esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results  
In this paper, a day-ahead DEWH load scheduling (from 0 a.m. to 12 p.m.) is considered. The length of each time step, \(\Delta t\), is set as 15 min. Chosen from the 2012 American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE,) Hand-book [27], the specific parameters in the thermal dynamic model of DEWH are listed in Table 1. The daily real-time prices are assumed to be known. The RTP values refer to [16], as shown in Figure 2.  
As mentioned above, the uncertain water demand and ambient temperature are transformed into intervals, and the consumers quantify the optimistic degree of the uncertainties by means of setting  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\mathbf{P_{r}}\) (kw) & \(\mathbf{Q}\) (kw) & \(\mathbf{R}\) (\({}^{\circ}\)C/kw) & \(\mathbf{C}\) (kwh/\({}^{\circ}\)C) & \(\mathbf{M}\) (L) & \(\mathbf{\theta}^{low}\) (\({}^{\circ}\)C) & \(\mathbf{\theta}^{up}\) (\({}^{\circ}\)C) \\ \hline
4.5 & 150 & 0.7623 & 431.7012 & 227.1 & 55 & 70 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the domestic electric water heaters (DEWH).  
Figure 2: Real-time pricing (RTP) prices for next day.  
the robust levels \(\Gamma_{d}\) and \(\Gamma_{\theta}\). In this paper, the forecast maximum intervals of hot water demand and ambient temperature for the next day are plotted in Figure 3, respectively, referring to [23]. As for the maximum values of robust levels, \(N_{d}\) and \(N_{\theta}\) are both assigned as ten, which denotes that the uncertainties of water demand and ambient temperature are divided into 10 different levels. At the same time, we should think over the deterministic situation where one of the robust levels is equal to zero or both of them are zero. Therefore, the number of the robust level pairs (\(\Gamma_{d}\),\(\Gamma_{\theta}\)) is 11 times 11, being 121 in total.  
Figure 3: Maximum forecast intervals for the next day. (**a**) Hot water demand; (**b**) Ambient temperature.
## 3 Research Results
N = 16} & \multirow{4}{*}{8.047–8.737 GHz} & \multirow{4}{*}{690 MHz} & \multirow{4}{*}{100 mm \(\times\)} & Rogers & & & \\  & & & & 100 mm \(\times\) & RT & High gain & & \\  & & & 100 mm & Duroid & Not & 1 & CubeSat & Not included \\  & & & & 2.736 \(\lambda\) & and & & provided & & \\  & & & & & RO3010 & & & & \\ \hline \multirow{4}{*}{[34]} & Printed monopole antenna. MIMO antenna. N = 4. & \multirow{4}{*}{3.37–3.61 GHz} & \multirow{4}{*}{240 MHz} & \multirow{4}{*}{60 mm \(\times\) 0.7 \(\lambda\)} & Rogers & Between & & \\  & & & & 60 mm \(\times\) & & 5870 & 2.71 dBi & & \\ \cline{1-1} \cline{6-10}  & Microstrip patch antenna. N = 4. & \multirow{4}{*}{3.54–3.65 GHz} & \multirow{4}{*}{110 MHz} & \multirow{4  
Firstly, the low 5G band is used for antennas and arrays in other applications, and they radiate with low gain as reported in [34; 35; 36; 37]. The arrays in [34; 36] are focused on multiple-input and multiple-output (MIMO) communications. Moreover, the work presented in [38]  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
has an aperture size slightly larger than 10 cm. These examples cannot be reused in modern nanosatellites. On the other hand, there exist arrays for CubeSats with higher gains concerning the proposed design in this research. Nevertheless, these arrays are configured for higher frequencies, as shown in [30; 39; 40]. Some arrays even use more than two ports for MIMO systems in [39; 41]. The interesting array in [42] obtains high gain with 256 antennas on a deployable technology that is larger than 10 cm. Finally, the arrays in [43; 44] exhibit lower gains in the 2.4 GHz frequency band for CubeSats. Here, the proposed design stands out mainly for considering a low-profile array with high gain performance. This is achieved by using 16 miniaturized elements and an FN in a 5G frequency band. Furthermore, the array was mounted on a CubeSat structure to test its performance. This design can be used for modern CubeSats in 5G applications.
## 4 Calibration Method
### Detection of Installation Eccentricity Error of Test Rods
The measured coordinates of test rod A and B conform to a circular distribution, as shown in Figure 6.  
