esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussions
### Evolution, Future Trends, and Open Research Areas in Blockchain-Based Data Storage
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Year** & **Data Storage and Processing Model** & **Application Domain** & **Key Applications** & **Underlying Technologies** & **Reference Number** \\ \hline
2017 & Hybrid & Education & EduChain (Education records) & Blockchain-based Education Systems & [7,49] \\ \hline
2018 & Off-chain & IoT & Blockchain-IoT Security & State Channels & [50,51] \\ \hline
2018 & Layer-2 & Finance & Plasma-Based Scaling Solutions & State Channels, Optimistic Rollups & [17,52] \\ \hline
2020 & Hybrid & Public & Blockchain for Government Services & e-Government, Digital Identity, Blockchain Identity Management & [175] \\ \hline
2021 & Layer-2 & Finance & Zero-Knowledge Rollups & zk-SNARKs, zk-STARKs, Validity Proofs & [178,179] \\ \hline
2022 & Hybrid & Cloud Computing & Hybrid Blockchain-Cloud Systems & Layer-2 Rollups, zk-SNARKs, Sharding & [42] \\ \hline
2023 & Hybrid & Supply Chain & Blockchain-based drug supply chain & Plasma, Optimistic Rollups, Zero-Knowledge Proofs & [160,173] \\ \hline
2023 & Layer-2 & Finance & Zero-Knowledge Rollups (ZKSync, StarkNet, Polygon zkEVM) & zk-SNARKs, Validity Proofs & [17,19] \\ \hline
2024–2025 & On-chain & Finance and AI & AI-based Smart Contracts & AI + Blockchain, Large Language Models (LLM) + Smart Contracts & [159] \\ \hline
2024–2025 & Hybrid & Interoperability Solutions & Cross-Blockchain Data Integration & Polkadot, Cosmos, Axel, Interoperability Protocols & [181, 179, 180, 181] \\ \hline
2024–2025 & Layer-2 & Finance and Enterprise & Modular Blockchains (ZeroLayer, EigenLayer, Celestia) & Restaking, Data Availability Layers, Rollup-as-a-Service & [182, 183, 184] \\ \hline
2025 & Layer-2 & IoT & Blockchain-IoT Integration, Smart Device Security & IoT Blockchain Net, Data Oracles, Privacy-Preserving Smart Contracts & [182, 183, 184] \\ \hline
2025+ & Edge-integrated Hybrid (Edge Storage + On-chain & Healthcare, Industry 5.0, Smart Cities, Autonomous Systems, IoE & Real-time data processing, Decentralized data storage, Edge data analytics, Secure IoT communications & [182, 183, 184] \\ \hline \hline \end{tabular}
\end{table} 
Table 13: _Cont._Over the years, blockchain-based data storage has evolved significantly, driven by the need for greater scalability, efficiency, and interoperability. While early implementations primarily relied on on-chain models, increasing demands for cost reduction and performance optimization led to the adoption of off-chain and hybrid approaches. As shown in Table 13, this evolution spans multiple sectors, including finance, healthcare, education, supply chain, IoT, and cloud computing, with advancements in Layer-2 solutions, zero-knowledge proofs (ZKPs), and interoperability protocols. These developments not only address scalability concerns but also enhance security and real-time data processing capabilities.
## 3 Security Issues Facing CVs and Solutions
### Vanet
#### 3.4.4. RSU Assisted Security and RSU Power Abuse  
Within the communication range of an RSU, multiple vehicles nodes may communicate with its neighbors via V2V or via V2I through RSU. As a result, the nodes will contend for the radio resources of the single RSU. When traffic density and message dissemination increase, the RSU may become saturated. Therefore, it is critical to make the communication near the RSU both secure and scalable. In practice, a challenging scheduling problem arises when RSU serves as a centralized scheduler for all the nodes within its coverage. On the other hand, RSU will possess great power to allocate resources to different nodes. It registers much private and secure information of each node within its range. Therefore, valid measures should be taken to block the power abuse of RSU, to prevent RSU from unfairly allocating resources to a certain node, and to maliciously invade or disclose the privacy of a node within its range. Either C-V2X or VANET represents a promising solution to support V2X communication. At the moment, VANET has the advantage. The 5.9 GHz band made available in the U.S. more than a decade ago remains reserved for it, the EU also intended to make 802.11p the basis of the radio standard for safety-related messages between vehicles within ITS-G5. However, Qualcomm, which supports DSRC and offered second-generation 802.11p DSRC chips in the past, introduced its first C-V2X commercial solution in 2017; China appears ready to mandate C-V2X for C-ITS and safety-related services. No matter which one will become the standard to support V2X finally, the security issues in both types of networks should be well considered before they are widely developed to support CAV application. Table 5 summarizes the features and security issues of C-V2X and VANET. As analyzed above, they possess different features and face different security issues accordingly. For example, as C-V2X potentially supports direct V2V communication, the direct secure communication without a priori configuration of the keys network is needed to benefit the network's security and efficiency. On the contrary, VANET natively supports V2V and V2I, but requires efficient authentication with privacy preservation and key management in group signature to achieve multi-vehicles' secure and efficient communication. Meanwhile, due to the risk of C-V2X being exposed to operators, the privacy protection (e.g., vehicle's location) and defense against operator power abuse will be indispensable. Accordingly, as VANET deep involves RSU or other infrastructure in its deployments, the security and power abuse of RSU or infrastructure need to be well considered. Moreover, since vehicular connectivity significantly increases, VANET produces an ever-increasing amount of data and it will impose significant challenges on the efficient, reliable, and secure data transmissions and processing in VANET and calls for further attention [58].
