esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Wireless Enabling Technologies
### Visible Light Communication (VLC)  
VLC allows high data rate transmission at the same time as illumination, by exploiting the same main principles of Bell's photophone transmissions dated more than 100 years ago. It is, in fact, sufficient to quickly switch on and off the light emitting diode (LED) to transmit a wide bandwidth signal invisible to the human eye and completely safe for the human body, by exploiting the wide and free optical spectrum. VLC is, thus, naturally suited to broadcast messages. Vehicles are already equipped with LED lights that could be easily exploited for V2V communications without interfering with electromagnetic devices and allowing a high reuse factors and lower interference from neighbors thanks to the limited penetration capabilities of light [6]. Besides these advantages, it has to be remarked that VLC is susceptible to ambient light and weather conditions, covers limited distances and does not cross obstacles [29; 30].  
The increasing interest in this kind of communication is also demonstrated by the development of the IEEE 802.15.7 standard, which defines the PHY and MAC layers for the visible light spectrum and allows for supporting audio and video services also in mobile visible links [31]. At the PHY layer, three are the available operating modes which differ in the adopted modulation and coding scheme [31; 32], allowing data rate from 11.67 to 96 Mb/s. At the MAC layer, the IEEE 802.15.7 standard foresees the use of CSMA/CA: each node transmits only if the medium is sensed as idle after a random backoff interval time; then, the message is considered correctly received only upon the reception of an acknowledgment at the transmitter. A revision of the standard, called IEEE 802.15.7r1, is foreseen to better address vehicular communications as a VLC use case. In this case, the standard considers the requirements of vehicular communications and aims to enhance mobility, data rates, robustness and the networking protocols [33].
## 3 Results and Discussion
### Finite Element Analysis

Table 1: Maximum temperatures, positions and stresses for four types of heat fluxes at t = 16.26 s.  
Figure 7: Comparison of temperatures, and stresses for four types of heat flux, t = 10.26 s. (**a**) Uniform, (**b**) Linear, (**c**) Quadratic, (**d**) Quartic. To determine the relation between the heat flux input profiles and temperature profiles using finite element analysis, the obtained temperature data were fitted to a curve with the same order as the heat flux input. All curve fits were carried out using Excel (MS Office 2012). Figure 9 shows that the correlation coefficients, \(R\) of the curve-fitting of the temperature data were near to one, which means that the temperature and heat flux profiles have similar types of profiles. When the heat flux input was linear in the radial direction, the temperature profile was also linear. In this case the coefficients of correlation (R) were R = 0.95-0.96. The temperature distribution was linear in the radial direction except near both edges. For quadratic heat flux, R = 0.99. The temperature profile in the radial direction retained a quadratic function. For the quartic heat flux input, the temperature profile maintained a quartic function. From the results it can be said that temperature profiles in the radial direction on the brake disc can be fitted by a function with the same order as the heat flux profile. Using temperature profiles of the disc surface obtained with an infrared camera, heat flux profiles can be inferred, and then pressure profiles can be estimated. [Note that heat flux = \(\mu p(r)rw\).] It seems that the results of this study provide a new approach to estimating the contact pressure between brake disc and pads and for other similar cases. Figure 8: Stress along the symmetric line on the surface of the disc at t = 10.26 s under uniform heat flux. Figure 9: _Cont._  
To simulate the temperature profiles of thermal images using the process introduced above, the thermal image in Figure 4a was taken as an example. First, the temperature data of the thermal image were curve-fitted to a sixth order polynomial, T(r). Figure 10a shows good correlation (R\({}^{2}\) = 0.88) between the two curves. Then the heat flux, q(r), was assumed to be a similar function to T(r): q(r) = b\({}_{1}\)[T(r) - T\({}_{\rm b}\)], where b\({}_{1}\), and T\({}_{\rm b}\) are constants. b\({}_{1}\) = 0.8, and T\({}_{\rm b}\) = 14 were taken for the simulation. Figure 10b shows the thermal fields at t = 4.8 s, which are very similar to the thermal image in Figure 4a. In Figure 10c finite element temperatures in the radial direction on the surface were compared with the thermal image data. The two results were in good agreement though there was some uncertainty in the data related to the simulation. When temperature profiles cannot be represented as a polynomial, or to simulate experimental data more exactly, some coding work will be necessary. This kind of research will be pursued in the future.
