esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Analysis of the Global Scientific Collaboration Network: Formation of a Dual-Core Structure
#### 3.2.2 Institutional Cooperation
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rank** & **Institution** & **Countries/Regions** & **Number of Publications** \\ \hline
1 & Chinese Academy of Sciences & China & 310 \\
2 & Ministry of Agriculture Rural Affairs & China & 148 \\
3 & University of Chinese Academy of Sciences & China & 140 \\
4 & United States Department of Agriculture & USA & 120 \\
5 & Wuhan University & China & 115 \\
6 & University of California System & USA & 96 \\
7 & Chinese Academy of Agricultural Sciences & China & 83 \\
8 & China Agricultural University, & China & 76 \\ Beijing Academy of Agriculture Forestry Sciences, & China, USA, Germany & 73, 73 \\
9 & Consejo Superior De Investigaciones Cientificas & China, USA, Germany & 73, 73, 73 \\ Helmholtz Association, Helmholtz Association & Italy & 69 \\
10 & Consiglio Nazionale Delle Ricerche & Italy & 67, 67 \\ Institute of Geographic Sciences and Natural & China, USA & 62 \\
11 & Resources Research, State University System of Florida & France & 62 \\
12 & Centre National De La Recherche Scientifique & China & 57, 57 \\
13 & Beijing Normal University, Northwest A\&F & USA, China & 54, 54 \\
14 & Texas A\&M University System, University of Florida & China & 53 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Top 15 research institutions by number of publications. In the United States, the United States Geological Survey (USGS) and the United States Department of Agriculture (USDA) exhibit strong collaborative ties, aligning with their research priorities in geological exploration and agricultural monitoring. These agencies have conducted extensive collaborative research on the application of UAV-RS technologies in geospatial surveys, land resource assessment, and crop monitoring. Additionally, the involvement of NASA and the U.S. Department of the Interior highlights the importance of UAV-RS in environmental protection, disaster prevention, and space exploration. These institutions jointly develop and apply UAV-RS technologies to enhance environmental monitoring and Earth observation. Compared with China, U.S. institutions show a more balanced spatial distribution and maintain closer inter-institutional linkages, fostering frequent collaborations across organizations. European research institutions such as the Centre National de la Recherche Scientifique (CNRS) in France and Wageningen University and Research in the Netherlands also hold prominent positions in the global collaboration network, particularly in areas such as precision agriculture, soil property analysis, and geoscience. It is also noteworthy that the collaboration network includes institutions focused on niche technical domains such as structure from motion (SfM), soil surface characteristics, and precision nitrogen management. These trends suggest that UAV-RS is increasingly intersecting with multiple disciplinary fields, resulting in a more expansive and integrated research collaboration ecosystem.
# Publishing Anonymized Set-Valued Data via Disassociation towards Analysis
## 5 Experiments
### Neighbor Datasets and Knowledge Discovery
#### 5.2.2 Mining Association Rules  
The discovery of association rules draws another view on the neighbor datasets. In their default definition, association rules represent a correlation between itemsets. An association rule is defined as an implication of the form \(X\implies Y\) where \(X\) and \(Y\) are two disjoint itemsets, \(X\cap Y=\emptyset\). They illustrate a concept of correlation between the two itemsets, where the presence of itemset \(X\) shows a strong presence of itemset \(Y\). The discovery of association rules is a more complex characterization of data that depends fundamentally on the discovery of frequent itemsets. We searched for the most frequent association rules by their support, which is relative to their count and the size of the dataset. For this experiment, we extracted the 50 most frequent association rules in the original dataset with a support greater than 0.001. Then, we calculated the average support for those association rules in the generated re-associated datasets, \(\mathcal{T}^{\prime}\).  
From Figure 6 we can see that the support of the most frequent association rules is well-preserved through probabilistic wheel re-association. This shows that neighbor datasets are a faithful representation of the original dataset, by preserving reflecting close support. We can notice a loss in 5 Association Rules (support = 0); in fact, this is not a real loss this is reflecting association rules that have a support less than the minimum threshold (0.001) after re-association. However, if the need is to preserve predefined associations, ant-driven clustering, investigated thoroughly in Section 2, can be used.
