esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Analysis of Photoluminescence Spectra
#### 4.2.1 Analysis of Optical Constants for Binary Materials in the NIR \(\rightarrow\) UV Region
Figure 10: (**a**) Comparison of simulated MDF results (blue lines) for R(E) and \(\alpha\)(E) with experimental data (Ref. [22]) for InP (indicated by red squares) in the energy range of 0–6 eV. Magenta vertical arrows indicate the critical point energies; (**b**) same key as (**a**) but for \(\alpha\)(E). Figure 9: (**a**) Comparison of simulated MDF results (blue lines) for R(E) and \(\alpha\)(E) with experimental data (Ref. [22]) for InSb (indicated by red squares) in the energy range of 0–6 eV. Magenta vertical arrows indicate the critical point energies; (**b**) same key as (**a**) but for \(\alpha\)(E). 2.2 Analysis of Optical Constants for InP\({}_{1-x}\)Sb\({}_{x}\) Alloys in the NIR \(\rightarrow\) UV Region  
To calculate the composition-dependent optical constants for InP\({}_{1-x}\)Sb\({}_{x}\) ternary alloys, we have meticulously obtained MDFs from the values (Table 2) of their binary counterparts (InP, InSb) by using appropriate quadratic expressions [18] involving x of the major CP energies, while the strength and broadening parameters are obtained deliberating a linear dependence articulation on x [38]. In Figure 11a-d, we have reported our simulated results of n(E), \(x\)(E); \(\varepsilon_{1}\)(E), \(\varepsilon_{2}\)(E); R(E); and \(\alpha\)(E), respectively, for InP\({}_{1-x}\)Sb\({}_{x}\) ternary alloys in the photon energy range of 6 eV \(\geq\) E \(\geq\) 0 by selecting the composition increment of x by 0.2. In each figure, the results have revealed an appropriate shift in the CP energies toward the low-energy side as x is increased, in corroboration with the observed changes from InP \(\rightarrow\) InSb band structures. Like n(E), \(\kappa\)(E); \(\varepsilon_{1}\)(E), \(\varepsilon_{2}\)(E) (indicated by black and green points in Figure 11a,b the near normal-incidence reflectivity R(E) (shown by sky-blue-colored points) and the absorption coefficient \(\alpha\)(E) (represented by brown points) in Figure 11c,d also revealed distinct composition-dependent shifts in CP structures. Our simulations of optical constants for the InP\({}_{0.67}\)Sb\({}_{0.33}\) alloy have provided the lowest energy bandgap value E\({}_{0}\)\(\sim\) 0.46 eV, in very good agreement with the existing first-principles calculation as well as our PL measurements (see Figure 6 and Table 3). Figure 11: (**a**) Calculated n(E) and \(\kappa\)(E) spectra of InP\({}_{1-x}\)Sb\({}_{x}\) alloys as a function of photon energy E, with composition x, increment by 0.2. (**b**) Same key as (**a**) but for \(\varepsilon_{1}\)(E) and \(\varepsilon_{2}\)(E). (**c**) Same key as (**a**) but for reflectivity R(E). (**d**) Same key as (**a**) but for absorption coefficient \(\alpha\)(E).
## 3 Message Digests for Simulatable Signatures
### Optimal Asymmetric Encryption Padding  
Optimal Asymmetric Encryption Padding (OAEP) is a procedure initially proposed to pad the plaintext prior to its asymmetric encryption [18]. We next describe OAEP when no plaintext-awareness is required (by setting parameter \(k_{1}\) of the original proposal to 0). Let \(m\) be the bitlength of the input plaintext, and let \(k_{0}\) be an integer parameter. OAEP is constructed from two of oracles,  
\(\mathcal{G}\) and \(\mathcal{H}\), producing \(m\) and \(k_{0}\) bit outputs, respectively.  
The padding procedure takes as input the original plaintext \(M\) and a random \(k_{0}\)-bit string \(r\):  
1. Compute \(X=M\oplus\mathcal{G}(r)\),
2. Compute \(Y=r\oplus\mathcal{H}(X)\),
3. Return \((X,Y)\).  
We will denote this process as \((X,Y)=\text{OAEP}_{m,k_{0}}(M,r)\). The resulting \(X\) and \(Y\) are \(m\) and \(k_{0}\) bits long, respectively. Along the paper, sub-indices \(m\) and \(k_{0}\) will be removed when deemed redundant. The reverse procedure returns the original \((M,r)\) pair from \((X,Y)\):  
1. Compute \(r=Y\oplus\mathcal{H}(X)\),
2. Compute \(M=X\oplus\mathcal{G}(r)\),
3. Return \((M,r)\).  
We will denote the reverse process as \((M,r)=\text{OAEP}_{m,k_{0}}^{-1}(X,Y)\).  
