esa-sceva/satcom-chunk-collection huggingface dataset:
# The Influence of Glacier Mass Balance on River Runoff in the Typical Alpine Basin
## 2 Data and Methods
### Methods
#### 2.3.2 InSAR DEM Accuracy Assessment  
At present, the accuracy of the DEM can be validated through various methods, primarily by comparing and evaluating it against high-precision ground control points [61], high-precision DEM models generated from airborne LiDAR point cloud data [62], and satellite-borne LiDAR height measurements [63; 64], which serve as reference data. Given the complex environmental conditions and the absence of ground control point data and high-precision airborne radar data for reference in the study area, this paper employs ICESat-2 satellite altimetry data along with NASA DEM and ALOS DEM to evaluate the accuracy of the InSAR DEM. The ICESat-2 footprints were used as reference points, and data from the non-glacial region of the study area between September 2021 and February 2022 were extracted. To compare the ICESat-2 GLAS data with the DEM data, it was necessary to transform the ICESat-2 footprints and the three types of DEM data to a common plane and elevation reference. The bilinear interpolation method was applied to correspond the elevation values of ICESat-2 points to the InSAR DEM, ALOS DEM, and NASA DEM. The elevation differences were then calculated by subtracting the corresponding elevation values of ICESat-2 points, ALOS DEM, and NASA DEM from the InSAR DEM. A total of 4608 ICESat-2 points from the non-glacial region were selected as reference data. The maximum, minimum, mean, standard deviation, and root mean square error of the elevation differences with the InSAR DEM were calculated for these reference points.
## 5 Implementation and Evaluation
### Benchmark Methods  
* **MIX-SA**[34]. Typically, the larger the connectivity of a network, the more stable and invulnerable it is. The size of the minimum cut represents the number of disjoint paths between two nodes in the network. The more disjoint paths there are, the stronger  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Orbital Parameters** & **Iridium** & **Globalstar** & **108-Star-Drone** \\ \hline Orbital altitude (km) & 1414 & 780 & 550 \\ Orbital inclination & 52\({}^{\circ}\) & 86.3\({}^{\circ}\) & 60\({}^{\circ}\) \\ Number of orbital & 8 & 6 & 6 \\ Number of satellites in a single orbit & 6 & 11 & 16 \\ Polar constellation & Nonpolar & Polar & Nonpolar \\ \hline \hline \end{tabular}
\end{table}
Table 1: The main parameters of the three constellation datasets.  
the network's ability to maintain communication, even if a link fails. Simulated annealing algorithms based on maximum flow minimum cut exchange ensure that the connectivity of the solution is not reduced during each search for a neighboring solution.
* **DLS-SA**[13]. Dual-link random switching, as a heuristic local search strategy, is commonly used in combination with simulated annealing algorithms to solve combinatorial optimization problems. Its main idea is to randomly select two paths or links and exchange a portion of them to generate a new solution. This strategy helps to escape from local optima and increases the exploration capability of the search space.
## 6 Seismic Response Characteristics by Nonlinear Dynamic Analyses
### Analysis of Maximum Displacement  
This study evaluated the effects of the bridge height on the maximum displacement of the bridge and the displacement spectrum of the ground motion. Figures 11 and 12 show the relationship of the maximum displacement and displacement spectrum according to the height of a bridge for near-fault and far-fault ground motions, respectively. The displacement of the bridge due to earthquakes showed a similar tendency of an increase or decrease with the displacement spectrum as the aspect ratio of the bridge increased. In particular, for a 5 m high bridge, little difference was found between the maximum displacement and displacement response. This indicates that when the height of a bridge is low, the type of ground motion dominates the seismic behavior of the bridge. As the height of a bridge increased, however, the maximum displacement became larger than the spectral response. This is because a real bridge composed of various structural and non-structural elements has a much larger mass and higher complexity than the structure assumed in spectral analysis.  
The maximum displacement was also compared with the yield displacement capacity of the bridge obtained from the nonlinear static analysis of the pier. When subjected to near-fault ground motion with a forward-directivity characteristic, the bridge with a low height of 5 m exhibited a maximum displacement of 17.72 mm, which was approximately 80.6% of the yield displacement capacity of 22.0 mm. For bridges higher than 5 m, the maximum displacements were within 67.4% of the yield displacement. On the other hand, the maximum displacement of the bridge under the filing-step near-fault ground motions reached up to approximately 154.3% of the yield displacement capacity for the 5 m high bridge. The results of the study show that the maximum displacement response of the bridge due to the near-fault ground motion with the filing-step effect was close to or larger than the yield displacement capacity but lower than the ultimate displacement capacity.  
