esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background and Related Work
### Delay-Tolerant Internet of Vehicles  
DT-IoV is an IoV with delay-tolerant capabilities to support long disruptions in network connectivity [2]. This concept was developed considering studies made with Mobile Ad Hoc Networks (MANETs), Vehicular Ad Hoc Networks (VANETs), DTNs, and the Internet of Things. MANETs bring the concept of establishing direct communication between mobile nodes, which do not rely on a fixed infrastructure [5]. VANETs use vehicles as nodes, but contrary to DT-IoV, they assume end-to-end connectivity exists through some path, which can be particularly challenging in high-mobility or very sparse scenarios such as rural or mountainous areas [3]. This is where the DTNs capabilities are brought in as in these environments, the traffic density is usually low, meaning fewer opportunities for contact, and as the vehicles travel at a considerable speed, the contacts will be short.  
VENIAM [6] is a company that is presently already implementing services using the DT-IoV strategy, like mobile data offloading using delay-tolerance.
## 4 Case Study  
### Case Description  
This study uses an integral roller compacted concrete gravity dam located in China as a case to verify the performance of the proposed coupled model. The dam axis is arranged in a broken line, the dam crest elevation is 145 m, the maximum dam height is 63 m, and the dam crest length is 196.62 m. The length of dam sections on the left and right banks are respectively 58.92 and 62.70 m, and there is a longitudinal drainage and grouting gallery in the dam body. The dam began to be closed for water storage in 1993 and passed the completion acceptance in 1995. In order to ensure the safe operation of the project, automatic monitoring equipment is used to continuously monitor the response of the dam structure. Meanwhile, for the realization of the horizontal displacement monitoring, nine measuring points are set on the top of the dam, numbered E01 to E09, as shown in Figure 6. The main distribution of measuring points is one on the left and right sections of the dam, two on the left and right abutments of the dam, five on the overflow section.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Models** & **Hyperparameters** & **Search Range** & **Optimal Value** & **Training: MAE** & **Test: MAE** \\ \hline SVM-poly & \(C\) & (15, 30) & 21 & & \\ \(d\) & [1,2,3,4,5] & 1 & 1.5504 & 1.3880 \\ \hline SVM-rbf & \(C\) & (15, 25) & 20 & & \\  & \(\gamma\) & (0.1, 0.5) & 0.2 & 0.5598 & 1.2799 \\ \hline RF & \begin{tabular}{c} \(n\_estimators\) \\ _max\_features_ \\ _min\_sample\_leaf_ \\ \end{tabular} & (25, 40) & 29 & & \\  & \(\text{max\_features}\) & (0,1) & 0.5 & 0.3987 & 1.2282 \\ \hline MLP & \
\begin{tabular}{c} \(u\) \\ _Dropout rate_ \\ \end{tabular} & [0.4–0.8] & 128 & & 0.6561 & 1.3364 \\ \hline LSTM &
\begin{tabular}{c} \(u\) \\ _Dropout rate_ \\ \end{tabular} & [0.4–0.8] & 256 & & \\  & \(w\) & [3, 15] & 9 & & \\ \hline \end{tabular}
\end{table}
Table 1: Basic information of model parameter tuning.  
The environmental monitoring of water level is divided into upstream water level and downstream water level. The upstream water level measuring station is located in front of the upstream dam, and the downstream water level measuring station is located near the tail water outlet of the powerhouse. The upstream and downstream water levels are monitored in real time by self-registering water level gauges, supplemented by manual observation of water gauges.  
Monitoring equipment collects data on dam deformation and related environmental variables once a day. In this study, since the E06 measuring point has the most obvious variation for the impact factor, it was chosen as an example for analysis. The DBSCAN method is used to preprocess the monitoring data from 1998 to 2021, and the dam deformation prediction performance of the proposed coupled model is studied.
## 4 Conclusions  
This study addresses the critical issue of overtopping collapse in aging agricultural reservoirs by introducing an overtopping prevention structure utilizing gabion retaining walls on the dam crest. Through comprehensive simulation analysis, model experiments, and measurements, the following significant conclusions have been drawn:  
1. The overtopping prevention structure effectively blocks water leakage during overtopping events and prevents erosion downstream. Efficient drainage through vertical filters maintains stability, with a decrease in pore water pressure inside the dam, even under constant overtopping. This underscores the practical effectiveness of the structural approach.
