esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 PSA in a DOF
### Analytical Model of the PSA: Single Pump Configuration
However, we can find that phase-matching can be achieved both in regime of anomalous average dispersion (\(\lambda_{p}>\lambda_{\text{zero}}\)) and in regime of normal average dispersion (\(\lambda_{p}<\lambda_{\text{zero}}\)). The sign of the right side of Equation (26) depends on the impact of the high-order dispersion terms. The left side of Equation (26) can be positive or negative, as the integer \(q\) can take on both positive and negative values. As a result, the phase-matching (26) can be satisfied with the appropriate sign of \(q\). To demonstrate the formation of the gain sidebands in regime of the normal and anomalous average dispersion we calculate the gain of the signal wave \(G_{S}\) (Figure 4) as  
\[\xi(z)=\sum_{k=1}^{p}\frac{2\Omega_{ps}^{2k}}{(2k)!}\int_{0}^{z}\beta_{2k}(z^{ \prime})dz^{\prime}, \tag{10}\]8  
where \(L=1.4\) km is the fiber length, \(A_{-1}(L)\) is calculated numerically from Equations (7) and (8). We use the Taylor expansion of \(\beta(\omega)\) up to the fourth order. The modulation period is \(z_{m}=40\) m, and the effective nonlinearity is \(\gamma=9.44\) (W km)\({}^{-1}\). The attenuation coefficient is assumed \(\alpha=0.092\) km\({}^{-1}\), which corresponds to \(0.4\) dB/km. Figure 4a shows the parametric gain in the regime of the anomalous average dispersion. To provide consistency between the analytical results and numerical simulations, \(q\) must be assigned negative values. For the description of the gain in the normal average dispersion (Figure 4b) we have to use in the analytical model the positive integer \(q\). The analytical three-wave model agrees well with the numerical solution of the full system given by Equations (7) and (8). The model accurately describes the line shapes of the gain. Figure 4c shows the gain in the fifth sideband, \(q=5\). The analytical model do not consider the pump depletion. Due to this feature, the parametric gain \(G_{q}\) is overestimated (Figure 4c).
## 2 Materials and Methods  
### Treatment Plans Data Set  
Planning data from five patients who had previously been treated with EBRT followed by cervical HDR brachytherapy with a Tandem-and-Ring (TR) applicator set was used as a representative sample in this retrospective study. Each patient who underwent brachytherapy procedures was placed in a supine position with a CT/MRI applicator clamp attached to a base plate, immobilized and transported during imaging and treatment. All patients received the EBRT dose of 50.40 Gray (Gy) in 28 fractions with a consecutive brachytherapy boost dose of 25 to 30 Gy in 5-6 fractions. For the first fraction of a brachytherapy patient's treatment, an MRI- or CT-based treatment plan was generated and delivered, and then for subsequent fractions (2-5 or 2-6). The mobile C-arm X-ray imaging unit (WHA-50N, Shimadzu, Japan) was used only for registration and applicator position verification prior to each fraction of treatment. It should be noted that the initial brachytherapy plan (made from the first fraction) was reused for subsequent fractions of treatment. This manually optimized \"nominal plan\" (also called \"initial plan\") was generated from the first fraction image and compared with that of a robust optimized treatment plan taking into account applicator displacement assumptions. To incorporate dose delivery uncertainty by potential applicator displacements, the translation of the applicator displacement set up 1 mm steps in the upper and lower bound of 5 mm in the \(x,y\) and \(z\) directions based on assumptions in the pre-study reports: the MRI-based 3D treatment plan for each fraction using only one MRI treatment, interfractional tandem displacement between multiple insertions [26], interapplication variation of doses and spatial location of the OAR [27] and several reports by other groups [20; 21; 22]. Then, we applied our robust optimization method to anatomical structures and applicator models delineated from the first fraction image to generate robust treatment plans for all treatment fractions.
