esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology for Precise Code Positioning
### Reference Satellite Selection Method  
The quality of smartphone GNSS observations is closely related to the CNR, as previously discussed. Hence, a reference satellite selection method based on CNR weighting is introduced. In CNR weighting, the process begins by computing the average CNR value for each satellite in continuous arcs. Subsequently, the average CNR of satellites within the frequency is calculated to determine the frequency CNR value (\(\overline{CNR}^{Q}_{j}\)). Finally, the satellite CNR value is computed using the following formula:  
\[CNR^{s,Q}_{w}=\sum\limits_{j=1}^{n}\left(CNR^{s,Q}_{j}\cdot\frac{\overline{ CNR^{Q}_{j}}}{\sum\limits_{j=1}^{n}\overline{CNR^{Q}_{j}}}\right) \tag{8}\]  
where \(CNR^{s,Q}_{w}\) refers to the CNR value of a satellite determined through CNR weighting. All other parameters remain consistent with those previously mentioned. It is worth noting that in cases where \(CNR^{Q}_{w}\) values are identical for different satellites, priority is given to select the one with a higher elevation. This method ensures that the selected satellite must have complete frequency pseudo-range observations.
# From Vine to Wine: Coloured Phenolics as Fingerprints
## 4 Discussion
### Multivariate Analysis  
#### 4.3.1 Principal Compound Analysis  
Figure 3 illustrates the separation of different anthocyanin derivatives based on their contributions to the two main factors derived from factor analysis. The arrangement of the anthocyanin compounds in the two-dimensional space highlights their differing roles and relationships in the red wines. Chemical differences among anthocyanin derivatives, produced by glucosylation, acetylation, or coumaroylation, could explain their separation through factor analysis.  
Thus, Factor 1, explaining the largest proportion of variance (48.7%), predominantly separates anthocyanins based on their structural complexity, such as acetylation or glucosylation. Compounds like Pt-6-ac-3-glc, M-6-ac-3-glc, and Acet are located on the far right of the Factor 1 axis, indicating a strong and positive correlation of acetylated anthocyaninswith this dimension. These compounds, characterized by their acetylated species, are likely critical for differentiating phenolic profiles of red wines by aging or specific winemaking practices which have increased their oxidation [38]. Conversely, less complex anthocyanins, such as those only from glucosides, appear closer to the centre or left side of Factor 1.  
Factor 2, which explains 22.1% of the variance, differentiates anthocyanins along a different axis, more related to specific environmental and aging influences. For instance, Glc is positioned prominently at the top of the axis, while Cou is situated in the bottom-left quadrant, far from the anthocyanin cluster. This position highlights its independence from most compounds, possibly reflecting that its synthesis is mediated by hydroxycinnamic acid present in red wines [39].  
A distinct clustering is observed among glucosylated anthocyanins, such as M-3-glc, Pt-3-glc, and Pe-3-glc, in the upper-right quadrant. This clustering is relevant, as it implies that monoglucylated anthocyanins may serve as useful markers for classifying red wines based on grape cultivar or region of origin, as previously reported [11]. In contrast, acetylated and coumaorylated anthocyanins, such as M-6-ac-3-glc and Pe-6-co-3-glc, are more dispersed along Factor 1, indicating greater variability. This variability might be due to influences of winemaking and environmental factors related with wine aging [37].  
In summary, acetylated anthocyanins tend to dominate the differentiation along Factor 1, while Glc and Cou anthocyanins contribute to the differentiation along Factor 2. This separation suggests that the anthocyanin profile might be a powerful tool for distinguishing red wines based on key variables like grape cultivar, geographical origin, or aging process.
