esa-sceva/satcom-chunk-collection huggingface dataset:
# Validating the Adoption of Heterogeneous Internet of Things with Blockchain
## 4 Methodology
### Hypotheses Testing  
Regression is used to test a proposed research model by presenting the model's constructs with their dependence relationships. Then, the hypotheses are tested to prove relationships between the different constructs. The hypothesis test was based on several related statistical tests, such as squared multiple correlations (\(R^{2}\)), which indicate the variance of the dependent constructs, and standardized path coefficients (\(\beta\)), which indicate the strengths of the relationships between the independent and dependent variables. The test also produces a \(t\)-value and \(p\)-value, which revealed that there is a significant difference regarding the effect of the acceptance and usage of IoT blockchain technology. On the basis of the results summarized in the Table 4, each of the hypotheses was either supported or rejected.  
Table 4 shows the squared multiple correlations of the various variables in the model \(R^{2}\) in the range 0.755 to 0.898, which means the model (Attitude-related factors; Social influence-related factors; Data-related factors; Security-related factors) can explain 76% to 90% of the variations of the adoption of IoT blockchain technology.  
According to Table 4, the perceived usefulness, perceived ease of use, personal competency, transaction intention, social network, and reputation had a positive and significant effect on behavioral intention. Further, factors related to behavioral intention and social factors had a positive effect on the behavioral intention to the adoption of IoT blockchain technologies, with the standardized path coefficient being (\(\beta\) = 0.832 and sig. = 0.000), (\(\beta\) = 0.828 and sig. = 0.002 ), respectively. In this manner, H1, H2, and H0A were emphasized as the main and sub-hypotheses. On the other hand, factors related to data in terms of integrity, validity, governance, and privacy had a positive effect (\(\beta\) = 0.940 and sig. = 0.000) on trust towards IoT blockchain technology adoption, thereby confirming H3. The risk of transaction and technology had a significant relationship with trust in IoT blockchain technology adoption (\(\beta\) = 0.728 and sig. = 0.000), thus H4 was supported.
## 2 Equivalent Circuit Modeling of the Lithium-Ion Batteries
### The Objective Function Formulation for OCP  
Defining and designing the objective function is essential for applying an optimization algorithm before utilizing a metaheuristic optimization approach. The fitness values of each particle were calculated by minimizing the value of the objective functions through the iterative process with random initial conditions for each particle. In previous studies [27; 28], charging time and energy loss were selected as the physical quantities of the objective function to achieve the dual benefits of reducing both charging time and temperature rise. While the charge time is closely related to the charging current at each stage, the direct relationship between charge temperature and energy loss, as expressed by (15), remains unclear. The energy loss function is primarily linked to the charging current, which may not directly correspond to variations in charging temperature. Thus, in this study, we consider the charge temperature parameter instead of energy loss as the objective function. This approach allows for a direct optimization of the charge temperature. To construct the fitness value function, the maximum and minimum values of the charge time and charge temperature were determined using the CC-CV charging method. A charging current range of 0.5C to 2C was applied within the Thevenin ECM-developed Simulink and Simscape platforms on MATLAB software. The values obtained are summarized in Table 2.  
The objective function \(J\) in this study is proposed as follows:  
\[J=\sqrt{\gamma\Big{(}\frac{t_{c}(\overline{I}_{c,n})-t_{c,\text{min}}}{t_{c, \text{max}}-t_{c,\text{min}}}\Big{)}^{2}+(1-\gamma)\Big{(}\frac{T_{c}( \overline{I}_{c,n})-T_{c,\text{min}}}{T_{c,\text{max}}-T_{c,\text{min}}}\Big{)} ^{2}}, \tag{16}\]  
where \(t_{c}(\overline{I}_{c,n})\) and \(T_{c}(\overline{I}_{c,n})\) denote the charge time and charge temperature, respectively, \(\overline{I}_{c,n}=[I_{c,1},\ I_{c,2},\ I_{c,3},\ I_{c,A},\ I_{c,5}]\) represent the charging current values, and \(\gamma\) indicates the weighting index between the charge time and the charge temperature. The optimization problem for obtaining the optimal charge currents in each charge state can be formulated as follows:  
\[\begin{array}{c}\text{\emph{Minimize}}\ J,\ \ \overline{I}_{c,n}\in FC\\ \text{\emph{Subject}}\ to\ t_{c,\text{min}}\leq t_{c}(\overline{I}_{c,n})\leq t _{c,\text{max}}\ \text{and}\ T_{c,\text{min}}\leq T_{c}(\overline{I}_{c,n})\leq T_{c,\text{ max}},\\ \ I_{c,n}\leq I_{c,n+1},\ \ 0\leq I_{c,n}\leq I_{c,\text{max}},\ \ \text{and}\ V_{c,n}\leq V_{c,\text{ max}},\ n=1,2,3,4,\end{array} \tag{17}\]  
where \(FC\) denotes the set of all feasible charging patterns, and \(I_{c,\text{max}}\) and \(V_{c,\text{max}}\) represent the allowable maximum charging current and voltage values. These parameters are set at 5.8 A and 4.2 V, respectively.
