esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology  
### Computational Modeling and Grid  
Figure 2 displays the three-dimensional numerical model (SVD). Considering the accuracy and cost of numerical simulations, this study adopts the same method as [10; 11], namely the multipassage calculation model, which includes three impeller passages, three diffuser passages, and three return channel passages for VD case and three impeller passages, four diffuser passages, and three return channel passages for TVD, SVD, and HVD cases, as shown in Figure 2a.  
All computational domains employ multiblock structured hexahedral meshes generated by the commercial code NUMECA/Autogrid. Figure 2b shows the computational grid for the impeller and diffuser passages. The grids near the end walls are refined to capture more flow details, which is crucial for compressible flows [11]. Therefore, high  
Figure 1: Meridional schematic of diffuser: (**a**) VD; (**b**) FVD; (**c**) SVD; (**d**) HVD.  
Figure 2: Computational domain and grid: (**a**) multi-passage model; (**b**) grid; (**c**) \(y\) plus.  
precision and meticulous care are exerted by controlling grid points close to the end walls to ensure sufficient resolution for compressible flow calculation. The first cell height is set to \(10^{-6}\) m corresponding to the dimensionless wall distance y+ below unity along all end walls (design point flow rate and low flow rate conditions, Figure 2c). The impeller passage grid contains 77, 165, and 82 points in the spanwise direction, streamwise direction, and pitchwise direction, respectively. The vaned diffuser passage grid contains 73, 169, and 141 points in the spanwise direction, streamwise direction, and pitchwise direction, respectively [12]. The maximum mesh skewness is about 0.63, which meets the grid accuracy to resolve the secondary flow. Considering the accuracy of computation results, a grid independence study was conducted using four different grids, fine, medium 1, medium 2, and coarse. The total pressure ratio (\(\epsilon\)) for the impeller and the static pressure recovery coefficient (\(C_{\text{p}}\)) for the vaned diffuser are taken as parameters between different grids. The difference between the medium 2 grid and the fine grid was less than 0.16% for the impeller and diffuser, as shown in Table 2. A similar analysis was performed for the VD, TVD, and HVD. Therefore, the medium 2 grids were selected for the simulations. In the computational model, the inlet extended domain, impeller domain, and return channel domain have 206,625, 1,367,430, and 2,585,178 nodes, respectively. VD, TVD, SVD, and HVD have 276,507, 3,193,312, 3,244,068 and 3,244,068 nodes, respectively.
# Study of 5G-NR-MIMO Links in the Presence of an Interferer
## 2 Diversity Transmission Using Alamouti MIMO Technique
 \tag{2}\]  
Substituting (1) into (2) yields [27]  
\[\tilde{s}_{1}= \left(\sum_{i=1}^{2}\sum_{j=1}^{2}\abs{h_{ij}}^{2}\right)s_{1}+h_ {11}^{*}n_{1}(1)+h_{12}n_{1}^{*}(2)+h_{21}^{*}n_{2}(1)+h_{22}n_{2}^{*}(2)\]  
and  
\[\tilde{s}_{2}= \left(\sum_{i=1}^{2}\sum_{j=1}^{2}\abs{h_{ij}}^{2}\right)s_{2}+h_ {12}^{*}n_{1}(1)-h_{11}n_{1}^{*}(2)+h_{22}^{*}n_{2}(1)-h_{21}n_{2}^{*}(2) \tag{3}\]  
The estimation of each symbol \(s_{1}\) and \(s_{2}\) is multiplied with the norm of each complex channel gain without the other symbol's presence. Finally, the last receive step is the ML estimate. The transmitted symbol is estimated as follows [27]:  
\[ \begin{split}\hat{s_{1}}&=argmin_{s_{1}}[\abs{\tilde {s_{1}}-s_{1}}^{2}]\\ \hat{s_{2}}&=argmin_{s_{2}}[\abs{\tilde{s_{2}}-s_{2}} ^{2}]\end{split}  \tag{4}\]  
Another way to display Equation (1) is through matrices. After mathematical operations of conjugation, the model will be [1; 27]  
