esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion on Wind-Sand Hazards and Sand Control along the Railway  
### Wind-Sand Hazards and Their Causes along the Railway  
Because of landforms, an aeolian sand environment, and the angle between the railway and sand-driving wind, the railway is exposed to different degrees and forms of wind-sand hazards. Sand-driving wind and sand sources are necessary conditions for wind-sand activities, greatly affecting the shape and movement of dunes. The annual average sand-moving wind velocity of the study area is 5.99-6.11 m/s, and its prevailing directions are NE and ENE. The magnitude and direction of DP are consistent with sand-moving wind frequency on multiple time scales and are greatly affected by sand-moving wind frequency [9]. Its annual RDDs are SW and WSW, meaning that sand particles are transported along the NE-SW direction, consistent with [PERSON]'s [28] analysis of dune movement in different areas of the Taklimakan Desert. The intensity of sand drift activities presents spatiotemporal differences. Temporally, the spring and the summer account for large proportions of sand-moving wind frequency and have intense sand-drift activities, while the autumn and the winter have low wind energies. Spatially, the sand-moving wind velocity and intensity of sand drift activities of Qiemo are much higher than those of Ruoqiang.  
Ruoqiang is mainly distributed with flat sandy land and shrub-coppice dunes. Barchan dunes and chains gradually appear from Ruoqiang to Qiemo. The movement direction of barchan dunes is consistent with RDD. Under the wind action, wind-sand flows strongly impact the railway and seriously affect driving safety. Sand-driving wind regime and DP show seasonal variation characteristics. Wind-sand activities are most frequent in the spring, and the intense sand-drift activities and stable sand-drift directions in the spring bring sufficient sand sources along the railway. Meanwhile, the passage of the railway disturbs local wind balance and destroys dune stability, for which the railway is inevitably exposed to sand hazards.  
The railway runs along the NE-SW direction, and the angle between the prevailing direction of the sand-moving wind and the dominant direction of sand drift is small (Figure 10). The railway trend directions of Ruoqiang and Qiemo are about 247 and 230\({}^{\circ}\), respectively. The RDD of Qiemo was smaller than its railway trend angle in each season (Figure 8). Sand drift activities mainly occur on one side of the railway, so sand control should be strengthened on the upwind side. In the summer, Ruoqiang has a highly changeable wind direction, and its RDD was 259.77, greater than the railway trend. In the spring, the autumn, and the winter, its RDD was smaller than the railway trend. That is, sand drift activities occur on both sides of the railway in Ruoqiang, and sand control on the downwind wide cannot be ignored.  
Figure 9: Characteristics of monthly DP: (**a**) Qiemo; (**b**) Ruoqiang.
## 2 Materials and Methods
### Aligners Selected
#### 2.2.1 Mechanical Properties of Polyamide  
Polyamide (PA) is a biocompatible linear, semi-crystalline, 3D-printable thermoplastic composite with excellent mechanical properties. Its modulus of elasticity (Young's modulus) ranges from 1.5 to 2.1 GPa, depending on the specific processing conditions and testing parameters. PA exhibits high tensile strength and flexibility, which contribute to its resilience under cyclic loads common in orthodontic applications. The flexural modulus of PA aligns well with its ability to retain shape under stress, making it a prime choice for aligners. Additionally, PA has a low density (1.01-1.03 g/cm\({}^{3}\)), enhancing comfort for patients during prolonged use. PA's thermal stability (up to 185 \({}^{\circ}\)C) allows for sterilization before clinical use. This is complemented by its low moisture absorption (<1%), ensuring minimal dimensional changes in humid oral environments, which is critical for maintaining precise aligner fit [17; 18].
