esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Analytical Model of the Drive and Transmission Mechanism
### Analytical Calculation of the Vehicle's Dynamic Parameters
The deviation can be caused, for example, by the missing hydrodynamic component in analytical calculations, different initial conditions (idle speeds), insufficient accuracy of the input data (technical parameters obtained from the vehicle's technical documentation), insufficient accuracy of the external characteristics of the IVECO F1 CE0489C engine in the technical documentation of the vehicle and others. Nevertheless, it was necessary to perform this analysis to verify the behavior of the given system and to evaluate its functionality and usability for the analysis of other parameters. For these purposes, the power characteristics, torque characteristics, traction characteristics with driving force characteristics, driving resistance characteristics and dynamic factor characteristics were determined. For the creation of the dependencies characterizing the dynamics of straight-line driving of the vehicle, the manufacturer's data overviewed in the technical documentation of the IVECO LMV 4 \(\times\) 4 M65 vehicle were used [50; 51]. For the calculations, not only the dimensional and weight data of the vehicle but also parameters characterizing the particular components of the driving and transmission mechanism, for example, gear ratios, efficiencies, dimensions and inertias, were applied. Among the inputs for the analysis are the technical parameters of the IVECO F1 CE0489C engine and its external revolution characteristics (Figure 2), which shows the dependence of the effective power and torque on the engine revolutions obtained from the technical documentation of the IVECO LMV 4 \(\times\) 4 M65 vehicle. The calculation procedure for the creation of the characteristics then begins with the selection of the characteristic points of the external engine characteristics, the determination of the magnitude of the torques at the given points and the corresponding engine revolutions and the determination of the coordinates [\(T_{e}\), \(n_{e}\)] and [\(P_{e}\), \(n_{e}\)]. The obtained values summarized in Table 2 are used for the transformation of the coordinates [\(T_{e}\), \(n_{e}\)] to [\(F_{T}\), \(v\)], [\(T_{e}\), \(v\)] and [\(P_{e}\), \(v\)]. Then, it is possible to apply the coordinates [\(T_{e}\), \(v\)] for the particular gears to plot the torque characteristics and to apply the coordinates [\(P_{e}\), \(v\)] for the particular gears to plot the power characteristics. Consequently, the characteristics of the power values needed for driving can be plotted in the form of power characteristics. The coordinates [\(F_{T}\), \(v\)] were used for plotting the traction characteristics of the particular gears and for the creation of the traction characteristics from the traction resistance values. Finally, dynamic factor \(D\) can be calculated and visualized.
## 4 BFS Technological Stack--Dual-Blockchain Framework
### Adaptation of Bitcoin and Corda Concepts in BFS  
The BFS architecture uses concepts from both Bitcoin and Corda blockchains, adapting and combining elements from each to create a novel dual-blockchain framework tailored for financial reporting and liquidity management. While inspired by Bitcoin's block structure for its filing history chain, the BFS system does not directly implement Bitcoin's proof-of-work consensus mechanism. Instead, it uses a custom \"funds verification consensus\" approach more suitable for financial validation. This consensus mechanism verifies the economic validity of transactions by comparing transaction values against available funds in the latest accounting records, ensuring sufficient funds exist without revealing sensitive details. From Corda, BFS adapts the concept of point-to-point messaging and transaction vaults, modifying these to fit the specific needs of secure, private financial transactions within the BFS ecosystem. Specifically, BFS implements a \"business transactions register\" inspired by Corda's Transaction Vault, serving as an off-ledger repository for transaction records. These adaptations allow the BFS system to balance the transparency requirements of financial reporting with the privacy needs of business transactions. The resulting hybrid architecture enables the BFS system to implement its unique features such as the integration of accounting processes with blockchain technology while addressing the specific challenges of financial reporting and liquidity management in ways that neither Bitcoin nor Corda could achieve independently.
## 2 Basic Equations for Anchor Load Transfer  
The steps involved in deducing the anchorage mechanism through mathematical formulas are as follows: Firstly, acquire the basic equations of load transfer by analyzing the stress balance of the microelement body. Next, solve the basic equations of load transfer using the bond slip model of the anchorage body. Finally, draw conclusions on the function expressions describing the distribution of axial force and shear stress.  
A microelement body with a length of \(dx\) is chosen as the subject of study in the anchored section of the anchor bar, as illustrated in Figure 1. Due to the small size of the microelement body, it is reasonable to approximate that the shear stress distribution on this microelement is uniform.  
