esa-sceva/satcom-chunk-collection huggingface dataset:
# CS-FL: Cross-Zone Secure Federated Learning with Blockchain and a Credibility Mechanism
## 4 CS-FL Method
### Selection Phase: \(\varepsilon\)-Greedy Node Selection Strategy  
The node selection strategy of CS-FL is integrated into the smart contract within Zone-Chain, automatically executed by the leader of each round upon receiving all messages, shown as Step 3-5 in Figure 3. CS-FL classifies ENs into three sets based on SCI values:  
* Fully reliable nodes (\(FR\)): \(SCI_{n,i}>\varepsilon\);
* Partially reliable nodes (\(PR\)): \(1-\varepsilon\leq SCI_{n,i}\leq\varepsilon\);
* Unreliable nodes (\(UR\)): \(SCI_{n,i}<1-\varepsilon\).  
where \(\varepsilon\in(0.5,1)\) is an adjustable parameter. The algorithm aims to select the top \(K\) optimal nodes. The selection strategy is divided into two phases by using \(\varepsilon\)-Greedy algorithm: Greedy Exploitation and Exploration. The details of the algorithm are provided in Algorithm 1.  
```
Input: selected number \(K\), round \(i\), \(View_{i}\), \(\varepsilon\) Output: selected EN set \(S_{i}\)
1 initialize Leader \(L=i\) mod \(|M|\), \(S_{i}=\{\ \}\)
2 for\(n\in View_{i}\)do
3 if\(SCI_{n,i}<1-\varepsilon\)then
4 Unreliable Set \(UR_{i}\leftarrow\mathrm{ID}_{n}\);
5
6 else
7 if\(1-\varepsilon\leq SCI_{n,i}\leq\varepsilon\)then
8 Partially Reliable Set \(PR_{i}\leftarrow\mathrm{ID}_{n}\);
9
10 else
11 Fully Reliable Set \(FR_{i}\leftarrow\mathrm{ID}_{n}\);
12
13 while\(|S_{i}|<K\)do
14 Greedy Exploitation: \(S_{i}\leftarrow\) select \(\mathrm{ID}_{n}\) randomly in \(FR_{i}\);
15 Exploration: \(S_{i}\leftarrow\) select \(\mathrm{ID}_{n}\) in \(PR_{i}\) with probability \(p_{n}=SCI_{n,i}\);
16 if\(|UR_{i}|>|N|-K\)then
17 break;
```  
**Algorithm 1**\(\varepsilon\)-Greedy Node Selection Strategy: GNSS
## 2 Optimization Problem and the Method of Finding the Optimal Solution
### Cuckoo Search Algorithm  
Cuckoo Search Algorithm is another metaheuristic algorithm which stands out by gradient free optimization method [27]. It is a model that describes the behavior of cuckoos during the specific nature of breeding. These birds do not take care of theirs own eggs and throw them to other nest. So the algorithm simulates the flight while looking for nests of other birds and laying eggs in there. Of course, there is also need to pay attention to the owner's response. In these model, some assumption must be done  
* Cuckoo is identified with the egg,
* Each cuckoo has one egg,
* The nest owner decides to keep or throw the egg out with the probability \(1-\lambda\in\langle 0,1\rangle\). If the egg is thrown out, the new cuckoo is replace these one and the position is chosen at random.  
At the beginning of the algorithm, an initial population is created in random way. Each cuckoo moves by making a flight which uses the random walk concept. It is modeled as  
\[\overline{x_{i}^{t+1}}=\overline{x_{i}^{t}}+\mu\cdot L(\varphi,\rho,\delta), \tag{18}\]  
where \(\mu\) is the length of random walk step with normal distribution \(N\left(\frac{\rho}{cuckoos};0.1\right)\) and \(L(\cdot)\) is Levy flight defined as  
\[L(\varphi,\rho,\delta)=\left\{\begin{array}{cc}\sqrt{\frac{\rho}{2\pi}} \frac{\exp\left[-\frac{\rho}{\delta(\varphi-\delta)}\right]}{\left(\varphi- \delta\right)^{\frac{1}{2}}},&0<\varphi<\delta<\infty\\ 0,&\text{other}\end{array}\right., \tag{19}\]  
where \(\varphi\) is the length of the step, \(\delta\) is the minimum step for random walk and \(\rho\) is a scaling parameter.  