\[\vec{R}= \begin{bmatrix}r_{1}(1)\\ r_{1}^{*}(2)\\ r_{2}(1)\\ r_{2}^{*}(2)\end{bmatrix} = \begin{bmatrix}h_{11}&h_{12}\\ h_{12}^{*}&-h_{11}^{*}\\ h_{21}&h_{22}\\ h_{22}^{*}&-h_{21}^{*}\end{bmatrix}\begin{bmatrix}s_{1}\\ s_{2}\end{bmatrix} + \begin{bmatrix}n_{1}(1)\\ n_{1}^{*}(2)\\ n_{2}(1)\\ n_{2}^{*}(2)\end{bmatrix}  \tag{5}\]  
In short,  
\[\vec{R}=H_{TR}\vec{S}+\vec{N} \tag{6}\]  
where \(\vec{R}\) is the receiver signal vector, \(\vec{S}\) is the symbol matrix, \(\vec{N}\) is the AWGN noise vector, and \(H_{TR}\) is the channel matrix between the transmitter and the receiver, shown as  
\[H_{TR}= \begin{bmatrix}h_{11}&h_{12}\\ h_{12}^{*}&-h_{11}^{*}\\ h_{21}&h_{22}\\ h_{22}^{*}&-h_{21}^{*}\end{bmatrix}  \tag{7}\]  
An important feature of this Alamouti coding matrix is the orthogonality between its columns [27]. If the condition  
\[column_{1}^{H}column_{2}=0 \tag{8}\]  
is met, the vectors are orthogonal. The operator \((\ \cdot\ )^{H}\) is the complex transpose. For the \(H_{TR}\) case,  
\[ \begin{bmatrix}h_{11}^{*}&h_{12}&h_{21}^{*}&h_{22}\end{bmatrix} \cdot \begin{bmatrix} h_{12}\\ -h_{11}^{*}\\ h_{22}\\ -h_{21}^{*}\end{bmatrix} =0.\]  
This critical feature allows the symbols to be decoded and Equation (3) to be obtained. It can also be seen by presenting Equation (2) as the following matrix:  
\[\vec{S}=(H_{TR})^{H}\vec{R}\]Next, (6) is substituted into the above equation to obtain  
\[\vec{S}=(H_{TR})^{H}(H_{TR}\vec{S}+\vec{N})=\sum_{i=1}^{2}\sum_{j=1}^{2}|h_{ij}| \big{|}^{2}\textbf{I}_{2}\vec{S}+(H_{TR})^{H}\vec{N}\]  
which is the same as Equation (3). \(\textbf{I}_{2}\) denotes an identity matrix of size 2. In the next section, we will present the impairment of this orthogonality through interference's.
###### Abstract  
The Eddy Covariance method (EC) is widely used for measuring carbon (C) and energy fluxes at high frequency between the atmosphere and the ecosystem, but has some methodological limitations and a spatial restriction to an area, called a footprint. Remotely sensed information is usually used in combination with eddy covariance data in order to estimate C fluxes over larger areas. In fact, spectral vegetation indices derived from available satellite data can be combined with EC measurements to estimate C fluxes outside of the tower footprint. Following this approach, the present study aimed to model C fluxes for a karst grassland in Slovenia. Three types of model were considered: (1) a linear relationship between Net Ecosystem Exchange (NEE) or Gross Primary Production (GPP) and each vegetation index; (2) a linear relationship between GPP and the product of a vegetation index with PAR (Photosynthetically Active Radiation); and (3) a simplified LUE (Light Use-Efficiency) model assuming a constant LUE. We compared the performance of several vegetation indices derived from two remote platforms (Landsat and Proba-V) as predictors of NEE and GPP, based on three accuracy metrics, the coefficient of determination (R\({}^{2}\)), the Root Mean Square Error (RMSE) and the Akaike Information Criterion (AIC). Two types of aggregation of flux data were explored: midday average and daily average fluxes. The vapor pressure deficit (VPD) was used to separate the growing season into two phases, a wet and a dry phase, which were considered separately in the modelling process, in addition to the growing season as a whole. The results showed that NDVI is the best predictor of GPP and NEE during the wet phase, whereas water-related vegetation indices, namely LSWI and MNDWI, were the best predictors during the dry phase, both for midday and daily aggregates. Model 1 (linear relationship) was found to be the best in many cases. The best regression equations obtained were used to map GPP and NEE for the whole study area. Digital maps obtained can practically contribute, in a cost-effective way to the management of karst grasslands.  
