esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantum 3D FFT in Tomography
## 3 Theory of Tomography
### _Sinogram and Fourier Section or Central Section Theorem_  
A sinogram is a way of displaying the data \(p(r,\varphi)\) resulting from applying the Radon transform to the object \(f(x,y)\). By having the function \(p(r,\varphi)\) and setting the distance r on the horizontal axis and the angle on the vertical axis, we receive an image, such as the one in Figure 4. The named sinogram is because if we plot the projection function of an image consisting of only one point, we will receive the graph of a sinusoidal function.  
The Fourier section or central section theorem is the foundation of tomographic imaging. The central intersection theorem in two dimensions states that the 1D Fourier transform P(\(\omega\)) of the projection \(p(r,\varphi)\) of a 2D function \(f(x,y)\) is equal to an intersection through the origin of the axes of the 2D Fourier transform \(F\big{(}\omega_{x},\omega_{y}\big{)}\) of this function and is parallel to this projection (Figure 4).  
If we project our object over a range of at least 180 degrees, the corresponding central intersection of the 2D Fourier transform \(F\big{(}\omega_{x},\omega_{y}\big{)}\) will be rotated simultaneously and cover the entire 2D Fourier space. In other words, we can measure the entire 2D Fourier transform of the image, thus now we can take the inverse 2D Fourier transform to receive the image \(f(x,y)\) (Figure 5).  
Figure 4: Central section theorem in two dimensions.  
If we simply fill in the 2D Fourier space with its various intersections, the density grows closer to the origin of the axes. In short, the low frequencies outweigh the high frequencies, thus our image will be a blurry version of the original. Accordingly, what we will receive with this process will not be our original image, but we have a blurring created by the uneven distribution of low and high frequencies. This inequality of frequency density is proportional to the quantity  
\[\frac{1}{\sqrt{\omega_{x}^{2}+\omega_{y}^{2}}} \tag{18}\]  
It is proportional to the distance from the origin of the axes. Thus, a simple way to restore equal frequency distribution is to multiply the reshaped 2D Fourier transform by \(\sqrt{1+\omega_{x}^{2}+\omega_{y}^{2}}\). If we take the inverse transform after, then we will have the true original image \(f(x,y)\).
# An Efficient BDS-3 Long-Range Undifferenced Network RTK Positioning Algorithm
## 2 Long-Range URTK Algorithm
### Undifferenced Error Correction Value Calculation
It is needed only to calculate the classification error for the reference station, and the classification error correction value of the rover station can be obtained using the inverse distance weighted interpolation algorithm, as follows:  
\[Class_{U,i}^{p}= \begin{bmatrix}a_{1}&a_{2}&a_{3}&b_{1}&b_{2}\\ \end{bmatrix}\begin{bmatrix}Dis_{A,i}^{p}&Dis_{B,i}^{p}&Dis_{C,i}^{p}&UDis_{A,i}^{ p}&UDis_{B,i}^{p}&UDis_{C,i}^{p}\end{bmatrix} ^{T} \tag{16}\]  
where \(Class_{U,i}^{p}\) is the classification error of a satellite \(p\) corresponding to the rover station \(U\); \(a_{1}\), \(a_{2}\), and \(a_{3}\) are the weighted coefficients for undifferenced dispersion error of reference stations \(A\), \(B\), and \(C\), respectively; \(b_{1}\), \(b_{2}\), and \(b_{3}\) are the weighted coefficients for the undifferenced non-dispersion error of reference stations \(A\), \(B\), and \(C\), respectively. The weighting coefficient here is obtained through weighting the geometric distance between each base station and the rover station. The equation for calculating the weighting coefficient of the non-dispersive error is as follows:  
\[\left\{ \begin{aligned} & a_{1}=\frac{1/d_{U,A}}{1/d_{U,A}+1/d_{U,B}+1/d_{ U,C}}\\ & a_{2}=\frac{1/d_{U,B}}{1/d_{U,A}+1/d_{U,B}+1/d_{U,C}}\\ & a_{3}=\frac{1/d_{U,C}}{1/d_{U,A}+1/d_{U,B}+1/d_{U,C}}\end{aligned} \right. \tag{17}\]  
where \(d_{U,A}\), \(d_{U,B}\), and \(d_{U,C}\) are the geometric distances from stations \(A\), \(B\), and \(C\), respectively, to rover station \(U\). The calculation method of the weighting coefficient of the dispersion error is the same as that of the non-dispersion error, but the interpolation plane is in the central ionosphere, and the geometric distance is calculated using the \(IPP\) of the station and the satellite. The classification error correction value of each frequency observation is generated in the same way as the error correction value of frequency \(i\). In addition, the interpolation method of the non-dispersive error is consistent with that of the ionospheric delay error, but their interpolation planes are different. The weighting coefficient of the ionospheric delay error is obtained using the location of the puncture point.
