esa-sceva/satcom-chunk-collection huggingface dataset:
# Reactive Powder Concrete: Durability and Applications
## 3 Results and Discussion
It was more than four times less in the RCP than in the C80 concrete (Figure 3). Therefore, a natural carbonation lower than 2 mm after 500 years in reactive powder concrete is expected. Four high strength concretes named A, B, C, and D, with 28-day compressive strengths of 192 MPa, 138 MPa, 127 MPa, and 123 MPa, and made with the same constituents (CEM I 52.5 R, siliceous aggregates, and a high range water reducer) were tested for natural carbonation. Figure 4 shows the carbonation rate results of these four high strength concretes, which were cured at four different conditions as follows:  
1. Twenty-eight days of curing under water;
2. Air-curing (50% RH, 20 \({}^{\circ}\)C);
3. Air-curing (50% RH, 50 \({}^{\circ}\)C);
4. Carbon dioxide pre-curing (5% CO\({}_{2}\), 60% RH, 20 \({}^{\circ}\)C). \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Concrete Type**} \\ \cline{2-4}
**Property** & **C30** & **C80** & **RPC200** \\ \hline Effective chloride diffusion coefficient (\(\times 10^{-12}\) m\({}^{2}\)/s) & 1.1 & 0.6 & 0.02 \\ Apparent chloride diffusion coefficient (\(\times 10^{-12}\) m\({}^{2}\)/s) & 12.4 & 1.11 & 0.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Chloride diffusion coefficients, effective and apparent, of the reactive powder concrete, C30, and C80. Figure 3: Two years natural carbonation depth results of concretes: **(a)** C30 and C80; **(b)** RPC200. Then, they were submitted to indoor natural carbonation conditions (50% RH and 20 \(\pm\) 2 \({}^{\circ}\)C) for 10 years. Carbonation rate, V\({}_{\text{co2}}\), is calculated from Equation (1). \[\text{x}=\text{V}_{\text{co2}}\surd t, \tag{1}\]  
where: x = average carbonation front, mm. t = time, years. The main findings are: (i) concrete A is the most resistant against carbonation, with expected carbonation depths at 50 years around 25 mm; (ii) concrete D is the least resistant, of the four mixes tested, with expected carbonation depths at 50 years of between 50-70 mm; (iii) concretes B and C behave similar, in regards to their carbonation resistance; then, carbonation depths lesser than 50 mm are expected at 50 years; (iv) 28-day wet curing does not contribute significantly to the carbonation resistance of high strength concretes. In regards to the risk of reinforcement corrosion--its resistivity is so high (Table 4) that corrosion is excluded as there is no capillary porosity. This is confirmed by means of electrochemical impedance spectroscopy (EIS). The response of the system (concrete cured at 100% RH and 25 \({}^{\circ}\)C for 230 days) is displayed in Nyquist format in Figure 5, as this system is inherently capacitive. With respect to the metallic fibers, when they were added, only corrosion was detected in the zones of the fibers not covered by the paste in the concrete surface.
## 2 The Application of Metamaterials
### Antennas  
Metamaterials are more and more used in creating antennas that are more efficient at transmitting and receiving signals. This can create more efficient wireless communication systems, which would be beneficial for a wide range of applications. The progress of technology in consumer electronics demands an antenna having a compact size, high gain and bandwidth, and multiple antennas at the transmitter to receiver junction to enhance channel capacity [42]. In the progress of new designs and optimization processes, one such technique is the use of metamaterials in antenna design [43].  
[PERSON] et al. [42] pointed out that loading an antenna with one, two, and three-dimensional MM structures, comprised of a periodic subwavelength unit cell, exhibits RLC resonant structures and allows us to manipulate electromagnetic waves in the antenna system.  
