esa-sceva/satcom-chunk-collection huggingface dataset:
# AdaptiveConv2d: A Novel Convolutional Module for Medical Image Segmentation
## 2 Materials and Methods
### Introduction of a Second Optimization Objective
#### 2.2.4 Synergy with the Main Segmentation Loss  
In medical image segmentation, the primary objective (e.g., Dice or cross-entropy loss) supervises the distinction between foreground and background pixels. However, it does not inherently account for how features are distributed within each channel. By adding \(\mathcal{L}_{\text{energy}}(X_{c})\) to the overall loss function, we can perform the following:  
* **Suppress Local Noise and Outliers:** Large or abnormal deviations are penalized more when the channel's variance is small, thereby improving the uniformity of feature maps and reducing noise impact.
* **Retain Critical Local Structures:** For channels with small variance, subtle changes are given a larger penalty. This helps the model better preserve nuanced boundaries or small lesion areas--these are vital in many diagnostic tasks.
* **Balance Global and Local Context:** Channels with naturally broad intensity ranges are not overly restricted, thus maintaining global coherence without sacrificing the ability to detect large-scale variations.  
Hence, the energy function and the primary segmentation loss complement each other: the former enforces channel-wise stability, while the latter ensures accurate pixel-level classification. This dual-layer supervision is especially advantageous in complex medical scenarios where both fine-grained detail and robust global representation are essential.
## 5 Results and Discussion
### Sensitivity Analysis
#### 5.3.1 Ensemble Size  
Sensitivity of the results to the importance sampling ensemble size was checked by computing estimates with a progressively smaller number of samples from the priors. Figure 7 presents the relative error, defined as the percentage difference in the estimate and experimental value, and uncertainty level for ensemble sizes ranging from 100 to 50,000. It was found that, on average, the estimated values stabilize as the ensemble size increases. The peaks which occur at approximately 15,000 and 25,000 samples are due to the sample-based nature of the estimate. Since the estimates are the sample mean of the resampled ensemble the exact estimated value for each parameter changes as more samples are included in the ensemble. As such, it is encouraging to note that the estimates do appear to stabilize. Specifically, the average error of the estimates stabilizes when using an ensemble size of approximately 15,000, with the maximum error stabilizing with approximately 40,000 samples. The uncertainty smoothly decreases as the ensemble increases, stabilizing when approximately 25,000 samples are used. Error in the estimates converges faster than the uncertainty since an accurate estimate of the mean is easier to attain than a stable estimate of the variance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\boldsymbol{p_{\text{sub}}}\) (kPa) & **0.91** & **1.00** & **1.09** & **1.18** \\ \hline Maximum & 405.8 & 825.7 & 1032.8 & 2000.9 \\ Mean & 246.5 & 475.8 & 662.6 & 1469.8 \\ Uncertainty & 13.87\% & 12.32\% & 10.14\% & 6.67\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Estimated average maximum and mean contact pressures along with a measure of uncertainty in the form of a standard deviation as a percentage of the mean estimated value for the cases with medial compression. All data are in (Pa).
# Estimation of Litho-Fluid Facies Distribution from Zero-Offset Acoustic and Shear Impedances
## 4 Methodology
### Calculation of the Anisotropy Parameters: Epsilon and Delta  
After comparing and validating \(Z_{P}\) and \(Z_{S}\), obtained from the isotropic inversion, statistical modeling, and MLFN, the best P- and S-impedance volumes were applied to [PERSON]'s anisotropy equations to solve for the parameters Epsilon and Delta. Both the partially inverted impedances, zero-offset impedances, and central angles of the angle stacks (10, 20, and 32.5 degrees) were applied to the impedance-domain [PERSON]'s equations in (30) and (31), yielding six equations, as shown below:  
\[Z_{P1}=Z_{P0}\big{[}1+\delta sin^{2}(10)cos^{2}(10)+\epsilon sin^{4}(10)\big{]} \tag{52}\]  
\[Z_{P2}=Z_{P0}\big{[}1+\delta sin^{2}(20)cos^{2}(20)+\epsilon sin^{4}(20)\big{]} \tag{53}\]  
\[Z_{P3}=Z_{P0}\big{[}1+\delta sin^{2}(32.5)cos^{2}(32.5)+\epsilon sin^{4}(32.5) \big{]} \tag{54}\]  
\[Z_{S1}=Z_{S0}\big{[}1+\left(\frac{Z_{P0}}{Z_{S0}}\right)^{2}(\epsilon-\delta sin ^{2}(10)cos^{2}(10)\big{]} \tag{55}\]  
Figure 5: A graphical representation of the MLFN for both the \(Z_{P}\) and \(Z_{S}\) models. The input layer consists of three nodes, which are the near, mid, and far impedances, the hidden layer consists of 20 nodes, and the output layer consists of one node, which is the zero-offset impedance.  