In order to better fit the motion trajectory of the test rods, the least square circle fitting method is used. The corresponding parametric equation of the fitted circular curve can be expressed as \(\left(x_{i}-O_{x}\right)^{2}+\left(y_{i}-O_{y}\right)^{2}=R^{2}\). Therein, \(\left(O_{x},O_{y}\right)\) is the coordinate of the center of the circle and the ideal position of the central axis of transit axis, and \(R\) is the radius of the center and the installation eccentricity error of test rod. Let us take the test rod A as an example. In order to obtain the estimation values of \(A(x_{i},y_{i})\) and \(R_{1}\), it is necessary to minimize the sum of squares of the distance \(\delta_{i}\) between the measured points \(A(x_{i},y_{i})\) and the circle:  
Figure 3:0  
Figure 3:1 The images of the central axis of test rod A and B displayed on monitor. Figure 3:2 The trajectory of the central axis of test rod A and B. Assume that the distance from the measured points \(A(x_{i},y_{i})\) to the center of the circle is given by \(d_{i}=\sqrt{\big{(}x_{i}-O_{x}\big{)}^{2}+\big{(}y_{i}-O_{y}\big{)}^{2}}(i=1,2 \ldots N)\). Then, the Equation (2) can be rewritten as  
Figure 3:3  
According to the least squares principle, \(\sum_{i=1}^{N}\delta_{i}^{2}\) can be expressed as  
Figure 3:4  
where the quantities are \(\alpha=-2x_{i}\), \(\beta=-2y_{i}\), and \(\gamma={O_{x}}^{2}+{O_{y}}^{2}-R_{1}^{2}\). In order to minimize the function of \(F(\alpha,\;\beta,\;\gamma)\), the partial derivative of function \(F(\alpha,\;\beta,\;\gamma)\) with respect to variables \(\alpha,\;\beta,\) and \(\gamma\) is shown by  
\[ \begin{cases}\frac{\partial F(\alpha,\beta,\gamma)}{\partial\alpha}=\sum_{i= 1}^{N}2x_{i}\big{(}{x_{i}}^{2}+{y_{i}}^{2}+\alpha x_{i}+\beta y_{i}+\gamma \big{)}=0\\ \frac{\partial F(\alpha,\beta,\gamma)}{\partial\beta}=\sum_{i=1}^{N}2y_{i} \big{(}{x_{i}}^{2}+{y_{i}}^{2}+\alpha x_{i}+\beta y_{i}+\gamma\big{)}=0\\ \frac{\partial F(\alpha,\;\beta,\;\gamma)}{\partial\gamma}=\sum_{i=1}^{N}2 \big{(}{x_{i}}^{2}+{y_{i}}^{2}+\alpha x_{i}+\beta y_{i}+\gamma\big{)}=0\end{cases}  \tag{5}\]  
The quantities \(O_{x}\), \(O_{y}\), and \(R\) are calculated using Equation (5):  
\[ \begin{cases}&O_{x}=-\frac{\alpha}{2}\\ &O_{y}=-\frac{\beta}{2}\\ &R_{1}=\frac{\sqrt{\alpha^{2}+\beta^{2}-4\gamma}}{2}\end{cases}  \tag{6}\]  
where \(\big{(}O_{x},O_{y}\big{)}\) in the Equation (6) is the ideal position of the transit axis, and \(R_{1}\) is the installation eccentricity error of test rod A. By analyzing the magnitude and sign change of the \(x\) value and \(y\) value of \(Ax_{i}\), \(Ay_{i}\) and \(Bx_{i}\), and \(By_{i}\) (\(i=1,2\ldots N\)) in the \(X^{\prime}O^{\prime}Y^{\prime}\) coordinate system, the installation eccentric angles (\(\theta_{1}\)_and_\(\theta_{2}\)) can be obtained.
## 3 Methodology
#### Data Source Description  
To understand DEMs, it is essential to emphasize their importance in accurately predicting climate change-linked soil deposition. Recognized as vital global geospatial data, DEMs are endorsed by prominent organizations such as the Spanish National Cartographic System, the INSPIRE Directive, and the United Nations [42]. In Spain, the national mapping agency, the _Instituto Geografico Nacional_ (IGN), has utilized LIDAR data from the PNOA photogrammetric flight to create the digital elevation model [34]. At a global scale, since 2000, NASA has conducted various SRTMs to determine global DEMs, with a grid spacing of 1 and 3 arc-seconds. In the current research, these DEMs were downloaded for the time range 2009-2020 from NASA's Earth Explorer ([[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)), accessed on 20 June 2024).  
