esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Adsorption Isotherns and Data Modeling
#### Linear Regression  
Data were also modeled with Langmuir, Dubinin-Radushkevich, and Hill adsorption isotherms with linear regression. These linear regressions are based on transformations of the observed \(c_{\rm e}\) and \(q_{\rm e}\) values as here indicated.  
Linearized form of Langmuir adsorption isotherm:  
\[\frac{c_{e}}{q_{e}}=\frac{1}{K_{L}q_{m}}+\frac{1}{q_{m}}c_{e} \tag{9}\]  
Linearized form of Dubinin-Radushkevich adsorption isotherm:  
\[lnq_{e}=lnq_{s}-\beta\varepsilon^{2} \tag{10}\]  
Linearized form of Hill's adsorption isotherm:  
\[ln\frac{q_{e}}{q_{m}-q_{e}}=n_{H}lnc_{e}-lnK_{D} \tag{11}\]  
Using linear forms of the Langmuir isotherm, a diagram \(c_{\rm e}\)_vs._\(c_{\rm e}/q_{\rm e}\) was created, and \(q_{\rm m}\) and \(K_{\rm L}\) were determined from the slope (1/\(q_{\rm m}\)) and intercept (1/\(K_{\rm L}q_{\rm m}\)) and reported. In the Dubinin-Radushkevich isotherm, \(\varepsilon\) was calculated according to equation 5 with value \(c_{\rm s}\) of 0.1 mg. Diagram \(\varepsilon^{2}\)_vs._\(\ln q_{e}\) was created, and \(\beta\) and \(q_{\rm s}\) were calculated from slope (\(\beta\)) and intercept (\(\ln q_{\rm s}\)). For [PERSON]'s isotherm, Equation (11) is an approximate linearized form valid for small \(K_{\rm D}\). A diagram of \(\ln c_{\rm e}\)_vs._\(\ln q_{\rm e}/(q_{\rm m}\)-\(q_{\rm e}\)) was created. The \(q_{\rm m}\) value was the \(q_{\rm m}\) constant from the Langmuir model. From the slope (\(n_{\rm H}\)) and intercept (\(\ln K_{\rm D}\)), parameters \(n_{\rm H}\) and \(K_{\rm D}\) were determined and reported.
## 4 Experimental Results
This is highlighted when comparing Figure 6c,f,i, where the modal value of the number of additional vehicles present near a collision increases from 0 to 1; that is to say that, for lower global vehicle densities, we typically observe that no additional vehicles are present at a collision site. Whilst for a large population of 200 vehicles, we observed that it is common for at least one other vehicle to be present. Furthermore, lower adherence results in higher local traffic densities near collision sites for 100 vehicles (Figure 6d-f) and 200 vehicles (Figure 6g-i) but not 50 vehicles (Figure 6a-c). Collisions, when they occur, appear to be more likely to take place in the presence of other vehicles both when global traffic density is increased and when adherence level is lowered. However, an increase in local traffic density alone does not appear to cause an increase in collisions; a similar pattern was observed in [69]. This can be seen by comparing local traffic density results for 100 vehicles and 200 vehicles, which Figure 4 shows to have a near-equal collision rate despite Figure 6 showing that 200 vehicles have a higher local traffic density near collision sites. However, according to Figure 6, there is also an increase in local traffic density as adherence decreases, which always results in more collisions. This indicates that local traffic density may have a contributory effect towards collisions when combined with low adherence to speed limits, a higher average speed, or greater speed variance. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Scenario** & **Speed (mph)** & **Spread (mph)** & **Vehicles under 5 mph (\%)** \\ \hline
50 v, ad 30\% & 13.49 & 5.69 & 7.6 \\
50 v, ad 60\% & 11.74 & 5.8 & 12.0 \\
50 v, ad 100\% & 9.96 & 3.34 & 6.0 \\
100 v, ad 30\% & 12.24 & 6.93 & 17.0 \\
100 v, ad 60\% & 11.21 & 5.84 & 13.4 \\
100 v, ad 100\% & 9.9 & 3.56 & 6.2 \\
200 v, ad 30\% & 11.59 & 6.85 & 20.6 \\
200 v, ad 60\% & 11.17 & 5.52 & 12.7 \\
200 v, ad 100\% & 9.42 & 4.08 & 11.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average speed, spread of speeds, and fraction of vehicles moving below 5 mph for each scenario (where v = vehicles and ad = adherence percentage).
