esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance of Payement Temperature Prediction Models
## 3 Results
#### 3.1.2 Predicted Minimum Temperature
\begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline \multirow{2}{*}{**Analysis**} & \multirow{2}{*}{**Material**} & \multicolumn{2}{c}{**Viljeon Model**} & \multicolumn{2}{c}{**SHRP Superpave Model**} & \multicolumn{2}{c}{**Diefenderfer Model**} \\ \cline{3-8}  & & **Before** & _After_ & **Before** & _After_ & **Before** & _After_ \\  & & **Calibration** & _Calibration_ & _Calibration_ & **Calibration_ & **Calibration** & _Calibration_ \\ \hline \multirow{4}{*}{\(R^{2}\)} & Asphalt & 0.6137 & _0.6137_ & 0.4674 & _0.4674_ & 0.3776 & _0.3776_ \\  & Gravel & 0.6702 & _0.6702_ & 0.7321 & _0.7321_ & 0.8471 & _0.8471_ \\  & Concrete & 0.6766 & _0.6766_ & 0.7572 & _0.7572_ & 0.8794 & _0.8794_ \\  & Block paving & 0.6678 & _0.6678_ & 0.6962 & _0.6962_ & 0.8939 & _0.8939_ \\ \hline \multirow{4}{*}{\(RMSE\)} & Asphalt & 4.8524 & _4.8316_ & 8.0644 & 5.3390 & 4.6242 & 4.5878 \\  & Gravel & 5.1873 & 2.6673 & 7.6755 & 2.5906 & 2.9683 & _1.7031_ \\  & Concrete & 5.5343 & 2.7527 & 8.6976 & _0.7572_ & 3.8005 & _1.8192_ \\  & Block paving & 6.1729 & 2.7966 & 8.7745 & 2.2907 & 3.7749 & _2.0786_ \\ \hline \multirow{4}{*}{\(MRE\) (\%)} & Asphalt & 219.06 & _271.73_ & 309.69 & _290.59_ & 83.69 & _84.40_ \\  & Gravel & 174.83 & _71.08_ & 231.78 & _77.86_ & 63.62 & 48.65 \\ \cline{1-1}  & Concrete & 102.98 & 58.36 & 214.77 & _2.3315_ & 95.46 & _47.58_ \\ \cline{1-1}  & Block paving & 118.74 & **54.72** & 208.96 & _48.36_ & 81.19 & 46.59 \\ \hline \multirow{4}{*}{\(VAF\) (\%)} & Asphalt & 51.30 & _\(-\)3.30_ & 35.43 & \(-\)13.29 & 30.48 & 24.20 \\ \cline{1-1}  & Gravel & 47.47 & 55.87 & 59.09 & _60.00_ & 78.04 & 82.43 \\ \cline{1-1}  & Concrete & 47.49 & 57.30 & 64.26 & _73.15_ & 75.33 & 79.50 \\ \cline{1-1}  & Block paving & 28.01 & 37.49 & 47.22 & 57.89 & 68.25 & 68.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Analysis of predicted minimum temperatures.
