esa-sceva/satcom-chunk-collection huggingface dataset:
# Evolutionary Game for Content Cache in a mm-Wave-Based Vehicular Fog
## 2 Related Works
### mm-Wave Communications  
The mm-Wave spectrum receives much attention for 5G communication which is attractive to support wide bandwidth for giga-bit data rate. The mm-Wave communication links can be used not only for access links, but also for backhaul links that constitute multi-hop mesh or ad-hoc networks [26; 27; 28; 29]. The feasibility of the mm-Wave communication had already been explored by many universities and companies using channel measurement and beam-forming/tracking experiments [30; 31; 32]. 3 GPP, global standardization organization completes 5G specification about a new radio (NR) applicable to sub-6 GHz band (FR1) and mm-Waves (FR2) spectrums in the Release 15, and now focus on convergence technologies such as multimedia priority service, V2X, 5G satellite access, etc as Phase 2 for Release 16 and 17.  
There have been many literatures to investigate MAC protocols for the mm-Wave communication. First, IEEE standards such as 802.15.3 Task Group 3c (TG3c) [33] and IEEE 802.11 ad [34] specify physical and MAC layer protocols of Carrier Sensing Multiple Access/Collision Avoidance (CSMA/CA) or TDMA and beam-forming at 60 GHz unlicensed bands. For the beam training and alignment, the access point (AP) schedules two different periods, Association Beamforming Training (A-BFT) during Beacon Header Interval (BHI) and Service Period (SP) during Data Transmission Interval (DTI). The A-BFT allows us to detect a sector by sweeping all directions and further beam refinement occurs during the SP by exchanging special Beam Refinement Protocol (BRP) packets.  
Based on the IEEE 802.11 ad standard, [PERSON] et al. [35] proposed a spatial reuse strategy for concurrent transmissions with directional antennas. [PERSON] et al. [36] propose a frame-based directive MAC protocol (FDMAC) which is a centralized scheduling algorithm based on greedy coloring providing multiple concurrent transmissions. [PERSON] et al. [37] proposed a memory-guided directional MAC (MDMAC), as a fully distributed MAC protocol, which achieves approximate TDM scheduling for wireless meshes using memory about transmission success and fail. Another distributed algorithm, directional to directional MAC (DtDMAC) [38] uses an exponential backoff procedure for asynchronous operation.
# Artificial Intelligence of Things as New Paradigm in Aviation Health Monitoring Systems
## 3 Results
### Sensors in an Aircraft Health Monitoring System  
The robustness and safety of modern aviation are significantly reliant on the capabilities of various sensors that constitute an aircraft health monitoring system [52; 53; 54; 55]. These sensors are the aircraft's nerve endings, collecting critical data that ensure the optimal performance and safety of the flying vessel.  
The general taxonomy of sensors in an aircraft health monitoring system (Figure 2) can be delineated based on their functional roles, the physical phenomena they measure, their locations, and their relevance to different aircraft systems. This taxonomy is integral to understanding how data are collected and analyzed to maintain aircraft safety and efficiency.
## 4 Discussion
The UV-Vis spectra of these complexes show analogous spectral features to those analyzed before, suggesting that these insoluble species are formed by the soluble complexes, possibly upon the additional inclusion of a ligand or a hydroxide in the metal coordination sphere, in order to obtain a neutral species of the type M\({}_{p}\)(OH)\({}_{q}\)(CCA)\({}_{r}\). All the complexes' spectra contain a broad band in the UV region (i.e., at \(\lambda\) = 320 nm), with tailing into the visible region. These bands were assigned to a ligand to metal change transfer transition. The molar absorption coefficients of the complexes are reported in Figure 6: for the two complexes of neodymium, i.e., Nd(OH)(CCA)\({}^{+}\) and Nd(OH)(CCA)\({}_{2}\), \(\varepsilon\) values of 5870 and 4360 M\({}^{-1}\)cm\({}^{-1}\), respectively, were recorded at \(\lambda_{\max}\) of 290 nm. Higher \(\varepsilon\) values are obtained for the uranyl-HCCA system, which are 4: \(\times 10^{5}\) for the neutral species and 7 \(\times 10^{5}\) for the charged one. Our results can only be partially compared with those proposed by [PERSON] et al. [15] and by [PERSON] et al. [29]; in these works, the complex of formula Nd(CCA)\({}_{2}\)(NO\({}_{3}\))(H\({}_{2}\)O) was synthesized and characterized by spectroscopic and computational methods. According to our hypothesis, the spectroscopic results suggest that CCA\({}^{-}\) is bidentate bound to the metal ion through the carboxylic and the carbonylic moiety. The model calculations of both bidentate modes of CCA\({}^{-}\) to Nd(III) showed that the bidentate binding form through the carboxylic and the carbonylic oxygen atoms is energetically preferred as compared to that through both the carboxylic atoms. Figure 5: UV-Vis spectra of (**a**) Nd\({}^{3+}\)-HCCA: free ligand (blue line), solution (brown line) and solid (red line); (**b**) UO\({}_{2}\)\({}^{2+}\)-HCCA: free ligand (violet line), solution (red line) and solid (blue line). **Author Contributions:** Conceptualization: E.F. Investigation: L.M., G.A.C. Resources: E.F. Writing--Original Draft: E.F., L.M. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Data sharing not applicable. **Acknowledgments:** We thank the University of Calabria. **Conflicts of Interest:** The authors declare no conflict of interest.
