esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 MSE Analysis of the Proposed Algorithm
### Discussion on \(M\)  
The spectral frequency resolution directly affects the algorithm's estimation accuracy. When the sampling frequency is determined, choosing a larger \(N\) can reduce the spectral frequency resolution and increase the spectral magnitude. However, this also increases the estimation time and computational complexity of the algorithm. Therefore, we first pad zeros after the signal samples before computing the corresponding DFTs. This approach can decrease the frequency resolution without increasing the observation time. The DFT and DTFT waveforms of samples with and without zero-padding are shown in Figure 3. The figure indicates that the DFT and DTFT waveforms of the signal are extended after the signal samples are padded with \(N\) zeros. The two green circles are closer to the peak than the two blue circles. The \(2N\)-point DFT around the peak, which is closer to the peak, can obtain a smaller RMSE/CRLB in Figure 4. In addition, as \(M/N\) increases, the improvement in the MSE decreases. Considering the computational complexity brought by \(M\), we choose \(M=2N\) as a compromise between computational complexity and the estimation accuracy.  
Figure 3: DFTs and DTFTs of complex exponential signals at \(M=N\) and \(M=2N\).  
Figure 2: The RMSE/CRLB of the proposed algorithm versus \(p\) for different values of \(\delta\) when the \(SNR=0\) dB and \(N=512\).
# A Novel Cryptography-Based Architecture for Secure Data Asset Sharing and Circulation Systems
## 6 System Implementation  
The data asset security sharing and circulation system designed in this paper uses Java as the main program development tool, MySQL as the main database tool, and Amap as the system visualization base map. By designing a security-outsourcing storage module, a security multi-party-sharing module, a security trusted operation module and a security cross-domain return module, the security protection and visual analysis of data assets in the process of transaction circulation, outsourcing storage and cross-border transmission are realized.  
Figure 8: Deployment scheme for secure data asset sharing and circulation system.  
Figure 7: Service application scheme for secure data asset sharing and circulation system.
# A Dynamic Distributed Deterministic Load-Balancer for Decentralized Hierarchical Infrastructures
## 7 Experimental Study
### Performance Evaluation for ART\({}^{+}\)
#### 7.2.1 Cost of Search Operations  
To measure the network performance for the search operations (single and range queries), we conducted experiments for different values of \(b\), 2, 4 and 16. For each \(N\), we executed 1000 single queries and 1000 range queries. The search cost is depicted in Figure 9. Both normal (beta, uniform) and worst cases (powlow, zipfian, weibull) are depicted in the same graph.  
Experiments confirm that the query performance of ART and ART\({}^{+}\) is \(O(\log_{b}^{2}\log N)\) and the slight performance divergences are because BATON\({}^{*}\), as the inner structure of ART's cluster node, performs better than \(D^{3}\)-Tree in search operations.  
In case of massive failures, the search algorithm finds alternative paths to overcome the unreachable nodes. Thus, an increase in node failures increases search costs. To evaluate the system in case of massive failures, we initialized the system with 10,000 nodes and let them randomly fail without recovering. At each step, we check if the network is partitioned or not. Since the backbone of ART and ART\({}^{+}\) remains unaffected WHP, the search cost is restricted inside a cluster node (BATON\({}^{*}\) or \(D^{3}\)-Tree respectively), meaning that \(b\) parameter does not affect the overall expected cost. Figure 9 illustrates the effect of massive failures.  
We observe that both structures are fault-tolerant since the failure percentage has to reach the threshold of 60% to partition them. Moreover, even in the worst-case scenario, the ART\({}^{+}\) maintains a lower search cost compared to ART, since \(D^{3}\)-Tree handles node failures more effectively than BATON\({}^{*}\).