# Effects of Matrix Modification on the Mechanical Properties of Wood-Polypropylene Composites
## 3 Results and Discussion
### Characteristics of the Composites
#### 3.2.3 Fracture Morphology Analysis  
The fracture morphology of unmodified and modified composites was analyzed by SEM to provide an insight into fiber-matrix interactions (Figure 9). In the unmodified composites, WF with a clean and smooth surface was devoid of the PP matrices and an interfacial separation (Figure 9a). This occurrence indicated that a weak interface was formed between WF and the matrix [32]. In the composites based on the modified PP matrix, broken fibers were embedded in the modified matrix, and the interfacial boundary became indistinct, suggesting that the main energy dissipation of the applied stress was borne by the fracture of WF itself, other than debonding (Figure 9b,c). This occurrence could be attributed to the improved interfacial compatibility between WF and the modified matrix, as confirmed by the mechanical properties [9]. However, a coarse fracture surface was observed, when DVB concentration was added with a dosage of 0.8%. In addition, noticeable gaps at the interfaces were observed, suggesting a weak fiber-matrix interface (Figure 9d). As indicated from aforementioned mechanical data and DMA curves, the lower MFI values and excessive entanglement structure in MPPD0.8 resulted in a marked increase in melt viscosity at a high percentage of WF, which would result in the insufficiency of WF by the matrix, thereby forming a weaker fiber-matrix interface than that of WMPD0.4. Therefore, the stress from the matrix could not be transferred sufficiently to WF at the weak fiber-matrix interface. This finding provides evidence for the lower mechanical properties obtained in WMPD0.8, compared with that in WMPD0.4.  
Figure 9: SEM micrographs of fracture surface of the composites. **(a)** WF; **(b)** WMPD0; **(c)** WMPD0.4; **(d)** WMPD0.8.
###### Abstract  
The main contents of this paper are to verify the environmental factors affecting the power generation of floating photovoltaic systems and to present the power generation prediction model considering environmental factors by using regression analysis and neural networks studied during the last decade. This study focused on a comparative analysis of which model is best suited for the power generation prediction of the floating photovoltaic (PV) system. To compare the power generation characteristics of a floating and a land-based PV system, two identical 2.5 kW PV systems were installed--one on the water surface in the Boryeong Dam, Korea, and the other nearby on dry land--and their performances were compared. The solar irradiance of the floating PV system was 1.1% lower than that of the land-based PV. Nevertheless, the floating PV module temperature was 4.9% lower than that of the land-based PV, generating approximately 3% more power. Using the correlation analysis of data mining techniques, environmental factors affecting the efficiency of the floating PV system were investigated. The correlation coefficient between the module temperature and water temperature was \(r=0.6317\) which proves that the high efficiency and low module temperature characteristics of the floating PV system, when compared with that of the land-based PV, are due to the water evaporation effect. Considering environmental factors, power-generation prediction models based on regression analysis and neural networks are presented, and their accuracies are compared. This comparison confirms that the accuracy of the power generation prediction model using neural networks was approximately 2.59% higher than that of the regression analysis method. As a result of adjusting the hidden nodes in the neural network algorithm, it was confirmed that a neural network algorithm with ten hidden nodes was most suitable for calculating the amount of power generation.  
**Keywords:** floating photovoltaic system; neural network; prediction power generation; regression analysis +
Footnote †: journal: Applied Soft 2020, 10, 4526; doi:10.3390/app10134526
# Sparse Data-Extended Fusion Method for Sea Surface Temperature Prediction on the East China Sea
## 4 Experimental Results and Analysis
Before data assimilation, the clustering method we choose comes from the complex network [24], the assimilation method comes from the method proposed in this paper, and the prediction methods are GRU and LSTM [25]. The experimental results of GRU and LSTM are shown in the Tables 2 and 3 below. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Time** & \multicolumn{2}{c}{**1 Day**} & \multicolumn{2}{c}{**3 Days**} & \multicolumn{2}{c}{**7 Days**} & \multicolumn{2}{c}{**14 Days**} \\ \hline
