esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Method Verification Results and Discussion
The result of vertex cover search was provided in a *.txt file and includes information on the minimum amount of vertexes and provides the list of all vertexes involved in a found vertex cover (Figure 10). Figure 8: ISO27002–PCI DSS–GDPR mapping graph (Partial view). Figure 9: Adjacency matrix for the mapping graph (**(a)** Matrix view, **(b)** Heat view). Since all obtained vertex covers with a minimum number of vertexes were equivalent, any of them could be selected for further processing. Based on the chosen vertex cover, unnecessary vertexes were removed from the mapping graph with the help of the Cytoscape application. As it can be seen, the number of vertexes was reduced significantly (from 1267 vertexes in the initial graph to 322 vertexes). The resulting MSB graph is presented in Figure 11.  
For MSB verification against controls already deployed by the organization a hypothetical organization, ACME Corporation was used. It was assumed that it has already implemented logging and monitoring and backup requirements. The DCG graph for ACME Corporation was created in Cytoscape tool (Figure 12). Figure 11: Minimum security baseline graph. Figure 10: List of potential vertex cover (Partial view). For identifying subgraph isomorphism between the received MSB graph and the created DCG graph for ACME Corporation, the Cytoscape plug-in \"Cylisomorphism\" was used [64]. The DCG graph was evaluated against the MSB graph to identify pattern similarity. In our experiment, only information about vertexes and their connections was used. Because of that more than one potential alignment was recognized by the Cytoscape tool. In order to solve this issue, additional criteria should be used in future. Manual review of alignments was performed during our experiment. The final result of DCG verification against MSB is provided on Figure 13.  
The controls already deployed by ACME Corporation are shown on MSB in a yellow colour. As can be seen, the presentation of MSB in the form of the graph provides a valuable tool for a security officer for evaluating the current state of ISMS. The performed experiment has proved the concept that vector cover and subgraph isomorphism can be used for optimizing the process of standards mapping, removing duplicated requirements, and evaluating the current state of security controls against the desired MSB level.
# On the Octonion Cross Wigner Distribution of 3-D Signals
## 5 Octonion Ambiguity Function  
The concept of the Wigner distribution (WD) is directly related to the definition of the (Woodward) ambiguity function (AF) [21]. It can be defined in many different ways, but it seems that in the case of an octonion approach, each of them will have considerable limitations (in the context of analogous relations between WD and AF in the classical case).  
Definition 2: The octonion cross right-sided ambiguity function (OAF) of the 3-D signals \(f,g\in L^{2}(\mathbb{R}^{3};\mathbb{O})\) is defined as  
\[A^{R}_{f,g}(\mathbf{x},\mathbf{\omega})=\int_{\mathbb{R}^{3}}f\Big{(}\mathbf{y}+ \frac{\mathbf{x}}{2}\Big{)}\overline{g\Big{(}\mathbf{y}-\frac{\mathbf{x}}{2} \Big{)}}\cdot e^{-2\pi\mathbf{e}_{1}\omega_{1}y_{1}}e^{-2\pi\mathbf{e}_{2} \omega_{2}y_{2}}e^{-2\pi\mathbf{e}_{4}\omega_{3}y_{3}}\,\mathrm{d}\mathbf{y},\]  
where \((\mathbf{x},\mathbf{\omega})\in\mathbb{R}^{3}\times\mathbb{R}^{3}\) and multiplication is performed from left to right.  