eddy covariance; carbon flux; GPP; NEE; vegetation indices; remote sensing; satellite data; GPP map +
Footnote †: journal: Remote Sensing, 2019, 11, 649; doi:10.3390/rs11060649
# Performance Analysis of Dual-Hop Hybrid RF-UOWC NOMA Systems
## 1 Introduction
Hybrid communication systems, where transmission propagates through different environments, are currently attracting a lot of attention. In this paper, to enhance the spectral efficiency, we propose a downlink NOMA-based dual-hop hybrid RF-UOWC system, where the source exploits NOMA to convey two messages intended for two underwater destinations in presence of imperfect SIC. To the best of our knowledge, none of the previous work in the literature has studied hybrid RF-underwater based on NOMA as a spectrum access technique. The authors in [15] have investigated the performance of a wireless/power-line communication system, while our work investigates another hybrid system where the relay works as an intermediate node between wireless and underwater mediums. There are a lot of differences between them in terms of the field of application of the two systems. Our proposed system can find applications in many underwater applications, such as offshore oil field exploration, oceanic monitoring, and data collection. On the other hand, the system in [15] may find applications in situations where the signals suffer from penetration loss within buildings and factories. In [15], the PLC link was assumed to undergo lognormal distribution with Bernoulli Gaussian noise, including both background and impulsive noise components, while this work investigated UOWC channels that are characterized by EGG fading with AWGN. The main contributions of this paper can be summarized as follows. (1) We derived a new closed-form and asymptotic expressions for the OPs and EC, assuming that the wireless channel is characterized by Rayleigh fading with an additive white Gaussian noise (AWGN) and the UOWC links are characterized by EGG fading with AWGN. (2) We analyzed the diversity order of the OPs. (3) We proposed and solved a power allocation optimization problem to obtain an outage-optimal power allocation factor. (4) We validated the analytical derivations through Monte-Carlo simulations for varying underwater scenarios of air bubbles level (\(BL\)) under thermally uniform and temperature gradient UOWC channels, then we analyzed the impact of system parameters on the system performance. (5) Finally, we carried out a comparison between the proposed system with an OMA-based benchmark system. The rest of the paper is organized as follows, the system model is introduced in Section 2. The performance of the considered system is analytically evaluated by deriving the OPs and ECs in Sections 3 and 4, respectively. The proposed power allocation algorithm is provided in Section 5. Analytical and simulation results are discussed and compared with a benchmark system in Section 6. Finally, conclusions are provided in Section 7.
## 2 CCHP System  
### Configuration of Heat Recovery System  
A schematic of the CCHP system investigated in this study is shown in Figure 1. By using natural gas as fuel, the gas engine produces electricity and recovers heat from the coolant jacket water and exhaust gas. The heat recovered by the heat recovery coolant is transferred to the heat storage tank and stored in the form of hot water. The hot water is supplied to the HDCS and used to regenerate the desiccant in summer. The CCHP system consisted of one engine, two heat storage tanks, and two HDCSs.  