## 3 Results and Discussion
### Simulations for Hierarchical Tree Structured Network
#### 3.2.4 Network Latency  
A similar analysis on network latency for tree structure can be performed as it was done for chain-based networks. The sink node can be considered to be in the center of a circle covered by other nodes of the network where the maximum length of the link is the radius of that circle. Therefore, N is the number of nodes in such a radius. To cover a similar area that was covered for chain, we can assume that maximum number of nodes in longest link of tree network as half of maximum nodes that can exist in chain network. That is, N is 6 and 51 for HANs and FANs, respectively, thus expecting the total length of the communication between furthest source node and sink node to be less than 250 m and 250 km for HANs and FANs, respectively. Therefore, maximum expected latency of both AVG and proposed methods reduce by almost half that of latency expected for chain-based networks. Therefore, for tree-based networks, conclusions on network latency of the proposed method and EBDKF are same as that of chain-based networks. But in tree structured networks the AVG method can also satisfy the latency requirement in HANs and violates the latency requirement of FANs when N is greater than or equal to 45 as the maximum length of communication substantially has been reduced compared to chain-based networks.
# Seismic Response of Aeolian Sand High Embankment Slopes in Shaking Table Tests
## 5 Conclusions  
In this paper, specimens of aeolian sand high embankments were designed and constructed, complying with similarity laws, and shaking table tests were performed to study the seismic response. The following primary conclusions can be drawn:  
(1) The horizontal acceleration magnification (HAM) of the three model embankments always exceeds 1.0, and shows an increasing trend with height. The amplification effects due to the dynamic response of the embankment triggered by both incident and reflected waves are the main reasons for these phenomena.  
(2) The HAM of model 1 (1:1.5) increases with increasing input peak acceleration. The HAM of model 2 (1:1.2) and model 3 (1:0.8) shows attenuation when the input peak acceleration reaches a certain threshold value (300 gal in this test)  
(3) The microcosmic failure mode of the Aeolian sand embankments can be defined in three stages: the reflected wave emergence (RWE) stage, the reflected wave strengthening (RWS) stage, and the acceleration magnification attenuation (AMA) stage. 300 gal (cm/s\({}^{2}\)) is the critical acceleration of model 2 and model 3 transform from the RWS stage to the AMA stage. Furthermore, model 1 only experienced the RWE stage and the RWS stage during the test. Therefore, this critical acceleration can provide a significant guide on quantitative estimation of the anti-seismic ability of aeolian sand high embankments.  
(4) The embankment instability mechanisms of model 2 and model 3 belong to the type of tension-shattering sliding, and a collapse area formed by the horizontal continuous cracks and vertical tensile cracks. Only partial collapse occurred in model 1. The slope ratio has a significant influence on the seismic capacity of aeolian sand high embankments.  
In general, this study shows that the slope ratio exerts a significant influence on the seismic response of aeolian sand high embankments. The critical acceleration of embankments during their transformation from the RWS stage to the AMA stage is a reference for the estimation of the anti-seismic ability of aeolian sand high embankments. The instability mechanism of the models under horizontal acceleration also informed the highly efficient seismic design of aeolian sand high embankments.  
Conceptualization, Z.Z., J.L., S.S., and T.L.; Data curation, Z.Z., J.L., S.S., and T.L.; Funding acquisition, Z.Z.; Investigation, J.L.; Methodology, J.L., S.S.; Writing--original draft, Z.Z., J.L., and T.L.  
This work is financially supported by the National Key R&D Program of China (no. 2018 YFC0808706).  
The authors declare there is no conflicts of interest of this regarding the publication of this paper.
## 1 Introduction
### Our Contribution  
Based on the above considerations, in this paper, we propose a secure driving location (trajectory) data sharing system for trajectory-based message delivery in VANETs which can prevent the shared location data on the cloud from being illegitimately exposed to others. We consider a location-aware service scenario in which a vehicle allows some socialspot RSUs designated by the vehicle to access its preferred driving trajectory data stored on the cloud. For secure vehicular communications, group signatures or pseudonymous credentials have been used for anonymous authentication so that the identity and location of a vehicle cannot be linked and tracked on VANETs. However, the main goal of our work is to securely share the trajectory data of a vehicle with only authorized entities from confidentiality view point and to control who can access the location data by vehicle itself. To achieve our goal, we present a system architecture for sharing the trajectory data of vehicles with the help of a service manager acting as a broker between vehicles and socialspot RSUs, then design a secure location-sharing and authenticated message delivery protocols by making use of a certificateless proxy re-encryption scheme and an id-based signature scheme with pseudonymous identities.  