# A Low Complexity Persistent Reconnaissance Algorithm for FANET
## 4 Results
### The Simulation Trajectory  
Figures 5-7 are the trajectory diagrams of each UAV in the continuous reconnaissance process using TA&CRFO. Only a period of FANET trajectory is shown to facilitate readers' identification. Since the whole network is in 3D space, a multi-angle display is necessary to clearly show the changes in the FANET in the continuous reconnaissance process. Thus, Figures 5-7 are presented as the process's top, left, and front views. Inside these pictures, the colors and shapes of the markings represent different types of UAVs. The lines in different colors represent the moving track of each relay UAV during this time period. Through those figures, it can be observed that the newly formed FANET can play a better relay role in monitoring UAVs at (900, 100, and 100) m and (900, 900, and 100) m, and keep away from positions in high-threat areas of the moment.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \hline \begin{tabular}{c} Scenario \\ Horizontal dimensions \\ Vertical dimension \\ Number of RUs \\ RUs’ speed \\ \end{tabular} & \begin{tabular}{c} 1000 \(\times\) 1000 m \\ [100–200] m \\ [7–19] \\ [0–15] m/s \\ \end{tabular} \\ \hline \hline \begin{tabular}{c} Distances \\ Minimum safe distance, \(r^{S}\) \\ Maximum link distance, \(r^{C}\) \\ UAV’s threat perception radius, \(r_{thr}\) \\ \end{tabular} & \begin{tabular}{c} 20 m \\ 200 m \\ 25 m \\ \end{tabular} \\ \hline \hline \begin{tabular}{c} PSO-related \\ Number of particles, \(N_{p}\) \\ Number of iterations \\ Inertia weight, \(w\) \\ Cognitive parameter, \(c_{1}\) \\ Social parameter, \(c_{1}\) \\ \end{tabular} & \begin{tabular}{c} 50 \\ 400 \\ 0.7192 \\ 1.4472 \\ 1.4472 \\ 1.4472 \\ \end{tabular} \\ \hline \hline \
\begin{tabular}{c} KKT and FANET-related \\ \(\xi\), \(\zeta_{m}\) \\ Connectivity weight, \(w^{C}\) \\ Weight of FANET threat metric, \(w^{T}\) \\ Weight, of edit operations, \(w_{1},w_{2},w_{3},w_{4}\) \\ TED’s threshold parameter, \(\lambda\) \\ \end{tabular} &
\begin{tabular}{c} 0.5 \\ 0.5 \\ 0.5 \\ 2.5 \\ 2, 2, 0.3, 40 \\ 5.23 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Setting of simulation parameters.  
Figure 4: UAVs’ topology using TA&CRFO algorithm at \(t_{i}\).  
Figure 5: Trajectory of the FANET in a certain time period (Top view).  
Figure 6: Trajectory of the FANET in a certain time period (Left view).
# Thermal Management Systems for Civil Aircraft Engines: Review, Challenges and Exploring the Future
###### Abstract  
This paper examines and analytically reviews the thermal management systems proposed over the past six decades for gas turbine civil aero engines. The objective is to establish the evident system shortcomings and to identify the remaining research questions that need to be addressed to enable this important technology to be adopted by next generation of aero engines with complicated designs. Future gas turbine aero engines will be more efficient, compact and will have more electric parts. As a result, more heat will be generated by the different electrical components and avionics. Consequently, alternative methods should be used to dissipate this extra heat as the current thermal management systems are already working on their limits. For this purpose, different structures and ideas in this field are stated in terms of considering engines architecture, the improved engine efficiency, the reduced emission level and the improved fuel economy. This is followed by a historical coverage of the proposed concepts dating back to 1958. Possible thermal management systems development concepts are then classified into four distinct classes: classic, centralized, revolutionary and cost-effective; and critically reviewed from challenges and implementation considerations points of view. Based on this analysis, the potential solutions for dealing with future challenges are proposed including combination of centralized and revolutionary developments and combination of classic and cost-effective developments. The effectiveness of the proposed solutions is also discussed with a complexity-impact correlation analysis.  
