esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proof of security of BB84 against collective attacks
### Proof of security  
Following the point of view of [5] we choose a code such that \(\frac{d_{r,m}}{2n}>p_{a}+\epsilon\) for some \(\epsilon\); the right-hand side of (48) is then less than \(P\left[\left(\frac{|\mathbf{C}_{I}|}{n}>p_{a}+\epsilon\right)\wedge\left(\frac {|\mathbf{C}_{T}|}{n}\leq p_{a}\right)\right]\) which itself is exponentially small in \(n\). For each particular string \(c_{1}\ldots c_{2n}\) corresponding to a measurement of all qubits in some admissible basis \(\mathbf{b}\) we can apply [PERSON]'s sampling (Theorem 10). Let \(\overline{X}=\frac{|\mathbf{C}_{I}|}{n}\) be the average of the sample corresponding to erroneous information bits; \(\mu=\frac{|\mathbf{C}_{I}|+|\mathbf{C}_{T}|}{2n}\) is the expectancy of \(\overline{X}\). \(\frac{|\mathbf{C}_{T}|}{n}\leq p_{a}\) is equivalent to \(2\mu-\overline{X}\leq p_{a}\), or equivalently, to \(\overline{X}-\mu\geq\mu-p_{a}\). For the population \(c_{1},\ldots,c_{2n}\) the conditions \(\left(\frac{|\mathbf{C}_{I}|}{n}>p_{a}+\epsilon\right)\) and \(\left(\frac{|\mathbf{C}_{T}|}{n}\leq p_{a}\right)\) then rewrite to  
\[\left(\overline{X}-\mu>\epsilon+p_{a}-\mu\right)\wedge\left(\overline{X}-\mu \geq\mu-p_{a}\right) \tag{50}\]  
which implies \(2(\overline{X}-\mu)>\epsilon\) and using [PERSON]'s theorem (Theorem 10)  
\[P\left[\left(\frac{|\mathbf{C}_{I}|}{n}>p_{a}+\epsilon\right)\wedge\left( \frac{|\mathbf{C}_{T}|}{n}\leq p_{a}\right)\right]\leq P\left[\overline{X}- \mu>\frac{\epsilon}{2}\right]\leq e^{-\frac{1}{2}n\epsilon^{2}}. \tag{51}\]  
The above discussion gives the following  
**Theorem 9**.: _Let us be given \(\delta>0\), \(R>0\) and, for infinitely many values of \(n\), a family \(\{v_{1}^{n},\ldots,v_{r_{n}+m_{n}}^{n}\}\) of linearly independent vectors in \(\mathbf{F}_{2}^{n}\) such that \(\delta\leq\frac{d_{r_{n},m_{n}}}{n}\) and \(\frac{m_{n}}{n}\leq R\). Then for any \(p_{a}>0\) and \(\epsilon_{\mathrm{sec}}>0\) such that \(p_{a}+\epsilon_{\mathrm{sec}}\leq\frac{\delta}{2}\), [PERSON]'s accessible information satisfies the following bound_  
\[\langle I_{\text{Eve}}^{(p_{a})}\rangle\leq 2R\,ne^{-\frac{2\epsilon_{\mathrm{ sec}}}{4}n}.\]  
All we need to guarantee security is thus vectors \(\{v_{1}^{n},\ldots,v_{r_{n}+m_{n}}^{n}\}\) satisfying the conditions of the theorem. Such families were proven to exist in [5].
## 2 Methodology
### A Multi-Criteria Decision-Making (MCDM) Using Fuzzy AHP and Fuzzy TOPSIS
#### 2.2.3 Combining AHP-TOPSIS with a Geographic Information System (GIS)  
An appropriate methodology for flood risk assessment uses aggregated values of inputs corresponding to their weights. The combination of MCDM and GIS showed its advantages in hazard risk assessment [36; 37]. The hybrid approach using fuzzy AHP-TOPSIS originally demonstrates a logically and systematically quantitative framework to initially indicate critical issues, thereafter, assigning associated relative priorities to these issues, selecting the best compromised alternatives, and lastly establishing communication towards general acceptance. The fuzzy AHP, fuzzy TOPSIS, and GIS are integrated into the geographical fuzzy decision making. The fuzzy AHP is used to calculate the weights of selected criteria based on expert opinions as shown in a structured questionnaire. The fuzzy TOPSIS is applied to rank the flood hazardous risks of the alternatives using the weights of the criteria. GIS is used to conduct the Map Algebra procedure with the Spatial Analyst module of ArcGIS software and to map flood hazard factors and flood hazards.  
