esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
[27] utilized DOPO-PGE to modify EP, and the results indicated that DOPO-PGE was a promising additive in the field of enhancing the LOC of the EP matrix and CFRP, but the mechanical properties of DOPO-PGE-modified EP need further research. Previous studies have investigated various methods to enhance LOC; however, the mechanism of flame retardants affecting the LOC of EP needs further study and the comprehensive mechanical properties need to be further enhanced. In the authors' previous research, a reactive phosphorus-/nitrogen-containing fire retardant (BSEA) was synthesized by a facile addition reaction between \(N\), \(N^{\prime}\)-bis(salicylicidene) ethylenediamine (NEA) and DOPO with high productivity [37]. BSEA was utilized as a multifunctional component to endow an EP/biscitraconimide resin (BCI) system with the expected flame retardancy and desirable tensile properties at RT and 77 K. This inspired us to apply this simple and effective flame retardant to the preparation of liquid oxygen tank materials, which is a very attractive research area. In the authors' current study, BSEA was introduced to enhance the LOC of EP and enhance the cryogenic mechanical properties. The LOC of EP composites was detected by 98] LOT, and the essence of BSEA affecting the LOC was analyzed in depth, combined with the authors' previous work. Moreover, the mechanical properties, such as bending properties and fracture toughness (K\({}_{\text{IC}}\)), as well as the impact property, of the EP composites in the cryogenic environment were analyzed in detail and contrasted with those at RT.
## 3 UAV Applications in Climate Change Research
### Data Collection and Processing Platforms
The results are interpreted to derive actionable insights based on the mission objectives and different application needs, as shown in Figure 11.  
The proliferation of UAV-based aerial mapping sparked the development of specialized photogrammetry software to process the captured imagery into 3D reconstructions, point clouds, and orthomosaic maps. As UAV adoption expands across sectors ranging from construction to conservation, selecting the appropriate data processing workflow is pivotal to efficiently transforming raw sensor data into actionable ideas. A range of photogrammetry solutions exists at present, ranging from user-friendly commercial packages designed for simplified mapping workflows to open-source options offering advanced customizations, as shown in Figure 12. Cloud computing has also created new web-based processing capabilities. Determining the optimal solutions requires aligning processing capabilities with program needs and resources. However, as sensors and airframes advance, the continued evolution of computation power and algorithms will further unleash the great potential of UAV-derived data across applications. Figure 12: Different processing platforms and their applications, advantages, and disadvantages [177; 178; 179]. Popular commercial platforms include Pix4D, DroneDeploy, PrecisionMapper, and ArcGIS Drone2 Map for general mapping needs [1]. These streamline workflows for common applications, such as agriculture, surveys, and inspections. Photogrammetry-focused, open-source options, such as OpenDroneMap, provide more flexibility for advanced users. Some sensor manufacturers also offer specialized processing suited to visual, thermal, LiDAR, or spectral data, as illustrated in the review provided in Figure 12.  
Cloud-based processing through web services offered by industry leaders, such as Pix4D and DroneDeploy, allows rapid analysis without local computing resources [2; 46]. However, cloud processing incurs ongoing subscription fees versus one-time software purchases. For projects dealing with sensitive data, local processing may be preferred or required to ensure security and prevent external data storage [40]. However, this requires an investment in high-performance workstations for intensive computation. Selecting optimal processing platforms depends on weighing factors, such as usage costs, processing times, data security needs, analysis requirements, and team experience level. For most inspection and mapping applications, user-friendly commercial photogrammetry suites require minimal training while providing speed, scalability, and robust outputs [24; 177]. Open-source tools better suit research needs for a customized analysis, such as environmental monitoring applications and climate change research. Cloud platforms offer convenience, but may lack security; however, weighing these tradeoffs is important to match processing solutions to application goals.
