esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Evaluation of Powder Bed Temperature by Thermal Imaging  
To capture the temperature distribution on the powder bed surface of the PBF-LB/P system, a FLIR T420 infrared camera was employed. The camera provides real-time thermal imaging with a resolution of 320 \(\times\) 240 pixels, 30 frames per second, and a 25\({}^{\circ}\) field of view. The installation outside the process chamber was inevitable due to a variety of reasons such as camera dimensions, a minimum focus distance of 400 mm, and process temperature. To capture the whole powder bed surface at the installation space, to prevent geometrical distortions, and in consideration of the parameters mentioned above, the camera was centrally mounted at the top inspection window of the PBF-LB/P system. Separation of the camera and the actual installation space was realized by installing a calcium fluoride (CaF\({}_{2}\)) optical window. In the actual spectral range, the transmittance of CaF\({}_{2}\) is more than 90%. To prevent coating effects at the optical window caused by vaporizing powder particles during laser exposure, a nitrogen (N\({}_{2}\)) flushing was implemented by branching a separate conduction at the integrated nitrogen generator. The calibration of the emissivity of PA12 powder was performed by aligning the temperature value at the inbuilt pyrometer and the value at thermal imaging. According to [8], an emissivity of 0.85 was defined for PA12, confirming correct temperature values. The temperature values are not quantitatively determinable during phase transition between powder, solid, and molten material due to a significant change of emissivity [46]. Figure 7 shows the thermal grayscale imaging and  
Figure 5: Comparison of a defective test part (**a**) and an accurate test part (**c**). Clear appearance of distortion caused by continuous curling during the process (**b**) at the free edges after systematic reduction of the process temperature to 162 \({}^{\circ}\)C.  
Figure 6: Different test parts in (**a**) as bases for further triangulation and thermal analysis: cross-, triangular-, and circular-shaped geometries and in (**b**) 6 identical stacked layers of 2 mm material thickness and a total height of 14 mm.  
the related color map of the test geometries on the powder bed after coating (a), surface heating time (b), and after laser exposure (c), leading to no induced curling at the current process stages. As can be seen in Figure 7, only small areas have been cut off the edges of thermal recordings, referable to the field of view and shadowing by the inspection window of the PBF-LB/P system (shading at left bottom part of images).  
Within this work, different color maps have been studied on thermal recordings to convert single value grayscale images, since the human eye is not very sensitive to small changes in grayscale intensities. According to related temperature fields, different color maps and temperature ranges showed clear differences in the visualization of details on the powder bed surface. The color map shown in Figure 7 was used from then on for further analysis.
# Analytical Algorithm for Oxygen Concentration of Aircraft Fuel Tank in Various Inerting Stages
## 3 Analytical Algorithm
### The Descent Phase Calculation Method  
During the descent phase, atmospheric pressure \(p_{\rm atm}\) can be higher than \(p_{U}\), because \(p_{\rm atm}\) increases with decreasing height while \(p_{U}\) decreases with fuel consumption. However, to keep the fuel tank functional, \(p_{U}\) should be bigger than \(p_{\rm atm}\). Hence, it is necessary to increase the pressure of the ullage space by injecting NEA. The variables that need to be calculated in this situation are \(z^{\prime}_{Uov}\), \(z^{\prime}_{UInv}\), and \(\hat{v}_{I}\). The ullage washing model can be expressed as:  