\[Z_{S2}=Z_{S0}\big{[}1+\bigg{(}\frac{Z_{P0}}{Z_{S0}}\bigg{)}^{2}(\epsilon-\delta) sin^{2}(20)cos^{2}(20)\big{]} \tag{56}\]  
\[Z_{S3}=Z_{S0}\big{[}1+\bigg{(}\frac{Z_{P0}}{Z_{S0}}\bigg{)}^{2}(\epsilon-\delta) sin^{2}(32.5)cos^{2}(32.5)\big{]} \tag{57}\]  
where \(Z_{P1}\), \(Z_{P2}\), and \(Z_{P3}\) are the P-impedance volumes at the near, mid, and far angles, respectively, while \(Z_{S1}\), \(Z_{S2}\), and \(Z_{S3}\) are the S-impedance volumes at the near, mid, and far angles, respectively. \(Z_{P0}\) and \(Z_{S0}\) are the zero-offset P- and S-impedances, respectively, and \(\epsilon\) and \(\delta\) are the anisotropy parameters.  
By adding Equations (52)-(54) to each other and Equations (55)-(57) to each other, two equations were obtained such that one of them related Epsilon and Delta to the \(Z_{P}\) volumes and the other one related Epsilon and Delta to the \(Z_{S}\) volumes. Finally, the two resulting equations were solved by MATLAB for the anisotropy parameters Epsilon and Delta, as shown below:  
\[\text{Epsilon}=2.3(Z_{P0}Z_{P1}+Z_{P0}Z_{P2}+Z_{P0}Z_{P3}+Z_{S0}Z_{S1}+Z_{S0}Z_{ S2}+Z_{S0}Z_{S3}-3Z_{P0}^{2}-3Z_{S0}^{2})/(Z_{P0}^{2}) \tag{58}\]  
\[\text{Delta}=2.3(Z_{P0}Z_{P1}+Z_{P0}Z_{P2}+Z_{P0}Z_{P3}-0.3Z_{S0}Z_{S1}-0.3Z_{S0 }Z_{S2}-0.3Z_{S0}Z_{S3}-3Z_{P0}^{2}+0.9Z_{S0}^{2})/(Z_{P0}^{2}) \tag{59}\]
# Impact of a Microbial Cocktail Used as a Starter Culture on Cocoa Fermentation and Chocolate Flavor
## 2 Results
### Chemical Changes During Fermentation
#### 2.2.1 Sugar Consumption and Metabolite Production  
During the six days of fermentation the concentrations of glucose, fructose, and citric acid were evaluated in the pulp, and the results are shown in Figure 2. Citric acid was fully metabolized at 24 h of fermentation in both assays (Figure 2A,B). In both fermentations, glucose and fructose were completely consumed at 72 h. After 24 h of inoculated fermentation the PH15 sample showed greater sugar consumption compared to the non-inoculated one, but in the following hours, both inoculated and non-inoculated fermentations presented the same profile of sugar consumption (Figure 2A,B).  
Ethanol, lactic acid, and acetic acid were evaluated in the pulp and inside the beans, and are shown in Figure 3. The inoculated fermentation of PH15 showed the maximum value of ethanol in the pulp (8.44 g/kg at 48 h), and this compound inside the beans also reached higher values (6.65 g/kg at 48 h) when compared to the control fermentation (PH15 NI) (Figure 3A). However, at the end of the fermentation, the ethanol concentration was higher (4.34 g/kg) inside the beans in the non-inoculated fermentation.  
The microbial inoculation accelerated the sugar consumption in the first 24 h of cocoa fermentation, whereas the ethanol production was accelerated in the first 48 h of fermentation (Figures 2B and 3A). The pulp of PH15 I showed the highest acetic acid concentration between 48 h and 120 h of cocoa fermentation, and the lowest concentration at the end of the fermentation (144 h, Figure 3C). In contrast, PH15 NI showed the highest acetic acid concentration in the residual pulp at the end of the fermentation (144 h, Figure 3C). Overall, the concentration of acetic acid was higher in the fermentation of PH15 I (Figure 3C).  