# Simulation of Two-Phase Flow of Shotcrete in a Bent Pipe Based on a CFD-DEM Coupling Model
## 2 Governing Equations
### CFD-DEM Coupling Model  
In each time step, the DEM is used to calculate the position and velocity of each particle. After the iterative DEM calculation, the porosity and particle-fluid interaction force per unit volume in the fluid grid are calculated. The resulting data are used to calculate the fluid flow field and drag force of the fluid acting on the individual particles. Subsequently, the iterative CFD simulation is run until the value of the result converges; the result is applied to the discrete elements to obtain information about the particle motion in the next time step. The coupled, iterative CFD-DEM calculation process is shown in Figure 2.  
The CFD-DEM gas-solid two-phase flow is embodied in the drag force between the gas-solid phases, and the gas-solid phase is coupled by the force acting between the two phases. In this study, the gas drag toward the particle phase and its reaction force were considered. Therefore, the commonly used Wen-Yu drag model was applied:  
\[\beta_{\mathrm{Wen-Yu}}=\frac{3}{4}\mathrm{C_{D}}\frac{\rho_{g}\epsilon_{g} \epsilon_{s}\big{|}\mathrm{U_{g}}-\mathrm{U_{s}}\big{|}}{\mathrm{d_{p}}} \epsilon_{g}^{-2.65} \tag{7}\]  
\[\mathrm{C_{D}}=\left\{\begin{array}{c}\frac{24}{\mathrm{Re}_{\mathrm{p}}} \left(1+0.15\mathrm{Re}_{\mathrm{p}}^{0.687}\right),\ \mathrm{Re}_{\mathrm{p}}<1000\\ 0.44,\mathrm{Re}_{\mathrm{p}}\geq 1000\end{array}\right. \tag{8}\]  
where \(\mathrm{Re}_{\mathrm{p}}\) is the Reynolds number of the particle (\(\mathrm{Re}_{\mathrm{p}}\)= \(\rho_{g}\epsilon_{g}\epsilon_{s}\big{|}\mathrm{U_{g}}-\mathrm{U_{s}}\big{|} \mathrm{d_{p}}/\mu_{g}\)), \(\mathrm{d_{p}}\) the particle diameter, \(\mathrm{C_{D}}\) the drag coefficient, \(\beta\) the phase momentum exchange coefficient, \(\rho_{g}\) the fluid density, \(\epsilon_{g}\) the fluid volume fraction, \(\epsilon_{s}\) the solid volume fraction, \(\mathrm{U_{g}}\) the fluid  
Figure 2: Flowchart of coupled, iterative CFD–DEM calculation.  
velocity, \(\mathrm{U_{s}}\) the minimal spouting velocity, F the drag force acting on the particle, and \(\upmu_{\mathrm{g}}\) the dynamic viscosity of the fluid.
# Non-Contact Measurement of Small-Module Gears Using Optical Coherence Tomography
## 2 Methods and Experiments
### Measurement of the Geometric Parameters of the Gears
#### 2.1.2 Calculation of Tooth Number  
After the tooth profile was obtained by optical coherence tomography imaging, the tooth number could be calculated according to the tooth tip or the root position of any three teeth. The calculation process is shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Priority Level** & **The First Series** & **The Second Series** \\ \hline Standard modulus series (mm) & 0.1, 0.12, 0.15, 0.2, 0.25, 0.3, 0.4, 0.5, & 0.35, 0.7, 0.9, 1.75, 2.25, 2.75, (3.25), \\  & 0.6, 0.8, 1, 1.25, 1.5, 2, 2.5, 3, 4, 5, 6, & 3.5, (3.75), 4.5, 5.5, (6.5), 7, 9, (11), \\  & 8, 10, 12, 16, 20, 25, 32, 40, 50 & 14, 18, 22, 28, (30), 36, 45 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Standard module series table (**GB/T 1357-1987**).  
Figure 4: Calculation of tooth number using optical coherence tomography imaging by employing the tooth tip or the root position of any three teeth. Labels of 1, 2, 3, and 4 are the tip positions. _Ra_: the radius of the addendum circle. \(R^{\prime}\): the perpendicular bisector of any two top-line connection.  