2. Horizontal and vertical displacements were minimal, within 0.5% and 0.2% of the dam height, respectively, even with decreasing water levels. This stability is attributed to the pressure effect from the structure's self-weight, indicating a low risk of damage or overtopping during changes in water level.
3. The integration of the dam crest core, horizontal and vertical filters, and toe drain effectively prevents saturation within the dam body, highlighting their indispensable role in maintaining the overtopping prevention structure. Optimizing parameters such as filter slope, thickness, permeability coefficient, and toe drain scale can further enhance stability and minimize risks during emergency situations.
4. Minor drying-shrinkage-induced deformations occurred in the gap between the dam crest core and gabion structure, but their impact on stability is minimal. No significant deformation from water leakage was observed, ensuring overall structural stability.  
The dam crest core, filters, and gabion structures can maintain stability during continuous overtopping. However, broader discussions are needed to establish this structure as a viable option for preventing overtopping during abnormal rainfall. Future research should focus on developing engineering standards, including analyzing parameters like flood potential and optimal design height. Additionally, a comprehensive seismic stability assessment is essential for integrating overtopping prevention measures into taller dam structures.  
Software, J.-H.R.; investigation, J.-S.H. and H.-S.S.; writing--review and editing, D.-W.L. and C.-H.K.; project administration, supervision, and conceptualization, D.-W.L. and Y.-H.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Chungnam National University (Grant number not assigned).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
We would like to thank the Research Management Department of Chungnam National University for generously providing research funds and administrative support in carrying out this study.  
The authors declare no conflicts of interest.
# FF-MR: A DoH-Encrypted DNS Covert Channel Detection Method Based on Feature Fusion
###### Abstract  
In this paper, in order to accurately detect Domain Name System (DNS) covert channels based on DNS over HTTPS (DoH) encryption and to solve the problems of weak single-feature differentiation and poor performance in the existing detection methods, we have designed a DoH-encrypted DNS covert channel detection method based on features fusion, called FF-MR. FF-MR is based on a Multi-Head Attention and Residual Neural Network. It fuses session statistical features with multi-channel session byte sequence features. Some important features that play a key role in the detection task are screened out of the fused features through the calculation of the Multi-Head Attention mechanism. Finally, a Multi-Layer Perceptron (MLP) is used to detect encrypted DNS covert channels. By considering both global and focused features, the main idea of FF-MR is that the degree of correlation between each feature and all other features is expressed as an attention weight. Thus, features are re-represented as the result of the weighted fusion of all features using the Multi-Head Attention mechanism. Focusing on certain important features according to the distribution of attention weights improves the detection performance. While detecting the traffic in encrypted DNS covert channels, FF-MR can also accurately identify encrypted traffic generated by the three DNS covert channel tools. Experiments on the CIRA-CIC-DoHBrw-2020 dataset show that the macro-averaging recall and precision of the FF-MR method reach 99.73% and 99.72%, respectively, and the macro-averaging F1-Score reached 0.9978, which is up to 4.56% higher than the existing methods compared in the paper. FF-MR achieves at most an 11.32% improvement in macro-averaging F1-Score in identifying three encrypted DNS covert channels, indicating that FF-MR has a strong ability to detect and identify DoH-encrypted DNS covert channels.  
