esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Statistics for GNSS Positioning under Harsh Conditions: A Useful Tool?
## 5 Test and Results
### Experimentation under Real Harsh Conditions
Part **A** corresponds to a highway where there is a succession of eight small bridges. The MM estimator results, in this case, completely unaffected by the multipath and NLOS effects. Part **B** corresponds to a national road surrounded by dense foliage, inducing damps on the received satellite signals. Again, the MM estimator avoids the effects of the contaminated observations, which drive the LS estimator to have errors of around 20 m.  
Figure 7: Number of GPS+Galileo satellites tracked and PDOP (**bottom left**). Squared positioning errors for the LS and MM estimators over time, and highlight on time spans **A** and **B** (**top left**). Histogram of positioning errors for LS and MM-estimator (**right**).
This technology can be used to image large cell populations at high speed and to discriminate specific cells in real time based on information processing technology. In an experiment to demonstrate the versatility of their technology, they imaged cells of different sizes (3-30 um) at high speed and identified potential cancer cells circulating in the blood of cancer patients. However, a manufacturing process has not yet been developed that can ensure high-quality and safe hCTP production. In this study, we constructed model systems for distinguishing abnormal cervical cancer cells (HeLa-GFP) when mixed together with human embryonic lung fibroblasts (MRC-5). Images of the cells at different stages of a continuous culture period were input to a DL algorithm. The prediction performance was improved in the late stage of the culture period. The prediction performances of the constructed models were further improved by optimizing the DL hyperparameters, namely, the batch size (BS) and learning rate (LR). These results suggest that DL-based prediction models can effectively classify different cell types in cell images; in particular, DL can automatically extract the meaningful features from complex morphological cell information.
## 2 Materials and Methods  
### Satellite Data  
There are many types of satellite-derived precipitation products, among which one in particular, Integrated Multi-satellite Retrievals for GPM (IMERG), was chosen for its good resolution, 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\), and the good results it has shown in the literature compared to other satellite products [17]. Satellite data were collected for this study from 2010 to 2021 through the use of the IMERG, from 2014 to 2021, and the Tropical Rainfall Measuring Mission (TRMM), from 2010 to 2014. The GPM is a constellation of 10 satellites, which uses an algorithm to obtain accurate estimates of precipitation over much of the Earth's surface [18]. The GPM constellation initiated by National Aeronautics and Space Administration (NASA) and Japan Aerospace Exploration Agency (JAXA) includes many other space agencies around the world. The satellites are equipped with two data acquisition instruments, a dual-frequency precipitation radar (DPR) and a GPM microwave imager (GMI). The DPR is a radar that provides 3D images of the structure and characteristics of the precipitation, while the GMI is a radiometer with a conical scanner that operates on several channels and provides indications of the water content of the precipitation. The IMERG algorithm in its latest version (V06) was applied to all available GPM and TRMM data in order to make the record as long and reliable as possible (2000-2021). In this study, the latest version of IMERG, V06, dated March 2019, was used, which has a very high spatial resolution of 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) and a minimum time interval of 30 min. The IMERG data are pre-calibrated by the Deutscher Wetterdienst (DWD) Global Precipitation Climatology Centre (GPCC) in order to control data bias.
# Motion-Induced Phase Error Compensation Using Three-Stream Neural Networks
## 3 Experiment
### Quantitative Evaluation
It is shown that the largest STD error of the two reconstructed spheres by two methods are 0.4001 mm and 0.1254 mm and the largest diameter error of two methods are 2.6746 mm and 0.5486 mm. The results demonstrate that the proposed method can efficiently reduce the motion-induced error. Figure 6: Measurement results of a translational scene in actual FPP system. (**a**) Standard ceramic plate translated in depth direction to be tested. (**b–d**) Reconstructed results of 12-step PSP, three-step PSP and proposed method, respectively. (**e**) Section line in 600 th row of the height (Z) distributions of three methods. Figure 7: Measurement results of two standard spheres in actual FPP system. Symbol d represents diameter, c represents center distance. (**a**) Standard spheres translated in-depth direction to be tested. (**b**) One of deformed fringe patterns. (**c,d**) Reconstructed results of three-step PSP and proposed method, respectively. In Figure 8, we measured a rotating sculpture with a speed of about 0.1 rad per frame. The experimental conditions are the same with the ceramic plate, except that the motorized linear stage was replaced with a rotation stage. The rotating sculpture and one of the deformed fringe patterns are shown in Figure 8a,c, respectively. It can be seen from Figure 8d,g that the three-step method has obvious periodical waves at nose area. The results in Figure 8e,h demonstrate that the proposed method can also deal with the motion-induced error caused by such rotational motion. Figure 8i exhibits the profile distribution of three methods along the same line.