Once the individuals in the population have completed their movement, decide if the egg stays at the current position should be made. It is a decision-making mechanism by the owner of the nest to which the eggs were thrown. It is modeled as  
\[H\left(\overline{x_{i}^{t+1}}\right)=\left\{\begin{array}{cl}1-\lambda& \text{drop the egg}\\ \lambda&\text{leave the egg}\end{array}\right., \tag{20}\]  
where \(\lambda\in\langle 0,1\rangle\) is a random value understood as the chance for egg to stay. Whole algorithm is described in Algorithm 5.  
```
Start,  Define all parameters \(\lambda\in\langle 0,1\rangle\), \(\varphi\), \(\rho\), \(\delta\), \(bestratio\), number of \(cuckoos\) and iterations \(T\),  Define fitness function \(f(\cdot)\),  Create an initial population,  t:=0, while\(t<T\)do  Move individuals to another position using Equations (18) and (19),  According to Equation (20), the nest host decides whether the cuckoo eggs remain,  Evaluate the whole population,  Sort the population according to fitness condition, \(t++\), endwhile  Return the best cuckoo,  Stop.
```  
**Algorithm 5**Cuckoo Search Algorithm
###### Abstract  
This paper proposes a steady-state thermal model for the passive cooling of photovoltaic (PV) modules integrated into a vertical building facade by means of a solar chimney, including an empirical correlation for turbulent free convection from a vertical isothermal plate. The proposed analytical model estimates the air velocities at the inlet and at the outlet of the ventilation channel of such a cooling system and the average temperature of the facade-integrated PV modules. A configuration composed of a maximum of six vertically installed PV modules and one solar chimney is considered. The air velocities at the inlet and at the outlet of the ventilation channel obtained for the case of installing PV modules on the building facade are compared with those calculated for the case where the PV modules are integrated into the roof with a slope of 37\({}^{\circ}\). By comparing each of the solutions with one PV module to the corresponding one with six PV modules, it was found that the increase in the air velocity due to the effects of the solar irradiance and the height difference between the two openings of the ventilation channel ranges between 41.05% in the case of \"Roo\" and 141.14% in the case of \"Facade\". In addition, it was obtained that an increase in the solar chimney height of 1 m leads to a decrease in the average PV section temperature by 1.95-7.21% and 0.65-2.92% in the cases of \"Roo\" and \"Facade\", respectively. Finally, the obtained results confirmed that the use of solar chimneys for passive cooling of facade-integrated PV modules is technically justified.  
air velocity; analytical model; photovoltaic (PV) module; solar chimney; steady-state thermal analysis; temperature Article
# New-Generation ASiR-V for Dose Reduction While Maintaining Image Quality in CT: A Phantom Study
## 2 Materials and Methods
### Data Reconstruction  
Post-processing reconstruction procedures were performed using FBP for high-dose acquisitions, standard ASiR for clinical acquisitions, and the novel ASiR-V algorithm for reduced-dose acquisitions. In particular, because ASiR-V is commercially unavailable on OPTIMA 660, GE Healthcare provided software support for ASiR-V image reconstruction (NI 22, 24, and 30).  
Percentage blending varied from 10% to 100% for both ASiR and ASiR-V. We defined the \"standard ASiR\" as the images acquired with the protocol clinically adopted in our institute and reconstructed with the ASiR algorithm at 40% of blending. This value was identified based on the literature [30; 31] and after an image quality evaluation by experimenting with varying percentages of ASiR and different scan techniques.