## 9 Performance Validation and Analysis  
Multiple scenarios and environmental circumstances are assessed to validate the performance of the proposed estimator. Considered scenarios comprise static as well as dynamic environments. Two static locations are rooftop (for line of sight signal propagation model) and a location surrounded by skyscrapers (for multipath in urban environment) in the downtown of shanghai. In Figure 3a,b, the two locations are shown. In open sky scenario (roof top) all signals are line of sight with no multipath or other interferences. In contrast, there are multipath as well as other radio interferences in urban canyon scenario.  
Figure 3: GNSS antenna position (**a**) open sky (rooftop) (**b**) urban canyon (downtown Shanghai).  
Figure 2: CNo of BeiDou and GPS-visible satellites for open sky data.  
For stationary scenarios, accuracy is examined in terms of 90% position error radius (RCEP = radial circular error position) and position error mean distance (PMD) from the center. Results are analyzed against LSE and WLSE (weighted least squares estimation) algorithms. In this paper, we applied the SIGMA \(-\varepsilon\) weight model [37] for observation weight estimation.  
\[\sigma_{l}^{2}(i)=a.\ \exp(-CNo/10) \tag{35}\]  
\(\sigma_{l}(i)\) is the variance of \(l\)th observation at \(i\)th epoch. \(a\) is the scaling constant and depends upon the noise characteristics of the tracking loop or carrier tracking loop noise bandwidth. And the WLSE is defined as;  
\[\Delta x=\left(\mathbf{H}^{T}\Sigma^{-1}\mathbf{H}\right)^{-1}\mathbf{H}^{T} \Sigma^{-1}\Delta\rho \tag{36}\]  
\(\Sigma^{-1}\) is the weight matrix where \(\Sigma\) is the diagonal observation variance-covariance matrix.  
\[\Sigma=\left[\begin{array}{cccc}\sigma_{1}^{2}&0&\ldots&0\\ 0&\sigma_{2}^{2}&\ldots&0\\ \vdots&\vdots&\ddots&\vdots\\ 0&0&\ldots&\sigma_{n}^{2}\end{array}\right] \tag{37}\]
## 6 Discussion  
The results demonstrated that the proposed innovative tuning method achieved satisfactory results compared to commercial platforms, even when different sensor technologies were used. The method aims to quickly determine the appropriate values for the **Q** matrix based on sensor datasheet values, thus eliminating the need for an experienced designer, as well as any heuristic assumptions or initial guesses.  
The datasheet information provided by manufacturers, especially for lower-cost sensors, often does not perfectly correspond to the actual performance of the sensors. This discrepancy necessitates the performance of new Allan variance tests to obtain values closer to real-world performance. These tests are time-consuming, require a controlled environment, and demand expertise from the person conducting them. The proposed method converts the datasheet values into more accurate real-world values, streamlining the process and removing the need for a specialist.  
Additionally, the implementation of the method on platforms with different sensor technologies, such as FOGs and MEMS, highlights the robustness and flexibility of the approach. Even in scenarios where the sensors have significantly different noise characteristics, the proposed tuning method successfully maintained trajectory accuracy, particularly during periods of GNSS signal degradation.  
Future work will focus on further optimizing the tuning parameters. Specifically, the **Q** matrix will be made adaptive to temperature variations, as inertial sensors are particularly sensitive to these changes. This adjustment would allow for even more precise tuning under variable operational conditions, potentially further improving the performance of inertial navigation systems.  
Moreover, incorporating Real-Time Kinematic (RTK) data in future experiments could provide an even higher level of accuracy and serve as a more precise reference for evaluating the proposed method. A direct comparison of the proposed method with other recent automatic Kalman filter tuning methods, including those that use machine learning or genetic algorithms, would also be beneficial.
## 4 Carols Airborne Data Analysis
### CAROLS Results
#### 4.2.3.1 Ocean Measurements  
Figure 12 shows the computed differences between the TRAP software simulations and the measured _Tx_ and _Ty_ values during the wing wag measurements. The mean differences are found to be less than 1 K for all flights, for the measurements sampled at 40 ms (with large std values).  
