esa-sceva/satcom-chunk-collection huggingface dataset:
# On-Line Tendency Control of Dissolved Oxygen Concentration during Aerobic Fed-Batch Fermentations
## 3 Results and Discussion
### Performance during C. glutamicum Fermentation  
The time profile of cell/glutamate concentrations, substrate concentration, and stirrer speed, as well as DO are depicted in Figure 4.  
Figure 3: DO control structure for _Pichia pastoris_ fermentation.  
Figure 4: _Cont._  
Glutamate production is a typical non-growth associated fermentation process. During growth phase (0-10 h), cells grows exponentially and glutamate production does not occur. In production phase (10-32/34 h), glutamate extensively accumulates and cell growth ceases [17].  
As can be seen from Figure 4, DO concentration started decreasing after about 2-3 h of fermentation and reached close to zero during the cell growth phase. The fall of DO in fermentation broths was due to the high demand for substrate utilization during the exponential phase of the organism, and the subsequent increase was due to lower demand. The gradual increase in the stirrer speed to a maximum (up to around 900 rpm) at about 400 min reflected this characteristic. Additionally, the agitation speed was chosen to render a proper mixing while avoiding excessive shear forces that may have caused cell rupture [26]. After crossing the maximum, the stirrer speed gradually decreased to about 550 rpm at 32 h, in the latter part of the fermentation process. Furthermore, as Figure 4D shows, two DO values were very high, this is because the substrate was depleted and the DO concentration began to rise sharply. DO in the broth was the result of a balance of its consumption rate in the cells and the rate of oxygen transfer from the gas to the liquid phase.
## 3 Overview of Physical Unclonable Functions
### PUF Implementation
#### 3.2.5 Butterfly PUF  
Butterfly PUFs exploit the variations in delay paths within a symmetric structure. The structure consists of two paths that resemble a butterfly shape, and the delays through these paths are sensitive to manufacturing variations.
## 1 _Article_
### Overtopping: Numerical Modelling  
The development of computational models for overtopping simulations progressed along with the advances in computing power. For example, some works analysed the wave-infrastructure interaction using 2D or 3D models to study overtopping [13; 14; 15]. Alternatively, other works use Machine Learning models to perform water flow estimations during overtopping [16] or predict overtopping events [17; 18].  
Public institutions and private companies have developed software which includes computational models for overtopping simulations. An example of such a tool is the widely used Computational Fluid Dynamics Toolbox OpenFoam [19]. OpenFOAM has many features to solve complex fluid flows involving chemical reactions, turbulence and heat transfer, acoustics, solid mechanics, and electromagnetics.  
Although not technically numerical modelling, it is worth mentioning that, alongside the first EurOtop manual in 2007, they also developed an online Calculation Tool to assist the user through a series of steps to establish overtopping predictions for embankments and dikes, rubble mount structures, and vertical structures. Due to lacking funds, they could not update the web-based Calculation Tool with the new formulae in the manual's latest edition. They removed the old calculation tool from the website but kept PC-Overtopping, a PC version of the tool [20].  
Also, in parallel with the EurOtop manual, the authors developed an Artificial Neural Network called the EurOtop ANN. This ANN can predict the mean overtopping discharge using the hydraulic and geometrical parameters of the structure as inputs. The authors created this ANN using the extensive EurOtop database extended from the CLASH database, which contains more than 13,000 tests on wave overtopping. The ANN and both databases are free and available through links on the website [21; 22].  
Although the evolution and support experienced by tools like OpenFOAM and the EurOtop tools have positioned them as a viable alternative to physical modelling due to their low cost and adaptability, they present the same limitations as physical modelling. These limitations are caused by adopting simplifications of reality in the model to maintain an acceptable computational cost. Another problem with these numerical models is that, given the phenomena's complexity, real data should be used to validate the models whenever possible.
## 3 Nanoparticles in EOR
### Iron Oxide  
With unique magnetic and electrical properties, iron oxide (Fe\({}_{2}\)O\({}_{3}\)/Fe\({}_{3}\)O\({}_{4}\)) NPs have been proposed as nano sensors [126]. However, there are only a few studies on the potential use of iron oxides for EOR. [PERSON] et al. [81] studied several metal oxide NPs such as Fe\({}_{2}\)O\({}_{3}\), CuO, and NiO on carbonate core plugs. The result for the iron oxide NPs was not so promising since it could only reach 57% ultimate recovery while other NPs could achieve up to 85%. However, others have shown that iron oxide NPs were able to increase recovery up to 24% (additional) when dispersed in water [122]. They can increase the viscosity of the displacing fluid, which leads to a higher sweep efficiency. [PERSON] et al. [86] reported that iron oxides were only able to recover 17% extra oil while other NPs (i.e., Al\({}_{2}\)O\({}_{3}\) and SiO\({}_{2}\)) recovered around 20% additional oil.  