# Fuzzy Evaluation Model of Process Improvement Capability with Costs Consideration
## 3 Fuzzy Testing for Process Improvement Capability Index
According to Equations (26) and (30), the length of the bottom \(d_{R}\)\(=UC_{R}-VL\) can be shown as follows:  
\[d_{R}=\left(3\gamma_{0}^{*}\sqrt{\frac{(n-1)R}{\chi_{0.0025n-1}^{2}}}\right)^ {-1}-VL\text{ {\it if }}0\ \in\ CI, \tag{36}\]  
Figure 2: Membership function \(\eta(x)\) with vertical line \(x=VL\). or  
\[d_{R}=\left(3\gamma_{0}^{*}\sqrt{Min\left\{\left(\frac{\delta_{0}^{*}}{\gamma_{0}^{ *}}-E_{0}(0.01)\right)^{2},\left(\frac{\delta_{0}^{*}}{\gamma_{0}^{*}}+E_{0}(0.0 1)\right)^{2}\right\}+\frac{(n-1)R}{\chi_{0.0025n-1}^{2}}}\right)^{-1}\ -\ VL\text{ if }0\
otin CI. \tag{37}\]  
Similarly, according to Equations (29) and (30), the length of the bottom \(d_{T}=UC_{R}-UC_{M}\) can be expressed below:  
\[d_{T}=\left(3\gamma_{0}^{*}\sqrt{\frac{(n-1)R}{\chi_{0.0025n-1}^{2}}}\right)^{ -1}-\left(3\gamma_{0}^{*}\sqrt{\frac{(n-1)R}{\chi_{0.5n-1}^{2}}}\right)^{-1} \text{ if }0\ \in\ CI, \tag{38}\]  
or  
\[d_{T}=\left(3\gamma_{0}^{*}\sqrt{Min\left\{\left(\frac{\delta_{0}^{*}}{\gamma_ {0}^{*}}-E_{0}(0.01)\right)^{2},\left(\frac{\delta_{0}^{*}}{\gamma_{0}^{*}}+E_ {0}(0.01)\right)^{2}\right\}+\frac{(n-1)R}{\chi_{0.0025n-1}^{2}}}\right)^{-1} -\left(3\sqrt{\left(\delta_{0}^{*}\right)^{2}+\frac{(n-1)R}{\chi_{0.5n-1}^{2} }\gamma_{0}^{*2}}\right)^{-1}\text{ if }0\
otin CI. \tag{39}\]  
Therefore, we let \(0\ <\phi_{1}<\phi_{2}<0.5\).The values of \(\phi_{1}\) and \(\phi_{2}\) were decided by experts and their accumulated experiences, it can improve the testing precision [15; 16; 18]. As noted by [PERSON] et al. [25], we may obtain the following fuzzy test rules:  
1. If \(d_{R}/2d_{T}\leq\phi_{1}\), then reject \(H_{0}\) and conclude \(C_{PIM}<VL\). 2. If \(\phi_{1}<d_{R}/2d_{T}<\phi_{2}\), then make no decision on whether to reject/not to reject \(H_{0}\). 3. If \(\phi_{2}<d_{R}/2d_{T}<\)0.5, then do not reject \(H_{0}\) and conclude \(C_{PIM}\geq VL\).
## 1 Introduction  
An ultrashort optical pulse allows the observation of ultrafast phenomena [1; 2]. Such a pulse finds a variety of applications, e.g., the ultra-high-precision machining of materials [3] and the sensitive detection of organic compounds in mass spectrometry [4]. Several approaches have been reported to date for their generation. One of the approaches, four-wave Raman mixing in a hydrogen gas, generates octave-spanning multicolor emissions in the optical region [5; 6; 7]. By Fourier synthesis of the multicolor emissions, intense sub-2-fs optical pulses can be generated. Ultrashort laser pulses with similar pulse durations have been generated by other schemes, as demonstrated by several groups [8; 9; 10]. For the characterization of such extremely short pulses, a diagnostic system covering a wide spectral bandwidth, and hence a sufficiently high time resolution, is required. Available techniques for this purpose include autocorrelation (AC) [11], frequency-resolved optical gating (FROG) [12; 13], and spectral phase interferometry for direct electric-field reconstruction (SPIDER) [14; 15]. Sub-4-fs pulses have already been characterized by the techniques of spatially encoded arrangement for SPIDER [16] and the dispersion scan (D-scan) [17]. The cross-correlation FROG (XFROG) [18] has been used for the characterization of ultrashort optical pulses as short as 2.4 fs [9]. XFROG is also used in characterizing ultrashort pulses generated by four-wave mixing (FWM) in a bulk medium [19], and ultrashort pulse trains [20]. Recently, methods to extract the information of a carrier envelope phase were reported [21; 22]. The phase-matching bandwidth of XFROG is extremely wide, and a few fs optical pulse can be measured when a properly selected nonlinear medium is employed. Note that a low-intensity optical pulse can be characterized using an intense reference pulse in XFROG. In measurements using XFROG, it is necessary to carefully characterize the reference pulse; otherwise, the pulse duration of the test pulse cannot be reliably evaluated.  
