esa-sceva/satcom-chunk-collection huggingface dataset:
# Similarity Model of a Rotary Lobe Expander Working with Various Fluids
## 9 Discussion  
The similarity model of the power \(P\) of a rotary lobe expander (Roots expander) is derived. It is based on the limiting power of no viscosity and on the similarity of the pressure losses. In this expander type, the volume transferred by the rotors from the inlet to the outlet is the same (the built-in volume ratio is equal to 1). The presented model concerns the similarity of pressure losses due to fluid viscosity, which decreases the power calculated for the ideal case of no viscosity. In this model, it is necessary to know the total volume transferred by the rotors within one shaft's revolution. The model was tested on the expander GGP16 XA from Armak Motors, which was disassembled and measured.  
The derived similarity model is rather simple. The intention was to develop an analytical, fast engineering tool for calculating the expander's power with acceptable accuracy as the more precise numerical calculation is expensive. In this model, the heat exchange with the ambient is not taken into account, along with the leakages. Two rotors are modeled by the single piston and the expander's geometry is represented by the volume \(\Delta V_{geom}\) transferred by the rotors during the rotor shaft's revolution. Pressure losses are modeled by the single pressure loss at the high-pressure side, between the inlet and the piston.  
The similarity model was tested both for the manufacturer's experimental power for Air and for the numerically calculated power for Air, R245 fa, SES36, and HFE100. Additionally, the simple thermodynamic analysis for these fluids is presented. It shows the difference among these fluids in the generated power in the limiting case of no viscosity.  
In the first application of the model, the manufacturer's values of power \(P_{mmf}\) (for five different inlet pressures and the rotational speed \(n=0...3000\) rpm) are approximated by the corresponding similarity power \(P_{sim}\). The maximum relative error of this approximation is \(\pm 5\%\).  
In the second application, the numerically calculated power from air \(P_{num,Air}\) for \(p_{in}=16\) bar(a) and the rotational speeds \(n=1500\) and \(3000\) rpm is used to calculate the corresponding similarity power \(P_{sim}\) for the fluids R245 fa, SES36, and HFE7100. The similarity power is compared with the numerically calculated power \(P_{num}\) for these fluids. The maximum relative error is again \(\pm 5\%\), which is probably a coincidence.  
Figure 11: Ratio of the similarity \(P_{sim,R}\) and the numerical power \(P_{num,R}\) vs. the rotational speed \(n\) for the various fluids for the comparison of \(P_{sim,R}\) and \(P_{num,R}\).  
The presented simple similarity model may be useful for quickly calculating the expander's power for various fluids, pressure conditions, and rotational speeds with acceptable accuracy.
## 2 Method for Analysing Losses in Centrifugal Pumps with Different Prewhirl Angles
### Setup of the CFD Simulation  
The CFD simulation was conducted using ANSYS-CFX 2022 R1 software, employing the finite volume method for discretization [29]. The simulation was performed as an Unsteady-Reynolds-Averaged-Navier-Stokes (URANS) [29] simulation. A stationary simulation was conducted for each operating point to initialize the simulation. Five operating points with varying flow rates of \(Q\in\left\{60\frac{\text{m}^{3}}{\text{h}};\,80\frac{\text{m}^{3}}{\text{h}};100 \frac{\text{m}^{3}}{\text{h}};120\frac{\text{m}^{3}}{\text{h}};140\frac{\text {m}^{3}}{\text{h}}\right\}\) were simulated for all three prewhirl body angles. Figure 6a depicts the entire flow area for the simulation. The boundary conditions at the inlet of the flow area (boundary inlet) and at the outlet of the flow area (boundary outlet) are indicated by arrows. A static pressure of \(p=0\) Pa and a medium turbulence intensity of 5% were considered for the boundary condition at the inlet. A constant mass flow (\(\dot{m}\)) normal to the surface was specified as the boundary condition at the outlet. The mass flow was calculated in accordance with Equation (4).  
\[\dot{m}=\rho Q \tag{4}\]  
It was assumed that the solid walls were smooth. The conditions do not apply to the surfaces depicted in Figure 6b. The equivalent sand grain roughnesses (\(k_{\text{s}}\)) were considered for these surfaces. These factors exert a non-negligible influence on the head, as demonstrated by [PERSON] et al. [28]. The surface of the prewhirl bodies is printed in layers, which results in a higher equivalent sand grain roughness compared to the other surfaces. The pump impeller, which is constructed from cast aluminium multi-material bronze (CuAl), also exhibits a higher degree of sand grain roughness. In addition to the surfaces depicted in Figure 6b, equivalent sand roughnesses with a value of \(k_{\text{s}}=0.3\) mm were also considered for the walls of the pump impeller in the impeller side gaps and the gap.  
