esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Near-Field Scanning Transmitarray Feed by a Cluster
### Near-Field Focused Transmitarray  
A transmitarray to focus the near-field on a single point is designed by applying geometrical optics. The phase distribution of the transmitarray elements is based on reaching a constant phase delay from the feed to the focused point. The phase delay of the transmitarray elements may compensate the different ray paths along its radius according to:  
\[\varphi_{inc}(\rho)+\varphi_{TA}(\rho)+\varphi_{out}(\rho)=\zeta \tag{2}\]  
where \(\varphi_{inc}(\rho)\) is the phase of the incident field at the transmitarray surface; \(\varphi_{TA}(\rho)\) is the phase shift introduced by the transmitarray elements; \(\varphi_{out}(\rho)\) is the phase delay because of the path from the outer transmitarray surface to the focusing point; and \(\zeta\) is a constant. The radius \(\rho\) is defined as \(\rho=\sqrt{x_{m}^{2}+y_{m}^{2}}\), regarding the center of the transmitarray and \((x_{m},y_{n})\) the coordinates of the center of the element. Considering \(\zeta=0\) and applying geometrical optics, the phase shift distribution of the transmitarray elements can be computed as:  
\[\varphi_{TA}(\rho)=k_{0}\bigg{(}\sqrt{r^{2}+\rho^{2}}+\sqrt{F_{TA-f_{0}}^{2}+ \rho^{2}}\bigg{)} \tag{3}\]  
where \(k_{0}\) is the vacuum wave number; \(r\) is the distance from the phase center of the feed to the center of the transmitarray; and \(F_{TA-f_{0}}\) the distance from this center to the focusing point.
## 2 Materials and Methods  
### Research Materials  
The materials used in this paper can be divided into two main parts: (1) the nautical chart data of the waterway outside Shantou Port in the Hanjiang underwater delta in the past 40 years; and (2) ten column samples from the Hanjiang underwater delta.  
The chart depth data came from the Navigation Assurance Department of the Naval Command of the Chinese People's Liberation Army and the Hydrographic Bureau of the Chinese People's Liberation Army. The scale was 1:15,000 to 1:25,000 (Table 1). The support projection method and the WGS 1984 coordinate system were used. The areas of charts 15,112 and 15,113 were the outer channels of Shantou Port (15,112 and 15,113 are charts of different periods in the same sea area, and the distribution area is shown in the shaded area in Figure 1).  
Figure 1: Map of columnar sample sites and chart areas in the study area.  
Among them, four nautical charts of the Shantou Port Outer Channel (Map Nos. 15,112 and 15,113) were published in 1984, 1993, 2013, and 2018, and the actual survey years for most of the areas were 1980, 1989, 2012, and 2016. The numbers of water depth points were 1583, 1784, 1692, and 2349, respectively.  
In August 2020 and July 2021, under the organization of the project \"Comprehensive Geological Survey of Chaoshan Coastal Zone\" carried out by the Haikou Marine Geological Survey Center of the China Geological Survey, 10 columnar samples were collected in the Hanjiang Estuary and coastal waters (Figure 1), and four of them were selected for \({}^{210}\)Pb dating and grain size analysis. The instrument used for columnar sample collection was a shallow sea gravity sampler, and the maximum working water depth was 50 m. The sampling diameter was 70 mm.
## 2 Experimental Procedure
### 3.2.1. Strength Deterioration of Wet Concrete  
Thorough the above experiments, the moisture content, strength, and strength deterioration results of wet concrete with different immersion times were obtained and are shown in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Specimen** \\ **Group** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Immersion** \\ **Time \(t\) (h)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Average Mess** \\ **\(m\) (kg)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Average** \\ **Moisture** \\ **Content \(k\)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Average** \\ **Suturation \(w\)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Strength \(f_{\mathbf{c}}\)** \\ **Ratio \(d_{\mathbf{s}}\)** \\ \end{tabular} } \\ \hline A & Baked & 7.180 & 0.000 & 0.000 & 43.620 & 0.000 \\ B & Dried in air & 7.256 & 0.0105 & 0.444 & 37.957 & 0.130 \\ C & Standard curing & / & / & / & 42.588 & / \\ D & 6 & 7.275 & 0.0132 & 0.558 & 38.169 & 0.125 \\ E & 12 & 7.333 & 0.0214 & 0.901 & 33.129 & 0.241 \\ F & 24 & 7.350 & 0.0237 & 0.999 & 35.642 & 0.183 \\ \hline \hline \end{tabular}
\end{table}
Table 6. Experimental results of the saturation, strength, and strength deterioration of wet concrete.  