## 3 Warehouse Delivery Problem
### Formalization
#### 3.1.2 Solution Formulation
These observations are reflected in the next section, where they are used to provide a richer and more efficient ASP encoding to match the formalization. Given a warehouse delivery problem consisting of a warehouse \(W=(V,E,f_{E},C,R,\)\(f_{H},f_{S})\) and task execution graph \((T,D,f_{D},f_{V})\), and a robot assignment \((f_{T},f_{W})\) with corresponding set of projections \(P\) that satisfies Conditions 7, 13 and 15, then:  
1. For any distinct tasks \(t,t^{\prime}\in T\) such that \((t,t^{\prime})\in D\) and \(f_{D}((t,t^{\prime}))=\mathit{deliver}\), and corresponding projection points \((v,a,e)\) in some \(\vec{p}\in P\) and \((v^{\prime},a^{\prime},e^{\prime})\) in some \(\vec{p^{\prime}}\in P\), it follows that \(a+\kappa\leq a^{\prime}\)
2. For distinct tasks \(t,t^{\prime}\in T\) such that \((t,t^{\prime})\in D\) and \(f_{D}((t,t^{\prime}))=\mathit{deliver}\), then: 1. there exists a robot \(r\in R\) such that \(\langle t,t^{\prime}\rangle\sqsubseteq f_{T}(r)\), and 2. for all \(r^{\prime}\in R\), where \(r\
eq r^{\prime}\), then \(t,t^{\prime}\
ot\in f_{T}(r)\). Conditions 13 and 15 establish that the delivery task pair \(t,t^{\prime}\) are consecutive tasks for some robot's task sequence (Observation 2a). Condition 7 ensures that there is only one such robot for any task pair (Observation 2b). Since a projection requires that the assigned robot is stationary at a task's vertex for at least time \(\kappa\) (Condition 1), hence there is a guarantee that the arrival at the vertex for task \(t^{\prime}\) is at least \(\kappa\) time after the arrival at \(t\)'s vertex (Observation 1). The key point of Observation 1 is that Condition 14 is implicitly satisfied for a pair of tasks involved in a delivery. In simple terms, to pickup an item and then to put it down implicitly means that the one must follow the other with a time gap of at least \(\kappa\). Consequently, for this special case it is unnecessary to explicitly enforce Condition 14, which we use to provide a more efficient encoding by avoiding these redundant constraints. These observations are reflected in the example scenario, where the projections of the timed walks for robot \(r_{1}\) and \(r_{2}\) contain arrival times that satisfy Observation 1 and the delivery task pairs form distinct subsequences for each robot's task sequences:  
\[\langle t_{1},t_{2}\rangle\sqsubseteq f_{T}(r_{1}),\ \langle t_{3},t_{4} \rangle\sqsubseteq f_{T}(r_{1}),\ \langle t_{5},t_{6}\rangle\sqsubseteq f_{T}(r_{2}),\ \text{and}\ \langle t_{7},t_{8}\rangle\sqsubseteq f_{T}(r_{2}) \tag{5}\]
## 4 Performance Evaluation
### Testbed Experiments  
Testbed experiments were also conducted to evaluate REL and compare it with AODV in terms of network lifetime and data delivery in experiments into real environment, as expected for IoT and ubiquitous computing applications. The experiments were set up to allow each node to send 1200 packets to the BS with an inter packet interval of 0.5 s. This configuration is common for most IoT applications that require a high data rate.  
Figure 6 shows the results of PDR for each node when they use REL or AODV as the routing protocol. It important to point out that although PDR ranges from 0% to 100%, Figure 6 establishes an interval from 90% to 100% to make the performance evaluation clearer. On the basis of the results of Figure 6, the total packet loss of AODV is 67.6% and REL is 28.3%. Thus, REL increases the PDR by 39.3%. This is due to the fact that REL selects routes on the basis of cross-layer information, with end-to-end link quality estimation (QoS-aware), unlike AODV that only chooses paths based on hop count. Thus, REL is able to provide data transmission with a higher degree of reliability in IoT scenarios.  
The PDR of node 5 has a higher value (13.3%) of packet loss using AODV as the routing protocol, and only 1.1% using REL. As depicted Figure 2, node 5 is the central node of the grid topology used for the experiments. REL selects routes together with load balance operation, and, thus avoids continually sending packets to the same node, which causes interference, collisions, and buffer overflow. On the other hand, AODV selects routes that are only based on the minimum number of hops, therefore nodes 7, 8 and 9 often send packets to node 5.  
