esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Equipment
### Characterizations  
FTIR measurement: A drop of glycerin or triglyceride triacetate was placed on the surface of cotton fiber, cellulose acetate fiber, and glass fiber, respectively. The FTIR spectra were detected by a VATAR FT-IR370 spectrometer after the sample was dry. The wavelength range was 400-4000 cm\({}^{-1}\). Sixty-four scans were conducted for each FTIR measurement.  
Scanning electron microscope (SEM) characterization: A JSM-7500F/JSM-7500F scanning electron microscope was used to photograph the surface morphology of cotton fiber, cellulose acetate fiber, glass fiber, respectively. The accelerating voltage was 10.0 kV. Before testing, the surface of each material was coated with gold by sputtering coater (Hitachi, E-1010, Hitachi International Ltd., Tokyo, Japan).  
XRD (X-ray diffraction): Cotton fiber filter, cellulose acetate fiber filter, and glass fiber filter were measured with an Empyrean instrument, respectively using Cu K\(\alpha\) radiation of wavelength 1.5406 A (40 kV, 40 mA). The scanning range was 10-50\({}^{\circ}\).  
DFT (density function theory) analysis: All structural optimizations and energy calculations were performed using Materials Studio's DMol3 module. The generalized gradient approximation (GGA)-Perdew-Burke-Ernzerhof (PBE) functional was used to calculate the exchange correlation energy. The basis set of double numerical orbits + orbital polarization function (DNP) was selected. In order to accelerate the convergence rate of self-consistent field (SCF) iteration and allow orbit relaxation during calculation, Fermi Smearing was set to 0.005 Ha. The convergence criteria of atomic energy change, maximum stress and displacement were set as 1 \(\times\) 10\({}^{-5}\) Ha, 2 \(\times\) 10\({}^{-3}\) Ha/A, and 5 \(\times\) 10\({}^{-3}\) A, respectively. After the convergence test, the orbital cutoff accuracy was set to 4.9 A; the binding energy (E\({}_{\text{b}}\)) calculation formula is as follows:  
\[\text{E}_{\text{b}}=\text{E}_{\text{total}}-\text{E}_{\text{G}}-\text{E}_{ \text{C}} \tag{1}\]  
where, Etotal, E\({}_{\text{G}}\), E\({}_{\text{C}}\) represent the total energy after combination and the monomer energy after structural optimization, respectively.
## 6 Conclusions and Future Works
This methodology can be used to solve other types of problems, such as (i) considering a 10- or 20-year planning perspective with regard to the coupling of D-STATCOMS and renewable energy sources in order to reduce the grid operation costs; (ii) employing the generalized normal optimizer and the arithmetic optimization algorithm, currently considered metaheuristic optimizers, to aid the solution of the master-slave optimization methods presented herein (moreover, for MINLP models, it is vital to consider the joint use of D-STATCOMs and batteries); (iii) considering longer time periods, be it weeks or months, and their effect on charging behaviors (a study dealing with uncertainties related to demand curves could address optimization issues); (iv) considering relevant load imbalances within three-phase networks caused by the installation of D-STATCOMs and evaluating their impact, as well as further analyzing the voltage stability of the network with these devices, along with the net present value (NPV), where the savings associated with network losses could be compared against the installation costs in order to assess the project's profitability and even calculate the break-even period; and (v) studying the network reconfiguration via sensitivity analysis in order to observe the effects of integrating D-STATCOMs into different network configurations. Conceptualization, methodology, software, and writing (review and editing), G.F.B.-P., B.C.-C. and O.D.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. This article is derived from the graduation project Optimal integration of D-STATCOMs in radial and meshed distribution networks using a MATLAB-GAMS interface, presented by the student German [PERSON] to the Electrical Engineering program of Universidad Distrital Francisco Jose de Caldas Engineering School as a partial requirement to obtain a degree in Electrical Engineering. The authors of this paper declare no conflict of interest.
# Determining Key Parameters with Data-Assisted Analysis of Conditionally Automated Driving
## 4 Results
### Safety Measures
#### 4.3.4 Time to Collision (TTC)  
Calculating minimal TTC is a well-established road-safety measurement [106]. It is determined as the minimum of all discrete TTCs measured between the moment of TOR and the moment of passing the roadblock. A current TTC is the time budget until collision assuming constant vehicle speed and acceleration. The longer the minimal TTC, the safer the take-over.  
