esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Controlling lipid oxidation is a major challenge in the case of both pH-shift processing [21] and enzymatic hydrolysis [22]. The polyunsaturated lipids in fish are highly susceptible to oxidation, especially due to the presence of pro-oxidants, mincing and high temperatures or extreme pH conditions used in the extraction. Lipid oxidation further promotes the oxidation of proteins and reduces sensory quality and nutritional value [23; 24]. The potential application is limited for a protein isolate or hydrolysate with strong fishy odor and flavor, mostly derived from lipid oxidation [19; 25; 26]. Not only lipid content, but also composition may affect the development of lipid oxidation, since the susceptibility of lipids to oxidation is dependent on the degree of unsaturation [23]. Phospholipids (PLs) are considered to be more susceptible than triacylglycerlycerols (TAGs) due to their high degree of unsaturation as well as their presence in cell membranes [27]. Previous studies on enzymatic hydrolysis or the pH-shift processing of fish have reported varying lipid contents and levels of lipid and protein oxidation in the resulting protein isolates [18; 21; 22; 28]. Despite relatively high lipid contents reported [9; 14; 17; 18], there is little information on the composition of lipids retained in the protein isolates from previous studies. The aim of this work was to study the potential of enzymatic hydrolysis and pH-shift processing in the valorization of proteins/peptides from small-sized, whole B. herring and roach. Therefore, the study focused on the quality of proteins/peptides and remaining lipids in isolated proteins and peptides produced by two different pH-shift methods (alkaline vs. acidic) and enzymatic hydrolysis using three different commercial endoproteases from B. herring and roach. The amino acid composition of produced isolates and hydrolysates was analyzed to estimate their nutritional value. The composition and quality of the lipids remaining in the isolates and hydrolysates was studied by analyzing fatty acid and lipid class composition. In addition, protein carbonyls, peroxide values and lipid-oxidation-derived volatile compounds were analyzed as markers of oxidation.
## 2 Materials and Methods
### Methods
#### 2.2.2 Covariance Matrix Decomposition  
An artificial dataset, with a distribution similar to that of the original dataset, was created using CMD. Let the original and artificial datasets be \(D\in R^{k\times N}\) and \(D_{c}\in R^{k\times n}\), respectively, where \(N\) is the number of original data points; \(n\) is the number of augmented  
Figure 3: (**a**) Probability and (**b**) cumulative distributions for GNDv2 with zero mean and unit standard deviation.  
data points; \(k\) is the number of features. The \(i\)th sample of the original dataset can be expressed as \(d^{(i)}=u+v^{(i)}\), and the \(i\)th sample of the artificial dataset can be expressed as \(d^{(i)}_{a}=u+v^{(i)}_{a}\), where \(u\) is a vector composed of mean values of \(Y\), and \(v^{(i)}\) and \(v^{(i)}_{c}\) are the stochastic components. Next, suppose that \(X\) is a matrix composed of random noise vectors as \(X=\left[x^{(1)}\ x^{(2)}\ldots x^{(n)}\right]\), where \(x^{(i)}\) is a random noise vector with zero mean and unit standard deviation. \(\text{Cov}[\cdot]\) can be defined as an operator that calculates the covariance matrix of the input vectors. If there exists a matrix \(L\), which relates \(v^{(i)}_{c}\) and random noise as \(v^{(i)}_{c}=Lx^{(i)}\), the covariance matrix of \(Y_{c}\) is equal to \(LL^{T}\) (Proof: \(\text{Cov}\!\left[LX\!\left(LX\!\right)^{T}\right]=\text{Cov}\!\left[LXX^{T}L ^{T}\right]=L\!\left(\text{Cov}\!\left[XX^{T}\right]\right)L^{T}=LIL^{T}=LL^{T}\), where \(I\) is the identity matrix. It is worth noting that this relation can be derived because the mean and standard deviation of the random noise are 0 and 1, respectively. To generate artificial data similar to the original data, the covariance matrices of \(Y\) and \(Y_{c}\) should be the same. Thus, \(L\) can be obtained when \(LL^{T}\) is the same as the covariance matrix of the original dataset.  
Figure 4 briefly presents the data augmentation pipeline used in this study. The artificial data generated by CMD and GND were combined with the original data in the training stage. Then, both were used for training. In the test stage, only the original data for testing were used to obtain the classification accuracy.  
