esa-sceva/satcom-chunk-collection huggingface dataset:
# Use of Evolutionary Computation to Improve Rock Slope Back Analysis
## 2 Genetic Algorithm (GA)  
Genetic algorithms (\(GA\)), first introduced by [PERSON] and his collaborators in the 1960s and 1970s, are population-based stochastic search algorithms inspired by [PERSON]'s theory of natural selection. [PERSON] was the first to adopt the use of crossover, recombination, mutation, and selection operators in the study of artificial and adaptive systems. These operators form the basis of the genetic algorithm as a problem-solving strategy. GA begins by randomly generating a population of potential solutions. Chromosomes, which are strings of DNA, represent each member of the population, as an encoding of the problem and its search space. Members of a population are selected for recombination based on their fitness and they recombined to form a new population, for the next generation. This process is repeated until some termination criteria are met, as shown in Figure 1.  
There are several control parameters in GA. The mutation probability \((p_{m})\), which is defined as the percentage of the total number of genes in the population and controls the probability with which new genes are introduced into the population. The purpose of the mutation operator is to maintain diversity within the population and suppress premature convergence. A simple way to achieve mutation would be to alter one or more genes. In GA, mutation serves the crucial role of either (a) replacing the genes lost from the population during the selection process so that they can be tried in a new context or (b) providing the genes that were not presented in the initial population. Mutation is the part of GA that is related to the \"exploration\" of the search space.  
The crossover operator plays an important role in the overall operation of GA. The performance of GA to a great extend depends on the performance of the crossover operator used. It is analogous to reproduction and biological crossover. In this, more than one parent is selected and one or more offspring are produced using the genetic material of the parents. The crossover probability \((P_{\mathbb{C}})\) is defined as the probability of the number of offspring produced in each generation of the population size. Crossover is usually applied in a GA with a high probability.  
Genetic algorithms are able to address complicated problems with many variables and a large number of possible outcomes by simulating the evolutionary process of \"survival of the fittest\" to reach a defined goal. They operate by producing many random answers to a problem, eliminating the worst and cross-pollinating better answers. This process of repeated elimination and regeneration gradually improves the quality of the answers to an optimal or near-optimal condition.
# A New Linear Model for the Calculation of Routing Metrics in 802.11s Using ns-3 and RStudio
## 3 Results
### Original Model and Its Modifications  
The statistical test results for Imesh1 and Imesh2 are summarized in Figure 5. These results were obtained using the STARGAZER function.  
Lmesh2 has an increment of the R2 and adjusted R2 values. The error decreases when the regressors are increased. This is all related to Imesh1. Therefore, it is possible to explain the observed variability to a great extent. The F-test shows that this variability is significant. The conditions for this type of regression are met. Furthermore, the Pr value in the ANOVA test shows that Imesh1 and Imesh2 are not the same, as they can be seen in Figure 6.  
The test STEPWIZE indicates that all the regressors remain within the model due to Akaike's information criterion (AIC) values. See Figure 7.  
In other words, the original model, IEEE 802.11s, improves when including the square, the FAILAVG cube, and additional regressors. The residual analysis for Imesh1 indicates that the data distribution is not Gaussian since the residual pattern is the same for the data. Therefore, the model is not linear, and it can be seen in Figure 8.  
When excluding the non-linearity data, the original model was modified in Imesh3, and the following results were produced, shown in Figure 9.  
Figure 5: STARGAZER of lmesh1 and lmesh2.  
Figure 6: ANOVA Imesh1-lmesh2.  
Figure 7: STEPWIZE summary.  