Figure 1. Experiments of the mechanical properties of concrete under compression load after different immersion times. (**a**) Universal test machine. (**b**) Representative broken specimens after testing.  
It was found that the compressive strength of the C30 concrete specimens deteriorated more obviously after immersion for more than 6 h and reaching a certain saturation (moisture content), and with the increase in the saturation, the deterioration rate of compressive strength tends to increase and then decrease (see Figures 2 and 3). Based on the theory of linear elastic fracture mechanics, according to the law of initiation and development of microcracks in concrete under compression, due to the influence of surface tension and volume compression deformation of water, the free water in the initial microdefects, such as cracks and pores, produces splitting and pulling effects on the cracks, which accelerates the initiation and development of cracks. For the material subjected to the same compression load, the more content of free water in the concrete, the more serious damage it suffers; thus, the higher the saturation of the concrete, the lower its static compressive strength.
# An Intrusion Resistant SCADA Framework Based on Quantum and Post-Quantum Scheme
## 4 Proposed Scheme
### Key Generation
#### 4.1.5 Classical Channel Authentication in QKD  
The primary contribution and focus of our research are to provide message authentication and integrity by using SPHINCS-256 with QRNG, instead of PRNG, in HORST and by considering quantum key derived from QKD.  
Our proposed algorithm has the basic steps which are predefined in QKD, namely, Raw key generation (via Quantum channel), Key Sifting, Error Correction Code (ECC), and Privacy Amplification (all three sub-phases via classical channel). The basic steps ensure the key distribution of the quantum key. However, the classical channel in QKD must be authenticated.  
To address the above concern, we have introduced SPHINCS-256 in QKD to authenticate the classical channel. Figures 2 and 3 provides an overall structure of our proposed algorithm. Figure 2 illustrates all the sub phases of QKD. After Raw Key Generation, the key sifting and QBER involves sending a series of wrong basis and extracted portion of sifted key from the receiver to the sender over classical channel. During QBER and error correction code phases, the sender sends the codeword over the classical channel. Each information exchanged over the classical channel during QKD is fed to SPHINCS-256 for message authentication.  
Our proposed algorithm includes symmetric key generation using quantum key distribution as opposed to relying on trusted third party or a pre-shared secret key scheme. The QKD, itself, does not rely on a pre-shared secret key scheme or a trusted third party. Moreover, the QKD uses a quantum-resistant post-quantum signature scheme during the classical communication phase. Thus, the proposed integration of QKD and SPHINCS-256, described in Figures 2 and 3, provides intrusion detection and security against both quantum and classical attacks, unlike other traditional algorithms. Moreover, for our future work, we will be performing comparative analysis of QKD authenticated by SPHINCS-256 and QKD authenticated by other universal hash algorithms.  
Figure 2: The structure of our proposed scheme. It includes symmetric key generation, Encryption and message authentication.