## 6 Case Study
### Results and Discussion  
#### 6.2.1 Extraction Module  
A sample of the extracted reporting requirements is illustrated in Figure 3. As detailed in Section 6.1, 340 individual reporting requirements and their submittal frequencies were identified from over 500 contract pages. Although quite high (88%, Table 4), the recall of the current extraction module is not 100%. Sufficient for planning purposes, the extraction module should not be used as the only means of requirement extraction during the execution phase of a project. A manual review during the execution phase should continue to be performed until the ability of the framework to consistently extract 100% of reporting requirements is achieved. Failing to determine the exhaustive list of submittals and information deliverables required by the client can result in claims and ligations, subjecting both parties to disputes and conflicts that could have been prevented. Nevertheless, manual extraction is also prone to error, and the use of the extraction module as an adjunct tool during the execution phase of a project is strongly recommended. The list of reporting requirements extracted manually and by the automated extraction module should be compared to identify requirements that may be missing from the manually extracted list.  
The 340 reporting requirements output by the extraction module were reviewed by the project team. It was determined that some of the reporting requirements were repetitive, requiring submission of the same report. A total of 70 distinct reports and information submittals was identified. A list of these reports was prepared and input into the prediction module.
## 3 Results
### Comparison with Field Measurements  
#### 3.2.1 Direct Validation  
Figure 6 compares the MODIS monthly C5 and C6 CIs with field measurements. All the field CI data (Table 1) are evaluated to be spatially representative for 500 m MODIS pixels by using a method developed by [PERSON] et al. (2009) [78] and [PERSON] et al. (2012, 2014, 2017) [104; 105; 106], which rationalizes a direct comparison between field-measured CIs and the corresponding MODIS CIs [26].  
In general, both versions of the MODIS CIs have a high correlation (\(R^{2}\geq 0.80\)) with the field CI data, while the C6 CI (\(R^{2}=0.89\), RMSE = 0.051, bias = 0.02, MAE = 0.03) better agrees with the field measurements than the C5 CI (\(R^{2}=0.80\), RMSE = 0.065, bias = 0.03, MAE = 0.05). Both CI versions slightly underestimate the surface CIs (bias = 0.02 and 0.03 for C6 and C5 CI), while the C6 CI shows an improvement over the C5 CI in the higher range of values (0.75-0.95). Much of the improvement in the C6 CI may be due to the improved data quality of the MODIS C6 BRDF/Albedo product. Compared with the MODIS C5 BRDF/Albedo product, the C6 implementation enhances the estimation of surface reflectance anisotropy and improves the inversion quality [35]. On the one hand, all valid clear-sky observations are used for each retrieval in the C6 BRDF/Albedo inversion algorithm, rather than up to four observations used for each retrieval with the C5 product [66]; thus, the C6 product provides more available observations for BRDF inversion. On the other hand, in the case of insufficient observations for a full inversion, the strategy of pixel-specific _a priori_ knowledge updated daily is used to improve the magnitude inversion of the C6 BRDF/Albedo product, instead of the static seasonal land cover BRDFs strategy used in the C5 inversion algorithm [107].