A constant speed of \(n=1470\) min\({}^{-1}\) was assumed for the flow area in the impeller, as well as for the walls of the impeller in the impeller side gaps and in the gap. The impeller is discretised with an angle of \(\Delta\vartheta:=3^{\circ}\) for each revolution. Four revolutions of the pump impeller were calculated on the basis of the initialising steady-state simulations. This yields a total of 480 time steps, calculated with a maximum iteration number of 10 iterations. An effective residual of \(5\times 10^{5}\) and a conversation target of 1% were assumed as convergence criteria. The shear stress transport (SST) [29] model, as proposed by [PERSON] in [30], was employed to map the turbulence.  
Figure 6: (**a**) Total flow area with details of the boundary conditions (boundary inlet and boundary outlet); (**b**) equivalent sand grain roughness (\(k_{\text{s}}\)) at the wall boundary conditions for the prewhirl body, the impeller and the prewhirl body hub.
## 1 Introduction
For the theoretical calculation of earth pressure on pipelines, a variety of calculation methods of earth pressure were reasonably applied in practical projects, i.e., the Marston method [23; 24] based on the limit equilibrium theory, the Hindy method [25], and the empirical coefficient method of earth pressure [26; 27; 28]. At present, the calculation method of earth pressure is commonly based on the Marston method. Moreover, [PERSON] and [PERSON] [29] carried out research on the mechanical model of pipelines in landslide conditions and calculated the stress distribution on pipelines using the theory of Winkler. For further research, the safety factor equation of pipelines subjected to impact force was summarized. [PERSON] et al. [30] analyzed the dynamic response of buried pipelines impacted by rockfalls by combining the theoretical calculation formula and LS-DYNA. The results showed that there was an approximate proportional relationship between the maximum impact force and velocity; the stress concentration existed on the upper and lower surfaces of the pipeline, and the vertical stress distributed along the longitudinal and transversal directions of the pipeline; then, it decreased gradually along both ends of the pipeline. Although the finite element analysis (FEA) of and theoretical research on buried pipelines impacted by rockfalls were conducted by many scholars in the past, the experimental investigation of buried pipelines under impact loads was absent due to the complexity of the non-linear contact problem and the difficulty of full-scale experiments. Furthermore, the effects of pipeline parameters on the strain behavior of buried pipelines under impact loads need to be further explored by combining theoretical derivation, experimental research, and FEA. Therefore, an experimental and numerical study on the strain behavior of a buried pipeline subjected to an impact load was established in this research through theoretical research, experimental study, and simulation. The purposes of this research were threefold. Firstly, the rockfall impact force on soil, and the loads and strains on a buried pipeline under an impact load were theoretically derived based on the Hertz collision theory and the Spangler formula. Secondly, a soil-box was designed to conduct a scale experiment of 10 buried pipelines under impact load using a drop hammer impact test machine. Thirdly, an FEA model of hammer-soil-pipeline was developed for further investigation of the dynamic response of the buried pipeline under an impact load, and its accuracy was verified by theoretical and experimental results. Finally, the effect of some important parameters (the wall thickness of the pipeline, the diameter of the pipeline, the buried depth, and the impact height) on the strain behavior of the buried pipeline under an impact load was discussed based on the theoretical, experimental, and FEA results.