The far-fault ground motion produced relatively lower displacements than the near-fault ground motion. The maximum displacement of a 10 m high bridge, which showed the highest value among the bridges, was 19.79 mm, which is approximately 22.5% of the yield displacement capacity. For other heights, the maximum lateral displacement was within 17.2% of the yield displacement. In general, the bridge showed more significant displacement when subjected to the near-fault ground motion. However, the far-fault ground motion EQ3_FF with a very low PGA (Peak Ground Acceleration) of 0.08 g generated the highest maximum displacement of 67 mm for the bridge with a height of 20 m. This is because the bridge and EQ3_FF were similarly in the low-frequency range. These results  
Figure 10: Variation in the moment along the length of a pier.  
indicate that the displacement response of a bridge is affected by the frequency characteristics of both the ground motion and structural system.
# A Hyper Heuristic Algorithm to Solve the Low-Carbon Location Routing Problem
## 4 Algorithm
### Low-Level Operator Design  
The operator library, \(\xi\), can be directly used to search the problem domain solution space, provided by problem domain experts. \(\xi\) can be regarded as an inoperable black box, divided into two categories: local search (LS) and mutation heuristic (MH). MH has a small perturbation to the current solution to prevent it from falling into a local optimal solution. The disadvantage is that a high-quality solution cannot be guaranteed. However, LS is used to improve the current solution and obtain a high-quality solution. This paper constructed six LS operators and nine MH operators.  
LS includes: 2-Opt inside one route (Inside-2 Opt), 2-Opt between different routes (Inter-2 Opt), swapping customers inside one route (Inside-Swap), swapping customers between different routes (Inter-Swap), shifting customers inside one route (Inside-Shift) and shifting customers inter different routes (Inter-Shift). For local optimization inside one route, a full optimization strategy is used [41]. Local optimization between different routes uses the K-1 optimization mechanism; that is, a route is randomly selected, and optimized with customers from other routes. Therefore, the operator complexity is reduced from \(K\)(\(K\)-1)/2 to \(K\)-1.  
MH was divided into two categories, one for vehicle route disturbances only, including Inside-2 Opt-M, Inside-Or-Opt, Inter-Shift-M, Inter-Swap-M, Shaw [36], Decompose, and Merge. The strategy of the first five perturbation operators is to randomly select the 1/4-1/2 route and randomly perturb the customers among the lines; the Decompose method involves decomposing a randomly selected route into two routes; Merge randomly chooses two routes and merges them into one. The other type of MH perturbs the selected warehouse, including Add-Swap and Relocation. The Add-Swap operator randomly selects a 1/3 to 2/3 route and allocates it to a new depot or closes a depot and arranges all the routes of this depot to another depot to prevent excessive convergence due to there being too few depots. Relocation collapses each driving route into a \"Super-Client\", and then inserts the depot into each route to obtain a minimum insertion cost [42].  
The above two types of operators must satisfy the constraint (7)-(20) to ensure the feasibility of the route and to avoid the use of repair techniques, and the LS operator must guarantee \(FIR\geq 0\).
## 4 Rare Earth Flotation from Secondary Resources
### Absorbing Colloid Flotation  
Another advanced flotation technique capable of extracting low quantities of REE ions from an aqueous solution is absorbing colloid flotation (ACF; Figure 3d). This method utilizes colloidal Fe(OH)\({}_{3}\) by adding ferric chloride (FeCl\({}_{3}\)) into the flotation system. Separation occurs when REE ions adsorb to colloids via physisorption called \"hydroxide flocs\", which have a positive charge. An anionic surfactant (NaDDS) is then added to collect the hydroxide flocs through their attachment to rising bubbles. [PERSON] et al. (1954), for example, investigated the application of ACF to recover Ce from an aqueous solution and found that maximum recovery reached 99.8% under acidic conditions.  
Based on the concept of ACF, [PERSON] and [PERSON] (2005) investigated the application of phosphine oxide as a carrier to recover La and Ce from an aqueous solution. In this previous work, the recovery of REE ions reached 90% under alkaline pH because of higher La\({}^{3+}\)- and Ce\({}^{3+}\)-hydroxocomplexes interacting with polar groups on phosphine oxide.