# Improvement of Heterogeneous Transfer Learning Efficiency by Using Hebbian Learning Principle
## 3 Hebbian Transfer Learning
### The Algorithm
The Hebbian trace \(Hebb_{i,j}\) is dynamic during an episode, and the baseline weights and the degree of plasticity are adjusted for each episode. We conducted the experiment with CNN architecture from [67], with the execution of stochastic gradient descent (SGD). The convolution neural network model has 64, 192, 384, 384, 256 filters in the five convolutional layers, followed by a dense classifier for both source domain training and transfer learning. The proposed Hebbian transfer learning algorithm can be described as follows:  
```
1:\(\text{D}_{\text{T}}\), \(\text{W}^{\text{S}}\)//target image datasets and source weights
2:\(\text{W}^{\text{T}}\)//target weights
3:Initialize Hebbian trace \(\text{Hebb}_{\text{i,j}}\)//Hebbian plasticity
4:\(\text{W}^{\text{T}}\)\(\leftarrow\)\(\text{W}^{\text{S}}\)//assigning source weights to target
5:\(\text{W}^{\text{T}}\)\(\leftarrow\)\(\text{W}^{\text{T}}-\text{W}^{\text{T}}_{\text{i,n}}\)//removing last layer weights of CNN
6:for episode in range(episodes) do: for batch_idx, (inputs, targets) in enumerate(trainloader):
7:\(\text{x}_{\text{j}}(t)=\sigma\left[\text{x}_{\text{i-inputs}}\big{[}\text{w} _{\text{i}}\text{x}_{\text{i}}(\;t-1)+\;\alpha_{\text{i,j}}\text{Hebb}_{\text {i,j}}(t)\big{]}\right]\)
8:\(\text{Hebb}_{\text{i,j}}(t+1)=\text{Hebb}_{\text{i,j}}(t)+\;\eta\text{x}_{\text {j}}(t)\Bigl{(}\text{x}_{\text{i}}(t-1)-\text{x}_{\text{j}}(t)\text{Hebb}_{ \text{i,j}}(t)\Bigr{)}\)
9:if episode % k == 0: //k is an adjustable parameter
10: Calculate B.C.E loss and Backpropagate //gradient descent
11:\(\text{hebb}=\text{model.module.initialZeroHebb}()\)
12:\(\text{W}^{\text{T}}\)\(\leftarrow\)\(\text{W}^{\text{T}}-\Delta\text{W}^{\text{T}}_{\text{i,j}}\)
13:return \(\text{W}^{\text{T}}\)
```  
**Algorithm** Hebbian Transfer Learning.
# An Improved Adaptive Iterative Extended Kalman Filter Based on Variational Bayesian
## 3 Illustrative Example
### Two-Dimensional Target Tracking
Table 1 shows that under noise \(\bar{R}=\text{diag}\big{(}(5\,\text{m})^{2},(0.05\,\text{rad})^{2}\big{)}\), the convergence speed was enhanced by 49.04%, demonstrating accelerated convergence and significantly enhanced estimation precision. This highlights the algorithm's capacity to provide reliable and accurate estimates.
## 5 Evaluation and Analysis
### Experimental Results and Analysis with IOMeter Tool  
To fully evaluate the proposed Asymmetric Programming and measure its performance with more comprehensive metrics, we conducted more experiments with IOMeter [39]. The system architecture is shown in Figure 11. IOMeter is widely used as a standard and configurable workload generator and it conducts the write and read operations to the experimental platform according to the configuration.  
In this section, we present the experimental results with an analysis with IOMeter. We also compared Asymmetric Programming with a representative scheme Meta-Cure [17] and the default error correction scheme in the Linux kernel (labeled as \"Baseline\") in terms of three performance metrics: the number of uncorrectable page errors, the I/O performance and the garbage collection efficiency. We also use 256 MB space in one NAND flash package to conduct the experiment and the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Scheme**} & \multicolumn{2}{c}{**Uncorrectable Page Errors**} & \multicolumn{2}{c}{**I/O Performance**} & \multicolumn{2}{c}{**Garbage Collection**} \\ \cline{2-7}  & \begin{tabular}{c} **\# of** \\ **Uncorrectable** \\ **Page Errors** \\ \end{tabular} & \begin{tabular}{c} **Reduction (\%)** \\ **(vs. BL)** \\ \end{tabular} & \begin{tabular}{c} **Average Response** \\ **Time (ns)** \\ \end{tabular} & \begin{tabular}{c} **Overhead (\%)** \\ **(vs. BL)** \\ \end{tabular} & \
\begin{tabular}{c} **\# of Block** \\ **Erase Counts** \\ \end{tabular} &
\begin{tabular}{c} **Overhead (\%)** \\ **(vs. BL)** \\ \end{tabular} \\ \hline BL & 2503 & — & 2,495,552 & — & 1,745 & — \\ MC & 983 & 60.70 & 2,727,154 & 9.28 & 1,866 & 6.93 \\ AP & 201 & 91.97 & 2,496,564 & 0.04 & 1,749 & 0.23 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The experimental results of Baseline, Meta-Cure, and Asymmetric Programming with Zynq on-board sensor system (BL: Base Line; MC: Meta-Cure; AP: Asymmetric Programming).  
NTFS is set as 8192 bytes. Thus, the size of each read or write request of the NTFS is the integer multiples of the allocation unit size and is aligned to the size of physical page which is also 8192 bytes.