The Figure 7 shows the energy-efficiency for this testbed. AODV presents unbalanced energy consumption between nodes and their possible premature death, as can be seen in Figure 7. Additionally, five nodes consumed up to 50% of the energy resources before the end of the experiment. As expected, these nodes are the same as those with the highest packet loss values. Thus, routes less reliable and more susceptible to errors require more retransmissions and spend more energy.  
In contrast, REL increases the network lifetime by more than 15% compared with AODV and also presents an almost uniform distribution of energy consumption, as shown in Figure 7. This is due to the fact that REL has a mechanism for opportunistic piggybacking, which leads to an overhead reduction  
Figure 6: Packet delivery ratio in testbed experiments.  
and, thus, improves the energy consumption. Additionally, REL uses a load balancing scheme that provided uniform energy consumption for all network nodes.  
Finally, the difference between the higher and lower energy consumption for REL was 6% (nodes 1 and 2), whereas for AODV this value exceeded 23% (nodes 3, 4, 6, 8, and 9). This is due to the reasons explained above.
# Unsupervised Cell Segmentation and Labelling in Neural Tissue Images
## 5 Conclusions  
In this work, we presented a fully automated and unsupervised cell segmentation and identification schema for microscopy fluorescent images. Specifically, we outlined an unsupervised multi-phase schema (called the USRL schema), avoiding the necessity of labelling training data, thus reducing the experts' workload. As a proof-of-principle, we applied an instantiation of this schema (called MagNoLia) to a dataset of ALS molecular pathology confocal images. However, MagNoLia can not only be applied to other immunostaining and tissue samples, as the USRL schema can also be instantiated for a large variety of other datasets and fields, from cancer pathology to basic cell biology analysis. In our proof-of-principle on the ALS dataset, we performed a comparison with a state-of-the-art machine learning segmentation tool called ilastik [14], summarised as Rec, Prec and F. As a conclusion, we can affirm that MagNoLia allows for an easy and intuitive incorporation of biological knowledge into the segmentation process, while providing more control to the experts. MagNoLia also yielded a higher Prec, yielding more trustworthy segmentations than ilastik did.  
Conceptualisation, D.G.-C., A.S. and C.L.-M.; methodology, D.G.-C., A.S., B.D.B. and C.L.-M.; software, S.I.-R. and F.A.-S.; validation, S.I.R.; formal analysis, S.I.R., H.B., B.D.B. and C.L.-M.; investigation, S.I.-R., F.A.-S. and C.L.-M.; resources, C.H., R.P. and A.S.; data curation, C.H., and R.P.; writing--original draft preparation, S.I.-R. and F.A.-S.; writing--review and editing, D.G.-C., A.S., B.D.B. and C.L.-M.; visualisation, C.H., S.I.-R. and F.A.-S.; supervision, D.G.-C., A.S., B.D.B. and C.L.-M.; project administration, D.G.-C., H.B., A.S. and C.L.-M.; funding acquisition, D.G.-C., H.B., A.S. and C.L.-M. All authors read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors gratefully acknowledge the financial support of the Spanish Ministry of Science (Project PID2019-108392 GB-100 AEI/FEDER, UE), the funding from the European Union's H2020 research and innovation programme under Marie Sklodowska-Curie Grant Agreement Number 801586, as well as that of Navarra de Servicios y Tecnologias, S.A. (NASERTIC). [PERSON] and C.H. wish to acknowledge the support of King's College London (Studentship \"LAMBDA: long axons for motor neurons in a bioengineered model of ALS\" from FoD0 CS) and the Wellcome Trust (213949/Z/18/Z). On behalf of [PERSON], [PERSON] and R.P., this research was funded in whole, or in part, by the Wellcome Trust (213949/Z/18/Z). For the purposes of open access, the authorhas applied a CC BY public copyright licence to any author-accepted manuscript version arising from this submission.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} NDs & Neurodegenerative Diseases \\ USRL & Unsupervised Segmentation Refinement Labelling \\ OS & Object Segmentation \\ REF & Refinement \\ LBL & Labelling \\ MN & Motor Neuron \\ \end{tabular}
## 1 Introduction  
Most carbon capture utilization and storage (CCUS) processes involve injections of carbon dioxide (CO\({}_{2}\)) into depleted oil and gas reservoirs [1]. One of the common advantages of these processes is the high-volumetric efficiency due to the high CO\({}_{2}\) injectivity into the highly permeable reservoirs. A common disadvantage of these processes is the risk of CO\({}_{2}\) leakage through wellbore due to the high mobility of CO\({}_{2}\) in the supercritical state [2,3]. Studies have shown that the risk of CO\({}_{2}\) leak from subsea geological storage is low due to the low mobility of CO\({}_{2}\) in liquid state [4]. Cases of CO\({}_{2}\) injection into sub-seabed water zones include the pioneer Slepinipr project in the Norwegian Sea initiated in 1996 [5] and the Hokkaido project initiated in 2016 [6, 7, 8]. Recently, it was proposed that CO\({}_{2}\) be injected into subsea water zones where the in-situ temperatures are below the hydrate-forming temperature of CO\({}_{2}\) to lock CO\({}_{2}\) inside the water zones in a solid hydrate state [9, 10, 11]. However, it is unclear if the CO\({}_{2}\) would form hydrates during the injection period.  