Because of the distance between the tooth center to the tip of any tooth is equal theoretically, the tooth center should be on the perpendicular bisector of the connection of the top of any two teeth. By calculating the intersection point of the perpendicular bisector of the 1-2 line and the 2-3 line, the location of the tooth center is determined. We also could determine other possible tooth centers by calculating 2-3-4, 1-3-4, or 1-2-4 lines. The error of the tooth center position was reduced by calculating the mean value of the O coordinate.  
Secondly, we could connect the tooth center O with any tooth tip position. Subsequently, the distance of the connected line was Ra of the tooth tip circle radius, which is calculated as \(R_{a}\)= 8.282 mm by:  
\[R_{a}=(z+2{h_{a}}^{*})m/2 \tag{2}\]  
where the value of the modulus was determined as 0.5 mm, \({h_{a}}^{*}\) value was determined as 1, and the number of teeth \(z\) was 30. The parameters of the gear were identical with the known parameters. When \(z\) was 30, the real addendum circle diameter \(R_{a-real}\) was 8.2 mm, as calculated by Equation (2) by the tooth number and the modulus obtained; hence, the relative error is \(|8.282-8.2|/8.2=1\%\).
## 2 Fundamental of Phase Space Reconstruction and Deep Neural Network
### Deep Neural Network
If the objective function \(E\Big{(}\mathbf{\omega}^{(k)}\Big{)}\) becomes lower in the \(k\)th iteration, \(\mu^{(k)}\) is divided by a factor \(\theta\) as \(\mu^{(k+1)}\) of the next iteration. If the objective function \(E\Big{(}\mathbf{\omega}^{(k)}\Big{)}\) becomes higher in the \(k\)th iteration, the iteration will be restarted and multiply \(\mu^{(k)}\) by the factor xxx as \(\theta\) of this iteration. \(\theta\) generally takes a number greater than 1, such as 4. 3 Ultrashort-Term Load Forecasting Model Based on Phase Space Reconstruction and Deep Neural Network  
The traditional load fluctuation is mainly caused by user fluctuations in power usage. Although the electricity consumption of the user is uncertain, there are certain rules in general, and the fluctuation range is not large. For ultrashort-term prediction, linear extrapolation, time series prediction, and other methods can usually achieve the required accuracy. With the massive access of distributed energy sources such as PV power plants, the net load can be expressed by Formula (14). \(p_{t}\) is the actual net load, \(p^{\prime}\) is the user's electricity load, and \(p_{t}^{\text{PV}}\) is the opposite number of PV power generation. \[p_{t}\ =\ p_{t}^{\prime}+p_{t}^{\text{PV}}. \tag{14}\]  
Since the amount of PV power generation is as uncertain as the power load and different from the traditional power supply with a known power output, the PV power generation can be considered a load, which reduces the dispatching burden of the system. As a result, the uncertainty of load increases, and the range of fluctuation enlarges, even the situation of power reversal will occur at noon on sunny days. If the traditional forecasting method is also used, it will produce larger errors and cannot accurately predict the load. Since the load is a non-linear time series with large fluctuations after the distributed energy access, it is difficult to directly predict. Therefore, the complex short-term prediction model may not satisfy the real-time requirements. In this paper, the phase space reconstruction is used to project the load time series into a time-varying and short-term regularity point in the high-dimensional phase space. Then, the non-linear fitting ability and fast convergence speed of LMBP DNN are used to fit and predict the locus of the points in the phase space to realize the ultrashort-term prediction of the load considering the distributed energy.
## 5 Study Limitations  
This investigation, based on an analysis of 28 scholarly articles, has inherent limitations. The selected papers, while significant, represent only a fraction of the rapidly expanding field of AI language models. Consequently, the insights and comparative analyses derived herein are limited to the content and scope of these specific studies, thus potentially omitting a broader spectrum of capabilities and deficiencies inherent to these technologies.  
Moreover, the study lacks original experimental data or empirical validation of the models' performances, thus making the findings reliant on the methodologies and conclusions of third-party researchers. The rapid evolution characteristic of AI development may also reduce the relevance of the conclusions as newer model iterations emerge. This highlights the critical need for continuous, updated evaluations to keep pace with advancements in the field.  
Furthermore, the heterogeneity in methodologies and focal points across the reviewed papers could introduce variability, thus complicating direct comparisons. The primary reliance on English language sources further limits the generalisability of the findings, as language models operate in a multilingual global context. Future studies would benefit from incorporating a broader linguistic perspective to attain a more comprehensive understanding of these models' capabilities across different languages and cultural contexts.  
