esa-sceva/satcom-chunk-collection huggingface dataset:
# The Impact of Inter-Modulation Components on Interferometric GNSS-Reflectometry
## 3 Impact on _i_GNSS-R Altimetric Performance
### Waveform Model  
The ocean reflected GNSS signal can be modelled as the sum of the returns from many scattering elements of the sea surface. Models of GNSS waveforms for reflections over the ocean are derived in [13; 26; 27]. After simplification, the useful signal component in the power waveform can be expressed as the convolution product  
\[\left\langle Z_{S}\left(\tau\right)\right\rangle=W\left(\tau\right)\otimes \Lambda_{B}^{2}\left(\tau\right) \tag{2}\]  
where the weight function \(W(\tau)\), known as the flat surface impulse response in conventional radar altimetry, represents the contribution to the waveform of the points with a delay \(\tau\) and \(\Lambda_{B}(\cdot)\) is the band-limited ACF of the GNSS signal.  
In order to intuitively show the difference between the interferometric waveform with and without the IM component, the average waveforms are simulated using Equation (2) and the scenario described  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Orbit Altitude & 600 & Km \\ Incidence Angle at the SP & 15 & deg \\ Wind Speed at 10 m (U10) & 7 & m/s \\ Antenna Directivity at Nadir & 22.6 & dB \\ Receiver Bandwidth & 12 & MHz \\ Coherent Integration Time & 1 & ms \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters For Simulation.  
Figure 4: Measured direct waveforms of the GPS L1 in-phase and quadrature components from PRN01. Coherent integration time of \(T_{\mathrm{c}}=10\) ms and incoherent integration time of \(T_{\mathrm{inc}}=1\) s have been used in this example.  
in Table 2. As shown in Figure 5, two main aspects should be noted through the comparison between the waveforms. First, including the extra IM signal component increases the total transmitted power and hence the amplitude of the reflected power waveform. Second, the IM component concentrates more power further away from the carrier, and the slope of the power waveform becomes steeper, leading to better altimetric performance.
# State-of-Charge Prediction Model for Ni-Cd Batteries Considering Temperature and Noise
## 3 APSO-GRNN Algorithm
### Prediction Process of Battery SOC Based on APSO-GRNN Model  
Although a GRNN is suitable for regression analysis, it is easily affected by the initial parameter matrix. To overcome this limitation, the APSO algorithm proposed in this paper has faster convergence and better global optimization ability than ordinary PSO. Optimizing the GRNN's parameters using APSO can compensate for the GRNN's defects. The GRNN model uses the battery terminal voltage \(V\), output current \(I\), and temperature \(T\) as input values, and the GRNN prediction model is used as the evaluation function \(f(x)\). The specific steps in this approach are as follows:  
1. Initialize the particle swarm algorithm parameters. The initial particle population size is \(m\times n\), where \(m\) is the number of particle vectors, \(n\) is the number of neurons in the summation layer, and the number of iterations is \(T\). The smoothing factor of each neuron in the summation layer is taken as the position attribute of the particle \(x_{i}\), and the particle position parameters and matrix parameters are randomly initialized. \[x=\begin{bmatrix}\sigma_{11}&...&\sigma_{1n}\\ \vdots&\vdots&\vdots\\ \sigma_{m1}&\cdots&\sigma_{mn}\end{bmatrix}v=\begin{bmatrix}v_{11}&....&v_{1n }\\ \vdots&\vdots&\vdots\\ v_{m1}&...&v_{mn}\end{bmatrix}\] (14)
2. Assuming that the GRNN model is a fitness calculation model, the error matrix of the particle swarm is calculated using Equations (9) and (13) as \(e\), and the error vector of a single particle is \(E_{i}\). \[e=\begin{bmatrix}E_{11}&\cdots&E_{1n}\\ \vdots&\ldots&\vdots\\ E_{m1}&\cdots&E_{mn}\end{bmatrix}E_{i}=\begin{bmatrix}E_{i1}\\ \vdots\\ E_{in}\end{bmatrix}\] (15)
3. The local optimal value \(p_{i}\) and each particle's global optimal value \(g_{M}\) are updated according to the error matrix. The particle swarm velocity weight matrix \(w\) is modified using Equation (13), and the particle swarm position vector and velocity vector are updated using Equations (7) and (8).