## 1 Introduction
For example, the terpene synthase gene _Phellandrene Synthes 1 (PHS1)_ is specifically expressed in glandular trichomes and is able to catalyze the synthesis of phellandrene as the main product and various other monoterenes [34]. The _TPS_ gene family has an important role in terpene biosynthesis. In _Arabidopsis thaliana_ and tomato, which are representative plants, the TPS gene family can be divided into seven main branches: _TPS_-a to _TPS_-h, among which TPS-a mainly contains sesquiterpene and diterpene synthases, and TPS-b and TPS-g branch mainly consists of monoterene synthases [35; 36]. Based on the protein structural characteristics, TPS can specifically catalyze the intermediates involved in the terpene biosynthesis pathway, such as dimethylallyl diphosphate (DMAPP and C5), geranyl diphosphate (GPP and C10), farnesyl diphosphate (FPP and C15), and geranylgeranyl diphosphate (GGPP and C20), thereby forming C5, C10, C15, and C20 terpene skeletons [23]. Taking sesquiterpenes as an example, the starting substrate of TPS is farnesyl diphosphate (FPP), and most of its TPS conserved motifs contain \"DDXXD/E\" and \"DTE/NSE\" [37]. For example, _AtTPS11_ and _AtTPS21_are involved in catalyzing the synthesis of various sesquiterpenes in flower volatiles of _Arabidopsis thaliana_[23]. In _T. sinensis_, _TsTPS18_ was found to be involved in the regulation of cyanophyllene synthesis [17]. Although the _TPS_ gene is of great significance in improving the flavor, currently, our understanding of TPS in _T. sinensis_ is still very limited. In this study, we characterized the distribution density changes and morphological structure of epidermal trichomes in the natural populations of _T. sinensis_ and performed a global gene expression analysis to identify the key genes that are associated with trichome development and secondary metabolic pathways. Combining gene expression and trichome distribution analysis, we identified and characterized two putative sesquiterpene synthase genes, _TsTPS466_ and _TsTPS1262_. Through heterologous transformation in model plants and _T. sinensis_ and compositional analysis of VOC components, we showed that these two TPS genes are important genes involved in sesquiterpene biosynthesis. Our research results provide a molecular basis for elucidating the formation of _T. sinensis_ flavor traits and provide basic information for the breeding of high-quality varieties.
## 3 Results
### Mission Design Algorithm in Representative Mission Example (PoC in CMSS): 3 rd Meth
The algorithm breaks down complex missions into manageable segments by including Points of Interest (POIs). This approach provides operational versatility to actors to understand and effectively execute their missions. Moreover, _clarity in operational roles and responsibilities_ is provided by identifying key intermediate objectives, and each actor knows their specific role at every stage of the mission, reducing confusion and increasing efficiency. Furthermore, _enhanced decision making_ due to the predesigned, well-defined POIs and mission segments aids actors to make decisions, especially in dynamic and rapidly changing situations. Lastly, _risk mitigation_ is achieved, since the algorithm allows planning for the potential risks and threats,  
Figure 13: Mission design algorithm in representative mission example (PoC in CMSS). enhancing the safety and probability of the success of the mission. Last, but not least, _flexibility in real-time adjustments_ is possible since the iterative nature of the algorithm allows for adjustments in response to unforeseen circumstances, even though the mission is preplanned. * _The benefits from a design engineer's standpoint_ include the _modularity and scalability_, due to the algorithm's divide and conquer approach which means that it can be adapted to various mission complexities, enhancing the system's scalability. Secondly, _data management and analysis are feasible_ because the systematic breakdown of the mission aids in avoiding information overloading via a targeted data requirement pertinent to actionable actors. The results of data organization and dissemination could be used as benchmarks for the SS's architecture design. Thirdly, _the minimized computational overhead_ is achievable because the algorithm may efficiently handle complex procedures due to the use of nested loops (external and internal). Moreover, _enhanced communication and coordination_ due to the compartmentalization of mission segments and POIs facilitate the design engineer to minimize the computational load related to the communication and coordination procedures in the SS. Finally, the CMSS provides a framework which allows for smoother operational transitions from one system to another. Thus, the integration with existing systems could be easier than before. The mission design algorithm, through its structured and iterative approach, not only simplifies the planning and execution of complex missions but also offers significant advantages in the integration of processes into the CMSS. This dual benefit streamlines mission execution and enhances the overall effectiveness and efficiency of operations. A detailed presentation of the aforementioned transportation mission algorithm is currently being developed by one of the authors ([PERSON]) and will be presented in the near future.