# Error Resilience for Block Compressed Sensing with Multiple-Channel Transmission
## 3 Proposed Algorithm
The elements in the descriptions \(D^{\prime\prime}_{k1,i}\) and \(D^{\prime\prime}_{k2,i}\), \(i=1,\ 2,\ \cdots,\frac{m}{2}\) were compensated as follows:  
\[D^{\prime\prime}_{k1,i}=\left(\frac{r_{1}\cos\theta_{1}\cos\theta_{2}\sigma_{ 1}^{2}+r_{1}\sin\theta_{1}\sin\theta_{2}\sigma_{2}^{2}}{-\cos\theta_{2}\sigma_ {1}^{2}+\sin\theta_{2}\sigma_{2}^{2}}\right)D^{\prime}_{k1,i}; \tag{10}\]  
\[D^{\prime\prime}_{k2,i}=\left(\frac{r_{2}\cos\theta_{1}\cos\theta_{2}\sigma_{ 1}^{2}+r_{2}\sin\theta_{1}\sin\theta_{2}\sigma_{2}^{2}}{\cos\theta_{2}\sigma_{ 1}^{2}-\sin\theta_{2}\sigma_{2}^{2}}\right)D^{\prime}_{k1,i}. \tag{11}\]  
By taking the inverse operations of Equation (9), compensated BCS coefficients were obtained in Equation (12):  
\[\left[ \begin{array}{c}C^{\prime\prime}_{k1,i}\\ C^{\prime\prime}_{k2,i}\end{array} \right]=\left[ \begin{array}{cc}r_{2}\cos \theta_{2}&-r_{2}\sin\theta_{2}\\ -r_{1}\cos\theta_{1}&r_{1}\sin\theta_{1}\end{array} \right]^{-1}\left[ \begin{array} []{c}D^{\prime\prime}_{k1,i}\\ D^{\prime\prime}_{k2,i}\end{array} \right]. \tag{12}\]  
By gathering all the elements together, \(\mathbf{C}^{\prime\prime}_{k1}\) and \(\mathbf{C}^{\prime\prime}_{k2}\) were formed from the compensated descriptions in Equation (12). Here we denote  
\[\mathbf{C}^{\prime\prime}_{k1}=\left\{C^{\prime\prime}_{k1,1},\ C^{\prime \prime}_{k1,2},\ \cdots,C^{\prime\prime}_{k1,\frac{m}{2}}\right\}=\left\{Y^{\prime\prime}_{k,1}, \ Y^{\prime\prime}_{k3},\ \cdots,Y^{\prime\prime}_{k,m-1}\right\} \tag{13}\]  
and  
\[\mathbf{C}^{\prime\prime}_{k2}=\left\{C^{\prime\prime}_{k2,1},\ C^{\prime \prime}_{k2,2},\ \cdots,C^{\prime\prime}_{k2,\frac{m}{2}}\right\}=\left\{Y^{\prime\prime}_{k2,2},\ Y^{\prime\prime}_{k4},\ \cdots,Y^{\prime\prime}_{k,m}\right\} \tag{14}\]After the combination of the even and odd indexed components in Equations (13) and (14) and Figure 1c, we obtained \(\mathbf{Y}_{k}^{\prime\prime}\). Finally, using BCS we reconstructed block \(\mathbf{X}_{k}^{\prime\prime}\). After completing the reconstruction of all the blocks, we composed the reconstructed image \(\mathbf{X}^{\prime}\).