Gas hydrates are solids formed at high-pressure and low-temperature conditions where gases of small molecules are trapped inside the cages of water molecules [12]. Formation of gas hydrates in static conditions has been thoroughly investigated in the past decade, both experimentally and theoretically. [PERSON] et al. [13] formulated the kinetics of the formation of methane and ethane gas hydrates. His model indicates that gas hydrate formation rate is directly proportional to the hydrate crystal particle area and the difference in fugacity of gas at the in situ condition and the fugacity of gas at the three-phase equilibrium conditions. [PERSON] and [PERSON] [14] modified [PERSON] et al.'s formula by replacing the particle area with crystal film in describing the mass transport during hydrate formation. [PERSON] [15] made a similar modification using chemical potential difference as the driving force. [PERSON] [16] defined the chemical potential difference as the oversaturation. [PERSON] and [PERSON] [17] defined the driving force as the temperature difference between the equilibrium and testing conditions, while [PERSON] [18] defined the driving force as the pressure (fugacity) difference between the equilibrium and testing conditions. [PERSON] et al. [19] applied the fugacity model to the dissociation process of gas hydrates.  
None of the above-mentioned works considered the dynamic conditions where the effect of moving gas on hydrate formation can be significant. This work fills the gap. CO\({}_{2}\) was injected into sandstone cores to investigate hydrate formation at different temperatures ranging from 0 \({}^{\circ}\)C to 5 \({}^{\circ}\)C in dynamic conditions. Results show that the required minimum pressure for CO\({}_{2}\) to form hydrates in dynamic conditions is about double the required hydrate-forming pressure in static conditions. More investigations are required to achieve a thorough understanding of the process.
# Estimation and Evaluation of Zenith Tropospheric Delay from Single and Multiple GNSS Observations
## 5 Discussion  
The GNSS application in meteorology is becoming more and more popular with its rapid development. The increase in observation systems and observable satellites provides enormous data assistance for GNSS meteorological study and applications. Previous studies were done prior to or during the early stages of the BDS-3 satellite network with fewer BDS-3 satellites. Unlike previous studies, our study aims to use more data sources to validate ZTD values under different multi-GNSS combined strategies, especially the full operation BDS-3. For this purpose, we used multi-GNSS clock and orbit products to estimate the ZTD values and evaluated the performance of ZTD for multi-system solutions, as shown in Figures 2 and 3. The correlations between GPS ZTD and the ZTD of the other three systems are very high, but BDS ZTD shows significant fluctuations at some times. In Section 4.2, we compared the ZTD of the four-system solution with single-system, two-system, and three-system solutions. A great improvement of ZTD in accuracy and stability can be obtained by multi-GNSS solutions in Figure 6. In Sections 4.3 and 4.4, we evaluated the ZTD for different solutions when compared with IGS products, radiosonde, and ERA5 data. The results proved that an improvement of accuracy can be obtained by two or more systems when compared to the single-system solution.  
With the establishment of more MGEX stations and the launch of more satellites, especially BDS-3, the number and quality of satellite observations have been improved. Therefore, higher accuracy in retrieving ZTD can be obtained from multi-GNSS observations, which will provide a good opportunity for GNSS atmospheric research and meteorological applications.