## 2 Methods  
### Model Architecture  
The process to estimate Cal and CPF values from NIR signals can be defined as follows. Let \(X\in\mathbb{R}^{1\times D}\) be the input, where \(X\) represents the NIR signals with a sensing range of 256 (i.e., \(D=256\)) for a given food item. The output \(Y\) comprises nutrient values \(\{Calorie,Crabohydrate,Protein,Fat\}\) corresponding to the input \(X\). The deep learning model is denoted by \(f_{\theta}()\), where \(\theta\) represents the model's training parameters. The primary objective of the model is to map the input to the output, expressed as \(Y=f_{\theta}(X)\). To achieve this objective, we designed the model architecture illustrated in Figure 1. The model consists of consecutive hidden layers that form the overall architecture. Each hidden layer is denoted by \(\sigma(z)\), where \(\sigma\) is the activation function, and \(z\) is the output from the previous layer. Each layer employs the Rectified Linear Unit (ReLU) function as the activation function, due to its widespread use in non-linear models. This model simply predicts the output corresponding to the given input, and it does not require other structural requirements, allowing for the creation of a simple network. During the prediction phase, the model utilizes our proposed uncertainty criterion to autonomously decide whether to accept or reject the result. Further details on this decision-making process are provided in Sections 2.2 and 2.3. During training, the model adapts to a specifically designed loss function aimed at estimating uncertainty. The specifics of this loss function are detailed in Section 2.4. This tailored approach ensures that the model not only predicts nutrient values with high accuracy but also assesses the reliability of its predictions, enhancing overall model robustness and performance.
## 2 Materials and Methods
### Characterization  
After synthesis of the powder, the study of its structural properties followed. A number of techniques including XRD, micro-Raman, XPS, and UV-Vis Spectroscopy were applied. For the XRD analysis (D8 Advance, Bruker, Germany), the measurements were conducted at a 2-theta angle with a 20\({}^{\circ}\) to 80\({}^{\circ}\) range and a 0.01\({}^{\circ}\)/0.5 sec scanning rate, employing Cu-K\(\alpha\) radiation (\(\lambda\) = 1.5418 A) at a voltage of 30 kV and current of 15 mA. The confocal microscope Raman apparatus (inVia, Renishaw, Wotton-under-Edge, Gloucestershire, UK) used an excitation source of a high power near infrared (NIR) diode laser (\(\lambda\) = 785 nm). All measurements were conducted at room temperature in a backscattering configuration. The laser beam was focused onto the samples using a x20 short distance magnification lens, with low excitation power, in order to secure low laser heating of the powder. An internal Si reference was used to calibrate the frequency shifts. Three spots were measured for each sample for repeatability purposes. The exposure time was 30 s, with three accumulations, 0.1% power, and a range of 100-1500 cm\({}^{-1}\). An analysis of the chemical states on the surface of the powder was assessed by X-ray Photoelectron Spectroscopy (Leybold SPECS, Pittsburgh, PA, USA). The photoemission experiments were executed in an ultra-high vacuum system (UHV) equipped with an X-ray gun for XPS measurements. Two analyzer pass energies of 15 eV and 40 eV and an unmonochromatized MgK\(\alpha\) line at 1253.6 eV were used. All XPS core-level spectra were evaluated using a fitting routine, which can decompose each spectrum into individual mixed Gaussian-Lorentzian peaks after a Shirley background subtraction. The sample was in powder form and pressed into a pellet, the analyzed area was a spot of 7.0 mm diameter, and the measurements were recorded at room temperature. A UV-Vis spectrometer (U-3010, Hitachi, Tokyo, Japan) was used to measure the band gap of the powder. The apparatus is equipped with a 50 mm integrating sphere, to allow diffuse reflectance measurements. The surface and morphological characteristics of the powder were investigated by a Field Emission Scanning Electron Microscope (FESEM, JSM-7401F, JEOL, Tokyo, Japan) and a Transmission Electron Microscope (TEM, CM20, Philips, Amsterdam, The Netherlands).