# A Novel Ship-Tracking Method for GF-4 Satellite Sequential Images
## 2 Proposed Method Framework
### Ship Tracking
#### 2.3.1 Ship Modeling
\begin{array}{c}\lambda_{k-1}+\dot{\lambda}_{k-1}T_{k-1}\sec( \varphi_{k-1})/60\\ \dot{\lambda}_{k-1}\\ \varphi_{k-1}+\dot{\varphi}_{k-1}T_{k-1}/60\\ \dot{\varphi}_{k-1}\\ a_{k-1}\end{array} \right]+\left[ \begin{array}{ccc}(T_{k-1})^{2}\sec( \varphi_{k-1})/120&0&0\\ T_{k-1}&0&0\\ 0&(T_{k-1})^{2}/120&0\\ 0&T_{k-1}&0\\ 0&0&1\end{array} \right]\left[ \begin{array}{c}\ddot{\lambda}_{k-1}\\ \ddot{\varphi}_{k-1}\\ 0\end{array} \right] \tag{19}\]  
with \(T_{k-1}=t_{k}-t_{k-1}\). The Equation (19) is nonlinear, so the first-order extended Kalman filter (EKF) is used for state prediction and update.
## 4 Experimental Results
### Event of 14 August 2018  
Results are presented as in the previous Section 4.1: in Figure 9, the received powers by the stations \"S2\(-\)Torcia\", \"PZS1\", \"Ufficio Ingr 1\", and \"Pala Eolica\" are shown along with the rainfall intensity estimated from the radar and the RG for the event on 14 August 2018. In Figure 10, the estimates of rainfall intensity returned by the stations \"S2\(-\)Torcia\", \"PZS1\", \"Ufficio Ingr\", and \"Pala Eolica\" are shown along with the ones returned by the radar and the RG for the event on 14 August 2018. Finally, the estimation errors are reported in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{_Site_} & \multicolumn{4}{c}{_Channel A_} & \multicolumn{4}{c}{_Channel B_} \\  & \(\mathbf{e_{r}^{in}}\) & \(\mathbf{e_{r}^{out}}\) & \(\mathbf{e_{g}^{in}}\) & \(\mathbf{e_{g}^{out}}\) & \(\mathbf{e_{r}^{in}}\) & \(\mathbf{e_{r}^{in}}\) & \(\mathbf{e_{g}^{out}}\) & \(\mathbf{e_{g}^{in}}\) \\ \hline Stazione S2 Torcia & 3.08 & 0.01 & 2.21 & 0.01 & 2.07 & 0.06 & 1.40 & 0.06 \\ PZS1 & 1.90 & 0 & 1.46 & 0 & 0.96 & 0.33 & 1.31 & 0.32 \\ Pala Eolica & 2.68 & 0.01 & 2.09 & 0.01 & 1.44 & 0.09 & 1.46 & 0.09 \\ Ufficii Ingr & 2.61 & 0.01 & 1.93 & 0.01 & 1.74 & 0.06 & 1.38 & 0.06 \\ \hline \end{tabular}
\end{table}
Table 2: Mean errors computed inside and outside the rainfall’s temporal windows achieved with the SRS sensors available at the Monte Scarpino landfill on 4 July 2018.  
Figure 8: Time series of rainfall intensity \(\mathcal{R}\) [mm/h] measured at the sites (**a**) S2\(-\)Torcia, (**b**) PZS1, (**c**) Pala Eolica, and (**d**) Ufficii Ingr on 4 July 2018. The vertical dotted lines delimit the time window \(W\).  
Figure 10: Time series of rainfall intensity \(\mathcal{R}\) [mm/h] measured at the sites (**a**) S2\(-\)Torcia, (**b**) PZS1, (**c**) Pala Eolica, and (**d**) Ulfici Ingr on 14 August 2018. The vertical dotted lines delimit the time window \(W\).  
Figure 9: Received power variation [dBm] at the sites (**a**) S2\(-\)Torcia, (**b**) PZS1, (**c**) Pala Eolica, and (**d**) Ulfici Ingr on 14 August 2018. The signal power variations for channels A and B refer to the right \(y\)-axis, whereas the rain intensities \(\mathcal{R}\) provided by the radar and the RG refer to the left \(y\)-axis.