## 2 Related Work
The study is performed at a frequency of 28 GHz under an omnidirectional pattern and implements simulations to obtain the Path Loss parameters. The infrastructure is identified as an RSU located at 5 m height. The data is fitted to a CI free-space model, also used in research previously reported as [40; 42]. Further studies have been performed in order to analyze and enhance wireless channel operation, by analyzing interference impact, the use of deep learning techniques to optimize beam forming parameters or the proposal of new effective receiver volume configurations, among others [45; 46; 47; 48; 49]. In this work, compared to the previously presented works, the wireless communication channel in a vehicular environment is analyzed in the mmWave frequency bands (28 GHz), with the aid of an in-house developed 3D-RL algorithm, a powerful tool to expand the scarcity of empirical data in these frequency bands. In comparison with [42], together with [41], they apply a RT technique in HST communication scenarios, a completely different environment from the urban vehicle setup presented in this work. In the case of [40; 43; 44], these studies are dedicated to V2I communication \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**REF** & **Type** & **Environment** & **Channel Metrics** & **Frequency** & **Year** \\ \hline
[36] & - & Indoor & PDP, PAS & 60 GHz & 2017 \\
[37] & Cellular & Urban Microcell & PL exponent & 28 GHz & 2018 \\
[38] & - & Inside a Bus & RMS DS, PL & 60 GHz & 2018 \\
[39] & Cellular & Urban & PL exponent, RMS DS & 72 GHz & 2013 \\
[40] & V2I & Urban & PL, RMS DS & 28 GHz & 2018 \\
[41] & HST & Several & PL, RMS DS, PDP, CT & 25.25 GHz & 2018 \\
[42] & HST & Rural railway & PL, RMS DS & 28 GHz & 2019 \\
[43] & V2I & Urban/Highway & KF, RMS DS, AS & 60 and 21.6 GHz & 2020 \\
[44] & V2I & Urban & PDP, KF, RMS DS, AS & 28 GHz & 2019 \\ \hline \hline \end{tabular} Power Delay Profile (PDP), Power angular spectrum (PAS), root-mean-square Delay Spread (RMS DS), Path Loss (PL), Rician K-factor (KF), Angular Spread (AS), Coherence Time (CT), High-Speed Trains (HST).  
\end{table} 
Table 1: RT/RL approaches for mmWave frequency bands. in different conditions. In the work carried out in [50], the communication channel is modeled for a V2V environment with the inclusion of obstructions, but the research is carried out in the 5 GHz band. Comprehensive surveys in relation to vehicular communications can also be found in [51; 52]. However, the presented work aims to increase the knowledge about the wireless communication channel at mmWave bands, with special focus on the impact of different obstructions in the V2V communication links, and its effects on large and small-scale fading parameters. The proposed methodology can be further extended with no loss of generality to multiple studies, such as interference analysis or coverage/capacity estimations, for different scenarios and applications.
## 4 Results and Discussions
### The Influence of PCM Melting Temperature
The variations of the PCM roof interior surface temperature, \(T_{n}\), and the corresponding environmental temperature, \(T_{e}\), are displayed in Figure 8. Although the interior surface temperature rise of the PCM roof with a melting temperature \(T_{m}=32\) could be delayed more than the other PCM roofs, its interior surface temperature, \(T_{n}\), during the nighttime was the highest. Furthermore, during the nighttime, the environmental temperature, \(T_{e}\), was much lower than the melting temperature of the PCM roofs in the current work, which means that the environmental temperature could cool down the temperature inside the roof. Based on this, a higher melting temperature of a PCM roof at nighttime has a negative contribution to the energy saving amount of the PCM roof. Hence, by balancing the trade-off influence of the PCM layer melting temperature during the daytime and nighttime, an optimum melting temperature, \(T_{m}\), exists, which was 31 in this work for a PCM roof to reduce the maximum amount of energy consumption. For the remainder of the paper, the melting temperature of \(T_{m}=31\) was used. Figure 6: The effect of the melting temperature on the PCM roof energy saving amount. Figure 7: The PCM layer average liquid fraction during the summer cooling season at different melting temperatures.
## 4 Discussion
### Alternator with Air Core  
The air core alternators demonstrated a high damping percentage, specifically 67%, indicating their ability to minimize losses due to magnetic hysteresis and eddy currents. This phenomenon can be explained by the absence of ferromagnetic material, which facilitates a more uniform magnetic field and reduces interference in the generation of electromotive force (EMF). The high resonance frequency of 501.50 Hz for these prototypes suggests optimal efficiency in high-frequency applications where quick response and the minimization of energy losses are crucial. However, the energy generation measured in terms of Vrms and Vpeak was the lowest among the three types.
## 4 Traffic Data
### Traffic Model  
In this measurement, we considered a highway scenario where the vehicles were driving on the M0 motorway. SUMO implements a microscopic traffic simulation, meaning that each vehicle's movement is simulated individually. To model the vehicles' movements, it was sufficient to specify where the vehicles entered and left the highway. After that, SUMO handled the exact modeling of the movement. Our road model consisted of one highway and highway driveways where the vehicles could enter, leave or pass on the highway (one road for each direction). First, we calculated the entry, exit, and passing traffic at each driveway (we named it traffic demand; see Figure 4). After that, we calculated the flows (practically each vehicle's start and end points). Eventually, we transformed these data into a format accepted by the traffic simulator. The entry and exit points were mapped with the map topology of the traffic simulator--SUMO--to interpret these points as the starting and ending points of a particular traffic flow. Using this model and the related calculations, we were able to replicate the traffic counter data in the simulation.