Analogous properties of this function can be proved as in the case of OWD, but from our point of view, the mutual dependencies between these functions will be more critical. Notice that  
\[A^{R}_{f,g}(2\mathbf{x},2\mathbf{\omega}) =\int_{\mathbb{R}^{3}}f(\mathbf{y}+\mathbf{x})\overline{g( \mathbf{y}-\mathbf{x})}\cdot e^{-2\pi\mathbf{e}_{1}\cdot 2\omega_{1}y_{1}}e^{-2\pi \mathbf{e}_{2}\cdot 2\omega_{2}y_{2}}e^{-2\pi\mathbf{e}_{4}\cdot 2\omega_{3}y_{3}}\, \mathrm{d}\mathbf{y}\] \[=\frac{1}{8}\int_{\mathbb{R}^{3}}f\Big{(}\mathbf{x}+\frac{ \mathbf{t}}{2}\Big{)}\overline{g\Big{(}\frac{\mathbf{t}}{2}-\mathbf{x}\Big{)} }\cdot e^{-2\pi\mathbf{e}_{1}\omega_{1}t_{1}}e^{-2\pi\mathbf{e}_{2}\omega_{2} t_{2}}e^{-2\pi\mathbf{e}_{4}\omega_{3}t_{3}}\,\mathrm{d}\mathbf{t}\]  
which is _almost_ the same formula, as in the definition of OWD. We conclude that  
* If \(g\) is even, then \(A^{R}_{f,g}(\mathbf{x},\mathbf{\omega})=\frac{1}{8}W^{R}_{f,g}\big{(}\frac{\mathbf{ \omega}}{2}\big{)}\);
* If \(g\) is odd, then \(A^{R}_{f,g}(\mathbf{x},\mathbf{\omega})=-\frac{1}{8}W^{R}_{f,g}\big{(}\frac{\mathbf{ \omega}}{2}\big{)}\),  
which is the same relation as in the classical case. Unfortunately, the interrelationships using Fourier transforms will not be true in the case of the octonion setup (due to non-associativity of octonion multiplication). We can only conclude that if we denote (as before)  
\[h(\mathbf{y};\mathbf{x})=f\Big{(}\mathbf{x}+\frac{\mathbf{y}}{2}\Big{)} \overline{g\Big{(}\mathbf{x}-\frac{\mathbf{y}}{2}\Big{)}},\]  
then \(W^{R}_{f,g}(\mathbf{x},\cdot)\) is the OFT of \(h(\cdot;\mathbf{x})\) and \(A^{R}_{f,g}(\mathbf{x},\cdot)\) is the OFT of \(h(\mathbf{x};\cdot)\).
## 3 Results
### Yield Estimation Basing on VIs
Since the florescence ratio and difference reflectance at the seventh day of the flowering stage had the highest correlation with the yield, it was considered as the best date for rice yield estimation basing on spectral information at the flowering stage. At the seventh day of the flowering stage, florescence ratio and difference reflectance from 520 nm to 704 nm were best correlated to rice yield, with the biggest correlation coefficient for florescence difference reflectance at 624 nm, and 664 nm for florescence ratio reflectance. The first derivative ratio reflectance (Figure 5) at 560 nm, 720 nm and 740 nm showed good correlation with yield, and they were 590 nm, 690 nm, 740 nm and 760 nm for the first derivative difference reflectance. Obviously, the first derivative reflectance were better correlated with yield than the difference reflectance or the ratio reflectance. The results showed that for the first derivative ratio reflectance, the sensitive bands were around green bands 560 nm-568 nm, yellow bands 576 nm-588 nm, red-edge bands 680 nm-760 nm and NIR bands around 840 nm; the highly sensitive bands (top 50% of sensitive bands) were located at 704-760 nm. For the first derivative difference reflectance, sensitive bands were centered at yellow bands 580 nm-596 nm, red bands 664 nm, red-edge bands 688 nm-696 nm, 724 nm-768 nm, and the NIR region 840 nm-864 nm; the highly sensitive bands (top 50% of sensitive bands) were more spread out than the ratio derivative reflectance, mainly centering at 590 nm, 690 nm, 736 nm-748 nm, and 760 nm-768 nm. Figure 4: Correlation between rice yield and the difference florescence reflectance (FDR), the rice yield and the ratio florescence reflectance (FDR).
