esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 BDS Precise Orbit and Clock Products Generation
### Processing Strategy  
The Position and Navigation Data Analyst (PANDA) developed by GNSS Research Center of Wuhan University [21] was employed to conduct POD, PCE, and PPP in this study. Table 2 summarizes the observation models, dynamical models, and estimated parameters for POD and PCE.  
BDS orbital parameters were generated only from BDS observations and fully independent from the other GNSS observations. Besides, epoch-wise satellite and receiver clock offsets, station coordinates, zenith troposphere delay (ZTD), as well as float ambiguities for each satellite were also estimated. There are 68 GNSS stations with 30 s intervals in Figure 1, and their observations from 22 to 28 April were processed. We took three consecutive days as one orbit arc in least-squares estimation. Zero-difference ionosphere-free observations of B1 and B2 phase and code observations were used to eliminate the ionosphere delays effect. The data processing interval was 30 s and the elevation cutoff angle was 7\({}^{\circ}\). To detect bad measurements and remaining cycle slips, raw measurements were further cleaned by analyzing posterior residuals derived from the estimation. The prior orbits were originated from the broadcast ephemeris as initial values. NNOR with an external atomic clock was selected as the reference clock. The BDS orbit and 30 s clock corrections were obtained synchronously, which were called BDS WHU orbits and WHU 30 s clocks, respectively.  
For the procedure of PCE, the BDS orbits and station coordinates were held fixed. GPS orbits came from the IGS final products. Besides satellite clocks, station clocks, ZTD, as well as float ambiguities were estimated in the PCE procedure. The BDS and GPS 1 s satellites' clock products were generated by the 1 s GNSS data from 13 stations, shown in Figure 1. Complete 24 h GNSS data starting from 10:00:00 24 April 2015 were processed. Initial satellite clocks were obtained from the broadcast ephemeris. It should be mentioned that the satellites clock corrections of BDS were processed independently, and the corresponding solutions were called WHU 1 s clock.  
Figure 1: Distribution of selected GNSS stations with 1 s (blue and yellow dots) and 30 s (red dots) sampling intervals data. The star is the epicenter of the 2015 Mw 7.8 Gorkha, Nepal, earthquake.
# Railway Lines across the Alps: Analysis of Their Usage through a New Railway Link Cost Function
## 2 The Proposed Cost Function
### Detailed Characteristics of the Alpine Railway Lines Taken into Account in This Study
#### 3.1.3. Turin--Lyon 'Frejus' Line  
On the Frejus line, on the Italian side, the maximum sum of grade and curve resistances is equal to 28 N/kN from Bussoleno to Salbertrand (22 km). From Salbertrand to Bardonecchia, the sum of grade and curve resistances varies and in particular it is equal to: 16 N/kN from Salbertrand to Oulx (6 km), 19.8 N/kN from Oulx to Beaulard (6 km) and 21.9 N/kN from Beaulard to Bardonecchia (5 km). Instead the sum of grade and curve resistances is equal to 31 N/kN, for only 3 km, between Bardonecchia and the beginning (on the Italian side) of the Frejus tunnel. On the French side, between Modane and the beginning of the Frejus tunnel, that is for 4 km, the maximum sum of grade and curve resistances is 31 N/kN; inside the tunnel, the maximum sum of grade and curve resistance is 31 N/kN for 7 km, from the beginning of the tunnel (on the French side) to the Italy/France border, while the rest of the tunnel (between the Italy/French border and the beginning of the tunnel on the Italian side), that is for 6.3 km, is flat. Between Modane (France) and Bussoleno (Italy), and vice versa, it is allowed: a maximum towable weight of 1150 t with double traction, and of 1600 t with triple traction (Osservatorio [35], in Ferrari [36]). The line on the French side, from St. Jean de Maurienne to Modane, shows a maximum sum of grade and curve resistances equal to 22 N/kN: this part of the line is operated with double traction with a maximum towable weight of 1600 t (source: interviews with Novatrans, one of the most important French MTOs, which operates on this line).  