Ferrofluids (Fe\({}_{3}\)O\({}_{4}\)) were proposed for magnetic heavy oil recovery by [PERSON] et al. [127]. The magnetic recovery mechanism works by having an in-situ injection of magnetic NPs and pushing the NPs toward the reservoir using a repelling magnetic force from downhole tools. Then, inward magnetic forces are applied toward the borehole to attract and recover the magnetic NPs that are already soaked with oil.
# Rotate Vector Reducer Fault Diagnosis Model Based on EEMD-MPA-KELM
###### Abstract  
With the increase of service time, the rotation period of rotating machinery may become irregular, and the Ensemble Empirical Mode Decomposition (EEMD)can effectively reflect its periodic state. In order to accurately evaluate the working state of the Rotate Vector (RV) reducer, the torque transfer formula of the RV reducer is first derived to theoretically prove periodicity of torque transfer in normal operation. Then, EEMD is able to effectively reflect the characteristics of data periodicity. A fault diagnosis model based on EEMD-MPA-KELM was proposed, and a bearing experimental dataset from Xi'an Jiaotong University was used to verify the performance of the model. In view of the characteristics of the industrial robot RV reducer fault was not obvious and the sample data is few, spectrum diagram was used to diagnose the fault from the RV reducer measured data. The EEMD decomposition was performed on the data measured by the RV reducer test platform to obtain several Intrinsic Mode Functions (IMF). After the overall average checking and optimization of each IMF, several groups of eigenvalues were obtained. The eigenvalues were input into the Kernel Extreme Learning Machine (KELM) optimized by the Marine Predators Algorithm (MPA), and the fault diagnosis model was established. Finally, compared with other models, the prediction results showed that the proposed model can judge the working state of RV reducer more effectively.  
RV reducer; EEMD; MPA; KELM; fault diagnosis Article
## 2 Materials and Methods
### Experimental Design and Statistical Analysis  
In this study, Response Surface Methodology (RSM) was used. Using the Box-Behnken design (BBD), the influence of three independent variables--Papain concentration (\(X_{1}\)), Temperature (\(X_{2}\)), and Hydrolysis time (\(X_{3}\))--on the DPPH and ABTS radical scavenging activity of obtained LPH was investigated. The coded and actual levels of the independent variables used in the RSM design are listed in Table 1. The dependent variables in this study are DPPH and ABTS. They are denoted from \(Y_{1}\) and \(Y_{2}\), respectively. A second-order polynomial equation was used to express these dependent variables as a function of the independent variables as follows:  
\[Y_{j}=\beta_{0}+\sum_{i=0}^{k}\beta_{i}X_{i}+\sum_{i=1}^{k}\beta_{ii}X_{i}^{2} +\sum_{i>0}^{k}\beta_{ij}X_{i}X_{j}+E, \tag{3}\]  
where \(Y_{j}\) (\(j=1\), 2) represents the responses to be modeled; \(\beta_{0}\) is the constant coefficient; \(\beta_{i}\) is the coefficient of the linear effect; \(\beta_{ij}\) is the coefficient of the interaction effect; \(\beta_{ii}\) is the coefficient of the squared effect; \(k\) is the number of variables; and \(X_{i}\) and \(X_{j}\) define the independent variables (papain concentration (\(X_{1}\)), Temperature (\(X_{2}\)), and Hydrolysis time (\(X_{3}\))). The statistical significance of the coefficients was verified using the Student's \(t\)-test (\(\alpha=0.05\)), goodness-of-fit was established as the determination coefficient (R\({}^{2}\)), and the model consistency was established by the Fisher F test (\(\alpha=0.05\)).  
The experimental design and statistical analysis were performed using Design-Expert software (Version 13.0.5.0, State-Ease Inc., Minneapolis, MN, USA). The complete exper  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Independent Variables**} & \multirow{2}{*}{**Symbols**} & \multicolumn{3}{c}{**Levels**} \\ \cline{3-5}  & & \(-\mathbf{1}\) & \(\mathbf{0}\) & \(\mathbf{+1}\) \\ \hline Papain concentration (\%) & \(X_{1}\) & 1.0 & 2.0 & 3.0 \\ Temperature (\({}^{\circ}\)C) & \(X_{2}\) & 20 & 30 & 40 \\ Hydrolysis time (min) & \(X_{3}\) & 60 & 120 & 180 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Coded and decoded levels of independent variables used in the RSM design.  
imental design consisted of 21 experimental runs, taken in a random order. The Center Points per Block were established at 5 to be able to estimate the pure error sum of squares.