In this work, we strategically placed 17 control points throughout the study area (Figure 3).  
DEM data from NASA's SRTM were augmented with the chosen control points and used to calculate the total water volume in the study area via iterative processes [43]. Likewise, an iterative data processing method was used to ascertain the average water depth, which was essential for gathering data on soil erosion in the D  
Figure 3: Control point distribution. Coordinates in km. Source: own elaboration from [34].  
Next, a thorough literature review was conducted to determine the potential availability of a sufficiently long precipitation record for deriving relevant information for this research [44]. In this context, based on [45], precipitation (daily rainfall data) and temperature (daily data) records (from 2006 to 2020) were obtained, enabling the extraction of variables (Figure 4) pertinent to the Donana salt marshes during the research period from 1 October 2008 (the start of the flooding cycle of 2008/2009) to 31 May 2020 (the end of the flooding cycle of 2019/2020).  
Furthermore, utilizing data mining to analyze soil deposition between 2009 and 2020 involved extracting significant information from extensive datasets to reveal patterns and trends, which was then organized in a comprehensible way for further use by modified genetic algorithms. These modified genetic algorithms [46] work as a parallel process, operating as follows: Initially, the extensive dataset to be categorized is stored in a central storage unit. Upon execution, individual segments are processed through separate mapping tasks, each one beginning by accessing the training dataset and training the classifier. Then, the trained classification model is used to classify the large dataset. The iterative training process for each mapping task is designed to minimize the computational impact, as the training dataset is small compared to the extensive one, which accounts for most of the computing time.
## 4 Counting the Metal-Protein Interactions with Bioinformatics: Metallproteomes
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Protein** & **Function** & **Example a** \\ \hline Membrane metal & Export, import—on the plasma & Zip (SLC39A) family of zinc \\ ion transport & membrane or intracellular & tranporters, influx into cytosol (Zn) \\  & membranes & ZnT (SLC30A) family of zinc \\  & & membranes & transporters, efflux from cytosol \\ \hline Transport & Cellular, extracellular translocation, & Tranferrin (Fe) \\  & distribution, and redistribution & Metallothioneeins (Zn,Cu) \\ \hline Storage & Long term, short term & Ferritin (Fe) Metallothioneeins (Zn, \\  & & Cu) \\ \hline Metal buffering & Adjusting steady-state levels of metal & \\  & ions and control of transients & \\  & employed in cellular signaling with & Metallothioneeins (Zn, Cu) \\  & metal ions. & \\ \hline \multirow{4}{*}{Chaperoning} & Safeguarding metal ions to avoid & Iron chaperone, poly(rC) binding \\  & adventitious reactions, including & protein (PCBP1) \\  & mineralization of proteins by & Copper chaperone (CCS) for \\  & transferring the correct metal ion to & cytosolic superoxide dismutase \\  & the protein that requires it. & Iron regulatory proteins (IRP-1, -2) \\ \hline \multirow{2}{*}{Sensing} & Gauging metal ion concentrations & \\  & and triggering transcriptional (Zn) or & Metal-responsive transcription \\  & post-transcriptional (Fe) responses for & factor-1 (MTF-1) (Zn) \\  & regulation. & \\ \hline Metallocorfactor & Insertases, enzymes that insert metal & \\  & ions into prosthetic groups or & Ferrochelatase (Fe) \\  & cofactors. & \\ \hline Metal acquisition & Metallophores—the majority are & Siderophores (Fe) \\  & low-molecular-weight compounds, & Chalcophores (Cu) \\  & but some are proteins. & Zincophores (Zn) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Functions of homeostatic proteins of metal metabolism. until recently. However, the recognition that at least twenty-four membrane transporters and at least a dozen metallothioneineins are involved in human cellular zinc homeostasis, and that all these proteins have multiple mutations, has resulted in a burgeoning field of genetic disorders of zinc metabolism with high importance for medicine [24]. Mutations do not necessarily affect the transport process itself but can interfere with the regulation of the transporter. A case in point is a particular mutation in ferroportin that results in the change of an amino acid that is involved in SUMOylation and therefore affects trafficking of this cellular iron exporter [25].