In this study, an XFROG system is developed for the reliable characterization of ultrashort optical pulses, in which self-diffraction (SD) FROG and second harmonic generation (SHG) FROG are used for the characterization of the reference pulse. In this paper, we report on the combination of SD FROG and XFROG; the reliability of SD FROG was further assured by SHG FROG. This approach is, to our knowledge, the first study in the FROG measurement. Due to the wide spectral domain available, this diagnostic tool would be useful for the measurements of a variety of laser sources emitting in the region extending from the visible to the near-infrared.
## 2 Methodology
### WSN Transmission Power  
The Friis formula is  
\[P_{R}=\frac{A_{r}A_{t}}{d^{2}\lambda^{2}}P_{t} \tag{1}\]  
which is a formula for receiving power with the effective cross-sectional area of an antenna (1).  
\[A_{\text{isotropic}}=\frac{\lambda^{2}}{4\pi} \tag{2}\]  
The formula for an isotropic antenna is  
\[P_{R}=\left(\frac{\lambda}{4\pi d}\right)^{2}P_{t} \tag{3}\]  
which is a simplified formula for receiving power with an isotropic antenna. The antenna used is isotropic (an ideal antenna that radiates with equal power in all directions) (2), so its gain is 1 or does not need to be calculated.  
The Friis formula is used as an objective function to find the optimal (lowest) transmission power value while meeting operational constraints (3). The objective function includes minimizing the transmitted power (\(P_{t}\)) while ensuring that the received power (\(P_{R}\)) is sufficient for reliable communication between nodes.  
To ensure connectivity between nodes, a threshold (\(\rho_{\text{th}}\)) check of \(-60\) dB is used (4).  
\[A=\begin{cases}1,&\text{if }P_{R}\geq\rho_{\text{th}}\\ 0,&\text{otherwise}\end{cases} \tag{4}\]
# Design of a Combined Redrawing-Ironing Process to Manufacture a CNG Pressure Vessel Liner
## 3 Finite Element Analysis for Combined Redrawing-Ironing Process
### Analysis Conditions and Material Property  
The behavior of the workpiece was set to a deformable domain with a volume mesh, which was periodically remeshed as the process progressed. The die and the punch were assumed as rigid bodies with a surface mesh, which means that their deformation is negligible. The punch velocity of 50 mm/s was suggested in the actual field, and the friction coefficient (\(\mu\)) between the workpiece and the die was defined as 0.05 [16]. The flow stress equation and the material properties were obtained through a tensile test of 34 CrMo4, as shown in Equation (24) and Table 2, respectively [16].  
\[\overline{\sigma}=887.02\overline{\varepsilon}^{0.123}[\text{MPa}] \tag{24}\]  
Forge Nxt 2.0 software uses the Hansel-Spitelt model shown in Equation (25), which is based on the relation of the variables of strain, strain rate, and temperature. Material constants of the equation (\(A\), \(m_{1}\), \(m_{2}\), \(m_{3}\), \(m_{4}\), \(m_{5}\), \(m_{6}\), \(m_{7}\), \(m_{8}\), and \(m_{9}\)) were obtained from the flow stress-strain data of Figure 8, as listed in Table 3.  