Frequency domain features are widely used to classify motor imagery brainwaves; therefore, the following pre-processing was applied in this study. Time series samples were transferred to the frequency domain via a fast Fourier transform. Then, amplitudes with frequencies between 8 and 16 Hz were collected, as this frequency range is closely related to motor imagery brainwaves [27]. Next, the amplitude values were normalized by integrating the amplitudes over the frequency.
# Calculation of Displacement-Dependent Active Earth Pressure for Deep Excavations in Soft Soil
## 2 Materials and Methods
### Test Apparatus  
Test equipment was required to measure the radial strain of a soil sample during the lateral unloading process. Existing methods for monitoring radial strain include the digital image measurements [40; 41], the Hall effect transducers measurement [42; 43], and calculation using the back volume change [44].  
GDSTTS, an advanced, automated stress path triaxial testing system, was used in the tests. The fundamental system hardware elements are shown in Figure 3. The GDS system provides two ways to measure lateral strain: one is a direct measurement by a Hall effect transducer, and the other involves a conversion using the vertical deformation. A Hall effect transducer needs to be installed in such a way that it passes through the middle of the sample, which causes heavy disturbance to the sample. The operation is difficult to complete, especially for soft soil, and it is an intensive process that can only measure the strain of a specified section. The second approach was used to obtain the equivalent uniform radial strain in this experiment. The equivalent radial strain can be converted using Equation (1), in which the axial displacement change \(\Delta h\) was monitored using an external LVDT, the volume change \(\Delta V\) was monitored by a back pressure controller, and other values included the initial parameters of the soil samples. It is worth noting that the radial strain obtained by this method is an average value of the diameter change rate along the whole height, which is different from the local strain measurement.  
\[\varepsilon=\frac{\Delta r}{r_{0}}=\sqrt{\frac{\Delta V+\pi r_{0}^{2}h}{\pi(h -\Delta h)r_{0}^{2}}}-1 \tag{1}\]  
Figure 2: Diagram of excavation.
# Technology Identification, Evaluation, Selection, and Optimization of a HALE Solar Aircraft
## 2 HALE Solar Aircraft Concepts  
### Reference Aircraft Configuration  
HALE solar aircrafts must obtain solar energy necessary for long endurance and, at the same time, have sufficient wing area to create lift for flight. Most solar aircrafts have large wingspan appropriate to secure sufficient area to attach solar cells and gain lift. In this study, a reference configuration was established as in Figure 1 using OpenVSP [13], a program employed for configuration modeling, by referring to the configuration of Zephyr S. Table 1 shows detailed specifications of the aircraft configuration.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Classification**} & **Value** & **Unit** \\ \hline \multirow{6}{*}{Main wing} & Wing span & 24.2 & m \\  & Chord length & 1.10 & m \\  & Area & 30.3 & m\({}^{2}\) \\  & Aspect ratio & 18.1 & – \\  & Airfoil & DAE-11 & – \\  & Taper ratio & 0.26 & – \\ \hline \multirow{6}{*}{Horizontal wing} & Wing span & 4.20 & m \\  & Chord length & 0.80 & m \\ \cline{1-1}  & Area & 3.36 & m\({}^{2}\) \\ \cline{1-1}  & Aspect ratio & 5.25 & – \\ \cline{1-1}  & Airfoil & NACA 0010 & – \\ \cline{1-1}  & Taper ratio & 1.00 & – \\ \hline \multirow{6}{*}{Vertical wing} & Wing span & 2.40 & m \\  & Chord length & 0.80 & m \\ \cline{1-1}  & Area & 1.92 & m\({}^{2}\) \\ \cline{1-1}  & Aspect ratio & 3.00 & – \\ \cline{1-1}  & Airfoil & NACA 0010 & – \\ \cline{1-1}  & Taper ratio & 1.00 & – \\ \hline \multicolumn{2}{c}{Fuselage length} & 8.15 & m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Reference HALE solar aircraft geometric data.  
Figure 1: Reference high altitude long endurance (HALE) solar aircraft configuration.