## 5 Conclusions  
This paper proposes a composite metric routing approach based on combining the distance and energy of the routing path for solving the problem of energy-efficient path planning for UGVs (unmanned ground vehicles) on natural off-road uneven mountainous real-life terrain. We present a greedy implementation of the composite metric approach. Additionally, the Terramechanics of the surface of the terrain between the UGV wheels and the soil is taken into account. The terrain slope is considered in the energy model to account for the UGV capability of passing over, based on the available power stored in the UGV batteries. As benchmarks for comparison, we use a recent energy minimization approach, in addition to an ant colony optimization (ACO) meta-heuristic. The observed results show that, under the composite metric greedy implementation, a better performance can be achieved in terms of creating shorter paths compared to directly minimizing the energy cost, with a negligible increase in the energy consumption. In fact, in some route pairs, the composite metric achieves a respectable 17.7% distance reduction with only a small energy consumption penalty of 3.5% compared to direct energy minimization. Moreover, our results also indicate that the proposed greedy algorithm strongly outperforms the ACO implementation in terms of the quality of the paths obtained and the algorithm running time. In fact, the running time of our proposed algorithm indicates its suitability for large natural terrain graphs with thousands of nodes and tens of thousands of links. Avenues for future research include relaxing the condition that the UGV velocity is constant and incorporating the energy consumption due to acceleration and deceleration in the model and algorithm.  
Conceptualization, M.S., S.A. and C.-T.C.; methodology, M.S. and A.I.S.; software, M.S. and A.I.S.; validation, M.S., A.I.S. and C.-T.C.; formal analysis, M.S.; investigation, M.S. and A.I.S.; resources, M.S. and A.E.-M.; data curation, M.S. and A.I.S.; writing--original draft preparation, M.S. and A.I.S.; writing--review and editing, M.S., A.I.S., S.A., A.E.-M. and C.-T.C.; visualization, A.I.S.; supervision, M.S. and S.A.; project administration, M.S. and A.E.-M.; funding acquisition, M.S. and A.E.-M. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the University of Sharjah, in part by Research Group Grant 150410.  
The authors declare no conflict of interest.  
Figure 8: A 3D terrain model that displays the constructed paths from using the greedy (Algorithm 1) and ACO implementation simulation results for the [C,D] route pair over the real-life land under consideration in this work.
# Enhanced Matching Game for Decoupled Uplink Downlink Context-Aware Handover
## 4 Proposed Model
### Decoupled Uplink-Downlink Handover Algorithm
if The \(UE_{k}\) triggers the handover process then Phase I--agent BS selection \(UE_{k}\) starts creating agent BS to satisfy QoS requirements based on the assignment method as described in Section 4.2 \(UE_{k}\) ranks the agent BS according to \(\succ_{k}\) if\(\mu^{k}_{m,i}\) the most preferred by \(\succ_{k}\)then \(UE_{k}\) sends a proposal to an agent \(BS_{i}\)  Each \(BS_{l}\) for agent \(BS_{i}\) updates \(U_{l}(\mu)\) based on matching \(\mu\) \(\mu^{\prime}\longleftarrow\mu\) if\(\forall BS_{l}\in BS_{i}(k,\mu^{k}_{ml})\succ_{l}(k,\mu)\)then \(N^{req}\longleftarrow N^{req}\cup k\) \(\mu^{\prime}\longleftarrow\mu^{\prime}\cup\mu^{k}_{ml}\) else \(N^{req}\longleftarrow N^{req}\cup k\) \(UE_{k}\) sends request to the next agent BS preference. endif endif Phase II--dismissing-matching if\(|N^{req}|\
eq 0\)then while\(U_{l}(\mu^{\prime})\) not strictly beneficial do  Delete the worst UE from \(N^{req}\) based on \(\succ_{l}\)  Update \(\mu^{\prime}\) based on \(\succ_{l}\) endwhile endif  Depending on the last \(N^{req}\), each UE is associated with agent \(BS_{i}\). ```  