# Integrating Blockchains with the IoT: A Review of Architectures and Marine Use Cases
## 3 BioT Architectures and Communication
### BioT Implementation Choices
EdgeChain enforces security policies through smart contracts, and experimental results show that this framework not only enhances security but also maintains a reasonable integration cost. Another approach is fog computing [30], a subset of edge computing that operates even closer to IoT devices by using local gateways. These gateways interact with each other and the cloud, supporting tasks that require immediate local processing as well as long-term cloud storage. This architecture is essential for efficiently managing the increasing volumeof IoT data and accommodating different blockchain applications. [PERSON] et al. (2018) advocate for decentralized architectures that leverage both edge computing and cloud resources, enabling more efficient processing, storage, and real-time decision-making across multiple network layers. By synthesizing these insights, it becomes evident that BIoT applications can leverage various computing paradigms to optimize their infrastructure based on specific requirements. This flexibility enables them to address unique application demands and effectively manage operational constraints. As IoT deployments keep increasing, traditional cloud-based architectures are increasingly seen as less favorable due to their inherent scalability limitations. In contrast, edge computing presents a compelling alternative. By processing data closer to their source, it minimizes the need for extensive data transmission, thereby reducing latency and network congestion ([PERSON] et al., 2019). Additionally, as an extension of edge computing, fog computing further enhances these advantages by supporting real-time data processing and consistent interaction with cloud or edge servers. Table 2 summarizes these architectural approaches-- cloud-based computing, edge computing, and fog computing--by comparing their advantages and disadvantages. This comparison allows for identifying strategic options for implementation styles in BIoT systems, highlighting the trade-offs between centralized and decentralized models. Evidently, the optimal architectural style for BIoT applications depends on the specific needs of each application. Full node architectures prioritize maximum security, ensuring the protection of data and transactions. On the other hand, lightweight and edge-assisted approaches are designed to optimize scalability and resource efficiency for IoT devices, accommodating their inherent constraints. Continued research into lightweight blockchain protocols and efficient computing solutions remains essential for effectively integrating blockchain technology with the evolving landscape of IoT applications and supporting a variety of use cases.
## 2 Materials and Methods
### Experimental Measurements and Battery Simulation
#### 2.3.1 Setup of the Electric Power Measurements  
To quantify the electric power needed to perform all of the mechanical cycles reported in Table 1, the electric measurements of the voltage and current, supplied by the battery pack during the three operation phases, were carried out. The measurements were performed only for one cycle of dumpster lift, garbage compaction, and load discharge. The measuring campaign was carried out through the use of the Yokogawa WT 5000 power analyzer, as shown in Figure 8. The instrument measured the voltage between the battery terminals, and a direct coupling was made between the battery terminals and the power analyzer (its accuracy is \(\pm\)0.02% of the reading value). The current supplied by the battery pack was measured via an amperometric clamp (Chauvin Arnoux E3N, its accuracy is \(\pm\)3% of the reading value) connected as an input to the power analyzer.
## 4 Results
### Bacterial Samples  
Effect of Rotational Speed and Concentration  
Similar experiments were done with cultured _E. coli_ mixed with the artificial mucous at various concentrations. The swab was dipped in the sample, air dried for 30 s, and eluted in DI water for 2 min at different rotational speeds. The recovery efficiency obtained at various rotational speed is shown in Figure 7. They show that the recovery efficiency increases with rotational speed initially and gradually plateaus after 500 RPM. Recovery efficiencies close to 85% are obtained at 500 RPM, while they are low at around 47% at 100 RPM. There was no statistical difference in the results that were obtained between 6.4 \(\times\) 10\({}^{9}\) and 6.3 \(\times\) 10\({}^{8}\) CFU/mL samples.  
Elution with lower concentrations of _E. coli_ in the sample show a similar increasing trend in recovery efficiency (Figure 7). For \(\sim\)2 \(\times\) 10\({}^{6}\) CFU/mL concentration, the average number of eluted particles range from \(\sim\)1-1.5 \(\times\) 10\({}^{6}\) CFU/mL over 100-500 RPM. This corresponds to a recovery increase from 49.1% to 86%, with an average error of \(\pm\)3.4%. For \(\sim\)4 \(\times\) 10\({}^{5}\) CFU/mL samples, the efficiencies increase from 50.8% to 86.6% over the same range of rotational speed, and almost coincides with the values of the previous curve for higher rotational speeds. Higher recovery (90.42%) can be obtained at 500 RPM with 3.3 \(\times\) 10\({}^{5}\) CFU/mL sample and also corresponds to a steeper slope than the sample with \(\sim\)1 \(\times\) 10\({}^{6}\) CFU/mL concentration Similar to the previous experiment with polystyrene beads, the average recovery efficiencies between the control case and that of different speeds are insignificant at 100 RPM (\(p\)-value = 0.3), but is statistically significant at 300 RPM (\(p\)-value = 0.006). Also, when compared between two different concentrations, the recovery efficiency values between control case of no agitation and lower rotational speeds are insignificant at 100 RPM (\(p\)-value = 0.41), but the values are close to statistically significance at higher rotational speeds (\(p\)-value = 0.067 at 400 RPM). As seen in the case of polystyrene beads, at higher rotational speeds (400 and 500 RPM), there is a significant increase in efficiency with lower concentrations than that at a higher concentration. Hence, working with a lower concentration of cells can be beneficial at higher rotational speeds.  
