esa-sceva/satcom-chunk-collection huggingface dataset:
# Stereo Digital Image Correlation in MATLAB
## 2 Framework Theory
### Calibration
#### 2.2.2 Radial Distortion Model  
The radial distortion model is sufficient to account for distortion in the images caused by imperfections of the lens system [60]. First the normalized, ideal image coordinates \(\hat{\mathbf{x}}_{\eta_{j}}=\left[\begin{array}{cc}\hat{x}_{\eta_{j}}&\hat{y}_ {\eta_{j}}\end{array}\right]^{T}\) are computed as  
\[\hat{\mathbf{x}}_{\eta_{j}}=\left[\begin{array}{cc}\hat{x}_{\eta_{j}}\\ \hat{y}_{\eta_{j}}\end{array}\right]=\Phi\Big{(}\mathbf{K}_{j}^{-1}\hat{\mathbf{x}}_{ s_{j}}\Big{)}. \tag{2}\]  
Figure 1: Schematic diagram of coordinate systems employed by the camera model.  
Thereafter, the unit-less radial distortion parameters, \(\kappa_{j}^{1}\) and \(\kappa_{j}^{2}\), are used to compute the normalized, distorted image coordinates, \(\mathbf{x}_{n_{j}}=\left[\begin{array}{cc}x_{n_{j}}&y_{n_{j}}\end{array}\right]^{T}\), as [61]  
\[\mathbf{x}_{n_{j}}=\bigg{(}1+\kappa_{j}^{1}\hat{\mathbf{x}}_{n_{j}}^{T}\hat{\mathbf{x}}_{n_{ j}}+\kappa_{j}^{2}\bigg{(}\hat{\mathbf{x}}_{n_{j}}^{T}\hat{\mathbf{x}}_{n_{j}}\bigg{)}^{2} \bigg{)}\hat{\mathbf{x}}_{n_{j}}. \tag{3}\]  
Finally, the distorted coordinates in the distorted sensor CS, \(\mathbf{x}_{s_{j}}=\left[\begin{array}{cc}x_{s_{j}}&y_{s_{j}}\end{array}\right]^ {T}\), are obtained as  
\[\mathbf{x}_{s_{j}}=\left[\begin{array}{c}x_{s_{j}}\\ y_{s_{j}}\end{array}\right]=\Phi\Bigg{(}\mathbf{K}_{j}\left[\begin{array}{c}x_{n _{j}}\\ y_{n_{j}}\\ 1\end{array}\right]\Bigg{)}. \tag{4}\]
# A Look at the Importance of Chirality in Drug Activity: Some Significative Examples
## 10 Anti-Histamine Drugs (H\({}_{1}\) Receptor Antagonists)
### Selective Serotonin Reuptake Inhibitors (SSRIs)  
The 4-arylpiperidines, such as paroxetine (**53**) and femoxetine (**54**), are chiral selective 5-HT reuptake inhibitors used for the treatment of depression, anxiety, and panic disorders [134]. Paroxetine contains two stereogenic centers and, hence, forms erythro and three (or _cis_ and _trans_) diastereoisomers, occurring as pair of enantiomers. The _cis_ isomers of paroxetine are significantly less potent than _trans_ ones and, among them, the _trans_-(-)-paroxetine ((35,4_R_)-3-(1,3-benzodioxol-5-yloxymethyl)-4-(4-fluorophenyl)piperidine) displays the highest 5-HT uptake [135]. Lately, studies on this compound have been necessarily increased because of the COVID-19 pandemic and now post-COVID syndrome in which the treatment of depression has become essential for numerous patients, regardless of gender [136; 137]. Femoxetine is a closely related compound and, as for paroxetine, the (35,4_R_)-femoxetine is the one responsible with an antidepressant activity [138].