## 2 Literature Review
### Feature Matching for Geolocalization  
A well-known algorithm for finding the most similar vectors is called k-nearest neighbors (k-NN) [67]. The distances between the query vector and the data are calculated to determine the nearest data vectors. Different distance metrics lead to different results. The most popular metric is the Euclidean distance [68]. The distance between two vectors, \(x\) and \(y\) with dimensionality \(j\) can be calculated as in Equation (1) for the Euclidean distance or Equation (2) for the Manhattan distance. When using these metrics, it is important to normalize the vectors when all features in the vectors are equally important.  
\[d_{x,y}=\sqrt{\sum_{i=1}^{j}(x_{i}-y_{i})^{2}} \tag{1}\]  
\[d_{x,y}=\sum_{i=1}^{j}\lvert x_{i}-y_{i}\rvert \tag{2}\]  
A set number of neighbors or a distance threshold determines whether a certain number of matches are found or not. k-NN works with both global and local descriptors. To use the information of local descriptors, a geometric similarity can be determined. Some models, such as DELF [63], extract local features, which means that vectors are extracted per feature in combination with the location of the feature. First, k-NN is used to determine the most similar keypoints. These keypoints can be used to determine the best geometric transformation between the two sets. The transformation is performed by fitting a transformation matrix using RANSAC (random sample consensus) [69]. The number of keypoints that follow the found transformation, called inliers, determines the similarity between images.
# Analysis of Slope Stability in Ion-Adsorption Rare Earth Mine Under In Situ Leaching Condition
## 3 Results and Discussion
### Optimization Measures  
Based on the slope instability mechanism revealed above and previous mining experience of in situ leaching mining for rare earth mines, it is evident that the leaching solution softens each soil layer of the slope, reducing the soil's shear strength, specifically decreasing cohesive force, and increasing the likelihood of slope failure. Furthermore, the increased moisture content in the slope soil exacerbates the self-weight stress, enhancing the downward sliding force of the slope soil. Therefore, a well-optimal designed leaching solution injection plan can effectively prevent landslides of the slope. The studied Rare Earth Mine slope is divided into four stratified layers: topsoil, fully weathered granite, semi-weathered granite, and bedrock. The interfaces between these layers, especially between the fully and semi-weathered granite layers, represent weak points. Damage is mainly concentrated in the topsoil and fully weathered granite layer, where the depth is relatively shallow, making surface and mid-layer landslides more likely. The potential for slope failure increases during the mid to late leaching stages. In summary, the following measures in Table 3 can be taken to optimize the stability of ionic rare earth mine slopes. In addition, the advantages and disadvantages of various slope reinforcement measures are also shown in Table 3. The intensity of the leaching solution injection is a crucial factor affecting slope stability and should be carefully regulated. Strengthen the slope displacement monitoring system, particularly during the mid to late injection stages. Focus on monitoring displacement in the topsoil and fully weathered granite layer, especially at the interface between fully and semi-weathered granite layers. Enhance the mine's drainage and flood control systems. Based on the terrain and geomorphological features, strategically place leachate collecting ditches and tunnels to ensure the timely removal of leaching solutions and rainwater from the mine. Finally, additional reinforcement methods can be employed if the aforementioned measures do not meet safety standards or if the safety reserves of steep slopes are insufficient. These additional methods include the construction of retaining walls, anti-slide piles, slope surface reinforcement, slope cutting and unloading, and ecological slope protection. Figure 10: Distribution of the plastic zone at different times during leaching (The yellow part in the figure represents the plastic zone, and the line represents the isoline of the maximum shear strain). Figure 11: Distribution of the plastic zone at different times during the push-water operation (The yellow part in the figure represents the plastic zone, and the line represents the isoline of the maximum shear strain).
## 4 Design and Implementation of the Optimization Framework
### iMASKO Options via Command Lines and GUI
* _Seeds Generator_: due to the quad-core CPUs of the server, each simulation actually consists of four parallel and independent (independent seeds) runs for average results, so the use of _-s1 <seeds1>_, _-s2 <seeds2>_, _-s3 <seeds3>_ and _-s4 <seeds5>_ (integers for _seeds1_, _seeds2_, _seeds3_, and _seeds4_) can specify different seeds for the four independent runs, and all the generated seeds can be stored and used in the optimization process to guarantee reproducibility of results
* _Result Saving_: the use of _-save <filename>_ can save the final result file with the user defined file name and with any suffix. As an example: >_/imasko -save GAresults.log_. As the requirements of different tasks are application-specific and vary all the time, the complete configuration of iMASK options via command lines not only satisfies the varying requirements, but also provides a flexible and effective way to set related parameters in the optimization/evaluation process for the given scenario. Since iMASKO command line options are not intuitive especially for non-experience designers and users, a MATLAB based GUI has been developed to link simulation and optimization, which facilitates configurations on both sides and makes the evaluation process visualizable. The GUI is shown in Figure 5, where all parameters mentioned in the previous section can be set in the corresponding edit box. Default values are used if the parameters are not set. All these parameters are passed to the simulation and GA evaluation engine by pressing the \"Apply\" button, and the evaluation will start when a specific platform is chosen on the right side of the GUI.