# Assessment of Transformer Fault Severity from Online Dissolved Gas Analysis Using Positive CUSUM
###### Abstract  
Dissolved gas analysis (DGA) is one of the transformer testing methods that has been widely used for a long time because it does not require the transformer to be offline for testing. The inspection can be conducted while the transformer is operating normally, and it is cost-effective. In research on fault severity, thermodynamic theory has been applied to find the energy index of the hydrocarbon gases generated by faults, CH\({}_{4}\), C\({}_{2}\)H\({}_{6}\), C\({}_{2}\)H\({}_{4}\) and C\({}_{2}\)H\({}_{2}\), referred to as the Normalised Energy Intensity (NEI). This study examines the application of the Positive Cumulative Sum of Difference (CUSUM) method to hydrocarbon NEI and carbon dioxide NEI for fault severity assessment. The study demonstrates the effectiveness of this approach by means of case studies on the basis of DGA results from two steel plants. In the case of NEI from hydrocarbon gases (NE\({}_{\rm HC}\)), the positive CUSUM is compared with the NEI, the NEI score, and the cumulative NEI. In the case of NEI from carbon oxide gases (NE\({}_{\rm CO}\)), Positive CUSUM is compared with NEI and the ratio of CO\({}_{2}\) to CO. It was found that Positive CUSUM for NE\({}_{\rm HC}\) is a much better indicator of the severity of defects than the NEI, the NEI score, and the cumulative NEI. In contrast, Positive CUSUM for NE\({}_{\rm CO}\) gave excessively high values. However, combining NEI with the CO\({}_{2}\)/CO ratio gave better monitoring results. Therefore, from an online DGA perspective, Positive CUSUM can be used to effectively monitor fault severity.  
transfer maintenance; dissolved gas analysis; fault severity; NEI; transformer monitoring Article
## 2 Materials and Methods
### Effect of LSZ on the Charging Behavior of CNHox  
The electrophoretic mobility of the CNHox in the presence of different concentrations of LSZ was measured to corroborate the charging behavior, and Equation (1) was used to determine the zeta potential. According to the manufacturer of Zetasizer Nano-ZS (Malvern, Australia), the detection limit of EPM for a protein of 15 kDa is 1 g/L. Therefore, the scattering from LSZ might be negligible. KCl was used as a background electrolyte and had a concentration of 10 mM after mixing. The lysozyme concentration ranged from 0.001 mg/L to 1 g/L, whereas the concentration of the CNHox was fixed at 2 mg/L.  
In Figure 6, each of the filled blue circles stands for the zeta potential at different mass ratio values of LSZ to CNHox. The magnitude of the zeta potential reduces somewhat as the mass ratio rises and reaches the isoelectric point (IEP) at about 0.0875 g/g. The charge reversal occurs with a steep slope as the mass ratio increases. For the IEP at a higher mass ratio of LSZ to CNHox, the slope gradually closes to zero where the mass ratio is around 50 g/g. Then, the low mass ratio, IEP, and high mass ratio can be determined from this result. These charging behaviors result from the adsorption of positively charged LSZ to the negatively charged CNHox surface. This behavior acts similarly to the neutralization and charge reversal of natural clay allophane and silica by counterions and LSZ, respectively [16; 23; 24; 39]. [PERSON] et al. [41] have simulated the adsorption of LSZ on hydrophobic surfaces and declared that LSZ can be adsorbed on a hydrophobic surface unless the interaction energy between protein and surface is strong enough to break the hydration shell barrier. Not only does the orientation of LSZ molecules relative to the hydrophobic surface play a part, but the attractive hydrophobic interaction plays a non-negligible role [42] in the adsorption of LSZ on a hydrophobic surface; one can also assume that these factors contribute to the strong charge reversal at high LSZ concentrations.  
Although we found the charge reversal and the saturation point, the adsorption amount of LSZ and whether or not the LSZ can enter in the cavity of CNHox are still unclear.