# Study of the Photo-Response of Doped GaAs with Aging
## 2 Materials and Methods  
In 1995, 10 \(\upmu\)m thick GaAs:Sn and GaAs:Ge films were grown on a GaAs substrate oriented in the (100) direction by Liquid Phase Epitaxy at a temperature of 800 \({}^{\circ}\)C. The precursor materials used to grow the film were Ga(7N), Ge(5N) and Sn(5N), including the purity in parentheses, and also GaAs to obtain As [13]. Through this method, the resulting films contained native defects such as V\({}_{\text{As}}\) and interstitial Ga due to excessive Ga [14]. To facilitate crystalline ordering and avoid deep defects or traps, thermal annealing was performed at 620 \({}^{\circ}\)C for 4 h in an H\({}_{2}\) environment. The Ge concentrations ranged from \(1\times 10^{16}\) atoms cm\({}^{-3}\) to \(1\times 10^{19}\) atoms cm\({}^{-3}\) and the Sn concentrations ranged from \(1\times 10^{17}\) atoms cm\({}^{-3}\) to \(2\times 10^{18}\) atoms cm\({}^{-3}\). The impurity concentrations were measured by the capacitance-voltage method. A C-V Plotter Princeton Applied Research Mod 410 was used to obtain the capacitance (C) at different applied voltages (V). Through the 1/C\({}^{2}\) vs. V graph, the slope of the line was measured, which is proportional to the inverse of the concentration N of the impurities. We have assumed that all impurities are ionized at room temperature due to their low ionization energies.  
In this study, PL and PR were measured in those samples. Subsequently, all GaAs epitaxial samples were stored in closed cabinets with the following environmental characteristics: atmospheric pressure of 1017 hPa, average temperature of 24 \({}^{\circ}\)C, relative humidity of 76% and darkness. In 2001, [PERSON] [14] re-measured PL at different temperatures and PR at 300 K and stored the samples in the same environment conditions for another 20 years. In 2021, again, we took the PL and PR spectra of these samples.  
The PL system in this work used an external excitation source, a laser with a wavelength of 488 nm focused on the sample by a cylindrical lens. A convergent lens in the entrance slit of a Horiba FHR1000 spectrophotometer focused the light emission from the sample under study. To remove the Rayleigh radiation reflected by the sample, an LP'488 filter was located in the entrance slit. The spectrophotometer has a diffraction grating of 1800 lines per millimeter, a focal distance of 100 cm and a spectral resolution of 0.010. A CCD camera with thermo-electric cooling, was used as a photodetector. The samples were placed inside a cold finger cooled through a closed circuit of liquid He. The temperature was measured through a carbon resistance. Measurements were made between 13 K and 300 K. The PR spectra were recorded by using a modulation source, a He-Ne laser with a wavelength of 488 nm, cut by a mechanical chopper attached to a lock-in amplifier. The radiation probe from a quartz-tungsten-halogen lamp was passed through a monochromator. The detector used in this technique was a PbS photoppin. Measurements were made at 300 K.
## 2 Material and Methods
### Quantitation of Odor-Active Compounds through Standard Addition  
Approximately 1 mg/mL stock solutions of odor-active compounds and internal standards (2-octanol and ethyl decanoate) for standard addition were prepared in absolute ethanol and stored at 4 \({}^{\circ}\)C. The compounds and internal standard were added to the tea infusions and stirred at 300 r/min for 10 min to equilibrate. The compounds were extracted through SE-SPE or desorbed through SPME according to their volatility. Data were collected using GC-MS in the selected-ion monitoring (SIM) or extracted-ion chromatograph mode (EIC). The qualifier and quantifier ions of the compounds are listed in Table 1. Quantitation was accomplished by building standard curves of the amount of each compound added versus the ratios of the peak areas of the quantifier ions and internal standard. Five points, including a blank, were used to build the standard curves.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **CAS** & **Quantifier Ion** & **Qualifier Ions** \\ \hline Dimethyl sulfide & 75-18-3 & 62 & 62; 47; 61 \\
2-Methyl-propanal & 78-84-2 & 72 & 43; 41; 72 \\
3-Methyl-Butanal & 590-86-3 & 44 & 44; 41; 43 \\
2-Methyl-Butanal & 96-17-3 & 57 & 57; 58; 41 \\ Dimethyl disulfide & 624-92-0 & 94 & 94; 79; 45 \\ Hexanal & 66-25-1 & 56 & 56; 44; 41 \\ Methional & 3268-49-3 & 48 & 48; 104; 47 \\ Dimethyl trisulfide & 3658-80-8 & 126 & 126; 79; 45 \\ Benzenacetaldehyde & 122-78-1 & 91 & 91; 120; 92 \\ Linalool & 78-70-6 & 71 & 71; 93; 55 \\ Phenylethyl alcohol & 60-12-8 & 91 & 91; 92; 122 \\ (E,Z)-2,6-Nonadienal & 557-48-2 & 69 & 41; 70; 69 \\ (E)-2-Nonenal & 18829-56-6 & 70 & 70; 43; 55 \\ Benzoic acid & 65-85-0 & 105 & 105; 122; 77 \\ (Z)-Linalool oxide (pyranoid) & 14009-71-3 & 68 & 68; 94; 59 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Qualifier and quantifier ions of key aroma volatiles.