The main specifications of this system are listed in Table 1. The maximum electric power was 115 kWe and 152 kW of waste heat was retrieved. Under the rated condition, the fuel (liquefied natural gas) consumption was 33.6 N-m3/h and the total efficiency including both electric power and heat recovery was 74.4%.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{tabular}{c} **Primary** \\ **Energy** \\ **Input** \\ **(kW)** \\ \end{tabular} & \begin{tabular}{c} **Mechanical** \\ **Power** \\ **(kW)** \\ \end{tabular} & \begin{tabular}{c} **Electric** \\ **Power** \\ **(kW)** \\ \end{tabular} & \begin{tabular}{c} **Heat** \\ **Recovery** \\ **(kW)** \\ \end{tabular} & \
\begin{tabular}{c} **Fuel** \\ **Consumption** \\ **(N-m3/h)** \\ \end{tabular} &
\begin{tabular}{c} **Total** \\ **(efficiency** \\ **(\%)** \\ \end{tabular} \\ \hline
359 & 126 & 115 & 152 & 33.6 & 74.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of gas engine CCHP system used in the present study.  
Figure 1: Schematic of the combined cooling, heat, and power (CCHP) system with a hybrid desiccant cooling system (HDCS).
###### Abstract  
In many Wireless Sensor Network (WSN) applications, the location of the nodes in the network is required. A logical method to find Unknown Nodes (UNNs) in the network is to use one or several mobile anchors (MAs) equipped with GPS units moving between UNNs and periodically broadcast their current location. The main challenge at this stage is to design an optimum path to estimate the locations of UNNs as accurately as possible, reach all nodes in the network, and complete the localization process as quickly as possible. This article proposes a new path planning approach for MA-based localization called Nested Hexagon Curves (NHexCurves). The proposed model's performance is compared with the performance of five existing static path planning models using Weighted Centroid Localization (WCL) and Accuracy Priority Trilateration (APT) localization techniques in the obstacle-presence scenario. With the obstacle-handling trajectories used for the models, the negative impact of the obstacle on the localization is reduced. The proposed model provides full coverage and high localization accuracy in the obstacle-presence scenario. The simulation results show the advantages of the proposed path planning model with the H-curve model over existing schemes.  
localization; mobile anchor node assisted localization; obstacle-handling; path planning; static path planning +
Footnote †: journal: _Sensors_  
**2021**, 21, 3697. [[https://doi.org/10.3390/s2113697](https://doi.org/10.3390/s2113697)]([https://doi.org/10.3390/s2113697](https://doi.org/10.3390/s2113697))
## 2 Materials and Methods
### M1 Outputs  
The results of the ANOVA confirmed small but significant within-subject differences in joint angles and moments (Figure 4A,B and Figure S3) associated with the use of different ankle models. These differences were not limited to the ankle joint but propagated proximally through the KC. Post hoc analysis highlighted that the three models differed for the hip kinematics, mostly during mid-and late stance and swing; also, M1 MC kinematics differed from the others during mid-and late stance and mid-swing at the knee, and from M1 MF during mid- and late stance at the ankle. Joint moments showed biggest differences  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Cohort Values**} & \multicolumn{3}{c}{**Individual Values**} \\ \cline{2-7}  & **MCJ** & **MFJ** & **SGJ** & **MCJ** & **MFJ** & **SGJ** \\ \hline
**Maximum CaFi** & \multirow{2}{*}{0.0} & \(-0.1\) & \(20.4\) & \(3.5\) & \(8.2\) & \(50.7\) \\
**elongation (\%)** & & & & & & \\
**Maximum TiCa** & \multirow{2}{*}{0.5} & \(3.5\) & \(5.7\) & \(5.9\) & \(17.9\) & \(32.6\) \\
**elogation (\%)** & & & & & & \\
**Maximum cartilage** & \multirow{2}{*}{\(-0.2\)} & \(-1.5\) & \(-4.1\) & \(-0.5\) & \(-2.4\) & \(-5.4\) \\
**co-penetration (mm)** & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Maximum ligaments elongations and cartilage co-penetration over the entire Range of Motion (ROM) for the average cohort and individuals.  