4. Repeat Steps 2 and 3 until the error of the global optimal particle is less than the target error \(E_{target}\) or the maximum number of iterations \(T_{max}\) and then end the iteration. The algorithm's structure is shown in Figure 3.
## 2 Results and Discussion
### Headspace SPME-GC/MS Analysis of VOCs  
A total of 29 VOCs, including nine aldehydes, five ketones, nine alcohols and six other compounds, were detected (Table 1). The trend in the VOCs at 4 \({}^{\circ}\)C was changed more than that at 0 \({}^{\circ}\)C, and various VOCs in the present study were reported previously as products of bacterial metabolism, enzymatic reaction or lipid autoxidation [PERSON] et al. (2017); [PERSON] et al. (2017). The accumulation of aldehydes, alcohols and ketones were due to the metabolites produced by spoilage microorganisms and fatty acids oxidation [PERSON] et al. (2017). Aldehydes and alcohols are major VOCs as products of chemical activity in fresh seafood. Ketones, acids, aromatic compounds, and other compounds were properly shown in the lower levels (Table 1). [PERSON] et al. (2007) and [PERSON] et al. (2013) [PERSON] et al. (2013) [PERSON] et al. (2017) made a similar conclusion that hexanal, heptanal, octanal, and nonanal were identified in fresh tuna, and this finding was probably related to derivation from oxidative degradation of polyunsaturated fatty acids [PERSON] et al. (2017). Figure 2: ATP (**a**), adenosine diphosphate (ADP) (**b**), adenosine monophosphate (AMP) (**c**), inosine monophosphate (IMP) (**d**), inosine (HxR) (**e**) and hypoxanthine (Hx) (**f**) content of bigeye tuna at 0 \({}^{\circ}\)C and 4 \({}^{\circ}\)C for 6 day (each point is the mean value of three determinations). Among aldehydes, hexanal, nonanal, heptanal and propanal were present at the highest concentration on day 0. The high content of hexanal could be closely associated with oxidation of linoleic acid and polyunsaturated fatty acid, which indicated seafood spoilage [28]. Moreover, heptanal, octanal, nonanal, (_E,Z_)-2,6-nonadien and tetradecanal appeared in early storage times, and a fishy odour was confirmed for the above substances [29]. Therefore, the changes in aldehydes can reflect the degree of oxidation of polyunsaturated fatty acids in bigeye tuna, which provides a basis for judging its freshness [8]. The compounds from the ketones were relatively low in fresh tuna (day 0). Ketones, acetone and 2,3-pentanedione were only detected on day 0, while 2-heptanone, 2-nonanone and 2-undecanone appeared on day 6 at 4 \({}^{\circ}\)C. The abundance of ketones at the end of the storage time was greater than that on day 0, which might have been due to the higher metabolic activity of _Pseudomonas_. The _Pseudomonas_ spp, which might increase the ketone concentration, were the most abundant species at the end of storage as shown in an earlier study [29]. Among alcohols, 2-octen-1-ol and (_E_)- and (_Z_)-2-penten-1-ol were not detected in fresh tuna (day 0).