\[\left\{\begin{array}{l}f_{o}\cdot z^{\prime}_{Uov}-\frac{p_{U}z_{low}\cdot T _{U}}{T_{I}}\cdot\hat{v}_{I}=g_{o}\\ f_{n}\cdot z^{\prime}_{UInv}-\frac{p_{U}z_{low}\cdot T_{U}}{T_{I}}\cdot\hat{v}_ {I}=g_{n}\\ z^{\prime}_{Uov}+z^{\prime}_{UInv}=-z^{\prime}_{Ufv}\end{array}\right. \tag{9}\]  
with  
\[\left\{\begin{array}{l}f_{o}=p_{U}\cdot v_{U}+\beta_{o}\cdot p_{U}\cdot v_{F }\\ f_{n}=p_{U}\cdot v_{U}+\beta_{n}\cdot p_{U}\cdot v_{F}\\ g_{o}=-p^{\prime}_{U}\cdot v_{U}\cdot z_{Uov}-\beta_{o}\cdot p^{\prime}_{U} \cdot v_{F}\cdot z_{Uov}\\ \quad\quad\quad-p_{U}\cdot v^{\prime}_{U}\cdot z_{Uov}-\beta_{o}\cdot p_{U} \cdot v^{\prime}_{F}\cdot z_{Uov}\\ g_{n}=-p^{\prime}_{U}\cdot v_{U}\cdot z_{UInv}-\beta_{n}\cdot p^{\prime}_{U} \cdot v_{F}\cdot z_{UInv}\\ \quad\quad\quad-p_{U}\cdot v^{\prime}_{U}\cdot z_{UInv}-\beta_{n}\cdot p_{U} \cdot v^{\prime}_{F}\cdot z_{UInv}\end{array}\right. \tag{10}\]  
By solving (9), as:  
\[\left[\begin{array}{c}z^{\prime}_{Uov}(i)\\ z^{\prime}_{UInv}(i)\\ \hat{v}_{I}(i)\end{array}\right]=A(i)^{-1}\cdot a(i) \tag{11}\]  
where  
\[A(i)=\left[\begin{array}{ccc}f_{o}(i)&0&-\frac{p_{I}(i)\cdot z_{low}(i) \cdot T_{U}(i)}{T_{I}(i)}\\ 0&f_{n}(i)&-\frac{p_{I}(i)\cdot z_{lm}(i)\cdot T_{U}(i)}{T_{I}(i)}\\ 1&1&0\end{array}\right] \tag{12}\]  
\[a(i)=\left[\begin{array}{c}g_{o}(i)\\ g_{n}(i)\\ -z^{\prime}_{Ufv}(i)\end{array}\right] \tag{13}\]  
with \(i\) standing for the \(i\)th moment. The iterative form of the ullage oxygen concentration can be expressed as:  
\[z_{Uov}(i+1)=z_{Uov}(i)+z^{\prime}_{Uov}(i)\cdot\Delta t \tag{14}\]
## 2 Materials and Methods
### Data Analysis  
The effect of pot treatment on the CPUE (Catch Per Unit Effort) of BSR was estimated using a generalized linear mixed effect model (GLMM) based on Poisson regression. The model structure was as follows:  
\[\begin{array}{c}\text{\emph{BSR Model}}=\text{\emph{glmmadm}}\text{\emph{ (CPUE}}\sim\text{\emph{treatments}}+\text{\emph{offset}}(\text{\emph{log}}( \text{\emph{soak}}))+(1\,\text{\emph{trial no}}),\\ \text{\emph{family}}=\text{``\emph{nbinom}}\text{\emph{\emph{\emph{\emph{ \emph{\emph{\emph{\emph{\emph{\emph{\emph{\emph{\emph{\emph{\emph{\emph{\emph{\emph{ \emphemphemphemphemph  
fish in total. The BSR catch varied according to the color of LED light attached to the pot. The \(BSR\) catch was the largest (\(n=273\)) for the pot with green light, followed by blue light (\(n=157\)), white light (\(n=133\)), red light (\(n=10\)), and the control without a light (\(n=2\)) (Kruskal-Wallis test, \(p<0.05\)) (Table 2).  
The monthly catch count in accordance with the total length class for the target BSR species between the control and experimental groups during the field experiment period is shown in Figure 4. The monthly catch showed the highest catch frequency in the pots with green, blue, and white LED lights. The catch per unit effort (CPUE) for the BSR pot of the control group was compared according to the BSR catch count per the total use of fishing gear, as shown in Figure 5. CPUE was the highest for the pot with green LED (2.84 \(\pm\) 3.21; mean \(\pm\) standard deviation, hereafter), followed by blue LED (1.64 \(\pm\) 3.21), white LED (1.39 \(\pm\) 1.46), and red LED (0.02 \(\pm\) 0.03). For BSR catch efficiency, the pots with green and blue LED lights showed the highest levels during the peak fishing season in October and December. The parameter estimates for the GLMM model for the BSR CPUE are shown in Table 3. The effect of treatment on CPUE was visualized using odds ratio plot in Figure 6.  