DNS over HTTPS; DNS covert channel; features fusion; multi-head attention mechanism +
Footnote †: journal: Applied
# Scalable Block Preconditioners for Linearized Navier-Stokes Equations at High Reynolds Number
## Low-Rank Update of the Block Preconditioner  
### Left Preconditioning  
Consider the generic linear system \(A\mathbf{x}=\mathbf{b}\). Following [26], given an initial preconditioner \(P^{(0)}\approx A^{-1}\) and a tall, full-rank, \(n\times p\) matrix \(V\), the Broyden-tuning preconditioner is defined as  
\[P=P^{(0)}-\left(P^{(0)}AV-V\right)\left(V^{T}P^{(0)}AV\right)^{-1}V^{T}P^{(0)} \tag{20}\]  
with the property that  
\[PAV=V. \tag{21}\]  
Hence, the preconditioned matrix has the eigenvalue \(1\) with multiplicity (at least) \(p\). The best choice of the columns of \(V\) is represented by the eigenvectors of \(P^{(0)}A\) corresponding to the outlier eigenvalues. Let us assume then that the columns of \(V\) are approximate eigenvectors of the initially preconditioned matrix:  
\[P^{(0)}AV=V\Lambda+E,\]  
with \(\Lambda=\text{diag}(\lambda_{1},\ldots,\lambda_{p})\) and \(\|E\|\) is small enough. The eigenvalues are decreasingly ordered with respect to the function \(f(\lambda)=|\lambda-1|\).  
Neglecting matrix \(E\) we can simplify the low-rank preconditioner as  
\[P = P^{(0)}-\left(P^{(0)}AV-V\right)\left(V^{T}P^{(0)}AV\right)^{-1} V^{T}P^{(0)} \tag{22}\] \[= P^{(0)}-\left(V\Lambda-V\right)\left(V^{T}V\Lambda\right)^{-1} V^{T}P^{(0)}\] \[= P^{(0)}-V(\Lambda-I_{p})\Lambda^{-1}(V^{T}V)^{-1}V^{T}P^{(0)}\] \[= P^{(0)}-V(I_{p}-\Lambda^{-1})(V^{T}V)^{-1}V^{T}P^{(0)}\] \[= P^{(0)}-VHV^{T}P^{(0)},\]  
with small matrix \(H=(I_{p}-\Lambda^{-1})(V^{T}V)^{-1}\) to be built once and for all before starting of the iteration process.
## 3 Results  
### Study Selection  
The study selection process followed PRISMA guidelines to ensure transparency and rigor. A systematic search across eight electronic databases--MEDLINE (PubMed) (\(n=331\)), Google Scholar (\(n=145\)), Scopus (\(n=189\)), EBSCOhost (\(n=94\)), Cochrane Library (\(n=49\)), Web of Science (WoS) (\(n=171\)), IEEE Xplore (\(n=97\)), and ProQuest (\(n=126\))--yielded 1 202 records. After removal of 920 duplicates, 282 unique records remained. These were screened by title and abstract (\(n=143\)), resulting in 139 full-text articles assessed for eligibility. Of these, 63 were excluded: 12 focused on adult populations, 19 did not employ AI-based BA models, 24 relied on alternative imaging techniques, and 8 lacked sufficient methodological detail. Ultimately, 76 studies satisfied all inclusion criteria and were included in the qualitative and quantitative synthesis. The PRISMA flow diagram in Figure 1 provides a visual summary of this process.
## 2 Radar Jamming Signal Analysis and Modeling
### Deception Jamming
#### 2.3.3 Chopping and Interleaving Jamming  
Chopping and Interleaving Jamming (CIJ) is another type of dense range gate pull-off jamming, which is also generated based on DRFM technology. Its generation process includes two steps--Chopping and Interleaving. The generation process is: the receiver stores the intercepted radar signal in the DRFM, and then uses a string of equally spaced rectangular pulses to sample the intercepted signal. This stage is called the Chopping stage; then the sampled signal is copied into adjacent empty slots until the slots are filled, which is the Interleaving stage. Finally, the spectrum of the jamming is shifted to the frequency band where the radar signal is located. Its mathematical model is as follows:  
\[J(t)=\sum_{k=0}^{n-1}p(t-kT/mn) \tag{10}\]  
where the expression for \(p(t)\) is:  
\[p(t)=s(t)\bigg{[}\text{rect}\bigg{(}\frac{t-\tau_{a}}{\tau_{a}}\bigg{)}*\sum_{i =0}^{m-1}\delta(t-iT_{a})\bigg{]} \tag{11}\]  
where \(T\) is the pulse width of the radar LFM signal, \(m\) is the number of rectangular pulse strings, \(n\) is the number of slots filled in each section, \(\tau_{a}\) is the pulse width of the rectangular pulse string, \(\delta(\cdot)\) is the impulse function, \(\text{rect}(\cdot)\) is the rectangular function, and \(T_{a}\) is the fundamental period of the rectangular pulse string.