Figure 13 shows the data collected over the ocean, influenced by wing wag movements with strongly varying incidence angles, for both antennas. The results indicate a strong degree of consistency between the data collected by the two antennas. The upper Tb values are for the X polarization, whereas the lower values correspond to the Y polarization. The nadir antenna data are plotted in red, and that with the slant antenna is plotted in black. For the same incidence angle, the variations between the nadir and slant antenna measurements are less than 0.2 K for the X polarization and less than 1 K for the Y polarization.  
In the case of the third and fourth two Stokes parameters (T3 and T4), the analysis must take into account the fact that the cables were not completely phase matched as a result of their long length. A correction was thus applied to eliminate the influence of this effect on the correlation between the horizontal and vertical measurements. Computation of the phase difference angle is based on the comparison of recordings made during wing wag movements, which enabled an angle of approximately 42\({}^{\circ}\) to be determined.  
Figure 14 provides a comparison between the corrected measurements and the modeled third and fourth Stokes parameters (using TRAP software). For the third parameter, a good agreement is observed: the CAROLS measurements fit the model estimations for all incidence angles, with variations of less than 2 K. In the case of the fourth Stokes parameter, although we find a good agreement for low incidence angles with nadir antenna, small but increasing discrepancies are found as the incidence angle increases; these differences reach a value of \(-10\) K with slant antenna. This is probably due to simulation errors resulting from the large antenna beam width, reaching very high angles at an incidence greater than 50\({}^{\circ}\).  
Figure 12: Comparison between measured and simulated antenna temperatures (in K)—for the Y (top) and X (bottom) polarizations, with the nadir antenna—as a function of view angle during the wing wag movements, for the three different flights (black: the first flight, red: the second flight, blue: the third flight) over the ocean during the CAROLS’2009 campaign.  
Figure 13: Tb (K) measured for the X and Y polarizations, for both nadir (red) and slant (black) antennas, as a function of incidence angle.
## 5 Conclusions  
The development stage is a crucial factor that determines the composition of SMs in _A. montana_ infloresences and the antitumor activity of the analyzed extracts. The development stage can modify the composition of SMs, such as SLs, Fs, and EOs in amica RM. The contents of Fs and EOs increased during flowering to the full flowering phase; however, the highest concentration of SLs was noted under full flowering and at the end of flowering. RM harvested in different development stages varies in their composition of SMs, and collecting this plant material at an appropriate stage of development can serve as a modifying factor of the chemical composition of arnica infloresences. The development stage and different extraction methods had an impact on apoptosis and necrosis in the human cervical carcinoma HeLa, human colon carcinoma HT29, and human colon metastatic carcinoma SW620 cell lines. More promising results, i.e., a relatively high level of apoptosis and a low level of necrosis induced by the arnica extracts, were demonstrated in the HeLa cell line (full flowering; concentration: 0.5 \(\mu\)L/mL), the HT29 cell line (beginning of flowering; concentration: 0.5 \(\mu\)L/mL), and the SW620 cell line (stage of yellow buds; concentration: 1 \(\mu\)L/mL). The present study is the first report on the anticancer activity of _A. montana_ water and ethanol extracts, with emphasis on the role of the development stage. Nevertheless, further studies are required to explore their potential for future medicinal purposes. In further studies, other factors and their interactions modifying the quality of _Arnica ffos_ should be analyzed, such as interactions of abiotic and biological factors increasing the SM yield and modification of the chemical composition of RM and, consequently, the biological activity of extracts. From the point of view of anticancer activity, water was a more effective solvent than ethanol. _A. montana_ provides a very broad range of RM (e.g., infloresences, rhizomes, roots, achenes, and all plant); therefore, attention should be paid to the water as an eluent in research on the biological activity of mountain arnica.  
Conceptualization, P.S., D.S. and J.J.-G.; methodology, P.S., J.J.-G., D.S., L.S., A.Z., M.P. and R.P.; formal analysis, P.S., D.S., J.J.-G.; investigation, D.S., P.S., J.J.-G., A.Z., M.P. and R.P.; resources, P.S., D.S. and [PERSON]-G.; data curation, D.S., P.S. and J.J.-G.; writing--original draft preparation, D.S., P.S., [PERSON]-G., A.Z., M.P., R.P. and L.S.; writing--review and editing, D.S., P.S., J.J.-G., A.Z., M.P., R.P. and L.S.; visualization, P.S., [PERSON]-G. and D.S. All authors have read and agreed to the published version of the manuscript.  
The research was financially supported by the Polish Ministry of Science and Higher Education.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to copyright protection.  
The authors declare no conflict of interest.