## Materials and Methods
### Task Cycle Time and Waiting Time  
The objective measurements made in this study included the cycle time and waiting time for completing an assembly task. The waiting time was further divided into the operator waiting time (OWT) and robot waiting time (RWT).  
Figure 3 presents a graph showing the speed and distance changes during an assembly task, including the distance between the two spotlights and the speed change of the LED spotlights on the robotic gripper and back of the operator's hand. A complete assembly task consisted of the assembly of three orange blocks, handover of a green block from the robot, assembly of the green block, and finally placement of the assembled blocks in the placement area.  
According to the hand speed variation curve in Figure 3 (V_Hand), the movement process for the first block (G1) that was taken from the building block area produced a waveform showing clear acceleration and then deceleration. A similar undulating waveform was generated when the hand returned from the building block area to the assembly point (R1), during the subsequent movements for the orange block assembly, and when the hand was extended to the handover point (RtR). At this time, the UR3 speed variation curve (V_UR3) shows acceleration and then deceleration as the robot moved to the handover point to prepare to perform the handover task (SD); however, the speed changed less substantially than that of the human operator's hand. When the robot reached the handover point and stopped, the operator took the block from the robot and returned to the assembly point (RtA). The robot returned to the starting point (FD) once the spotlight on the operator's hand had passed the virtual trigger plane. The cycle time is the time required to complete an assembly task and is defined as the interval between the two G1 events.  
During the handover process, both the RWT and OWT affected the task fluency. As illustrated in Figure 3, RtR occurred earlier than SD. When the hand speed was close to 0 during the RtR event, the operator's hand had arrived at the handover point. When the robot's speed was close to 0 during the SD event, the robot had arrived at the handover point. The OWT is the duration between the speed of the operator's hand reaching 0 during the handover event (RtR) and the robot arriving at the handover point. By contrast, the RWT is the duration between the robot arriving at the handover point and the operator completing the orange block assembly tasks and moving to the handover point (RtR). This case is illustrated in Figure 4: SD occurred earlier than RtR, and the interval between them is the RWT.  
Figure 4: Robot waiting time (RWT) in the assembly cycle. Abbreviations: see caption of Figure 3.  
Figure 3: Speed variation of the operator’s hand and robot during the handover process, with \(G_{\text{x}}\) = reach out to grab the \(x\)th building block, \(x\) = 1, 2, 3; \(R_{\text{x}}\) = return to the assembly area with the \(x\)th building block; RtR = reach to the robot; RtA = return to the assembly area with the handed over building block; SD = start handover by robot; FD = finish handover by robot.