# Full Three-Dimensional Inverse Design Method for S-Ducts Using a New Dimensionless Flow Parameter
## 5 Conclusions  
In this research, an upgraded BSA was three-dimensionally developed for the inverse design of 3-D S-ducts with different cross-sectional shapes. The pressure distribution on the wall of a 3-D duct is influenced by the centerline curvature, area, and cross-sectional shape. If the original BSA based on the pressure difference is used for the geometry correction of a 3-D duct, the inverse design diverges. To converge the 3-D inverse design, the displacement of the wall nodes was defined based on a new dimensionless pressure parameter to eliminate the effect of area using the streamline curvature.  
The proposed method was validated by a straight duct with arbitrary cross-sectional area converging to the target geometry. Finally, the 3D inverse design was used to eliminate the current separation and reduce the distortion in a diffuser due to increasing the aspect ratio. The main results of this study are summarized as follows:  
The 3-D diffuser was replaced by several 2-D planar ducts. Changing the upper and lower lines of each planar duct changes the total cross-sectional area and thus affects the pressure contour of the other 2-D planar ducts.  
Due to the effect of cross-sectional area on the pressure contour, the use of the pressure difference is not a suitable criterion for the displacement of control points in 3-D inverse design and causes a divergent solution.  
The pressure contour of a 3-D diffuser was nondimensionalized based on the flow curvature equation. The dimensionless pressure is only a function of the duct curvature and the height of the diffuser cross-sections so that the effect of the area is eliminated.  
Using the dimensionless pressure, the duct curvature is corrected in a lower number of iterations.  
Dimensionless pressure is a suitable criterion for moving the control points and correcting cross-sections in the inverse design of 3-D ducts.  
Increasing the duct curvature increases the difference between the pressure along the upper and lower lines in the bending areas and creates a secondary flow. By correcting the dimensionless pressure in a high-pressure-gradient region and correcting the cross-section, the pressure difference decreases, which improves duct performance.  
Conceptualization, M.N.-A.; validation, K.C.K.; formal analysis, A.K.; investigation, A.K.; resources, K.C.K.; writing--original draft preparation, A.K.; writing--review and editing, M.N.-A. and K.C.K.; supervision, M.N.-A. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Brain Pool Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2019H1D3A2A01061428). This work was also supported by the National Research Foundation of Korea (NRF) grant, which is funded by the Korean government (MSIT) (No. 2020R1A5A8018822).  
The authors declare no conflict of interests.
# Quantum Time-Memory-Data Tradeoff Attacks on the SNOW-V Stream Cipher
###### Abstract  
Symmetric cryptosystems (i.e., stream ciphers and block ciphers) have always played an important part in securing the various generations of 3 GPP (3 rd Generation Partnership Project) mobile telephony systems. The SNOW-V stream cipher, published in September 2019, is the most recent member of the well-known SNOW family of ciphers. It is designed to provide confidentiality and integrity for 5G communications. There have been no time-memory-data tradeoff (TMDTO) attacks on the cipher published so far. By combining with the BSW sampling technique, we propose TMDTO attacks on SNOW-V. The results show that the attacker can mount a TMDTO attack, where none of the online time complexity, the memory complexity and the offline time complexity are bigger than \(2^{256}\), if the keystream sequences generated by the secret key, together with different IVs, are provided to the attacker. Furthermore, we analyze the security of SNOW-V against quantum TMDTO attacks, and the results show that a quantum TMDTO attack offers, strictly, better online time complexity than [PERSON]'s algorithm, when the available memory space is bigger than \(2^{170.67}\). These results are helpful in evaluating the security of SNOW-V against (quantum) TMDTO attacks.  
