image contrast significantly, thereby maximizing the visibility of objects under severely limited visibility conditions. Our results suggest potential applications in areas such as low visibility imaging, medical imaging, and underwater imaging. By integrating this algorithm, it is possible to significantly enhance object detection capabilities in adverse weather conditions.  
Conceptualization, L.C.; methodology, L.C.; software, L.C. and J.L.; validation, L.C. and J.L.; formal analysis, L.C.; investigation, L.C. and Q.B.; resources, Y.L. and J.Z.; data curation, L.C. and J.L.; writing--original draft preparation, L.C.; writing--review and editing, L.C., J.L., Q.B., Y.L. and J.Z.; visualization, L.C.; supervision, Y.L.; project administration, Y.L.; funding acquisition, Y.L. and J.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Guangdong Major Project of Basic and Applied Basic Research (2020B0301030009); National Natural Science Foundation of China (61991452); and the National Key Research and Development Program of China (2022 YFA140430402).  
Not applicable.  
The original data presented in this study are openly available in Chen-Lubbon/Image-enhencement-code (github.com), accessed on 7 September 2024.  
The authors declare no conflicts of interest.
# Algorithm for Fatigue Crack Initiation Assessment Based on Industrial Photogrammetry
## 2 Materials and Methods
### Algorithm Validation  
In the work of [PERSON] [11], the assessment of crack initiation is determined using data from the hydraulic press. The datasets from the press itself are high-frequency (17 Hz) compared to the data obtained using the industrial photogrammetry approach (0.1 Hz). The data of the press correspond to the actual degree of deformation of the specimen in a given cycle (moment). Summarizing the fact that the data from the hydraulic press are high-frequency measurement data of the specimen, while the data from the photogrammetric measurement are of lower frequency due to the imperfection of the equipment and were obtained without contact, it can be concluded that the data from [11] can be taken as a reference. In the reference paper, the cracking moment was determined by analyzing the recorded displacements of the hydraulic press jaws and analyzing the images. Still, in this paper, the reference method of the cracking moment is chosen based on the recorded displacements of the press jaws.  
In order to determine the period of crack initiation N\({}_{\text{i}}\) and the beginning of crack propagation Np\({}_{\text{P}}\), the recorded displacements of the hydraulic press jaws were used in addition to the analysis of cyclic test imageries of the weld edge. When analyzing the recorded displacements, three different phases became apparent (see Figure 4). In phase I, a slight increase in displacements can be observed, followed by stabilization. After a certain time, in phase II, the displacements stabilize with a very slight increase. Finally, in phase III, the displacements progressively increase until failure occurs. Phases I and II represent the initiation period, while phase III refers to the propagation of the technical crack.  
To refine the distinction between phases I and II and phase III, [PERSON] [11] assumed that phase I is defined as the initial 20% of the total fatigue life N\({}_{\text{F}}\), during which the values of jaw displacements are disregarded. Following this, the arithmetic mean of displacements over the subsequent 20% of N\({}_{\text{F}}\) is computed, yielding the value \(\delta_{\text{av}}\)[11]. Additionally, the instantaneous displacement \(\delta_{0,4\text{NF}}\) at N = 0.4 \(\times\) N\({}_{\text{F}}\) cycles is determined. The ratio RA = \(\delta_{\text{av}}/\delta_{0,4\text{NF}}\) is then calculated [11]. By monitoring deviations from this ratio by 1.5%,  
Figure 4: Phases in the displacement record of the jaws.  
the cycle where R = \(\delta/\delta_{0.4\text{NF}}\) diverges signifies the transition from phase II to phase III, which marks the commencement of the crack initiation period N\({}_{\text{i}}\) and the crack propagation period N\({}_{\text{P}}\)[11].