**Algorithm 1** A context-aware handover algorithm.
## 5 System Overview
### Replica Node  
Replica nodes, which are used to increase the system's fault tolerance in the DFS as an indispensable backup method, are also used and are much easier to process on this system. First, these nodes are used to make copies of files that are stored by data nodes. The data nodes asynchronously send the uploaded files to replica nodes. Replica nodes store these files in the underlying operating system file structure. Here, no special file structure or any other file processing strategies are used, but the operating system's filing service is done. This means replica nodes run as known file servers that are only responsible for storing files on the operating system volume-directory structure. The replica count was set at three as the default replica count for DFS, for all nodes that replica count can be raised to eight.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**File Size (KB)** & **KUSDFS (ms.)** & **OS (ms.)** & **NoSQL (ms.)** & **RDBMS (ms.)** & **Hadoop (ms.)** \\ \hline
30 & 0.01 & 0.04 & 0.60 & 0.75 & 0.80 \\
1000 & 0.92 & 1.43 & 2.74 & 3.15 & 4.01 \\
10000 & 4.40 & 4.48 & 8.44 & 9.97 & 11.15 \\
20000 & 4.48 & 11.19 & 18.01 & 18.16 & 20.45 \\
30000 & 11.36 & 14.80 & 27.22 & 27.80 & 30.15 \\
50000 & 22.60 & 24.54 & 43.95 & 44.08 & 47.88 \\ \hline \end{tabular}
\end{table}
Table 2: The response times given by the systems for different file size (ms.)  
Figure 8: The response times given by the systems for different file size (ms.).  
All replica nodes have a service responsible for storing, deleting, updating, forwarding, and searching the files in the system. So, when a file search arises or is needed in one of data nodes or client, SearchFile command is sent to all replica nodes, then, at the same time replica nodes starts to search targeted file in their volume structure as grid computing, at the end of concurrent search process, each node returns the search results indicating that search process is successful or not. In other words, when a file wants to be searched by a node, SearchFile messages are sent to all replicas, replicas work in parallel, and the result is transmitted to the data node requesting the file. Replica nodes not only make file searches but they also process ReplicaWrite, ReplicaRead, and ReplicaDelete commands which denote the operations applied to a file in writing, reading, deleting, and updating on the replica node, respectively.
## 2 Composition of LEO Satellite Network and APINT Model
### APINT Model  
The awareness of target objects in the network mainly include various resources used by nodes to provide network services, such as bandwidth, computing, storage, and queue resources, as well as ISL status information [16]. Each node in the network has awareness capability [17]. Based on programmable technology, active telemetry and passive telemetry can be performed, and the awareness process can be designed, modified, and operated [18].
# Experimental Validation of a GNSS Receiver Antenna Absolute Field Calibration System
## 3 Results and Discussion
### Results of Short-Baseline Validation  
In line with the goal of this research to experimentally validate the LMMT calibration facility and corresponding calibration results by means of a short-baseline test, and according to the described validation methodology (Section 2.3.2), a 6 h long static GNSS survey on four stations (Figure 7), three independent short-baselines, was conducted on 7 June 2023. Each of the four antennas was connected to a Trimble NetR5 infrastructure GNSS receiver whereby only GPS carrier-phase observations were registered with a standard elevation mask of 10\({}^{\circ}\) and an observation interval of 1 s (1 Hz). Table 5 gives a summarized overview of the GNSS (GPS) survey. Figure 12: PCC differences (\(\Delta\)_PCC_) between individual calibrations obtained by Geo++ GmbH and LMMT for the TRM57971.00 NONE antenna (S/N: 30739001, I/L: Z1) at G01 and G02 frequencies. To estimate the impact of the receiver antenna PCC model (type-mean by IGS and individual by LMMT) on baseline accuracy, short-baseline solutions, i.e., geocentric (ITRF2020) and topocentric coordinate differences, were calculated two times using identical input data and processing parameters, different only in the applied antenna PCC models. Short-baselines were calculated in a relative positioning approach in single-frequency (only GPS L1 and only GPS L2) and dual-frequency (ionosphere-free linear combination GPS L0) modes utilizing IGS precise orbits and ionospheric corrections based on the IGS global ionospheric maps (GIMs). Processing of GPS-only observations was performed using the WaSoft software package [43]. Results of short-baseline postprocessing are presented in Table 6.  
Furthermore, based on the obtained short-baseline solutions (Table 6) and the predetermined reference values of significantly higher accuracy (Table 3), short-baseline errors, per frequency and PCC model, were calculated and are presented in Table 7. Short-baseline errors are expressed along each coordinate axis individually, as well as in terms of the horizontal (2D) and total spatial (3D) errors.