# Deep-Learning Based Positron Range Correction of PET Images
## 2 Materials and Methods
### Application and Quantitative Analysis  
The trained models were saved as Keras models in hdf5 format. The models were then loaded and applied to a simulated study not included in either the training or the validation datasets.  
The input was adapted to the specific characteristics of each trained network:  
* Selecting the corresponding input slices--in the case of the models with three and five input slices, the slices closer to the edges of the axial FOV were extended to avoid truncation.
* Zero-padding the images to obtain 160 \(\times\) 160 pixels in each slice.
* Normalizing the values to be between \(-1\) and 1. This normalization was restored in the output, so that the PRC preserved the appropriate units.  
A quantitative analysis of the resulting image was performed to obtain the mean (\(\upmu\)) and standard deviation (\(\upsigma\)) in different organs. The noise was defined as the \(\upsigma\):\(\upmu\) ratio in uniform regions away from any boundaries and edges. The recovery coefficients were obtained by defining regions over the whole organs, and their values were then normalized respective to the reference reconstruction with \({}^{18}\)F. The differences between the \({}^{68}\)Ga images before and after the proposed PRC could be easily evaluated from the obtained coefficients.
## 5 Experiments and Discussion
### Deviation Analysis of Simplified Method
Figure 9 presents a 3D visualization of the computation deviation for the backward-squint image motion compensation approximation algorithm and the exact algorithm as functions of two variables. The color gradient from yellow to blue indicates an increasing compensation deviation. The 3D visualization clearly shows that the primary influencing factor of compensation deviation is the roll variation angle \(\Delta\phi\), while the initial pitch angle has a relatively smaller effect, which is consistent with the single-variable simulation validation discussed above. In practical airborne operations, considering both computational efficiency and compensation accuracy, a combination of the two formulas can be used:  
\[ \begin{split}&\theta=\arctan(\frac{\tan\theta_{start}}{\cos( \Delta\phi)})\\ & 2\kappa=-\Delta\phi\cos\theta_{start}\end{split}  \tag{22}\]  
Figure 8: Comparison of 1.28\({}^{\circ}\) camera roll angle: (**a**) compensation angle; (**b**) pitch angle; (**c**) compensation angle deviation; (**d**) pitch angle deviation.
## 3 Cooperative Schemes
### Introduction to Cooperative Schemes  
This section presents a thorough survey of the cooperative VLC schemes, with the main focus on mitigating inter-cell interference as well as overcoming the damaging effect of LoS-link blockage [26]. These two phenomena have been already studied in RF systems,  
Figure 12: Types of footprint scenarios. Dashed circumferences represent coverage areas rather than illumination areas.  
but they present notable differences when working in VLC, and then, they deserve a completely different study.  
_Inter-cell interference_: RF networks are typically deployed by qualified staff, minimizing overlapping areas and so decreasing the inter-cell interference. Besides, directional antennas make transmission be under control pointing to a specific area. On the contrary, for VLC, the deployment of the optical APs are usually given and the system aims for providing the lighting infrastructure with communication capabilities. Additionally, VLC networks must comply with lighting standards, which impose the overlap of illumination areas.  