## 3 Basic Definitions and System Framework
### Name Registration Process and Content Publish or Query  
Users who want to publish contents need to obtain legal public keys and certificates from the Audit Institutions and then send their own user ID (UID) and user's public key to the resolution node. The node verifies the validity of time stamp and the public key, and it checks whether the public key is in the PKBL list, then verifying the validity of the UID by self-certifying naming. The resolution node generates a random number encrypted by the user's public key and sends it to the user. The user uses the private key to decrypt the random number and sends it back to the resolution node, so the node can verify the user identity. After obtaining registration permission, the user gets permission to publish or query content by content ID (CID). Content integrity and publisher authenticity can be checked by self-certification. Figure 2 shows this registration and content publish or query process.  
In the next chapter, we will describe how to discover and revoke invalid content and malicious users through voting algorithms.
# A Connectivity-Based Clustering Scheme for Intelligent Vehicles
## 6 Simulation Results and Discussions
### Assessment of the Route Connectivity of the Proposed Clustering Scheme  
In this subsection, we assess and compare the route connectivity of the suggested clustering scheme with the previous TCRP and reliability-based routing scheme [3]. Here, the simulation environment is the same as that which we used above in the assessment of RRMs and average packet loss, i.e., a congested traffic scenario with continuous arrival of vehicles. It is indicated in Figure 5 that the link connectivity of the suggested clustering scheme is higher than the linear routing scheme and TCRP. The link connectivity of the proposed connectivity-based clustering is high due to the heuristic approach of spectral clustering in dividing the network into an optimal number of clusters. Additionally, our suggested scheme forms a large number of clusters in highly dense networks that subsequently reduce the number of cluster members. As a result, it reduces the overhead as well as combined strongly connected vehicles in clusters, which subsequently positively affects the selection of route connectivity. The performance of the linear scheme in terms of link connectivity is even worse than TCRP because of the lack of clustering. In the linear scheme, there is no boundary to limit the broadcast storm; thus, a simple beacon message can flood the whole network and subsequently affect the link connectivity.  
Our simulation results show that the proposed clustering scheme performs well in terms of the selected performance metrics, i.e., number of clusters, route connectivity, and route RRMs, as mentioned in the aforementioned sections (Sections 6.1-6.3). Thus, on the basis of the simulation findings, we determine that the link connectivity metric and heuristic approach of spectral clustering in our suggested scheme obtain better performance compared with the existing routing schemes for high evolving networks. As a result, we determine that the heuristic approach of spectral clustering as well as the connectivity metric play vital roles in forming long-lasting and stable clusters.
# Variable Responses to a Marine Heat Wave in Five Fringing Reefs of Southern Taiwan
## 3 Results
### Inter-Site Analysis  
When averaging across survey months in each year (Figure 8), there was not a statistically significant effect of site (Figure 8A) on bleaching prevalence for either survey year (one-way ANOVA, \(p>0.02\)). However, when looking _only_ at the data from September 2020 (when the bleaching event was ongoing), there was a statistically significant site effect (\(p<0.001\)) on percentage of sampled coral tissue areas bleached; see Figure 4 for the inter-site _post-hoc_ comparisons. Bleaching prevalence was significantly more widespread in September 2020 (Figure 8B) when compared to other months, though 30% of corals were still bleached in December 2020. When averaging across survey times within each year, bleaching prevalence was not significantly higher in the shallow reefs versus the deep one (Figure 8C); only when analyzing the September 2020 bleaching event data alone was a depth effect evident (student's \(t\)-test, \(p<0.0001\); 78 & 8.5% of coral tissues sampled at 3 & 7 m, respectively, were bleached.). In contrast, the bleaching prevalence did not differ between the three natural and two artificial reefs, either when pooling across survey times within year (\(p=0.11\); Figure 8D) or when looking at the September 2020 data only (56 & 77%, respectively; \(p=0.17\)). However, this lack of significance is because one of the three natural reefs, ILO, generally resisted bleached. Recovery was far more pronounced at the two artificial reefs vs. the two natural reefs located at the same depth (3 m).