Figure 5: Catch Per Unit Effort (CPUE) comparison between the control and treatment pots for BSR.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Treatment** & **Estimate** & **S.E.** & **\(z\)-Value** & **95\% CI** & **\(p\)-Value** \\ \hline (Intercept) & 0.41 & 0.37 & 1.13 & 0.74–3.09 & 0.26 \\ Control & \(-\)0.72 & 1.18 & \(-\)0.61 & 0.05–4.87 & 0.54 \\ Green & 0.52 & 0.10 & 4.95 & 1.37–2.05 & 0.00 \\ Red & \(-\)2.28 & 0.33 & \(-\)6.93 & 0.05–0.19 & 0.00 \\ White & \(-\)0.17 & 0.12 & \(-\)1.44 & 0.66–1.06 & 0.15 \\ Random effect & & Variable & Variance & S.D. \\ Trial no. & & Intercept & 2.28 & 2.279 \\ \hline \hline \end{tabular}  
* The number of pots were 2300. S.E. Standard error of the estimate; S.D. standard deviation.  
\end{table}
Table 3: Parameter estimates, fit statistics, and variation from the random effects of a GLMM model for BSR based on trial number as a random factor (\(n=21\)).  
Figure 6: Odds ratios plot for GLMM pot treatment comparison. *** shows significancy of \(p\)-value.
## 2 Materials and Methods
### Mann-Kendall Test  
Mann-Kendall trend analysis is a robust approach to assess non-parametric trends, effectively reflecting how consistently a trend either declines or grows [39]. It has proven to be effective in detecting trends within time series data [20; 40]. This study utilized Mann-Kendall trend analysis to evaluate the spatio-temporal trends in greenness and productivity as well as climatic factors, i.e., Radia, Temp, Rainfall, during 2001-2020. The significance of these trends was tested by the Z value, and the specific formulas are shown as:  
\[S=\sum_{i=1}^{n-1}\sum_{j=i+1}^{n}sgn\big{(}x_{j}-x_{i}\big{)} \tag{1}\]  
\[\text{sgn}(x_{j}-x_{i})=\begin{cases}1&x_{j}-x_{i}>0\\ 0&x_{j}-x_{i}=0\\ -1&x_{j}-x_{i}<0\end{cases} \tag{2}\]  
\[Z=\begin{cases}\frac{S-1}{\sqrt{a^{2}}}&S>0\\ 0&S=0\\ \frac{S+1}{\sqrt{a^{2}}}&S<0\end{cases} \tag{3}\]  
\[a^{2}=\frac{n(n-1)(2n+5)}{18} \tag{4}\]  
where \(x_{i}\) represents LAI or GPP or the climatic factor sequence; \(n\) represents the number of data; \(S\) is the sum of the step function values of the difference between two different points in the time series; and \(a^{2}\) is the variance. We adopt a significance level of 0.05, where a z-score below \(-1.96\) signifies a statistically significant decline trend, a z-score above 1.96 indicates a significant growth trend, and a z-score falling between \(-1.96\) and 1.96 implies an insignificant trend.
# Spot Weld Inspections Using Active Thermography
## 4 Discussion  
In this manuscript, a comparison is performed between different active thermography techniques in order to find the most suitable solution for the inspection of spot welds. The samples are heated using light heating and induction heating and processed applying both fast Fourier transform and principle component analysis. As light heating excitation, both lock-in thermography and pulse thermography are investigated. For each excitation method, multiple parameter sets are explored, such as frequency variations for lock-in thermography and variations in pulse duration for pulse thermography. Induction heating shows to be the most suitable technique for spot weld inspections since it results in clear data images and low inspection times. Improving the measurements using induction thermography can be possible by minimizing the distance between the coil and the sample and increasing the amount of windings of the excitation coil. Both front and back heating result in good quality images in inductive thermography, making it possible to be used in the manufacturing process. In decent spot welds, a clear contour can be detected, and the region inside the circular boundary has a different thermal behavior in comparison to the material around the spot weld. Considering the samples used in this research, we conclude that samples 1A, 2A, 1B and 2B are the only valid spot welds.