## 4 Direct Cooperation via In-Band Energy Level Patterns  
### Introduction  
This section describes cooperation techniques between co-located LTE and Wi-Fi networks that operate in the same frequency band, using in-band pattern recognition in order to enhance the spectral efficiency of the coexisting networks. A cooperation scheme that uses in-band pattern recognition can be applied, when the co-located networks do not have the ability to communicate between each other (e.g., via a coordinator) in order explicitly to express their requirements. The in-band pattern recognition methodology allows direct cooperation between different wireless technologies, as it can be used for technology identification and inter-RAT synchronization. Moreover, a wireless technology can use one or more in-band special patterns in order to inform other technologies about different actions that it performs. Upon the recognition of such a pattern, a wireless network will be able to adapt its behavior towards an increased performance (e.g., higher throughput) and more advanced spectrum usage. Figure 7 depicts an example of an in-band pattern recognition. In this example, a predefined energy level pattern is transmitted by the LTE eNB. This pattern is used for the identification of the LTE network by a Wi-Fi AP.  
For a technique of such a nature, the complexity of the design and the implementation is relatively low, as only small modifications of the current protocols of each wireless technology are required in order to transmit and interpret such energy level patterns. Nevertheless, the low complexity of the methodologies implies also a limited flexibility, meaning that upon sensing a co-located wireless technology, each network takes some predefined actions that can contribute to more efficient spectrum sharing and/or performs readjustment and tuning of existing coexistence techniques.
# Analysis of Acoustic Flow Field Characteristics of Key Components of High-Speed Train Bogies
## 2 Calculation Model
### Body and Bogie Model
#### 2.2.2 Bogie Model  
The bogie, located between the track and the car body, is an essential component of a high-speed train. It supports the entire car body, ensures the normal operation of the train, and enables smooth movements on both straight tracks and curves [34; 35]. Therefore, various parameters of the bogie directly affect the safety and stability of the train. Common train bogies today typically consist of a frame, gearbox, traction motor, and axle box (as shown in Figure 2).  
From Figure 2, it can be seen that the bogie structure is intricately composed, and that certain components are constructed with small dimensions. If the un-simplified 3D solid model of the bogie is used directly for analysis, calculation and, meshing, it will greatly increase the computational effort of the latest finite element analysis, increase the possibility of errors, and finally will not improve the accuracy of the final results. Therefore, this paper would simplify the bogie model as follows, as shown in Figure 3.  
Figure 1: The geometry of the train (unit: m).  
Figure 3: Comparison chart of bogie model.  
Figure 2: Bogie structure diagram.  
Simplifying the bogie model  
As the goal of this paper is to examine the key components of the bogie, a simplified model will be used to improve computational efficiency without affecting the analysis of the sound field simulation. The model retained the main components and removed the components that have less influence on the results. The reserved parts were the bogie frame, traction motor, traction motor frame, gearbox, axle box, wheel pair, and air spring. The removed parts were the single traction tie rod, swivel arm positioning device, anti-snake damper, motor fan, and screws and nuts.  
Smoothing of key components  
The surfaces of key components, such as bogie axle boxes and motors, are often uneven, especially at the connection between axle box and axle box cover and at the surface of the gearbox motor. These details often have a small impact on the final result, and if they are not simplified, they will increase the calculation volume significantly, wasting both time and effort. Therefore, the fundamental components of the bogie were smoothed to improve the efficiency of the calculation.  
By simplifying the model in the above way, the key components that have a large impact on the final simulation results were retained, and the computational resources were greatly saved, which improves the running speed and efficiency of the finite element analysis at a later stage, and also ensures the accuracy of the final results. The final simplified bogie model is shown in Figure 3, with a bogie wheel diameter of 0.9 m and a bogie wheelbase of 2.5 m.