## 3 Results
### Estimation Based on Linear Regression
Table 10 shows the estimated figures of merit (MSE, MAE, and RMSE) for EV accuracy in linear regression for the implemented methods described earlier. Statistical error figures supported the visual inspection findings, offering better results for the model that incorporates the endogenous and exogenous variables simultaneously. In particular, lower error was obtained when considering both sets of variables, showing a RMSE of 6.51. Figure 6 and Table 11 shows the results of the linear regression modeling coefficients with the relationship among parameters (endogenous and exogenous variables) and the valuation itself. In the first method including only the endogenous variables, the last FCF, together with the actual debt, were the most influencing parameters in EV, whilst, when considering both spectra of variables, the most influencing ones were FCF, GDP, and WACC. The results obtained here are consistent with theoretical concepts experienced earlier and published in the literature. Figure 7 and Table 12 show the results and the statistical analysis, respectively, for EV estimation by sector. Utilities sector was confirmed to be the most accurately followed by the deployed model, with an RMSE of 1.1746 being the smallest one. This fact was visually verified in Panels (m,n,o). In these specific companies, the estimated EV (in blue) and real EV (in red) drew closer to each other than other represented sectors, and it was especially remarkable when endogenous and exogenous variables were simultaneously used, see Panel (o). \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Variables** & **MSE** & **RMSE** & **MAE** & **R\({}^{2}\)** \\ \hline
**Endogenous** & 48.89 & 6.99 & 4.93 & 0.17 \\
**Exogenous** & 52.53 & 7.24 & 4.24 & 0.10 \\
**Endogenous and Exogenous** & 42.50 & 6.51 & 4.49 & 0.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Estimated EV errors by linear regression. Figure 6: Actual estimated EV using (**a**) endogenous, (**b**) exogenous, (**c**) endogenous and exogenous variables. Figure 7: EV estimated by sector. Reading from top to bottom, we find Panels (**a–c**) for Consumer Cyclicals; (**d–f**) for Consumer Non- Cyclicals; (**g–i**) for Industrial; (**j–l**) for Technology, and (**m–o**) for Utilities. Reading from left to right, we have endogenous, exogenous, and endogenous and exogenous variables in the input space.
# Semantic-Aware Low-Light Image Enhancement by Learning from Multiple Color Spaces
## 4 Model Details
### Semantically Guided Multi-Color Space Loss
#### 4.5.2 Semantically Guided Color Histogram Loss  
The color histogram is a statistical tool for describing the color distribution in images, often used for color analysis. However, traditional color histograms primarily represent global color distributions, overlooking distinctions between different local instances. This can lead to local color deviation and loss of details.  
To mitigate these issues, we first generate a semantic segmentation map using an HRNet semantic segmentation network. We then compute semantically guided color loss across different color spaces.  
In the RGB space, we employ the Semantically Guided Color Histogram (SCH) Loss, as previously described, and thus omit redundant details here.  
In the LAB color space, we compute losses separately for the L channel (lightness) and AB channels (color). The L-channel loss is measured using Mean Squared Error (MSE). The A and B channel losses are calculated using a histogram distance-based probability distribution method. The formulation is as follows:  
\[L^{I_{gt}},A^{I_{gt}},B^{I_{gt}}=RGB2 LAB(I_{gt}) \tag{18}\]  
\[L^{I_{out}},A^{I_{out}},B^{I_{out}}=RGB2 LAB(I_{out}) \tag{19}\]  
\[\mathcal{L}_{lab}=E_{I_{out},I_{gt}}[(L_{Ig}-L_{I_{out}})^{2}-\sum_{i=1}^{n}Q( A_{i}^{I_{gt}})log(Q(A_{i}^{I_{out}}))-\sum_{i=1}^{n}Q(B_{i}^{I_{gt}})log(Q(B_{i}^{I_{ out}}))] \tag{20}\]  
where \(I_{gt}\) and \(I_{out}\) represent the ground truth and enhanced images, respectively. \(Q\) denotes the quantization operator, and \(RGB2 LAB\) represents the color space conversion function.  