_LoS-link blockage_: RF systems are more resilient to blockages due to the intrinsic physical characteristics of such frequency bands, i.e., transmission, reflection, diffraction and refraction, among others. Proper signal power levels can be received even in case of LoS-link blockages between the transmitter and receiver. In contrast, visible light wavelengths cannot go through opaque objects, and the contribution of NLoS links is minimal with respect to LoS.  
In the following, we introduce the cooperative schemes presented in the literature to overcome both inter-cell interference and, in some cases, LoS-link blockages.
# Anisotropic Shear Strength Behavior of Soil-Geogrid Interfaces
## 2 Experimental Program
### Testing Procedures  
All the biaxial geogrids (SS20, SS30 and SS40) and triaxial geogrids (TX150, TX160 and TX170) were cut into nearly circular specimens with a diameter of 300 mm. Soil was first compacted in the lower shear box, and then the geogrid specimen and gasket were fixed on the lower box in sequence. The upper shear box was then installed on the gasket and filled with soil. The shear displacement was set as 33 mm for the 200 mm diameter upper box to reach a shear strain of 16.5% for the shear plane length. The specimen dimensions were slightly larger than the shear box for the convenience of fixing the geogrid on the shear  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Geogrid**} & \multicolumn{5}{c}{**Direction of Tensile Loading**} \\ \cline{2-6}  & \(\mathbf{0}^{\circ}\) & \(\mathbf{22.5}^{\circ}\) & \(\mathbf{45}^{\circ}\) & \(\mathbf{67.5}^{\circ}\) & \(\mathbf{90}^{\circ}\) \\ \hline SS20 & 26.29 & 17.90 & 10.72 & 17.98 & 26.68 \\ SS30 & 33.97 & 22.14 & 12.95 & 21.50 & 34.58 \\ SS40 & 48.26 & 28.29 & 17.98 & 29.53 & 47.82 \\ TX150 & 15.97 & 13.15 & 12.83 & 12.26 & 17.55 \\ TX160 & 21.57 & 16.20 & 13.47 & 13.81 & 19.28 \\ TX170 & 23.82 & 21.43 & 22.39 & 21.71 & 25.15 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tensile strength of geogrids for loading in different directions (kN/m).  
Figure 1: Gravel used in the direct shear test.  
box. The geogrid was fixed at the elevation of the shear plane and remained fixed during shearing, and the contact area between the geogrid and soil of each test was the same for different shear directions. Direct shear tests on soil-geogrid interfaces were conducted with the shear loading applied horizontally on the lower box at a constant rate of 1 mm/min in the directions of 0\({}^{\circ}\), 45\({}^{\circ}\) and 90\({}^{\circ}\) (direction indicated in Figure 3), respectively. For each geogrid and shear direction, a series of interface shear tests were performed under the normal stresses of 50 kPa, 100 kPa, 150 kPa and 200 kPa to obtain the failure envelope.
# Optimal Estimation Retrieval of Aerosol Fine-Mode Fraction from Ground-Based Sky Light Measurements
## 2 Model and Methods
### Modeling for Ground-Based Observation  
Generally, for the modeling of downward radiation, the uncertainty of surface reflectance can be ignored for the reason that the change in downward atmospheric radiation mainly depends on the properties of aerosols and molecule scattering [22]. Therefore, the Lambertian surface hypothesis is adopted to describe the contribution of the surface in this study. In addition, the ground-based measured radiation is mainly affected by the entire atmospheric column and is not strongly dependent on the vertical distribution of aerosols. Considering that atmospheric aerosols are usually trapped in the planetary boundary layer (PBL), we use the exponentially shaped profile distribution model and set the scale height to 2 km in this study [37; 38].  