\[\overline{\sigma}=Ae^{m_{1}T}T^{m_{9}}\varepsilon^{m_{2}}e^{\frac{m_{4}}{T}}(1+ \varepsilon)^{m_{5}T}e^{m_{9}\varepsilon}\varepsilon^{m_{3}}e^{m_{8}T} \tag{25}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Case** & \multicolumn{3}{c}{**Gap of Loads (\%)**} \\ \hline  & **Between 13\({}^{\circ}\) and 14\({}^{\circ}\)** & **Between 14\({}^{\circ}\) and 15\({}^{\circ}\)** \\ \hline DIR 1.1 & 0.61 & 0.50 \\ DIR 1.2 & 0.54 & 0.47 \\ DIR 1.3 & 0.48 & 0.42 \\ DIR 1.4 & 0.43 & 0.38 \\ DIR 1.5 & 0.39 & 0.34 \\ DIR 1.6 & 0.39 & 0.31 \\ DIR 1.7 & 0.33 & 0.29 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Gaps of the combined redrawing-ironing loads by varying DIR.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Young’ modulus** & 205 GPa \\ \hline
**Yield strength** & 303 MPa \\ \hline
**Tensile strength** & 516 MPa \\ \hline
**Poisson’s ratio** & 0.28 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material properties of steel liner (34 CrMo4).  
Figure 7: Combined redrawing-ironing load according to the die angle (1–15\({}^{\circ}\)) at each DIR (1.1).
# Computational Fluid Dynamics Applied to
## 5 Results
### Categorization of Relevant Papers Exploiting Meshless Approaches  
In Table 1, all the relevant papers exploiting meshless approaches are listed and classified based on the variables presented in Section 4.2. In the list, nine scientific works, published since 2009, have been considered pertinent for the goal of this paper. In particular, it is possible to notice that seven papers out of nine have been published in the last two years. In addition, six out of nine papers have been experimentally validated but three out of six have been verified only qualitatively. The other three works show that the difference between the CFD analysis and the experimental results differ widely more than 10%. Indeed, in Figure 1, it is possible to notice that even if the system simulated through meshless approaches can achieve a good level of complexity (generally between 3 and 4), the accuracy of the results is mostly not verified or demonstrated only qualitatively. It is interesting to note that no bearing simulations have been found in the pertinent literature. Moreover, all the simulations were carried out with commercial software. In particular, ParticleWorks(tm) software was used in [12; 14; 63] where the MPS method was exploited. In [64] DPM was used while the SPH was exploited in the other works. Among the others, it is possible to mention the software ANSYS FLUENT, used for example in [15].  
In [15; 64] pumping systems were studied. More specifically, the two-phase flow (air-oil mixture) in a pump for the aeroengine lubrication was simulated in [64]. In [15] meshless approaches were applied in a pump for mineral processing. In [12; 16] the meshless approaches were exploited for studying the oil flow in ordinary gearboxes.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Paper** & **Year** & **Software** & **System** & **Target** & **Validation** & **Accuracy** & **Complexity** \\ \hline
[15] & 2009 & Comm. & Pump & Both & No & N.A. & 3 \\
[64] & 2011 & Comm. & Pump & Fluxes & No & N.A. & 1 \\
[16] & 2018 & Comm. & Gears & Fluxes & Yes & Qualitat. & 4 \\
[65] & 2019 & Comm. & Gears & Both & No & N.A. & 3 \\
[66] & 2019 & Comm. & Gears & Both & Yes & Qualitat. & 4 \\
[17] & 2019 & Comm. & Gears & Both & Yes & \(<\)80\% & 3 \\
[14] & 2020 & Comm. & Gears & Both & Yes & 80\%–90\% & 4 \\
[12] & 2020 & Comm. & Gears & Fluxes & Yes & Qualitat. & 4 \\
[63] & 2020 & Comm. & Gears & Both & Yes & 80\%–90\% & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification of the pertinent papers exploiting meshless approaches.  
The target of the analysis performed in [12; 16] is to simulate the oil flow in gearboxes and the results obtained have been validated qualitatively. Modelling a multiphase gearbox was the goal of [65] but the results have not validated yet. In [17; 66], the meshless approaches were used to estimate the churning losses in gearboxes. In [63] a speed reducer with complex shapes have been simulated.