# Legumes as Functional Food for Cardiovascular Disease
## 3 Legumes as Functional Food for Cardiovascular Diseases
### Lentil (Lens Culinaris Medik.)  
Bioactive peptides derived from lentils (_Lens culinaris_ Medik.) were shown to be anti-hypertensive, ACE inhibitory and anti-oxidative [187; 188]. In the study by [PERSON] et al., (2016), Wistar-Kyoto and spontaneously hypertensive rats showed significantly reduced levels of TC and LDL-C (\(p<0.05\)) after whole lentils feeding for eight weeks [186].  
The lentil convicillin storage protein is ascribed to inhibit ACE and dipeptidyl aminopeptidase III and IV peptidases, key enzymes involved in the complex interactions between cyclic nucleotides and the calcium-dependent secondary messenger systems [188] and stimulate vasoactive substances [187]. Three oligopeptides (LLSGTQNQPSFLSGF, NSLTLPILRYL, TLEPNSVFLPVLLH) derived from the Savinase-digestion of cultivated lentil proteins were identified to have the highest potentials against ACE activity in vitro. The peptides' antihypertensive benefit was linked to the primary structure of the hexepeptides at the C-terminal end as these residues affect ACE inhibition [189]. Enzyme-hydrolyzed protein isolates also yielded oligopeptides of less than six amino acids that strongly inhibited ACE [190; 191; 192; 193].
# Retrieval of Similar Evolution Patterns from Satellite Image Time Series +
## 4 Discussion
### Discovery of Similar Patterns in SITS
In the case of DTW distance images presented in (**a,b**), dark color represents similar evolutions and bright color represents non-similar evolutions, whereas in (**c,d**), white pixels correspond to spatio-temporal patterns that are similar to the query, which was selected from the region of interest.
# Identification of Rice Sheath Blight through Spectral Responses Using Hyperspectral Images
## 2 Materials and Methods
### \"Three-Edge\" Parameters and Narrow-Band Vegetation Indices  
In order to fully explore spectral features of ShB lesions on the leaf blade and the leaf sheath, two spectral analysis methods, \"three-edge\" parameters and narrow-band vegetation indexes [20; 21; 22], were used in this study.  
Diseases can cause changes in physiological and chemical substances and cellular structures in plants, resulting in spectral changes in the regions of the blue edge, green peak, yellow edge, red valley, red edge and near-infrared. The blue edge, green peak, yellow edge, red valley, red edge and near-infrared band are in the range of 490-530 nm, 510-560 nm, 550-582 nm, 640-680 nm, 670-750 nm and 783-890 nm, respectively. An increase in the amount of chlorophyll, for example, causes a red-edge shift towards longer wavelengths. A series of spectral characteristic parameters are constructed in these ranges, including position, amplitude, area, ratio and normalized ratio [20; 23; 24; 25]. These are termed \"three-edge\" parameters. A total of 36 representative \"three-edge\" parameters are included in Appendix A, with abbreviations and definitions of each variable.  
High spectral resolution amplifies spectral information. Narrow-band vegetation indices are designed for hyperspectral data. Some indices closely related to diseases have been proved to be useful to detect the changes in plant parameters, such as pigment content, nitrogen content, water content, and leaf area or biomass. The selected vegetation indexes are shown in Appendix B [26; 27; 28; 29; 30; 31; 32; 33; 34; 35].