[PERSON] et al. [44] presented applications of metamaterials in design to enhance antenna parameters and confirmed that MM can be applied within the environment. He pointed out that, depending on the parameters of the desired antenna, MM can be applied to improve bandwidth, power gain, or to create compact, multifrequency band antennas. The effectiveness of improving the parameters depends on the structure, size, quantity, and method of use in the unit cell of MM. The application of MM in an antenna can increase their gain \(\geq\) 2 dB, bandwidth \(\geq\) 100%, reduce size \(\geq\) 50%, or create additional frequency bands for multicommunication system-operated antennas [45; 46; 47].
# Joint Client and Resource Optimization for Federated Learning in Wireless IoT Networks
## 2 System Model
### Learning Process  
In the considered network, an ML model of interest is trained in a distributed manner by the cooperation of the FL server and IoT nodes. The goal of the training is to obtain the model parameter \(\mathbf{w}\) by minimizing the loss function \(f(\mathbf{w})\) on the data set \(S\). The minimization can be expressed as  
Figure 1: System model of FL-aided wireless IoT networks.  
\[\min_{\mathbf{w}}f(\mathbf{w})\triangleq\min_{\mathbf{w}}\frac{1}{|S|}\sum_{i=1}^{M }\sum_{l=1}^{|S_{i}|}f_{i}(\mathbf{w},S_{l}), \tag{1}\]  
where \(f_{i}(\mathbf{w},S_{l})\) is the local loss function of IoT node \(i\) on sample \(S_{l}\). We focus on the widely used federated averaging learning framework [2], where the training communication round is periodical. The \(t\)th communication round consists of the broadcasting phase, the local training phase and the aggregating phase. In the broadcasting phase, the FL server broadcasts the global model parameter \(\mathbf{w}^{t}\) to all the IoT nodes via the wireless down link (DL) of BS. Then, in the local training phase, each IoT node \(i\) calculates the gradient of the local loss function \(\
abla f_{i}(\mathbf{w}^{t},S_{i})\), and then, \(E\) epochs of the gradient descent method are used to obtain \(\mathbf{w}^{t+1}\) as  
\[\mathbf{w}^{t+1}_{i}=\mathbf{w}^{t}-\zeta_{i}\
abla f_{i}(\mathbf{w}^{t},S_{i }), \tag{2}\]  
where \(\zeta_{i}\) is the learning rate of IoT node \(i\). Finally, in the aggregating phase, each IoT node \(i\) transmits its local model parameter \(\mathbf{w}^{t+1}_{i}\) to BS via the wireless up link (UL), while the FL server updates the global model parameter as  
\[\mathbf{w}^{t+1}=\sum_{i=1}^{M}\frac{|S_{i}|}{|S|}\mathbf{w}^{t+1}_{i}. \tag{3}\]  
The learning process terminates when the following condition holds.  
\[f(\mathbf{w}^{t})-f(\mathbf{w}^{t+1})\leq\Lambda, \tag{4}\]  
where \(\Lambda\) is the learning termination threshold.
## 4 Autonomous Shovel Technology
### Shovel Trajectory Planning
#### 4.2.2 Self-Learning-Guided Trajectory Planning Methods  
With the development of artificial intelligence technology, many scholars have tried to solve the complex and changeable problems during the shovel loading process of LHD by applying learning methods. Scholars from the University of Arizona [61] applied fuzzy logic control to perform mechanical mining in unstructured and unpredictable environments for the first time and combined fuzzy logic with neural networks to simulate robotic autonomous mining experiments. [PERSON] et al. [62] developed an automatic dig control system (ADCS) based on the combination of both a behavior-controlled and fuzzy-logic-controlled model. Tests were conducted on a Cutler wheel loader and found to excavate comparably to manual operation but required longer mining time. [PERSON] et al. [63] proposed an earthwork excavation control method for the excavator based on a combination control of iterative learning and impedance. [PERSON] et al. [64] proposed an autonomous mining method based on reinforcement learning (RL), which is suitable for the mining of different pile types and ore shapes. This method worked well with steady loading weight, cycle time and fuel efficiency [44]. [PERSON] et al. [65] advanced an iterative learning-based admittance control algorithm, which could automatically update the control parameters according to the target bucket filling weight.  