Figure 7: Effect of RPM on recovery of _E. coli_ samples at different concentrations, 120 s elution time.
## 3 Materials and Methods
### Geodetic Analysis
#### 3.5.2 DInSAR 1993-2020  
DInSAR data were obtained from four different satellite constellations that have covered the investigated area since 1993 (Table 2). In detail, data was obtained from the ERS1/2 and ENVISAT constellations of the European Space Agency (ESA) for the period 1993-2010; the Cosmo-SkyMed satellite constellation of the Italian Space Agency (ASI) for the period 2012-2013, using and interpreting data obtained as part of the \"II Not-ordinary Plan of Environmental Remote Sensing\" [65; 66]; and, finally, ESA's SENTINEL-1 data, for the period 2016-2020, were processed and interpreted using SUBSIDENCE software, which implements the coherent pixel technique-temporal phase coherence (CPT-TPC) approach [67; 68], developed at the Remote Sensing Laboratory (RSLab) of the Universitat Politecnica de Catalunya of Barcelona.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Geometry of Acquisition** & **Period** & **No. of Images** \\ \hline \multirow{2}{*}{GNSS} & 3D topocentric & 01 May 2003 & \multirow{2}{*}{n/a} \\  & (north, east, up) & 29 August 2020 & \\ \hline \multirow{2}{*}{ERS1/2} & \multirow{2}{*}{Ascending/descending} & 10 January 1993 & \multirow{2}{*}{66/72} \\  & & 13 December 2000 & \\ \hline \multirow{2}{*}{ENVISAT} & \multirow{2}{*}{Ascending/descending} & 13 December 2002 & \multirow{2}{*}{65/40} \\  & & 14 July 2010 & \\ \hline \multirow{2}{*}{Cosmo-SkyMed} & \multirow{2}{*}{Descending} & 20 February2012 & \multirow{2}{*}{35} \\  & & 23 December 2013 & \\ \hline \multirow{2}{*}{SENTINEL-1} & \multirow{2}{*}{Descending} & 13 January 2016 & \multirow{2}{*}{138} \\  & & 31 August 2020 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Geodetic data sets used in this study.  
DInSAR data produce relative LoS motion measurements with respect to a reference point. To refer the GNSS and SAR motions to the same frame, the 3D GNSS vectors defined in the local reference system have to be projected into the SAR LoS using the relationship: GNSS LoS = sin (\(\theta\)) cos (\(\phi\))E + sin (\(\theta\)) sin (\(\phi\))N + cos (\(\theta\)) UP [69]  
Where, (\(\theta\)) and (\(\phi\)) are the incidence angle and ground track, respectively, and E, N, UP satellite are the components (east, north, and up) of the GNSS displacement.  
When just the vertical components of the GNSS vectors are of interest, the projection into the SAR LoS can be performed using the simplified relationship from [69]: GNSS LoS = cos\(\theta\)\(*\) UP
## 2 Materials and Methods
### Study Areas and Materials
#### 2.1.2 Materials  
The satellite data used in this study are listed in Table 1:  
1. HJ-1A/HJ-1B data: the two-level system geometric correction products of HJ-1A/HJ-1B satellites, which have spatial resolution of 30 m, a revisit period of 4 days, viewing swath width of 700 km, and include a total of four bands (blue, green, red, and near-infrared).
2. MODIS NBAR products: the MODIS NBAR products (MOD43A4), which have spatial resolution of 500 m, three-level terrestrial standard, and 16 days of synthetic data products.
3. MODIS daily products: the MODIS global daily reflectivity products (MOD09 GA), for which the spatial resolution is 500 m.
4. The surface measured data: the measured reflectance data collected at the same time as the test sites, measured by ASD spectrometer.  
The red (R) and near-infrared (NIR) bands of the NBAR data were chosen for the time series analysis, as they were sensitive to the vegetation growth and were typically used in most quantifications of vegetation inversion, such as LAI and NDVI, among others.