After 144 h, the pulp of PH15 I showed greater concentration of lactic acid than the pulp of PH15 NI (Figure 3B). In both PH15 I and PH15 NI samples there was no lactic acid penetration in the cotyledon. Acetic acid was higher inside the beans in PH15 I at later stages, at 120 h (2.70 g/kg), and at 144 h (2.53 g/kg).  
Figure 3: Detection by HPLC of ethanol **(A)**; lactic acid **(B)**; and acetic acid **(C)** during 144 h of fermentation of PH15 NI () and PH15 I (); full symbols correspond to metabolites detected in the pulp while open symbols to those detected inside the beans.
# Adaptive-Size Dictionary Learning Using Information Theoretic Criteria
## 2 Ingredients  
### Information Theoretic Criteria  
ITC serve for assessing the adequacy of a model to a process described by experimental data, by combining its goodness of fit (approximation error) with its complexity. The underlying model in Equation (1) is \(\mathbf{Y}=\mathbf{D}\mathbf{X}+\mathbf{U}\), where \(\mathbf{U}\) is a matrix with entries that follow a Gaussian distribution with zero mean and an unknown variance (the same for all entries) [19]. Denoting \(T=mN\), the goodness of fit is expressed via the Root Mean Square Error  
\[\text{RMSE}=\frac{1}{\sqrt{T}}\|\mathbf{Y}-\mathbf{D}\mathbf{X}\|_{F}. \tag{2}\]  
The complexity depends primarily on the number of parameters; in the DL or DR case, this is  
\[\text{NoP}=sN+(m-1)n. \tag{3}\]  
The first term corresponds to the number of nonzeros in \(\mathbf{X}\). The second term is the number of independent elements of the dictionary; we subtract \(n\) from the total of \(mn\) elements to account for the atom normalization constraints.  
After preliminary investigation with several ITC, we kept only two for our DL approach. The first is Bayesian Information Criterion (BIC) [20], extended to the form  
\[\text{EBIC}=2\log\text{RMSE}+\frac{\log T}{T}\text{NoP}+\frac{2N}{T}\log \binom{n}{s}. \tag{4}\]  
The first two terms are the standard ones; we have added a third term to account for all possible positions of the nonzero entries in the matrix \(\mathbf{X}\), inspired by [21].  
The second ITC is Renormalized Maximum Likelihood (RNML) [22; 23], adapted as in [24] to our context, to which we have added the same combinatorial term as above:  
\[\begin{split}\text{ERNML}_{1}&=(T-\text{NoP})\log \frac{\text{RMSE}^{2}}{T-\text{NoP}}+\text{NoP}\log\frac{\|\mathbf{D}\mathbf{x}\|_{2}^{2 }}{T\text{-NoP}}\\ &\quad+\ \log[\text{NoP}(T-\text{NoP})]+2N\log\binom{n}{s}.\end{split} \tag{5}\]  
We note that a different version, \(\text{ERNML}_{2}\), derived in [25], further analyzed in [26] and adapted as Equation (5), gave results similar to those of \(\text{ERNML}_{1}\), thus we do not report it here. The ITC dismissed after preliminary (and thus possibly insufficient) investigation are those from [27; 28].
## 4 Slp-macgt Model Design
### Secure Data Transmission Based on Fountain Codes
#### 4.4.1 Data Encoding  
In the data transmission phase, the message to be transmitted by the source node is first divided into \(K(s_{1},s_{2},\ldots,s_{K})\) packets, and the length of each packet is \(n\). The degree \(d_{n}\) of each code element is randomly selected by a random number generator according to a specific degree probability distribution function \(\rho(d)\), and \(1\leq d_{n}\leq K\) represents the number of packets that a set of messages must contain. The \(d_{n}\) code elements involved in encoding are also random, and the output of the encoder \(t_{n}\) is obtained from an XOR operation of any \(d_{n}s_{k}\).  
The degree distribution function of LT codes is determined by the ideal solitary wave distribution first proposed by [PERSON] [32]:  
\[\rho(d)=\left\{\begin{array}{ll}\frac{1}{K},&d=1\\ \frac{d}{d(d-1)},&d=2,3,\ldots,K\end{array}\right. \tag{2}\]  
After determining the degree of an encoding, it is also necessary to determine which \(s_{k}\) values participate in the XOR coding operations. \(s_{k}\) can be selected in the same way as the determination of degree. The encoding process of LT codes is shown in Figure 3.