Figure 7: Manipulator model for reverse six-bar linkage mechanism: 1—bucket; 2—pull rod; 3—rocker arm; 4—bucket cylinder; 5—arm; 6—front frame; 7—boom cylinder.  
It was verified to be effective for both fragmented rock and gravel shoveling after field testing with a 14-ton scraper. In addition, the performance on the fragmented rock piles was better than that on gravel piles.  
The self-learning-guided shovel trajectory planning method has obvious advantages when working in the uncertain underground roadway. Through interactive learning about the environment, the bucket trajectory planning can be better completed. However, a large amount of data and long time of study are required for training to implement good trajectory planning.  
In general, the shovel loading trajectory needs to be planned according to the actual real-time loading conditions, either guided by resistance force or self-learning. The ultimate aim is to excavate successfully with optimized shovel time and energy consumption. If the loading trajectory is planned improperly, as shown in Figure 8, the shovel fails as the lump block is too big.
# Measurement and Modeling of Narrowband Channels for Ultrasonic Underwater Communications
## 3 Ultrasonic Measurements for UAC
### Measurement Results
The procedure to estimate the fading does not avoid some in-band noise; however, noise was also registered alone at the measurement location, and the signal to noise ratio obtained at the output of the signal processing algorithms was larger than 30 dB in the worst conditions (for a center frequency of 128 kHz and link distance of 200 m); these results allow one to disregard the noise influence on the estimated fading CDF. The estimated CDF of the fading of some of the measured channels is shown in Figure 5, for illustrative purposes only. As stated before, the AoF is related to the variance of the distribution that, in turn, determines the steepness of the slope of the corresponding CDF. This means that the higher the steepness, the lower the AoF. In general, it is observed that the CDF shape exhibits a stronger frequency dependence in shorter links [21]. As seen in Figure 5a, the transducers' depth seems to have influence also on the fading statistics, because the curve for the transducers at less depth shows \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Channel Code** & **Channel Attenuation (dB)** & **Doppler Spread (Hz)** & **Coherence Time (ms)** \\ \hline A6-32 & & 1.7 & 116.3 \\ \hline A6-64 & 30–50 & 4.0 & 50.2 \\ \hline A6-128 & & 6.0 & 33.5 \\ \hline B6-32 & & 2.5 & 78.9 \\ \hline B6-64 & 35–65 & 4.2 & 47.4 \\ \hline B6-128 & & 6.7 & 30.0 \\ \hline C3-64 & & 2.8 & 70.5 \\ \hline C9-32 & 50–80 & 1.7 & 119.6 \\ \hline C9-64 & & 2.6 & 76.8 \\ \hline C9-128 & & 4.0 & 50.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Measured channel attenuation, Doppler spread and coherence time. Figure 5: Cumulative distribution function (CDF) of the power gain estimated from measurements: **(a)** for two different transducers’ depth and the same link distance (200 m) and sounding frequency (64 kHz); **(b)** for two different link distances and the same transducers’ depth (6 m) and sounding frequency (64 kHz). less dispersion in the distribution (it has a steeper slope). In Figure 5b, the CDF of two channels of a 50-m and a 100-m link distance is shown where the sounding signal frequency is 64 kHz in both measurements. As is observed, in this case, the longer channel exhibits more fading than the shorter one. This is not necessarily the general case, as the fading level depends not only on the link distance, but also on the environment and the deployment geometry. In the next section, these measurements are used to estimate the channel parameters according to the \(\kappa\)-\(\mu\) shadowed model.