Figure 3 shows the logical framework of the flood risk assessment based on this approach. The first step is determining the criteria. This study uses 10 criteria, representing the terminology \"vulnerability\" in flood studies in the selected areas (Figure 2). The method of fuzzy AHP combined with fuzzy TOPSIS estimates the weights of the aforementioned criteria, and allows one to prioritize alternative sites based on the intensity of flood hazards. ArcGIS is the final step to aggregate the analyzed outcome of the fuzzy AHP-TOPSIS in a map.  
Figure 3: Logical framework of the study. AHP—analytic hierarchy process, TOPSIS—technique for order of preference by similarity to ideal solution.  
Figure 2: Factors affecting the likelihood of flood hazards.
## 3 LOS Guidance Algorithms  
The LOS algorithm is used to convert the desired path to reference heading angle. Meanwhile, in order to be more universal, we assume that the sideslip angle cannot be measured directly, so the adaptive method is employed to estimate sideslip angle to compensate the difference between heading angle and course angle. Finally, the fuzzy algorithm is used to adjust the value of lookahead distance \(\Delta(t)>0\) to realize a better control effect.
# A Fast Wavelet-Based Bridge Condition Assessment Approach Using Only Moving Vehicle Measurements
## 3 Method for Bridge Damage Detection  
When bridge health conditions change, the dynamic parameters of the bridge will change to a certain extent. As shown above, the vehicle responses will change as well. However, due to the limited VBI effect time, FFT-based extracted bridge frequencies have a low resolution in the frequency domain that cannot reflect the change of the frequencies. This paper proposes to use wavelet energy for bridge damage detection using vehicle responses.
# Multi-Antenna Jammer-Assisted Secure Short Packet Communications in IoT Networks
## Appendix A
Noticing that \(\gamma_{b}^{ZFB}\) is an exponentially distributed random variable, its CDF is given by:  
\[F_{\gamma_{b}^{ZFB}}(x)=1-e^{-\frac{y}{\lambda_{a}\tilde{\tau}_{ae}}}.\] (A4)  
On the other hand, in order to derive the PDF of \(\gamma_{e}^{ZFB}\), we first give the PDF of the random variable \(Z=\left|\mathbf{h}_{je}^{\dagger}\mathbf{w_{ZF}}\right|^{2}\) as [37]:  
\[f_{Z}(z)=\frac{z^{N_{j}-2}}{\left(N_{j}-2\right)!\gamma_{je}^{N_{j}-1}}e^{- \frac{z}{\gamma_{b}^{Z}}}.\] (A5)  
Then, the CDF of \(\gamma_{e}^{ZFB}\) is given by:  
\[F_{\gamma_{e}^{ZFB}}(y) =\int_{0}^{\infty}\left(1-e^{-\frac{\lambda_{j}\tilde{\tau}_{ae} \tilde{\tau}_{ae}}{\lambda_{a}\tilde{\tau}_{ae}}}\right)f_{Z}(z)dz\] \[=1-\frac{e^{-\frac{y}{\lambda_{a}\tilde{\tau}_{ae}}}}{\tilde{ \gamma}_{je}^{N_{j}-1}}\left(\frac{\lambda_{j}y}{\lambda_{a}\tilde{\tau}_{ae} }+\frac{1}{\tilde{\gamma}_{je}}\right)^{1-N_{j}}.\] (A6)  
Taking the derivative of (A6) with respect to \(y\), the PDF of \(\gamma_{e}^{ZFB}\) is given by:  
\[f_{\gamma_{e}^{ZFB}}(y) =\frac{e^{-\frac{y}{\lambda_{a}\tilde{\tau}_{ae}}}}{\lambda_{a} \tilde{\tau}_{ae}\gamma_{je}^{N_{j}-1}}\left(\frac{\lambda_{j}y}{\lambda_{a} \tilde{\tau}_{ae}}+\frac{1}{\tilde{\gamma}_{je}}\right)^{1-N_{j}}\] \[+\frac{\lambda_{j}\left(N_{j}-1\right)}{\lambda_{a}\tilde{\tau}_ {ae}\tilde{\tau}_{je}^{N_{j}-1}}e^{-\frac{y}{\lambda_{a}\tilde{\tau}_{ae}}} \left(\frac{\lambda_{j}y}{\lambda_{a}\tilde{\tau}_{ae}}+\frac{1}{\tilde{\gamma }_{je}}\right)^{-N_{j}}.\] (A7)  
Substituting (A4) and (A7) into (A3), we have:  
\[\stackrel{{(a)}}{{\approx}}\frac{B}{N}\int_{0}^{ \infty}\int_{0}^{\infty}\Big{(}1-\epsilon^{ZFB}(x,y)\Big{)}f_{\gamma_{b}^{ZFB }}(x)f_{\gamma_{e}^{ZFB}}(y)dxdy\]0 (A8)  
To simplify the integral in (A8), we introduce a sufficiently large parameter \(M_{1}\) to ensure \(V_{e}^{ZFB}\approx 1\), when \(\gamma_{e}^{ZFB}>M_{1}\), and then (A8) can be approximated as:  