According to the axial force, equilibrium can be obtained:  
\[dp(x)+U\tau(s)dx=0 \tag{1}\]  
\[\frac{dp(x)}{dx}=-U\tau(s) \tag{2}\]  
where \(p(x)\) is the axial load of the anchorage body, \(\tau(s(x))\) is the shear stress of the anchorage interface, and \(U\) is the perimeter of the anchorage body.  
And the micrometric body satisfies  
\[p(x)=EA\varepsilon(x)=-EA\frac{ds}{dx} \tag{3}\]  
where \(\varepsilon(x)\) is the axial strain of the anchorage body, and \(E\) and \(A\) are the Young's modulus of elasticity and cross-sectional area of the anchorage body.  
The basic equations of load transfer can be obtained by substituting Equation (1) into Equation (3),  
\[\frac{d^{2}s(x)}{dx^{2}}-\frac{U}{EA}\tau(s(x))=0 \tag{4}\]  
where \(s(x)\) is the shear displacement of the anchorage interface, and the value of the shear displacement \(s(x)\) is equal to the value of the axial displacement \(u(x)\), under the action of the axial load.
## 3 TVWS-DTV Coexistence: Field Measurements
### Field Tests Considering Short Distances
#### 3.1.2 Test Environment and Results
Therefore, the evaluation of these parameters is essential to determine operational limits that guarantee both the protection of DTV systems and the viability of TVWS services. Table 5 presents the results of the measurements obtained for distances of 15 m and 50 m under the operational limit condition of each STB. The conditions of PR registered refer to the incidence of interfering signals on channels \(n+1\) and \(n-1\). The highest PR value recorded in this test was \(-32.7\) dB for STBs 1 and 2 under interference on channel \(n+1\) for the 50 m distance tests. On the other hand, the lowest recorded PR value was \(-35.5\) dB, obtained with STB 3 under interference on channel \(n+1\) and for a 15 m distance between the stations. From the measured PR values, a PR\({}_{\rm avg}\) value can be calculated for the incidence of interfering signals on channels \(n+1\) and \(n-1\). The PR\({}_{\rm avg}\) value for interference on channel \(n+1\) is \(-33.73\) dB, while for channel interference \(n-1\), it is approximately \(-34.32\) dB. The difference between these values, about 0.65 dB, may be influenced by the fact that the interfering signal has a lower ACPR value in the upper adjacent channel. Table 6 presents the results obtained under the conditions of interference applied to channels \(n+2\) and \(n-2\) for distances of 15 m and 50 m between the stations. In these tests, an inferior performance of STB 1 can be observed, with PR values approximately 10 dB higher than the other two STBs under the operational limit condition. The PR values for STB 1 under interference on channels \(n+2\) and \(n-2\) were around \(-46.05\) dB and \(-45.1\) dB, respectively, considering that these values represent the average between the PR values obtained for 15 m and 50 m. On the other hand, STBs 2 and 3 showed lower PR values, indicating better performance as they support an interfering signal power about 10 times higher than the interfering signal power supported by STB 1 under interference in the conditions of channels \(n+2\) and \(n-2\). Under the interference \(n+2\) condition, the measured PR values for STBs 2 and 3 were around \(-55.08\) dB, while under the interference \(n-2\) condition, the values were approximately \(-55.15\) dB. Similarly to the calculation performed under interference on channels \(n+1\) and \(n-1\), the PR\({}_{\rm avg}\) between the three STBs can be obtained. The PR\({}_{\rm avg}\) for the three STBs obtained under interference applied to channel \(n+2\) was \(-52.06\) dB, and for the interference applied to channel \(n-2\), a value of \(-51.8\) dB was calculated.
## 3 Numerical Method
### Mesh and Boundary Conditions  
The quality of the mesh plays a significant role in the accuracy and stability of the numerical computation. The computational mesh composed of tetrahedral cells is shown in Figure 4. To increase the simulation accuracy, grid refining is used at the elbow and Venturi tube. Since the difficulty and calculation amount of numerical simulation will be greatly increased with the addition of a wind turbine, and because this paper focuses on the aerodynamic performance of the wind gathering device itself, the simulation in this paper does not involve a wind turbine.  
The computational domain and boundary conditions are shown in Figure 5. The length, width and height of the computational domain are 40, 40 and 50 \(Dv\) respectively, where \(Dv\) is the diameter of the Venturi tube. The angle \(\theta\) is defined as the angle between the incoming flow direction and the axis of the Venturi tube. \(\theta\) is equal to \(0^{\circ}\) when the outlet of the Invelox system has its back to the incoming wind, and \(180^{\circ}\) when the outlet is facing the incoming wind. The inlet of the computational domain is the velocity inlet boundary condition, the outlet is the pressure outlet, and the two sides and the top are the slip boundary.  