## 2 Effect of Spoofing Signals on Acquisition
### Acquisition Performance Analysis
The conditional probability that GNSS signal is detected by target receiver and signal is a spoofing signal is used as the basis for spoofing signal acquisition performance [26], so that authentic signal and spoofing signal detection probabilities of the \(i\) th channel are \(P_{d,j}^{a}\) and \(P_{d,j}^{s}\), respectively. This conditional probability is called the relative acquisition probability \(P_{i}\) of spoofing signal:  
\[P_{i}=\frac{P_{d,j}^{s}}{P_{d,j}^{s}+P_{d,j}^{a}} \tag{13}\]Each branch spoofing signal satisfies the high acquisition performance while being constrained by noise floor and relative acquisition probability. Figure 3 shows the relationship between total power of spoofing signal and the probability of signal acquisition. Figure 3 shows that when total power of spoofing signal increases from \(-170\) dBW to -159 dBW, the acquisition probability of the spoofing signal increases from 0.86 to 1, the relative acquisition probability of the spoofing signal increases from 0.46 to 0.5, and the authentic signal acquisition probability is always 1. When total power of spoofing signal increases from \(-159\) dBW to \(-143\) dBW, the acquisition probability of spoofing signal can be calculated signal, relative acquisition probability of the spoofing signal and acquisition probability of the authentic signal remain unchanged. When total power of spoofing signal increases from \(-143\) dBW to \(-135\) dBW, acquisition probability of the spoofing signal is always 1, while the acquisition probability of the authentic signal decreases from 1 to 0, and relative acquisition probability of spoofing signal increases from 0.5 to 1. When the total power of spoofing signal is \(-170\) dBW, because authentic signal is the default value \(-158\) dBW, the acquisition probability of spoofing signal can be calculated as 0.86 according to Equation (7). However, it should be noted that this does not mean that the receiver is easier to acquire spoofing signal, because the acquisition probability of authentic signal is 1 and the relative acquisition probability of the spoofing signal is about 0.44. Therefore, we believe that the receiver still preferentially captures authentic signal, that is to say, it still depends on the relative acquisition probability of the spoofing signal. In Figure 3, because authentic signal is the default value \(-158\) dBW, when the 'Total Spoofing Power' is \(-175\) dBW, the power of the authentic signal is \(-158\) dBW.
## 3 Overview of Underwater Acoustic Micro-Modem  
Figure 2 illustrates the block diagram of the overall structure of our underwater acoustic micro-modem. The modem consists of a digital board, two analog boards and a transducer and it can be interconnected with external devices such as personal computer (PC) by the universal asynchronous receiver and transmitter (UART) protocol.  
Figure 2: Block diagram of our micro-modem.  
For data transmission, the digital board gets information data from the PC, makes a physical layer frame and delivers it to the analog transmission board. At the analog transmission board, a modulated signal is generated and then amplified. The electric signal is transformed to acoustic waves at the transducer and sent through an underwater channel. On the contrary, when acoustic waves arrive at the transducer, they are converted to electric signals. After signal amplification and detection at the analog reception board, the estimates of information data are reproduced at the digital board and are transferred to a monitoring terminal.
### Data Processing
The study area shown in Figure 1 and the nearest grid data to the epicenter were chosen to represent the data at the epicenter. The original OLR data were organized in an array of 180 \(\times\) 360 pixels. The time dimension was continuous. The a-plot in Figure 2 shows the OLR values for the region of China from 22 April 2022 to 21 May 2022. The b-plot in Figure 2 shows the Yangbi earthquake in the \(5^{\circ}\times 5^{\circ}\) area (5 \(\times\) 5 pixels). Figure 2 was drawn in MATLAB. Extracting the experimental area from the original data was the main pre-processing method in this experiment. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Time (UTC + 8)** & **Longitude/\({}^{\circ}\) E** & **Latitude /\({}^{\circ}\) N** & **Depth/KM** & **Magnitude/\(M_{\rm S}\)** & **Location** \\ \hline
21 May 2021 21:48 & 99.87 & 25.67 & 8 & 6.4 &
\begin{tabular}{c} Yangbi County, \\ Yunnan Province \\ \end{tabular} \\
5 September 2022 12:52 & 102.08 & 29.59 & 16 & 6.8 &
\begin{tabular}{c} Luding County, \\ Sichuan Province \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 1: Detailed information of earthquake examples. Figure 1: Research region. The pentagrams indicate the locations of epicenters, the gray area indicates the \(5^{\circ}\times 5^{\circ}\) study area, and the pink lines represent the fault zones.