time-memory-data tradeoff attack; quantum time-memory-data tradeoff attack; BSW sampling; SNOW-V; stream cipher +
Footnote †: journal:  
Article
## 4 Bibliometric Analysis Results
### Predictive Maintenance
#### 4.1.3 Period 2008-2016
In addition, the identified techniques were manually classified into the following four categories:  
* _Technical_. The approach focuses on application-specific technical parameters. * _Stochastic_. The approach emphasizes the importance of stochastic issues. * _Generic_. The approach deals with adaptive and learning methods. * _Process_. The approach focuses on the maintenance lifecycle. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**1990-2000**} & \multicolumn{2}{c}{**2000-2008**} & \multicolumn{4}{c}{**2008-2016**} \\ \hline \# & **Key** & **\%** & **Key** & **\%** & **\(\Delta\)** & **Key** & **\%** & **\(\Delta\)** \\ \hline
1 & predictive maintenance & 17.99 & predictive maintenance & 11.86 & 0 & predictive maintenance & 9.61 & 0 \\
2 & condition monitoring & 8.63 & maintenance & 3.83 & +1 & condition monitoring & 3.17 & +1 \\
3 & maintenance & 5.76 & condition monitoring & 3.35 & -1 & maintenance & 2.73 & -1 \\
4 & diagnosis & 5.04 & neural network & 2.75 & +3 & reliability & 2.12 & +1 \\
5 & monitoring & 4.32 & reliability & 2.51 & +27 & condition-based maintenance & 1.5 & +7 \\
6 & models & 4.32 & fault detection & 1.92 & +7 & fault diagnosis & 1.32 & +11 \\
7 & neural networks & 3.6 & preventive maintenance & 1.8 & +2 & prognostics & 1.28 & +2 \\
8 & vibration & 3.6 & genetic algorithm & 1.56 & n & prediction & 1.15 & +36 \\
9 & preventive maintenance & 2.88 & prognostics & 1.44 & n & model predictive control & 0.93 & +67 \\
10 & emissivity & 2.16 & partial discharges & 1.32 & n & data mining & 0.93 & +8 \\
11 & thermography & 2.16 & condition-based maintenance & 1.32 & n & preventive maintenance & 0.93 & \(-4\) \\
12 & fault diagnosis & 2.16 & diagnostics & 1.2 & +22 & fault detection & 0.88 & \(-6\) \\
13 & fault detection & 2.16 & modelling & 1.08 & n & optimization & 0.79 & +48 \\
14 & failure prediction & 2.16 & sensors & 0.96 & n & wind turbine & 0.75 & n \\
15 & failures & 1.44 & maintenance management & 0.96 & n & monitoring & 0.71 & +7 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Keyword ranking. Figure 12: Research Front Flow Diagram (Color and Font Sizes). Based on these categories, the identified techniques are visualized in a timeline (cf. Figure 13) to obtain further insights. In general, we can observe a dominance of technical approaches. Thereby, most research focuses on concrete machine use cases, whereas general frameworks are less considered.
# Geant4 Simulations of a Scintillator Cosmic-Ray Detector
## 3 Estimation of Sensitivity
### Calculation of Signal's Amplitude
The values of these parameters, along with the gain, \(G\), and the capacitance, \(C\), are available from the documentation of the photomultiplier but are also listed in Table 3.  
Figure 3: Photon Detection Efficiency, PDE, as a function of photons’ wavelength, \(\lambda\), for \(V_{br}\ =\ 24.5\) V and three different values of \(V_{SiPM}\). \begin{table}
\begin{tabular}{l l l l l l} \hline \(\mathbf{V}_{br}\) [\(\mathbf{V}\)] & \(\mathbf{G}\) & \(\mathbf{C}\) [\(\mathbf{pF}\)] & \(\Delta\) \(\mathbf{V}\) [\(\mathbf{V}\)] & \(\mathbf{M}\) & \(\mathbf{\epsilon}_{p}\) [\%] \\ \hline
24.5 & \(3\cdot 10^{6}\) & 3400 & 0.00014 & 18,980 & 64 \\ \hline \end{tabular}
\end{table} 
Table 3: Values of parameters used to calculate amplitude of signal produced in detector at \(V_{SiPM}\ =\ 29.4\) V and \(T\ =\ 21\)\({}^{\circ}\)C.  