There are two kinds of boundary conditions at the ground. When the incoming velocity is uniform, the turbulence intensity and length scales were set as 5% and 1 m respectively, and the ground boundary condition is the slip boundary. When the inlet velocity is the ABL wind profile, the ground boundary condition is a non-slip boundary.  
For the ABL wind profile, we applied the power-law equation to describe the wind profile [26].  
\[u=u_{\text{ref}}\left(\frac{z}{z_{\text{ref}}}\right)^{\alpha} \tag{3}\]  
where \(z_{\text{ref}}\) is the reference height and \(u_{\text{ref}}\) is the wind speed at reference height, and \(\alpha\) is the wind profile index. In this study, we set the reference height as 30 m (center of Invelox system entry) and  
Figure 4: Computational mesh of the Invelox wind turbine system.  
specified the reference wind speed as 6.7 m/s. The profile of turbulent kinetic energy \(k\) imposed at the inlet boundary is calculated as follows:  
\[k=\frac{3}{2}(I\cdot u)^{2} \tag{4}\]  
\[I=0.1\bigg{(}\frac{z}{z_{\mathrm{G}}}\bigg{)}^{(-\alpha-0.5)} \tag{5}\]  
where \(z_{\mathrm{G}}\) is the gradient wind height set as 450 m. The turbulent dissipation rate \(\varepsilon\) at the inlet boundary is suggested as follows:  
\[\varepsilon=C_{\mu}^{1/2}k\frac{u_{\mathrm{ref}}}{z_{\mathrm{ref}}}a\bigg{(} \frac{z}{z_{\mathrm{ref}}}\bigg{)}^{(\alpha-1)} \tag{6}\]  
where \(C_{\mu}\) is a constant with the value of 0.09.  
The user-defined function is compiled according to the above formulas and is imported into the fluent to realize the non-uniform velocity inlet boundary. In addition, the other software settings are consistent with that in the uniform incoming flow.
## 6 Stress Analysis of the Bare Tower Subjected to Solar Radiation Variation
### Analysis of Temperature Change at Each Time  
The variation values of three temperature measuring points on the inner and outer walls of 12 columns within a day were extracted, and the variation trend is shown in Figure 11. As can be seen, the temperature of the outer wall of the four sides of the tower in the daytime is greater than that of the inner wall, forming a positive temperature difference. The main reason is that the heat accumulates in the outer wall of the tower due to the increase of solar radiation and atmospheric temperature in the daytime, while the heat conduction rate of concrete is slow, and the temperature cannot be transferred to the inside immediately. During the night, the solar radiation disappears, and the atmospherictemperature drops rapidly, so the temperature of the outer tower wall drops. Moreover, after the heat transfer effect of concrete from outside to inside for a long time during the day, the temperature of the inner wall will gradually be higher than that of the outer wall, forming a negative temperature difference. During the whole day, the maximum positive temperature difference of the east, south, and west tower walls is 18.6 \({}^{\circ}\)C, 18.1 \({}^{\circ}\)C and 19.1 \({}^{\circ}\)C at 14:00, 16:00 and 18:00, while the maximum negative temperature difference of the east and west tower walls is 2.1 \({}^{\circ}\)C and 4.7 \({}^{\circ}\)C at 3:00 and 7:00, respectively. Since the north tower wall has not been directly exposed to the sun, the main source of its heat is the heat transfer of the east and west tower walls, so the temperature fluctuation range of the inner and outer walls is small, the maximum is 0.2 \({}^{\circ}\)C, and there is no temperature difference along the wall thickness.  
Therefore, to ensure that the main tower will not be damaged by high temperatures in future operation and use, cooling measures can be set at the tower wall in the summer sunshine area, with the focus on the south outer tower wall. The inner wall is not equipped  
Figure 11: Temperature variation of each side wall of the tower column at any time.  
with cooling facilities because the temperature difference is small, and the temperature is consistent with the ambient temperature.