**Method** & _mae_ & _rmse_ & _mae_ & _rmse_ & _mae_ & _rmse_ & _mae_ & _rmse_ \\ \hline GBDT & 0.852513307 & 1.23915234 & 1.300753798 & 1.720802339 & 1.611730345 & 2.147783524 & 2.186371979 & 2.778341512 \\ RF & 2.288058902 & 5.90082053 & 3.597995729 & 7.272703867 & 4.777261762 & 8.969278431 & 5.374765925 & 9.815889435 \\ KF & 0.389958846 & 0.618940732 & 0.646802982 & 1.080479235 & 0.900576924 & 1.46066052 & 1.260270091 & 1.842596171 \\ Our method & 0.373859393 & 0.542795005 & 0.57908497 & 0.866673458 & 0.766950071 & 1.196595851 & 1.248104149 & 1.819827343 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of experimental results of different methods. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**GRU** & **Situation** & **1 Day** & **3 Days** & **7 Days** & **14 Days** \\ \hline All points & Before assimilation & 0.6249 & 1.1209 & 1.8523 & 3.0078 \\  & After assimilation & 0.5490 & 0.9415 & 1.5366 & 2.7531 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Temperature prediction results under different prediction aging before assimilation. Figure 6: Average temperature in March, April, and May after assimilation. (**a**) Average temperature in March. (**b**) Average temperature in April. (**c**) Average temperature in May. We found that by calculating the average error of different methods, when the data are fused, regardless of the prediction time limit of 1, 3, 7, and 14 days, the results of temperature prediction are much better than those before fusion, which shows that the method proposed in this paper is more effective. By calculating the average error of different methods, we find that based on the above analysis, considering that the satellite remote sensing data has a strong correlation locally, we cluster according to this correlation, then use the measured data to assimilate the remote sensing data in the cluster, and use the assimilation method proposed in this paper to assimilate the temperature data. Finally, the accuracy of temperature prediction results can be greatly improved.
# Parent and PHY Selection in Slot Bonding IEEE 802.15.4e TSCH Networks
## 5 Evaluation
### Experiment Methodology & Setup
#### 5.1.1 Simulator Experiments  
In the simulation experiments, a comparison was carried out between the proposed parent and PHY heuristic of Section 3 and the near-optimal, but computationally complex, GA scheduler that was introduced in our previous work [7]. The experiment workflow using the GA consisted of four consecutive steps. In the first step, the 6 TiSCH simulator was used to generate the network topologies [25]. Then, in the second step, these topologies were used as input to the GA. While optimizing the packet delivery ratio and keeping the radio on time to a minimum, the GA solved the slot bonding problem, based on the generated topologies, in a near-optimal manner. The simulator and the GA are publicly available ([[https://github.com/imec-idlab/tsch-slotbonding-ga-simulator](https://github.com/imec-idlab/tsch-slotbonding-ga-simulator)]([https://github.com/imec-idlab/tsch-slotbonding-ga-simulator](https://github.com/imec-idlab/tsch-slotbonding-ga-simulator)), accessed on 8 May 2021). During the third step, our so-called feasibility algorithm generated an overall TSCH schedule for all network nodes, using the best possible solution found by the GA (including the parent and PHY selection and the number of slots for each node). In the fourth and last step, the resulting schedule was used in the 6 TiSCH simulator as a centralized schedule to make static schedule allocations in the TSCH experiments. For a more elaborate description and illustration of this experiment setup, the reader is referred back to our previous work [7].  
The proposed heuristic only makes parent and PHY selections and does not allocate (bonded) slots. Therefore, using the network topology created by the 6 TiSCH simulator in the first step of the experiment process, the proposed heuristic made the parent and PHY selections and fixed these selections, and afterwards the GA was configured to find the best slot allocation solution for those parent and PHY choices. The reason for keeping the GA scheduler for the slot allocations was to have a fair comparison for the PHY and parent selection, without having influence from (sub-optimal) slot allocation. This implementation is publicly available ([[https://github.com/imec-idlab/tsch-slotbonding](https://github.com/imec-idlab/tsch-slotbonding)]([https://github.com/imec-idlab/tsch-slotbonding](https://github.com/imec-idlab/tsch-slotbonding)), accessed on 8 May 2021).  
Figure 4: Illustration of the slot timings for the 1000 kbps and 50 kbps (bonded) slots.
