esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Initial Material Characterization
It quantifies material hardening or even softening that occurs when it begins to yield. In elastic-perfectly-plastic cases, the tangent modulus tends to be zero. Rare materials exhibit elastic-perfectly-plastic behaviors, so most materials follow the multilinear behavior with included tangent modulus. Since the tangent modulus indicates a downward slope of the stress-strain curve beyond the yielding point, the material has low stiffness features in the plastic region. Thus, it is to be expected that lattice diagrams follow the theoretical shape indicated in ISO 37:2024(en) [35] when the stress-strain curve drops downward, excluding any hardening after yielding. Material mechanical properties (Table 2) were therefore determined through standard tensile experiments using FDM-fabricated tensile specimens and validated through a comparison of available parameters in literature [39; 40; 41]. In constructing the FEA model, the multilinear plasticity hardening approach accounts for the material's behavior beyond the elastic region. The model incorporates a series of linear segments representing the stress-strain relationship, each defined by a tangent modulus. This approach accurately captures the material's decreasing stiffness and potential strain hardening or softening. A true strain-stress condition of test results was defined to determine a multilinear plasticity hardening model for FEA analysis. A true mean strain stress has been segmented afterward to define a multilinear plasticity hardening model for Ansys (Figure 10). By integrating these detailed material properties into the FEA model, the analysis can predict the mechanical response of lattice structures under various loading conditions. The model's accuracy is ensured by adhering to the theoretical framework provided by standards such as ISO 37:2024(en), which describes the expected behavior of materials under stress. \begin{table}
\begin{tabular}{c c} \hline \hline
**Major Material Parameters** & **Value** \\ \hline Young’s modulus \(E\) & 2080 MPa \\ \hline Poisson’s ratio \(\mu\) & 0.49 \\ \hline Yield stress \(S_{y}\) & 32.87 MPa \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The critical ASA parameters from the characterization used in the multilinear isotropic hardening model.
# Seismic Performance of Reinforced Concrete Short Columns Subjected to Freeze-Thaw Cycles
## 2 Experimental Program
### Material Properties  
The mechanical properties of steel were tested. The yield strength and ultimate strength of the longitudinal bar were 366 MPa and 514 MPa, respectively. The elastic modulus of the longitudinal bar (\(E_{\text{s}}\)) was \(2.0\times 10^{5}\) MPa. As for the transverse bar, the corresponding yield strength, ultimate strength, and elastic modulus were 303 MPa, 470 MPa, and \(2.1\times 10^{5}\) MPa, respectively.  
Table 2 shows the detailed mixture of concrete composition for columns. The ordinary Portland cement, with a 28 day nominal compressive strength of 42.5 MPa, was used in this research. The air-entraining agent was not included. The standard compression test [28] for reference concrete cubes with a side length of 150 mm was carried out after subjecting every 50 numbers of freeze-thaw cycles. Each set has three cubes for obtaining the average strength. The mean cube concrete strength values \(f_{cu}\) are reported in Table 2 and were converted to the cylinders strength \(f_{\text{c}}\)' by the empirical formula [25].  
Since the freeze-thaw condition described in this paper is rather different from the ones described in the commonly adopted standards, including ASTM 666 [29] and RILEM TC176-IDC [30], the microstructure of concrete under varying FTCs was also investigated using a scanning electron microscope (SEM) after compressive tests. The images of the SEM tests are shown in Figure 3. We observed that the cement hydration products of undamaged concrete have high density and is tightly connected. After 100 FTCs, the pore structure was partially destroyed, and microcracks appeared mainly due to the periodic pressure induced by water freezing and thawing. Then, the cement hydration products became loose, and the C-S-H gel changed from a whole part into acicular island structures, which were intertwined to form a three-dimensional network as a result of 200 FTCs. In addition, the complete pore structure disappeared.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**w/c**} & **Water** & **Cement** & **Sand** & **Stone** & \(f_{\text{cu},\emptyset}\) & \(f_{\text{cu},\text{50}}\) & \(f_{\text{cu},\text{100}}\) & \(f_{\text{cu},\text{150}}\) & \(f_{\text{cu},\text{200}}\) \\  & **(kg/m\({}^{3}\))** & **(kg/m\({}^{3}\))** & **(kg/m\({}^{3}\))** & **(kg/m\({}^{3}\))** & **(MPa)** & **(MPa)** & **(MPa)** & **(MPa)** & **(MPa)** \\ \hline
0.42 & 203 & 483 & 688 & 1076 & 56.7 & 54.3 & 51.48 & 47.85 & 43.15 \\ \hline \hline \end{tabular}  
* Notes: \(f_{\text{cu},\text{4}}\) means the compressive strength of cubic concrete specimens after number \(i\) FTCs, where \(i=0,50,100\), 150, and 200.  