call:  
1m(formula = METRIC \(\sim\) COMP + I(COMP*2) + I(COMP*3) + MXN + ML_STEP + M_PACKET + INTERFACES + L4, data = mesh1)  
Residuals:  
Min 1Q Median 3Q Max  
-175.057 -0.021 0.213 0.471 1.207  
Coefficients:  
Estimate std. Error t value Pr(>|t|)  
(Intercept) -1.355e+00 1.553e-01 -8.729 < 2e-16 ***  
COMP 1.512e+02 1.878e-01 804.931 < 2e-16 ***  
I(COMP*2) -1.429e-01 6.859e-02 -2.084 0.0372 =  
I(COMP*3) 1.037e-02 7.074e-03 1.466 0.1426  
MON 8.295e-03 2.561e-03 3.239 0.0012 **  
M_STEP -6.870e-03 1.327e-03 -5.178 2.25e-07 ***  
M_PACKET 2.583e-05 5.454e-06 4.737 2.17e-06 ***  
INTERFACES -2.693e-02 1.988e-02 -1.354 0.1756  
L4 UDP 7.248e-02 2.977e-02 2.435 0.0149 =  
---  
Signif. codes: 0 ***' 0.001 ***' 0.01 **' 0.05 '.' 0.1'' 1  
Residual standard error: 4.13 on 82039 degrees of freedom  
Multiple R-squared: 0.9981, Adjusted R-squared: 0.9981  
F-statistic: 5.323e+06 on 8 and 82039 OF, p-value: < 2.2e-16  
**Figure 8. Adjusted residuals for Imesh1.  
Figure 10. Adjusted residual for Imesh3.  
Figure 9. Imesh3 summary.  
When calculating a new regressor, COMP, as the reverse complement to 1 of _ef,_ the GAUSSIANA distribution model is obtained, and it better fits the LM where the selected regressors significantly contribute to the metric calculation. Nonetheless, this contribution is limited since the increment in R2 and R2 adjusted is very low.
## Materials and Methods
### Input Datasets
Precipitation data are used as a limiting factor of soil moisture availability and interception and is sourced from the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) dataset [39]. ETo is estimated using the weather, atmospheric transmissivity, and solar radiation data and has no satellite data input. Detailed information on the methodology and processing can be found in the WaPOR methodology documents [36].
# A Taxonomy for Security Flaws in Event-Based Systems
## 3 Taxonomy
### Relationship between Security Flaws and Event Attacks
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & \begin{tabular}{c} **Security Flaw** \\ **in EBS** \\ \end{tabular} & \
\begin{tabular}{c} **Event** \\ **Attack** \\ \end{tabular} &
\begin{tabular}{c} **Existing** \\ **Solution** \\ \end{tabular} \\ \hline F1 & Trapdoor & - & - \\ \hline F2 & Conspirator & A5 & - Detection of information leaks [46; 60; 142] \\ \hline F3 & Logic/Time Bomb & - & - \\ \hline F4 & Covert Channel & - & - \\ \hline F5 & Open Event Channel & A1-7 & - Encryption of events [41] \\  & & - Policy enforcement [46; 47; 71; 143] \\ \hline F6 & Inadequate Concurrency & A1 & - Detection of event anomalies [68] \\ \hline F7 & Unsafe Event & A2,3,7 & - Role-based access control [39; 135] \\  & & - Encryption of events [41] \\  & & - Detection of vulnerable components [22; 45; 46; 142] \\  & & - Policy enforcement [46; 47; 71; 143] \\ \hline F8 & Unsafe Event Interface & A1,4,6 & - Role-based access control [39; 135] \\  & & - Detection of vulnerable components [22; 45; 46; 142] \\  & & - Policy enforcement [46; 47; 71; 143] \\ \hline F9 & Inadequate Authentication & A1-7 & - Security policy validation [39; 144] \\ \hline F10 & Inadequate Resource Management & A6-7 & - Analysis of runtime events and resources [145; 146] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: EBS Security Flaws, Event Attacks, and Existing Solutions. vulnerable to event attacks, they suffer from inaccuracy and scalability issues in their analysis. To further secure EBSs, advanced approaches that combine detecting flaws and preventing attacks are required.