## 4 Simulation Results
### Binary Phase Shift Keying (BPSK)  
#### 4.1.1 Coherent BPSK  
The model implements or simulates a Binary Phase Shift Keying (BPSK) system using MATLAB and obtains its BER vs. SNR. To evaluate the performance of the system, a simulation is carried out for \(10^{5}\) bits, and SNR equal to 0 to 9 dB as used in most of the simulations carried out for the BPSK system. An AWGN is added of variance equal to \(1/\left(10^{\text{(SNR/10)}}\right)\), then, the noisy signal is sent over the Gaussian channel. After that, the AdaBoost algorithm is added at the receiver, where the observation weights \(w_{i}=1/n\), i = 1, 2, \(\ldots\), \(n\), and \(n\) is the length of the input bits. The number of weak classifiers (\(T\)) used for BPSK is equal to 5 weak learners, where it is the number of trials after which the desired  
Figure 3: Flowchart of the proposed system. BPSK = binary phase shift keying; QPSK = quadrature phase shift keying.  
result is achieved. The number of classes (\(K\)) is equal to 2 because in the case of BPSK, we are only interested in dividing the data into two classes. The AdaBoost technique separates the data into two classes achieving the BPSK concept as shown in Figure 4.  
Figure 5 shows the relation between both simulated and theoretical BER versus SNR of the coherent BPSK system. The results of simulated and analytical BER match very well. It is seen that as the value of the SNR increases, the corresponding value of the BER decreases. This makes sense since the SNR is equal to the power of the signal/power of the noise, so the value of the SNR increases. As a result, both curves coincide with each other. This is caused by adding the AdaBoost algorithm that helps in detecting the original data from the noisy received signal and reconstructing the constellation diagram and, as a result, the BER decreases. Hence, this verifies the improving effect of the AdaBoost algorithm on the used modulation technique.  
Figure 4: BPSK modulation technique.  
Figure 5: BER vs. SNR for coherent BPSK modulation over the AWGN channel.
## 3 Results
### Spatial Distribution of Currents  
The depth-averaged maximum velocities over 35 days were calculated at each model node to analyze the magnitudes of strong currents resulting from constricted channels in the SIS (Figure 7). Areas where high velocities occurred are shown close-up in Figure 7b-d: Osaka Bay (Naruto Strait), Aki-Nada (Hiuchi-Nada), and Bisan-Seto. Nada is a long-established Japanese word that means that an area is difficult to navigate due to strong currents or wave heights. Currently, it denotes a similar concept as a basin [11].  
The Naruto Strait in Figure 7a is the area famous for Naruto Whirlpools caused by the phase differences of tides and steep topographic gradients [31]. Even though our model cannot reproduce whirlpools due to the lack of nonlinearity during the simulation process, depth-averaged currents with a magnitude of approximately 5 m/s were calculated (Figure 7b). The Akashi Strait is the main channel connecting the Kii Channel and Harima-Nada through Osaka Bay. Along this strait, a depth-averaged maximum velocity of approximately 2.8 m/s was calculated.  
Figure 7b-d demonstrates the velocities around the basin in the middle of the SIS, including in the Hiuchi-Nada and Bingo-Nada regions. On the left side of the basin, sea water from Aki-Nada flowed into Hiuchi-Nada through four straits. Two of the southernmost straits showed velocities approximately 1.5 m/s stronger than those in the northern straits. This seemed to have been caused by the different widths of the channels around each strait. If a relatively wide channel became constricted rapidly as seawater flowed in, the depth-averaged current speed exceeded 4 m/s before the entrance of Hiuchi-Nada was reached. On the right side of the central basin, the Bisan-Seto strait connects Bingo-Nada and Harima-Nada (Figure 7d). The coastal lines and shape of this area are relatively straight and simple compared to those along the left side. Therefore, a velocity of approximately 2 m/s was calculated here.  