\[\stackrel{{(a)}}{{\approx}}\frac{B}{N}\int_{0}^{ \infty}\int_{0}^{\infty}\Big{(}1-\epsilon^{ZFB}(x,y)\Big{)}f_{\gamma_{b}^{ZFB }}(x)f_{\gamma_{e}^{ZFB}}(y)dxdy\]1 (A9)  
where \(\alpha_{1}=e^{\frac{Q^{-1}(\tilde{\tau})}{\sqrt{N}}+\frac{\tilde{\tau}}{N} \ln 2}\). By using Gaussian-Chebyshev quadrature [38], the integral \(\Xi_{1}\) can be approximated as:  
\[\stackrel{{(a)}}{{\approx}}\frac{B}{N}\int_{0}^{ \infty}\int_{0}^{\infty}\Big{(}1-\epsilon^{ZFB}(x,y)\Big{)}f_{\gamma_{b}^{ZFB }}(x)f_{\gamma_{e}^{ZFB}}(y)dxdy\]2 \[\stackrel{{(a)}}{{\approx}}\frac{B}{N}\int_{0}^{ \infty}\int_{0}^{\infty}\Big{(}1-\epsilon^{ZFB}(x,y)\Big{)}f_{\gamma_{b}^{ZFB }}(x)f_{\gamma_{e}^{ZFB}}(y)dxdy\]3 (A10)where \(M_{2}\) is a parameter for the complexity accuracy tradeoff, \(t_{m}=\cos\!\left(\frac{2m-1}{2M_{2}}\tau\right)\), and \(\varpi_{m}=e^{\sqrt{\frac{1-\left(1+M_{1}\left(t_{m}+1\right)/2\right)^{-2}}{N} }}Q^{-1\left(\delta\right)+\frac{B}{N}\ln 2}\left(1+\frac{M_{1}}{2}(t_{m}+1) \right)-1\).
## 3 Results and Analysis
### Complexity Analysis  
When computing the shortest paths of node pairs within 2-hops neighbors for the target node in NBF algorithm and ego betweenness, we just compute the square and cube of the adjacency matrix of the network and check the elements in the new matrix instead of using a Dijkstra algorithm, which makes it much faster. The method is also used in [30].  
The computational complexity of the six methods is shown in Table 2, where \(n\) is the total number of nodes in the network, \(m\) is the number of edges and \(\langle k\rangle\) is the average degree of the network. From the table, we can see that the computational complexity of k-shell is \(O(m)\), which is the lowest, but, from the experimental results, one can see that it performs the worst. WL algorithm and degree centrality have the second lowest computational complexity, but the attack effect is also not good.  
The computational complexity of NBF algorithm is \(O(n\langle k\rangle^{2})\), which is equal to that of ego betweenness and LLS algorithm. Although NBF algorithm and ego betweenness have the same computational complexity, ego betweenness just considers neighbor nodes within 1-hop, while the NBF algorithm takes into account neighbor nodes within 2-hops. The actual computing time of ego betweenness is less than that of the NBF algorithm. Both the NBF algorithm and LLS algorithm consider neighbor nodes within 2-hops, and the computational cost of the two algorithms is almost the same. One can see from the experimental results that the NBF algorithm, ego betweenness and LLS  
Figure 5: The decline rate of network efficiency (\(\mu\)) subjects with different dynamic attack strategies. The decline rate of network efficiency (\(\mu\)) on the \(y\)-axis against the rate of removed node (\(p\)) on the \(x\)-axis. (**a**) the USAir network; (**b**) the Netscience network; (**c**) the Infectious network; (**d**) the USAirport network; (**e**) the Yeast network; (**f**) the Power network; (**g**) the synthetic network WS.  
algorithm outperform the other algorithms in most cases and the NBF algorithm performs the best in almost all cases. In summary, NBF can get the best attack effect in reasonable time when compared with other outstanding algorithms, which makes it applicable in large-scale networks.
# Antiference: New Concept for Evolutive Mitigation of RFI to GNSS +
## 8 Conclusions  
The Antiference project succeeded in the implementation of a prototype, showing the high potential of the application of ML methods to RFI detection and classification. Significant improvements are still needed for the commercialization of such a system. It was convincingly demonstrated that the usage of signal fingerprints and a fingerprint database is feasible and beneficial in documenting the history and streamlining of the performance for subsequent encounters with the same source of RFI.  