\end{table}
Table 2: Composition and strength of concrete (units: kg/m\({}^{3}\) and MPa).  
Figure 2: Details of environmental chamber.
## 1 Introduction  
High-energy and high repetition frequency lasers are urgently required for applications in Optical Parametric Chirped-Pulse Amplification (OPCPA) pump sources, particle acceleration, medical treatment and material processing. The gain medium used in the laser is an important subject for consideration. Good gain media need to have high gain characteristics and maintain high optical quality. However, in order to reach high energy and high repetition frequency, the unavoidable accumulation of heat occurs in the gain medium. Only a part of the energy is used in laser amplification, and most of the energy is absorbed by the medium, generating excessive heat. This will not only cause the phenomenon of thermal lensing [1; 2] with the beam quality affected, but can also cause the gain medium to rupture and be damaged.  
The applications such as laser inertial confinement nuclear fusion, laser shock strengthening technology and ultrastrong ultrashort pulse laser creation require high energy and a high repetition frequency. Amongst existing laser materials, phosphate neodymium-doped glass (Nd:glass) can achieve high energy storage and output, but it cannot support high repetition frequencies. The high thermal conductivity of Nd-doped yttrium aluminum garnet (Nd:YAG) supports high repetition frequency operation, but it is not conducive to high energy output [3].  
In recent years, the Nd-doped silica glass (NDSG) has gradually gained attention. It not only has the theoretical parameters for generating high energies [4; 5], but also presents the prospect of high repetition frequency lasers because of its high thermal shock resistance parameter. As high a level as 40 J has been reported for the laser output energy generated by a NDSG oscillator system [6]. The preparation method of NDSG has also gradually developed, from the traditional glass melting method [7] through the chemical vapor deposition (CVD) method [8], the sol-gel method [9], the zeolite method [10], and many others. Moreover, the preparation technology is still continuously improving.  
In these previous studies, the laser amplification in NDSG was based on the flash lamp pumping. In comparison, the laser diode (LD) pumping is characterized by a narrower illumination wavelength, which can more effectively match the absorption spectrum of the gain medium. The LD pumping also produces less thermal deposition, and its thermal management is better. Despite these advantages, the LD pumping has not been widely used due to cost and other factors.  
This study is aimed at the characterization of the laser amplification in NDSG with LD pumping, and the description of the wavefront distortion under different clamping conditions. Then, the results of testing the high thermal toughness of NDSG and the uniformity of the material are described. Finally, the potential and current deficiencies of NDSG are discussed.
## 3 Public Datasets for Scene Understanding Methods
### Dataset Quality  
A backlog in data quality (Figure 4), caused by poor sensor technologies used in the process of capturing indoor/building datasets, is a relevant issue for industrial application and research. Most indoor datasets originate between 2015 and 2017, while most large-scale outdoor datasets originate from the period between 2019 and today. This observation correlates with the finding in Figure 3b, where indoor datasets tend to be captured on RGB-D devices, while most urban datasets rely on laser scanning technology. Sensor arrays and stationary rotating cameras help with registration accuracy and scene completeness as they produce 360\({}^{\circ}\) panoramic depth images like in the S3 DIS dataset [59], but they still suffer from higher measuring inaccuracy if compared to Light Detection And Ranging (LiDAR) (manufacturer's information Matterport: precision of \(\pm 1\%\) within max. 4.5 m range [87]). Most outdoor datasets use costly mobile or terrestrial laser scanning technologies in combination with synchronized panoramic cameras to color the point clouds, Global Positioning System (GPS) for georeferencing, and inertial measurement units (IMUs) for motion correction. The industry standard rotating LiDAR sensors for mobile laser scanning (MLS) applications from companies like Velodyne Inc. and Ouster Inc. claim range accuracy down to \(\pm 0.5\) cm within 35 m (down to \(\pm 2.0\) cm within max. 200 m) range [88]. Laser sensors for TLS applications from companies like Leica Geosystems AG sell laser scanners with 3D accuracy down to \(\pm 3.2\) mm within 35 m (\(\pm 5.6\) mm within 100 m) range [89]. By utilizing laser scanners and multi-sensor-fusion, the latest urban datasets allow significantly higher data quality [37; 82].