## 2 Differential Radar Interferometry for Deformation Monitoring in Cultural Heritage Sites
### Local-Scale Structural Deformation Monitoring for Heritage Monuments
#### 2.2.2 Differential SAR Tomography  
In cultural heritage sites with a large density of vertical structures, it is difficult for space-borne DInSAR to interpret and differentiate scatterers in the vertical direction due to height ambiguities arising from the irregular and under-sampled spatial distribution of the imaging positions. SAR tomography (TomoSAR), enabling a real geometric resolution capability in the vertical direction, has proven to be capable of separating interfering targets and even solving misinterpretations in SAR images caused by layover and foreshortening effects [33]. The differential SAR tomography (d-TomoSAR), integrating DInSAR, tomography and multi-temporal technologies, extends the capabilities of TomoSAR to deformation monitoring. It performs a four-dimensional (4D) space-velocity SAR imaging that makes use of the multi-baseline and multi-temporal characteristics of repeat-track data to separate interfering scatterers at different heights, as well as to distinguish their single slow displacement velocities [57; 58]. [PERSON] _et al._[34] applied d-TomoSAR to deformation monitoring over the Grotta Perfetta area in the city of Rome. Even though they used medium-resolution SAR data over the target area, the multiple scatterers interfering in the same radar pixel were resolved, and their single time series displacement values were correctly estimated. The results demonstrated the potentiality of this approach for monitoring complex targets in cultural heritage sites. Furthermore, [PERSON] and [PERSON] [59] proposed a \"time warp\" method to convert any nonlinear multiple (M) component motion into a linear one by rearranging the SAR acquisition data. By projecting the temporal baseline to the artificial temporal baseline, the \"time warp\" method provides the possibility to focus the desired parameter, e.g., the amplitude of periodic motion, to the coefficient space [60]. It enables the deformation estimation for complex motion models, including linear and periodic motion models, opening the possibility for d-TomoSAR developing into an (M + 1)-dimensional deformation monitoring approach.
## 6 Onboard Steering Commands via Minimal-Cost-Variance Control
Thus, for \(\beta_{i}(k)\triangleq A_{i}(h)s_{i}(k)+B_{i}(h)\ \gamma_{i}\ (k)\), it follows that  
\[z_{i}(k) =s_{i}(k)\,, \tag{92}\]3 \[z_{i}(k) =s_{i}(k)\,, \tag{92}\]4  
Aside from the relevance of \(S_{i}(k)\triangleq Q_{i}(k)+M_{i}(k+1)\) for \(n_{0}\leq k\leq N_{i}-1\), to the terminal conditions given by \(m_{i}(N_{i})=0\), \(M_{i}(N_{i})=0\), \(v_{i}^{*}(N_{i})=0\), and \(V_{i}^{*}(N_{i})=0\), some mathematical manipulations further yield  
\[z_{i}(k) =s_{i}(k)\,, \tag{92}\]5 \[z_{i}(k) =s_{i}(k)\,, \tag{92}\]6 \[z_{i}(k) =s_{i}(k)\,, \tag{92}\]7  
Performing the minimization with respect to \(\gamma_{i}(k)\) for the \(i\)-th clock steering, the optimal minimal-cost-variance controller \(\gamma_{i}^{*}(k)\) for clock and time scale adjustments are given by  
\[z_{i}(k) =s_{i}(k)\,, \tag{92}\]8  
where, for \(n_{0}\leq k\leq N_{i}-1\),  
\[z_{i}(k) =s_{i}(k)\,, \tag{92}\]9  
and  
\[J_{i}(n_{0}) =\sum_{k=n_{0}+1}^{N_{i}}L_{i}(k,s_{i}(k),u_{i}(k-1)) \tag{93}\]0 \[J_{i}(n_{0}) =\sum_{k=n_{0}+1}^{N_{i}}L_{i}(k,s_{i}(k),u_{i}(k-1)) \tag{93}\]1  
where \(A_{i}^{*}(h)\triangleq A_{i}(h)+B_{i}(h)K_{i}^{*}(k)\) and \(n_{0}\leq k\leq N_{i}-1\). Currently, concerns regarding the control solution and its ability to explain the minimum nature of the expected value of a finite-time horizon quadratic cost--similar to the minimum mean cost control--have taken a back seat in the investigation of new clock steering methods presented here. The primary focus has shifted to the minimum variance cost problem. For each selection of \(\mu_{i}(k)\) within the range \(n_{0}\leq k\leq N_{i}-1\), solving the recursion equations leads to a mean value of the performance measure, along with its corresponding minimum variance and optimal control law. A key objective is to determine effective values of \(\mu_{i}(k)\), \(n_{0}\leq k\leq N_{i}-1\), to validate several sets of expected values, minimum variances, and optimal steering laws. In contemporary terms, it can be asserted that the minimum mean problem is a specific instance of the problem addressed here; it represents the solution to the recursion equations in the limit as \(\mu_{i}(k)\) approaches infinity, for \(n_{0}\leq k\leq N_{i}-1\). Under suitable conditions where both process and measurement noises are Gaussian, the design of stochastic control herein can be further extended and thus divided into two separate problems, one of optimal control with full state information and one of filtering. The remainder of the research investigation is to present a framework for the separation principle, which is more in line with basic engineering thinking, e.g., the optimal feedback law for steering commands is linear in the data feedback and given by  
\[J_{i}(n_{0}) =\sum_{k=n_{0}+1}^{N_{i}}L_{i}(k,s_{i}(k),u_{i}(k-1)) \tag{93}\]2  
where \(\hat{s}_{i}(t_{k})\) is obtained by a local Kalman state estimator. Each platform \(i\) in Figure 11 uses the steering command (112) based on the Kalman state estimator for feedback frequency adjustments to ensure that the phase offset between the steerable OCXO and the IEM estimate is driven to zero.