Keywords:thermal management systems; next generation of gas turbine engines; aero engines; engine efficiency; performance enhancement +
Footnote †: journal: Applied Sciences
## 1 Introduction
Several researchers developed cryptographic algorithms based on ECs, such as the random numbers generator [26; 27], the substitution box generator [28; 29; 30], image encryption [2; 31; 32], and text encryption [33]. [PERSON] et al. [33] presented a text encryption algorithm, where the Pell sequence and elliptic curves serve as the foundation for the proposed approach. Initially, the plaintext is diffused by shifting the symbols in a cyclic manner, making the resulting text meaningless. In the second step, the diffused plaintext is concealed from potential attackers through the use of the Pell sequence, a weight function, and a binary sequence to translate each element of the diffused plaintext into real numbers. The third step involves generating permutations over elliptic curves to further obscure the encoded, diffused plaintext. The focus of our research is to investigate how elliptic curves can be incorporated into the max-plus algebra-based wavelet transform. The aim is to improve the performance of the transform by utilizing the mathematical properties of elliptic curves. For our purpose, we design a novel text encryption scheme based on ECs and MP wavelets. The proposed text encryption scheme involves three primary phases. The initial phase uses ASCII characters for encoding the plaintext, which enables us to represent the data in a standardized way. Additionally, we introduce diffusion in the representation, which adds randomness to the data and makes it more difficult to anticipate the pattern of the data. In the second stage, we generate points on an elliptic curve, followed by applying a mapping approach to enhance the level of randomness into the data, thereby increasing the security of the encryption process. This stage incorporates an extra layer of complexity that further increases the difficulty for a potential attacker to decrypt the encoded information. In the third and final phase, we break down the output using MP wavelets. This procedure involves breaking down the information into smaller portions and employing a specific mathematical method to generate encrypted text. This final stage yields a secure result that can be safely transmitted or stored without any concern of unauthorized access. Moreover, we conduct a thorough security analysis of our scheme and compare it to various state-of-the-art schemes. This paper is structured as follows: Section 2 includes some introductory material. In Section 3, we introduce a new scheme for encrypting text. Section 4 contains a comprehensive evaluation of the security of our proposed scheme, along with a thorough comparison with existing text encryption schemes [10; 11; 12; 13; 14; 15; 16; 17; 18]. Finally, in Section 5, we provide concluding remarks.
# An Analysis of Mechanical Properties for Ultrasonically Welded Multiple C1220-Al1050 Layers
## 3 Results of the Experiment
### Evaluation of Mechanical Joint Performance
#### 3.1.1 Evaluation of Mechanical Joint Performance  
When the multi-layer ultrasonic welds are simultaneously tensioned, the fracture type can occur in five different shapes, as shown in Figure 8. Figure 8a is a case in which interface fracturing occurred in all of the welds. In case b, the break occurs at the end of the indentation, and then the interface fracture occurs in all of the welds. In case c, an indentation fracture occurs in weld zone 1 and an interface fracture occurs in weld zone 2. In addition, a fracture occurs in the aluminum base metal between the copper plates. In such a case, the shear strength is similar to the tensile strength of the aluminum base material. In case d, the primary fracture occurs in the indentation of the upper aluminum, and both interface and aluminum fractures coexist. In cases c and d, fracture types of aluminum--indentation fracture, interface fracture, and aluminum base material fracture--exist in the same way, but the location of the interface fracture is different. In case e, only two fracture types are shown. In this case, the second aluminum sheet is fractured in a state where the aluminum indentation fracture type of weld zone 1, and weld zone 2 and 3 are bonded together. However, when the welds were tensioned separately, a total of four types of fracture were observed, similar to the case of simultaneous tensioning. In Figure 9a, interface fractures occurred in all welds, and in case b, the aluminum base material fracture occurred in Al1-Cu1. Subsequently, interface fractures occurred in all of the other welds. In case c, aluminum indentation fracture occurred in All-Cu1, and aluminum base material fracture occurred in the Cu1-Al2 weld. In case d, aluminum fracture occurred because of indentation in the Al1-Cu1 weld, and interface fracture occurred in weld zones 2 and 3. Observing the welds separately, aluminum base material fractures, aluminum indentation fractures, and interface fractures were all observed in weld zone 1, and aluminum base material fractures and interface fractures occurred in weld zone 2. For weld zone 3, no type of fracture other than interface fracturing was observed.  
Figure 8: Five kinds of fracture types in a one-step shear strength test. (**a**) Interface fracture at all weld zones, (**b**) Combination fracture of Al indentation and interface, (**c,d**) Combination fracture of Al indentation, interface and Al base metal, (**e**) Combination fracture of Al indentation and Al base metal.