# Mixed-Integer-Based Path and Morphing Planning for a Tensegrity Drone
###### Abstract  
This paper proposes a method for simultaneously planning a path and a sequence of deformations for a tensegrity drone. Previous work in the field required the use of bounding surfaces, making the planning more conservative. The proposed method takes advantage of the need to use mixed-integer variables in choosing the drone path (using big-M relaxation) to simultaneously choose the configuration of the drone, eliminating the need to use semidefinite matrices to encode configurations, as was done previously. The numerical properties of the algorithm are demonstrated in numerical studies. To show the viability of tensegrity drones, the first tensegrity quadrotor Tensodrone was build. The Tensodrone is based on a six-bar tensegrity structure that is inherently compliant and can withstand crash landings and frontal collisions with obstacles. This makes the robot safe for the humans around it and protects the drone itself during aggressive maneuvers in constrained and cluttered environments, a feature that is becoming increasingly important for challenging applications that include cave exploration and indoor disaster response.  
tensegrity drone; morphing planning; mixed-integer programming; big-M relaxation; configuration dataset +
Footnote †: journal: Applied sciences
## 2 System Design
### Ranging Measurement in Two Links  
The principle of dual one-way ranging (DOWR) measuring based on signal propagation delay in the inter-satellite link (ISL) is shown in Figure 4. The MS and SS simultaneously send GNSS-like signals with their own time tags to each other, and then exchange their own measured pseudo-range and other information, such as the satellite number, subframe number, and local time [18; 19].  
By receiving the opposite satellite's signal, two one-way pseudo ranges can be described as  
\[\rho_{12}(t_{1})=c\cdot(-\Delta t(t_{1})+\tau_{s1}+\tau_{12}(t_{1})+\tau_{r2}+ \varepsilon_{12}), \tag{5}\]  
\[\rho_{21}(t_{2})=c\cdot(\Delta t(t_{1})+\tau_{s2}+\tau_{21}(t_{2})+\tau_{r1}+ \varepsilon_{21}), \tag{6}\]  
where \(\Delta t\) is the clock error between the MS and SS, \(\tau_{12}(t_{1})\) and \(\tau_{21}(t_{2})\) are the signal propagation delays, and \(\tau_{s1}\), \(\tau_{s2}\), \(\tau_{r1}\), \(\tau_{r12}\) refer to the transceiver's time delay of the MS and SS, respectively. \(\varepsilon_{12}\) and \(\varepsilon_{21}\) are measurement noises, which contain antenna phase center movement, device delay error, the noise of transmitter and receiver, ionospheric delay error, multipath error, and other noise sources.  
The instantaneous relative distance \(D_{12}(t_{1})\) and instantaneous clock error \(\Delta t(t_{1})\) can be calculated by adding and differencing Equations (5) and (6) as  
\[D_{12}(t_{1})\approx\frac{\rho_{12}(t_{1})+\rho_{21}(t_{2})}{2}=c\Bigg{[} \frac{\tau_{12}(t_{1})+\tau_{21}(t_{2})}{2}+\frac{\tau_{s1}+\tau_{s2}}{2}+ \frac{\tau_{r2}+\tau_{r1}}{2}+\frac{\varepsilon_{12}+\varepsilon_{21}}{2} \Bigg{]}, \tag{7}\]  
\[\Delta t(t_{1})=\frac{\rho_{21}(t_{2})-\rho_{12}(t_{1})}{2c}-\frac{\tau_{21}( t_{2})-\tau_{12}(t_{1})}{2}-\frac{\tau_{s2}-\tau_{s1}}{2}-\frac{\tau_{r1}- \tau_{r2}}{2}-\frac{\varepsilon_{21}-\varepsilon_{12}}{2}. \tag{8}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Synchronization**} & \multicolumn{4}{c}{**Frame Guide (48 bits)**} & \multicolumn{4}{c}{**Duty Section (9\% bits)**} \\ \cline{2-10}  & **Satellite** & **Local** & **Subframe** & **Reserved** & **Opposite** & **Massuing** & **Opposite Subframe** & **Local Pseudo** & **Reserved** \\  & **ID** & **Time** & **Number** & **Bits** & **Satellite ID** & **Opposite Time** & **Number** & **Range** & **Bits** \\ \hline
1100010010 & 4 bits & 32 bits & 4 bits & 8 bits & 4 bits & 32 bits & 4 bits & 48 bits & 8 bits \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detail structure of inter-satellite transmission subframe.  
Figure 4: Principle of dual one-way ranging measuring.  