## 4 Experimental Results
### Computational Results  
In this study, the metaheuristics were implemented using the Python programming language [33] under PyCharm IDE [34] version 2020.3.1 with Windows 10 as the operating system. The programs ran on an Intel(r) CoreTM i5-4690 CPU 3.50 GHz platform with 32 GB of RAM. The performances of the algorithms were evaluated using the dataset derived from a real case study of postman delivery in association with the Chiang Rai post office for a period of 50 days. Table 3 shows the VRP solutions (total travel distance) obtained from the proposed metaheuristics, which include the best, average and standard deviation of the total distance from 10 replicated runs for each instance. The average time duration to run one optimization cycle for each instance is also reported. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Parameter Value** \\ \hline Population Size & 50 \\ Iterations & 50 \\ Scale Factor (\(F\)) & Random between 1.5 and 2.5 \\ Crossover rate (\(Cr\)) & Linearly Increasing from 0.2 to 0.8 \\ Crossover Type & Binomial Crossover \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The setting of DE parameters. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Instance**} & **Current** & \multicolumn{2}{c}{**PSO**} & \multicolumn{2}{c}{**Time**} & \multicolumn{2}{c}{**DE**} & \multicolumn{2}{c}{**Time**} \\ \cline{3-10}  & **Practice** & **Best** & **Average** & **SD** & **(s)** & **Best** & **Average** & **SD** & **(s)** \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results of total distance. To further analyze the effectiveness of the metaheuristics for solving the postman delivery routing problem, the percentage deviation (\(PD\)) between solutions obtained from the metaheuristics and those from the current practices were calculated using Equation (10). The results for the \(PD\) are shown in Table 4:  
\[PD=((Sol_{current\_practice}-Sol_{heu})/Sol_{current\_practice})\times 100, \tag{10}\]  
where \(Sol_{current\_practice}\) is the solution obtained from the current practice and \(Sol_{heu}\) is the solution obtained from a metaheuristic. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Instance**} & \multirow{2}{*}{**Current Practice**} & \multicolumn{2}{c}{**PSO**} & \multicolumn{2}{c}{**DE**} \\ \cline{3-6}  & & **Best** & _PD_ & **Best** & _PD_ \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results of the percentage deviation (\(PD\)).
## 2 Assimilation Method and Experiment Configuration
### Construction of Four-Dimension Ensemble Samples  
The satellite observations are assimilated with a \(\pm 3\)-h assimilation window. Taking 0000 UTC 30 June 2017 as the analysis time, for example, the assimilation window for the satellite is from 2100 UTC  
Figure 1: The components of the Proper Orthogonal Decomposition-based ensemble four-dimensional variational (POD-4 DenVar) system. See text for details.  
29 June to 0300 UTC 30 June 2017. All observations and ensemble samples are processed and grouped in 7 sub-windows (slot 1 through slot 7), shown in Figure 2. The construction of ensemble samples is shown in Figure 3. Firstly, two predictions (R1 and R2) are made from different sets of initial time (before 6 and 12 h) to 0600 UTC 29 June. Then the two prediction results are used as initial conditions, making two 36-h integrations from 0600 UTC 29 to 1800 UTC 30 June with model output every 1 h. According to the grouped seven sub-windows within the 6-h assimilation window, every ensemble sample consists of seven model output every 1 h. Thus, 62 ensemble samples are obtained through sliding the time point of 1-h model output.  
Since the observations of radar are obtained every 6 min, the radar assimilation window is set as 1 h from 0000 UTC 30 June to 0100 UTC 30 June 2017, with the analysis time at the beginning of the time window. All observations and ensemble samples are processed and grouped in 11 sub-windows. Like a satellite, the construction of radar ensemble samples is as follows. Firstly, two predictions (r1 and r2) are made from different initial time (before 8 and 14 h) to 2000 UTC 29 June 2017. After that, r1 and r2 results are used as initial conditions, making two 8-h integrations with the model output every 6 min, which corresponds to the radar scanning frequency. Each ensemble sample should consist of 11 ensemble members, since the model generates output every 6 min during the 1-h time window.  
Figure 3: The method of historical sampling for satellite data assimilation. See text for details.  
Figure 2: The sub-windows in the satellite data assimilation time window. See text for details.  
Thus, 142 ensemble samples are obtained through slide the time point from 2000 UTC 29 June to 0400 UTC 30 June 2017, which is like satellite ensemble samples.
## 2 Numerical Simulation Calculation Methods and Model Validation
### Numerical Simulation Calculation Methods
#### 2.1.3 Calculation Method of Sound Field Prediction  
The FW-H method [27] of large eddy simulation (LES) combined with acoustic analogy theory is used for noise simulation. The steady calculation results are used as the initial flow field of unsteady calculation, and the time required for impeller rotation \(1^{\circ}\) is used as the time step \(\Delta t\), namely \(\Delta t=1/(6n)\). Therefore, the time step in this study is set to 0.0001 s to obtain the flow field information in the near field. FLUENT solved the FW-H equation by calculating the time-domain integral and area integral to get the far-field noise of the fan. The FW-H equation is as follows:  
\[\frac{1}{C_{0}^{2}}\frac{\partial^{2}p^{\prime}}{\partial t^{2}}-\frac{ \partial^{2}p^{\prime}}{\partial x_{i}^{2}}=\frac{\partial^{2}}{\partial x_{i }\partial x_{j}}\big{[}T_{ij}H(f)\big{]}-\frac{\partial}{\partial x_{i}}[n_{i }p\phi(f)\Delta f]+\frac{\partial}{\partial t}[pv_{n}\delta(f)\Delta f] \tag{5}\]  
where \(C_{0}\) is the sound velocity of the undisturbed fluid, \(p^{\prime}\) is the sound pressure, \(x_{i}\) is the space fixed coordinate system, \(v_{n}\) is the normal velocity, and \(T_{ij}\) is the Lighthill stress tensor. The three terms on the right side of the equation represent the quadrupole, dipole, and monopole.