Figure 3: FiCa (**A**) and TiCa (**B**) and minimum cartilage-to-cartilage distance (**C**) values for the average cohort, plotted versus ankle flexion angle for the three ankle models. Bottom gray bars represent the flexion angle values for which post hoc tests were significant.  
at the ankle joint, where the M1 SG differed from the others throughout the whole stance; M1 MF knee moments differed from those of the other models during mid- and late stance; hip moments presented differences across the three models limited to small portions of the gait cycle.  
For what concerns muscle forces (Figure 4C), post hoc analysis highlighted significant differences mostly attributable to the M1 SG, i.e., Medial Gastrocnemius, Soleus, Lateral Gastrocnemius muscles, during stance and late swing. Peronei muscles presented differences between each of the three models during mid-and late stance.  
Figure 4: M1 outputs for the average cohort, considering the three ankle models: joint kinematics (**A**); joint moments (**B**); forces exerted for significant muscles at the ankle (**C**); joint contact force (**D**). Bottom gray bars represent the region of the gait cycle where post hoc tests were significant.  
M1 SG showed significant differences with respect to the other two models also in the joint contact forces at both the knee (mid- and late stance) and ankle (stance and late swing), also differing from the M1 MF during hip mid- and late stance (Figure 4D).
## 1 Introduction
However, because these parts are often subjected to prolonged loads, creep analysis is difficult and has obvious limitations. Therefore, rapid and reliable prediction of the plastics' creep behavior is very important for designing plastic products subjected to prolonged loads [3]. Many simplified constitutive equations have been proposed for describing the time-dependent transient and steady-state creep behavior, starting with instantaneous elastic strain of plastics after loading [3; 4; 5; 6; 7; 8]. [PERSON] et al. [6] presented short-term (600 s) creep test results for selected polymers, in terms of hyperbolic sine and exponential functions. [PERSON] et al. [7] predicted the long-term creep behavior of a certain polyethylene using a time-temperature superposition and an analytical formula for 30-120 min-long creep tests. [PERSON] et al. [8] also attempted to predict the long-term creep behavior of polymeric materials from short-term (2 h) creep test results. [PERSON] et al. [9; 10] has published an  
Figure 1: Typical creep behavior of a material, and the corresponding Burgers model. empirical equation for predicting long-term creep behavior from creep data measured by applying a compressive stress of (0.25-0.45) \(\sigma_{10\%}\) to EPS. [PERSON] [11] described all three phases of creep (i.e., primary, secondary, and tertiary creep phases) for high-density polyethylene, using a novel unifying mathematical model derived from the relationship between the constant strain rate, creep, and stress relaxation. [PERSON] et al. [12] modeled the primary creep behavior of polypropylene using the Maxwell model in the FEA framework, and reported that the experimental and FEA results agreed well at low stress levels, but the discrepancy was large at high stress levels. To date, studies on EPS have mainly been conducted on an experimental analysis of creep behavior, or an analytical formula for predicting long-term creep behavior from short-term creep measurements, using EPS as a construction material (e.g., underground structures). On the other hand, when EPS is used as a packaging cushioning material, the loading period is short, and especially in the case of home appliance packaging, it is calculated as about one week due to the distribution characteristics of the product. Research results on the behavior of EPS when it is used as a cushioning packaging material, or the material properties for CAE of the EPS-based cushioning system, are insufficient. The purpose of this study was to validate the EPS creep behavior, for Improving FEA reliability with respect to EPS-based cushioning systems. Because this study was conducted as a part of the computer-aided engineering (CAE) prediction technology development for preventing fall accidents of packaged appliances stacked in multiple stages (unitized loads) in logistics warehouses, the variables and the scope of the study are reflected this scenario.