# Design and Evaluation of Flooding-Based Location Service in Vehicular Ad Hoc Networks+
## 3 Semiflooding Location Service  
The semiflooding location service (SFLS) was developed to take advantage of both flooding- and rendezvous-based location services for VANETs while avoiding their drawbacks. The advantage of flooding-based solutions is that each node in the network is aware of the position of all the other nodes, while the advantage of rendezvous-based solutions is the low number of messages exchanged to keep the positions of the nodes up to date. The drawback of flooding solutions is the high number of messages exchanged between nodes (location messages are forwarded without any change to the boundary of the network), while the drawback of rendezvous solutions is their difficulty to handle node disappearance and communication problems. The SFS allows all nodes in the network to have accurate-enough location information about all other nodes in the network, while not overwhelming the network with location packets. Moreover, SFS is resilient to node disappearances and network problems.
# Assessment of BeiDou-3 and Multi-GNSS Precise Point Positioning Performance
## 2 Multi-GNSS Ionospheric-Free PPP Model
\begin{array}{l}\mathrm{P}_{\mathrm{IF}}^{\mathrm{SYS_{1}}}=\mathrm{R}_{ \mathrm{r}}^{\mathrm{s}}+\left(\mathrm{c}\cdot\delta\mathrm{t}_{\mathrm{r}}+ \mathrm{l}_{\mathrm{bp}}^{\mathrm{SYS_{1}}}\right)-\mathrm{c}\cdot\delta \mathrm{t}_{\mathrm{IF}}^{\mathrm{s}}+\mathrm{T}_{\mathrm{r}}^{\mathrm{s}}+ \mathrm{\epsilon}_{\mathrm{P}}\\ \vdots\\ \mathrm{P}_{\mathrm{IF}}^{\mathrm{SYS_{1}}}=\mathrm{R}_{\mathrm{r}}^{\mathrm{s}}+ \left(\mathrm{c}\cdot\delta\mathrm{t}_{\mathrm{r}}+\mathrm{l}_{\mathrm{bp}}^{ \mathrm{SYS_{1}}}\right)-\mathrm{c}\cdot\delta\mathrm{t}_{\mathrm{IF}}^{\mathrm{ s}}+\mathrm{ISB}_{\mathrm{r}}^{\mathrm{SYS_{1}}-\mathrm{SYS_{1}}}+\mathrm{T}_{\mathrm{r}}^{ \mathrm{s}}+\mathrm{\epsilon}_{\mathrm{P}}\\ \Phi_{\mathrm{IF}}^{\mathrm{SYS_{1}}}=\mathrm{R}_{\mathrm{r}}^{\mathrm{s}}+ \left(\mathrm{c}\cdot\delta\mathrm{t}_{\mathrm{r}}+\mathrm{l}_{\mathrm{bp}}^{ \mathrm{SYS_{1}}}\right)-\mathrm{c}\cdot\delta\mathrm{t}_{\mathrm{IF}}^{\mathrm{ s}}+\mathrm{T}_{\mathrm{r}}^{\mathrm{s}}+\mathrm{\lambda}_{\mathrm{IF}}^{\mathrm{SYS_{1}}} \cdot\mathrm{N}_{\mathrm{IF}}^{\mathrm{SYS_{1}}}+\mathrm{\epsilon}_{\mathrm{ \Phi}}\\ \vdots\\ \Phi_{\mathrm{IF}}^{\mathrm{SYS_{1}}}=\mathrm{R}_{\mathrm{r}}^{\mathrm{s}}+ \left(\mathrm{c}\cdot\delta\mathrm{t}_{\mathrm{r}}+\mathrm{l}_{\mathrm{bp}}^{ \mathrm{SYS_{1}}}\right)-\mathrm{c}\cdot\delta\mathrm{t}_{\mathrm{IF}}^{\mathrm{ s}}+\mathrm{ISB}_{\mathrm{r}}^{\mathrm{SYS_{1}}-\mathrm{SYS_{1}}}+\mathrm{T}_{\mathrm{r}}^{ \mathrm{s}}+\mathrm{\lambda}_{\mathrm{IF}}^{\mathrm{SYS_{1}}}\cdot\mathrm{N}_{ \mathrm{IF}}^{\mathrm{SYS_{1}}}+\mathrm{\epsilon}_{\mathrm{\Phi}}\end{array} \right. \tag{6}\]  
with  
\[\mathrm{ISB}_{\mathrm{r}}^{\mathrm{SYS_{1}}-\mathrm{SYS_{1}}}=\mathrm{l}_{ \mathrm{bp}}^{\mathrm{SYS_{1}}}-\mathrm{l}_{\mathrm{bp}}^{\mathrm{SYS_{1}}} \tag{7}\]  
where \(\mathrm{ISB}_{\mathrm{r}}^{\mathrm{SYS_{1}}-\mathrm{SYS_{1}}}\) is the inter-system bias (ISB) between the navigation system \(\mathrm{SYS_{i}}\) and \(\mathrm{SYS_{1}}\).