The following lemma states a property of OAEP, which is crucial for our construction.  
**Lemma 1**: _Given a message \(M\) and a \(k_{0}\)-bit string \(Y\), it is hard to find an \(\{r,X\}\) pair so that \((X,Y)=\text{OAEP}_{m,k_{0}}(M,r)\)._  
Proof.: Given \(M\) and \(Y\), one must find a bitstring \(r\) satisfying Equation (1).  
\[Y=r\oplus\mathcal{H}(M\oplus\mathcal{G}(r)). \tag{1}\]  
Since \(\mathcal{G}\) acts as a random oracle, after choosing \(r\), the output of \(\mathcal{G}(r)\) is assumed to be random so that \(M\oplus\mathcal{G}(r)\) is also random. Function \(\mathcal{H}\) is also a random oracle so that \(\mathcal{H}(M\oplus\mathcal{G}(r))\) is random and so \(r\oplus\mathcal{H}(M\oplus\mathcal{G}(r))\) is. Hence, the probability that the resulting random string matches \(Y\) is \(2^{-k_{0}}\) so that the expected number of trials needed for finding such an \(r\) is \(2^{k_{0}}\) which is unfeasible if \(k_{0}\) is large enough. Given that \(r\) is \(k_{0}\) bits long, there are exactly \(2^{k_{0}}\) candidates so that such an \(r\) may not exist.  
Another possibility is to search for an \(X\) satisfying Equation (2).  
\[M=X\oplus\mathcal{G}(\mathcal{H}(X)\oplus Y). \tag{2}\]  
In this case, an equivalent analysis can be applied leading to a \(2^{-m}\) success probability, which is harder, as typically \(m\gg k_{0}\).
## 3 Illustrative Example  
To illustrate the analyzed optimization model we consider the sample project shown in Figure 1. The project consists of 8 activities and dummy activities 0 and 9 representing the starting and ending node in the graph \(G\) (\(V,E\)) respectively. Activities are executed using one type of limited, renewable resources of availability of 10. Discount rate used in calculations is \(\alpha=0.01\).  
Assume that the baseline schedule, which is the basis for determining the stage settlement of the project between the client and the contractor, is shown in Figure 2. It can be generated, for example, using the schedule generation scheme (SGS) serial, for the activity list [1; 5; 2; 3; 6; 4; 7; 8].  
Figure 1: Example of project in activity-on-node (AON) representation.  
Assume that based on the baseline schedule of Figure 2 three contractual stages of work that are distinguished that have the following completion times: \(MD_{1}=4\), \(MD_{2}=8\), \(MD_{3}=12\). Activity sets to be completed in further stages are as follows \(MA_{1}=\{1,\,2\}\), \(MA_{2}=\{3,\,4,\,5\}\), \(MA_{3}=\{6,\,7,\,8\}\). The cash flow related to \(CFA_{i}\) activities and the data for stage project settlements such as: contractual completion times \(MD_{m}\), client contractual payment amounts \(MP_{m}\) and delay costs \(MC_{m}\), are shown in the AON representation in Figure 3. Milestone data is denoted by the \"square\" symbol. The same colours show the nodes representing activities from the same stage of the project. The stage settlements should be arranged to encourage the contractor to execute the project as quickly as possible and not to exceed \(MD_{m}\) completion times.  
Once the stage settlement has been completed, the contractor will prepare a schedule which maximizes the sum of the discounted cash flows function \(F\). It is advisable to delay the expenditure (negative cash flow) incurred at the start of activities (lower discounted value) and accelerate client payments for project stages (positive cash flows) acquired at the time of their completion (higher discounted value). The baseline schedule shown in Figure 2 can be improved, for example, by the deferred start of activities 6 and 8, which will not delay the completion of stage 3. It is also possible to complete the work stages earlier. The proposed techniques of generating solutions taking into account the specificity of the discussed problem, are described further in this work.