# Design and Performance Analysis of Navigation Signal Based on OFDM
## 3 ACF and PSD Derivation Verification of OFDM Navigation Signal Modulation System
### PSD and ACF Parsed Expression Experimental Verification  
This section verifies the correctness of the ACF of the OFDM navigation signal and the PSD analysis expression through simulation, such as using Equations (13) and (14). The ACF and PSD generation process in the form of simulation are as follows: a time domain signal is generated according to the OFDM modulation model, then the autocorrelation operation is performed and the simulated ACF is obtained. The simulated power spectral density can be obtained by performing a Fourier transform on the simulated ACF.  
Figure 2 shows the results of the OFDM navigation signal's ACF and PSD simulations compared with the parsing expression. As can be seen from Figure 2a,b, the ACF of the OFDM navigation signal has a narrow and sharp main peak; next to the main peak are multiple secondary peaks, but the peak of the secondary peak is lower and is obviously suppressed. When the OFDM navigation signal selects the cyclic prefix mode, it can be found that at abscissa \(1\)us and \(-1\)us, there is a pair of left-right symmetrical secondary peaks, which are caused by the cyclic prefixes. In zero-padding mode, there is no similar secondary peak. As can be seen from Figure 2c,d, the PSD of the OFDM navigation signal presents an approximately flat form, and there is no obvious distinction between the main and secondary lobes; Compared with the PSD in the cyclic prefix and zero-padding modes, it can be found that the PSD energy of the zero-padding mode is primarily concentrated in the main lobe, and the zero-padding model is more concentrated than the PSD energy in the cyclic prefix mode.  
According to the experimental results, we find that the parsing expression is consistent with the numerical simulation results, and the correctness of the parsing expression is verified. In the simulation process, the analytical expression can save many operations compared with the numerical simulation, and the operation efficiency is high, which provides convenience for the signal analysis.
###### Abstract  
Electrically conductive and flexible thermoplastic polyurethane/graphene (TPU/GE) porous structures were successfully fabricated by selective laser sintering (SLS) technique starting from graphene (GE)-wrapped thermoplastic polyurethane (TPU) powders. Several 3D mathematically defined architectures, with porosities from 20% to 80%, were designed by using triply periodic minimal surfaces (TMPS) equations corresponding to Schwarz (S), Diamond (D), and Gyroid (G) unit cells. The resulting three-dimensional porous structures exhibit an effective conductive network due to the segregation of graphene nanoplatelets previously assembled onto the TPU powder surface. GE nanoplatelets improve the thermal stability of the TPU matrix, also increasing its glass transition temperature. Moreover, the porous structures realized by S geometry display higher elastic modulus values in comparison to D and G-based structures. Upon cyclic compression tests, all porous structures exhibit a robust negative piezoresistive behavior, regardless of their porosity and geometry, with outstanding strain sensitivity. Gauge factor (GF) values of 12.4 at 8% strain are achieved for S structures at 40 and 60% porosity, and GF values up to 60 are obtained for deformation extents lower than 5%. Thermal conductivity of the TPU/GE structures significantly decreases with increasing porosity, while the effect of the structure architecture is less relevant. The TPU/GE porous structures herein reported hold great potential as flexible, highly sensitive, and stable strain sensors in wearable or implantable devices, as well as dielectric elastomer actuators.  