## 2 Signal Temporal Logic
  
Note that the truth value of an STL formula \(\phi\) over a trajectory \(\tau\) at time \(t\) thus can be decided at time \(t+\text{duration}(\phi)\) if the values \(\tau(k)(x)\) are known for all time instants \(k\in\{t,\ldots,t+\text{duration}(\phi)\}\) and all variable names \(x\) occurring in \(\phi\), where \(\text{duration}(\phi)\) is defined as follows:  
\[\text{duration}(\top) = 0,\] \[\text{duration}(g\geq c) = 0,\] \[\text{duration}(\
eg\phi) = \text{duration}(\phi),\] \[\text{duration}(\phi\vee\psi) = \text{max}(\text{duration}(\phi),\text{duration}(\psi)),\] \[\text{duration}(\phi\mathbf{U}_{[t_{1},t_{2}]}\psi) = \text{max}(t_{2}-1+\text{duration}(\phi),t_{2}+\text{duration}( \psi)).\]  
Unfortunately, the ground-truth values of \(\tau(k)(x)\) are frequently not directly accessible and have to be retrieved via environmental sensing, which is bound to be inexact due to measurement error and partial due to economic and physical constraints on sensor deployment and capabilities. Inaccessibility of the ground truth renders direct decision of STL properties based on the above semantics elusive; we rather need to infer, as far as this is possible, the truth value of an STL monitoring condition \(\phi\) from the vague evidence provided by mostly partial and inexact sensing.
# Cross-Technology Interference-Aware Rate Adaptation in Time-Triggered Wireless Local Area Networks
## 2 Related Work  
As mentioned earlier in the introduction, the core of the proposed CTI-aware rate adaptation framework lies in CTI identification and rate adaptation based on the identified results. This section reviews related studies on these two key techniques.
## 4 Results and Discussion
### Solar Irradiation Data  
The meteorological conditions of the location under study are a key factor in the comparison of \(PV\) module mounting systems. Therefore, the solar irradiance data measured by pyranometers 1, 2, and 3 were used in the study under real operating conditions. On the other hand, the Hottel and Liu-Jordan models were used in the theoretical study. Table 2 shows the monthly beam and diffuse horizontal solar irradiation in real operating conditions and in theoretical conditions.  
Figure 6 shows daily beam and diffuse solar irradiation on a horizontal surface under real operating conditions and under theoretical conditions:  
\begin{table}
\begin{tabular}{c c c c} \hline
**Specifications** & \multicolumn{2}{c}{**Theoretical Study**} & \multicolumn{2}{c}{**Real Operating Conditions**} \\ \hline
**(kWh/m\({}^{2}\))** & \(H_{bh}\) & \(H_{dh}\) & \(H_{bh}\) & \(H_{dh}\) \\ \hline January & 40.61 & 18.72 & 32.49 & 18.71 \\ February & 60.46 & 21.25 & 39.80 & 25.48 \\ March & 107.37 & 29.29 & 34.68 & 43.57 \\ April & 145.29 & 33.44 & 76.77 & 48.20 \\ May & 181.50 & 38.35 & 85.39 & 53.73 \\ June & 188.72 & 38.76 & 71.84 & 63.20 \\ July & 188.63 & 39.16 & 110.49 & 56.94 \\ August & 163.34 & 35.95 & 65.80 & 51.70 \\ September & 120.78 & 30.26 & 68.96 & 38.03 \\ October & 82.16 & 25.66 & 35.47 & 33.40 \\ November & 46.99 & 19.57 & 25.63 & 20.61 \\ December & 33.95 & 17.24 & 15.82 & 17.67 \\ \hline \end{tabular}
\end{table}
Table 2: Monthly beam and diffuse horizontal solar irradiation in real operating conditions and in theoretical conditions.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Specifications** & \multicolumn{2}{c}{**PV Module Mounting System**} \\ \hline  & **Fixed Tilt Angle System** & **Scenario 1** & **Scenario 2** \\ \hline Location & Gijon, (Spain) & Gijon, (Spain) & Gijon, (Spain) \\ Latitude & 43°31′22′′ N & 43°31′22′′ N & 43°31′22′′ N \\ Longitude & 05°43′07′′ W & 05°43′07′′ W & 05°43′07′′ W \\ Altitude (m) & 28.0 & 28.0 & 28.0 \\ Fixed tilt angle (°) & 33.5 & - & - \\ Tracking type & - & Horiz. single-axis tracker & Horiz. single-axis tracker \\ Type of control & - & Astronomical algorithm & Astronomical algorithm \\ Backtracking mode & - & Yes & No \\ Rotation angle (\(\beta_{\text{max}}\)) (°) & - & \(\pm\)60 & \(\pm\)90 \\ Configuration & 2 Vx2 & 1 Vx8 & 1 Vx8 \\ Pitch (\(e_{t}\)) (m) & 6.5 & 6.5 & 6.5 \\ Reflectance (\(\rho_{S}\)) & 0.3 & 0.3 & 0.3 \\ \hline \end{tabular}
\end{table}
Table 1: Specifications of the PV module mounting systems.  