## 2 Materials and Methods
### Electron Relaxation Model
#### 2.2.3 Low Pump Intensity Approximation  
The system of Equations (32)-(34) has to be solved numerically, and the computation of the transient evolution of \(F_{e}\), \(F_{h}\), and \(T\) is rather time-consuming, mostly because of the spatial integration in Equations (27) and (28). To address this issue, we can employ an approximation. Since Equation (15) is nonlinear, separating the spatial and temporal components in the equation is not possible. However, if the pump intensities used are not too high so that the term \(n_{\mathrm{nt}}/n_{\mathrm{nt}}^{\mathrm{max}}\) is small, we can assume that the spatial distribution is minimally affected in the initial tens of femtoseconds. Under this assumption, the concentration of non-thermalized carriers can approximately be expressed as  
\[n_{\rm nt}(\vec{r},t)\approx\exp{\left(-\frac{\ln 2}{w_{0}^{2}}r^{2}\right)}\bar{n}_{ \rm nt}(t)\,, \tag{35}\]  
where \(\bar{n}_{\rm nt}\) is the solution of Equation (15) neglecting the spatial part. Therefore, the spatial integrals in Equations (27) and (28) can be solved analytically and one obtains  
\[n_{t}(t) =n_{0}+\frac{w_{0}^{2}}{\tau_{ee}}\int_{-\infty}^{t}\frac{e^{-(t- t^{\prime})/\tau_{b}}\bar{n}_{\rm nt}(t^{\prime})}{\left(2w_{0}^{2}+4\ln(2)D(t-t^{ \prime})\right)}\exp{\left[-\frac{\ln(2)r_{0}^{2}}{2w_{0}^{2}+4\ln(2)D(t-t^{ \prime})}\right]}dt^{\prime}\;; \tag{36}\] \[\varepsilon_{t}(t) =e^{\prime}_{0}(t)+\frac{h\omega_{b}}{2}\frac{w_{0}^{2}}{\tau_{ee }}\int_{-\infty}^{t}\frac{e^{-(t-t^{\prime})/\tau_{b}}\bar{n}_{\rm nt}(t^{ \prime})}{2w_{0}^{2}+4\ln(2)D_{c}(t-t^{\prime})}\exp{\left[-\frac{\ln(2)r_{0}^ {2}}{2w_{0}^{2}+4\ln(2)D_{c}(t-t^{\prime})}\right]}dt^{\prime}\,. \tag{37}\]  
By employing these expressions, we can assess the transient evolution of the parameters of interest more efficiently. Figure 3 shows the variation of \(F_{e}\), \(F_{h}\), and \(T\) with time for different values of the pump spot radius (which determines the importance of the diffusion term in the balance equations). In the left panel, the solid (dashed) lines depict the variation of the quasi-Fermi level for electrons (holes), while the right panel illustrates the transient evolution of temperature. It is clear from the presented results that, for stronger focused beams, the diffusion effect is more pronounced and the transient evolution of the considered parameters is faster, as a larger number of electrons escape from the excitation spot and do not contribute to the spatial averages that determine the observable quantities, such as probe beam's reflectivity.
# Evaluation on Improvement Zone of Foundation after Dynamic Compaction
## 5 Discussion and Conclusions  
In this paper, the numerical investigation is carried out using 3D FEM. An improved soil cap model realizes the variation of elastic parameters during the DC process. The feasibility of the models is supported by comparison with centrifuge model results. Based on the numerical results, the following conclusions can be made:  
1. As for single-point and multipoint tamping, the drop energy and drop momentum exert significant effects on the effective improvement depth and soil improvement between the impact points, whereas the influence of the drop number and the tamper radius is relatively smaller.
2. There exists saturated drop energy beyond which further energy application has little effect on soil improvement. Under the same condition of drop energy, a low drop of a heavy tamper can generate a larger improvement area, a deeper improvement depth, and is more conducive to the soil improvement between impact points.
3. The increase in tamping distance is more beneficial to the improvement area but less to the improvement depth and soil improvement between impact points. Beyond a certain tamping distance, the impact at points 1 and 2 slightly influences the soil improvement between impact points, and soil improvement between impact points will be relatively poor.
4. The soil compacted between impact points by the previous impact point will be improved to a lesser extent with impact at subsequent impact points.
5. The most significant finding from this paper is the proposed method of evaluating the sandy soil improvement effect of DC according to specified engineering requirements. It not only predicts the degree of final improvement with depth both below impact point and between impact points but also provides the optimal technological parameters for DC construction.  
It is important to note, however, that DC partly relies on soil liquefying locally and densifying through the dissipation of the excess pore pressures. These features of the response cannot be replicated with the adopted constitutive model. Thus, the proposed method is applied strictly only to moist and dry granular soils. Developing a more practical model deserves further investigation in the future.  
Methodology, writing--original draft, C.Z.; software, validation, C.Z., H.L. and C.Y.; conceptualization, formal analysis, writing--review and editing, C.Z.; supervision, K.Y. and Z.Y.; project administration, H.Q., K.W. and P.J. All authors have read and agreed to the published version of the manuscript.  