# Improved Charge Injection and Transport of Light-Emitting Diodes Based on Two-Dimensional Materials
## 2 Two-Dimensional (2D) Materials in Organic Light Emitting Diodes (OLEDs)
### Composite Layers Based on 2D Materials
#### 2.2.2 Composite HTL Based on 2D Materials  
[PERSON] reported PLEDs with ultra large GO (UL-GO) sheet (0-0.08 wt.%)/PEDOT:PSS composite HTL, in which the average lateral size of UL-GO sheets is more than 20 \(\upmu\)m, as shown in Figure 4b [52]. The improved conductivity of PEDOT:PSS:GO HTL leads to the enhanced injection and transport of holes. Moreover, it can effectively block electrons from EML to ITO because of the optimized work function. The optimal PLEDs with a PEDOT:PSS:GO (0.04 wt.%) composite HTL show a maximum CE of 725.6 cd/m\({}^{2}\), which is improved by \(\sim\)11% compared with the reference device without the doping of GO. Diker reported blue OLEDs with PEDOT:PSS:GO composite HTL [55]. All the devices with PEDOT:PSS:GO composite HTLs showed better performance compared with the reference device with the pure PEDOT:PSS HTL. The maximum CE and EQE values of the device containing PEDOT:PSS:GO (0.1 mg/mL) HTL are 0.156 cd/A and 0.102%, which are 120% and 50% higher than that of the reference, respectively. As for OLEDs with 2D materials doped HTLs, the performance of device may be enhanced from several aspects including the improved work function, effective electron blocking and reduced surface roughness of ITO.
# Algorithm for Generating S-Boxes with Prescribed Differential Properties
## 2 Methods for Generating Cryptographic S-Boxes
### Mathematical Construction  
A different approach to the construction of S-boxes is to use mathematical (or algebraic) construction of S-boxes. Mathematically constructed S-boxes are based on using a family of Boolean functions with known security properties [2], such as the Gold functions [27], the Kasami functions [28], the Bracken-Leander functions [29], Dillon's permutation [30],and others. New functions with good properties can be derived mathematically from these known constructions [31].  
One of the most commonly used families of functions is based on inverse mapping. As an example, the function \(x^{254}\) in \(GF(2^{8})\) is known to be four-differentially uniform [32] and to have high nonlinearity and algebraic degree. The S-box of the current Advanced Encryption Standard (AES) was constructed by applying an affine transformation to this function. Another way of mathematical constructing S-boxes is using cubic polynomial mapping [33].  
In [34; 35; 36; 37], the authors applied various optimizations to AES S-box generation. A key-dependent mechanism to generate S-boxes with good cryptographic properties was used in [38; 39]. A similar approach combined with other proposed optimizations was applied by the authors of [40; 41; 42; 43].  
A different approach was used in [44; 45]. The authors replaced the binary representation of S-boxes with a domain quasigroup \(G\), making it possible to find functions that are at the same time both balanced and perfectly nonlinear. Such functions have a completely flat difference distribution table.