## 2 FDEM Modelling
### Contact Forces  
In the FDEM, the contact force \(\mathbf{f}\) was evaluated and integrated through the overlapping area \(A\). As shown in Figure 2, the infinitesimal contact force \(d\mathbf{f}\) due to the penetration of elemental area \(dA\) is defined in Equation (4), as  
\[d\mathbf{f}=-d\mathbf{f_{t}}+\mathbf{f_{c}} \tag{4}\]  
in which \(d\mathbf{f_{t}}\) and \(d\mathbf{f_{c}}\) are the force components of \(d\mathbf{f}\) from the target and the contactor, respectively, as  
\[d\mathbf{f_{t}}=-E_{p}grad\varphi_{\text{c}}(\text{P_{c}})dA \tag{5}\]  
\[d\mathbf{f_{c}}=-E_{p}grad\varphi_{\text{t}}(\text{P_{t}})dA \tag{6}\]  
where \(\text{P_{c}}\) and \(\text{P_{t}}\) are the points on the contactor and the target, respectively, and they share the same coordinate within \(A\); \(\varphi_{\text{c}}\) and \(\varphi_{\text{t}}\) are predefined potentials; _grad_ is the gradient; and \(E_{p}\) is the contact penalty and is usually 100 times of the Lame's first constant. Thus, the contact force \(\mathbf{f}\) can be obtained by integrating \(dA\) over \(A\), as  
\[\mathbf{f}=E_{p}\int_{A}[grad\varphi_{c}(\text{P_{c}})-grad\varphi_{t}(\text{ P_{t}})]dA \tag{7}\]  
Further details on the contact forces can be referred to [PERSON] [28].
# Deep-Sea Underwater Cooperative Operation of
## 6 Conclusions  
In this study, three cooperative operational modes using combinations of HOV, USVs, landers, and AUVs were developed for different application scenarios in the deep sea, and an on-site command system of manned and unmanned submersibles was introduced. Aiming at the cooperative operating mode of manned and unmanned submersibles, a timing allocation mechanism to avoid mutual interference between positioning and communication was designed. The cooperative operation of manned and unmanned submersibles can change the manner in which AUV detection operations are conducted while the HOV is diving. The AUV detection results can provide support for the selection of the HOV diving site.  
The time required to complete the same task is only one-third of the traditional operational mode. The cooperative operational mode improves the overall operational efficiency of scientific research work. Finally, through the analysis of a typical underwater target search task, the operational time and the required number of dives for the three cooperative operational modes were determined. If sea conditions permit, the cooperative operation mode based on a USV is the most suitable for underwater target detection tasks.  
Figure 18: (**a**) Operation based on USV mode. (**b**) Work based on lander mode.  
**Author Contributions:** Conceptualization, L.Y. and T.Z.; methodology, X.W., S.Z. and T.Z.; writing--original draft preparation, L.Y., S.Z. and X.W.; writing--review and editing, P.S. and T.Z.; visualization, S.Z. and X.W.; supervision, L.Y. and T.Z.; project administration, L.Y. and T.Z.; funding acquisition, L.Y. and T.Z. All authors have read and agreed to the published version of the manuscript.  
This work was partially funded by the National Key Research and Development Program of China (Grant No. 2021 YFC2803000); the Open Foundation of Key Laboratory of Submarine Geosciences, MNR (Grant No. KLSG2003); the National Natural Science Foundation of China (Grant No. 41976182, 51979041); and the Taishan Scholar Project Funding (Grant No. TSQN202103137).  
Not applicable.  
The authors declare no conflict of interest.
# A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation
###### Abstract  
To address the issue of data integrity and reliability caused by sparse vessel trajectory data, this paper proposes a multi-step restoration method for sparse vessel trajectory based on feature correlation. First, we preserved the overall trend of the trajectory by detecting and marking the sparse and abnormal vessel trajectories points and using the cubic spline interpolation method for preliminary restoration. Then, we established a composite indicator of feature correlation for selecting highly correlated trajectory features as inputs to the model, reducing data redundancy while improving the key correlation between trajectory features. Finally, we developed a vessel trajectory restoration model based on the Seq2 Seq network for secondary restoration of the trajectory to ensure the accurate restoration of the vessel trajectory. Through comparison and ablation experiments, we demonstrate that the method can efficiently extract highly correlated features from vessel trajectories and combines the advantages of the interpolation method and neural network model to effectively improve the accuracy of trajectory restoration and ensure the integrity and accuracy of trajectory data. The research results could provide crucial technical support for the subsequent mining of vessel behavior patterns and assisted decision-making, which holds significant application prospects and potential value.  