# An Overview of Modelling and Energy Management Strategies for Hybrid Electric Vehicles
## 3 Research and Current Status of Energy Management Strategies for HEVs
### Optimization-Based Energy Management Strategies
#### 3.2.3 Model Predictive Control Strategy  
Model predictive control (MPC), used to be known as rolling time domain control, is a powerful theoretical tool for solving non-linear strongly constrained control problems [136, 137, 138, 139, 140, 141]. This method keeps the computational effort within a feasible range within a predefined finite moving sight distance. It also improves control accuracy and performance through reasonable prediction of future work disturbances. MPC has now been widely studied and applied in the field of control, especially in the control of motors [142, 143]. The method works by rapidly calculating the optimal control of the forecast level and applying only the first element. The forecast horizon is then shifted to the future in the subsequent time step [144].  
[PERSON] proposed a model predictive controller for a PHEB with two electric motors that were online-capable. The goal was to reduce the fuel consumption while performing an anticipated driving objective [145]. The proposed multi-level controller structure is indicated in Figure 11. This method integrated a static Hamiltonian function solution with a convex optimization problem while accounting for dynamic states. For a typical driving cycle of 30 min, it took around 3 s to solve the MPC optimization. Meanwhile, the driver-behavior-aware modified stochastic MPC was proposed for EMS in PHEBs [146]. The modified stochastic MPC defeats the limitations of the traditional rolling optimization approach used in MPC.  
In [147], [PERSON] suggested an approach based on MPC that possessed high computational efficiency to obtain the optimal torque distribution and gear shift timing. Figure 12 illustrates the overall schematic of the EMS. As a result of this, it is possible to find the roughly global optimal solution with a seemingly reduced processing overhead, indicating a tremendous potential for real-time applications.
## Materials and Methods  
### Characteristics of the Test Sites  
The study area lies within a regional geomorphological unit known as the Warsaw Basin. All test sites are located in the Vistula River valley or in the vicinity of the valley (Figure 1). The first test site at Stegny is the place where a wide research program was executed by the Warsaw University of Life Sciences and the University of Warsaw during the last few years. The main purpose of these investigations was to estimate the physical properties and mechanical parameters of Pliocene clays. The Stegny site is located in the southern district of Warsaw. Its stratigraphy includes Quaternary deposits developed as fine and medium-dense sands of a thickness not exceeding 4.5 m, underlain by overconsolidated Pliocene clays. The free groundwater table was measured at a depth of 3.2 m.  
The second test site at the SGGW Campus covers an area in the margin of a denudated moraine plateau. The site is located in the marginal zone of the southern part of the Warsaw moraine upland in Ursynow district. Here, these sediments form a continuous, distinct layer, consisting mostly of fine sands. Above the fluvioglacial sediments lies a layer of melt-out sediments from the Odranian Glaciation, represented by Glacial till. In situ (boreholes, RCPTU tests) and laboratory investigations have allowed to conclude that the surface zone consists of overconsolidated sandy clays, grey and brown in color.  
Figure 2 presents the grain-size distribution curves for several soil samples: fine and medium sands (FSa, MSa), and clays (Cl and siCl) taken from the subsoil of the Stegny site, and sandy clays (sisaCl) and clayy sands (clsa) taken from the SGGW Campus. The graph contains also the grain-size distribution curves that have been tested in the laboratory, including sandy clays (sisaCl) from the SGGW Campus tested in a modified oedometer and samples of fine, medium and coarse sands (FSa, MSa and CSa) tested in the column apparatus.  
Figure 1: Location map of the test sites.  
The last test site is the modernized right embankment of the Vistula River, located in the Maciejowicka Valley. Renovation works carried out in the recent years were enforced by numerous filtration deformations, which in extreme cases lead to instabilities. A section of the embankment examined before the modernization was built of locally available material, which was poorly graded fine and silty sands. Control tests using ERT and BAT-groundwater monitoring system, were carried out on selected sections of the flood embankment in which a diaphragm was installed.
# Marine Internet for Internetworking in Oceans: A Tutorial
## 5 Internetworking Strategies of Marine Internet
### Integrated Coastline Network (ICN)
#### 5.1.2 Communication Links  
Typically, there are two types of nodes present in water areas: a terminal (T), such as a mobile phone, and a BS. A terminal cannot relay signals from other nodes such as other terminals but a BS can. As illustrated in Figure 6, the following communication links may co-exist in oceans:  
* Terminal to terminal (T-T): Two terminals communicate with each other directly in an ad hoc mode. For example, on small boats without BSs, as in Figure 6, users onthe boats can use their handsets to communicate with each other directly.