## Appendix A
\begin{split} r_{11}=& c_{234}c\partial_{1}\ ;\,r_{21}=c_{234}s \partial_{1}\ ;\,r_{31}=s_{234}\\ r_{12}=&-s_{234}c\partial_{1}\ ;\,r_{22}=-s_{234}s\partial_{1}\ ;\,r_{32}=c_{234}\\ r_{13}=& s\theta_{1}\ ;\,r_{23}=-c\partial_{1}\ ;\,r_{33}=0\\ d_{x}=& c\partial_{1}(a_{3}c_{23}+a_{2}c\partial_{2}+a_{4}c_{234})\\ d_{y}=& s\theta_{1}(a_{3}c_{23}+a_{2}c\partial_{2}+a_{4}c_{234})\\ d_{z}=& a_{3}c_{23}+a_{2}c\partial_{2}+a_{4}c_{234}\end{split}  \tag{11}\]  
The inverse kinematics of the robot manipulators is described as giving the desired end effector's configuration, namely, position and orientation in the operational space, to determine the joint variables in the joint space. This means we can calculate the required rotation angle of each joint. The inverse kinematics of our robot arm is derived as follows:  
\[\left(T_{1}^{0}\right)^{-1}\times T_{4}^{0}=T_{2}^{1}\times T_{3}^{2}\times T _{4}^{3} \tag{12}\]  
\[ \begin{bmatrix}c\theta_{1}&s\theta_{1}&0&0\\ 0&0&1&0\\ s\theta_{1}&-c\theta_{1}&0&0\\ 0&0&0&1\end{bmatrix} \times\left[ \begin{matrix}r_{11}&r_{12}&r_{13}&d_{x}\\ r_{21}&r_{22}&r_{23}&d_{y}\\ r_{31}&r_{32}&r_{33}&d_{z}\\ 0&0&0&1\end{matrix} \right]\\ =&\left[ \begin{matrix}c\theta_{1}&s\theta_{1}&0&0\\ 0&0&1&0\\ s\theta_{1}&-c\theta_{1}&0&0\\ 0&0&0&1\end{matrix} \right]\times\left[ \begin{matrix}c_{234}c_{1}&-s_{234}c_{1}& s_{1}&c_{1}(a_{3}c_{23}+a_{2}c_{2}+a_{4}c_{234})\\ c_{234}s_{1}&-s_{234}s_{1}&-c_{1}&s_{1}(a_{3}c_{23}+a_{2}c_{2}+a_{4}c_{234})\\ s_{234}&c_{234}&0&a_{3}c_{23}+a_{2}c_{2}+a_{4}c_{234}\\ 0&0&0&1\end{matrix} \right]\\ \end{bmatrix} \tag{13}\]  
From Equation (A19), we have:  
\[ \begin{cases}d_{x}c\theta_{1}+d_{y}s\theta_{1}-a_{4}c_{234}=a_{3}\left(c\theta_{2 }c\theta_{3}-s\theta_{2}s\theta_{3}\right)+a_{2}c\theta_{2}\\ d_{z}-a_{4}s_{234}=a_{3}\left(s\theta_{2}c\theta_{3}+c\theta_{2}s\theta_{3} \right)+a_{2}s\theta_{2}\end{cases} \] (A23)  
\[ \begin{cases}d_{x}c\theta_{1}+d_{y}s\theta_{1}-a_{4}c_{234}=-a_{3}s\theta_{2}s \theta_{3}+c\theta_{2}\left(a_{3}c\theta_{3}+a_{2}\right)\\ d_{z}-a_{4}s_{234}=s\theta_{2}\left(a_{3}c\theta_{3}+a_{2}\right)+a_{3}s\theta _{3}c\theta_{2}\end{cases} \] (A24)  
\[s\theta_{2}=\frac{d_{z}-a_{4}s_{234}-a_{3}s\theta_{3}c\theta_{2}}{a_{3}c\theta _{3}+a_{2}}\] (A25)  
\[c\theta_{2}=\frac{d_{x}c\theta_{1}+d_{y}s\theta_{1}-a_{4}c_{234}+a_{3}s\theta _{2}s\theta_{3}}{a_{3}c\theta_{3}+a_{2}}\] (A26)  
\[\tan\theta_{2}=\frac{s\theta_{2}}{c\theta_{2}}=\frac{d_{z}-a_{4}s_{234}-a_{3}s \theta_{3}c\theta_{2}}{d_{x}c\theta_{1}+d_{y}s\theta_{1}-a_{4}c_{234}+a_{3}s \theta_{2}s\theta_{3}}\] (A27)  
\[\theta_{2}=\tan^{-1}\left(\frac{d_{z}-a_{4}s_{234}-a_{3}s\theta_{3}c\theta_{2} }{d_{x}c\theta_{1}+d_{y}s\theta_{1}-a_{4}c_{234}+a_{3}s\theta_{2}s\theta_{3}} \right)\] (A28)  
From Equations (A17), (A22) and (A28), we have:  
\[\theta_{4}=\theta_{234}-\theta_{2}-\theta_{3}\] (A29)
## 2 SPT-Based Determination Method for Constitutive Parameters
### Parameter Analysis of Constitutive Model  
The HS and HSS constitutive models were adopted to simulate the braced excavation of soft soil, which contain 11 and 13 model parameters, respectively According to the literature review [11; 15; 24; 28], among the constitutive parameters of the HS and HSS models, the strength parameters cohesion \(c\) and internal friction angle \(\varphi\) have little effect on the deformation of the retaining structure, while the large strain parameters, namely reference secant stiffness \(E_{50}^{ref}\), reference tangent stiffness \(E_{oed}^{ref}\), and reference Young's modulus for unload-reload \(E_{ur}^{ref}\), as well as the small strain parameters, namely initial shear modulus \(G_{0}^{ref}\) and shear strain \(\gamma_{0.7}\), are important parameters. Therefore, this study focuses on these important parameters to predict the deformation induced by braced excavation. On the other hand, a proportional relationship exists among the important parameters of the HS and HSS models. Using inverse analysis for all the parameters will easily lead to multiple solutions and lack of intrinsic correlations among these important parameters. Therefore, a sensitivity analysis was conducted in this study to obtain the most sensitive parameters to the soil displacement. The correlation analyses of the constitutive parameters were then analyzed to obtain the key parameters that could accurately characterize the soil displacement caused by braced excavation.  