We predicted the minimal TTC (P7) using Attributes A1-A33 and P1-P6. The histogram of measured minimal TTC is plotted in Figure 4b. The regression model with the lowest RRSE = 66.1% for the approximation of the minimal TTC was created with SVM (see Table 10). It proved to be significantly better (\(p\) < 0.001) than the baseline, but not significantly better than the multiple linear regression (RRSE = 72.1%, \(p\) = 0.427) or the M5' tree of linear regression models (RRSE = 73.7%, \(p\) = 0.160). The five attributes with the largest weights are _reaction time (P2)_, weight: \(-\)0.54; _First reaction = none (A23)_, weight: \(-\)0.42; _reaction before the obstacle became visible (A20)_, weight: 0.34; _TOR-to-minimal-speed interval (A27)_, weight: \(-\)0.26; and _maximal acceleration (A29)_, weight: 0.24.
## 9 Performance Comparison  
The performance of the algorithm was compared with algorithms used for the detection of faults in the presence of solar energy using DWT [11], WPT [10] and Stockwell transform [12]. It is observed that the performance of the algorithms based on the DWT and WPT was affected by the presence of noise. The DWT technique was found to work well for a noise level up to 50 dB, SNR whereas the WPT gives better results with a noise level of 32 dB SNR. These two techniques did not affect a high level of noise such as 10 dB SNR. However, the performance of the proposed algorithm is effective even in the presence of a high noise level of 10 dB SNR. Further, the Stockwell transform-based algorithm reported in [12] uses the patterns of various contours and plots for the fault detection, which range from a half cycle to one cycle. However, the proposed algorithm is effective to detect faults in the presence of SE within a quarter-cycle time period. Hence, it is established that the performance of the algorithm proposed is superior to DWT [11], WPT [10] and Stockwell transform [12]-based methods.
# Physics-Based Noise Modeling and Deep Learning for Denoising Permanently Shadowed Lunar Images
## 3 Methods
### Overall Pipeline  
As shown in Figure 6, the proposed FRET is a hierarchical network with a U-shaped structure, where full-scale skip connections are employed between the decoder and encoder to expand the receptive field and capture multi-scale information. The specific process is as follows:  
Figure 4: Illustration of simulated images. (**a**) Noiseless images with sufficient lighting; (**b**) Synthesized noisy images.  
Figure 5: Real images of PSRs.  
Given a noisy lunar PSR image \(\widetilde{\Gamma}\in\mathbb{R}^{1\times\text{H}\times\text{W}}\), the FRET first applies a convolution operation to map the input image to low-level features. In the encoding phase, following the U-shaped design, the feature map undergoes N encoding layers with downsampling, where each encoding layer is a REWin Transformer block.  
For the decoding phase, the proposed FRET consists of N-1 decoding layers, each also being a REWin Transformer block. Due to the limitations of simple skip connections in fully capturing information from various scales, we introduce the concept of full-scale skip connections in the decoding part of the network to enhance its ability to learn multi-scale information. The full-scale skip connections modify the interconnections between the encoder and decoder, as well as within the decoder subnet [17]. This approach allows each decoding layer to merge feature maps from smaller and same-scale ones in the encoder, as well as from larger scales in the decoder. As a result, the feature map of each decoder layer is capable of capturing complex and extensive semantics across all scales. As shown in Figure 6, when i denotes the i-th downsampling layer along the encoding direction, the output feature map \(X_{De}^{i}\) can be expressed by Equation (3):  
\[X_{De}^{i}=\left\{\begin{aligned} X_{En}^{i},& i=N\\ FLWin\left(\underbrace{\left[C\left(D\left(X_{En}^{k}\right) \right)\right]_{k=1}^{i-1},C\left(X_{En}^{i}\right)}_{\text{Scales: }1^{th}\sim i^{th}},& \underbrace{C\left(U\left(X_{De}^{k}\right)\right)_{k=i+1}^{N}}_{\text{ Scales: }(i+1)^{th}\sim N^{th}}\right),& i=1, \cdots,N-1\end{aligned}\right. \tag{3}\]  
In this equation, the function \(C(.)\) represents the convolution operation, \(FLWin(.)\) denotes a REWin Transformer block, and \(D(.)\) and \(U(.)\) represent the upsampling and downsampling operations, respectively. The notation [] indicates concatenation along the channel dimension.  