## Appendix E  
The Laplace transform of some very practical functions can be found in a simpler way (by skipping evaluation of the Bromwich integral), using the infinite series representation of ratios of the modified Bessel functions discussed in Section 5:  
\[\frac{1}{2\sqrt{s}}\frac{I_{p+1}\big{(}\sqrt{s}\big{)}}{I_{p}\big{(}\sqrt{s} \big{)}}=\sum_{k=1}^{\infty}\frac{1}{j_{p,k}^{2}+s} \tag{11}\]  
where \(j_{p,k}^{2}\) are the consecutive zeros (roots) of the standard Bessel function of the p order \(J_{p}\big{(}j_{p,k}\big{)}=0\). 1. The Laplace transform of the formula found in [PERSON]'s work [30], which defines the relaxation memory function \(\Phi_{p}(t)\) for a general class of viscoelastic models is as follows: \[\widetilde{\Phi}_{p}(s)=\frac{4(p+1)}{s}\frac{1}{2\sqrt{s}}\frac{I_{p+1}\big{(} \sqrt{s}\big{)}}{I_{p}\big{(}\sqrt{s}\big{)}}=F_{1}(s)\cdot F_{2}(s)\] (12)  
This can be archived with help of Equation (11). The inverse Laplace transform of both frequency domain functions \(F_{1}(s)\) and \(F_{2}(s)\) is well known from elementary Laplace transforms tables  
\[\mathcal{L}^{-1}\{F_{1}(s)\}=\mathcal{L}^{-1}\bigg{\{}\frac{4(p+1)}{s}\bigg{\}} =f_{1}(t)=4(p+1) \tag{13}\]\[\mathcal{L}^{-1}\{F_{2}(s)\}=\mathcal{L}^{-1}\Bigg{\{}\sum_{k=1}^{\infty}\frac{1}{j _{p,k}^{2}+s}\Bigg{\}}=f_{2}(t)=\sum_{k=1}^{\infty}e^{-\beta_{p,k}^{2}t} \tag{104}\]  
The time domain solution of the function is Equation (104) is convolution integral from above time domain Functions (105) and (106):  
\[\Phi_{p}(t)=\int_{0}^{t}f_{1}(t-u)\cdot f_{2}(u)du=\int_{0}^{t}4(p+1)\cdot\sum _{k=1}^{\infty}e^{-\beta_{p,k}^{2}u}du=4(p+1)\int_{0}^{t}\sum_{k=1}^{\infty}e^ {-\beta_{p,k}^{2}u}du \tag{105}\]  
As can be seen from the final form of the above formula, the problem comes down only to determining the definite integral from the infinite series of the exponential function:  
\[\int_{0}^{t}\sum_{k=1}^{\infty}e^{-\beta_{p,k}^{2}u}du=\sum_{k=1}^{\infty}\frac {1-e^{-\beta_{p,k}^{2}t}}{j_{p,k}^{2}}=\sum_{k=1}^{\infty}\frac{1}{j_{p,k}^{2} }-\sum_{k=1}^{\infty}\frac{e^{-\beta_{p,k}^{2}t}}{j_{p,k}^{2}} \tag{106}\]  
The first r.h.s. series from the above solution is the Rayleigh series, the solution of which is presented in the main part of this work under Equation (1). The final result, therefore, takes the following form:  
\[\Phi_{p}(t)=4(p+1)\left[\sum_{k=1}^{\infty}\frac{1}{j_{p,k}^{2}}-\sum_{k=1}^{ \infty}\frac{e^{-\beta_{p,k}^{2}t}}{j_{p,k}^{2}}\right]=4(p+1)\Bigg{[}\frac{1 }{4(p+1)}-\sum_{k=1}^{\infty}\frac{e^{-\beta_{p,k}^{2}t}}{j_{p,k}^{2}}\Bigg{]} =1-4(p+1)\sum_{k=1}^{\infty}\frac{e^{-\beta_{p,k}^{2}t}}{j_{p,k}^{2}} \tag{107}\]  
As can be seen from the above solution, it is identical to the one obtained by [PERSON] [30]. However, at the stage of its derivation, the need to use the complex procedure to solve the Bromwich integral was eliminated. 1.