While the system model developed during the project was a local model, the extended of the general architecture to a cloud-based system can be envisioned, where the detection, classification, and RFI characterization can be carried out remotely on the equipment. The design of very small fingerprints and detection/classification features that can be communicated easily via networks facilitates this approach.  
On the other hand, having fingerprints that are explicitly readable by humans facilitates the combination and sharing of databases between equipment. This can result in a better understanding of the growing threats that affect all GNSS receivers in terms of their daily performance and a better cataloguing of the types and frequency of occurrence of different types of RFI.  
Conceptualization, S.A., V.B., A.B. and B.O.; software, V.B. and U.B.; validation, V.B. and U.B.; formal analysis, B.O.; data curation, S.A.; writing--original draft preparation, S.A.; writing--review and editing, A.B., B.O. and S.B.; supervision, S.B.; project administration, S.A.; funding acquisition, S.A., A.B. and B.O. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the European Space Agency, under the program NAVISP Element 1, grant number 4000133535/20/NL/GP.  
Not applicable  
Not datasets are publicly available.  
The reported work was carried out under ESA NAVISP Element 1, which is devoted to the development of innovative PNT, systems, technologies, algorithms, and techniques.  
The authors declare no conflicts of interest. All involved parties including OHB Digital Solutions, S&T, as well as Integricom declare no conflicts of interest.
## 4 Discussion
### Installing Time Interval  
The second factor is the time interval after borehole is drilled before the injected cement mortar solidified. Beside the time interval of installment, the setting time of grout also has an effect on the stress measurement. To discuss the relationship between grout setting time and rate of stress recovery, seven different grout setting time t\({}_{0}\), including 0 h, 10 h, 20 h, 50 h, 100 h, 200 h, and 500 h, were set. As shown in Figure 20, for different grout setting time t\({}_{0}\), the rates of stress recovery increases with the time and finally near to 1.0, which means that recovered stresses (or measured stresses) can nearly reach to the initial stress. Therefore, for the Burgers viscoelastic model, the recovery stress can reach to 100% of the applied regardless of grout setting time. These results indicate that the time interval for grout setting time have effect on measuring time, but little on the measured in situ stress.  
[PERSON] et al. [20] investigated the relationship between grouting time and recovery stress and found that the longer solidification time of slurry materials, the smaller the recovery stress of the sensing column will eventually be, so grouting should be carried out immediately after the sensing column has been installed and the use of quick-setting grout materials is appropriate. Therefore, considering that the stress recovery rate is very high during the first few hours, a short time interval for installation will definitely increase the accuracy of the measurement.
## 3 Experiments and Analysis
### Qualitative Comparison  
The qualitative detection results, shown in Figures 7 and 8, include a selection of detection results with corresponding 3D diagrams. Detected targets are highlighted by red boxes and enlarged in the image corners. The backgrounds of Seq.1, Seq.2, Seq.3, and Seq.6 are complex, containing point-like clutter and bright elements. In contrast, Seq.4 and Seq.5 have simpler backgrounds but some feature very weak and multiple targets. The performance of different methods is analyzed as shown below.  
Figure 7: Detection results and corresponding 3D visualizations for Seq.1 to Seq.3 using different algorithms. The red rectangle indicates the target location, with an enlarged view in the corner. A missing rectangle indicates a missed target in the frame. Rows 1, 3, and 5 (**a1–j1**, **a3–j3**, **a5–j5**) correspond to detection results for Seq.1, Seq.2, and Seq.3, respectively, while rows 2, 4, and 6 (**a2–j2**, **a4–j4**, **a6–j6**) show the 3D mesh visualizations for Seq.1, Seq.2, and Seq.3.
# Acoustic Parametric Signal Generation for Underwater Communication
## 4 Results
### First Studies
(**a**) second time derivative of envelope for bit 1; (**b**) second time derivative of envelope for bit 0; (**c**) Cross correlation signal with bit 1; (**d**) Cross correlation signal with bit 0. In Figure 5, the \(RA\) parameter averaged for all bit positions is presented as a function of the directivity angle of the emitter. We can observe that for this case all \(RA\) values are above the noise level region, which is between \(\pm\)0.45, so the detection is produced for all angles (\(\pm\) 25\({}^{\circ}\)).