Similarly, in the LCH color space, we compute losses for the L, C, and H channels: The L and C channel losses use MSE loss. The H channel loss is computed using cross-entropy based on probability distributions. The corresponding formulations are as follows:  
\[L^{lst},C^{lst},H^{lst}=RGB2 LCH(I_{gt}) \tag{21}\]  
\[L^{lat},C^{lat},H^{lat}=RGB2 LCH(I_{out}) \tag{22}\]  
\[\mathcal{L}_{lch}=E_{Iout,lst}[-\sum_{i=1}^{n}Q(L_{i}^{lst})log(Q(L_{i}^{lat}) )+(C_{Igt}-C_{Iout})^{2}+(H_{Igt}-H_{Iout})^{2}] \tag{23}\]  
where \(I_{gt}\) and \(I_{out}\) denote the ground truth and enhanced images, respectively, and RGB2 LCH is the color space conversion function.  
Total Semantically Guided Multi-Color Space Loss By integrating the losses across multiple color spaces, we define the total semantically guided multi-color space loss as follows:  
\[\mathcal{L}_{color}=\mathcal{L}_{rgb}+\mathcal{L}_{lab}+\mathcal{L}_{lch} \tag{24}\]  
In the improved SKF framework, we incorporate this semantically guided multi-color space loss into the overall loss function, which is formulated as follows:  
\[\mathcal{L}_{all}=\mathcal{L}_{recon}+\lambda_{color}\mathcal{L}_{color}+ \lambda_{SA}\mathcal{L}_{SA} \tag{25}\]  
where \(\mathcal{L}_{recon}\) represents the reconstruction loss. \(\lambda_{color}\) and \(\lambda_{SA}\) are hyperparameters that balance the contributions of the different loss terms.
# Monte-Carlo-Based Estimation of the X-ray Energy Spectrum for CT Artifact Reduction
## 2 Materials and Methods
### Maximum Likelihood Expectation Maximization Method (MLEM)
#### 2.2.2 Experiments  
In order to provide the required data for the MLEM method, an experimental setup was established to scan a polyvinyl chloride (PVC) step wedge using FelXCT, a micro tomography imaging system. It is worth mentioning that X-ray micro tomography imaging systems have been widely used for a variety of applications [19; 36; 37; 38]. A geometric sketch of the step wedge is shown in Figure 3.  
Since the FleXCT scanner produces an X-ray cone beam and the MLEM method assumes that the X-rays pass the sample in a parallel way, the experimental setup should be arranged in such a way that it optimally fulfils the MLEM method prerequisites. The FleXCT X-ray scanner instrument has 10 motorized axes to move the sample, detector and X-ray tube with respect to each other, allowing not only conventional radiography or CT, but a wide range of other scanning geometries (e.g., laminography, dynamic zooming, helical CT, 4 DCT) [19]. This versatility of the instrument could be optimally used to approach parallel beam conditions. To accomplish this, the flat panel detector was placed at a distance of 1 m from the X-ray source, after which the PVC step wedge was put as close as possible to the detector (see Figure 4). In the next step, the axis center of the X-ray cone beam and center of the detector was aligned with the center of each step wedge separately, after which a radiograph was taken for each tube voltage (60-200 kV, 96 scans). In order to reduce the radiograph noise, 10 replicates were made for each radiograph, resulting in a total measuring time of 500 ms per step. After scanning all 12 steps for each tube voltage, the step wedge was removed, after which both flat and dark field projections were taken. After flat field and dark field correction, a region of interest (ROI) was defined in the center of each step (approx. 1 \(\times\) 1 mm\({}^{2}\)), of which the mean value was used in the MLEM calculations. Figure 5 shows normalized projections of the step wedge for the center of the beam set in the center of step wedge no. 11 for tube voltages of 60, 100 and 180 kV.