# TPSQLi: Test Prioritization for SQL Injection Vulnerability Detection in Web Applications
## 4 Methodology and Implementation
### Test Prioritization Algorithm
#### 4.4.1 Pseudocode  
Algorithm 1 outlines the test prioritization algorithm for SQL injection vulnerability detection in web applications. The algorithm takes as input an \(n\)-dimensional Strength-Weakness (SW) vector, where \(n\) represents different techniques, and a payload for each technique. The process of our algorithm is detailed below:  
1. Initialization (Set Initial Variables):  
Figure 5: Our test prioritization algorithm unit. * Strength-Weakness Vector ('SW_Vector'): Initialize a fundamental score for each technique using a function discussed in the subsequent subsection. This vector quantifies each SQL injection technique's relative strengths or weaknesses based on historical data or predefined metrics. * Exploit Time ('exploit_time'): Initialize this variable to record the fastest exploit time for each technique. For instance, if technique A exploits a vulnerability in 2 s, while technique A takes 5 s, this would influence the prioritization of these techniques. * Is Exploit ('is_exploit'): Initialize this boolean variable to record the success status of each technique. If a technique fails, it will be marked as False, affecting its priority in future tests. * Execution Loop:
* Payload Selection: For each payload that failed in the previous round (higher risk payloads), determine whether it can be successfully exploited. If not, reduce its risk and select the payload with the highest fundamental score for testing. * Execution: Start the execution loop by recording the start time to establish a baseline duration. Execute the selected payload, performing the specific task assigned by the system. After the payload execution, record the end time to calculate the total execution time, which is critical for performance analysis and optimization. * Outcome Evaluation: Determine whether the exploit was successful. There are two possible outcomes: Failure Case:
* If 'is_exploit' for the technique is False, add the execution time and subtract one point from the fundamental score. * If 'is_exploit' is True, only subtract one point from the fundamental score without adding the execution time. Success Case:
* Set the payload's risk to high. * If 'is_exploit' for the technique is False, add the execution time and set 'is_exploit' to True. * Iteration and Update:
* Iterate: Continue testing the next payload until all payloads have been executed. * Update: After executing all payloads, update the SW-vector based on the recorded exploit time and the 'is_exploit' status. Our proposed algorithm ensures that the payloads with higher risks are prioritized and the execution times are minimized by continuously updating and adapting the strength-weakness vector based on the performance of each technique. For an example of the algorithm in action, please refer to Section 4.4.4.
# Laser Cooling and Trapping of Rare-Earth-Doped Particles
## 3 Optical Refrigeration of Rare-Earth-Doped Particles
### Laser Trapping and Cooling Rare-Earth-Doped Particles
\begin{table}
\begin{tabular}{c c c c} \hline
**Sample** & **Pump Wavelength (nm)** & **\(\Delta\)T (K)** & **Reference** \\ \hline Yb\({}^{3+}\):YLF & 1020 & 23.6 (vacuum) & [30] \\ \hline Yb\({}^{3+}\):YLF & 1020 & 19 (D\({}_{2}\)O) & [38] \\ \hline Yb\({}^{3+}\):YLF & 1031 & 126 (vacuum) & [32] \\ \hline Yb\({}^{3+}\):LLF & 1020 & 20.4 (vacuum) & [33] \\ \hline Yb\({}^{3+}\):LLF & 1020 & 15 (air), 5 (water) & [34] \\ \hline Yb\({}^{3+}\):NaYF\({}_{4}\) & 1020 & 9 (D\({}_{2}\)O) & [36] \\ \hline Yb\({}^{3+}\):NaYF\({}_{4}\) & 1020 & 42 (vacuum) & [39] \\ \hline Yb\({}^{3+}\):KLaF\({}_{4}\) & 1020 & 8.6 (vacuum) & [41] \\ \hline \end{tabular}
\end{table} 
Table 1: Reported results in optical refrigeration of nanoparticles. \(\Delta\)T (K) is the temperature drop. Figure 13: The lowest temperatures of seven nanocrystals (left axis). The decreases in the crystal’s temperature reached by seven nanocrystals (right axis). The scheme in Figure 14 consists of a Paul trap for nanocrystal spatial localization [40; 44] and a 3D standing light wave for the optical cooling of nanocrystals [45]. The standing light wave can be replaced by a single light wave. Indeed, as is evident from the foregoing, RE-doped nanocrystals can be optically cooled with a single optical wave. In the theoretical analysis of optical cooling presented in Figure 14, 5% Yb\({}^{3+}\)-CsF\({}_{2}\) nanocrystals were considered. It was assumed that the nanocrystal of mass \(M=6.4\times 10^{-19}\)g was placed in the center of the Paul trap (\(x_{0}=y_{0}=z_{0}=0\)) at initial velocities of \(v_{0x}=v_{0y}=v_{0z}=10\) cm \(\cdot\) s\({}^{-1}\). The nanocrystal dynamics consist of the motions of two kinds: the secular motion and the small oscillations about the secular path (Figure 14). The secular motion takes place along the smooth trajectory, which converge to the center of the Paul trap. The interaction between the charged nanocrystals and radio-frequency field results in small oscillations about the secular path. The numerical simulations presented in [42] prove that the Yb\({}^{3+}\)-CsF\({}_{2}\) nanocrystal mechanical action can reach a value \(\sim 10\hbar\), which is close to the quantum limit of \(\hbar\).