# Permanent Deformation Mechanism of Steel Bridge Deck
## 1 Introduction
### Review of Relevant Research Literature  
#### 1.2.1. Putting Behavior of SBDP Investigated Through Experimental Study  
When the concept of rutting was first introduced, it was widely accepted that rutting primarily originated from structural deformation induced by traffic loading [4]. [PERSON] [5] categorized rutting-related deformation into two fundamental types: densification and shear flow deformation. Building upon this framework, [PERSON] et al. [6] delineated the rutting evolution process into two distinct stages: an initial stage characterized by vertical deformation due to material compaction, followed by a secondary stage dominated by shear deformation resulting from the lateral flow of asphalt mixtures.  
In comparison with conventional asphalt pavements, the rutting progression in SBDPs is significantly more complex, owing to the distinctive substructure of steel decks and the severe constraints imposed by construction conditions [7]. Conventional laboratory testing methods have proven inadequate for accurately replicating the in-service conditions of SBDP or capturing the detailed evolution of rutting behavior. To address these limitations, full-scale accelerated pavement testing has been adopted by [PERSON] and [PERSON] [8], [PERSON] et al. [9], and [PERSON] et al. [10] to better simulate realistic operational environments and assess rutting development. Nevertheless, experimental investigations are inherently challenged by the difficulty of achieving consistent results under nominally identical conditions. Due to the inability to fully standardize all influencing parameters, measurement uncertainties are inevitable, leading to considerable variability in repeat trials. Moreover, the extended time requirements, high financial costs, and limited controllability associated with full-scale SBDP testing have increasingly constrained their practical application. As a result, numerical simulation has emerged as a promising alternative, offering greater efficiency, repeatability, and parameter control in the analysis of mechanical responses and damage evolution in SBDP systems.
## 3 Results and Discussion
### Computational Complexity Analysis  
This section provides the computational complexity analysis of the proposed joint strategy, i.e., WHT+NOMA+IRC, in comparison to the conventional NOMA approach. Computational complexity plays a vital role in analyzing the receiver performance. Theoretically, it is understandable that the complexity of the proposed system in this work should increase with the increment in the number of operations as compared to conventional NOMA. With the inclusion of IRC, the system becomes more complex due to the nature of the IRC weight matrix calculation, given from (34) and (36). Therefore, in this section, the complexity comparison, in terms of the required number of multiplications, is calculated for WHT-NOMA with IRC, compared to conventional NOMA, and is shown in Table 4 and Figure 22[10; 34; 37], where \(N_{C}\) represents the number of sub carriers and \(K\) represents the number of UEs performing IRC in the small-cell. It is evident from the results in Figure 22 that the computational complexity of the proposed technique is much higher than that of the conventional NOMA approach. Thus, the advantages of better user throughput and reduced BER comes at the expense of higher computational complexity.
## 2 Methods  
### Physical Setup  
The physical setup of the experiment was identical to the author's previous VLT study while using natural sources [9]. The experiment was conducted in an ITU-R BS.1116 [18]- compliant critical listening room (6.2 \(\times\) 5.2 \(\times\) 3.5 m; RT = 0.25 s; NR = 12) at the Applied Psychoacoustics Lab of the University of Huddersfield. Figure 1 shows the loudspeaker configuration for the experiment. The loudspeakers were arranged in a vertical quadraphonic configuration that was based on the front part of the Auro-3D 9.1 loudspeaker format [2] (without the centre channel). The L and R loudspeakers, which had the horizontal base angle of 60\({}^{\circ}\), were raised at the listener's ear height (0\({}^{\circ}\) elevation). The HL and HR loudspeakers in the height layer were placed right above the L and R loudspeakers and elevated at 30\({}^{\circ}\) from the listener position. The loudspeaker positions also comply with recommendations in ITU-R BS.2152 [19]. The distance between the listening position and each of these loudspeakers was 2 m. The direct-to-reverberant (D/R) energy ratios were 7.7 dB and 6.5 dB for the L and HL loudspeaker, respectively, when measured while using an omni-directional microphone at the listening position. Furthermore, within 15 ms after the direct sound, there was no reflection that exceeded \(-\)14 dB when compared to the direct sound level, which meets the requirement of ITU-R BS. 1116. Therefore, it can be considered that the influence of reflections on the perception of VIC would be minimal.  
Figure 1: Loudspeaker setup used for the listening test. The main layer loudspeakers are configured with the standard 60\({}^{\circ}\) subtended angle from the listening position. The distance between the listener and each of the main layer speakers was 2 m. The height layer loudspeakers are placed right above the main layer ones, with 30\({}^{\circ}\) elevation from the listening position, based on the Auro-3D configuration [2].  
A light-emitting diode (LED) strip was directly positioned in front of the listening position. This was located behind the acoustically transparent curtain and it was to be used by subjects when making their localisation judgments.