In addition to these limitations, there are technical challenges and application-specific considerations that warrant discussion. For example, ensuring data security and preventing unauthorised access to sensitive information is a critical concern, particularly in healthcareapplications like Llama's ChatDoctor [38]. Implementing robust encryption protocols and conducting regular security audits are essential to mitigate these risks.  
Moreover, the performance and applicability of AI chatbots may vary across different languages and cultures. While models like Ernie excel in comprehending Chinese dialects [4], their effectiveness in other linguistic contexts remains uncertain. Developing chatbots that can navigate cultural nuances and adapt to regional preferences is an ongoing challenge that requires further research and development.  
Despite these limitations, this study provides a valuable foundation for future research by synthesising the current state of knowledge and identifying key areas for further investigation. As the field of AI language models continues to evolve, it is crucial to build upon these findings and conduct more extensive, empirical studies to validate and expand the understanding of these technologies.
## 2 Materials and Methods
### IVIM Fitting Procedure
#### 2.1.1 Clustering Approach  
In the proposed approach, a clustering technique is employed to determine the cluster \(\mathcal{C}_{ij}\) associated with each voxel \((i,j)\in\mathcal{I}\). Then, a Bayesian CAR fitting is performed, where the CAR prior specification depends on the clustering solution \(\big{\{}C_{ij},(i,j)\in\mathcal{I}\big{\}}\).  
The \(k\)-means clustering approach is chosen for its simplicity and low computational cost, and because it is one of the most widely adopted clustering methods for medical image segmentation. It is employed by assuming as the distance between any pair of voxels the \(L1\) distance between the vectors of their signal intensities over \(b\in B\). Moreover, the number of clusters \(N_{\mathcal{C}}\) is set equal to 9, which is reasonable from a theoretical viewpoint, as it represents the combinations of two levels (low or high) for the three coefficients to be estimated plus an additional case for the background (\(2^{3}+1=9\)); therefore, it gives enough flexibility to recognize these macroscopically different behaviors.  
The results of the clustering procedure is the cluster \(\mathcal{C}_{ij}\in[1,N_{\mathcal{C}}]\) of each voxel \((i,j)\in\mathcal{I}\), where all voxels belonging to the same cluster have the same \(C_{ij}\) value.  
The clustering was implemented in MATLAB (R2020a, The MathWorks Inc., Natick, MA, USA), with the function \"kmeans\", imposing 50 replicates with alternative initial cluster centroid positions.
# A Semi-Analytical Approach for the Linearized Vibration of
## 4 Numerical Results and Discussions
### Results and Discussion
#### 4.2.3 The Effect of Combined Static and Thermal Load
The peak frequency of acceleration response shifts with the mode frequencies, and there is no significant impact on the peak value of acceleration response. The position of the anti-resonance peak of structural response changes consistently with the mode frequencies. It shifts towards lower frequencies with an increase in thermal loads and towards higher frequencies with an increase in static loads. In the analyzed load range, the effect of thermal loads is greater than that of static loads. The RMS of the acceleration response considering the influence of combined loads is shown in Figure 15. Upon analysis, it has been determined that alterations in mode shapes have resulted in a minor fluctuation in the acceleration response within the central area of the beam [100, 200]. However, the RMS acceleration response in the two lateral regions ([50, 100] and [200, 250]) has demonstrated a significant rise in correlation with the augmentation of static deformation. The structural acceleration response is more affected by the combined loads than by the linear superposition of the individual loads. Figure 14: PSD curves of acceleration in the z-direction under different combined loads at various locations: (**a**) PSD curves for point located at \(x=150\) mm (**b**) PSD curves for point located at \(x=100\) mm; (**c**) PSD curves for point located at \(x=75\) mm.
# Advancing Crop Yield Predictions: AQUACROP Model Application in Poland's JECAM Fields
## 1 Introduction
Its key aspects include the utilization of not only in-situ data collected during field campaigns or by farmers, as well as the integration of high-resolution satellite data from the Copernicus Land Monitoring Service, sourced from the Sentinel-2 satellite. This approach minimizes personal intervention by farmers in the fields and has proven crucial in today's era for the advancement of precision agriculture. The AquaCrop model, at the core of this study, operates as a planning tool to support management decisions in rainfed agriculture. Given Wielkopolska's susceptibility to drought, the model's ability to simulate the growth and yield of crops becomes invaluable. The study also emphasizes the AquaCrop model's versatility in considering atmospheric conditions, soil characteristics, and field management components. As a region exposed to the challenges of changing climate patterns, Wielkopolska's agricultural landscape necessitates a robust monitoring system. The AquaCrop model, integrated with in-situ measurements and tailored for the specific conditions of the JECAM area in Wielkopolska, emerges as a crucial tool in understanding and predicting crop performance. This article delves into the detailed materials, methods, and data processing techniques employed in this study, shedding light on the intricate interplay between agriculture, climate, and technology in the context of the Wielkopolska region's agricultural resilience.