## 4 Discussion
However, further analysis is needed to determine the capability of SIF/PAR to capture the impact of drought events or heatwaves on transpiration. Estimates of SIF/PAR can be incorporated into transpiration retrieval models (such as GLEAM) or offline land surface models via data assimilation. Finally, our results support the potential of SIF observations to benchmark climate models. This applies not just to the representation of ecosystem carbon exchange, but also to the estimates of surface water and energy fluxes, at a time in which ecosystem stress is still incorporated in climate models via empirical parameterizations. The following are available online at [[http://www.mdpi.com/2072-4292/11/4/413/s1](http://www.mdpi.com/2072-4292/11/4/413/s1)]([http://www.mdpi.com/2072-4292/11/4/413/s1](http://www.mdpi.com/2072-4292/11/4/413/s1)), Figure S1: Global map of FLUXNET towers used for comparisons, Figure S2: Taylor diagrams comparing transpiration and potential evaporation from each earth2 Observe model with FLUXNET towers, Figure S3: SIF/PAR correlations against tower \(\tau\) grouped by IGBP classifications, Figure S4: Average values for \(\tau\) from each earth2 Observe model globally. Table S1: List of adjusted alpha values implemented per biome type, Table S2: List of FLUXNET sites used for this analysis. Conceptualization, B.R.P., W.H.M. and D.G.M. ; Formal Analysis, B.R.P. and W.H.M. ; Methodology, B.R.P., W.H.M. and D.G.M. ; Resources, W.H.M. and B.M. ; Writing--Original Draft, B.R.P. ; Writing--Review and Editing, B.R.P., W.H.M., P.G., B.M. and D.G.M. ; Supervision: P.G. and D.G.M. This work is funded by the European Research Council (ERC) under grant agreement n\({}^{\circ}\) 715254 (DRY-2-DRY) and the Belgian Science Policy Office (BELSPO) in the framework of the STEREO III project SR/02/329 (STR3S). B.R.P. acknowledges partial funding from the United States Department of Agriculture (USDA) and Water Resource and Policy Initiative's (WRPI) Water Resources Fellowship and the American Geophysical Union (AGU) Horton Hydrology Research Grant. This work used eddy-covariance data acquired and shared by the FLUXNET community, including these networks: AmerFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, Carboltaly, CarboMont, ChinaFlux, FLUXNET-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia, and USCCC. The FLUXNET eddy covariance data processing and harmonization was carried out by the European Fluxes Database Cluster, AmeriFlux Management Project, and Fluxdata project of FLUXNET, with the support of CDIAC and ICOS Ecosystem Thematic Center, and the OzFlux, ChinaFlux and AsiaFlux offices. MODIS MCDI3 MCD42 data were retrieved from the online Google Earth Engine, courtesy of the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC). We thank [PERSON] for the useful discussions. The authors declare no conflict of interest.
## 3 Results and Discussion
### Catalytic Activity: Transesterification of Glycerol with Ethylene Carbonate
#### 3.3.3 Effect of Temperature  
The effect of reaction temperature in glycerol conversion and in the initial rate of transesterification reaction with Tunisian smectite in the range 80-110 \({}^{\circ}\)C is presented in Figure 12. As indicated by the collision theory, higher temperatures lead to increased collisions between the reactant molecules, resulting in a higher frequency of reactions. Therefore,  
Figure 11: (**A**) Initial reagent molar ratio influence setting the reaction temperature at 80 \({}^{\circ}\)C and the catalyst mass load at 2%. Here the glycerol conversion versus reaction time with CE/Gly ratio (M) is observed \(\blacksquare\) M = 1.5; \(\bullet\) M = 2; \(\blacktriangle\) M = 3 (**B**) Initial reaction rate at diverse initial CE/Gly molar ratio.  
the rising temperature in the interval between 80 \({}^{\circ}\)C and 110 \({}^{\circ}\)C increased the collision number between reactive molecules, thus improving conversion percentage [11; 18; 43]. We appreciated the conversion of glycerol at 4 h was about 47% at 80 \({}^{\circ}\)C, a conversion that climbed up to 99.3% with at 110 \({}^{\circ}\)C, in both cases with 2% \(w/w\) catalyst/glycerol and EC/Gly molar ratio set at 2. Thus, to ensure a high conversion of glycerol at 4 h, 110 \({}^{\circ}\)C was the necessary temperature. In this condition, we achieved this, as well, a very high yield of GC. Figure 12B shows that there is an exponential increase of the initial reaction rate with the increase in the temperature between 80 \({}^{\circ}\)C and 110 \({}^{\circ}\)C, as can be expected if the Arrhenius equation is valid.  