A 3D surface model was then interpolated, and the flood depth was derived by calculating the difference between the 3D surface and the DEM. A similar approach was also used in [24]. Despite these recent advances, most recent works remain focused on case studies rather than operational monitoring. While some operational services have emerged, they are often characterized by a rather narrow product portfolio (e.g., surface water extent only), providing only limited information about an ongoing event. Furthermore, most existing systems lack a scalable computing architecture, limiting their ability to continue to provide low-latency information as the size of an event increases. Lastly, many current efforts do not emphasize the co-development and transitioning of developed technology to an in-region partner when defining product portfolios, processing solutions, and delivery mechanisms of their services. This may limit the impact of their work on decision-making practices in a specific target region. In this paper, we introduce HydroSAR, a cloud-based SAR data analysis service which was co-developed with end user partners for the monitoring of hydrometeorological events in the Hindu Kush Himalaya. Funded through a NASA SERVIR Applied Science Team grant, HydroSAR includes a series of SAR-based value-added products for the monitoring of surface hydrology (image time series, flood extent, water depth, and affected agriculture) in a region. Product algorithms were selected, adapted, and operationalized based on a review of the recent literature and in collaboration with end user partners in the flood-prone Hindu Kush Himalaya. The goal of HydroSAR was to develop a more complete set of hazard data layers than most comparable systems, enabling simultaneous assessment of the surface water extent, water depth, and flood impacts on agriculture. After illustrating the motivation of our work with a summary of the flood mapping needs in the HKH (Section 2), we discuss our co-development strategy, which is implemented to ensure that HydroSAR meets the needs of the end users in the region (Section 3). The selected HydroSAR product portfolio and product algorithms are summarized in Section 4. After presenting our visualization and data sharing concepts in Section 5, Section 6 introduces the cloud-based implementation of HydroSAR, providing the capability to scale automatically with the event size and enabling near real-time access to the production of actionable information even for events of extremely large spatial scales. Section 7 provides a performance assessment of HydroSAR product algorithms by comparing SAR-derived products to reference information provided by in-region partners or derived from different sensors. Finally, we demonstrate the impact of our service by applying HydroSAR products to the 2023 monsoon season (Section 8). A summary and outlook conclude this paper.