# Dynamic Segmentation for Physical Activity Recognition Using a Single Wearable Sensor
###### Abstract  
Data segmentation is an essential process in activity recognition when using machine learning techniques. Previous studies on physical activity recognition have mostly relied on the sliding window approach for segmentation. However, choosing a fixed window size for multiple activities with different durations may affect recognition accuracy, especially when the activities belong to the same category (i.e., dynamic or static). This paper presents and verifies a new method for dynamic segmentation of physical activities performed during the rehabilitation of individuals with spinal cord injuries. To adaptively segment the raw data, signal characteristics are analyzed to determine the suitable type of boundaries. Then, the algorithm identifies the time boundaries to represent the start- and endpoints of each activity. To verify the method and build a predictive model, an experiment was conducted in which data were collected using a single wrist-worn accelerometer sensor. The experimental results were compared with the sliding window approach, indicating that the proposed method outperformed the sliding window approach in terms of overall accuracy, which exceeded 5%, as well as model robustness. The results also demonstrated efficient physical activity segmentation using the proposed method, resulting in high classification performance for all activities considered.  
activity recognition; machine learning; wearable sensors; spinal cord injury; telem rehabilitation
# Electromagnetic Assessment of UHF-RFID Devices in Healthcare Environment
## 2 Materials and Methods
### Simulations of Exposure from the AG-RFID Reader by Means of an In-House 3D-RL Algorithm  
An in-house simulation MATLAB(r) algorithm was used and E-field estimations were obtained. Far-field conditions were supposed during all the processed simulations, and the parameters like dielectric permittivity and conductivity were obtained for specific elements. The simulation results exhibit uncertainty in the 2 dB to 5 dB range, owing to the 3D scenario mapping process.  
Specific materials were considered with variables in Table 3. Other simulation parameters are shown in Table 4, apart from free space losses, reflection, refraction or diffraction. The emission was produced by the devices at the same positions as in the real-life scenario. A concise and precise description of the experimental work is presented here. The complete and detailed information is available in [13].  
The graphic representation of the information on the EM levels is considered a well-known tool for clear depiction of the information as presented in previous works drafted by [6, 13, 14].  
This paper has been prepared with the use of the methodology for the production of 2D contour maps that offer graphic, immediate and accurate representation of the EMF distribution. The data collected were imported using graphic software (Surfer 8) to draw 2D contour maps showing the previously measured field strength values. The selected measurement points are displayed in Figure 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Operation Frequency & 865–868 MHz \\ Transmitted Power & 0.00316–2 W \\ Horizontal angular resolution (\(\Delta\Phi\)) & 1\({}^{\circ}\) \\ Vertical angular resolution (\(\Delta\Theta\)) & 1\({}^{\circ}\) \\ Permitted maximum reflections & 6 \\ Cuboids size (Mesh resolution) & 0.5 m \(\times\) 0.5 m \(\times\) 0.5 m \\ Diffraction phenomenon & Activated \\ \hline \hline Simulation uncertainty: 2 dB to 5 dB, owing to the 3D scenario mapping process. \\ \end{tabular}
\end{table}
Table 4: Variable Configuration for 3D Ray Launching [13].  
Figure 4: Laboratory measurements of the EMF emitted by RFID readers inside the anechoic chamber: (**a**) measurement scenario, (**b**) measurement of a MO-RFID reader, (**c**) measurements of AG-RFID reader.
## 3 Results
### Peri-implant Circular Fluid Cytokine Levels and Self-Perceived Pain  
PICF levels of TNF-\(\alpha\), MMP-1, and IL-8 were raised significantly among group-1 and group-2 than the control group (Table 3).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Characteristics** & **Group-1** & **Group-II** & **Group-III** \\ \hline Total number of samples ’\(n\)’ & 20 & 20 & 20 \\ Mean age (\(\pm\)SD) & 41.7 \(\pm\) 6.6 & 39.4 \(\pm\) 5.8 & 38.2 \(\pm\) 7.3 \\ Gender (M/F) & 11/9 & 8/12 & 10/10 \\ Number of implants assessed & 42 & 36 & 28 \\ Duration of implants in months (\(\pm\)SD) & 37.4 \(\pm\) 3.2 & 31.6 \(\pm\) 2.1 & 29.9 \(\pm\) 5.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Demographics of the study groups.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variables (Mean \(\pm\) SD)** & \begin{tabular}{c} **Group-I** \\ _(\(n\) = 20)_ \\ \end{tabular} & \
\begin{tabular}{c} **Group-II** \\ _(\(n\) = 20)_ \\ \end{tabular} &
\begin{tabular}{c} **Group-III** \\ _(\(n\) = 20)_ \\ \end{tabular} \\ \hline PI in \% (\(\pm\)SD) & 41.5 \(\pm\) 8.3 a & 46.6 \(\pm\) 11.4 a & 13.7 \(\pm\) 5.1 b \\ BOP in \% (\(\pm\)SD) & 21.6 \(\pm\) 6.9 c & 53.5 \(\pm\) 7.7 d & 15.8 \(\pm\) 7.0 e \\ PD in mm (\(\pm\)SD) & 5.0 \(\pm\) 0.5 f & 4.8 \(\pm\) 0.3 f & 1.2 \(\pm\) 0.8 g \\ CBL in mm (\(\pm\)SD) & 2.9 \(\pm\) 0.6 h & 2.7 \(\pm\) 0.4 h & 0.7 \(\pm\) 0.02 i \\ SPP score & 1.3 \(\pm\) 1 j & 3.4 \(\pm\) 1 k & 0 \(\pm\) 0 j \\ \hline \hline \end{tabular} Dissimilar lowercase letters indicate a statistically significant difference (\(p\) < 0.05) between groups along rows. Abbreviations: BOP = bleeding on probing; CBL = crestal bone loss; PD = probing depth; PI = plaque index; SPP = self-perceived pain.  