# IMPA versus Cloud Analysis and IDA: Different Methods to Evaluate Structural Seismic Fragility
## 2 Methodology
### Performed Nonlinear Analysis
#### 2.3.3 Incremental Modal Pushover Analysis (IMPA)  
IMPA is a novel nonlinear static procedure proposed by [PERSON] and others first for buildings [40; 55; 56], and later adjusted also for bridges [57; 58; 59]. This procedure takes advantage of the simplicity of static analysis, but at the same time it grants the definition of the seismic demand for a certain range of intensity levels by scaling down response spectra. Conceptually, the procedure to find the maximum expected demand for the \(j\)th element of \(l\)th mechanism \(D_{jl}\) is the same as that describe for MPA-CA in sub-Section 2.3.1, yet in IMPA procedure response spectra are scaled to multiple levels of a chosen monotonic scalable intensity measure as \(S_{a}\), PGA, PGV (herein \(IM=S_{a}(T_{1}\zeta=5\%)\)) (Figure 4). For each intensity level, the performance point (P.P.) can be determined and the demand measure \(D_{jl}\) combined if two or more modes of vibration are considered, to define a multimodal \(DCR_{LS}\). The output of the analysis can be represented as a collection of \"IMPA\" curves, which are a plot of the recorded \(DCR_{LS}\) against \(IM\), wholly similar to IDA curves. The intersection of IMPA curves and the chosen threshold of the limit state (\(DCR_{LS}=1\)) generates the empirical distribution of the random variable (\(IM\)) for the probabilistic model of the fragility aforementioned in Section 2.3.1 (Equations (12) and (13)).
## 2 Materials and Methods
### Model Calibration and Validation  
Here, the FGM was recalibrated based on eddy flux tower data from FLUXNET2015 obtained at 40 sites in EuroFLUX networks. Given that temporally and spatially continuous global V\({}_{\text{cmax}}\) maps have been published recently, we recalibrated the FGM using one of these up-to-date datasets. The site-level data were used to calibrate the FGM 500 m GLASS-V5 LAI, and 500 m leaf chlorophyll content-based V\({}_{\text{cmax}}\) from 2001 to 2016. After data screening [28], a total of 2256 monthly records covering 9 biomes were selected. We randomly split the flux sites and used half of the sites within each biome to calibrate the biome-specific parameter g1, which describes instantaneous water use efficiency in FGM [28] (Table S1). The data collected at reserved sites were used to evaluate the model at the biome and site levels. Overall, the FGM explained 87.5% of the variations in GPP derived from eddy covariance data, with a root mean square error (RMSE) ranging from 0.9 to 2.73 g C m\({}^{-2}\) d\({}^{-1}\) for different PFTs (Figure S1). The GPP estimated by FGM in China [12] and Europe [28] was comparable to most existing remote sensing-based GPP products. Additional details about model validation against tower-based GPP and other GPP products can be found in previous publications [12; 28].  
The solid mechanisms of the process-based model, low computational demand, consideration of LULCC and key environmental constraints from temperature, water pressure deficit, and solar radiation, and an improved representation of spatial- and temporal-dynamic V\({}_{\text{cmax}}\) all make the FGM particularly useful for disentangling the regional climate change impacts on GPP in Europe.
# Deterministic Propagation Modeling for Intelligent Vehicle Communication in Smart Cities
## 5 Measurements Results  
To have a complete characterization of the considered scenario, the radio wave electromagnetic propagation analysis presented in the above sections has been complemented with a network parameter assessment with the aid of real measurements implemented in the same scenario. The description of the campaign of measurements was presented in Section 2.3.