In our proposed system, a vehicle can upload driving trajectory data encrypted under its own public key to a semi-trusted service manager on the cloud. The uploaded trajectory data are coupled with re-encryption keys associated with the designated socialspot RSUs so that the manager re-encrypts vehicle's trajectory data and distributes to the intended socialspot RSUs. Then, the socialspot RSUs can send service messages to the vehicle by way of some RSUs along the driving route of the vehicle. We make the vehicle revoke the access rights of unwanted socialspot RSUs by changing vehicle's public key and re-encryption keys but the vehicle does not need to obtain a certificate for the new public key. Therefore, the proposed system can be self-controllable and more flexible.  
The rest of this paper is organized as follows: We first present a system architecture and security considerations in Section 2 and cryptographic building blocks to design the proposed system in Section 3. Then, we describe our proposed secure location-sharing and authenticated message delivery protocols in Section 4. We discuss the security and performance of our protocol in Section 5, and finally conclude this paper in Section 6.
# A Novel Encryption Scheme in Ship Remote Control against Differential Fault Attack
## 3 Prevention of DFA
### Combined Transform  
The optimization scheme can improve the difficulty of DFA to obtain \(\bigtriangleup a_{i,j}\). The algorithm optimization scheme is as follows:  
1. Taking the last round of encryption and reverse transform in the encryption process as an example (the following descriptions take this round as an example). \(Y_{3}\) is no longer directly shifted from the original \(X_{32}\), but first combined with \(X_{33}\) and \(X_{34}\) through transform \(F(X_{32},X_{33},X_{34})=(T_{0},T_{1},T_{2})\), as shown in Figure 4.
2. After an S-box transform and XOR with the round key, that is \((S(T_{0})\oplus rk_{31},S(T_{1})\oplus rk_{31},S(T_{2})\oplus rk_{31})=(F_{0}, F_{1},F_{2})\).
3. Finally, the output ciphertext \(R(F_{0},F_{1},F_{2},X_{35})=R(Y_{3},Y_{2},Y_{1},Y_{0})=(Y_{0},Y_{1},Y_{2},Y_{3})\) is obtained by reverse transform.
# Research on Satellite Selection Strategy for Receiver Autonomous Integrity Monitoring Applications
## 3 Methodology
### Performance-Requirement-Driven Fast Satellite Selection Algorithm  
According to the performance impact analyses in Section 3.1, a performance-requirement-driven fast satellite selection (PRDFSS) algorithm can be carried out to construct a relatively small subset that satisfies both the accuracy and integrity requirements. The main idea of the algorithm is constructing a small size initial subset through a geometric selection strategy, then improving the performance by adding the valuable satellite to the subset sequentially until the required performance is reached, thus a small feasible subset is obtained.  
The proposed PRDFSS algorithm is comprised of three parts which are illustrated as follows:
## 6 TTL Estimation
### Results under S-Type Offset  
#### 6.1.1 Impact of Pointing Angle Noise  
Assuming \(\Delta x=\Delta y=\Delta z=0.5\,\mathrm{mm}\) for each satellite, the impact under different noise levels of pointing angle measurement was tested in the TTL estimation. Only four linear coupling factors \(\{p_{y,1},p_{z,1},p_{y,2},p_{z,2}\}\) were estimated.  
The ASD of the input and estimated TTL error are shown in red and blue, respectively, in Figure 8. In the cases that white noise with levels of \(0.01\,\mathrm{\SIUnitSymbolMicro rad}/\mathrm{Hz}^{1/2}\) and \(0.1\,\mathrm{\SIUnitSymbolMicro rad}/\mathrm{Hz}^{1/2}\) were added to the pointing angles, the estimation errors shown in black are similar, which can also be seen in Table 3, where the estimated coupling factors are at the same level. In case (c), the angle noise level is similar to that of the GRACE-FO LSM1B data and the estimation error is a bit higher than the TTL error of GRACE-FO (green). When the angle noise is up to the level of \(10\,\mathrm{\SIUnitSymbolMicro rad}/\mathrm{Hz}^{1/2}\), poor estimation results can be seen. One can notice that the estimation errors at high frequencies are limited by the white angle noise  
Figure 7: Flowchart of the TTL estimation process.  
instead of the estimation error of the coupling factors. It can be concluded that the noise of the angle measurement should be better than \(1\,\mathrm{\SIUnitSymbolMicro rad}/\mathrm{Hz}^{1/2}\) for successful TTL estimation.