The new Turin-Lyon line is composed of [35]:  
* The new Frejus base tunnel (sometimes called also Moncensio base tunnel), between Bussoleno and St. Jean De Maurienne, 57.5 km long, of which 45 in France and 12.5 in Italy, with a maximum sum of grade and curve resistances of 13 N/kN (which does not take place in the tunnel itself but in the two, short, adduction ramps on both side of the tunnel). This tunnel will be operated in single traction by the majority of MTOs, but given the locomotive taken into account in this paper, double traction has been considered.
* A new line connecting the freight village of Turin/Orbassano with Bussoleno: this line will be constructed in order to shorten the path between these two places;
* A new line connecting S. Jean de Maurienne with Avressieux, and the duplication of the existing line between Avressieux and Saint Andre Le Gaz: this line will also shorten considerably the current path, which follows the valley floors therefore it is quite long.  
In the 'project scenario', however, only the Frejus base tunnel was taken into account, and not the new lines on the Italian side and on the French side. In a future step of this research, the entire new Brenner and Frejus lines will be taken into account.
# A New Orbiting Deployable System for Small Satellite Observations for Ecology and Earth Observation
## 2 Small Satellites for VIS, TIR, and MW Remote Sensing
### Evolution of Small Satellites
It represented a major milestone in the EO industry, for being the first fully commercial operational class EO system using a constellation of five micro-satellites (~150 kg), which provided exceptional performance for their class and a full end-to-end system. A dedicated Spacecraft Control Centre and an 80 Mb /s X-band data downlink ground station service was able to organize, acquire, and process up to 5,000,000 km\({}^{2}\) of images every day from the five-band multispectral imager (RGB, Red Edge, and NIR bands), with 6.5 m GSD to generate land information products [41]. In 2013, SkyBox, with its micro-satellite SkySat-1, provided 0.9 m resolution images in Panchromatic filters and became the first company ever to capture and release HD videos acquired in space [42]. The resolution of the SkySat satellite images and videos is high enough to observe objects that impact the global economy, such as terrain, vehicles, and shipping containers [43]. Concurrently to the progress of SmallSats and the widespread of constellations, the turn of the XXI century also represented a milestone for a particular class of \"very\" small satellites, i.e., the CubeSats. CubeSats were born as a collaborative project between [PERSON] at Stanford University's Space Systems Development Laboratory (SSDL) and Prof. [PERSON] at California Polytechnic State University (Cal Poly) [44]. The idea was to rapidly develop a pico-satellite that could give university students accessibility to space for conducting scientific experiments and testing out new technologies [45; 46; 47]. Indeed, the standardized design and compact dimensions allowed to reduce both costs and developing time, as well as to use the same type of deployment system (Poly-PicoSatellite Orbital Deployed (P-POD)), which provides a verified and reliable procedure, preventing any potential challenges in the phase of launch as a secondary payload [48; 49]. These characteristics, together with the advancement in the miniaturizing technology, have made CubeSats the key element in the space industry, not only for the EO [50; 51; 52], but also for the exploration of our solar system [53; 54; 55].
# Joint Location-Allocation Model for Multi-Level Maintenance Service Network in Agriculture
## 3 Problem Description and Model Formulation
### Nomenclature  
As for the proposed MLFLAP, two subproblems, such as the service station location and allocation problem and the spare part centre location problem, are considered in this paper, which can be seen as a joint optimization problem integrating with the P-region problem and set covering location problems. Thus, this paper formulates an MILP-based solution model. Several parameters and variables are defined first:  
(1) Sets:  
\(I\)  
Set of service units indexed by \(i\) or \(i^{\prime}\);  
\(J\)  
Set of the potential location of service stations indexed by \(j\), \(J\subseteq I\);  
\(K\)  
Set of the potential location of spare part centres indexed by \(k\), \(K\subseteq I\);  
\(A\)  
Set of adjacent service unit pairs.  