sparse trajectory restoration; feature correlation indicator construction; trajectory sparsity and anomaly detection; Seq2 Seq neural network Article
## 5 Test Results and Interpretation
### Displacements of Pylons  
The laser scanner surveys during the proof load phases provide 3D images of the deformed shape of the overall bridge. The 3D coordinates of many points that were scattered throughout the bridge can be acquired at different loading conditions to get the well-known point clouds from which direct measurements of the bridge displacements can be obtained. The approach is promising to control the overall deformed configuration of a bridge and particularly to measure the displacements of points that are inaccessible for sensor positioning. The laser scanner acquisition was adopted to control the pylons displacements. In detail, the acquisitions were performed at unloaded conditions (phase L1) and during configuration C1, corresponding to the application of the maximum load (load phase L5), which is expected to produce the higher displacements of the mid-span deck and pylons. By comparing the acquired point clouds, the displacements that are reported in Figure 12 were obtained.  
As can be observed, some parts of the bridge are absent from the cloud because they fall in shadow areas with respect to the laser scanner position. All the four pylons are well visible, hence the laser scanner acquisitions are used to analyze the pylon displacements due to loads of configuration C1. In particular, the longitudinal (Y) and transverse (X) displacements of the south and north pylons at the higher stay anchorages, labelled with SP and NP, respectively, are taken into account and reported in Figure 12. Displacements indicate that the pylons moved toward the middle of the bridge deck, both in the longitudinal and transverse directions, consistently with the applied loads (in the mid-span of the bridge on the downstream side).  
The displacements of the same points that were obtained from the FE model in C1 load configuration are listed in Table 3, together with the relevant experimental ones. The  
Figure 11: Experimental stay cable axial load variations during the C1 load configuration.  
Figure 12: Laser scanner point cloud that was acquired during C1.  
reported numerical displacements are calculated by subtracting the contribution of the unload bridge self-weight from the total displacement that was obtained in C1.  
It can be observed that the theoretical displacements are similar to the experimental ones, especially the transverse ones, demonstrating that the numerical model is capable of well-describing the behavior of the pylons. Moreover, the longitudinal numerical displacements are always higher than the relevant experimental ones. Finally, the longitudinal symmetric behavior of the structure is also proved, consistently with the applied loads.
## 8 Conclusions
### Future Directions  
While this paper presents a novel approach combining the BACA and SA for optimizing coverage and energy efficiency in WSNs, there remain several promising directions for future research in this area. These potential extensions and improvements can further enhance the performance and applicability of the proposed approach:  
* Three-dimensional coverage optimization: The current approach focuses on optimizing coverage in a two-dimensional (2D) field. Extending the research to three-dimensional environments will be valuable, as many real-world scenarios, such as monitoring buildings or underground structures, require WSNs to operate in 3D spaces. Investigating how the BACA and HC/SA can be adapted or combined to tackle 3D coverage challenges will be a significant step forward.
* Dynamic deployment and mobility of nodes: In practical WSN deployments, sensor nodes may experience mobility or need to be dynamically deployed to adapt to changing environmental conditions. Exploring how the proposed approach can be extended to handle dynamic node deployment and mobility scenarios is crucial for achieving robust and adaptable WSNs.
* Heterogeneous sensor nodes: In many WSNs, sensor nodes may have varying capabilities and energy constraints. Researching how the proposed approach can be adapted to handle heterogeneous nodes will cater to diverse deployment scenarios and enable more versatile applications.  
Conceptualization, A.M.K. and M.J.O.; methodology, A.M.K.; software, M.J.O.; validation, A.M.K.; formal analysis, A.M.K.; investigation, A.M.K. and M.J.O.; resources, A.M.K. and M.J.O.; data curation, A.M.K. and M.J.O.; writing--original draft preparation, A.M.K. and M.J.O.; writing--review and editing, A.M.K. and M.J.O.; visualization, A.M.K. and M.J.O.; supervision, H.M.A.; project administration, H.M.A.; funding acquisition, H.M.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Science Foundation grant number 2338521.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to future research based on the same dataset.  
The first two authors would like to thank [PERSON] for all the work he has put towards his students and for his helpful comments and ideas during their participation as undergraduate students in his NSF REU Site in Summer 2023.  
The authors declare no conflicts of interest.