# Set-Based Group Search Optimizer for Stochastic Many-Objective Optimal Power Flow
## 4 Case Study
### Simulation Results on the Different Proportion of REs
In a parallel vein, to fortify the robustness of our results, we executed ten independent runs of both the SetGA and SetGSO, employing the IEEE-57 bus and IEEE-118 bus systems as illustrative benchmarks. Based on the statistical results across three distinct test scenarios presented in Tables 7 and 8, encompassing MED, HV, and \(I_{\text{ave}}(X)\) indicators, it can be concluded that SetGSO exhibits the optimal convergence average. This signifies that the proposed algorithm notably ameliorates imprecision whilst concurrently upholding the convergence and diversity of hyper-volume. While SetGA achieves the best outcome in terms of the \(I_{\text{ave}}(X)\) metric, SetGSO demonstrates relatively low result fluctuations, with even its worst outcomes surpassing those of SetGA. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Scenario**} & \multicolumn{3}{c}{**SetGSO**} & \multicolumn{3}{c}{**SetGA**} \\ \cline{2-7}  & **HV** & \(I_{\text{ave}}(X)\) & **MED** & **HV** & \(I_{\text{ave}}(X)\) & **MED** \\ \hline
30\% REs & **6,248,200** & **0.13** & **2268.2** & 4,587,000 & 0.14 & 3055.5 \\
40\% REs & **6,983,400** & **0.30** & **2374.0** & 5,028,900 & 0.31 & 3079.5 \\
50\% REs & **6,969,600** & 0.42 & **1298.2** & 6,848,600 & **0.38** & 1586.4 \\ \hline \end{tabular} Bold text represents superior values.  
\end{table} 
Table 6: Results obtained by SetGA and SetGSO on different proportions of REs in the modified IEEE-118 Bus system. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Scenario**} & \multicolumn{3}{c}{**SetGSO**} & \multicolumn{3}{c}{**SetGA**} \\ \cline{2-7}  & **HV** & \(I_{\text{ave}}(X)\) & **MED** & **HV** & \(I_{\text{ave}}(X)\) & **MED** \\ \hline
30\% REs & **215,720** & **0.45** & **2485.2** & 173,910 & 0.56 & 2586.1 \\
40\% REs & **201,360** & **0.61** & **2341.8** & 160,850 & 1.74 & 2477.3 \\
50\% REs & **254,180** & 1.78 & **2142.4** & 202,080 & **1.37** & 2280.0 \\ \hline \end{tabular} Bold text represents superior values.  
\end{table} 
Table 5: Results obtained by SetGA and SetGSO on different proportions of REs in the modified IEEE-57 Bus system.
## 3 Analysis and Results
### Visualization Analysis of Researchers
The article offers a deep exploration of the potential technological revolution in construction robotics, setting the stage for subsequent studies [28]. [PERSON]'s work (2022) \"Theory of mind for a humanoid robot\" has been cited 200 times. This paper explores the concept of \"Theory of Mind\" in robotics, particularly how humanoid robots can be endowed with the ability to understand and simulate human mental states. Although focused on humanoid robots, it provides valuable insights for construction robotics, especially in enhancing collaboration efficiency between robots and humans [29]. [PERSON] and [PERSON] (2019) published \"Lean production theory-based simulation of modular construction processes\", which has been cited 135 times. This paper, based on lean production theory, explores the application of construction robots in modular construction processes through simulation, proposing effective ways to optimize construction processes using robots, particularly in enhancing production efficiency [29]. Figure 1: Co-authorship network of researchers in construction robotics. Nodes represent authors (size = number of publications) and edges indicate collaboration strength (thickness = co-author frequency). Generated using CiteSpace 6.3.R1 with data from Web of Science Core Collection (2002–2024). These highly cited works reveal a strong interdisciplinary collaboration in construction robotics, incorporating fields such as artificial intelligence, virtual reality, and environmental sustainability [30]. For instance, the research by [PERSON] and [PERSON] combines computer vision with construction robots, while [PERSON] introduces virtual reality for training purposes. As construction robotics continues to evolve, collaboration among scholars is expected to deepen. Future research will likely focus on applied research in construction robotics, exploring how robots can be effectively implemented in the construction industry. Moreover, interdisciplinary and inter-regional collaboration will be pivotal to the future development of this field [31]. Meanwhile, these results also reflect industry-academia collaboration and commercialization. For example, Built Robotics (USA) focuses on the deployment of autonomous excavators in infrastructure projects, reducing labor costs by 30%. The China State Construction Engineering Corporation (CSCEC) has adopted 3D-printing robots for prefabricated housing, achieving a 50% reduction in material waste. Komatsu's Smart Construction Initiative has resulted in the integration of AI-guided bulldozers and drones in Japan, improving site efficiency by 40%.