(2) Parameters:  
\(n\)  
The number of service units;  
\(m\)  
The number of service vehicles;  
\(l\)  
The number of chosen service stations;  
\(d_{i}^{i}\)  
Distance from the potential location of service station \(j\) to service units \(i\);  
Distance between the potential location of service station \(j\) and potential  
\(D_{j}^{k}\)  
location of spare part centre \(k\);  
\(c_{1}\)  
The construction costs per spare part centre;  
\(c_{2}\)  
The usage costs per service vehicle;  
\(c_{3}\)  
The service mileage costs for service stations to service units per kilometre;  
\(r_{i}\)  
Maintenance demand requirements in each service unit \(i\);  
\(D1_{max}\)  
Maximal service distance for service stations to service units;  
\(D2_{max}\)  
Maximal service distance for spare part centres;  
\(C\)  
The maximal service capacity per service vehicle;  
\(M\)  
A constant more than the number of service units.  
(3) Variables:  
\(x_{k}\)  
If the spare part centre is set in location \(k\), the value is 1; otherwise, it is 0;  
\(y_{j}\)  
If the service station is set in location \(j\), the value is 1; otherwise, it is 0;  
\(z_{i}^{j}\)  
If service unit \(i\) is served by the potential location of service station \(j\), the value is 1; otherwise, it is 0;  
\(v_{j}\)  
The number of service vehicles allocated to potential locations \(j\);  
\(u_{j}^{k}\)  
If the potential location of service station \(j\) can be served by the potential  
location of spare part centre \(k\), the value is 1; otherwise, it is 0;  
The contiguity evaluation for optimal location-allocation results. The  
\(w_{ii^{\prime}}^{j}\)  
variable for contiguity evaluation to indicate the imaginary flow from  
service unit \(i\) to service unit \(i^{\prime}\), which is served by service station \(j\).  
Figure 1: A multi-level service network case.
## 4 Results and Discussion
### Verification of Angular and Axial Dependences
Above Re = \(3\times 10^{4}\), the value of \(A_{\rm 0,axi}\)  
Figure 4: Observed mean maximum amplitudes (circles) and \(\sigma\)s (bars) obtained from four different axial positions: (**a**) D0.2n3, (**b**) D0.2n30, (**c**) D0.2n50 and (**d**) D0.2n70 at \(P_{0}=2\) bar (black), 3 bar (red) and 4 bar (green) and simulated amplitudes (lines). increases rapidly with increasing Re. The \(A_{0,\text{axi}}\) values are fitted by an exponential growth \(A_{0,\text{axi}}(Re)=A_{0}^{Re}e^{Re/\tau}+A_{0}\). As shown in Figure 5f, the observed values are in good agreement with the values calculated by the equation with \(A_{0}^{Re}\) = 1.07 \(\times\) 10\({}^{-3}\), \(\tau\) = 5658 and \(A_{0}=0.199\) (\(R^{2}\) = 0.972). For a given value of \(\vartheta\), the \(u_{leak}\) displacement in Equations (50) and (51) is exponentially attenuated by the axial distance \(\eta\) via \(F_{\zeta z}^{4}\) or \(F_{\eta z}^{4}\) derived from the [PERSON]'s function. Note that from Equations (4), (23) and (27), \(F_{\zeta z}^{4}(\eta)\propto\kappa_{\eta}e^{-\kappa_{\eta}|\eta|}\) and \(F_{\eta z}^{4}(\eta)\propto\kappa_{\eta}^{2}e^{-\kappa_{\eta}|\eta|}\). This leads us to propose a semi-empirical equation to fit the observed axial dependence of \(A_{\text{obs}}\) as follows:  
\[A_{\text{sim}}(\eta)=Ae^{-\kappa_{z}|\eta|}+A_{0,\text{axi}}, \tag{57}\]  
where \(A\) is the pre-exponential factor, given as \((A_{\text{obs,max}}-A_{0,\text{axi}})\). This equation reproduces exactly the same values as in the case of Equation (56); henceforth, we will use Equation (57) instead of Equation (56) to analyze the axial dependence of \(A_{\text{obs}}\). Figure 5: (**a–e**) Observed mean maximum amplitudes (circles) and \(\alpha\)s (bars) obtained from five different hole diameters: (**a**) D0.2\(\theta\)180, (**b**) D0.3\(\theta\)180, (**c**) D0.5\(\theta\)180, (**d**) D0.8\(\theta\)180 and (**e**) D1.2\(\theta\)180 at P = 2 bar (black), 3 bar (red) and 4 bar (green), simulated amplitudes (lines) and (**f**) plot of \(A_{0,\text{axi}}\) values (circles) vs. Reynolds NO and the fit (line) by one-phase exponential growth.