# Antennas for Licensed Shared Access in 5G Communications with LTE Mid- and High-Band Coverage
## 2 A Planar Antenna for LSA Bands
### Design Procedure and Working Principle  
The design of this LSA antenna starts with a simple L-shaped antenna, which is shown as Step 1 in Figure 2. This antenna has a \(-6\) dB reflection coefficient bandwidth of 670 MHz (1.39-2.06 GHz), as shown in Figure 2, and resonates at 1.58 GHz. The effective length of the antenna (Step-1) is 44 mm.  
Figure 1: Configuration of the planar antenna (Ant#1) for LSA operation (1452–1492 and 2300–2400 MHz): **(a)** top view and **(b)** bottom view. All dimensions are in millimeters.  
Figure 2: Two steps of the evolution of the planar antenna (Ant#1) to cover the two LSA bands.  
In the next step, our target is to achieve an operating band that could cover the higher LSA band (2.3 GHz band), together with the lower LSA band (1.5 GHz band). In order to have another resonance at the higher LSA band, an L-shaped right element is added to the antenna element in Step 1, and the reflection coefficient curve is shown in Figure 2. The second antenna (Step 2) has a wide \(-6\) dB reflection coefficient bandwidth of 1170 MHz (1.38-2.55 GHz) and covers both LSA bands, together with 12 FDD and 7 TDD LTE bands in the LTE mid band. The antenna resonates at 1.71 and 2.38 GHz within the 1.5 and 2.3 GHz LSA bands, respectively. The effective length of the right element is 31 mm, which is equal to a quarter wavelength at 2.38 GHz, and that of the left element is 44 mm, which is equal to a quarter wavelength at 1.71 GHz. Note that the electrical lengths are calculated with respect to the free-space wavelength.  
Now let us study the surface current distribution, as shown in Figure 3, at 1.8 and 2.6 GHz in order to further understand the operation of the antenna. At both frequencies, the surface current is at a maximum around the feed point and a minimum at the end of the left or right element, meaning that the antenna resonates in the quarter-wavelength modes at both frequencies. The left element has strong currents at 1.8 GHz and the right element has strong currents at 2.6 GHz. However, in both cases, moderate currents are observed in the ground plane close to the antenna structure.  
This planar antenna covers both the LSA bands but only a part of the mid-LTE band. In the next section, a dual-band antenna is presented to cover the entire LTE mid and high bands, together with the two LSA bands.