# Security Threats and Cryptographic Protocols for Medical Wearables
## 2 Security in Medical Devices
By doing this, the attacker can not only obtain critical health-related information of the patient, but also know the activities he has performed, the places he has visited and the format in which the WHMS stores the data [41]. * Replay attack: the attacker can go one step further from eavesdropping, introducing previous, eavesdropped data to compromise WHMS readings or gain illegitimate access to the patient's devices. For example, if a wearable device has the permission to unlock the patient's smartphone, the adversary might be able to unlock it by replaying information that has previously eavesdropped [29]. * Data tampering/modification: this attack occurs when the attacker modifies the measured data without having the proper authorization. To do this, the attacker must know the format in which the WHMS reads and transmits information, therefore this type of attack starts with an eavesdropping attack [34]. * Denial of service (DoS): the purpose of this attack is to alter the correct functioning of the WHMS by collapsing it with continuous orders. In this way, the battery of the device will run out very rapidly, or the patient will be unable to use it. A distributed DoS (DDoS) attack performed by botnets could lead many nodes to be involved in the attack [38; 42]. * Side-channel attack: in this type of attack, the adversary uses information from the implementation of a WHMS to gather critical details of the patient. For example, an electromagnetic analysis can be conducted to derive the power consumption of a WHMS and, hence, extract the activities it is executing [43]. Likewise, it has been shown that information acquired from motion sensors of wearable devices can be used to profile the patient [39]. The rest of this section was divided into three parts, one for each attack vector previously defined (firmware vulnerabilities, communication protocols, and applications and sensors). For each vector, the current literature was studied, and a brief description of the studies that have successfully executed an attack on a WHMS was included. Since some works use more than one attack vector in their methodology, each article was allocated in the group of its principal attack vector.
# Parametric Analysis of Compliant End Face Gas Film Seals Considering Slip Flow Effects
## 4 Results and Discussion  
### Pressure Distribution and End Face Deformation  
Based on the mathematical model established in the previous section, calculations are performed to obtain the deformation distribution of the compliant end face, as shown in Figure 6. The distribution of gas film pressure along the radius position is also obtained, as shown in Figure 7.  
By comparing Figure 6a,b, it can be found that the distribution of the deformation of the compliant end face is basically the same in both cases of considering the slip flow effect and not considering the slip flow effect, and the amount of deformation is basically the same at the same radius, and the area with the largest gradient of change is at the outer radius of the sealing ring, with a value of about 1.72 \(\upmu\)m. At the inner radius of the sealing ring, since the outlet pressure of the medium is ambient atmospheric pressure, the sealing ring is basically not deformed, and the gas film thickness is the minimum gas film thickness \(h_{0}\). This trend makes the thickness of the gas film gradually decrease from the outer radius to the inner radius, thus forming a wedge-shaped gas film between the friction partners and effectively enhancing the sealing performance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Validation Model I** & **Validation Model II** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Spiral groove dry gas seal construction parameters.  
From the lubrication gas film pressure distribution diagram in Figure 7, it can be seen that the pressure change trend is basically the same in two cases, considering the slip flow effect and not considering the slip flow effect, which is shown as a gradual decline from the outer radius to the inner radius, and the first decline is slower, and then decline faster. This is due to the gas film pressure in addition to the sealing gap, but also with the inlet pressure, the inlet pressure directly affects the flow field pressure distribution. From the overall distribution of pressure variation with radius, the pressure without considering the slip flow effect is slightly larger than the pressure considering the slip flow effect. There is a maximum pressure difference when the radius value is 59.87 mm, which is close to the low-pressure side, and from this radius position, the closer to the inner radius and outer radius of the seal ring, the smaller the difference between the two. When the radius value is 74.39 mm, the calculation results considering the slip flow effect and not considering the slip flow effect are basically the same, and the slip flow effect basically does not play a role in this region. It can be seen that the slip flow effect is more significant in the low-pressure region of the lubricated gas film, which is manifested in the gradual weakening of the slip flow effect with an increase in the pressure inside the flow field.  
Figure 6: Compliant end deformation distribution. (**a**) without slip flow; (**b**) with slip flow.  
Figure 7: Lubricated gas film pressure distribution.