Keywords:selective laser sintering (SLS); thermoplastic polyurethane (TPU); graphene (GE); mathematically defined structures; piezoresistivity; strain sensors +
Footnote †: journal: Applied Materials Science
## 4 Discussion
However, in our view, the most interesting extension would be to extend the framework to a transfer learning setting. The concept of transfer learning is to use a pre-trained model and fix the weights of the first few layers and re-train with an entirely new data set. Transfer learning has been used with success in computer vision and other tasks, and the key benefit is that the need for data is reduced drastically. Translation of this concept to binary TSC, where the pre-trained model would be trained on simulation data, should be investigated closer. Conceptualization, K.G. ; methodology, K.G., A.O. and N.B. ; software, K.G. ; validation, K.G. ; formal analysis, K.G., N.B. and A.O. ; investigation, K.G. ; data curation, K.G. ; writing-original draft preparation, K.G., A.O. and N.B. ; writing-review and editing, K.G., A.O., N.B. and G.A. ; visualization, K.G. ; supervision, G.A., N.B., A.O., Resources; G.A. ; Funding acquisition, G.A. ; Project administration, K.G. and G.A. All authors have read and agreed to the published version of the manuscript. This project has received funding from the European Unions's Horizon 2020 research and innovation programme under grant agreement No. 654462 and the Research Council of Norway (project no. 254711 BayMoDe). In addition, this work is part of the project ACTOM, funded through the ACT programme (Accelerating CCS Technologies, Horizon2020 Project No 294766). Financial contributions made from; The Research Council of Norway, (RCN), Norway, Netherlands Enterprise Agency (RVO), Netherlands, Department for Business, Energy & Industrial Strategy (BEIS) together with extra funding from NERC and EPSRC research councils, United Kingdom, US-Department of Energy (US-DOE), USA. In-kind contributions from the University of Bergen are gratefully acknowledged. The authors would like to acknowledge NVIDIA Corporation for providing their GPUs in the academic GPU Grant Program. We would also acknowledge [PERSON], Plymouth Marine Laboratory, for providing the data set. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. The following abbreviations are used in this manuscript: \begin{tabular}{l l} AUC & Area Under the Curve \\ BCNN & Bayesian Convolutional Neural Network \\ CCS & Carbon Capture and Storage \\ CO\({}_{2}\) & Carbon dioxide \\ CNN & Convolutional Neural Network \\ DOAJ & Directory of open access journals \\ DTW & Dynamic Time Warping \\ ERSEM & European Regional Seas Ecosystem Model \\ FVCOM & Finite-Volume Community Model \\ IOC & Intergovernmental Oceanographic Commission \\ KDE & Kernel Density Estimate \\ MAP & Maximum A Posteriori \\ MDPI & Multidisciplinary Digital Publishing Institute \\ MC & Monte Carlo \\ \end{tabular}
## 1 Introduction
Traditional vegetation indices have proven inadequate for these purposes, whereas SIF shows a stronger correlation with vegetation physiology. Elucidating SIF-GPP/ET relationships is vital for developing accurate SIF-based estimation models. Field measurements and analyses are essential for investigating the variations in SIF, GPP, and ET in coastal wetlands under the influence of tidal inundation, as well as their responses to meteorological factors. Simultaneously, model simulation serves as an important complementary approach to estimate SIF, GPP, and ET under different meteorological factors and across various spatial and temporal scales, while also offering deeper insights into the underlying mechanisms driving these processes. Among various models, process-based ecosystem models, which are grounded in a theoretical understanding of relevant processes, facilitate the exploration of mechanisms underlying carbon and water flux dynamics and predict ecosystem responses to environmental changes. The SoilCanopy Observation, Photochemistry, and Energy Fluxes (SCOPE) model [26] is based on the vegetation-soil-atmosphere continuum and has been applied to diverse vegetation types, such as grasslands and forests [27; 28]. It has become one of the most commonly used process models for the co-simulation of SIF and GPP across different biomes. Although this process model does not explicitly incorporate tidal processes, its simulation results can be interpreted as indicative of SIF, GPP, and ET in the absence of periodic tidal inundation. In this study, we investigate the variations in SIF, GPP, and ET under tidal inundation, their responses to meteorological factors, and the impact of tidal inundation on the ability of SIF to track these changes. Field measurements of GPP, ET, and SIF were conducted at the Shanghai Jiuduansha Wetland National Nature Reserve in the Changjiang Estuary, China. Our specific objectives are to (1) calibrate the leaf chlorophyll fluorescence modules of the SCOPE model using the photosynthetic gas exchange and chlorophyll fluorescence measurements in a coastal salt marsh wetland; (2) compare the measured and simulated canopy SIF\({}_{760}\), GPP, and ET under different meteorological and tidal conditions to analyze their responses to meteorological factors during tidal inundation and across salinity gradients; and (3) explore the variations and underlying mechanisms in the observed SIF\({}_{760}\)-GPP and SIF\({}_{760}\)-ET correlations caused by tidal flooding and the changes in meteorological factors. This study aims not only to enhance the application of SIF in indicating variations in carbon and water cycles in coastal wetlands but also to provide scientific support for the accurate estimation of GPP and ET in coastal wetland ecosystems across multiple temporal and spatial scales.