# Experimental Approaches in Delineating mTOR Signaling
## 4 Identification and Characterization of mTOR Signaling Pathway Components
### Protein Motif/Sequence Homology Search-Mediated Identification of mTOR Signaling Components
#### 4.5.3. PI(3,4,5)P\({}_{3}\) Was Identified to Bridge PI3K/mTORC2 Through a Protein Motif Search  
It is well characterized that PI3K is one of the major mTORC2 upstream activator [92], while the molecular connection between PI3K and mTORC2 remains elusive. Given that a PH domain was denoted in the C-terminus of the mTORC2 essential component, Sin1, and PH domains have long been characterized as phospho-lipid binding domains, [PERSON] et al. examined if the Sin1-PH domain recognized PI(3,4,5)P\({}_{3}\) produced by PI3K to promote mTORC2 activation. To this end, [PERSON] et al. found that (1) Sin1-PH domain could functionally replace Akt-PH domain for Akt activation, suggesting Sin-PH was a functional PH domain in recognizing phospho-lipids; (2) recombinant Sin1-PH domain proteins recognized PI(3,4,5)P\({}_{3}\) in lipid overlay assays; (3) overlaying Akt-PH and Sin1-PH structures led to identification of key PI(3,4,5)P\({}_{3}\) binding resides; and (4) mutating PI(3,4,5)P\({}_{3}\) binding residues in Sin1-PH led to reduced localization of Sin1-PH on plasma membrane in cells by immunofluorescence, attenuated Sin1-PH binding to PI(3,4,5)P\({}_{3}\), and subsequently decreased mTORC2/Akt signaling in cells [93].  
5.4. Motif-Search-Based Identification of SLC38A9 as a Lysosomal Cholesterol Sensor for mTORC1 Activation  
Given that mTORC1 promotes lipid synthesis through the mTORC1/Lipin1/SREBP1 signaling [94], [PERSON] et al. examined if dietary lipids control mTORC1 activity. This was based on several observations such as: (1) depletion of cellular cholesterol reduced mTORC1 activity in HEK293T cells, whereas exogenous LDL addition activated mTORC1 signaling; (2) sterol depletion still reduced mTORC1 activity in _TSC2\({}^{\sfrac{-}{-}}\)_ MEFs; (3) manipulation of cholesterol levels had minimal effects on cellular amino acid levels; and (4) depletion of cholesterol weakened Regulator binding to Rags. Thus, [PERSON] et al. hypothesized that cholesterol activated mTORC1 in a Rag GTPases dependent, but PI3K/Akt independent, manner (given this regulation occurs in _TSC2\({}^{\sfrac{-}{-}}\)_ MEFs where the PI3K/Akt signaling disconnects from mTORC1 regulation) [95]. To nail down cholesterol targets in controlling mTORC1, [PERSON] et al. screened for cholesterol-regulated motifs among all the mTORC1 scaffolding complexes, which led to identification of a putative Cholesterol Recognition Amino Acid Consensus (CRAC) motif in SLC38A9. Using an ultraviolet light (UV)-photoactivatable cholesterol analogue, 7-Azi-27-yne, [PERSON] et al. proved a direct interaction between cholesterol and SLC38A9 in cells, and further biochemical studies supported a role of SLC38A9 in mediating cholesterol-induced mTORC1 activation [95].
# HERAS: A Modular Matlab Tool Using Physical Optics for the Analysis of Reflector Antennas
## 5 Conclusions  
The HERAS software for the analysis of reflector antennas using physical optics has been presented. This tool, with source code in MATLAB, is available freely for educational and research purposes and may be of interest to antenna engineers for parametric studies and custom reflector antenna system design. HERAS may also be of interest to satellite communicationspayload and system engineers due to its adaptability and applicability in end-to-end performance investigations. Some projects where the tool has been of contribution related to VHTS systems have been presented, showing the potential of the tool for the design, analysis, and optimisation of such systems, which employ thousands of beams to achieve higher capacities. Specifically, this tool has been used for the efficient estimation of SFPB antenna systems, where several acceleration techniques were introduced in order to reduce the computational cost of the calculation of thousands of beams. In addition, the tool has also demonstrated its potential for the analysis of AFR configurations in combination with several signal processing techniques such as beam-forming and precoding for the analysis of these systems in terms of the final system performance.  