# Large Eddy Simulation and Dynamic Mode Decomposition of Turbulent Mixing Layers
## 2 Large Eddy Simulation of Turbulent Mixing Layer and Dynamic Mode Decomposition
### Dynamic Mode Decomposition Method
By the singular value decomposition (15), the high-dimensional system \(x_{i}\) can be mapped to the subspace \(z_{i}\)  
\[z_{i}=U^{H}x_{i} \tag{22}\]  
The governing equation of the reduced order system is obtained as follows:  
\[z_{i+1}=U^{H}x_{i+1}=U^{H}Ax_{i}=U^{H}AUz_{i}=\widetilde{A}z_{i} \tag{23}\]  
Let \(W\) be a matrix whose column vector is the eigenvector \(\mathbf{w}_{j}\), and let \(N\) be a diagonal matrix \(\widetilde{A}\) containing singular values, then the feature decomposition can be expressed as follows:  
\[\widetilde{A}=WNW^{-1},N=diag(\lambda_{1},\cdots,\lambda_{r}) \tag{24}\]  
Therefore, the snapshot at any time can be estimated as  
\[ \begin{array}{c}x_{i}=Ax_{i-1}=U\widetilde{A}U^{H}x_{i-1}=UWNW^{-1}U^{H}x_{ i-1}\\ =UWN^{i-1}W^{-1}U^{H}x_{1}\end{array}  \tag{25}\]  
Each column of \(\Phi\) is defined as a DMD mode,  
According to Formula (15), there is  
\[\Phi=UW \tag{26}\]  
The modal amplitude \(\alpha\) is defined as  
\[\alpha=W^{-1}z_{1}=W^{-1}U^{H}x_{1},\ \alpha=[\alpha_{1},\alpha_{2},\cdots, \alpha_{r}] \tag{27}\]  
where \(\alpha_{i}\) is the amplitude of the \(i\)th mode, which represents the contribution of the mode to the initial snapshot \(\mathbf{x}_{1}\). For the standard DMD method, the DMD modes are sorted according to the amplitude. If Equations (26) and (27) are brought into Equation (25), the flow field can be predicted at any time  
\[x_{i}=\Phi\Lambda^{i-1}\alpha=\sum_{j=1}^{r}\Phi_{j}(\lambda_{j})^{i-1}\alpha _{j} \tag{28}\]
# Progressive Poling of Large Area, High r\({}_{33}\) Electro-Optic Polymer SEO100c
## 2 Device Geometry  
Single mode rib waveguides were fabricated utilizing a core layer of the EO polymer SEO100c with cladding layers of NOA73. The Single mode rib waveguide geometry is shown in Figure 1. To ensure single mode operation the ratios of the height of the EO slab (h) and EO waveguide (H) need to be properly selected. A h/H ratio of 0.75 was chosen to as described by [PERSON] et al. [12]. This single mode operation was then confirmed by calculating the effective index of the fundamental slab mode and the 1 st order rib mode and verifying that the fundamental slab mode effective index was smaller. This geometry was designed around minimizing coupling loss between the input fiber (Thorlabs P1-980 PM) and the waveguide. The cladding layers were made thick enough to ensure no optical mode loss from the proximity of the electrodes [13]. The radial bend design providesnumerous path lengths on a single device. The waveguide length of the devices ranged from 1.74 cm to 3.63 cm. These longer path lengths allow small \(r_{33}\) values to be measured. Designing waveguides with a large radius of curvature (>0.5 cm) the resulting bending loss is insignificant.
## 4 Results and Discussion
### Homogeneous Isotropic Turbulence
#### 4.2.1 Effects of Polynomial Orders  
Figure 7 shows time evolution of the total volume-averaged kinetic energy Ek of HIT simulation, it can be observed that for iLES or each model, results at \(p3\) are more close to reference than their \(p1\) counterparts, which indicates that numerical error is lower at \(p3\). Figure 8 plots time evolution of the total dissipation rate. Note that at the beginning of the simulation the total dissipation rate \(\epsilon_{total}\) meets a sharp oscillation. This is caused by the given initial conditions, which are not in acoustic equilibrium and therefore lead to pressure fluctuation from the initial field as the pressure becomes consistent with the velocity [58; 59]. The initial fluctuation in flow field leads to the strong oscillation of \(\epsilon_{d}\) at the beginning, which is nearly unaffected by the order of solution polynomial or simulation methodologies, therefore not plotted. The closer view of \(\epsilon_{total}\) at the beginning moment in Figure 8 shows that peak values of the initial oscillation all decrease at higher solution order. Figure 9 plots the solenoidal dissipation \(\epsilon_{s}\). The \(p3\) simulations produces higher levels of solenoidal dissipation, which indicates a wider range of resolved scales. Figure 10 plots time evolution of the modeling dissipation and is discussed in detail in Section 4.2.2.  