# The Current State of Research of Wire Arc Additive Manufacturing (WAAM): A Review
## 3 Modeling
### Process-Related Modeling  
The weld bead geometry has a significant influence on the geometrical correctness of the build part. The prior estimation of the bead's behavior can be beneficial for the path planning and manufacturing process. Therefore, [PERSON] et al. derived a thermo-capillary-gravity model for a WAAM process which includes the material properties of the melt and the process variables of the manufacturing process, which have an influence on the weld bead shape (Figure 33) [8].  
They showed that the developed model had good correlation with the experiments for mild steel. [PERSON] and [PERSON] conducted some work on adjustment of the melt pool and the commonly used [PERSON]'s heat source for additive manufacturing [405].  
Figure 33: Input and output flowchart of the thermo-capillary-gravity model by [PERSON] [8].  
[PERSON] et al. described a modeling approach with a thermal conductivity model in detail and compared their modeling approach with experimental results for a simplified test piece [59]. Using such models can lead to a reduction of heat accumulation for honeycomb-like structures. The understanding and prediction of material transition is one of the challenging parts in welding-related process simulations and is currently under development not only for WAAM but welding in general [35; 380; 381; 407]. [PERSON] et al. developed a model for heat transfer and fluid flow and compared it to experiments with good correlation [381; 407]. A similar model has been developed by [PERSON] et al. for heat transfer, mass transfer, fluid flow and geometry morphology [408].  
Due to the distortion occurring in arc-based additive manufacturing, methods for reducing distortion are the subject of modeling. Here, different fixation variants are investigated in terms of their suitability for distortion reduction and the resulting residual stress distribution [409]. [PERSON] et al. found, based on numerical analysis, that less restraints reduced the amount of distortion and residual stresses and that edge fixation was the most suitable fixation strategy for the wall-like structure in their analysis [409]. Furthermore, they developed a model which could be used to avoid heat accumulation, one of the key problems in WAAM of small-to-large parts [404]. This heat accumulation leads to long interlayer waiting times and unwanted inhomogeneous mechanical properties across the part. Aside from the modeling of the WAAM process itself, different post-processes for WAAM-made parts are also subject to modeling activity (e.g., rolling processes [104; 410]). The commonly used part of WAAM is the modeling and prediction of the building process regarding layer height and width.  
Overall, modeling of WAAM is one of the key points to predict the final part properties and needs to be integrated into the process chain to fully utilize the benefits of WAAM.
# An Advanced Shear Strength Criterion for Rock Discontinuities Considering Size and Low Shear Rate
## Appendix A  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample Number** & **Normal Stress (kPa)** & **Shear Rate (mm/s)** & **Peak Shear Strength (kPa)** \\ \hline \multirow{8}{*}{Sample 1\#} & & 0.05 & 123.85 \\  & & 0.1 & 135.4 \\  & & 0.15 & 195.7 \\  & & 0.2 & 205.55 \\  & & 0.3 & 202.25 \\  & & 0.4 & 199.5 \\  & & 0.5 & 200.6  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample Type** & \begin{tabular}{c} **Shear Rate** \\ **V (mm/s)** \\ \end{tabular}  & \begin{tabular}{c} **Peak Shear** \\ **Strength** \\ **\(\tau\) **(MPa)** \\ \end{tabular}  & \begin{tabular}{c} **Normal Stress** \\ **\(\sigma\) **(MPa)** \\ \end{tabular}  & \ \begin{tabular}{c} **Arctan (\(\tau\)/o)** \\ **(\({}^{\circ}\))** \\ \end{tabular}  &
\begin{tabular}{c} **\(J\)\(\mathrm{\bf{{\rm{}}}}\)\(\mathrm{\bf{{\rm{}} \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample Type**} & \multicolumn{2}{c}{**Shear Rate**} & \multirow{2}{*}{**Normal Stress**} & \multirow{2}{*}{**Arctan (\(\tau/o\))**} & \multirow{2}{*}{**\(JRC_{n}\)-log\({}_{10}\)**} \\  & \(V\) **(mm/s)** & **Strength** & \(\sigma\) **(MPa)** & **(\({}^{\circ}\))** & **(\({}^{\circ}\))** \\ \hline  & 0.02 & 1.695 & 1 & 14.213 & 17.737 \\  & 0.02 & 2.39 & 1 & 20.762 & 25.911 \\  & 0.02 & 2.71 & 1 & 40.309 & 50.306 \\  & 0.02 & 3.11 & 2 & 14.213 & 13.459 \\  & 0.02 & 4.208 & 2 & 20.762 & 19.661 \\  & 0.02 & 4.58 & 2 & 40.309 & 38.171 \\  & 0.02 & 4.45 & 3 & 14.213 & 10.956 \\  & 0.02 & 6.289 & 3 & 20.762 & 16.005 \\ Cement & 0.02 & 6.69 & 3 & 40.309 & 31.073 \\ discontinuity & 0.1 & 1.675 & 1 & 14.213 & 17.737 \\  & 0.1 & 2.33 & 1 & 20.762 & 25.911 \\  & 0.1 & 2.63 & 1 & 40.309 & 50.306 \\  & 0.1 & 3.02 & 2 & 14.213 & 13.459 \\  & 0.1 & 4.02 & 2 & 20.762 & 19.661 \\  & 0.1 & 4.31 & 2 & 40.309 & 38.171 \\  & 0.1 & 4.3 & 3 & 14.213 & 10.956 \\  & 0.1 & 5.96 & 3 & 20.762 & 16.005 \\  & 0.1 & 6.27 & 3 & 40.309 & 31.073 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Test data of different cement discontinuity samples with the shear rate varying from 0.02 mm/s to 1 mm/s (after [51]). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sample Type**} & \multirow{2}{*}{**Shear Rate**} & \multirow{2}{*}{**Normal Stress**} & \multirow{2}{*}{**Arctan (\(\tau/o\))**} & \(JRC_{n}\)-log\({}_{10}\)** \\  & \(V\) **(mm/s)** & **Strength** & \(\sigma\) **(MPa)** & **(\({}^{\circ}\))** & **(\({}^{\circ}\))** \\ \hline  & 0.033 & 0.117 & 0.1 & 49.428 & 34.363 \\  & 0.05 & 0.117 & 0.1 & 49.398 & 34.363 \\ Granite & 0.083 & 0.115 & 0.1 & 48.960 & 34.363 \\ discontinuity & 0.1 & 0.123 & 0.1 & 51.001 & 34.363 \\ (\(13^{\circ}\)) & 0.13 & 0.126 & 0.1 & 51.595 & 34.363 \\  & 0.2 & 0.134 & 0.1 & 53.181 & 34.363 \\  & 0.25 & 0.139 & 0.1 & 54.339 & 34.363 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average values of rate-dependent friction angle \(\varphi_{\text{v}}\) for different granite discontinuity samples with the shear rate varying from 0.03 mm/s to 1 mm/s.
## 2 Results and Discussion
### Future Directions  
Despite the potential advantages, more work is needed to optimize the resin approach. The use of resin beads is convenient for comparing properties such as binding capacity and fouling for both commercial and our own laboratory-synthesized resins. Here we used a commercially available strong cation exchange resin where sulfuric acid/methanol is used to elute the resin. It is entirely feasible to use a weakly basic anion exchange resin where raising the pH releases the algae from the resin [25]. At lower pH (7-8) these resins are positively charged and effectively bind algae. Raising the pH to \(\sim\)pH 10 or above deprotonates the resin rendering it uncharged such that algae are released. Sodium methoxide in methanol, a strongly basic commercial transesterification reagent, would raise the pH, thereby releasing the algae and catalyzing the transesterification reaction. Here also, it would be critical to use a nonporous resin that did not entrain water.  
Future directions are aimed at determining which approach, using strong or weak anion exchange resins is more effective in terms of binding capacity, fouling, and FAME yields. It will also be important to develop new modes for use of the resins such as thin film coatings or resin coated particles that do not entrain water. Finally, more studies of the transesterification reaction are in order since, as shown with _Neochloris_, the one step conversion to FAME was not quite complete and was further affected by the nitrogen starvation.