In the end, the amplitude of the produced signal is calculated using the following formula [19]:  
\[V_{S}\ =\ \Delta V\cdot c_{p}\cdot M\bigg{(}1-\exp\bigg{(}-\frac{N_{reg}}{M} \bigg{)}\bigg{)}. \tag{3}\]  
To estimate the sensitivity of the detector, a large number of events had to be simulated and the percentage of those events that produced signals with sufficiently high amplitude had to be counted. The threshold for this amplitude is determined by an electronic circuit that further processes this signal [20]. The average amplitude of signals from an SiPM is of the order of a few tens of mV and in a real detector it has to be amplified first. In this work it was assumed that such amplification was linear and could be described by a single factor, \(k_{AMP}\). The electronics required some minimal amplitude of input signals, \(V_{th}\). For the currently developed prototype, \(V_{th}\approx 1\) V and \(k_{AMP}\approx 45\) and these values were used to obtain results presented in the next section. Noise in the electronics was much lower, about 10 mV. Even if such noise signal occurred before the amplifier and was processed by it, it should not have passed the threshold. The considered SiPM in this mode produced a signal that had a rising time around 10 ns and its length was of the order of hundreds of ns. In this analysis it was assumed that the signal shape was not affected by the amplifying circuit and the only quantity important for the detection of the signal was its amplitude at the peak. The statistical approach gives the formula to calculate the sensitivity of the detector:  
\[\eta\ =\ \frac{n(k_{AMP}\cdot V_{S}>V_{th})}{n_{all}}, \tag{4}\]  
Its uncertainty is  
\[\Delta\eta\ =\ \frac{\sqrt{n(k_{AMP}\cdot V_{S}>V_{th})}}{n_{all}}, \tag{5}\]  
where \(n(k_{AMP}\cdot V_{S}>V_{th})\) is the number of particles that caused a signal with sufficiently high amplitude and \(n_{all}\) is the total number of simulated particles.
## 3 Methodologies
### Machine Learning Models
#### 3.5.3 Support Vector Machine  
A support vector machine (SVM) is a non-parametric supervised machine learning algorithm designed to address nonlinear classification and regression problems using mathematical tools. The SVM is grounded in the statistical learning theory developed by [PERSON] and [PERSON] [34]. It achieves its desired output by transforming input data into a high-dimensional space using kernel functions and subsequently mapping the results back to a two-dimensional space [35]. This algorithm is particularly suitable for binary classification problems involving both positive and negative landslide samples. In such cases, the SVM seeks to identify the optimal separating hyperplane in the feature space that maximizes the margin between the two classes.  
Spatial prediction of geological disasters is a binary classification problem, where outcomes are categorized as either \"present\" or \"absent\". The core principle of the SVM lies in identifying a hyperplane within the training dataset that separates the two classes while maximizing the margin between them. The hyperplane is mathematically represented as:  
\[W^{T}x+b=0, \tag{7}\]  
where \(W\) is the normal vector that determines the orientation of the hyperplane, \(x\) is the input feature vector, and \(b\) is the bias term controlling the offset of the hyperplane from the origin. This boundary partitions the feature space, enabling the classification of data points.  
The hyperplane that achieves the maximum margin can be determined by minimizing the following objective function:  
\[min\frac{1}{2}\|w\|^{2}s.t.,y_{i}(w^{T}x_{i}+b)\geq 1,i=1,2\cdots n, \tag{8}\]  
where \(y_{i}\) represents the label of the \(i\)-th sample, and \(x_{i}\) is its feature vector. To maximize the margin between the two classes while ensuring that all points are correctly classified and positioned outside the margin, the norm \(\|w\|^{2}\) is minimized.  
The objective function can be reformulated by incorporating a Lagrange multiplier, resulting in the following expression:  
\[\underset{\alpha_{i}\geq 0}{\text{Max}}(w,b,\alpha)=\underset{\alpha_{i}\geq 0 }{\text{max}}(\frac{1}{2}\|w\|^{2}-\sum_{i=1}^{n}\alpha_{i}(y_{i}(w^{T}x_{i}+b )-1)), \tag{9}\]  
\[s.t.,\ \alpha\geq 0,i=1,\cdots,n, \tag{10}\]  
\[\sum_{i=1}^{n}\alpha_{i}y_{i}=0, \tag{11}\]\[y_{i}(w^{T}x_{i}+b)\geq 1,i=1,\cdots,n, \tag{12}\] \[w=\sum_{i=1}^{n}\alpha_{i}y_{i}x_{i}, \tag{13}\]  
where \(\alpha_{i}\) is the Lagrange multipliers, \(y_{i}\) is the label of the \(i\)-th sample, and \(w^{T}x_{i}\) is the dot product (or kernel function) between the \(i\)-th sample and the new sample \(x\).  
By eliminating the parameter \(\omega\), the solution requires the determination of \(\alpha_{i}\) and \(b\) and utilization of \(\alpha_{i}\) kernels to enable nonlinear separation.  
The advantages of the SVM include its foundation in minimizing structural risk and its robust performance, which is ensured by solving a constrained quadratic optimization problem.