Conceptualisation, A.B., S.M.-P., G.G., A.M. and N.J.G.F.; Software, A.B. and S.M.-P.; Validation, A.B., S.M.-P., G.G., A.M. and N.J.G.F.; Investigation, A.B., S.M.-P., G.G., A.M. and N.J.G.F.; Writing--original draft preparation, A.B.; Writing--review and editing, A.B., S.M.-P., G.G., A.M. and N.J.G.F. All authors have read and agreed to the published version of the manuscript.  
This work has been partly funded by the UK EPSRC under a DTP scholarship. The work of [PERSON] and [PERSON] was funded by the European Space Agency under contract no. 4000127115/19/NL/FE.  
Not applicable.  
The source code of HERAS and data reported in this paper are available from the corresponding author, upon reasonable request.  
The authors would like to thank [PERSON] for his contribution in the development of this software tool.  
The authors declare no conflict of interest.  
Figure 12: Beam far-field in a four frequency reuse scheme using (**a**) CFM and (**b**) ZF techniques.
# Reshaping the Digital Twin Construct with Levels of Digital Twinning (LoDT)
## 2 Functional Architecture of Digital Twinning
### The Architecture
#### 2.2.1 Creation  
In the creation phase, data is collected according to the parameters chosen to assess the performance of the physical asset (flow rate, frequency, strain, voltage, etc.) and the environment in which it operates (air temperature, precipitation, humidity, wind speed, etc.). These measurements can be made in a more manual way (e.g., visual inspection) or more automated, using built-in sensors or instrumentation installed on the respective physical asset, or even from external sources. Data can also be generated and controlled by entities external to the organisation (e.g., atmospheric data, satellite-based InSAR data). Data can be integrated within the organisation and managed as information assets. However, organisations should identify and address the risks associated with dependence on external data sources. Sensors generate signals that, before being transmitted to the virtual space, can be transformed into secure messages through encoders [40], increasing the security mechanisms (and their complexity) associated with managing these non-physical assets.  
In this phase, other factors should also be addressed, such as the variables to be measured, the type of sensors, and the choice of data acquisition frequency. Based on the defined objectives and the existing knowledge about the asset behaviour, the most relevant parameters for instrumentation should be selected, as well as the means and locations for their measurements. Factors such as the cost of instrumentation, accessibility for installation and replacement, and exposure to external agents (e.g., vandalism or the influence of nearby electromagnetic fields) should be considered in this decision. Phenomena that can distort the quality of the final information due to an incorrect data acquisition frequency (such as the aliasing effect) should be studied and prevented. The organisation should also aim to optimise the acquisition frequency considering the associated costs. Depending on the cases, a higher frequency rate may not add value to the phenomenon being measured and yet significantly increase the costs associated with data collection, communication, storage, processing, and analysis.
## 2 Materials and Methods
### Total Phenolic Content of G. hederaca Extracts  
The total phenolic content (TPC) of _G. hederaca_ extracts was measured using the Folin-Ciocalteu assay [39]. In a test tube, 1.60 mL of distilled water was mixed with 20 \(\upmu\)L of the sample extract, blank or standard to be analyzed. After that, 100 \(\upmu\)L of the Folin-Ciocalteu reagent was added and vortexed. After 3 min, 300 \(\upmu\)L of 75 g/L Na\({}_{2}\)CO\({}_{3}\) was added and stirred vigorously. After incubating the solution at room temperature for 45 min in the absence of light, absorbance values at 760 nm were acquired using a Varian Cary 300 bio UV-VIS spectrophotometer (Agilent, Santa Clara, CA, USA). Gallic acid was used as a standard and a calibration curve was plotted in the range 50-500 mg/mL [40] and the blank sample was distilled water. All measurements were performed at least in triplicate and the total phenolic content estimation was calculated using the following formula according to [PERSON] et al. (2018) [41]:  
\[\text{TPC}=\text{c}\times\text{v}/\text{m} \tag{1}\]  
where c is the concentration of gallic acid established from the calibration curve (mg/mL), v is the volume of extract solution (mL) and m is the weight of the sample extract (g). The results were expressed as mg gallic acid equivalent per gram of the extract (mg GAE/g of extract).