## Conventional GTR and GBR
### Membranes with Zone-Dependent bioactivity
#### 2.3.2 Multilayer Membranes with a Functionally Graded Structure  
For true regeneration during GTR and GBR, the membrane needs to modulate new periodontitis by enhancing alveolar bone growth while preventing the downgrowth of gingival tissue [48]. By using the e-spinning technique, each layer can be fabricated with different materials with different features [46; 47; 48]. The functionally graded membrane (FGM) consists of a core layer (CL) and two functional surface layers (SL), each facing bone (nano-HA (hydroxyapatite)) and epithelial (MET (metronidazoleae)) tissue. The CL is composed of a poly (D,L-lactide-\(co\)-caprolactone) (PLCL) layer surrounded by two composite layers composed of a gelatin/polymer ternary blend [29]. The features of CL, such as favorable elasticity and high tensile strength under hydration (8.7 MPa), make the membrane stable during the regeneration period [48]. However, the other two SL layers showed decreased tensile strength under dry or hydrated conditions [48].  
The schematic structure and degradation process are described in Figure 1. The 3D structure is formed with different nanofibers in various directions according to their manufacturing condition, and they have 3D space within their structure for revascularization and cell infiltration. The structure can also be understood from its sagittal view, and has two surface layers and one core layer [29]. These structural features induce an appropriate degradation process started by hydration and revascularization, and when some degree of volume loss of scaffold is obtained, progenitor cell invasion follows. After cells progressively differentiate into tissue-specific cells, the membrane itself is metabolized to make space for newly formed tissues. The phase of degradation of 3D scaffolds is important due to the harmony of new tissue formation.  
Figure 1: The schematic structure and degradation process of three-dimensional (3D) scaffolds. **(a)** Schematic structure of 3D scaffold with three different nanofibers; they form an inner space with a different biological feature for zone-dependent reaction during the regeneration period. **(b)** Sectional layers of a functionally graded membrane; two surface layers and one core layer with a different function are shown. **(c)** The degradation process of 3D scaffolds related to biologic reactions within a tissue. **(d)** The change of fibers consisting of a 3D structure according to the degradation procedure; space is not a hollow space but newly regenerated tissue.
## 4 An Evolution Equation of Damage Accumulation for Welding Materials  
Damage variable \(D\) is used to represent the damage degree of material in damage theory [59]. Meanwhile, the experimental results show that the macroscopic mechanical behavior of the damage of the welding material can be expressed in both energy and deformation. Therefore, in order to describe the microscopic structural damage mechanism of the material in macroscopic mechanics, the evolution equation of the damage variable \(D\) of the welding material under cyclic loading is established based on the experimental results. As shown in Equation (2), the Park-Ang [58] model is suitable for structural damage cumulative analysis, in which \(\delta_{m}\) and \(\delta_{u}\) is the maximum deformation with loading and ultimate deformation of material respectively, \(\int dE\) is the cumulative dissipation of plastic energy, \(F_{y}\delta_{u}\) is equivalent to the ultimate hysteretic energy under the monotonic load of perfect elastic-plastic condition.  