## 12 Results
### Quantitative Evaluation
A notable benefit of the proposed approach is its ability to substantially reduce total examination time in clinical workflows. By enabling high-quality image reconstruction from fewer input samples, the framework inherently decreases patient time in scanners, mitigating discomfort and increasing throughput in busy imaging centers. Additionally, integrated motion correction algorithms address a critical challenge in hybrid imaging--the pervasive issue of patient movement and physiological motion. By jointly optimizing motion compensation with super-resolution reconstruction, the method curtails motion-induced artifacts while maintaining high spatial resolution, a feat supported by the statistically significant reduction in target registration error (TRE) displayed in Table 11.  
The comparative studies in Tables 2 and 6-8 establish the superior quality of the proposed SRR algorithm relative to a diverse array of both classical and contemporary reconstruction techniques. These include traditional spline-based methods and well-established deep learning approaches such as Transformer-based networks and diffusion models. Not only does the proposed algorithm achieve higher PSNR and Structural Similarity Index (SSIM) scores, but it also consistently outperforms competitors on Normalized Mean Squared Error (NMSE) and Visual Information Fidelity (VIF) metrics. These improvements translate to more faithful anatomical representations, as qualitatively corroborated by expert radiologist assessments who observed enhanced delineation of fine structures and improved contrast in reconstructed images. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Method** & **PSNR \(\uparrow\)** & **SSIM \(\uparrow\)** & **RMSE \(\downarrow\)** & **NMSE \(\downarrow\)** & **VIF \(\uparrow\)** & **LPIPS \(\downarrow\)** & **MS-SSIM \(\uparrow\)** \\ \hline Sparse MRI (CS-MRI) [8] & 27.3 & 0.80 & 0.040 & 0.012 & 0.76 & 0.120 & 0.82 \\ TV Regularization [66] & 29.0 & 0.86 & 0.037 & 0.010 & 0.78 & 0.115 & 0.83 \\ DeepJoint [64] & 30.8 & 0.88 & 0.031 & 0.008 & 0.82 & 0.100 & 0.86 \\ DeepCascade MRI [32] & 30.25 & 0.87 & 0.028 & 0.009 & 0.85 & 0.098 & 0.85 \\ DAGAN [58] & 31.4 & 0.89 & 0.025 & 0.007 & 0.87 & 0.092 & 0.87 \\ E2E-VarNet [59] & 31.75 & 0.90 & 0.023 & 0.006 & 0.88 & 0.088 & 0.88 \\ TransMR [60] & 32.10 & 0.91 & 0.022 & 0.006 & 0.89 & 0.084 & 0.89 \\ MTrans [65] & 32.60 & 0.92 & 0.020 & 0.005 & 0.90 & 0.082 & 0.90 \\ Diffusion MR-PET Reconstruction [61] & 33.20 & 0.92 & 0.019 & 0.004 & 0.91 & 0.080 & 0.91 \\ Hybrid GAN-CS [62] & 33.45 & 0.92 & 0.018 & 0.004 & 0.91 & 0.078 & 0.91 \\ VoxResNet [63] & 33.60 & 0.93 & 0.017 & 0.004 & 0.92 & 0.076 & 0.92 \\ DeblurGAN [49] & 33.65 & 0.93 & 0.016 & 0.004 & 0.92 & 0.074 & 0.92 \\ TransMR-PET [57] & 33.75 & 0.93 & 0.015 & 0.004 & 0.92 & 0.073 & 0.92 \\ Proposed SRR Algorithm & 33.98 & 0.93 & 0.016 & 0.004 & 0.93 & 0.070 & 0.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Extended quality metrics comparison including perceptual metrics such as LPIPS and MS-SSIM. The methods are consistent with Table 2.