* Base station to terminal (B-T): BS-terminal communication is the primary communication scenarios here, such as between mobile phones and cellular BSs or APs. BSs can be installed along the coastline, on islands (called terrestrial BS-TBS) and vessels or even on HAPs (called vehicular BS-VBS).
* Base station to base station (B-B): This kind of link is used to relay signals between BSs with the following combinations: TBS-to-TBS, TBS-to-VBS and VBS-to-VBS. TBS-to-TBS can be either a wired or wireless link, depending on geographical conditions and deployment cost. Both TBS-to-VBS and VBS-to-VBS are wireless links used for communications with various types of vehicles such as ships and cars.
* Satellite to base station (S-B): This kind of link is used to connect satellites with BSs to access terrestrial Internet or communicate with vessels or floating platforms in oceans.
* Hydrophone to hydrophone (H-H): It is an underwater acoustic link used to connect acoustic communication nodes. Electronic-acoustic signal conversion is carried out between acoustic nodes and RF nodes on water surface.  
In some cases, multiple types of links may co-exist such as base station to base station/terminal (B-B,T) and terminal/base station to base station/terminal (T,B-B,T). As illustrated in Figure 6, when a ship is close to the coastline, users therein can communicate the TBS directly, while the onboard VBS can also communicate with it. The aerial unit in Figure 6 refers to a collection of air planes, helicopters, balloons and drones mentioned above, some of which can have BSs while others may not. In this case, T-T, T-B and B-B links may exist.
## 3 Methods
### Data Processing
#### 3.3.1 Signal Smoothing  
Signal smoothing was primarily performed to remove unwanted spikes in the stretch sensor data, which were caused mainly by the unstable condition of the circuit. The transient or unwanted spike commonly occurred frequently and randomly over time. Therefore, one method to eliminate noise is to apply a low-pass filter, which removes higher-frequency signals [41]. As shown in Figure 8, the raw signal of the stretch sensor contained several transient spikes, which were commonly caused by the unstable condition of the circuit. In this study, a 4 th-order Butterworth low-pass digital filter with a threshold frequency of 2 Hz was employed.
# XCO\({}_{2}\) Super-Resolution Reconstruction Based on Spatial Extreme Random Trees
## 3 Results
### Spatial Extreme Random Trees Validation  
In this study, based on the spatial extreme random trees model, the 10 km-resolution XCO\({}_{2}\) spatiotemporal dataset for China from 2016 to 2020 was reconstructed at a 1 km resolution. The model fitting results for each year are depicted in Figure 5. The fitting R\({}^{2}\) values range from 0.949 (2017) to 0.964 (2018), indicating high precision in both RMSE and MAE results. The accuracy of the prediction results demonstrates that the spatial extreme random trees model can reliably predict XCO\({}_{2}\) at relatively fine resolutions.
## 4 Transmission Channel Analysis
### Fresnel Zone
Let approximate the Earth as a perfect sphere, having radius \(R=6371\) km, with no terrain irregularities: such hypothesis is a suitable approximation when links take place over the sea. Moreover, suppose that the transmitting antenna is mounted at an altitude \(h_{TX}\) above sea level (see Figure 3 bearing in mind that it is not in scale since its  
Figure 2: First Fresnel zone example. purpose is merely qualitative). Then, the distance from the horizon \(D_{h}\) expressed in \(m\), provided that either \(R\) and \(h_{TX}\) are so, is  
\[D_{h}=\sqrt{(R+h_{TX})^{2}-R^{2}}=\sqrt{h_{TX}^{2}+2 Rh_{TX}}. \tag{6}\]  
Earth curvature should be taken into consideration whenever long distance links (i.e., more than few kilometers) are designed. In particular, the bulge amid the two endpoints could obstruct the first Fresnel zone. The maximum Earth bulge height \(H\) expressed in m, which is experienced in the midpoint of the link (see Figure 4 bearing in mind that it is not in scale since its purpose is merely qualitative), is computed as  
\[H=\frac{1000D^{2}}{8 kR}=\frac{125D^{2}}{\frac{4}{3}R}=\frac{375D^{2}}{4R} \tag{7}\]  
where \(D\) is the distance between the endpoints expressed in km, \(R\) is the Earth radius and \(k\) is a factor which accounts for the bending effect on radio waves caused by the declining of atmospheric pressure. Practically speaking, this phenomenon implies an enlargement of the Earth radius by factor \(k\). Actually, \(k\) also depends on weather conditions, but it is averagely equal to \(4/3\).