# Advanced Optimization Techniques for Federated Learning on Non-IID Data
## 5 Experimental Results
### Results of Federated Learning with Cyclical Learning Rate
#### 5.2.2 CLR Results on Fashion MNIST  
Moving on to the Fashion MNIST dataset, we observe the optimization of federated training achieved using a cyclic learning rate under non-IID conditions.  
First, we performed the learning rate range test (LRRT).  
The learning rate range test identified 0.01 as the minimum learning rate, where the loss began to decrease, and 0.07 as the maximum, where the loss started to slow down, as illustrated in Figure 8. These values were used as limits for the CLR strategy during federated training.  
Next, we compared the performance of FedAvg with a fixed learning rate and FedAvg with CLR on the Fashion MNIST non-IID dataset.  
Figure 9 illustrates the evaluation accuracy over the training rounds for FedAvg with a fixed learning rate set at 0.01 and FedAvg with a cyclical learning rate policy, where the learning rate oscillates between a minimum of 0.01 and a maximum of 0.07.  
Table 6 highlights the differences in evaluation accuracy between these two strategies per 10 rounds of training.  
Applying the cyclical learning rate strategy allowed the model to reach a higher accuracy of 78.3% in 200 rounds, compared to 71.2% with a fixed learning rate.  
Also, as shown in Table 7, CLR allowed the model to achieve the target test accuracy much faster than the fixed learning rate, showing a substantial improvement in training speed.  
Figure 8: Learning rate range test for fashion MNIST.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Max Accuracy (\%)** & **Rounds to 94\%** & **Speedup (Rounds)** \\ \hline mnist\_sgd\_fixed & 94.04 & 195 & x1 \\ \hline mnist\_sgd\_clr & 97.01 & 64 & x3.04 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Maximum accuracy and rounds to 94%.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Communication Rounds** & **SGD with Fixed Lr** & **SGD with Cyclical Lr** \\ \hline \hline  
\hline \end{tabular}
\end{table}
Table 6: Evaluation accuracy differences per 10 training rounds.  
Figure 9: Fashion MNIST non-IID with fixed learning rate vs. Fashion MNIST non-IID with CLR.
# LightMFF: A Simple and Efficient Ultra-Lightweight Multi-Focus Image Fusion Network
## 4 Experiments
### Ablation Study  
To validate the effectiveness of our proposed components and design choices, we conduct comprehensive ablation studies on the Lytro dataset [46]. Specifically, we investigate how the initial decision map prior and edge information contribute to the network's performance. We design four experimental settings:  
1. Group 1 (Baseline): Only source images are fed into the network, representing the conventional approach where the network directly learns to distinguish focused from defocused regions.
2. Group 2 (w/ Decision Map): Source images and the initial decision map are used as input. This setting evaluates the effectiveness of our proposed paradigm shift from direct classification to decision map refinement.
3. Group 3 (w/ Edge Map): Source images are complemented with edge maps. This setting examines the impact of explicit boundary information without decision map guidance.
4. Group 4 (Full Model): Our complete model, incorporating both the initial decision map and edge information, demonstrates the synergistic effect of all components.  