In this study, a one-factor sensitivity analysis was used, in which only one of the corresponding parameters was varied and the rest of the parameters were kept unchanged during the sensitivity analysis. The parameters were assumed to be independent of each other in the one-factor sensitivity analysis. The correlation coefficients of the parameters reflect whether the estimated parameter values are unique and whether the parameters should be optimized simultaneously. The Pearson's correlation coefficient (PCC) was adopted to measure the strength and direction of the linear relationship between two variables. If the absolute value of the PCC between two parameters is greater than 0.9, these two parameters cannot be optimized at the same time. The definition formula for the PCC is:  
\[\text{PCC}=cor(i,j)=\frac{\text{cov}(i,j)}{\text{var}(i)^{1/2}\bullet\text{var }(j)^{1/2}} \tag{1}\]  
where cov is the covariance between the parameters, and var is the standard deviation of the parameters.  
Figure 1: The flowchart for the SPT-based step-by-step inverse method for constitutive parameters.
# Requirements Engineering for a Drone-Enabled Integrated Humanitarian Logistics Platform
## 6 Results
### Analysis of Open-Ended Responses  
Regarding the open-ended questions in the questionnaire, participants provided valuable insights into their familiarity with similar platforms and challenges they encountered while using them. They also offered justifications for their ratings and provided insightful feedback.  
In terms of familiarity, approximately 30.7% of the participants, dispersed across all professional backgrounds, had prior experience with similar disaster management platforms and functionalities. When requested to provide details about the challenges they faced during their experience, subject matter experts highlighted the need for real-time monitoring of hazardous agents during chemical, biological, radiological and nuclear (CBRN) accidents and the use of drones for flood events. Technology experts mentioned challenges related to data standards and coordination as well as security and scalability during disasters. Some end-users raised concerns about interoperability and security, while others did not report any challenges despite their familiarity. Neither stakeholders nor disaster management professionals specified any challenges, despite their experience with pertinent systems.  
The justifications provided for the ratings played a role of paramount importance in adding qualitative context to the quantitative analysis of the results. Some participants even looked ahead to the subsequent design and development phases, raising concerns about changing regulations and legal frameworks governing the use of drones in specific geographical regions and emphasizing the need for adaptability. Participants also addressed the configurability of the platform. Providing customizable configurations within the platform would enable different departments to tailor the system to their specific operational needs. Such comments were made by both end-users and disaster management professionals.  
Finally, some participants suggested new requirements. A firefighter expressed the desire for real-time updates and notifications related to firefighting equipment and gear. They envisioned a system that could monitor the status of breathing apparatus, firefighting suits, and safety gear. In case of equipment malfunctions or resource depletion, such as low air levels in the breathing apparatus, they suggested implementing an emergency distress signal feature. This feature would trigger an immediate response from the command center and nearby first responders to provide prompt assistance during life-threatening situations. Moreover, an end-user made a comment emphasizing the necessity for UIs to be designed to accommodate first responders wearing gloves and other protective gear.