# Deep Learning-Based Autoscaling Using Bidirectional Long Short-Term Memory for Kubernetes
## 6 Experiment and Evaluation
### Experiment Results
#### 6.4.2 FIFA Worldcup 98 Dataset  
Table 5 shows the experimental results obtained for the FIFA Worldcup 98 dataset. The Bi-LSTM model outperforms all other models, including the statistical method ARIMA and feed-forward LSTM models, because it has the smallest prediction error values on MSE, RMSE MAE metrics in both one-step and five-step predictions. In five-step prediction, RMSE of the Bi-LSTM is 1.31 and 1.25 times smaller than those of ARIMA and forward LSTM, respectively, which indicates better accuracy. The Bi-LSTM not only has a better prediction accuracy but also has a prediction speed 600 times faster than that of the well-known short-term statistical ARIMA in one-step predictions. Although the prediction speed of Bi-LSTM is higher than that of the feed-forward LSTM, the speed is measured in milliseconds, which is too minor to consider.  
Figures 10-12 indicate the results obtained for the testing set. As the difference between these results is negligible, we focus on a small part of this dataset to obtain a better observation. We use the results obtained from 1 July 1998 20:31:00 to 2 July 1998 05:00:00, as shown in Figures 13-15. Figure 13 shows that the ARIMA model tends to over-predict and under-predict most of the time. While the LSTM model shown in Figure 14 tends to over-predict through the time, the predicted values provided by Bi-LSTM model in Figure 15 nearly match the actual values. Thus, the Bi-LSTM model has better prediction accuracy than ARIMA and the forward LSTM model.  
Figure 11: Forward LSTM with one-step prediction on the testing set.  
Figure 12: Bi-LSTM with one-step prediction on the testing set.  
Figure 13: ARIMA with one-step prediction on the testing set (from 1 July 1998 20:31:00 to 2 July 1998 05:00:00).
###### Abstract  
Due to the inherent characteristics of the electromagnetic wave scattering phenomenon, synthetic aperture radar (SAR) images are directly degraded by high-frequency multiplicative speckle (HMS) noise, which makes image de-speckling filter application and selection a challenge. In this regard, an adverse effects analysis of the HMS under implementation of seven different spatial de-speckling filters on a reference SAR image is considered in this paper. The investigation includes the formulation of the backscattered data and the HMS based on the pixel statistics and their distribution as an image noise behavioral analysis method. The resulting complex behavioral model is used for HMS power spectral density (PSD) function modeling. This paper also includes HMS system resolution effects analysis on the raw data generation (RDG) and the received frequency profile (RFP). An objective quality assessment procedure was also carried out to investigate both the de-speckled image resolution and the spatial filter evaluation in the presence of the HMS. As a result, the simulations verify that speckles are embedded within the high-frequency parts of the raw data, directly affecting the spatial resolution and the image resolution with non-specific patterns. The results also show that no spatial de-speckling filter consistently outperforms others, and their implementation is completely dependent on the texture, the system parameters, and their evaluation index. As a novel approach, HMS spectral behavioral modeling within the filtered images, as well as the proposed spatial de-speckling filter evaluation methods, are the proper techniques for optimum filter selection and specific purpose applications. The results are very helpful for remote sensing image restoration and data preservation when dealing with SAR images with a less fine resolution, such as ice-covered areas, coastal change detection, vegetation texture detection, geological structures mapping, and so forth. The SAR system resolution analysis is completed based on inversed problem solution (IPS) and with the help of a hybrid-domain image formation algorithm (IFA).  
s 2019 11 1041 10.3390/rs11091041 10.3390/rs11091041 10.3390/rs11091041
###### Abstract  
To assess structural sealant damage in hidden frame glass curtain walls (HFGCWs) during service, damage states were simulated by controlled cutting with varying incision lengths. Quantitative identification challenges were investigated through natural frequency and curvature modal difference (CMD) analyses at multiple test points. The results indicate that natural frequency decreases with increasing damage severity, while the first-order curvature mode difference (FCMD) exhibits localized abrupt changes in damaged regions. Boundary modes provide more targeted and accurate damage identification. The peak value of the FCMD mutation region enables precise damage localization. A quantitative damage identification threshold of 0.1205 was derived from FCMD distribution characteristics in boundary regions. By leveraging boundary mode features, modal testing efficiency is optimized, reducing the required acquisition nodes and effectively guiding structural sealant damage detection in engineering applications.  
curtain wall; structural sealant; quantitative damage identification; natural frequency; curvature mode difference Article