Table 3 and Figure 12 showcase the results of our ablation study. The baseline model (Group 1) performed less effectively, highlighting the inherent difficulties of directly classifying focus/defocus regions with an ultra-lightweight architecture. Adding the initial decision map (Group 2) significantly boosted performance. This confirms our hypothesis that reframing the learning objective to decision map refinement genuinely reduces task complexity. The edge information (Group 3) also brought noticeable improvements over the baseline, affirming that explicit boundary cues help the network better identify focus transitions. These ablation studies confirm that our task transformation approach and targeted guidance signals successfully reduce computational complexity while maintaining superior multi-focus fusion (MFF) performance.  
Figure 12: Visualization of ablation study results on the Lytro dataset [46].
## 3 Results and Discussions
### Effect of Microstructural Evolution on Mechanical Properties  
Tensile test curves are shown in Figure 7. The mechanical properties in terms of Ys, Rm, and A% of the UFH processed material are summarized in Table 6.  
According to the Hall-Petch relationship (Equation (2)), the tensile behavior of a material depends on the average grain size:  
\[\sigma_{y}=\sigma_{0}+\frac{k_{y}}{\sqrt{d}} \tag{2}\]  
where \(\sigma_{y}\) is the yield strength, \(\sigma_{0}\) is the intrinsic strength of the material (corresponding to a single crystal structure), and \(k_{y}\) is a constant depending on the alloy. Figure 8 shows the dependence trend between the average grain size and yield strength, showing a linear dependence with \(k_{y}\) = 32.7 MPa mm\({}^{-1/2}\) and \(\sigma_{0}\) = 245 MPa.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Heat Treatment** & **Process Conditions** & **Average Grain Size (\(\mu\)m)** & **HV1** \\ \hline CA & Tpeak: 1050 \({}^{\circ}\)C & 120.5 \(\pm\) 70.4 & 192.7 \(\pm\) 2.7 \\ M310-50A & Industrial reference & 104.7 \(\pm\) 52.7 & 186.3 \(\pm\) 2.3 \\  & annealing process & 104.7 \(\pm\) 52.7 & 186.3 \(\pm\) 2.3 \\ UFH & HR: 206 \({}^{\circ}\)C/s, Tpeak: 1038 \({}^{\circ}\)C & 56.2 \(\pm\) 30.6 & 199.2 \(\pm\) 5.5 \\ UFH & HR: 264 \({}^{\circ}\)C/s, Tpeak: 1085 \({}^{\circ}\)C & 62.5 \(\pm\) 32.3 & 201.6 \(\pm\) 1.3 \\ \hline \end{tabular}
\end{table}
Table 5: Average grain size comparison between samples that have undergone CA and UFH heat treatments.  
Figure 7: Stress–strain curves for materials that have undergone UFH treatment.  
From the results shown, it follows that the best mechanical resistance corresponds to a T\({}_{\text{peak}}\) of 1000 \({}^{\circ}\)C. For this sample, the values of Rm, Ys, and A% are highly comparable with those found in the literature for sheets with the same thickness and chemical composition as that investigated in this work [55; 72; 73]. Therefore, these literature works support the ability to successfully process NGO electrical steels through UFH treatment, providing materials with microstructure and mechanical characteristics comparable to industrial products manufactured with conventional technologies.
# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
## 4 Results and Discussion
### Discussion of mRSU and sRSU for Propagation Models regarding Number of Vehicles  
The propagation models using a consistent routing protocol, an interfacing protocol, and an increasing number of nodes are evaluated in this scenario. A realistic situation is used, with 49, 79, and 109 vehicles interacting through 500 KB packets. The mRSU in the three-log distance and Friis propagation models has the best performance since it has the least latency, as seen in Figure 11(a). In the event of a delay, the sRSU and mRSU for the Nakagami propagation model operate similarly. The highest packet delivery ratio is shown in Figure 11(c) using the mRSU for the Nakagami, Rician, and Rayleigh propagation models.  