Figure 7 shows hourly beam and diffuse solar irradiation on a horizontal surface under real operating conditions and under theoretical conditions:
## 5 Conclusions  
In this study, we evaluated the performance of two Sentinel-3A OLCI products (i.e., FAPAR and OTCI) in estimating the GPP across seven biomes in 2017-2018. OLCI FAPAR and OTCI products in combination with meteorological data were integrated into the MODIS GPP algorithm and three OTCI-driven models respectively to simulate GPP. GPP\({}_{\text{OLCI-FAPAR}}\) and GPP\({}_{\text{OTCI}}\) were compared with the GPP\({}_{\text{EC}}\) for each site, and a comparison between GPP\({}_{\text{OLCI-FAPAR}}\) and GPP\({}_{\text{MODIS-FAPAR}}\) was established. The main conclusions are summarized as follows:  
(1) The relationship between GPP\({}_{\text{OLCI-FAPAR}}\) and GPP\({}_{\text{EC}}\) is significant across all sites. The GPP\({}_{\text{OLCI-FAPAR}}\) correlated best with GPP\({}_{\text{EC}}\) at the US-WCr site (R\({}^{2}\) = 0.76) while it performed worst at the US-KFS site (R\({}^{2}\) = 0.45).  
(2) The inclusion of APAR data in OTCI-driven models exhibited significant relationship with GPP\({}_{\text{EC}}\) for all sites. Nonetheless, the model using only OTCI provided the most varied performance, with the relationship between GPP\({}_{\text{OTCI}}\) and GPP\({}_{\text{EC}}\) from strong to nonsignificant.  
(3) The comparison of GPP\({}_{\text{OLCI-FAPAR}}\) and GPP\({}_{\text{MODIS-FAPAR}}\) suggested that they all produced reasonable GPP estimates. In terms of R\({}^{2}\), GPP\({}_{\text{OLCI-FAPAR}}\) performed better than the GPP\({}_{\text{MODIS-FAPAR}}\) across all biomes. In the aspect of RMSE and bias, GPP\({}_{\text{MODIS-FAPAR}}\) was superior than GPP\({}_{\text{OLCI-FAPAR}}\). The main difference between GPP\({}_{\text{OLCI-FAPAR}}\) and GPP\({}_{\text{MODIS-FAPAR}}\) result from the inherent retrieval algorithm and assumptions in the FAPAR product.  
In conclusion, the results of this study demonstrate the potential of OLCI FAPAR and OTCI products in GPP estimation, and these results open the possibility of applying them to GPP monitoring at regional and global scales for future studies.  
Conceptualization, Z.Z.; methodology, Z.Z.; software, Z.Z.; validation, Z.Z. and L.Z.; formal analysis, L.Z.; data curation, Z.Z.; writing--original draft preparation, Z.Z.; writing--review and editing, L.Z.; supervision, A.L.; funding acquisition, L.Z. and A.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (41301586) and the Chinese National Funding of Social Sciences (awards 18 ZDA040).  
The authors would like to thank the AmeriFlux community for the eddy covariance data they provided for free, as well as the science teammembers who produce and manageable MODIS and Sentinel-3 data.  
The authors declare no conflict of interest.
# Extreme Ground Snow Loads in Europe from 1951 to 2100
## 3 Results and Discussion
### Evaluation of Current and Future Trends at Selected Weather Stations  
A first comparison against point observations was carried out to assess the ability of climate projections to reproduce current and past trends. To the aims of the present study, 42 weathers station in Germany and Switzerland, characterized by a high-quality data series of snow measurements in the period 1951-2020, were considered (see Figure 8). Obviously, to obtain a general assessment for the whole European territory, more uniformly distributed weather stations should be considered. However, this is not an easy task since snow data are still poorly documented and long series of high-quality data, from which trend can be evaluated, are available for few countries in Europe [62]. Moreover, the number of reliable weather station can vary from site to site, so that the coverage of various areas is non-uniform. Figure 8: Elevation map and geographical locations of the 42 weather stations in Table 3.  