## 2 Study Area and Data
### Data Description
#### 2.2.2 SSMIS Microwave Radiometer  
A microwave radiometer sensor is mounted onto HY-2A, but the radiometer data quality accuracy is lower than that of the microwave scatterometer. Therefore, a microwave radiometer with the closest possible matching time to the HY-2A transit time is needed. The HY-2A microwave scatterometer crosses the equator time onto its descending node at 6:00 local time, and on its ascending node at 18:00 local time. Therefore, a sensor that crosses the equator time at around 6:00, or 18:00 local time, is a priority when choosing a microwave radiometer. Therefore, the F17 SSMIS, carried onboard the Defense Meteorological Satellite Program (DMSP) satellites, was selected.  
Figure 1: Locations of tropical cyclones (TCs) and their basins.  
Figure 2: Satellite observation of Typhoon Fitow (1323) in 2 October 2013, (**a**) HY-2A, and (**b**) SSMIS 19 GHz H-polarization.  
SSMIS is a seven-channel passive microwave radiometer operating at four frequencies (19.35, 22.235, 37.0, and 91 GHz) and dual-polarization (except at 22.235 GHz which is V-polarization only) [31; 32]. SSMIS data are available through the NOAA Data Center's Comprehensive Large Array-data Stewardship System (CLASS). Figure 2b is an SSMIS 19 GHz H-polarization observation of Typhoon Fitow.
# Bounding the Likelihood of Exceeding Ward Capacity in Stochastic Surgery Scheduling
## 2 Model Development
### Robust Ward Optimization  
In robust optimization, distributional information about the LOS is ignored [2]. Instead, constraints that reflect the worst-case realization of uncertainty are added. The difficulty of describing the worst-case realization, the so-called uncertainty set, is the challenge remaining. The subject matter experts, knowing the patients' conditions, may be able to estimate the worst-case scenario. Depending on the risk-attitude of the hospital, a probabilistic guarantee for the feasibility can be made.  
Let us assume that the decision-maker is very conservative and requires an \(\omega\) level of certainty that the number of staffed ward beds occupied in the ward are kept below their capacity \(M^{A}\). Let the probability of patient \(i\) being in the ward on day \(d\) be denoted by \(\rho^{\prime}_{i,d}\). Then, the worst-case realization should satisfy the following condition:  
\[\bar{n}_{d}+\sum_{\begin{subarray}{c}p\in D_{J}\in\{0,\ldots,M^{W}-1\},r\in R :\\ (d-j),p,r\}\in DPR\end{subarray}x_{d-j,p,r}\Big{(}\sum_{i\in I_{p}}\mathbb{1}_ {\omega\leq\rho^{\prime}_{i,j}}\Big{)}\leq M^{A},\quad\forall d\in D \tag{24}\]  
where \(\bar{n}_{d}\) are the patients with certainty \(\omega\) in the ward from the previous plan and \(\mathbb{1}_{\omega\leq\rho^{\prime}_{i,j}}\) takes the value \(1\) when \(\omega\leq\rho^{\prime}_{i,j}\); otherwise, it is \(0\). Constraint (24) replaces constraints (12)-(19) described in the previous section; all other details of the MIP model remain the same.  
In Figure 2, one can see the distribution for the same surgery type as presented in Figure 1, but now, for the worst-case LOS using \(\omega=0.25\), as illustrated by the dashed-line. In this example, the patient is in ward from days \(0\) to \(7\) (\(\mathbb{1}_{\omega\leq\rho^{\prime}_{i,j}}=1\)) but has left the ward on day \(8\) (\(\mathbb{1}_{\omega\leq\rho^{\prime}_{i,j}}=0\)). The figure shows that the approach is conservative and may reduce the number of scheduling possibilities.  
Although the worst case for the LOS for a single patient is used, there is still the chance that we will exceed the staffed ward beds with a high probability when there are many patients in the ward. In the implementation of this constraint in [2], a slack is introduced to allow for additional ward beds and thus guarantee feasibility. We do not consider this an  
Figure 2: Approximating the expected length of stay (LOS) with the worst-case scenario as illustrated by a dashed-line using certainty \(\omega=0.25\).  
option and suggest that the strategy in practice would be to increase the value of \(\omega\) until feasibility is met.