Figure 11 shows the decay of \(Re_{\lambda}\) for the various simulations in log-log coordinates. A power law of the decay, which is previously fitted by [PERSON] et al. [51], is plotted here as reference. Note that higher value of \(Re_{\lambda}\) indicates higher Taylor microscale, which is related to the range of domain dominated by viscous effects. It is shown that the \(p1\) results deviate more from the reference because of the stronger effects of numerical viscosity. The 3D energy spectrum at \(t/\tau=1\) is shown in Figure 12. The energy at high resolvable wavenumber at \(p1\) is obviously over dissipated because of the low order of solution. The \(p3\) results recover the ideal slope better but also fall off the ideal slope at high wavenumber.
# Using an Uptake Enhancer to Mitigate Nitrogen Leaching While Enhancing Uptake Efficiency
## 3 Results
### Effect of Uptake Enhancer Application on Vegetative Growth of Oats
#### 3.2.2 Physiological Parameters and Yield Component Measurements  
The effect of uptake enhancer application on oats physiological and yield parameters is presented in Table 4.  
While oats displayed a declining trend in leaf chlorophyll content over time, a significant boost attributed to the uptake enhancer emerged at week 8, yet consistently higher chlorophyll content resulted from fertilizer application with the enhancer across all sam  
\begin{table}
\begin{tabular}{l l l l l l l l l l l l l} \hline \hline
**Sampling Time** & **4 Weeks** & \multicolumn{4}{c}{**8 Weeks**} & \multicolumn{4}{c}{**12 Weeks**} & \multicolumn{4}{c}{**16 Weeks**} \\ \hline
**Treatment** & **C** & **CF** & **CFA** & **C** & **CF** & **CFA** & **C** & **CF** & **CFA** & **C** & **CF** & **CFA** \\ \hline Chlorophyll content & 15.8 \({}^{\text{a}}\) & 18.0 \({}^{\text{a}}\) & 17.6 \({}^{\text{a}}\) & **10.3 \({}^{\text{a}}\)** & **16.2 \({}^{\text{b}}\)** & **14.1 \({}^{\text{ab}}\)** & 3.4 \({}^{\text{a}}\) & 5.1 \({}^  
pling times. Additionally, the application of fertilizer and uptake enhancer at weeks 12 and 16 generally increased both panicle fresh weight (FM) and dry matter (DM), with significance observed only at week 16. Notably, the DM of panicles in the CFA treatment surpassed that of treatment C by 81% and 41% at weeks 12 and 16, respectively.  
Number of Seeds per Plant  
While statistical significance was observed for seed numbers among treatments at week 16, indicating a difference between treatment groups, week 12 showed no statistically significant differences; however, there was still a notable difference between treatment C and CFA. Adding the uptake enhancer increased the seed numbers of oats in weeks 12 and 16, with week 12 showing a 36% increase and week 16 demonstrating a 47% increase compared to treatment C.  
Total Yield  
Even though the influence of the fertilizer and uptake enhancer was notable at both sampling times, it was only significant at week 16. Adding the uptake enhancer increased the seed mass of oats in both weeks 12 and 16. Week 12 had a 41% increase in seed mass, with treatment CF displaying the highest increase of 89%. On the other hand, the CFA treatment had a slightly lower increase of 81% in week 12. Week 16 demonstrated the highest increase in seed mass, with a 41% increase in the CFA treatment.  
Plant Analysis for N  
Although not statistically significant, there was a trend of decreasing N content in oats over time. Notably, the uptake enhancer had a substantial and consistent impact in all the sampling times, demonstrating its impact on N uptake by the plant.