FC were evaluated at the selected weather stations for characteristic values of ground snow loads \(q_{k}\), mean of annual maxima \(\overline{Q}\) and the coefficient of variation of annual maxima \(\mathrm{COV}_{Q}\) for the available time windows in the observation period (1951-1990, 1961-2000, 1971-2010 and 1981-2020). Results are summarized in Figure 9 for FC of \(q_{k}\), in Figure 10a for FC of \(\overline{Q}\) and in Figure 10b for FC of \(\mathrm{COV}_{Q}\), considering the average \(\mu\) and the confidence interval \(\mu\pm 1.645\sigma\). Observed trends (in red) were compared against those obtained by climate projections for the corresponding cells for the historical and future period (in blue and in green for the RCP4.5 and 8.5, respectively).
# Research on Computational Method of Supersonic Inlet/Isolator Internal Flow
## 2 RANS Computation Method
### Spatial Discretization
#### 2.2.2 WENO Scheme  
The WENO scheme is developed from the ENO scheme. A key idea of WENO is the linear combination of lower order fluxes or reconstruction to obtain a higher order approximation.  
\[f_{i+1/2}=\sum_{j=1}^{k}\omega_{j}f_{j}(x_{i+1/2}), \tag{12}\]  
where \(k\) is the number of stencils, \(\omega_{j}\) is the nonlinear weighting coefficient, \(f_{j}\) is flux on the different stencil, and \(f_{i+1/2}\) is the numerical flux at interface \(i+1/2\).  
At present, the most widely used WENO scheme is the 5 th-order WENO-JS [24]. It introduces a parameter characterizing the smoothness of the interpolation function  
Figure 1: Diagram of 2D structural grid reconstruction.  
corresponding to the interpolation template--the smoothness factor \(\beta_{j}\), which is specifically defined as follows:  
\[\beta_{j}=\sum_{l=1}^{k}\int_{\gamma_{i-1/2}}^{\gamma_{i+1/2}}\bigtriangleup x^{2l -1}\left(\frac{\partial^{l}f_{j}(x)}{\partial^{l}x}\right)^{2}dx, \tag{13}\]  
where the \(\bigtriangleup x\) is cell size.  
Based on Equation (13), the nonlinear weighting coefficient \(\omega_{j}\) can be obtained:  
\[\omega_{j}=\frac{\tilde{\omega}_{j}}{\sum_{j}\tilde{\omega}_{j}},\quad\tilde{ \omega}_{j}=\frac{\gamma_{j}}{(\varepsilon+\beta_{j})^{2}}, \tag{14}\]  
where \(\gamma_{j}\) is a linear weighting factor, and \(\varepsilon\) has a small value preventing denominator from being zero, such as \(10^{-6}\). For structural mesh, the finite volume methods have the same format for one and several space dimensions [25].
###### Abstract  
The Republic of Korea has played a leading role in the development of next-generation long-term evolution (LTE) public safety networks. The LTE-based public safety (PS-LTE) network, the LTE-based high-speed railway (LTE-R) network, and the LTE-based maritime (LTE-M) network use the same 700 MHz frequency band. That results in severe co-channel interference (CCI), so there is a dire need for practical research into resolving the CCI issue. Moreover, unplanned deployment of the mobile personal cell (mPC) generates serious user-association issues owing to its movement, which leads to severe co-channel interference in coexisting PS-LTE and LTE-R networks. Indeed, it is important to satisfy users' quality of service (QoS) requirements during resource allocation in specific public safety situations. Therefore, we address the CCI issues through wise deployment of the mPC for user association and load balancing in overlapping PS-LTE and LTE-R networks. In this paper, we propose a QoS mPC deployment (QoS_mPCD) scheme for priority-based load balancing and interference reduction in coexisting PS-LTE and LTE-R networks. The proposed scheme efficiently manages the user-association and load-balancing problems, and allocates the best resources to high-priority users based on defined service priority levels. Moreover, we employ an enhanced inter-cell interference coordination (eICIC) scheme that further reduces the interference with the users offloaded onto an mPC. System-level simulations are performed to evaluate the proposed QoS_mPCD scheme by considering important performance matrices such as user equipment (UE) throughput, UE received interference, and UE outage probabilities.  
Keywords:public safety network; railway network; coexistence; mobile personal cell; priority gate model +
Footnote †: journal: Electronics