# A Novel Covert Channel for IEEE 802.11 Networks Utilizing MAC Address Randomization
## 3 MAC Address Randomization
### Current Status  
The IEEE 802.11 committee established a dedicated group called the Randomized and Changing MAC Addresses Topic Interest Group (RCM TIG) [38] to standardize MAC address randomization. The primary focus of RCM TIG is to investigate existing MAC randomization techniques, evaluate the impact of MAC randomization on IEEE 802.11 networks, and work toward potential standardization efforts. Since 2014, the organization has actively discussed and addressed MAC address randomization during various meetings [39]. Regarding MAC address randomization, the IEEE 802.11 aq pre-association discovery amendment [40] provides recommendations for STA (station) behavior. It suggests that an STA should frequently change its MAC address while not associated with a BSS to enhance privacy. The amendment also advises two additional measures: resetting the sequence number whenever the MAC address changes and avoiding active scanning or sending probe requests with an empty SSID field if active scanning is enabled. The specific interval at which an STA should change its MAC address during scanning is left to the vendor's discretion and falls outside the scope of IEEE standards. In 2019, two topic interest groups (TIGs) emerged within the IEEE 802.11 committee: IEEE 802.11 bh (enhanced service with randomized MAC addresses) and IEEE 802.11 bi (enhanced service with data privacy protection). These TIGs aim to safeguard the functionality of network services that the use of MAC address randomization might compromise, while also enhancing data privacy in IEEE 802.11 networks [41].  
A study conducted between 2013 and 2017 examined the adoption of MAC randomization. Researchers collected a substantial dataset of nearly 374 million PR frames from various European cities and locations. The findings revealed that the practice of MAC randomization has expanded since its introduction. However, over 95% of the analyzed MAC addresses utilized unregistered organizationally unique identifiers (OUIs), which deviate from the IEEE Registration Authority (RA) recommendations. The adoption rate of MAC randomization remains low, with less than 3% of PR frames in the dataset employing this technique [42]. One contributing factor to the low adoption rate is the need for more familiarity among users regarding the MAC randomization feature on their devices or the need to understand how to enable it [43]. In response, vendors actively engage in initiatives to raise awareness about MAC randomization and enable it as the default device setting [44].
## 4 Discussion
### Comprehensive Service Life Based on Analytic Hierarchy Process
#### 4.2.2 Hierarchical Structure for RABs  
To simplify the evaluation of aging grades of reclaimed asphalt binders, the concept comprehensive service life was put forward to represent the comprehensive aging degree and calculated by using the AHP method.  
The first step of AHP was to divide the various factors into an orderly hierarchical structure according to their interaction and affiliation. Then, a judgment matrix is established by connecting analysts' judgments with performance results. Finally, the composition weight of each layer to the system goal is obtained by calculating the maximum eigenvalue and the eigenvector of the judgment matrix.  
As shown in Figure 14, the comprehensive service life is affected by the surface layers and their respective service years.
## 3 Results
### AFM Study of the Effect of Pulsed Pressure  
AFM images of HRP, adsorbed onto the mica surface from its solutions before and after the impact of the shock wave, were obtained, and corresponding distributions of the visualized objects with height were plotted after processing the AFM images.  
Figure 4 displays typical AFM images, and the \(\rho\)(_h_) distribution plots obtained for HRP adsorbed from solutions before and after the impact of the shock wave. The curves shown in Figure 4 indicate a slight increase in the number of objects with a height from 1.5 to 6 nm after the shock wave impact--in comparison to the case with control experiments. The appearance of larger objects indicates that a tendency of HRP protein to adsorb onto mica with an increase in the degree of aggregation occurs after the impact of the shock wave on its solution.  