## 4 Algorithm Design
### Fast Rerouting Mechanism
(2017) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]: A hybrid global-local load balancing routing scheme for the Internet of Things through satellite networks. _Int. J. Distrib. Sens. Netw._**2017**, _13_, 1550147717692586. [CrossRef]
* [PERSON] et al. (2015) [PERSON]; [PERSON]; [PERSON]; [PERSON] The generation and update algorithm of routing table in satellite network. In Proceedings of the 2015 IEEE International Conference on Communication Problem-Solving (ICCP), Guilin, China, 16-18 October 2015; pp. 619-622. * [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON] Routing algorithm design of satellite network architecture based on SDN and ICN. _Int. J. Satell. Commun. Netw._**2020**, _38_, 1-15. [CrossRef]
* [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] An Energy Sensitive and Congestion Balance Routing Scheme for Non-Terrestrial-Satellite-Network (NTSN). _Remote Sens._**2023**, _15_, 585. [CrossRef]
* [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Software defined network-based multipath state-aware routing with traffic prediction in satellite network. _Trans. Emerg. Telecommun. Technol._**2023**, _34_, e4819. [CrossRef]
* Kempton (2020) Kempton, B.S. _A Simulation Tool to Study Routing in Large Broadband Satellite Networks_; Christopher Newport University: Newport News, VA, USA, 2020. Disclaimer/Publisher's Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
# The Standardization Method and Application of the BIM Model for Interchanges
###### Abstract  
Research on the standardization of interchange BIM components is required to encourage the use of BIM technology in interchange and quicken the development and improvement of relevant standards and specifications. The depth level of the model, classification, and coding of interchange BIM components, and the design and administration of the component library are the main topics of this work. First, the concept and main research content on the standardization of interchange BIM components were proposed. Second, the common forms of interchange were summarized. The basic components of interchanges include through lanes, ramps, connections, overpass bridges, and traffic safety facilities, and can be divided into 14 second-level components, such as the main line, the minor cross highway, the speed-change lane, etc. On this basis, a method for interchange BIM components classification based on faceted classification and a coding method based on a combination code was proposed. The model depth grading of the BIM component of the interchange was studied, and grading was carried out considering three aspects: the level of detail (LOD), geometric accuracy, and information level. Finally, the process of creating interchange BIM component libraries was proposed. The results showed that the interchange BIM components can adopt a three-level coding method, namely \"the overall spatial code-classification code-instance code\", and the LOD, geometric accuracy, and information level of the interchange BIM components can be divided into four levels. Additionally, interchange BIM component libraries can be created using five steps: an overall plan of component resources, standard determination, component creation, component audit, component storage, and application.  
Keywords:road engineering; standardization; faceted classification; BIM; interchanges; components +
Footnote †: journal:
## 4 Handover and Mobility Process in 5G HetNet
### Advantages of Mobility Management in 5G HetNet  
Mobility is the maximum speed of a UE in km/h at which a specified QoS can be achieved. Mobility performance is critical for any WCS, especially for URLLC services that require low latency and high reliability [94]. Efficient mobility management is essential to achieve the demands of 5G HetNet and includes enhanced capacity, reduced network congestion, lower latency, and uninterrupted connectivity. Previous studies have shown that the demand for mobile services is increasing with the advent of new mobility paradigms, such as self-driving vehicles, drones, and mobile small cells [95].  
There are two types of mobility in WCS: cell-level and beam-level. In cell-level mobility, the UE switches from one cell to another and establishes a new connection with the new cell. In beam-level mobility, UEs move from one beam to another within the same cell coverage, establishing a new data path to the new beam. The 5G HetNet supports both cell-level and beam-level mobility to ensure seamless connectivity and high reliability, while LTE-A (4G) only supports cell-level mobility.  
Reducing the HOIT and the HOF rate can improve mobility performance in 5G-NR. In 5G-NR, the deployment of UD-HetNet reduces the size of the HO region, which consequently increases the HOF rate due to FHO. The conditional handover (CHO) technique can improve mobility performance, where the UE receives the HO command before the RLF from the S-gNB but does not execute the HO immediately. The DC-based HO, where UE  
Figure 7: The 5G HetNet with different mobility users and their related issues.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Speed Scenario** & **TTT** & **HOM** & **HOPP** & **HOF** \\ \hline High & Minimum & Minimum & Low & Low \\ High & Maximum & Minimum & Low & High \\ High & Maximum & Maximum & Low & High \\ High & Minimum & Maximum & Low & High \\ Low & Minimum & Minimum & High & High \\ Low & Maximum & Minimum & Low & High \\ Low & Minimum & Maximum & High & Low \\ \hline \hline \end{tabular}
\end{table}
Table 5: HO KPI performance for various UE speeds and HCP settings.  
connects with both S-gNB and T-gNB, also reduces HOFs, but it involves high complexity and UE cost [96].  