Financial support from the Shandong HI-SPEED Group is gratefully acknowledged.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
## 3 Results
### Characterization of HZ Polygon  
#### 3.4.1 PCA and Cluster Identification  
PCA analysis revealed that 16 principal components are necessary to capture 90% of the HZ spectral-temporal data variability (Figure 12). Subsequently, score matrices were generated for each of the 16 principal components and pixels, and these matrices were subjected to the elbow method (Figure 13) to assess the optimal number of clusters, determined to be N = 3.  
Consequently, a K-means algorithm with three clusters was applied to the reduced dataset, assigning each pixel its corresponding cluster number as depicted in Figure 14.
# Absorption Enhancement in Hyperbolic Metamaterials by Means of Magnetic Plasma
## 4 Conclusions  
To conclude, we studied the main characteristics of SPPs that can be excited in a metamaterial-magnetic plasma structure. Taking on board two different types of hyperbolic metamaterials, i.e., nanolayered and nanowire structures, we depicted both normal and absorbing dispersion relations of SPPs for TM polarization. We conclude that employment of magnetic plasmas into the structure under investigation gives rise to the tunable intriguing features of SPPs. It can be concluded that changes in the external magnetic field allow for the shift in the dispersion curves to the higher-frequency range. The former takes place by increasing the value of the \(\omega_{c}/\omega_{p}\) ratio. Moreover, SPPs at the boundary of nanowire composites possess an intriguing behavior. Between the regime of the bound and radiative modes, a frequency gap region with purely imaginary \(\beta\) prohibiting propagation exists. The case of \(\omega_{c}/\omega_{p}\) = 2 possesses some discrepancies in this specific region with \(\beta\) being not purely imaginary. Moreover, it can clearly be observed from Figure 2b that \(\beta\) is the complex number with \(\text{Re}(\beta)\
eq 0\) around \(\omega\) = 6.2 \(\times\) 10\({}^{14}\) Hz. The presence of the real part of the propagation constant between regimes of the bound and radiative modes is treated as the exotic behavior of the proposed structure. It can be concluded that inclusion of magnetic plasma gives rise to the absorption enhancement that is pivotal and desirable for antenna design applications. Absorption enhancement can be controlled by varying the permittivity of the dielectric medium.  
Conceptualization, T.G. and E.R.; methodology, T.G.; software, T.G.; validation, T.G. and E.R.; formal analysis, T.G.; investigation, T.G.; resources, E.R.; data curation, E.R.; writing--original draft preparation, T.G. and E.R.; writing--review and editing, T.G. and E.R.; visualization, T.G.; supervision, E.R.; project administration, T.G. and E.R.; funding acquisition, T.G. and E.R. Both authors have read and agreed to the published version of the manuscript.  
This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska Curie grant agreement No 713694 and from Engineering and Physical Sciences Research Council (EPSRC) (Grant No. EP/R024898/1). The work of [PERSON] was partially funded by the Ministry of Science and Higher Education of the Russian Federation as part of World-class Research Center program: Advanced Digital Technologies (contract No. 075-15-2020-934 dated 17 November 2020).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 5: Dispersion relations of SPPs for changeable nanowire metamaterial geometry: (**a**) \(S\) = 70 nm, (**b**) \(d\) = 30 nm.
## 3 System Model and Problem Formulation
### Problem Formulation  
In this section, we formulate the transmission optimization problem. For a WSN using CB technique in CPSS, the following features should be considered. Firstly, since social sensor nodes have dynamic mobility, the CB transmission in WSN needs to consider the integration of the fixed physical sensors and the mobile social sensor nodes. Secondly, the location of each node determines the CB performance. Thus, the proposed method is required to capture the dynamic mobility for social nodes and reduce its impact on the beampattern performance of CB in WSN. In addition, to increase data transmission rate, the SNR performance at the intended BS needs to be considered, and the INR at unintended BS is required to meet the specific requirement. From Equation (9), if the transmission power of each node only has two levels, i.e., zero and \(P_{max}\), then the number of the selected collaborative nodes determines the value of SNR. Therefore, the objective of our transmission optimization problem is to maximize the SNR at intended BS while considering the dynamic mobility of social sensor nodes, given the requirement that the INR at unintended BS should be greater than a specific value \(INR_{0}\). The optimization problem can be formulated as follows:  
\[\max_{c} \sum_{t=1}^{T}\mathcal{S}(\varphi_{0},\textbf{cP})\] (19) subject to \[\mathcal{INR}(\varphi_{l},\textbf{cP})<INR_{0} \tag{20}\] \[\mathcal{P}_{i}(s_{j}|s_{k})=\mathcal{F}_{jk^{\prime}}^{i}i\in 1, \cdots,O\] (21) \[c_{i}=0\ or\ 1,\quad i=1,2,...,M \tag{22}\]  