Figure 4: Results of AFM study of the effect of the shock wave impact on the HRP aggregation. Representative AFM images (**A**,**C**) and cross-section profiles (**B**,**D**) of the surface of mica substrate obtained after its incubation in 0.1 uM aqueous HRP solution, which was either not subjected (**A**,**B**) or subjected (**C**,**D**) to the shock wave impact. The cross-section profiles correspond to the lines in the respective AFM images. (**E**) Distribution plots of the AFM-visualized objects with height _p(h)_, obtained for the solution subjected to the shock wave impact (red curve) and for the control HRP solution (black curve). Experimental conditions: the pressure behind the reflected shock wave was 10 atm for 4 ms, the rise-time of the shock wave front was about 80 nsec.  
Spectrophotometry-Based Estimation of the Effect of Pulsed Electromagnetic and Presssure Field on the Enzymatic Activity of HRP  
The enzymatic activity of HRP was estimated by monitoring the time dependencies of the solution absorbance at 405 nm (\(A_{405}(t)\)) upon the reaction of HRP with ABTS substrate employing a technique developed by [PERSON] et al. [66]. These spectrophotometric measurements were performed for both the HRP sample, exposed to a 640 kV/m PEMF with a pulse repetition rate of 16 Hz for 40 min, and the control HRP sample, which was not exposed to PEMF. That is, the HRP solutions studied by spectrophotometry and those studied by AFM were treated under the same conditions. Figure 5 displays the obtained \(A_{405}(t)\) curves.  
The \(A_{405}(t)\) curves shown in Figure 5 indicate that the enzymatic activity of HRP, whose solution was irradiated in PEMF for 40 min, decreased significantly (by 46%) in comparison with the control sample. In the case of shock wave impact, we did not observe any change in the enzymatic activity of HRP.
# Using Graph Partitioning for Scalable Distributed Quantum Molecular Dynamics
## 4 Experiments
### A Collection of Test Graphs Derived from Molecular Systems  
This section uses a selection of physical test systems to evaluate all algorithms of Section 3, which were chosen to represent a variety of realistic scenarios where graph partitioning can be applied to MD simulations. Those test systems are available from the authors upon request. We demonstrate how the graph structure influences the results (Section 4.3) and additionally provide insights into the physics of each test system.  
A dendrimer molecule with 22 covalently bonded phenyl groups of solely C and H atoms is schematically shown in Figure 2 (left). The graph of the dendrimer molecule has 730 vertices, composed of 262 atoms and 730 orbitals.  
Figure 2: Molecular representation of phenyl dendrimer (**left**) using cyan and white spheres for carbon and hydrogen atoms, respectively. Hamiltonian matrix as 2D representation (**middle**) and thresholded density matrix (**right**). All plots show \(\log_{10}\) of the absolute values of all matrix elements. The SM-SP2 algorithm was used to compute the density matrix. Figure taken from [12]. Copyright ©2016 Society for Industrial and Applied Mathematics. Reprinted with permission. All rights reserved.  
Figure 2 (middle) displays the absolute values of the Hamiltonian matrix for the dendrimer system. Figure 2 (right) displays the density matrix encoding the physical properties of the system, which is obtained by applying the SM-SP2 algorithm to the Hamiltonian.  
We threshold the density matrix at \(10^{-5}\) to convert it into a graph needed to find meaningful physical components via graph partitioning. This is done with all systems of Table 1 to arrive at their adjacency matrices.  
Table 1 displays the molecule name (first column), the number of vertices \(n\) and edges \(m\) (second and third column) of its graph representation, and its average vertex degree \(m/n\) (fourth column). A short description of the molecule can be found in the last column.  
We consider four topologically different types of molecular systems (see Figure 3) as test beds for the partitioning algorithms. This is to provide a wider range of test systems for our algorithms. Figure 3 displays a one-dimensional system in the first panel (polyethyene linear chain with repeated CH\({}_{2}\) units), an anisotropic pristine 3D urea crystal (second panel), a polyalanine molecule solvated in water (third panel) with a typical \(\alpha\)-helix secondary structure, and a dendrimeric system with a fractal arrangement of phenyl rings (fourth panel) to challenge our partitioning algorithms.