Hence, differencing Equations (5) and (6) eliminates the clock error between the two satellites, and the instantaneous clock error is calculated by Equation (7), compensation approaches can be employed to mitigate, or even remove the influence of clock error.
## 1 Introduction
### National Estuarine Research Reserve Program and Rachel Carson Reserve Study Area  
In order to combat the degradation of estuaries in the United States over the last century, the Coastal Zone Management Act of 1972 was passed to help conserve these critical systems. This act resulted in the creation of the National Estuarine Research Reserve System (NERRS), a system of reserves meant to foster long-term research and monitoring, education, and stewardship of our estuarine natural resources. A major purpose of these reserves is to provide an undeveloped estuarine testbed, informing local, regional and national management decisions. To accomplish this goal, NERRS has implemented system-wide research and monitoring programs to address various stewardship issues, which includes a Habitat Mapping and Change plan [29]. The Habitat Mapping and Change Technical Committee advises reserves to conduct change analyses at least once every 10 years.  
The North Carolina (NC) NERR is one of 28 reserves currently in the NERRS, consisting of several sites along the NC coast that encompass the gamut of physical and ecological environments present in NC's extensive estuarine network. Centrally located along the coast of NC, the Rachel Carson Reserve (RCR, Figure 1) was created in the 1980s and consists of several fetch-limited barrier islands (Bird Shoal, Town Marsh, Carrot Island, and Horse Island) and a saltmarsh island complex (Middle Marsh). The area is subject to semidiurnal tides with a 0.9 m tidal range, exhibiting extensive saltmarsh platforms, intertidal oyster reefs, shallow seagrass beds, and tidal flats across a low-lying landscape. Vegetated dunes and upland habitats occur along parts of Bird Shoal, Town Marsh, and Carrot Island, which have been historically augmented by dredge spoil. Not only is the RCR ecologically significant [30], as fetch-limited barrier islands [31], the RCR islands help buffer the town of Beaufort from wave energy and storm surge, making their persistence critical for the coastal community.  
Figure 1: The Rachel Carson Reserve is located in the Southeastern United States, in the coastal state of North Carolina in Carteret County off the coast of the town of Beaufort. RapidEye, WorldView-3, and unoccupied aircraft systems (UAS) imagery is shown in true color for a section of emergent wetland and scrub-shrub in the Northwestern corner of the study site for comparison. Satellite imagery courtesy of DigitalGlobe Foundation and Planet Inc. Note: Color needed for this figure.
# Wavelength Selection for Satellite Quantum Key Distribution
## 2 Transmittance and Spectral Radiance
### Atmospheric Windows  
The International Commission on Illumination classifies optical radiation into three categories, IR-A (700-1400 nm), IR-B (1400-3000 nm), and IR-C (3000 nm-1 mm), sub-classified into [50]: near-infrared (NIR) 750 nm-1.4 \(\upmu\)m; short-wavelength infrared (SWIR) 1.4-3 \(\upmu\)m; mid-wavelength infrared (MWIR) 3-8 \(\upmu\)m; long-wavelength infrared (LWIR) 8-15 \(\upmu\)m; and far-infrared (FIR) 15 \(\upmu\)m-1 mm. We adopt these sub-classifications, with the addition of the visible region (400-750 nm).
# An Efficient Framework Model for Optimizing Routing Performance in VANETs
## 5 Simulation and Performance Analysis
### Results and Discussion
#### 5.3.5 Networks with Common Characteristics  
The behavior of each algorithm shows the capability of delivering, gathering, and processing route knowledge of different dynamic mobility patterns. The effectiveness of each approach is described by the functional relationship between the QoS metrics that influence the performance routing protocol and the mathematical approach. The proposed protocol help to improve the operations functionality to reduces link failures and conjunction problem by collecting accurate and up-to-date data about VANET network timely. Therefore, the proposed protocol enhances the stability and flexibility of the network by balancing between the QoS requirements and the OLSR configuration constraints. Table 9 demonstrates the summary of the main performance metrics obtained from each protocol in terms of packet loss, NRL, PDR, and E2 ED. The table interprets the median results in Tables 5-8. The median results were chosen because they are real values unlike the mean values.