# A Novel 64 QAM-OFDM Optical Access System Based on Bit Reconstruction
## 2 Theory and Principle
At this point the signal has a slightly higher probability of being mapped to the yellow-sphere region, which is closer to the origin of the constellation diagram than to the blue-sphere region. This is the rationale for the division between the high- and low-power areas. The last two bits of each symbol are maintained to be specific (the signals mentioned above that do not enter the logic code circuit). For example, the last two bits of each symbol from the first to the fourth quadrant are 11, 01, 00, and 10, respectively. Then the bit reconstruction is complete. In the OFDM module, an I/Q modulator with two MZMs is used to load real part and imaginary part information of the OFDM signal. According to the transmission function of MZM [18], the output optical signal is:  
\[ \begin{array}{ll}E(t)&=\ Acos(\dfrac{\pi}{2}\dfrac{\
u_{I}+\
u_{dc}}{\
u_{ \pi}})exp(j\omega_{LD}t+j\varphi_{LD})\\ &=\ Acos(\dfrac{\pi}{2}\dfrac{\
u_{Q}+\
u_{dc}}{\
u_{\pi}})exp(j\omega_{LD}t+ j+\dfrac{\pi}{2})\end{array}  \tag{8}\]  
where \(A\) is a constant, \(\
u_{dc}\) is the DC bias voltage of the MZM, \(\
u_{\pi}\) is the half-wave voltage of the MZM, and \(\omega_{LD}\) and \(\varphi_{LD}\) are the frequency and phase of the transmitting laser, respectively. Suppose that only subcarriers with frequencies \(\omega_{1}\) and \(\omega_{2}\) exist in the signal, and the amplitude \(\mu_{p1}\) is a bit reconstruction signal which is mapped. The \(\
u_{I}\) and \(\
u_{Q}\) can be expressed as:  
\[ \begin{cases}\
u_{I}=j\omega_{LD}t+j\varphi_{LD}(cos\omega_{1}t+cos\omega_{2} t),\\ \
u_{Q}=\mu_{p1}(sin\omega_{1}t+sin\omega_{2}t).\end{cases}  \tag{9}\]By defining \(\
u_{I}\) and \(\
u_{Q}\), we can obtain:  
\[ \begin{split} E(t)&=\textit{A}cos[\frac{M}{2}(cos \omega_{1}t+cos\omega_{2}t)+\frac{\phi}{2}]\\ &+jcos[\frac{M}{2}(sin\omega_{1}t+sin\omega_{2}t)+\frac{\phi}{2}] exp(j\omega_{LD}t+j\varphi_{LD})\\ &=\textit{A}exp(j\omega_{LD}t+j\varphi_{LD})E_{B}(t)\end{split}  \tag{10}\]  
where \(M=\frac{\mu_{I}\pi}{\
u_{\pi}}\) is the optical modulation index, and \(\phi=\frac{\
u_{d}\pi}{\
u_{\pi}}\) is the phase offset point of the modulator. \(E_{B}(t)\) can represent the baseband signal and can be expanded by the first-order Bessel function. When \(\phi=\pi\), the linear mapping of the system is optimal, so the output optical OFDM signal can be approximated as:  
\[E_{s}(t)=E(t)=\frac{AM}{2}(\
u_{I}+\
u_{Q})e^{i(\omega_{LD}t+\varphi_{LD})} \tag{11}\]  
Typically, TX side preprocessing is performed in the OFDM module. The specific process is to map the modulation signal to multiple subcarriers, and convert the frequency domain signal on the parallel subcarriers to the time domain through the inverse fast Fourier transform. The cyclic prefix is inserted before each OFDM symbol. Finally, the time domain signals of multiple subcarriers are superimposed through the parallel-serial conversion to form the 64 QAM-OFDM transmission signal in the OLT.
# Echelle Grating Spectroscopic Technology for High-Resolution and Broadband Spectral Measurement
## 7 Conclusions  
Echelle grating provides high spectral resolving power and high diffraction efficiency in the broadband wavelength range by the Littrow mode. The spectrometer with a cross-dispersed echelle scheme achieves a two-dimensional spatial distribution of the spectrum by one exposure without scanning in the broadband spectral range. It is the fastest and most sensitive spectroscopic technology as of now, and it has been extensively applied in commercial and astronomical spectrometers. In the future, the echelle will have more applications in many fields.  
Conceptualization, Y.Z.; investigation, Y.Z., W.L. and W.D.; writing--original draft preparation, W.L. and W.D.; writing--review and editing, Y.Z., W.L. and H.Y.; supervision, Z.H. and H.Y.; funding acquisition, Y.Z. and H.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Scientific Instrument and Equipment Development Project of China (2014 YQ510403); the National Natural Science Foundation of China (NSFC) (61505143); and the Strategic Priority Research of the Chinese Academy of Sciences (CAS) (XDB09040100).  
Not applicable.  
All data generated or analyzed during this study are included in this published article.  
The authors declare no conflict of interest.
## 3 Proposed Solution
### Architecture Proposed
#### 3.1.3 Congestion Detection  
The system contemplates two possible types of congestion: recurrent and non-recurrent. The proposal has a defined strategy for each of them.  