# Emerging Trends in Research on Food Compounds and Women's Fertility: A Systematic Review
## 3 Results  
A total of 4609 studies were screened for inclusion in this systematic review. After the elimination process (Figure 1), a total of 25 qualitative studies and 4 quantitative studies were included. The studies were performed internationally and included the following countries USA (n = 20) [15; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45], Australia (n = 1) [46], New Zealand (n = 1) [46], Ireland (n = 1) [46], United Kingdom (n = 2) [46; 47], China (n = 1) [48], Denmark (n = 4) [41; 43; 49; 50], Greece (n = 1) [14], Iran (n = 2) [51; 52], Brazil (n = 2) [53; 54], Canada (n = 1) [43], Russia (n = 1) [55], Italy (n = 2) [56; 57], Spain (n = 1) [58]. The articles concerned female fertility and the intake of a Mediterranean dietary pattern (n = 2) [57; 51], fruit, vegetables and whole grain intake (n = 3) [38; 46; 53], fish (n = 1) [42], dairy (n = 3) [15; 43; 44], types of fatty acids (n = 4) [39; 40; 41], soy (n = 2) [32; 33], caffeine (n = 3) [45; 50; 54], folate and B12 (n = 4) [34; 35; 59; 60], melatonin (n = 1) [58], CoQ10 (n = 1) [48] as well as a combination of different compounds (n = 4) [37; 47; 52; 55]. The women who participated in the studies were either planning pregnancy (n = 6) [36; 40; 41; 45; 46; 55], infertile (n = 3) [49; 51; 55], undergoing or subjected to assistive reproductive technology (ART) therapy (n = 16) [14; 15; 31; 34; 35; 39; 42; 44; 50; 52; 53; 54; 57; 58]. The main results of the studies have been summarized in Table 1 (qualitative studies) and Table 2 (quantitative studies). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Sample** & **Assessment Tool** & **Results** \\ \hline \hline
**Dietary Patterns** & & There were no consistent associations between adherence to a Mediterranean diet and successful obstetrics outcomes. There was an effect of the average adherence score on a Mediterranean diet on oocyte number and clinical pregnancy in women \textgreater{}35 years old but no effect on live birth. \\ \hline
**[PERSON] et al.** & & High adherence to the healthy dietary pattern was associated with a high average number of oocytes when compared with low adherence. \\ \hline
**[PERSON] et al.** & n = 244 non-obese & FFQ & Mediterranean diet score was positively related to clinical pregnancy and live birth among women \textgreater{}35 years old but not among women \textgreater{}35 years old but not among women \textgreater{}35 years \\ \hline
**[PERSON] et al.** & & Pro-fertility dietary pattern (supplemental intake of folate, B12, low-pestic residue produce, high intake of whole grains, seafood, dairy, soy foods) has a higher likelihood of live birth. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Qualitative studies on bioactive food components and women’s fertility included in the review process. Figure 1: Preferred reporting items for systematic reviews (PRISMA) Study selection process diagram.
# Improved Remote Sensing Image Classification Based on Multi-Scale Feature Fusion
## 2 Datasets
### The Aerial Image Labeling Dataset  
The aerial image labeling dataset was downloaded from [[https://project.inria.fr/aerialimagelabeling/](https://project.inria.fr/aerialimagelabeling/)]([https://project.inria.fr/aerialimagelabeling/](https://project.inria.fr/aerialimagelabeling/)) [58]. The images cover dissimilar urban settlements, ranging from densely populated areas (e.g., the financial district of San Francisco) to alpine towns (e.g., Lienz in the Austrian Tyrol). The dataset features were as follows:  
Coverage of 810 km2  
Aerial orthorectified color imagery with a spatial resolution of 0.3 m  
Ground truth data for two semantic classes: building and no building  
The original aerial image labeling dataset contained 180 color image tiles, \(5000\times 5000\) pixels in size, which we cropped into small image patches, each with a size of \(500\times 500\) pixels. An image-label pair example is provided in Figure 4.  
Figure 4: Example of image-label pair: (**a**) original image and (**b**) classified image.  
Figure 3: Example of land-use classification: (**a**) original image and (**b**) classified image.
## 5 Simulation Results and Discussion
### Energy Consumption  
The wireless sensor nodes are usually powered by batteries with limited capacity that cannot be easily replaced or recharged promptly. Therefore, effectively reducing energy consumption is crucial for ensuring efficient resource utilization and achieving a sustainable network. Enhancing the system's sustainability can effectively reduce the frequency and cost of maintenance for networks requiring long-term detection [41]. Figure 9 compares our proposed protocol's energy consumption with four other latest protocols across four scenarios.  
Frequent updates of CHs will increase network energy consumption. Our protocol adds the process of candidate CHs to ensure that the selected CHs are higher than the average remaining energy of existing nodes, prolonging the survival time of CHs and minimizing energy consumption. The FRNSEER and OAFS-IMFO protocols do not consider the degree of cluster compactness, which may cause an uneven distribution of clusters. FC-RBAT used fuzzy C-means for clustering, and its accuracy in selecting reasonable CHs is significantly lower than that of FQA. The parameters considered by the BOA-ACO protocol are more comprehensive, thereby increasing the complexity of calculations. Under identical conditions, our protocol can achieve superior results in terms of clustering and routing.  
Figure 8: Network lifetime comparison in different scenarios: (**a**) scenario 1; (**b**) scenario 2; (**c**) scenario 3; (**d**) scenario 4.  
In the experiment, FQA consumes half of the energy in 880, 752, 518, 503 rounds, which is 43.79%, 96.86%, 58.41%, 93.46% lower than OAFS-IMFO; 37.72%, 49.80%, 49.28%, 40.50% lower than FC-RBAT; 31.54%, 47.75%, 7.25%, 22.68% lower than FRNSEER; and 1.49%, 20.51%, 5.715, 10.55% lower than the BOA-ACO protocol in the four scenarios. FQA has more evident advantages in saving energy.