HOIT is the duration during which a UE neither transmits nor receives any data. In 4G/LTE-A, the HOIT is about 10 ms, which results in a significant transmission delay. However, 5G-NR technology has reduced this delay to 1 ms by utilizing soft handover and RACH-less handover techniques. If the HOIT is large, an HO mechanism that filters out the effect of shadowing and does not attempt a handover is preferable. However, if the HOIT is small, an HO to the best cell, exploiting the shadowing effect, can be beneficial for both cell association and system throughput.
# Prediction of Soil-Water Retention Curves of Road Base Aggregate with Various Clay Fine Contents
## 1 Introduction  
Interlayers are created by the interpenetration of a base layer and subgrade layer, of which fine content (\(FC\)) varies with depth [1; 2; 3; 4]. Variations in \(FC\) results in changes in water retention properties [5; 6] and soil properties [7; 8; 9; 10; 11; 12]. In addition, the soil water retention curve (SWRC) has been successfully used in the prediction of the mechanical properties of soil in the unsaturated state, i.e., shear behaviour [13; 14; 15] and resilient behaviour [16; 17; 18]. Therefore, it is necessary to study soil water retention properties in depth.  
With an increase in \(FC\), the microstructure and water retention properties change [19; 20; 21]. Generally, \(FC\) was found to increase along with the depth, and the interlayer is made up two parts: the upper part, characterized by a coarse-grain skeleton, and the lower part, characterized by a fine matrix [1; 22]. In the case of soil characterized by a fine matrix, [PERSON] et al. [6] pointed out that the SWRC of the mixture is only dependent on the dry density of fines; the coarse-grain content has a limited effect on the water retention properties of the mixture. Therefore, to obtain the SWRC of the mixture with different \(F\)s, the prediction curve built for fines grain soil at different dry densities can be adopted, as established by previous researchers [23; 24; 25; 26; 27]. In the case of coarse-grain, a limited SWRC prediction equation has been proposed, accounting for the effects of fine content. Previously established equations are focused on capturing the bimodal feature of SWRC, which is caused by the discontinuity of the pore size distribution (PSD) between inter-aggregate pores (formed by coarse grain) and intra-aggregate pores (formed by fines) [28; 29; 30; 31]. Since the effects of matrix suction on the mechanical behaviour of soils can be intensified by fines [2; 4; 11], it appears important to propose a prediction equation to address the fine content, which isessential for the prediction of the dynamic behaviour as well as shear behaviour of the base layer soils.  
In the study, the water retention properties of soils with different \(F\)Cs were tested by a large-scale SWRC test apparatus which was designed by the authors. Then, the evolution of the pore size distribution (PSD) with the increase in \(F\)C was emphatically analysed. After this, the SWRC prediction equation, which is able to consider the effect of \(F\)C, was proposed and validated by comparing the measured and predicted SWRCs.
## 4 Case Study: Racer/Cruiser Yacht Configurator
### Metrics  
The configuration experience for each pair enabled the collection of data through the following metrics:  
* User **age**, **gender**, and previous **VR experience** (yes/no);
* **Success rate**: Measures the effectiveness of the VR configuration process in terms of the percentage of successful paired sessions;
* **Early exit**: Number of pairs who quit the session before the planned test time;
* **SUS**[20]: Standard questionnaire for evaluating system usability;
* **Open-ended feedback**: Evaluation of the implemented features, the level of immersion, the graphical rendering, and the emotional impact, including any improvement suggestions.  
Figure 9: Double setup for the paired test (**left** image). Collaborator visualization of the shared scene, specifically the field of view of the female participant (**right** image).  