# Anti-Hyperglycemic Activity of Major Compounds from _Calea terifolia_
## 2 Results and Discussion
### Oral Sucrose Tolerance Test of Compounds **1**, **4**, **5** and Essential Oil  
In a previous work, we demonstrated that compounds **1**, **4** and **5** inhibited the activity of \(\alpha\)-glucosidase [11; 12]. Since \(\alpha\)-glucosidase inhibitors are anti-diabetic agents that improve postprandial hyperglycemia, an OSTT was performed in normoglycemic and hyperglycemic (nicotinamide/streptomycinZ), 40/100 mg/kg) mice; these experiments would provide  
Figure 2: ORTEP drawing of the crystal structure of calein C (**5**).  
additional evidences of the \(\alpha\)-glucosidase inhibitory properties in vivo of these compounds [27]. Caleins A (**4**) and C (**5**) (3.16-10 mg/kg) attenuated the postprandial peak (\(p<0.05\)) in both cases (Figure S1, Tables S6 and S7, Supplementary Materials; Figure 3). The effect was more evident in normal mice, which could be due to the metabolic imbalances and pathological changes that occurred in a hyperglyic state [28; 29; 30]. Since compounds **4** and **5** exerted similar effects, the nature of the ester moiety at C-8 had no impact in their in vivo action. Likewise, chromene **1** provoked a significant decrease of the postprandial peak at all doses administered (5.6, 10 and 31.6 mg/kg of BW) in both types of animals (Figure 3; Table S8, Supplementary Materials). These results are important since \(\alpha\)-glucosidase inhibitors prevent postprandial hypersecretion of insulin and reactive hypoglycemia.  
The essential oil (31.6, 100 and 316 mg/kg of BW) also produced an important decrement of the postprandial peak in both normoglycemic (Figure S1, Table S9, Supplementary Materials) and hyperglyic mice (Figure 3, Table S9, Supplementary Materials) after the sucrose challenge at all doses tested. It is important to point out that the NA-STZ model was selected because it portrays a similar biochemical blood profile and pathogenesis to type 2 diabetes mellitus in humans such as hyperglycemia, glucose intolerance, insulin resistance, dyslipidemia, reduced pancreatic insulin secretion; in addition this model has been validated using anti-type 2 diabetes mellitus drugs [31]. It should be noted that delay absorption of glucose could also involve a change in incretin secretion, an incretin like effect which augments insulin secretion following oral administration of glucose or a  
Figure 3: Effect of (**a**) callein A (**4**); (**b**) callein C (**5**); (**c**) chromene **1** and (**d**) essential oil in NA-STZ (40/100 mg/kg) hyperglyic mice, after a normal sucrose load (3 g/kg). * \(p<0.05\) significantly different ANOVA followed by [PERSON]’s t test for comparison with respect to vehicle.  
stimulation of insulin signaling pathways in peripheral tissues [32]. Further work will be necessary to answer these questions.
## 4 Results and Discussion
### Results for Non-IID Data Studies

Table 5: Publication venues with more than one selected study on providing communication efficiency in federated learning. In FL, clients train their local model by applying one of the learning techniques. The selected studies used different learning models to examine their proposed approach, with some studies using more than one learning model. Figure 9 shows the selected studies' top ten most used learning models. The results indicate that the convolutional neural network (CNN) is the most widely used model. However, the different studies utilize different numbers of layers when constructing the network; other studies utilize deeper networks such as ResNet and VGG when evaluating their work. Studies focusing on supervised learning methods such as FL often employ this approach. The selected studies used different datasets to examine their proposed approach, with some studies utilizing more than one dataset. Figure 10 shows the percentage of the top ten most commonly used datasets in the 93 selected studies. The figure indicates that the Cifar-10 dataset is the most widely used; also, we can see that most of the studies utilize an image dataset. In Appendix A Table A3, we map the topmost commonly used learning models with the respective datasets utilized when employing these models. We demonstrate only the datasets that are used in more than one study. As we can see, Cifar-10 is widely used among the different local models, as Cifar-10 was proven to be the most used dataset in non-IID studies, as shown in Figures 10 and 11. In Figure 11, the greener color indicates that more studies utilized the dataset with their respective learning models, while the red indicates that the dataset was not utilized with their respective learning models. Figure 8: Percentage of the top ten techniques commonly used to solve the non-IID data problem. Figure 7: Non-IID types simulated in the non-IID selected studies.