###### Abstract  
In urban areas, deep excavation-induced ground deformations may damage adjacent existing structures and are conventionally evaluated by levelling at installed settlement points. However, a small number of measurements cannot represent the total changes in ground deformations adjacent to excavation sites. Furthermore, significant local subisidences may occur in places where settlement points have not been installed and only noticed after an accident. For deep excavation sites located in urban areas, paved pedestrian sidewalks are often located adjacent to sites, and construction activities can cause these paving blocks to become displaced. This study introduces a method to detect paving block displacements adjacent to deep excavation sites using terrestrial photogrammetry. A digital camera creating point cloud data (PCD) and an acquisition method satisfying the frontal and side overlap requirements were demonstrated. To investigate the displacement detections and measurement capabilities by PCD analysis, an experimental program was conducted, including a PCD comparison containing the uplift, settlement, and horizontal paving block displacement and reference data. The cloud-to-cloud distance computation algorithm was adopted for PCD comparisons. Paving block displacements were detected for displacements of 5, 7.5, and 10 mm in the uplift, settlement, and horizontal directions; however, the horizontal displacements were less clear. PCD analysis enabled satisfactory measurements between 0.024 and 0.881 mm for the vertical-displacement cases, but significant errors were observed for the horizontal-displacement cases owing to the cloud-comparison algorithm. The measurement blind spot of limited settlement points was overcome by the proposed method that detected and measured paving block displacements adjacent to excavation sites.  
terrestrial photogrammetry; deep excavation; paving block; displacement detection Article  
[PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Detecting and Evaluating Displacements of Paving Blocks Adjacent to Deep Excavation Sites Using Terrestrial Photogrammetry
## 6 UOWC Security Concerns and Challenges and Future Directions
### Challenges and Future Directions
\begin{table}
\begin{tabular}{c c c c} \hline  & **Challenges** & **Future Directions** & **Ref.** \\ \hline  & & & Explore advanced \\  & & modulation and coding & \\  & **High Attenuation:** Optical signals & & schemes, such as \\  & & experience rapid attenuation in water, & \\  & & limiting communication range. & \\
2. Range and Power Efficiency & & **Power Constraints:** UOWC devices often & \\  & & operate on battery power, making energy & \\  & & efficiency crucial. & \(\bullet\) & Investigate low-power \\  & & & underwater optical \\  & & & transmitters and \\  & & & photodetectors. \\ \hline  & & & Utilise wavelength-division \\  & & **Trade-off Between Range and Bandwidth:** & \\  & & Increasing data rates often results in reduced & \\
3. Data Rate and Bandwidth & & communication range. & \\  & & **Turbidity Effects:** High turbidity & \\  & significantly reduces usable bandwidth. & & \\  & & & \\  & & & \\ \hline  & & **Precision Alignment:** Maintaining precise & \(\bullet\) & Implement auto-tracking \\  & & alignment between the transmitter and & systems for dynamic beam \\  & & receiver in dynamic underwater conditions is & alignment. \\
4. Beam Alignment and Stability & & challenging. & \(\bullet\) & Develop omnidirectional or \\  & & **Misalignment Due to Mobility:** The & \\  & & movement of underwater vehicles or the & \\  & & drifting of sensors can disrupt the & \\  & & communication link. & \\ \hline  & & **Eavesdropping** Highly directional optical & \(\bullet\) & Leverage physical layer \\  & & beams reduce eavesdropping risks but do & security techniques to \\  & & not eliminate them entirely. & protect data without relying \\
5. Security and Privacy & & **Jamming and Spoofing** UOWC systems are & \(\bullet\) & solely on encryption. \\  & & vulnerable to jamming and malicious signal injection. & \(\bullet\) & Investigate quantum \\  & & injection. & \\  & & & \\ \hline  & & & \(\bullet\) & Combine UOWC with \\  & & & **Limited Range:** Single-hop links are & acoustic or RF \\
6. Multi-hop and Networked & & constrained by water’s optical properties. & \\  & & **Synchronisation and Routing:** Designing & \\  & & efficient routing protocols for multi-hop & \\  & & UOWC networks is nontrivial. & \(\bullet\) & Develop protocols \\  & & & specifically for underwater \\  & & & \\ \hline  & & **Dynamic Mobility:** Autonomous and & \(\bullet\) & Create adaptive UOWC \\  & & remotely operated underwater vehicles & systems capable of \\  & & require robust, dynamic communication & maintaining links with \\