We will start by talking about the strategy for recurrent congestion. Every 300 s, the Zone Agent receives a new forecast from the Network Agent. The ZA analyzes the forecast in search of streets that may have an average delay greater than the limit determined by the system. Once these streets are detected, the Zone Agent sends a message of congestion to vehicles that have one or more of these streets on their route (Figure 4). The Zone Agent sends a route update message to the Route Guidance Agent and all Vehicle Agents who have one or more of the congested streets on their routes. Any VA that receives a route update message should verify how far it is from the congested highway. If the distance is less than a limit defined by the system, it will send a route suggestion message to the Zone Agent. In this case, the message will indicate the current position of the vehicle as point of origin. Meanwhile, as soon as RGA receives the route update message, it invalidates all previously generated kSPs.  
With regard to non-recurrent congestion, the Probe Car Agent carries out detection. If the PCA detects an excessive number of STOP events while crossing a street, it will proceed to send a non-recurrent congestion message to both the Zone Agent and the Network Agent. This message contains a timestamp, street ID, and delay experienced so far. The Zone Agent forwards a non-recurrent congestion message to the Route Guidance Agent and to all Vehicle Agents that that street has on its route. In the case of the RGA, it will verify if the message has updated information and, if so, it will invalidate only the kSP containing the ID of the congested street and update the information of that street in its forecast. Any VA that receives a non-recurrent congestion message should verify how far away it is from the congested road. If the distance is less than a limit defined by the system, it will send a route suggestion message to the Zone Agent. In this case, the message will indicate the current position of the vehicle as point of origin. For its part, the NA will keep this report for later use, during the process of generating a new forecast.
###### Abstract  
Earth observation provides timely and spatially explicit information about crop phenology and vegetation dynamics that can support decision making and sustainable agricultural land management. Vegetation spectral indices calculated from optical multispectral satellite sensors have been largely used to monitor vegetation status. In addition, techniques to retrieve biophysical parameters from satellite acquisitions, such as the Leaf Area Index (LAI), have allowed to assimilate Earth observation time series in numerical modeling for the analysis of several land surface processes related to agroecosystem dynamics. More recently, biophysical processors used to estimate biophysical parameters from satellite acquisitions have been calibrated for retrieval from sensors with different high spatial resolution and spectral characteristics. Virtual constellations of satellite sensors allow the generation of denser LAI time series, contributing to improve vegetation phenology estimation accuracy and, consequently, enhancing agroecosystems monitoring capacity. This research study compares LAI estimates over croplands using different biophysical processors from Sentinel-2 MSI and Landsat-8 OLI satellite sensors. The results are used to demonstrate the capacity of virtual satellite constellation to strengthen LAI time series to derive important cropland use information over large areas.  
Leaf Area Index; Earth observation; Sentinel-2; Landsat-8; vegetation phenology +
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## 4 Discussion
### Limitations to the Study  
Although our study demonstrates the improvement of mass flow rate/velocity and wall stress after endograft implantation, it has some limitations. Firstly, unlike in humans, we treated the artery wall as a rigid body, therefore assuming that it had negligible influence on blood flow. However, this approach is in line with [PERSON] et al. [38], who showed that differences between blood flow distribution for rigid and pulsating walls are comparable. Secondly, we described blood rheological properties using the non-Newtonian shear-thinning Quemada model. This is in accordance with [PERSON] et al. [39], who showed that non-Newtonian fluid reflects the real properties of blood and does not artificially increase WSS as the Newtonian approach does. Also, we analysed only acute type IIIb aortic dissection and the obtained data may not be applicable for other types of aortic dissections without initial verification. Moreover, small sample size could influence the obtained results. However, thepatients were carefully selected; hence, we believe the obtained results may be applicable to similar cases. Finally, the CFD simulation accuracy depends on the resolution of CTA and USG-Doppler data. The higher the resolution, the better the 3d model and the final results of blood hemodynamic reconstruction. In our study we used 1 mm slides from CT scans, which gives an acceptable but not a perfect resolution. Therefore, it might influence the final results. However, given the highly viscous nature of the flow, it is likely that the overall flow patterns would be very similar to those presented here. The obvious next step would be to extend this study and analyse a wider group of patients with TBAD and determine the usefulness of the CFD technique in post-operative patient evaluation.