# Removal of Heavy Metal Cd Element from Paddy Soil by Geo-Electrochemical Technology
## 3 Materials and Methods
### Experiment Method  
The experiment started on 29 July 2021, in the paddy field at the Agricultural Science Institute in Quanzhou County. There were three treatments and two repetitions for each treatment. The experimental groups with different conditions were named T1, T2, and T3; the corresponding experimental conditions are listed in Table 2. T1 was the electrokinetic remediation experiment of the GT on paddy soil; T2 was the same experiment but on the paddy soil-rice system; and T3 was the control experiment with rice plants alone and no GT. When installing the GT device, the cathode and anode extraction electrodes were separately buried into the paddy soil, approximately 20 cm deep, with the distance between the electrodes controlled at approximately 20 cm. Figure 2 shows the mechanism related to removing the Cd element from paddy soil by the GT.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Element:** & **Cr** & **Ni** & **Cu** & **Zn** & **As** & **Cd** & **Hg** & **Pb** \\ \hline Mean & 53.59 & 19.58 & 31.99 & 85.60 & 20.97 & 0.58 & 0.16 & 63.25 \\ Standard deviation & 1.20 & 0.41 & 1.31 & 4.52 & 1.45 & 0.04 & 0.01 & 2.70 \\ The legal limit for paddy fields & 250 & 70 & 50 & 200 & 30 & 0.4 & 0.5 & 100 \\ \hline \hline \end{tabular} Note: Units are mg/kg.  
\end{table}
Table 1: Heavy metal content of paddy soil in the Agricultural Science Institute experiment site.  
of about 1 cm and a height of about 80 cm. For T1 and T2, where GT was applied, the battery and adsorbent materials of the device were replaced every 7 days. T2 and T3 were the rice-planting experimental groups; considering local planting habits, the rice variety was uniformly Nongxiang 32, with consistent seeding and transplanting density. Manual weeding was used within the entire planting area without chemical herbicides; pest control was performed based on local pest information; and the irrigation water was tested free from Cd contamination. No high-concentration pesticides were used, and fertilization and other cultivation management techniques complied with local standards. The experiment began with the rice at the transplanting stage, and sampling was conducted on 25 October 2021, when the rice was mature, with the total duration of the experiment being 89 days.
## 4 Conclusions  
The microstructure, phase characteristics and mechanical properties with Ta addition in CrCoNiTa\({}_{x}\) MEAs are studied. The major conclusion are as follows:  
1. [leftmargin=*]
2. CrCoNiTa\({}_{x}\) alloys contain \(\gamma\) phase with FCC structure and Laves phase with C14 HCP structure. With increasing Ta content, it can be seen a complete transformation from hypoeutectic (Ta\({}_{0.1}\) and Ta\({}_{0.3}\)) to hypereutectic (Ta\({}_{0.5}\) and Ta\({}_{0.7}\)). As for Ta\({}_{0.4}\) alloy, the Ta\({}_{0.4}\) alloy exhibits a lamellar eutectic composition.
3. Assessment on the phase evolution is established on thermodynamic parameters (\(\Omega\)), atomic size differences (\(\delta\)), valence electron concentration (VEC) which are useful parameters to determine the formation of eutectic microstructure with FCC and Laves phase. The Laves phase evolves when \(\delta>2.2\) and VEC \(<8.25\).
4. With increasing Ta addition, the microhardness of the alloys increased and the hardness of the \(\gamma\) phase increased. While the Laves phase hardness stayed unchanged. Ta\(0.4\) alloy has the highest nano-hardness and elastic modulus due to the full complete eutectic structure with small lamellar spacing and enhanced solid solution strengthening.
5. With Ta addition increasing, the hard and brittle Laves phase is increasing, the plasticity of the alloy is decreasing, the compressive strength is increasing and then decreasing, The CrCoNiTa\({}_{0.4}\) EMEA with complete eutectic has the best balance of microstructure and properties, highest compressive strength (2502 MPa) and plastic strain (20.6%).  
Conceptualization--[PERSON]; methodology--[PERSON] and [PERSON]. ([PERSON]); supervision--[PERSON]. ([PERSON]) and [PERSON]; writing--original draft preparation, [PERSON] and [PERSON]. ([PERSON]).; writing--review and editing, [PERSON] All authors have read and agreed to the published version of the manuscript.  
Figure 9: Compression strength and plastic strain of some typical EHEAs compared with CrCoNiTa\({}_{x}\) EMEA: CoFeNiV\({}_{0.5}\)Nb\({}_{x}\)[30], Co2 Mo\({}_{x}\)Ni2 VW\({}_{x}\)[32], CrCoFeNiNb\({}_{x}\)[11], CoCrFeNiTa\({}_{x}\)[12], CoFeNiVMo\({}_{x}\)[33] and CrCoNiTa\({}_{x}\)(this wok).  