7. Integration with Autonomous & & links. & \\
**Systems** & & **Latency Sensitivity:** Many autonomous & \(\bullet\) & Explore artificial \\  & & underwater vehicle operations are & intelligence-based \\  & & time-sensitive and require low-latency & approaches to optimise \\  & & communication & communication \\
7. Integration with Autonomous & & & performance in real-time. \\ \hline \end{tabular}
\end{table} 
Table 11: _Cont._
# Effect of Aging and Moisture Damage on Fatigue Cracking Properties in Asphalt Mixtures
###### Abstract  
Recently, damage to asphalt pavements in South Korea has increased because of direct and indirect factors caused by abnormal climatic changes, such as torrential rains, prolonged heatwaves in summer, and heavy snowfall in winter. Additionally, the use of medium and heavy vehicles is also a contributing factor. Therefore, an experimental procedure to study the moisture damage and fracture properties of asphalt pavements considering the recent changes in precipitation properties, deterioration of road pavement, and traffic conditions in South Korea was developed in this study. Additionally, changes in material properties according to the indoor aging process and fatigue crack resistance were evaluated for hot mix and warm mix asphalt mixtures of the same grade and different initial production and construction temperatures. To evaluate the effect of aging and moisture damage on fatigue cracking, the experimental coefficient values of the predictive model were calculated for each condition. It was observed that the material properties of hot mix asphalt mixtures changed with an increase in aging and moisture damage. The service life of the pavement was reduced by approximately 40-80% owing to moisture damage, whereas aging had a greater effect on fatigue life as the service life increased.  
asphalt pavement; moisture damage; fracture properties; aging; hot mix asphalt (HMA); warm mix asphalt (WMA) +
Footnote †: journal: Applied Sciences
## 3 Numerical Simulation
### Axle Information Identification  
The vehicle's load is applied on the deck along the longitudinal direction to simulate a vehicle passing on bridge. Here, a three-axle vehicle with a gross weight of 35 kN is taken to pass the bridge, and the strain response at the position of FAD sensors shown in Figure 1 is calculated by finite element analyses (FEA). The weight of the front, middle, and rear axles is 5 kN, 13 kN, and 17 kN, respectively. The wheel track of the vehicle is180 cm. The front and rear wheelbase is 300 cm and 140 cm, respectively. To consider the noise effect, a noise of 20% (defined as noise variance/signal variance) is added to the strain response, and the response of part of the sensors is shown in Figure 6.  
Figure 4: The half-span cross-section of an actual orthotropic deck steel box girder.  
Figure 5: The entire finite element model.  
Figure 6: The strain response at the position of FAD sensors.  
As observed the figure, three pronounced peaks occur when the axle passes through the FAD sensor, which correspond to three axles. The identified wheelbases by the proposed algorithm are 3.0 m and 1.2 m, respectively, which validated the effectiveness of the proposed method.
## 4 The Proposed Model
### Graph Attention Embedding Representation
#### 4.3.1 Interaction Graph and Input Embeddings  
Given the LWS-optimized embeddings \(e_{u}^{(L)}\) and \(e_{i}^{(L)}\) for user \(u\) and item \(I\), we apply a GAT layer to compute attention coefficients \(\alpha_{ui}\) reflecting the relevance of each neighbor. This attention mechanism dynamically adjusts the importance of recent interactions, allowing the model to capture fine-grained, temporally adaptive user preferences. By focusing on more relevant and recent interactions, GAT refines the model's understanding of user behavior and preferences, complementing the global smoothing provided by LWS. The combination of VRKG's semantic abstraction and GAT's attention mechanism allows GLARA to address both global semantic consistency and local behavioral dynamics, offering an innovative approach that adapts to evolving user preferences over time.:  
\[\alpha_{ui}=\frac{\exp(\text{LeakyReLU}(\mathbf{a}^{T}[\mathbf{e}_{u}^{(L)}|| \mathbf{e}_{i}^{(L)}]))}{\sum_{j\in\mathcal{N}(u)}\exp(\text{LeakyReLU}( \mathbf{a}^{T}[\mathbf{e}_{u}^{(L)}||\mathbf{e}_{j}^{(L)}]))} \tag{22}\]  
where \(\mid\mid\) denotes vector concatenation, \(a\) is a learnable weight vector, and \(\mathcal{N}(u)\) is the set of items user \(u\) has interacted with.  
This mechanism allows the model to assign higher weights to recently or frequently interacted items, thus capturing fine-grained user interest shifts.