In Figure 13, an exponential increase in the initial reaction rate with temperature is evident, an expected Arrhenius tendency. Therefore, after the usual linearization, we can appreciate a linear dependence of In (r\({}_{0}\)) with the inverse of the absolute temperature 1/T. As usual, the slope allows for the estimation of an approximate activation energy, obtaining a value of 75.42 kJ-mol\({}^{-1}\). Values between 32 and 42 kJ-mol\({}^{-1}\) can be found for the reaction under study when using a homogeneous catalyst, K\({}_{2}\)CO\({}_{3}\)[44]. If zinc stearate was employed as an interfacial catalyst in similar conditions, an activation energy of 69.2 kJ-mol\({}^{-1}\) was calculated [45].  
Figure 12: (**A**) Temperature influence on the transcarbonatation of Gly with CE at 2% catalyst loading and CE/Gly molar ratio of 2. In this subfigure \(\blacksquare\) is for 80 \({}^{\circ}\)C; \(\bullet\) 90 \({}^{\circ}\)C; \(\blacktriangle\) 100 \({}^{\circ}\)C and \(\blacktriangledown\) 110 \({}^{\circ}\)C. (**B**) Initial reaction rates at studied temperatures.  
Figure 13: Initial reaction rate dependence with temperature (Arrhenius plot).
# On the Lock-In Phenomena near the Transonic Buffet Onset of a Prescribed Pitching Airfoil
## 3 Numerical Setup and Validation  
### Test Case Description  
[PERSON] and [PERSON] [25] conducted experiments on the supercritical airfoil OAT15A in the S3 Ch wind tunnel in Onera's Chalais Meudon Center. The test section of the wind tunnel has a square cross section, and the size of the test chamber is \(0.78\times 0.78\) m\({}^{2}\). The chord length of the OAT15A airfoil is \(c=230\) mm. The thickness of the airfoil is \(12.5\%\), and the thickness of the trailing edge is \(0.5\%\) of the chord length. The transition locations are fixed at \(0.07c\) on both the upper and lower surfaces. The incoming Mach number is \(Ma=0.73\), Reynolds number \(Re\approx 3\times 10^{6}\), the stagnation point pressure \(P_{\infty}=10,000\) Pa, and the far field temperature \(T_{\infty}=300\) K. The \(AoA\) ranges from \(2.5^{\circ}\) to \(3.9^{\circ}\). The experiments showed that the buffet onset was \(3.25^{\circ}\) at the designed condition, and the larger the \(AoA\), the more drastic the shock oscillation. Buffet frequencies, in all cases, remained around \(69\)-\(70\) Hz (the Strouhal number \(St=f_{b}c/U_{\infty}=0.062\)). Further LDV measurements were carried out at an \(AoA\) of \(3.5^{\circ}\). The \(AoA\) of \(3.5^{\circ}\) is chosen for numerical verification of the fixed airfoil in this study.  
For prescribed airfoil pitch motion validation, we test the simulation against the wind tunnel experiments of the oscillating NACA0012 airfoil (AGARD CT5 case) conducted by [PERSON] [26]. The mean \(AoA\) is \(0.016^{\circ}\) while the maximum pitch amplitude is \(2.51^{\circ}\), with the reduced frequency \(k_{p}=\frac{\pi f_{p}c}{U_{\infty}}=0.08\), where \(f_{p}\) denotes the forced pitching frequency and \(U_{\infty}\) denotes the incoming velocity. In this test case, the chord length \(c\) is set at \(1\) m, with the pivot point at \(0.25c\), \(Ma=0.755\), \(Re\approx 10\times 10^{6}\).
## 1 Introduction
#### Motivation of the Study  
This review paper aims to provide a comprehensive understanding of the state-of-the-art in the machining of additively manufactured Ti6 Al4V parts. The areas to be addressed include the following.  
* Significance of Ti6 Al4V alloy
* Advancement and integration of additive manufacturing with conventional/traditional machining will help to bridge the gap between the two manufacturing processes and enhance process efficiency.
* A review of cutting parameters, including cutting speed, feed rate, and depth of cut, is crucial for improving surface roughness and tool life. By considering existing studies, this analysis can inform future research and enhance industrial practices. Optimizing these parameters can reduce cutting energy, contributing to sustainable manufacturing and supporting economic viability.
* Technical challenges such as tool wear and tool life: Ti6 Al4V alloy is known as a difficult-to-cut material, posing significant tool wear. A review of existing studies will help to determine a durable material for cutting AM Ti6 Al4V parts.
* Research gaps: A review of existing studies will highlight areas where current research is lacking and suggest the direction for future studies.