After the N-1 decoding stages, we apply a convolution to obtain the residual image \(\text{R}\in\mathbb{R}^{1\times H\times W}\). Finally, the recovered image is obtained as \(\widetilde{\Gamma}^{{}^{\prime}}\in\widetilde{\Gamma}+R\). We use the mean Squared Error (MSE) loss to train FRET, where D represents the size of the dataset:  
\[Loss(\widetilde{\Gamma}^{{}^{\prime}},\widetilde{S})=\frac{1}{D}\sum_{i=1}^{d }\widetilde{(\Gamma}^{{}^{\prime}}-\widetilde{S})^{2} \tag{4}\]  
Figure 6: The structure of the FRET.
## 5 Conclusions and Outlook
Even so, the model-based algorithm had relative deviations of at most \(-20.2\%\), which, in addition, can be reduced by correcting the systematic error that was determined according to the algorithm characterization. As a result, a clear improvement in the detection of turbulence wedges and their areas compared to the state-of-the-art algorithm was achieved. Hence, a more accurate analysis of the impact turbulence wedges have on the efficiency and the annual energy production of the wind turbine is enabled. As an outlook, the detection of the turbulence wedges' positions still can be improved. This concerns, in particular, turbulence wedges near the edge of the thermogram, which currently have a lower probability of being detected, but which could be solved by padding the thermogram. Additionally, the determination of the area can be improved. Instead of using linearly decreasing weights for the weighted correlation evaluation, the actual intensity gradient in the thermogram (in particular at the wedge tip) needs to be automatically recognized and then taken into account in the weighting. Furthermore, the wedge detection algorithm can be enhanced to work with subpixel accuracy by using interpolated thermograms and templates. Lastly, an automatic determination of the wedge angle is desirable in the future so that the algorithm then works completely automatically. A further next step is the application of the algorithm for intensive field measurements of wind turbines in motion to investigate the additional area with turbulent flow due to the turbulence wedges over a one year operation and longer. On a shorter time scale, the algorithm will be used to study dynamic flow effects and how they influence the turbulence wedges. Conceptualization, A.-M.P., M.S. and A.F. ; methodology, A.-M.P., D.G. and A.F. ; software, A.-M.P. ; formal analysis, A.-M.P. ; investigation, D.G. ; writing--original draft preparation, A.-M.P. ; writing--review and editing, A.-M.P., D.G., M.S. and A.F. ; visualization, A.-M.P. ; supervision, A.F. and M.S. ; project administration, M.S. ; funding acquisition, D.G., M.S. and A.F. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the German Federal Ministry for Economic Affairs and Energy (BMWi) within the project of PreciWind, grant number 03 EE3013A. **Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Implementation and Results  
In this study we evaluate the performance of multimedia streaming in a VANET scenario. The mobility model we use is the Manhattan Mobility Model [12] and EvalVid [13] generates the multimedia traffic. We perform the simulation with help of NS-2 [14] on Cygwin [15] and the parameters used in the simulation are listed in Table 1.
## 3 Results and Discussion
### Adsorption Experiments of Pb(II)/Cd(II) with O-MWCNT\({}_{S}\)
#### 3.2.3 Initial Concentration
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **adsorption** \\ **Conditions** \\ \end{tabular} } & \multirow{2}{*}{**Heavy Metal Ions**} & \multicolumn{4}{c}{**Langmuir**} & \multicolumn{4}{c}{**Freundlich**} \\ \cline{3-8}  & & **Q\({}_{\text{m}}\) (mg:g\({}^{-1}\))** & **K\({}_{\text{L}}\) (L\(\cdot\)mg\({}^{-1}\))** & **R\({}^{2}\)** & **n\({}_{\text{F}}\)** & **K\({}_{\text{F}}\)** & **R\({}^{2}\)** \\ \hline \multirow{2}{*}{Single} & Pb(II) & 5.73 & 3.12 & 0.99 & 6.67 & 3.97 & 0.73 \\  & Cd(II) & 3.34 & 0.29 & 0.91 & 2.86 & 1.09 & 0.47 \\ \hline \multirow{2}{*}{Competitiveness} & Pb(II) & 7.11 & 0.44 & 0.96 & 3.13 & 2.69 & 0.95 \\  & Cd(II) & 0.78 & 4.84 & 0.94 & \(-12.5\) & 0.97 & 0.2 \\ \hline \hline \end{tabular} Note: Here is only listed the parameters of the reaction temperature of Pb/Cd as 298 K and the competitive adsorption of Pb and Cd.  