# Sequential and Comprehensive Algorithm for Fault Detection in Semiconductor Sensors
## 2 Materials and Methods
### Regression Analysis  
The relational inspection was used for identifying a relationship between two sensors. Since all the physical quantities were not independent and related to other physical quantities, the relational inspection should be considered. The linear and exponential relationships between two sensor data showed that the two data were interrelated under any physical or chemical laws. The regression analysis was useful for investigating such a relationship by finding an equation, known as a regression equation, that could elucidate the correlation between the two sensors. Based on the regression equation, we could detect the semiconductor faults by establishing a tolerance for this relation. For the training SVR, we generated some artificial outliers to represent the defect in the semiconductor manufacturing. The SVR was trained for each pair of sensors to detect the artificial outlier as a fault. An upper bound on the fraction of the training errors and a lower bound of the fraction of support vectors should be in the interval (0,1). In our study, the parameter 'nu' is 0.3 and 'kernel' is linear. To determine whether the two sensors are in correlation or not, the coefficient of determination (R\({}^{2}\)) is important. We can declare the correlation of the two sensors when their R\({}^{2}\) is more than 0.7. This threshold R\({}^{2}\) was based on the practical experience. When we performed the regression analysis by choosing any two sensors among our dataset, the results were clearly classified into two groups: below 0.3 and above 0.7.
# Spectra-Based Selective Searching for Hyperspectral Anomaly Detection
## 4 Discussion
### Parameter Setting of the Comparison Algorithms  
The experiments of other comparison methods were carried out by the corresponding source code on GitHub and default parameters given by the authors. See the supplementary materials for the details. Among the five algorithms for comparison, LSAD-CR-IDW and LRSRD needed to preset parameters based on the data sets. Therefore, we chose the optimal parameters of the comparison algorithms LSAD-CR-IDW and LRSRD for the two test images used in the experiment in order to ensure fair comparison. Firstly, for the local algorithms, such as LSAD-CR-IDW, the sizes of the dual-window depended on the size of the anomaly in the image of each dataset. According to the principle of CRD involved in LSAD-CR-IDW, the inner window should contain all abnormal pixels near the under-tested pixel, and the size of the outer window should be larger than the size of the inner window. At the same time, it should ensure that there was no anomaly in the remaining pixel set after the outer window minus the inner window. Improper selection of dual-window sizes would cause the background set to be polluted by abnormal pixels in the calculation process, thus decreasing the detection performance. The anomalies were three aircraft which were composed of 23-25 pixels for the AVIRIS San Diego airport hyperspectral dataset, and the anomaly for the HYDICE urban hyperspectral dataset were the vehicles embedded in different backgrounds which contained 1, 2, 2, 2, 2, 3 and 4 pixels. Therefore, according to the size of the anomaly in the above two datasets and the selection principle of the inner and outer window sizes, we determined the inner and outer window sizes of the two datasets in a reasonable range. For the San Diego dataset, the size of the outer window ranged from 7 to 19, and the inner window size ranged from 5 to 13. For the urban dataset, the outer window size ranged from 5 to 17, and the inner window size ranged from 3 to 11. In our comparison experiment, the detection result graph and AUC value were used to judge anomaly detection performance. In order to compare the detection performance of the algorithms fairly, the optimal window sizes for the local anomaly detector LSAD-CR-IDW corresponded to the best detection result and the highest AUC value. Furthermore, regularization parameter \(\lambda\) of the LSAD-CR-IDW detector was set as 100 in our comparison experiments, as suggested in [40]. For LRSRD, the number of clusters \(K\) and the number of pixels \(P\) picked up for background dictionary construction in each cluster were two important parameters that affected the performance of anomaly detection. According to the original literature [38], \(K\) and \(P\) were fixed to 15 and 20, respectively.