## 3 Materials and Methods
### Justification of the Expression for Estimating \(\sigma_{p}\)  
Equation (12) takes an _average_ path duration variance from \(\sigma_{J}\) and \(\sigma_{K}\). Then, it applies a correction factor that takes into account the reduction/increase (with \(K_{2}\) and \(S\)) as a function of the estimated total number of existing critical + subcritical paths (with \(N_{p}\)).  
As described earlier, in the PERT technique, the project duration standard deviation corresponds to the square root of the sum of all critical activity duration variances, that is, \(\sigma_{K}=\sqrt{\sum\sigma_{k}^{2}}\). This is exactly like our \(\sigma_{K}\) variable from Equation (7), but always assuming the existence of a single critical path (\(\sum d_{k}=PD\)). However, as real projects may have more than one critical or subcritical path, this would significantly bias the \(\sigma_{p}\) estimate. In Equation (12), by averaging the variances of \(\sigma_{K}\) and \(\sigma_{J}\), it is more likely that our estimate of the actual project duration standard deviation will improve because the magnitude of \(\sigma_{J}\), in most cases, is lower than \(\sigma_{K}\).  
Finally, the term \(\left[1+K_{2}\cdot LN\left(S\cdot N_{p}\right)\right]\) in Equation (12), constitutes a simplified linear regression of the \(\sigma_{p}\) reduction, as there are more paths in parallel. In this term, \(N_{p}\) takes care of counting the number of those paths, and \(S\) takes care of reducing their importance as a function of how large their average slack is. \(K_{2}\) again just centers the regression expression to maximize accuracy for cases when \(N_{p}<5\). As expected then, the estimate of \(\sigma_{p}\) keeps losing accuracy as \(N_{p}\) grows beyond five.
# Zero-TCE: Zero Reference Tri-Curve Enhancement for Low-Light Images
## 3 Materials and Methods  
Based on the inherent mapping relationship between low-light image enhancement and deep curve estimation, we propose a zero-reference tri-curve image enhancement (Zero-TCE) network grounded in deep curve estimation. This network primarily comprises a tri-curve enhancement for light (TCE-L) module, a tri-curve enhancement for contrast (TCE-C) module, and a multi-scale feature fusion module (MFF). In this model, low-light images are first processed through two parameter estimation modules to generate corresponding parameter maps. These parameter maps are then input into the enhancement modules, where they undergo mapping and iterative calculations across the RGB channels of all input image pixels to obtain brightness-enhanced and contrast-enhanced images. Finally, in the HSV color space, leveraging the brightness distribution characteristics of the image, the preliminary enhanced images are fused with the original image through multi-scale feature fusion, yielding the final enhanced image. The overall network framework is illustrated in Figure 1.
## 3 Results and Discussion
### Analysis of Amino Acid Composition of Digestive Products of GCPs  
To investigate the impact of simulated in vitro digestion experiments on the amino acid composition of GCPs, GCP-II--which exhibited the highest overall antioxidant activity in previous experiments--and its digestive products were selected as samples for further research and discussion. As shown in Table 4, glycine and arginine were abundant in GCP-II, with concentrations reaching 7.032 g/100 g and 2.819 g/100 g, respectively. These two amino acids were often considered to have a strong correlation with the antioxidant activity of substances, indicating that the notable antioxidant capacity of GCP-II. During the simulated in vitro digestion experiments, the amino acid composition of the peptides may undergo a series of changes, primarily influenced by the enzymatic action during the simulated digestion process, the digestion conditions, and the intrinsic properties of the peptides themselves. Following a series of digestive reactions, the total amino  
Figure 3: The effect of in vitro simulated digestion on the secondary structure of GCP-II under the circular dichroism.  
acid content in GCP-II decreased to 1.448 g/100 g, with nearly all detected amino acids showing substantial reductions in their concentrations. The contents of glycine, arginine, and hydrophobic amino acids such as phenylalanine and valine, which were known to influence antioxidant activity, also decreased significantly. Therefore, it was speculated that the in vitro antioxidant capacity of the digestive products of GCP-II will be markedly reduced after digestion treatment.