# Research on Flexible Traceability System of _Agaricus bisporus_ Supply Chain
## 2 Analysis of Traditional Traceability Models
### Problems  
Modern agricultural supply chains often comprise multiple stages, involving numerous participants, and are continuously expanding. In a flexible traceability system, through the application of concepts such as modularity and microservices, supply chain management becomes more adaptable, capable of responding flexibly to evolving demands and environments. Currently, there are two main issues present in traditional traceability systems.  
1. Issue of Low Query Efficiency  
The immutability of blockchain also gives rise to the problem of low query efficiency. Once data are recorded on the blockchain, it cannot be modified or deleted. Therefore, querying data requires a slow process of verification starting from the genesis block of the blockchain. This can significantly slow down the query speed, especially when dealing with large-scale data processing. Blockchain-based traceability systems require consensus mechanism verification and encryption calculations, which consume a substantial amount of computational resources, leading to low query efficiency.
2. Incompatibility with the Diverse Needs of Traceability Subjects  
Each blockchain ledger mixes traceability data aimed at multiple traceability subjects. If different traceability subjects want to obtain specific data according to their respective needs, complex data filtering and screening are required. This can affect the accuracy of data traceability. For a traceability subject, only their own traceability data are of concern. In this scenario, storing all traceability data together in one ledger not only increases the size and complexity of the ledger but also leads to data redundancy and wastage. Since each traceability subject needs to maintain a complete ledger, it consumes a significant amount of storage space and computational resources. This not only results in high maintenance costs for the ledger but also leads to performance degradation and system instability.
# Three-Dimensional Wind Measurements with the Fibered Airborne Coherent Doppler Wind Lidar LIVE
## 2 Experiments
### Lidar Signal Processing
The closer the assumption is from the real value, the stronger the signal peak, hence the name of the algorithm: maximum of the function of accumulated spectra. The remaining frequency offset from zero Doppler frequency leads to an estimate of vertical wind. Accumulated spectra also allow us to estimate the lidar CNR. The accuracy of the MFAS algorithm is related to the number of assumptions being computed, i.e., the computing time allocated to the algorithm. It is wise to use a rough MFAS estimate in order to initiate a WVML algorithm dedicated to wind accuracy. The WVML algorithm is a more common algorithm which fits a wind parametric model over spectrogram data thanks to an optimization algorithm. We then proceeded with wind vector estimation for all lidar ranges. At this stage, the wind has been estimated relative only to the aircraft. In order to obtain the wind relative to the ground, we must add aircraft speed relative to the ground (or cancel ground speed relative to the aircraft). The accuracy of aircraft attitude from FTI (Flight Test Instrumentation) and lidar orientation relative to the aircraft is not high enough to safely add aircraft speed. For an aircraft speed of 100 m.s\({}^{-1}\), an error of orientation of a single degree will lead to a residual aircraft speed of 1.75 m.s\({}^{-1}\) on axes perpendicular to aircraft motion. The desired wind speed accuracy is 0.5 m.s\({}^{-1}\), and this goal requires a precise ground speed relative to the aircraft cancelling procedure. We have chosen to work with ground speed measured from the lidar ground level return. Since both measurements are done within the same frame of reference, there is no lidar orientation issue. As the signal level from the  
Figure 4: Spectrogram data at a low carrier to noise ratio (CNR), and presence of the lidar signal (black dotted trail) in accordance with the maximum of the function of the accumulated spectra (MFAS) algorithm estimation. Figure 5: Doppler compensated spectrogram and presence of lidar signal (black dotted trail) in accordance with MFAS estimation (**left**). Function of accumulated spectra (**right**). ground return is stronger than the signal level from the atmospheric return, we use a more common VAD algorithm to retrieve ground speed relative to the aircraft.
# Effect of P+ Source Pattern in 4H-SiC Trench-Gate MOSFETs on Low Specific On-Resistance
## 3 Results and Discussion
This is not critical in deciding the blocking voltage in avalanche breakdown conditions; thus, high breakdown voltages remained regardless of the \(\mathrm{L_{PP}}\), as shown in Figure 5b. Figure 4: (a) Output characteristics of grid pattern with \(\mathrm{L_{PP}}\) of 0.5 to 2.5 \(\upmu\)m with 0.5 \(\upmu\)m steps; (b) specific on-resistance with square, stripe, and grid patterns with cell pitches of 3.0 \(\upmu\)m, 4.0 \(\upmu\)m, and 4.6 \(\upmu\)m.  