\end{table} 
Table 2: Parameters fitted to the adsorption process using the Langmuir and Freundlich models. Figure 6: Adsorption isothermal linear fitting. **(a,b)** Single system; **(c,d)** competitiveness system. comparing k\({}_{1}\) and k\({}_{2}\), it is evident that Pb(II) demonstrates a greater rate of physisorption than Cd(II) in both single and competitive systems (k\({}_{\rm{Pb1}}\)\(>\) k\({}_{\rm{Cd1}}\)), but, on the contrary, the chemisorption rate of Cd(II) is greater than that of Pb(II) (k\({}_{\rm{Cd2}}\)\(>\) k\({}_{\rm{Pb2}}\)) [27; 44; 45].
# MEASegNet: 3D U-Net with Multiple Efficient Attention for Segmentation of Brain Tumor Images
## 4 Experiments and Results  
### Datasets and Preprocessing  
To evaluate the segmentation performance on brain tumor MRI images, we selected the publicly available and freely usable BraTS (Brain Tumor Segmentation) challenge dataset, which includes BraTS2019, BraTS2020, and BraTS2021 [29; 30; 31]. Among the three datasets, the BraTS2019 dataset includes 335 training cases and 125 validation cases, the BraTS2020 dataset includes 369 training cases and 125 validation cases, and the BraTS2021 dataset includes 1251 training cases and 219 validation cases. All training data included images manually annotated by neuroradiologists. The validation data used for online validation contained manually annotated images that were not publicly accessible. During validation, the model was first trained on a local server. Upon completion of training, online validation was performed on the Synapse platform, where the final evaluation results were obtained. The Synapse platform is accessible via [[https://www.synapse.org/#platform](https://www.synapse.org/#platform)]([https://www.synapse.org/#platform](https://www.synapse.org/#platform)) (accessed on 14 March 2025).  
Figure 4: An illustration of the building blocks of the SLRFB block.  
The images in the BraTS dataset contain a significant amount of black background. To focus more on the analysis and processing of brain images, the black background was removed as much as possible during the preprocessing stage. In this process, the input image volume was cropped to the minimum bounding box containing nonzero voxels, and then the image size was cropped from 240 \(\times\) 240 \(\times\) 155 to 128 \(\times\) 128 \(\times\) 128 using the random cropping method, and then the input image was preprocessed by z-score normalization. To minimize the impact on segmentation performance, all intensity values were clipped to the 1 st and 99 th percentiles of the non-zero voxel distribution within the volume. Data augmentation was also employed in this study, including random Gaussian noise, rand smoothing, random flip, random shift, random contrast intensity adjustment, and random rotation of 90 degree.
## 6 Clinical and Pre-Clinical Studies
Reports in the literature have shown that collagen/chitosan/graphene oxide films with the addition of basic fibroblast growth factor (bFGF) have been studied for wound healing applications [76]. bFGF was observed to provide continuous release for at least 28 days. No cytotoxic effects were found by adding GO to the films and, in fact, the addition of GO actually improved cytocompatibility. This in vivo murine wound study confirmed that the GO prototype films were capable of accelerating the wound healing process in comparison with the control group. Alkylated chitosan/GO sponges have also been studied for their hemostatic effects, which can be used in wound healing applications [77]. The results of a rabbit femoral injury study showed higher hemostatic efficacy and promoted the adhesion of erythrocytes and platelets compared to the control samples. Additionally, by increasing the concentration of GO, blood clotting efficiency, platelet activation levels, and the release of intracellular Ca\({}^{2+}\) all increased.
## 5 Case Study Settings
#### Systems under Test
PutInDSpToAspQue() function is used whenever data is transmitted through a serial port, shown in Figure 19, and the routerTxMbx mailbox is posted whenever data are received via a serial port in the SDR embedded system, shown in Figure 20.  