## 1 Introduction
Various micro machining processes (wet/dry etching, laser drilling, and so on) for polymer films have been proposed for making patterned thru-holes using laser beam, chemical etching and dry etching processes for use as a shadow mask or a peel-off stencil mask in production of optoelectronic devices [20; 21; 22]. However, it has been challenging to make tiny thru-holes with small (under 10 \(\upmu\)m) distances between adjacent holes in polymer films due to the weak heat resistance of polymer materials during dry etching process. The laser drilling technique may not be appropriate to produce large area films economically. In the case of wet etching, the distance between holes cannot be minimized due to the isotropic etching property. To the best of our knowledge, it has been almost impossible to realize thru-holes on polyimide films with the ion ethching process. The ion collision process generates the heat, which causes a change in the polymer film during the process. In addition, the thin polymer film cannot be easily attached at the bottom of the sample holder during the process. Even if the film can be attached by special equipment (i.e., electrostatic chuck), the damage of high cost bottom holder cannot be ignored. Nano-porous polymeric films and membranes having holes with diameters of few tens of nanometers that were etched out selectively by acid hydrolysis after self-assembly of the block copolymers were also proposed for nanomaterial synthesis, size-selective catalyst supports, and advanced separation [23]. In this study, we report the realization of Kapton films with patterned thru-holes by ion etching process and the application of the Kapton air filter for use as an active air filter by exploiting its own superior properties of flexibility, transparency, heat resistance, mechanical durability, and reusability. We developed a new ion punching machine and process for the polymer film. The characteristics can be summarized as follows: (1) the process temperature is low, which does not elevate the temperature of the sample and (2) the adhesive material, which is soluble in acetone, is inserted between a polymer film and a sample holder to attach the film during the process. The small thru-holes effectively filter PM10 and the high electrostatic field of the Kapton film effectively captures PM2.5. The intrinsic characteristics of the film (tough, washable, and heat resistant) allow it to maintain its initial performance after a simple cleaning process using tap water. Furthermore, the thinness of the single layer Kapton films and 25% opening ratio significantly reduce the pressure difference between the inlet and outlet, allowing effective air flow through the filters.
## 3 Potential of Intelligent Algorithms for Hydrodynamic
### Data-Driven Optimization Algorithms  
(1) Deep Learning (DL)  
Deep learning, as an important branch of machine learning, achieves automatic learning of complex nonlinear mapping relationships by using large amounts of data and high-performance computing resources, and it has demonstrated extremely high effectiveness in pattern recognition and data prediction. In hydrodynamic noise research, deep learning can not only efficiently capture the implicit noise features in the flow field, but also has significant advantages in noise field prediction and noise source identification, thus providing strong support for high-precision fluid noise modelling.  
Compared to traditional numerical simulation methods, large-scale data-driven deep learning can significantly reduce the reliance on computationally resource-intensive simulations, while improving prediction efficiency. For example, [PERSON] [74] proposed a data-driven deep neural network (DNN) method to achieve fast aerodynamic noise prediction  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Advantage** & **Shortcoming** & \begin{tabular}{c} **Scope of** \\ **Application** \\ \end{tabular} \\ \hline DNS & \begin{tabular}{c} High accuracy, \\ capturing all scales \\ of turbulence, \\ suitable for basic \\ research and \\ validation \\ \end{tabular} & \begin{tabular}{c} The computational \\ cost is extremely high \\ and can only be used \\ for small-scale and \\ simplified flow \\ simulation. \\ \end{tabular} & \begin{tabular}{c} Small-scale \\ high-precision \\ research \\ \end{tabular} \\ \hline LES & \begin{tabular}{c} Balances \\ calculation \\ accuracy and cost \\ for most complex \\ flows \\ \end{tabular} & \begin{tabular}{c} Small-scale eddies \\ need to be modeled, \\ and the amount of \\ calculation is large. \\ \end{tabular} & \begin{tabular}{c} Engineering \\ applications, \\ complex flow \\ \end{tabular} \\ \hline RANS & \begin{tabular}{c} High \\ computational \\ efficiency, suitable \\ for large-scale flow \\ simulation \\ \end{tabular} & \
\begin{tabular}{c} Inability to capture \\ turbulence transient \\ characteristics and \\ strong dependencies. \\ \end{tabular} &
\begin{tabular}{c} Industrial flows, \\ large scale \\ simulation \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of simulation-based numerical methods for hydrodynamic noise.  
with guaranteed accuracy, which opened a new approach for high-precision real-time noise prediction. The successful application of this method demonstrates that deep learning can not only automatically extract high-level features in fluid noise, but also achieve dynamic and intelligent noise control in practical applications, thus promoting the continuous progress of fluid noise optimization and control technology.  