# Temperature-Insensitive Imaging Properties of a Broadband Terahertz Nonlinear Quantum Cascade Laser
###### Abstract  
Terahertz (THz) quantum cascade laser sources based on optical nonlinearity are the only electrically pumped monolithic semiconductor sources operable at room temperature in the 0.6-6 THz range. We investigated the temperature dependence of the imaging characteristics of a broadband THz nonlinear quantum cascade laser and evaluated several important properties: the spectrum, far-field pattern and THz imaging results. Consequently, we found that the far-field patterns were single-lobed Gaussian-like, and THz images were well-resolved despite the lower operating temperature of the device. The stable temperature-performance indicates that this broadband THz source is promising for THz imaging applications.  
terahertz; quantum cascade laser; nonlinear optics; imaging +
Footnote †: journal: Applied Sciences
## 4 Results and Discussion  
The particle-based model presented in Section 3 is validated using experimental measurements from Section 2. The model allows calculating the total charge in the flow channel, while the total charge in the experimental study is evaluated by integrating the current at the constant voltage of 0.75 V. This validation of the particle charging model includes the optimization of the critical modeling parameters and testing its sensitivity for the charging progress.
## 2 System Model
### FSO Channel Model  
As Figure 1 shows, R to D indicates that FSO links and adopts an M distribution. Atmospheric fading and pointing errors are compromising factors for the FSO communication link performance [24; 25; 26]. The M distribution channel PDF and CDF expressions are as follows [27]:  
\[f_{RD}(\gamma)=\frac{\xi^{2}A}{2\gamma}\sum\limits_{m^{\prime}=1}^{\beta}b_{ m^{\prime}}G_{1,3}^{3,0}\left[\frac{B\gamma_{RD}}{\overline{\gamma}_{RD}} |\frac{\xi^{2}+1}{\xi^{2},\alpha,m^{\prime}}\right] \tag{13}\]  
\[F_{RD}(\gamma)=\frac{\xi^{2}A}{2}\sum\limits_{m^{\prime}=1}^{\beta}b_{m^{ \prime}}G_{2,4}^{3,1}\left[\frac{B\gamma_{RD}}{\overline{\gamma}_{RD}}|\frac{ 1,1+\xi^{2}}{\xi^{2},\alpha,m^{\prime},0}\right] \tag{14}\]  
where:  
\[A=\frac{2\alpha^{\alpha/2}}{g^{1+\frac{\alpha}{2}}\Gamma(\alpha)}\left(\frac{ g\beta}{g\beta+\Omega_{0}^{\prime}}\right)^{\beta+\frac{\alpha}{2}} \tag{15}\]  
\[B=\frac{\xi^{2}\alpha\beta\left(g+\Omega_{0}^{\prime}\right)}{(\xi^{2}+1)\left( g\beta+\Omega_{0}^{\prime}\right)} \tag{16}\]  
\[b_{m^{\prime}}=\left(\begin{array}{c}\beta-1\\ m^{\prime}-1\end{array}\right)\frac{\left(g\beta+\Omega_{0}^{\prime}\right)^{1 -\frac{m^{\prime}}{2}}}{(m^{\prime}-1)!}\left(\frac{\Omega_{0}^{\prime}}{g} \right)^{m^{\prime}-1}\left(\frac{\alpha}{\beta}\right)^{\frac{\alpha^{\prime}}{2 }}\left(\frac{\alpha\beta}{g\beta+\Omega_{0}^{\prime}}\right)^{-\frac{gfor the fading parameter, respectively. The ratio of the equivalent beam width to the pointing error displacement standard deviation at the receiver is represented by \(\xi\), g is the average power of the scattering component, \(\Omega_{0}^{\prime}\) represents the average power of the coherent contribution, and \(G_{p,q}^{m,n}[\cdot]\) is the [PERSON]'s G function.