Based on the above model, an evolution equation of damage variable \(D\) (2) for calculating the cumulative damage of the welding material is established, as seen in Equation (3), in which the effects of plastic strain and cumulative plastic energy dissipation on the damage accumulation of the welding material are considered comprehensively. In Equation (3), \(\varepsilon_{m}^{p}\) is the maximum plastic strain in cyclic process, \(\varepsilon_{u}^{p}\) is the ultimate plastic strain of materials that can be obtained from the uniaxial tensile test, \(E_{i}\) is the plastic deformation energy dissipation of No. \(i\) semi-cycles, which is equal to the area of the hysteresis loop in numerical value, \(E_{u}\) is the ultimate hysteretic energy under monotonic loading, which can be calculated from the area enclosed by the stress-strain curves of the monotonic tensile test and the coordinate axis, \(\lambda\) is a parameter means the weight of cumulative plastic energy dissipation, which can be calculated by the cyclic loading test results. Compared with Equation (2), the energy part of the model in Equation (3) is no longer confined to the perfect elastic-plastic condition, and the fact that the value of damage variable \(D\) is always less than or equal to 1 until fractured is ensured.  
\[D=\frac{\delta_{m}}{\delta_{u}}+\beta\frac{\int dE}{F_{y}\delta_{u}} \tag{2}\]  
\[D=(1-\lambda)\frac{\varepsilon_{m}^{p}}{\varepsilon_{u}^{p}}+\sum_{i=n_{1}}^{ n}\lambda\frac{E_{i}}{E_{u}} \tag{3}\]
## 3 Results and Discussion
### Properties of the Nanocomposite Films
#### 3.2.4 Thermostability  
Thermogravimetric analysis (TGA) of the gelatin and gelatin/CuSNP films and its derivative (DTG) is shown in Figure 5. The first weight loss occurred at \(\sim\)60-120 \({}^{\circ}\)C owing to the disappearance of physisorbed moisture. The subsequent decomposition appeared around 200 \({}^{\circ}\)C and touched a maximum of about 250 \({}^{\circ}\)C due to the degradation of glycerol. The third decomposition was due to the thermal degradation of the gelatin matrix, observed at 310 \({}^{\circ}\)C in the neat gelatin film and 310 and 323 \({}^{\circ}\)C in the Gel/CuS\({}^{0.5}\) and gelatin/CuSNP\({}^{2.0}\) films, respectively. The addition of a lower concentration of nanoparticles (0.5 wt %) did not change the gelatin degradation temperature. However, the addition of a high content of CuSNP (2.0 wt %) slightly increased the decomposition temperature of gelatin, which may be due to the good crosslinking interactivity of CuSNP with gelatin [47].  
In contrast to this result, it was reported that the thermal properties of the agar-based film were not significantly changed by the inclusion of CuSNP [40]. Conversely, the thermal stability of the gelatin film has been reported to be substantially increased by the addition of ZnONP and AgNP [17; 19]. The final residues at 600 \({}^{\circ}\)C of the gelatin, gelatin/CuSNP\({}^{0.5}\), and gelatin/CuSNP\({}^{2.0}\) nanocomposite films were 26.2, 28.3, and 29.9%, and the increase was presumably due to the content of heat-stable nanoparticles. The relatively high residual content of gelatin-based films is likely due to the non-flammable raw materials and impurities of the polymer [40].