## 5 Experimental Results and Discussion
### Databases
#### 5.1.2 UBIRIS.v2  
The UBIRIS [49] database was published by SOCIA Lab (Soft Computing and Image Analysis Group of the University of Beira Interior, Portugal) which was established in 2004 for the purpose of evaluating images taken under uncontrolled conditions. The peculiarity of this database is that the images are acquired in visible contrary to the usual databases that are acquired in NIR. UBIRIS-v2 [50] contains a total of 11,102 images resolution 300 \(\times\) 400 pixels in TIFF format with a typical camera Canon EOS 5D Camera representing 500 subjects of different world origins (70 countries). UBIRIS-v2 is acquired remotely (between 4 and 8 m).  
Figure 7: Classification phase by CNN.
## 3 Results and Discussions
### Results and Analysis of the Grape Segmentation Test
#### 3.1.3 Model Evaluation  
We randomly selected 20 grape images from the validation set and counted the number of detected positive samples (_TP_), the number of falsely detected positive samples (_FP_), and the number of undetected positive samples (_FN_). Then, we calculated the precision rate and recall rate and finally obtained the value of \(F_{1}\). The results are shown in Table 1.  
The results show that the grape segmentation model trained with Mask R-CNN is effective and reliable. It can be used for the rapid extraction of the RoI in hyperspectral images.
## 3 Results
### Accuracy of Object Detection  
vePOD aims to enhance object-detection CNNs to process secret shares to protect vehicles' data privacy. The implementation of the secure functions should not affect the functional correctness of the object-detection network. In this set of experiments, we compared the detection accuracy of the vePOD CNNs with that of the original CNNs.  
Figure 4 presents the detection results in single-object and multi-object scenarios. We find that the vePOD CNN detects all the objects in each image that the original CNN does, achieving the same accuracy. This implies that the structure and secure functions of vePOD do not alter the detection accuracy.  
To quantitatively evaluate the detection accuracy, we calculated the error rate as the difference between the outputs from the vePOD CNN and those from the original CNN;i.e., \(|(d_{v\text{POD}}-d_{CNN})/d_{CNN}|\), where \(d_{CNN}\) and \(d_{vePOD}\) are the object-detection output (includes classification score and bounding box's offset) from the CNN and vePOD CNN, respectively. In our experiments, the average error rate was within \(e^{-7}\). That is, vePOD CNN has the same detection capability as the original CNN.
# Analysis of Slope Stability in Ion-Adsorption Rare Earth Mine Under In Situ Leaching Condition
## 3 Results and Discussion
### Analysis of Plastic Zone and Displacement Characteristics in Rare Earth Mine Slopes
During the initial leaching stage, the leaching solution in the slope soil was relatively limited, resulting in a slow exchange reaction between the leaching solution and rare earth ions. Consequently, the overall strength of the slope was minimally affected, maintaining stability. However, after 15 days of leaching, the moisture content in the slope substantially increased, and most weathered layers hosting the ore body were saturated with the leaching solution. This led to intense ion exchange reactions and, coupled with the self-weight of the solution, caused considerable displacement and further decreased slope stability.
# Construction of Air Quality Level Prediction Model Based on STEPDISC-PCA-BP
## 3 Results and Discussion  
### Data Pre-Processing  
From Tables 1 and 2, it can be seen that the meteorological and air pollutant factors in the samples have different parameter units and order of magnitude differences, which are directly used to train the network model to make it easy to get the same order of magnitude differences in weights, making the network model unstable. Normalization can make all factors have equal weights and better data convergence conditions. Equation (1) is used to standardize the original data.
## 5 Experimental Results
### Performance Metrics  
We determine the performance of the proposed WBCA and compared with the state-of-the-art contending programs. Therefore, the following parameters are used for conducting the experiments:  
* Consistency
* Quality Ratio
* User Response Time
* Solution Efficiency
* Threat Avoidance Capacity