# Groundwater Prediction Using Machine-Learning Tools
## 3 Techniques Used
### Gaussian Mixture Models  
Gaussian mixture models (GMMs) may be used for clustering [56] or as parametric models of the probability distribution of continuous features [57]. The user specifies the number of Gaussians in the model, and the means and covariances of the Gaussians are automatically computed using an expectation maximization (EM) algorithm [58].
# Hyperspectral Image Classification with Imbalanced Data Based on Semi-Supervised Learning
## 3 Materials and Experimental Setup
### Experimental Setup
#### 3.2.1 Sampling the Imbalanced Data Subset  
In the case of hyperspectral image methods, the processing of imbalanced datasets with varying degrees of imbalance may vary. Thus, to compare the influence of hyperspectral image methods on the classification of datasets with different degrees of imbalance,  
Figure 1: Hyperspectral remote sensing image and ground truth labels of Xiongan New Area. (**a**) RGB image and (**b**) ground truth labels.  
we designed three training subsets: **T1**, **T2**, and **T3** (Table 1). R (Max/Min), the ratio of maximum to minimum number of classes, is the difference between the three training subsets, which can be used to measure the degree of imbalance. **T1** represents an extremely imbalanced training dataset with R (Max/Min) = 14.0, **T2** represents a comparatively imbalanced training dataset with R (Max/Min) = 3.5, and **T3** represents a slightly imbalanced training dataset with R (Max/Min) = 1.6 (Table 1). In addition, as shown in Table 1, when designing the three training subsets, we aimed to avoid the repetition of minority classes appearing in the same class. Thus, the results remain unaffected by the data quality of a certain class.  
According to the designs in Table 1, we randomly performed multiple sampling to generate imbalanced training and testing datasets on a per-pixel basis without being returned to the dataset. The training and test datasets were two disjoint datasets. In addition, the number of testing data selected for each class was fixed at 2/7 of the number of classes with the maximum training data. As shown in Table 1, we sampled 800 entities from each class equally to construct the testing dataset and avoid the effects of imbalanced data during the testing process.
# Implementation and Security Test of Zero-Knowledge Protocols on SSI Blockchain
## 5 Classes Architecture
### Prover Class
#### 5.3.2 Prover Interface  
The Prover interface is activated with the python main.py p command. It allows the user to choose the ZKP protocol to be used, as well as the program to run:  
* Authenticate with Verifier: The Prover generates public/private keys according to the chosen ZKP method and executes the authentication protocol with the global parameters shared by the Verifier.
* Brute-force attack: The Prover acts as an impostor, generating a public key and trying to get the Verifier to authenticate it without using the private key, but rather by predicting the challenge that it will send and preparing the verification variables accordingly.  
In both cases, for a correct TCP connection it is necessary to enter the Verifier's IP address and listening port. This screen is shown in Figure 4.  
Figure 4: Prover Home Screen.  
Figure 3: Verifier Screen.  
When in Authenticate with Verifier mode, the Prover screen shows the automatically generated public and private keys and each round of the protocol, as shown in Figure 5a.  
When in Brute-Force Attack mode (Figure 5b), a message is displayed for each round indicating whether the Verifier challenge has been successfully passed. If all rounds have been passed and, therefore, it has been possible to authenticate successfully, the number of attempts that have been necessary is displayed. This allows the user to compare the security of the protocol for different values of the parameters that can be configured from the Verifier screen, as well as make comparisons between the three protocols.