# Seismic Stability of Dual Tunnels in Cohesive-Frictional Soil Subjected to Surcharge Loading
## 2 Problem Definition  
Figure 1 presents the main influential factors of a dual tunnels system. Following previous studies [6; 7; 8; 9; 12; 15; 16; 22; 23; 24; 25; 26; 29], tunnel length is considered as infinite; therefore, the plain strain model is suitable for this issue. The size of this numerical model is \(60D\) in width and \(30D\) in depth, which is big enough to avoid the boundary effect. The diameter of the tunnels is \(D\). The dimensionless horizontal distance between the tunnels is \(S/D\). The dimensionless cover depth of the voids is \(H/D\). The continuous distributed load \(\sigma_{\mathrm{s}}\) is applied to the surface of the soil domain. This study assumes the soil to be isotropic and homogeneous. Additionally, the soil obeys the Mohr-Coulomb yield criterion and associated flow rule with cohesion \(c\), internal friction angle \(\varphi\) and unit weight \(\gamma\). Following previous studies [29], the above parameters have the following values: \(\varphi=5^{\circ}\), \(10^{\circ}\), \(15^{\circ}\), \(20^{\circ}\); \(\gamma D/c=0\) and \(1\). The numerical model is a rigid half space, and the top surface is free. Additionally, seismic action is simulated by pseudostatic method, i.e., applying an earthquake acceleration factor \(k_{h}\) to the soil domain [7; 16]. Additionally, the cases of \(k_{h}=0\), \(0.05\), \(0.1\), \(0.15\) and \(0.2\) are considered in this study. For a more convenient expression of the seismic bearing capacity, a stability factor \(Nc\)s = \(\sigma_{\mathrm{s}}/c\)[34] is introduced, which can be expressed as follows:  
\[N_{\mathrm{cs}}=\frac{\sigma_{\mathrm{s}}}{c}=f\bigg{(}\frac{H}{D},\frac{S}{D}, \frac{\gamma D}{c},\phi,k_{h}\bigg{)} \tag{1}\]
## 5 Parametric Study on the Thermomechanical Performance
### Influence of Core Thickness  
It is obvious that structural flexibility depends on the thickness of the corrugated core layer. Here we performed several parametric investigations to explore the effect of different core thicknesses on the thermomechanical of the UCS and BCS. For this purpose, two sandwich structures (UCS and BCS) with two core thicknesses (0.1 mm and 0.3 mm) were considered. The emissivity of the outer face sheets was 0.6. The weight of the whole sandwich structure was decreased by 14.4% with a 0.1 mm thick core and increased by 14.4% with a 0.3 mm thick core.  
Figure 20 shows the deflections of the UCS and BCS with the two different core thicknesses. Figures 16 and 17 reveal that the general tendency of the deflection-time curves remained unchanged when the core thickness was decreased or increased. The maximum deflections increased with a decrease in the core thickness and decreased with an increase in the core thickness. The UCS and BCS with a 0.1 mm thick core resulted in maximum deflections of approximately 2 mm and 2.5 mm, respectively, which we considered to be close to the deflection limit. At the same time, the sandwich structures with a 0.3 mm thick core resulted in a significant decrease in the maximum deflection. Meanwhile, the von Mises stress of the sandwich structures was obtained to assess the localized failure. Figure 21 shows the von Mises stress distribution over both sandwich structures with the two core thicknesses. The maximum stress of 390 MPa was located in the webs close to edges and four corners of the sandwich structures. However, this high stress was very localized in the UCS and BCS with a 0.3 mm thick core, while it was distributed in more areas in the UCS and BCS with a 0.1 mm thick core. Specifically, the high stress was mostly located entirely in the two end webs of the UCS, as shown in Figure 21a, and was located in all the cells at the corners of the BCS, as shown in Figure 21b. The high stress of 390 MPa exceeds the yield strength of stainless steel (210 MPa at 25 \({}^{\circ}\)C), so there would still be much plastic deformation in the core of the sandwich structures with the 0.1 mm thick core. Figure 22 shows the equivalent plastic strain of the sandwich structures. Our results demonstrated that the plastic strain was significant in the areas near the edges and corners. The high stress of 390 MPa did not reach the ultimate strength of the material, but for the reusability of the sandwich structures, it would reach fatigue and failure after several operating missions. Thus, we concluded that the core thickness of 0.1 mm would not be suitable for this design and the sandwich structures with the 0.2 mm thick core are the minimum thickness for the design of the UCS and BCS under the high heating rate in this study.  
Figure 19: As-fabricated TPS panel with the BCS.