\end{table}
Table 2: Peri-implant clinical parameters of the research groups.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cytokines (pg/mL)** & \begin{tabular}{c} **Group-I** \\ _(\(n\) = 20)_ \\ \end{tabular} & \
\begin{tabular}{c} **Group-II** \\ _(\(n\) = 20)_ \\ \end{tabular} &
\begin{tabular}{c} **Group-III** \\ _(\(n\) = 20)_ \\ \end{tabular} \\ \hline TNF-\(\alpha\) & 648 \(\pm\) 335 a & 395 \(\pm\) 182 b & 87 \(\pm\) 23 c \\ MMP-1 & 4612 \(\pm\) 2296 d & 3179 \(\pm\) 1498 e & 174 \(\pm\) 82 f \\ IL-8 & 442 \(\pm\) 202 g & 387 \(\pm\) 164 g & 53 \(\pm\) 22 h \\ \hline \hline \end{tabular}
\end{table}
Table 3: Peri-implant circular fluid cytokine levels of the study groups.  
Statistically, a significant association was observed between SPP and TNF-\(\alpha\), MMP-1, and IL-8 expression in the PICF of test groups and control group as per bivariate multiple logistic regression analysis (Table 4).
## 3 Materials and Methods
### Practical Verification of Measurement Methodology  
Due to the need to verify the proposed methodology of measurement and data acquisition, extensive experimental measurements were also carried out according to the proposed methodology.  
The measurements' objective is to determine the center of gravity's position of two different vehicles with a various load distribution. The vehicles' technical parameters are given in Table 2.
## 3 Results and Discussion
### Dielectric Heating Experiment
Despite minor variations in tissue temperature, attributed to natural differences in dielectric properties and slight inconsistencies in antenna placement, the overall trend is consistent across all tests. Shorter distances lead to rapid heating in both types, while longer distances delay or reduce heating, especially in muscle tissue. Figure 9: Time-based temperature profiles of fish scales and muscle during dielectric heating at different antenna distances (\(n=3\)) recorded using embedded thermocouples. From a practical perspective, the results indicate that 9 cm offers the best balance. At this range, the fish scales reliably reach 70 \({}^{\circ}\)C--sufficient for parasite inactivation--while the underlying muscle remains below 50 \({}^{\circ}\)C, preserving its raw texture. While 3 cm and 6 cm also achieve high scale temperatures, they cause excessive heating in the muscle. Conversely, 12 cm results in insufficient heating altogether. Therefore, 6 cm and 9 cm are the most effective distances for dielectric heating, with 9 cm providing the greatest selectivity between parasite-rich scales and heat-sensitive muscle tissue, making it ideal for targeted aquaculture applications.
## 4 Results
### Training and Evaluating the Performance of the Models
#### 4.3.4 Predicting Qualifications for First-Year Students  
Figure 8 shows the performance metrics for different models applied to first-year students' data (for both the mainstream and the extended programmes). The model used APS, mathematics and physical sciences as input parameters and qualification as the target. Regarding accuracy, all models showcase high values, ranging from 96.1% to 96.3%. The decision tree, LGBM, and XGB models all achieve an accuracy of 96.2%, closely followed by the KNN and random forest models, with an accuracy of 96.3%. Precision and recall scores exhibit consistency across the models, ranging from 96.1% to 96.3%. The decision tree, KNN, LGBM, random forest, and XGB models demonstrate precision and recall rates of 96.1% to 96.3%.  