However, the throughput of the sRSU in the Friis propagation model decreases as the number of nodes increases, but the throughput of the mRSU rises as the number of nodes increases. For the mRSU, the packet delivery ratios in the Friis, three-log distance, and Rayleigh propagation models show the best outcomes, but the performance of the mRSU in Nakagami improves dramatically as the number of nodes increases, while the sRSU delivers the opposite results. In terms of energy consumption, when the number of nodes is varied, the FreeSpace and shadowing propagation models produce nearly identical performance for both sRSUs and mRSUs, whereas the sRSU's energy consumption is lower than that of the mRSU in the range propagation and Nakagami propagation models. However, the precise energy quota to be saved for emergency packets for mRSUs may be obtained using the related computations. Ultimately, from the perspective of the end-to-end delay and packet delivery ratio, the three-log distance and Friis models surpass the other models, while Nakagami, Rayleigh, and Rician have higher throughput.
## 3 Results and Discussion  
### The Effective Infiltration Depth  
Figure 3 shows the evolution of the infiltration depth of the polymer solution or water. Water infiltrated the soil faster than the polymer solution. Approximately three hours were required for water to completely infiltrate the entire sample (50 mm). Within the same period, the 0.5% ADNB solution infiltrated 30 mm, which was 3/5 of the whole length.  
However, the observed infiltration depth of the solution may not be exactly the effective infiltration depth of the polymer, which may be characterized by the remaining length of the soil samples after 10 min of immersion (shown in Figure 4). The soil sample treated with water completely disintegrated within 1 min. The sample treated with 2% ADNB had a maximum effective infiltration depth of 5.5 mm (Figure 5).  
Figure 3: Evolution of the infiltration depth of the polymer solution or water.  
When the concentration of the polymer emulsion was lower than 2% ADNB, the water-stable length of the soil sample increased with the concentration of the polymer emulsion. However, a polymer emulsion concentration higher than 2% resulted in an adverse effect: the effective water-stable length of the soil sample treated with 5% ADNB was only 2.5 mm, which was 45% of the maximum. Since the polymer emulsion was viscous and easily adhered to the soil particles, during the infiltration and adherence of the polymer emulsion to the soil, small particles agglomerated and the number of pores in the soil decreased. The polymer solution with a high concentration easily reached the infiltration limit where the pores of the surface soil layer were completely filled with polymer solution and an impermeable layer had formed through which polymers could not pass. Thus, the additional polymer emulsion was prevented from infiltrating and accumulated to a certain thickness on the surface (Figure 6). The higher the concentration of the polymer solution was, the quicker the impermeable layer formed, and the less the polymer solution could infiltrate the soil; as a result, a decrease in the effective water-stable depth was observed.  
Figure 4: The effective infiltration depth of ADNB.  
Figure 5: Residuals of the different soil samples after immersion.  
Figure 6: Accumulation of the polymer emulsion on the surface.
## 2 Related Work
### Node Activity Scheduling  
Since the sleeping state is the least energy consuming state, keeping nodes in the sleeping state is a good way to save energy. However, this should be accompanied by node activity scheduling to prevent network partition and message loss when some nodes are sleeping. We can distinguish two types of solutions according to their dependency to the medium access.
# Studies of General Precipitation Features with TRMM PR Data: An Extensive Overview
###### Abstract  
The Precipitation Radar (PR), the first space-borne precipitation radar onboard the Tropical Rainfall Measuring Mission (TRMM) satellite, could observe three-dimensional precipitation in global tropical regions and acquire continuous rainfall information with moderate temporal and high spatial resolutions. TRMM PR had carried out 17 years of observations and ended collecting data in April, 2015. So far, comprehensive and abundant research results related to the application of PR data have been analyzed in the current literature, but overall precipitation features are not yet identified, a gap that this review intends to fill. Studies on comparisons with ground-based radars and rain gauges are first introduced to summarize the reliability of PR observations or estimates. Then, this paper focuses on general precipitation features abstracted from about 130 studies, from 2000 to 2018, regarding lightning analysis, latent heat retrieval, and rainfall observation by PR data. Finally, we describe the existing problems and limitations as well as the future prospects of the space-borne precipitation radar data.  
tropical rainfall measuring mission; precipitation radar; precipitation features; latent heat; lightning 2019 11 80 10.3390/rs11010080 10.  
After over 17 years of data collecting, the instruments on TRMM were turned off on 8 April 2015. The spacecraft rushed into the Earth's atmosphere over the South Indian Ocean on 15 June 2015 and most of the spacecraft was burned up.