So far, the numerical model, a base model to establish a DT system for the SIS, has been validated in terms of tidal elevation, water temperature, and tidal current. Our aim is to apply this model to extreme natural hazards in the SIS to demonstrate its effectiveness, robustness and applicability, as we continue with further validation, encompassing  
Figure 7: (**a**) Depth-averaged maximum current speed over the 35-day simulation period in the SIS. (**b–d**) Enlarged images of each subdomain.  
variables such as wind waves, salinity, particle tracking, and suspended sediment in a future study.
# Design Analysis of a Passive Buffer System for Space-Assembled Segmented Mirrors
## 5 Conclusions  
In this paper, a passive buffer system for the space-assembled optical telescope is investigated. Through a theoretical research, the \"double-elastic contact model\" of the probe-cone docking mechanism in the capture stage, is established and the solution of the maximum contact compressive stress, and the maximum buffer deformation, are also derived. Based on this model, a design, and implementation of a passive buffer system is presented in which, both the pin and the cone wall, are elastically designed. Compared with the small-sized traditional docking mechanism, the dynamic simulation results prove that the impact energy is well-absorbed by increasing the deformation of the flexible structure. The proposed connection mechanism SMALL, using this buffer system, is light-weight, compact, and considerably power efficient, which can meet the needs of on-orbit assembled telescope very well.  
Figure 8: The contact force of a typical rigid collision system. The red line indicates the contact force between the first set of pin and hole while the blue line indicates the contact force between the second group.  
Figure 9: The contact force of the double-elastic collision system. The red line indicates the contact force between the first set of pin and hole while the blue line indicates the contact force between the second group.  
**Author Contributions:** Conceptualization, X.L. and W.W.; Methodology, X.L.; Software, Y.Q.; Validation, X.L., W.W. and Y.Q.; Formal Analysis, X.L.; Investigation, X.L.; Resources, X.L. and F.S.; Data Curation, X.L.; Writing-Original Draft Preparation, X.L.; Writing-Review & Editing, X.L.; Visualization, W.W.; Supervision, W.W. and X.F.; Project Administration, F.S.; Funding Acquisition, F.S.  
**Funding:** This research and the APC was funded by West Light Foundation of The Chinese Academy of Sciences grant number [XAB2016B24].  
**Acknowledgments:** The authors would like to thank three PHD candidates, [PERSON], [PERSON] and [PERSON] in University of Chinese Academy of Sciences, for their enthusiastic participation in our research.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Primary User Traffic Pattern Based Opportunistic Spectrum Handoff in Cognitive Radio Networks
###### Abstract  
Through the expeditious expansion of the wireless network, the unlicensed bandwidth-based devices are growing substantially as compared to the present vacant bandwidth. Cognitive radio networks present a proficient solution to the spectrum shortage diminution hitch by allowing the usage of the vacant part of the spectrum that is not currently in use of the Primary User licensed bandwidth to the secondary user or cognitive radio user. Spectrum management procedure in cognitive radio network comprises of spectrum sharing, sensing and handoff. Spectrum handoff plays a vital role in spectrum management and primarily focuses on single handoff strategies. This paper presents a primary user traffic pattern-based opportunistic spectrum handoff (PUTPOSH) approach to use in the cognitive radio networks. PUTPOSH permits a secondary user to sense the arrival of a primary user and use an opportunistic handoff scheme. The opportunistic handoff scheme firstly detects the arrival of the primary users by energy detection sensing and secondly, it allows a cognitive radio user to decide whether to do handoff or not contingent upon the overall service time to reduce the unused handoffs. The handoffs can either be reactive or proactive based on the arrival rate of the primary user. The simulation results show that the presented PUTPOSH approach (a) minimizes the number of handoffs and the overall service time, and (b) maintains the channel utilization and throughput of the system at a maximal point.  
spectrum handoff; spectrum sensing; cognitive radio networks; cognitive radio user; primary and secondary user +
Footnote †: journal: Accepted: 21 February 2020; Published: 2 March 2020