Overall, deep learning, with its powerful feature learning capability and efficient data processing advantages, provides a new solution for the prediction, identification, and control of fluid noise, demonstrating great potential for improving noise prediction accuracy and real-time response capabilities.
# Secure Internal Data Markets
###### Abstract  
The data market concept has gained a lot of momentum in recent years, fuelled by initiatives to set up such markets, e.g., on the European level. Still, the typical data market concept aims at providing a centralised platform with all of its positive and negative side effects. Internal data markets, also called local or on-premise data markets, on the other hand, are set up to allow data trade inside an institution (e.g., between divisions of a large company) or between members of a small, well-defined consortium, thus allowing the remuneration of providing data inside these structures. Still, while research on securing global data markets has garnered some attention throughout recent years, the internal data markets have been treated as being more or less similar in this respect. In this paper, we outline the major differences between global and internal data markets with respect to security and why further research is required. Furthermore, we provide a fundamental model for a secure internal data market that can be used as a starting point for the generation of concrete internal data market models. Finally, we provide an overview on the research questions we deem most pressing in order to make the internal data market concept work securely, thus allowing for more widespread adoption.  
local data market; internal data market; on-premise data market; secure data market +
Footnote †: journal:  
environment in which data are basically made available to everyone and, thus, follows a \"one size fits all\" approach. In the novel basic model provided in this paper, we provide a much more abstract view on the topic of internal data markets that allows specific tailoring to the requirements of the companies in question. This can range from security- and privacy-relevant aspects, such as data anonymisation stages, to issues of whether to charge money for data transfers, to resort to a karma point system, or to even not charge anything. In this paper, we provide the following contributions:  
* A comparison of global and internal data markets with respect to security-relevant parameters from a general perspective.
* A novel abstract basic model for a secure internal data market, including audit and control.
* Flexible tailoring capabilities in the abstract model through the inclusion of pre- and post-processing capabilities.
* A set of open research questions that need consideration in order to make internal data markets work securely in environments with limited resources.
## 4 Results and Discussion
### Monitoring Results
#### 4.1.2 Microcell Corrosion Current (iCORR,MICRO)  
Figure 6 shows the corrosion intensity results per microcell, which were obtained during the 4-month monitoring period.  
As shown, the results reveal a similar tendency to that noted in Section 4.1.1. For the column not affected by the presence of chlorides (Figure 6A), the corrosion levels in both sensors were similar and below the threshold of 0.1 mA/cm\({}^{2}\), set to consider if a rebar  
Figure 5: Macrocell corrosion monitoring: (**A**) the column free of aggressive agents; (**B**) the column with the chloride-contaminated lower zone; (**C**) the column with the carbonated lower zone.  
is no longer in the passive state and corrosion intensity is no longer negligible [40; 64]. Conversely, the behaviour of columns B and C differed, and distinct results were obtained between the sensors in the LZ and the UZ.  
For column B (with the chloride-contaminated LZ), the sensors in the UZ showed a first phase with very different results, which stabilised after the first 30-40 days and whose values were below 0.1-0.2 \(\mu\)A/cm\({}^{2}\). On the contrary, the values for the sensors in the LZ were between five- and seven-fold higher, and even exceeded 0.8 \(\mu\)A/cm\({}^{2}\) in some cases, which are moderate-high corrosion levels [40; 64]. These data reflect the influence of including chlorides in the column zone to produce localised rebar depassivation. The presence of oxygen (high-porosity concrete, Table 2) and humidity (the LZ was periodically wetted) favoured high corrosion levels being maintained during the 4-month monitoring period.  
In column C, an equally clear tendency was displayed, but it remained more stable over time. Although very different results were obtained for the first 15 days, values were  
Figure 6: Microcell corrosion monitoring: (**A**) the column free of aggressive agents; (**B**) the column with the chloride-contaminated lower zone; (**C**) the column with the carbonated lower zone.  
high in the final study phase in the sensors placed in the LZ (sometimes above 1.0 \(\mu\)A/cm\({}^{2}\)), while the values for those in the UZ remained below 0.1 \(\mu\)A/cm\({}^{2}\).