**Funding:** The authors gratefully acknowledge the financial support by the National Natural Science Foundation of China (52071267, 51631008), the National Key Research and Development Program of China (No.2016 YFB0701400).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Proposed Non-Linear Diffusion Method
### Performance Evaluation  
To evaluate the effectiveness of the suggested despeckling method, various commonly used quality metrics are utilized. These metrics include an SNR, which measures the ratio of the signal power to the noise power in the image. The PSNR is also employed to measure the peak signal power-to-noise power ratio. The SSIM evaluates the similarity between the original and despeckled images based on luminance, contrast, and structure. Furthermore, a relatively new metric called the UIQI is used [50].  
SNR is a quantitative parameter employed to compute the discriminations of an imaging system. The mathematical expression of the SNR is as follows:  
\[\text{SNR}=10.\text{log}_{10}\left[\frac{\text{V}_{\text{ar}[\text{g}]}}{ \text{MSE}}\right] \tag{8}\]  
where \(\text{V}_{\text{ar}}[\text{g}]\) refers to the reference image variance.  
PSNR is quite a common performance metric employed to verify the effectiveness of despeckling methods. A greater value of the PSNR better indicates the results. It is calculated as follows:  
\[\text{PSNR}=20\text{log}_{10}(\text{Max})-\text{log}_{10}(\text{MSE}) \tag{9}\]  
SSIM helps to compute the equivalence of the despeckled and the reference SAR image. It relies on three elements: luminance, contrast, and shape. It is derived as follows:  
\[\text{SSIM}=\frac{(2\mu_{\text{a}}\mu_{\text{b}}+\text{P}_{1})(2\sigma_{\text {ab}}+\text{P}_{2})}{(\mu_{\text{a}}^{2}+\mu_{\text{b}}^{2}+\text{P}_{1})( \sigma_{\text{a}}^{2}+\sigma_{\text{b}}^{2}+\text{P}_{2})} \tag{10}\]  
where \(\mu_{\text{a}},\mu_{\text{b}}\), \(\sigma_{\text{a}}\), \(\sigma_{\text{b}}\), and \(\sigma_{\text{ab}}\) denote local means, standard deviation, and cross variance for images a and b, respectively.  
\[\text{P}1=(0.01*\text{L})^{2}\text{and P}2=(0.03*\text{L})^{2} \tag{11}\]  
where L signifies specified dynamic scale value.  
Figure 3: (**a**) Speckled SAR-1 image; (**b**) speckled SAR-2 image.  
UIQI is derived using three coefficients: degree of linear correlation, mean proximity to light, and contrast similarity in images. The scale of these three factors lies from zero to one. Therefore, the last limit of UIQI lies between 0 and 1. If the obtained UIQI value of the despeckled images is one then the image quality is better, and vice-versa: 0 represents the lowest image quality.  
\[\text{UIQI}=\frac{\sigma_{\text{xy}}}{\sigma_{\text{x}}\sigma_{y}}.\frac{2 \text{xy}}{\text{x}^{2}+\text{y}^{2}}.\frac{2\sigma_{\text{x}}\sigma_{y}}{ \sigma_{x}^{2}+\sigma_{y}{}^{2}} \tag{12}\]
# A Review of Traffic Flow Prediction Methods in Intelligent Transportation System Construction
## 5 Traffic Prediction-Related Datasets
### Common Evaluation Indicators  
Traffic flow prediction, as a typical regression problem, is commonly evaluated using performance metrics such as the MAE, RMSE, and MAPE to assess the accuracy of the model's predictions.  
Mean Absolute Error (MAE): The MAE is the average of the absolute differences between the observed and true values. It is the most intuitive method to represent the error by calculating the average absolute error between the predicted and actual traffic flow values. Smaller MAE values indicate better model performance, where n represents the number of traffic prediction test samples. The formula is as follows:  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-\hat{y}_{i}|. \tag{16}\]  
Root Mean Square Error (RMSE): The RMSE is the square root of the mean of the squared differences between the observed and true values. By squaring the errors before averaging, larger deviations from the true values incur a greater penalty, making this metric sensitive to large errors. A smaller RMSE indicates better model performance, where n represents the number of traffic prediction test samples. The formula is as follows:  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\ (y_{i}-\hat{y}_{i})^{2}}. \tag{17}\]  
Mean Absolute Percentage Error (MAPE): The MAPE measures the relative error between the observed and true values as a percentage. The values range from 0 to 1 and reflect the accuracy of predictions in proportional terms. A smaller MAPE indicates better model performance, where n represents the number of traffic prediction test samples. The formula is as follows:  
\[\text{MAPE}=\frac{1}{n}\sum_{i=1}^{n}\bigg{|}\frac{y_{i}-\hat{y}_{i}}{y_{i}} \bigg{|}. \tag{18}\]