The equivalent circuit for the TMOSFETs with an integral p-body diode is shown in Figure 6a. To examine the impact of P+ source areas on the third-quadrant characteristics, the grid P+ source TMOSFETs with different LPP are compared in Figure 6b. As expected, an increase in LPP produces an increase in current density through the body diode in the third quadrant. Figure 6c shows the third-quadrant behaviors of TMOSFETs with square, stripe, and grid patterns and LPP of 0.5 \(\upmu\)m, 1.5 \(\upmu\)m, and 2.5 \(\upmu\)m. A shorter LPP (0.5 \(\upmu\)m) in the grid TMOSFETs produces smaller quadrant behaviors than with conventional square and stripe TMOSFETs due to the small P+ source areas. However, a longer LPP (1.5 \(\upmu\)m) in the grid TMOSFETs produces a larger current density than in the square p-body TMOSFETs due to the larger p-body area. In addition, all LPP values produce smaller quadrant current than with the stripe p-body TMOSFETs. Figure 5: (**a**) Forward blocking behaviors of square, stripe, and grid patterns with cell pitches of 3.0 \(\upmu\)m, 4.0 \(\upmu\)m, and 4.6 \(\upmu\)m; (**b**) breakdown voltage of grid pattern with cell pitches of 3.0 \(\upmu\)m, 4.0 \(\upmu\)m, and 4.6 \(\upmu\)m, and LPP of 0.5–2.5 \(\upmu\)m.  
The static characteristics of TMOSFETs with square, stripe, and grid patterns and all simulated results for \(\rm R_{on,sp}\), forward blocking behavior (BV), and figure-of-merit (FOM, \(\rm BV^{2}/R_{on,sp}\)) are compared in Table 2. Cell pitch (CP) is the most critical factor affecting \(\rm R_{on,sp}\). The smaller cell pitch of the TMOSFETs with a grid pattern produces a lower \(\rm R_{on,sp}\). For the same cell pitch, the channel area has a greater influence on \(\rm R_{on,sp}\). The P+ source for the stripe and grid patterns is influenced by greater forward blocking characteristics than the P+ source for the square pattern. Figure 6: (**a**) Equivalent circuit for MOSFETs with body diode; (**b**) third-quadrant behaviors for grid pattern with \(\rm L_{PP}\) of 0.5–2.5 \(\rm\upmu m\); (**c**) third-quadrant behaviors of square, stripe, and grid pattern with \(\rm L_{PP}\) of 0.5 \(\rm\upmu m\), 1.5 \(\rm\upmu m\), and 2.5 \(\rm\upmu m\).
# Simulation of a Narrowband Power Line Communications System over Medium Voltage
## 5 Simulation Results and Discussion
### Evaluation of \(\gamma(f)\) and \(H(f)\)  
The transfer function of the PLC channel examined is determined based on Section 2. The simulations for the real and imaginary part of the complex propagation constant \(\gamma(f)\) give rise to the curves plotted in Figure 2a,b which are very close to the theoretical curves [9].  
Figure 1: Wavelet-OFDM transmission [19]. (**a**) IDWT for transmission; (**b**) DWT for reception.  
In Figure 3, the transfer function amplitudes of the three modes supported by a three-wire MTL are plotted for an indicative distance of 2 km.  
\begin{table}
\begin{tabular}{c c c c} \hline Number of Channels & 119 & Conductor Permeability & \(\mu_{0}\) \\ \hline Symbol duration & 8.192 ms & Conductor permittivity & \(2.3\times 10^{3}\)\(\epsilon_{0}\) \\ \hline Modulation order & 2 or 4 & Conductor conductivity & \(3.8\times 10^{7}\) S/m \\ \hline Number of symbols & 10,000 & Ground conductivity & \(5\times 10^{-3}\) S/m \\ \hline Digital up-sampling factor & 16 & Ground permittivity & \(13\)\(\epsilon_{0}\) \\ \hline SNR & 7.5 to 20 dB & Ground permeability & \(\mu_{0}\) \\ \hline Carrier frequency & 75.75 kHz & Height of wires & 10 m \\ \hline Nyquist filter roll-off & 0.25 & Radius of wires & 2 mm \\ \hline Wavelet type & Haar & Distance between wires & 0.7 m \\ \hline W-OFDM overlap factor & 8 & Number of wires & 3 \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of the simulated system.  
Figure 3: Amplitude of the transfer function for the three modes supported by a three-wire MTL at 2 km from the injection point.  
Figure 2: Frequency dependence of the complex propagation constant \(\gamma(f)\) of MV-PLC channels. (a) \(\alpha(f)=\text{Re}\left(\gamma(f)\right)\); (b) \(\beta(f)=\text{Im}\left(\gamma(f)\right)\).