## 2 Overview of Whirlpool Hash Function
### Features of Whirlpool
#### 2.2.2 Using Miyaguchi-Preneel Scheme to Construct Hash Function  
Traditional hash functions, for example MD5 and SHA-1, usually make use of the Davies-Meyer scheme to construct hash function, whereas Whirlpool adopts Miyaguchi-Preneel scheme to construct hash function. Miyaguchi-Preneel scheme is one of the few still unbroken methods to construct a hash function from an underlying block cipher [23]. In this scheme, the message block and key is equal in status, that is to say, the message block and key can be exchanged, which makes Whirlpool more flexible than hash functions constructed from Davies-Meyer.
## 3 Simulation and Optimization of the Joint  
### Finite Element Modelling  
Previous finite element simulation studies on threads have the following limitations: (1) they are mostly 2D models that are unable to reflect the true three-dimensional force state and (2) transforming an asymmetrical model into a symmetrical model ignores the effect of the helix angle. Although this can simplify the finite element calculation process, the results do not truly reflect the stress variation in the thread. In this paper, a three-dimensional asymmetric model is used to study the conical thread of a core joint. This three-dimensional model can more accurately reflect the connection state of the thread and visually reveal the thread stress distribution.  
A 1:1 three-dimensional model of the threaded joint is established, as shown in Figure 3. The joint material is AISI 4145H. The material parameters are as follows: the elastic modulus is \(2.05\times 10^{11}\) Pa, the Poisson's ratio is 0.29, the tensile strength is 965 MPa, and the yield strength is 820 MPa.
## 3 Characteristics of Wavefield Response in Layer Models
### Multilayer Model with Complex Velocity and Density Profiles  
Here, we present a comparative analysis of the PP-wave seismic responses for a multilayered model using different methods. The multilayered models in Figure 14 are derived from the well-known overthrust model, with the S-wave and density models computed using empirical formulae. The forward wavelet used is a Ricker wavelet with a dominant frequency of 45 Hz, and the incident angle ranges from 0\({}^{\circ}\) to 40\({}^{\circ}\). The model size is nz \(\times\) nx = 187 \(\times\) 101, with a grid size of dx = 40 (\(m\)) and dz = 5 (\(m\)). The parameter \(\lambda\) = \(\alpha\) = 1 \(\times\) 10\({}^{-8}\), the time step dt = 1 \(\times\) 10\({}^{-3}\) (s), and the total number of time steps is nt = 1000. Figure 15 shows the seismic responses computed using different methods. Figure 15a represents the PP-wave response obtained through theoretical AVA coefficient spectra calculation. Figure 15b displays the PP-wave seismic response computed using the modified propagation matrix theory. Figure 15c shows the PP-wave seismic response obtained via propagation matrix calculations, whereas Figure 15d exhibits the PP-wave seismic response derived from the Zoeppritz equation. The error comparison in Figure 16 indicates that errors in different methods increase with an increment in the incident angle. A comparison of Figure 16a,b reveals that the modified propagation matrix method provides accurate AVA seismic responses. Furthermore, the error comparison in Figure 16c shows that, as the number of geological layers increases, the influence of the transmission loss gradually amplifies, resulting in an increasing error in AVA seismic data based on the Zoeppritz equation. Additionally, the Zoeppritz equation cannot account for the impact of multiple reflections on the seismic response. Table 1 presents a comparison of computational resource consumption for three forward modelling approaches. As shown in the table, the revised wave propagation matrix method introduces an appreciable increase in computational intensity. This highlights one limitation of our proposed approach.  
Figure 13: The reflection coefficient of the second reflecting interface of Figure 3. (**a**) PP-wave amplitude. (**b**) PS-wave amplitude.  
Figure 16: Data error. (**a**) shows the new propagation matrix to calculate the error of the seismic response. (**b**) shows the error of the seismic response calculated by the traditional propagation matrix. (**c**) shows the error value of the seismic response calculated by the Zoeppritz equation.  
Figure 14: Multilayered models with complex velocity and density profiles. (**a**) \(V_{\text{P}}\); (**b**) \(V_{\text{S}}\); (**c**) density.  
Figure 15: Synthetic PP-wave seismogram record of Figure 14. (**a**) Synthetic AVA PP records. (**b**) Synthetically modified PM PP records. (**c**) Synthetic PM PP records. (**d**) Synthetic Zoeppritz PP records.