Figure 7: Predicting qualifications selected using all students’ data for features—APS, mathematics and physical sciences.  
F1 scores range from 96.0% to 96.2% for the models. The decision tree, KNN, LGBM, random forest, and XGB models achieve F1 scores of 96.0% to 96.2%. The RMSE values range from 0.645 to 0.667, and the MAE values range from 0.109 to 0.116. The models display consistently high accuracy, precision, recall, and F1 scores. They showcase the capability to predict the qualification of first-year data students accurately. The RMSE and MAE values suggest a relatively low level of prediction error, further underlining the models' accuracy in classification tasks.  
When comparing Figure 7 with Figure 8, the models demonstrate high accuracy, precision, recall, and F1 scores. However, when predicting qualification for first-year data students, the models exhibit slightly higher accuracy and more accurate predictions based on RMSE and MAE values. These findings suggest that the models perform well in accurately predicting qualifications in both contexts, with a slightly more robust performance predicting qualifications specifically for first-year data students.
# Soil Moisture Monitoring at Kilometer Scale: Assimilation of Sentinel-1 Products in ISBA
## 2 Model and Data
### Land Cover  
The land cover data were obtained from the global CGLS dataset based on PROBA-V observations at a spatial resolution of 100 m \(\times\) 100 m. Since the S\({}^{2}\)MP inversion model is only applicable to agricultural areas and grasslands (vineyards and orchards are excluded), the CGLS-LC100 map was filtered to retain only the agricultural areas listed in CGLS-LC100: cropland (map code \"40\") and herbaceous vegetation (map code \"30\"). In CGLS-LC100, cropland is defined as land covered with temporary crops followed by harvest and a period of bare soil. Note that perennial woody crops are classified as the appropriate forest or shrub land cover type. Herbaceous vegetation is defined in CGLS-LC100 as follows: \"Plants without persistent stems or shoots above ground and lacking a definite firm structure (tree and shrub cover is less than 10%)\".  
Figure 3 shows the land cover classification for the Montpellier region. This region is mainly covered by open and closed forests, with cropland and urban areas located mainly in the south of the Mediterranean coast. Agricultural areas can also be found in the northeast of the region, around the city of Ales. The open forest and shrub classes may include crop fields, vineyards, and orchards. The global irrigation map of [PERSON] et al. [51] shows that irrigation is common in this region.  
Figure 3: Land cover (CGLS) for (**a**) the Montpellier region and for (**b**) the Ales sub-domain (4\({}^{\circ}\)-4.3\({}^{\circ}\)E, 43.95\({}^{\circ}\)-44.25\({}^{\circ}\)N), including the city of Ales. Lands cover classes: open forest (“OF”), closed forest (“CF”), herbaceous wetland (“HW”), permanent water bodies (“WB”), sparse vegetation (“SV”), urban (“U”), cropland (“C”), herbaceous vegetation (“HV”), and shrubs (“S”). “OF” and “S” may include crop fields, vineyards, and orchards. The locations of the Pezenas, La Grand Combe, and Villevieille SMOGANIA stations (PZN, LGC, and VLV, respectively) are shown.  
Figure 2: S1 SSM product maps for one day in May around 18:00 UTC for (**a**) Toulouse, (**b**) Montpellier, and (**c**) Salamanca. SMOSMAIA and REMEDHUS stations are represented by red and blue dots, respectively. The Meteorole-Flux station is represented by a green dot. White is used to mask missing data.
# Mapping with Pleiades--End-to-End Workflow
## 4 Methods
### Digital Surface Model Generation
#### 4.2.2 Image Matching
The couple of 2D image coordinates is then transformed into the epipolar images using the forward addresses (cf. Figure 8). The column difference of the epipolar pixel coordinates then serves as prediction for image matching. With such disparity prediction the search space can be significantly reduced at the top level. Like others, we found that the simple parabola subpixel interpolation yields a bias in the distribution of fractional disparities (cf. Reference [118]). This bias causes incorrect reconstructions of tilted surfaces. To reverse this effect we calculate a mapping between the current fractional distribution and a uniform distribution. It is then applied efficiently via a lookup table. This effect is depicted in Figure 11.