# Effects of Precipitation and Fire on Land Surface Phenology in the Brazilian Savannas (Cerrado)
## 3 Results  
### Identification of the Driest and Wettest Seasonal Cycles at the Park  
The analysis of long-term average monthly precipitation (1983-2023) at the ENP confirmed the occurrence of a rainy season from October to May and a dry season from June to September (Figure 4). During the dry season, monthly rainfall typically falls below 50 mm. In the more recent years, between 2018 and 2021, no rainfall exceeding 6 mm was recorded for June, July, and August, indicating a notably local dry winter. However, as shown by the outliers in Figure 4, there were a few years, especially between 2014 and 2017, when the accumulated rainfall in these months unusually exceeded 100 mm with the majority of precipitation occurring in June.  
Interannual variations in accumulated precipitation on each cycle during the period of MODIS data analysis (2002/2003 to 2022/2023 cycles), compared to the long-term 1983-2023 period, are shown in the boxplot of Figure 1 (Appendix A). The results from the boxplot analysis of long-term rainfall data (1983-2023) from the meteorological station identified the following intervals of accumulated precipitation per cycle: severe dry cycle (<1153 mm), dry (1153-1433 mm), regular (1433-1814 mm), wet (1814-2057 mm), and severe wet cycle (>2057 mm). However, as shown in Figure 5, no severe dry or wet cycles were detected during the period of MODIS data analysis. The seven dry cycles that deviated from the Climatological Normal were 2002/2003, 2004/2005, 2005/2006, 2006/2007, 2020/2021, 2021/2022, and 2022/2023, as indicated by the red color in Figure 5. The eight wet cycles detected in our analysis were 2009/2010, 2010/2011, 2011/2012, 2013/2014, 2014/2015, 2015/2016, 2018/2019, and 2019/2020 (blue color in Figure 5). The remaining six cycles were classified as regular cycles in the boxplot analysis when compared to the 1983-2023 long-term rainfall (gray color in Figure 5).  
Figure 4: Variations in average long-term monthly precipitation (1983–2023) at Emas National Park (ENP). The mean values are represented by crosses, while the median values are shown as horizontal lines within the boxes for each month. Outliers are depicted as circles.
## 5 Experiments  
### Test Methodology and Parameters  
The main goal of the experiments was to compare the application execution time on Docker and Host, but in order to make a thorough comparison, many variables need to be taken into consideration. The course of the tests was influenced by:  
**Program, Paradigm**: --described in Section 3:  
1. DAC;
2. MasterSlave;
3. SPMD.
**Compilation parameters, parameters, flags**: --options used during compilation:  
1. No optimization parameter;
2. O2 (-O2);
3. O3 (-O3);
4. O3_native (-O3 -march=native).  
Tests without any optimization parameter will not be described in further analysis because such execution times were the longest in these cases (several times longer) and stand out from the rest.
**Runtime machine**: --environment in which the tasks were run:  
1. Docker (installed on bare-metal host),
2. Host (bare-metal).
**Computing processes**: --number of processes performing operations: 1-64 with step 4, plus 128 and 256, but only 1, 2, 4, 8, 16, 32, 64, 128, 256 calculation processes will bepresented and 12, 20, 24, 28, 36, 40, 44, 48, 52, 56, 60 calculation processes will be omitted. The available hardware test platform used HyperThreading in order to run 128 and oversubscribe mode to run 256 processes.  
The DAC program requires that the number of calculation processes be always equal to a power of two, so it uses from 2\({}^{0}\) to 2\({}^{8}\) calculation processes. The MasterSlave program requires one additional master process that collects results from the slave calculation processes. The number of calculation processes without the master process will always be given in this publication.  
The tests were performed as a combination of the above, and each test was repeated 10 times. Before starting the analysis of the results, all tests were completed. Graphs and tables containing median times of program execution in particular configurations are presented in subsequent subsections. Apart from median times, we also present Q1 and Q3 data, interquartile ranges and outliers.