## 1 Introduction
[16] conducted a quantitative analysis of the shoreline change rate of sandbars in the Nakdong River estuary using aerial photographs. Global studies have also used remote sensing data, such as aerial photographs and artificial satellite images, to analyze coastal terrain changes [19]. Following the coronavirus disease 2019 (COVID-19) lockdowns, when industrial activities increased again, the technique of order of preference by similarity to ideal solution method was used to evaluate a water quality-monitoring network for 34 tributaries in the Nakdong River basin, and information necessary for setting up and managing a water quality-monitoring system was presented [20]. The analysis of coastal seafloor changes using remote sensing data has the advantage of easy acquisition of comprehensive data, but it has the disadvantage of low accuracy compared to direct survey data. Studies on the correlation between environmental factors and changes in the topography of estuarine areas have also been conducted. [PERSON] et al. [21] studied the basics of the formation and development of topography in sandbars in the Nakdong River estuary by analyzing the surface sediment transport rate. [PERSON] and [PERSON] [22] analyzed the ocean circulation in the Nakdong River estuary to elucidate the patterns of marine environmental changes. [PERSON] and [PERSON] [23] also analyzed the factors affecting geomorphic changes in the estuary, specifically the influence of environmental factors, such as the discharge rate of the estuarine dike and meteorological factors. In this study, the estuary area is an important environmental and ecological area, for migratory bird arrivals and is associated with a seashore. And past studies have focused on monitoring changes. Therefore, we would like to discuss monitoring changes according to the cause through research. We aimed to elucidate the patterns of coastal topographical changes in the Nakdong River estuary from 2018 to 2022 using bathymetric data and shoreline survey data obtained during this period, as well as precipitation data from the Republic of Korea Meteorological Administration [24] and water discharge and suspended sediment discharge data from the Nakdong River estuary drive managed by K-Water Corporation [25]. We conducted a comprehensive comparative analysis of these data, taking seasonal variations into account.
# Continuous Fiber-Reinforced Material Extrusion with Hybrid Composites of Carbon and Aramid Fibers
### Hybrid Effect  
Using the Rule of Hybrid Mixture (RoHM) equations, the properties of hybrid composites can be predicted based on the material properties of the individual composite systems [47]. The assumption is that the hybrid system consists of two individual composite systems and that there are no interactions between the individual systems. The validity of the RoHM equations, especially for the Young's modulus of unidirectionally reinforced composites, has been demonstrated in numerous studies [48; 49; 50; 51; 52; 53]. The Young's modulus of the hybrid composite \(E_{hyb}\) can be determined with the following equation:  
\[E_{hyb}=\sum_{i=1}^{2}E_{ci}V_{ci} \tag{1}\]  
\(V_{c}\) is the relative volume fraction of a composite with one fiber material in the total volume and \(E_{c}\) is the associated Young's modulus. For hybrid composites with two composite systems, the following equation applies:  
\[V_{t}=V_{c1}+V_{c2}=1 \tag{2}\]  
where \(V_{t}\) is the total volume of the composite and \(V_{c1}\) or \(V_{c2}\) is the relative hybrid volume fraction of the first or second system, respectively. For the calculation of the Young's modulus of a hybrid composite with two different fiber materials, taking equation 2 into account, the following equation is obtained:  
\[E_{hyb}=E_{c1}\times V_{c1}+E_{c2}\times(1-V_{c1}) \tag{3}\]  
Analogous to Equation (3), this can also be predicted for the determination of stress (see Equation (4) for an example) or impact strength at different mixing ratios [37; 54; 55].  
\[\sigma_{hyb}=\sigma_{c1}\times V_{c1}+\sigma_{c2}\times(1-V_{c1}) \tag{4}\]An approach often used in the literature in connection with the Rule of Hybrid Mixture is the distinction between a positive and negative hybrid effect. Here, an upward deviation of the experimentally determined material properties compared to the predicted RoHM values is considered a positive effect. A downward deviation is referred to as a negative hybrid effect. The hybrid effects can be determined for strength (\(\lambda_{c}\)), Young's modulus (\(\lambda_{E}\)) and impact strength (\(\lambda_{ac}\)) according to Equations (5)-(7).  
\[\lambda_{\sigma}=\frac{\sigma_{exp}}{\sigma_{hyb}}-1 \tag{5}\]  
\[\lambda_{E}=\frac{E_{exp}}{E_{hyb}}-1 \tag{6}\]  
\[\lambda_{a_{c}}=\frac{a_{c\;exp}}{a_{c\;hyb}}-1 \tag{7}\]  
For \(\lambda_{\sigma}>1\), \(\lambda_{E}>1\) and \(\lambda_{ac}>1\) the hybridization with the respective mixing ratio has a positive hybrid effect. For \(\lambda_{\sigma}<1\), \(\lambda_{E}<1\) and \(\lambda_{ac}<1\) it indicates a negative hybrid effect [37; 56].