###### Abstract  
The automatic identification system (AIS) is an essential tool for modern ships, enabling the broadcast of identification and location information. However, the current AIS standard lacks security features, meaning that messages exchanged via AISs are transmitted in plaintext, which leads to security issues such as privacy leakage. Most existing solutions rely on public key cryptography. This paper proposes a physical-layer key generation protocol based on the current AIS standard (ITU-R M.1371-5). In the case of unicast AIS communication, the protocol utilizes channel randomness to generate symmetric keys for securing communications. Compared to public key cryptography, the proposed protocol offers advantages such as low overhead, elimination of third parties, and ease of implementation. Finally, this paper discusses the security of the protocol against various threats as well as evaluates its performance and overhead. Under common speed and signal-to-noise ratio (SNR) conditions, The protocol generates Advanced Encryption Standard (AES) keys of different lengths in under 4000 ms, and these keys successfully pass the National Institute of Standards and Technology (NIST) randomness test.  
Keywords:physical-layer key generation; AIS security; symmetric encryption; secret key exchange +
Footnote †:: journal: Marine Science and Engineering
## 3 Methodology
### Preliminaries
To address this, we introduce additional parameters into the predictive model in Equation (3) to capture and correct for these systematic deviations. One way to enhance the model is by incorporating a trend-corrected residual component into the loss function. Instead of minimizing only the MSE, we introduce a regularized residual correction term that accounts for systematic biases. This modification ensures that persistent trends in prediction errors, such as seasonal variations in deformation or external stress accumulation, are explicitly modeled. Mathematically, we redefine the predictive function as  
\[Y^{*}=f(X_{s},X_{t},A;\Theta)+g(X_{t},E) \tag{5}\]  
where \(g(X_{t},E)\) represents a learnable residual correction term that captures systematic deviations using external covariates \(E\) (e.g., seasonal effects, geological conditions, and maintenance history). The revised loss function then becomes  
\[L=\frac{1}{NT^{\prime}}\sum_{i=1}^{N}\sum_{t=1}^{T^{\prime}}\left(y_{i,t}-(y_{ i,t}^{*}+g(X_{t},E))\right)^{2}+\lambda\|\Theta\|^{2} \tag{6}\]  
where \(\lambda\|\Theta\|^{2}\) is an additional regularization term to prevent overfitting. This correction improves long-term predictive accuracy by adapting to structured biases that may otherwise remain unaccounted for in a purely MSE-based approach.
## 2 Data Source
### Data Augmentation
The MI value is equal to zero if two variables are independent, and higher values indicate stronger dependency. MI is computed using Equation (4). Unlike the Pearson correlation, the use of the MI approach can capture non-linear dependencies without assuming any specific data distribution. Given the non-linear nature of the dataset, MI regression is suitable for computing the correlation between variables. As shown in Figure 1, four features exhibit a strong relationship with X\({}_{50}\), i.e., BD, X\({}_{\text{B}}\), HB, and E.  
\[I(X;Y)=\iint p(x,y)\log\biggl{(}\frac{p(x,y)}{p(x)p(y)}\biggr{)}dxdy \tag{4}\]  
where \(I(X;Y)\) denotes the mutual information between X and Y. \(p(x,y)\) denotes the joint probability density function (pdf) of X and Y. \(p(x)\), \(p(y)\) denotes marginal pdfs of X and Y. \begin{table}
\begin{tabular}{c c c c c} \hline
**Parameters** & **Min. Value** & **Max. Value** & **Mean** & **Standard Deviation** \\ \hline S/B & 0.9267 & 1.7921 & 1.1788 & 0.1093 \\ H/B & 1.2498 & 6.8683 & 3.2153 & 1.3955 \\ B/D & 17.9408 & 52.2242 & 29.3455 & 4.7601 \\ T/B & 0.4353 & 4.7513 & 1.0477 & 0.5782 \\ PF (kg/m\({}^{3}\)) & 0.1625 & 2.5717 & 1.0259 & 0.6302 \\ X\({}_{\text{B}}\) (m) & 0.0307 & 2.8724 & 1.2029 & 0.4764 \\ E (GPa) & 8.8334 & 60.0957 & 23.7790 & 16.2551 \\ X\({}_{50}\) (m) & 0.0184 & 0.9930 & 0.3175 & 0.1574 \\ \hline \end{tabular}
\end{table} 
Table 3: Large-scale data summary statistics.