The analysis of demographics such as age, VR experience, and gender may provide valuable insights into factors influencing usability. Below is a concise explanation of their potential impact:  
* _Age_: Older users, who may resemble the actual clientele (e.g., boat owners), could prioritize configuration feasibility from a purchasing perspective, while younger, tech-savvy users might offer insights to improve features and enhance gamification.
* _VR experience_: Experienced users might focus on advanced features and provide technical feedback on immersion and interactivity, while inexperienced users could highlight accessibility gaps and the learning curve.
* _Gender_: Gender might influence interaction preferences, such as task approach and priorities, ensuring that the system design is inclusive and appealing to all user groups.
# Simulation of Real Defect Geometry and Its Detection Using Passive Magnetic Inspection (PMI) Method
## 2 Theoretical Background and Methodology
\[divH_{d}=-\mathrm{div}(\frac{J}{\mu_{0}}) \tag{1}\]  
\[E_{d}=\frac{1}{2}\mu_{0}\int\limits_{all\ space}H_{d}^{2}dV=-\frac{1}{2}\mu_{0 }\int\limits_{sample}H_{d}\cdot JdV\  
media with their own magnetization values and a specific vector perpendicular to the separation plane of those materials (_n_):  
\[\lambda_{V}=-\text{div}M \tag{3}\]  
\[\sigma_{5}=M\cdot n \tag{4}\]  
\[\sigma_{5}=(M_{1}-M_{2})\cdot n \tag{5}\]  
\[M(r)=J(r)/J_{s} \tag{6}\]  
According to Equation (7), the stray field energy at a position (_r_) can be also calculated through the negative gradient of the potential of the stray field energy at a place (\(\Phi_{d}(r)\)) [55], where \(\Phi_{d}(r)\)--Equation (8)--is a function of magnetization saturation (\(J_{s}\)), volume charge density (\(\lambda_{V}\)), surface charge density (\(\sigma_{5}\)), and the derivative of the position vector (\(r^{\prime}\)). Next, the magnetic field energy is obtained from Equation (9) through the integration functions of surface charge density and volume charge density over the volume and surface, respectively. \[H_{d}(r)=-grad\Phi_{d}(r) \tag{7}\]  
\[\Phi_{d}(r)=\frac{J_{s}}{4\pi\mu_{0}}\bigg{[}\int\frac{\lambda_{V}(r^{\prime}) }{|r-r^{\prime}|}dV^{\prime}+\int\frac{\sigma_{5}(r^{\prime})}{|r-r^{\prime}| }dS^{\prime}\bigg{]} \tag{8}\]  
\[E_{d}=J_{s}\bigg{[}\int\lambda_{V}(r)\Phi_{d}(r)dV+\int\sigma_{5}(r)\Phi_{d}( r)dS\bigg{]} \tag{9}\]  
For conducting this research article, we scanned 373.87 mm of the surface of a ferromagnetic rebar (low-carbon steel), with a diameter of 16 mm, and two artificial defects (Table 1) [37], using a high-resolution 3D laser scanner (Figure 1a) [56]. The shape of the rebar was created with cloud points (Figure 1b) that were modified and converted to a mesh by Mesh Lab V1.3.2 ([[http://meshlab.sourceforge.net/](http://meshlab.sourceforge.net/)]([http://meshlab.sourceforge.net/](http://meshlab.sourceforge.net/))). Subsequently, the produced mesh was imported to COMSOL\({}^{R}\) software and converted to a discretized surface and solid, respectively (Figure 1c). The solid rebar was simulated via COMSOL\({}^{R}\) software with regard to the magnetic field of the Earth, different components of magnetic flux density were investigated at different spacing, related simulation results were compared with our previous experimental results, and statistical approaches were introduced. Figure 1: Process of converting the rebar geometry to a solid model: (**a**) scanning the rebar with three-dimensional (3D) laser scanner; (**b**) cloud points of rebar, presented in MeshLab; (**c**) solid illustration of rebar.