where c=\(\{c_{1},c_{2},\cdots,c_{M}\}\) (with \(c_{i}\in\{0,1\}\) the vector of node selection), \(c_{i}=1\) denotes that the \(i\)th node is selected into the candidate collaborative node set, and \(\mathcal{F}_{jk}^{i}\) represents the probability from the location \(k\) return to location \(j\) for social sensor node \(z_{i}\). In Equation (19), we assume the time period is divided into \(T\) time slots. Thus, the \(T\) in Equation (19) denotes the number of time slots in the time period. From above objective function, we can see that this is a non-linear and non-convex optimization problem. As a result, it is difficult to derive an optimal algorithm to solve this problem as the computational complexity grows exponentially with the number of nodes and BSs. Furthermore, the location of social sensor nodes is changing based on the probability of social dynamic mobility \(\mathcal{F}_{j,k}^{i}\), which is stochastic in nature. At the same time, the characteristics of social dynamic mobility for social sensor nodes have great impacts on the INR. To address these issues, we use a stochastic game approach in this paper to maximize the SNR performance under the INR constraint in the directions of unintended BSs (\(INR_{0}\)). In addition, considering the practical implementation WSN, it is necessary to develop an effective learning algorithm to tackle the social dynamic mobility.
## 2 Methodology
### Meshing and Mesh Quality Analysis  
The literature [32] shows that for straight pipes, the use of a hexahedral mesh scale decreases the computation time and increases the accuracy of solution. For that reason, the hexahedral meshing technique was used to generate the mesh. The multizone method was selected, where pure hexahedral mesh was assigned in all places possible while unstructured mesh is placed in the remaining regions, as shown in Figure 2. To capture two-phase flow behavior near the boundary walls properly and accurately, five inflation layers using the smooth transition option were applied, as shown in Figure 2b. The transition ratio used was 0.1 with a growth rate of 1.1 to get appropriate inflation layers.  
The quality of the mesh is very important, and it plays a big role in stability and the accuracy of the numerical solution. The parameters used to check mesh quality are skewness, orthogonal quality, and aspect ratio. According to the definition of skewness, an equilateral cell has a value of 0 (best) and an entirely degenerate cell has value of 1 (worst). Highly skewed cells can destabilize the solution. A skewness value of 0.15 was recorded in a current study. The orthogonal quality represents how close the angles between adjoining element edges are to some optimal angle. In the case of orthogonal quality, 1 represents good and 0 represents bad quality. In the current study, the average orthogonal quality was 0.97. The ratio of the longest to shortest edge length is known as the aspect ratio. The minimum and maximum value of the aspect ratio recorded was 1.2 and 8.8, which is in a very good zone for capturing two-phase flow near the boundary layer as well as in the bulk flow region [31].
# HP-LSTM: Hawkes Process-LSTM-Based Detection of DDoS Attack for In-Vehicle Network
## 2 Related Work
### Hawkes Process for Modeling Network Attack  
Hawkes processes, as a type of self-exciting point process, have found increasingly widespread applications in the field of network attack modeling, particularly in financial security and social network security [23; 24; 25; 26]. [PERSON] et al. [23] developed a novel classifier named HawkesEye, which utilizes time windows for prediction. This classifier combines Hawkes processes with specific topic models for analyses based on time and textual information. [PERSON] et al. [24] proposed a social network witch attack detection model based on multi-stimulus Hawkes processes. This model simulates other users' endorsement behaviors toward a particular user through external stimuli and reflects the user's own reposting and commenting behaviors through internal stimuli, effectively identifying witch attacks in social networks. [PERSON] et al. [25] applied Hawkes processes to zombie network detection in the Internet of Things (IoT). Using multivariate Hawkes processes, they identified potential connections among attackers by exploiting the mutually excitatory characteristics. Additionally, in the field of vehicular network security, research teams have explored the feasibility and superiority of Hawkes processes. Specifically, for the issue of safety in autonomous driving vehicles, [PERSON] et al. [26] proposed a multi-stage Hawkes process (MSHP). This approach aims to predict the occurrence probability of errors in each stage of the perception system and their potential impacts on subsequent stages by treating errors at each stage as events in a Hawkes process, thus achieving the accurate prediction of errors in the perception system of autonomous driving vehicles. These research findings not only demonstrate the strong potential of Hawkes processes in both theory and practice but also provide insights for research on network security, social network monitoring, IoT security, and the safety of autonomous driving vehicles.