The proposed framework has been assessed in this study and determined the synchronizations bugs in each interface, as well as the combination of these interfaces. These bugs could not force into the SDR embedded system, so we induced these bugs in MATLAB and created a knowledge base. The ramp signal was used because it is the best test signal to test these interfaces. All possible synchronization bugs mentioned in Table 1 were simulated in MATLAB. Several bugs were induced to see how the shape of the ramp signal changes. Therefore, we created all possible waveforms of the ramp signal. The knowledge base was created by inducing bugs on a single interface and in a combination of interfaces. Once the knowledge base was created, the next step was the emulators. On the emulator side, different PEs simultaneously ran with interfaces. Every emulator of the PEs was connected to different PCs. Therefore, several screens of the PE interfaces showed what happens inside the PEs using emulators. The ramp signal was sent to all processors as it is visually the easiest signal to identify for bugs. In this way, bugs were detected in the interfaces and then compared with a knowledge base. Once the system was in the development phase, we could not see through an emulator. In this case, the input was sent the final output of the system could be seen. Moreover, if the output was incorrect, the only way to go back and fix the bug was by using the proposed approach. Whenever a bug occurs during the inter-processor communication, we could refer to our knowledge base to identify the possible interface/interfaces causing the errors. Figure 19: Code used whenever data are transmitted through serial port. The proposed framework should effectively simulate communication across multiple PEs for detecting and localizing bugs in real-time communication.
# Evaluation of Fatigue Life of Asphalt Concrete Mixtures with Reclaimed Asphalt Pavement
## 2 Description of Test Methods  
As part of laboratory tests, a number of research methods covered by European standards were applied. In the scope of binder tests, penetration (PN-EN 1426) and softening temperature (PN-EN 1427) tests were carried out. In terms of basic physical properties of mineral-bituminous mixtures, the density (PN-EN 12697-6), maximum density (PN-EN 12697-5), air void content (PN-EN 12697-8), and soluble binder content (PN-EN 12697-1) were tested.  
The fatigue life was determined using the four-point bending beam test (4 PB) according to PN-EN 12697-24 standard. Tests were performed in constant strain mode (several strain amplitudes), at a frequency of 10 Hz and a temperature of 10 \({}^{\circ}\)C. These are typical testing conditions used in Poland. Beams with size of 50 \(\times\) 63 \(\times\) 380 mm were cut from plates compacted by laboratory roller compactor (PN-EN 12697-33). The analysis of fatigue life most often evaluates the \(\varepsilon_{6}\) parameter, which determines the strain under the fatigue test, during which the life of 1 million load cycles is obtained. A higher value means potentially better fatigue properties. Additional information is provided by the fatigue characteristic, which is described by the formula:  
\[N=A\cdot e^{b}, \tag{1}\]  
where: \(N\)--fatigue life, \(\epsilon\)--strain during fatigue test; \(A\), \(b\)--linear regression parameters, and \(b\) is the inclination of the fatigue line.  
Prior to fatigue tests, the same beams were subjected to frequency sweep test, which enabled estimation of the stiffness modulus according to PN-EN 12697-26.
# A Novel Deep Learning Network Model for Extracting Lake Water Bodies from Remote Sensing Images
## 3 Results and Discussion
### Model Performance Comparison
#### 3.4.2 Comparison of Model Performance Evaluation Metrics  
In order to make a more accurate comparison, we take 1354 remote sensing images as a test set to test the model for 120 times, establish the confusion matrix between the observed label and the predicted label and calculate the optimal values of the R50A3-LWBENet model and the U-Net, AU-Net, RU-Net, ARU-Net, SER34 AUNet, and MU-Net models in the three evaluation indexes of _PA_, _MPA_, and _MIoU_ based on the confusion matrix, and the calculation results are shown in Table 4.  
As can be seen from Table 4, the top 3 models in ascending order of time spent are U-Net, SER34 AUNet, and R50A3-LWBENet. Although R50A3-LWBENet time performance is not optimal, it is improved over the remaining six models in the three performance evaluation metrics of _PA_, _MPA_, and _MIoU_. The improvement results are: 1.4, 1.35, and 2.8 percentage points higher than the U-Net model; 1.1, 1.2, and 2.3 percentage points higher than the AU-Net model; 4.9, 5.7, and 9.6 percentage points higher than the RU-Net model; and 0.5, 0.45, and 1.1 percentage points higher than the ARU-Net model; and 0.3, 0.25, and 0.6 percentage points higher than the SER34 AUNet model; and 0.2, 0.15, and 0.4 percentage points higher than the MU-Net model. The R50A3-LWBENet model has better performance in dealing with multi-scale features, noise interference and boundary ambiguities, and the model is more robust with better generalization ability.