With respect to the calculation of radiation transfer, the Unified Linearized Vector Radiative Transfer Model (UNL-VRTM) is employed as the forward model, which was specifically designed for  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Mode** & \(\mathbf{r_{\rm eff}}\) **(\(\mathbf{\mu}\))** & \(\mathbf{v_{\rm eff}}\) & \(\mathbf{m_{\rm r}}\) & \(\mathbf{m_{\rm i}}\) \\ \hline Fine & 0.155 & 0.284 & 1.39, 1.40, 1.40, 1.42, 1.41 & 0.0079, 0.0075, 0.0066, 0.0066, 0.0067 \\ Coarse & 2.213 & 0.482 & 1.53, 1.54, 1.55, 1.54, 1.50 & 0.0049, 0.0041, 0.0023, 0.0019, 0.0009 \\ \hline \end{tabular}
\end{table}
Table 2: The aerosol model parameters adopted for simulation and inversion in this paper.  
Figure 1: The relationship between aerosol spectral FMF\({}_{\rm o}\) and FMF\({}_{\rm v}\).  
the simulation of atmospheric remote sensing observations and for the inversion of corresponding properties from these observations [39]. The outputs of UNL-VRTM include not only the four elements of the Stokes vector but also their sensitivities (Jacobians) with respect to aerosol and surface model parameters. The UNL-VRTM is widely used to develop the inversion algorithm for retrieval of aerosol and surface properties from ground-based, airborne, and satellite measurements [31; 40; 41; 42; 43].
# A Novel Radio Network Information Service (RNIS) to MEC Framework in B5G Networks
## 4 Performance Evaluation and Results
### Results
There is a bottleneck related to the number of simultaneous requests. The relationship with the increase in the number of users is linked to the capacity of the RNIS to serve them all. With fewer requests handled, there are fewer requests to be processed and, consequently, less processing time required. It is important to bear in mind that, in both scenarios, the time it takes to process and deliver information can take a few milliseconds, and that the number of unfulfilled requests can increase as more users use the system. However, it is possible to improve processing times when there are fewer requests to deal with. In the scenario with eight MEC applications (Figures 9-12), the longest processing times stand out, reaching up to 4 s even in the scenario with 100 connected users and all requests answered. The behavior of unanswered requests starts after 400 users, resulting in a reduction in processing time. However, the times remain considerably high, with averages ranging from 48 to 200 ms. This pattern continues for larger numbers of users connected to the network, indicating that, with eight MEC applications, current resources are not providing acceptable times. The results indicate consistent performance in the scenarios analyzed, providing valuable insights into the performance of RNIS when processing different types of information under varying conditions. The proposal is capable of efficiently handling and processing up to 200 users, sending information every 1 s, with four consumer MEC apps. It meets all demands and provides information in times of less than 19,133 ms with the basic configuration of the i5 machine. With 200 users and two MEC apps, processing and delivery times are even better, with times of less than 13,058 ms. Taking into account that the MEC app is in the same implementation environment as the RNIS, it takes less time because it is in this condition. However, for higher loads, additional considerations are needed when sizing the system to optimize RNIS performance under more demanding conditions. When evaluating the performance of RNIS at different load levels, such as 100 and 200 users, the system showed acceptable processing times and total times up to the MEC app client, with all requests fulfilled. However, at higher loads, especially above 400 users, there is an increase in unfulfilled requests and processing times, suggesting limitations in sizing the system to handle high loads. Considering the context of consumer MEC applications and the configurations of the Intel Core i5-10300H (CPU @ 2.50 GHz octa-core and 8 GB of RAM, Intel, Santa Clara, CA, USA), it is feasible to serve up to four simultaneous MEC applications, as each MEC app can handle several types of UE. However, in order to meet the growing demand for MEC apps, it will be necessary to improve the machine's specifications to ensure optimal performance.
## 3 Results and Discussion
### Pure Water Permeability  
Figure 6 shows the membrane module of a dead-end microfiltration system installed in the laboratory used for the pure water permeability test.  
Figure 5: Pore size distribution of extruded support layer and alumina microfiltration (MF) membrane.  
Figure 6: (**a,b**) Images of multichannel cylindrical-tube-type microfiltration filter. (**c**) Image of microfiltration filter mounting module for water permeability test.  
Using this system, the pure water permeability was measured. As shown in Figure 7, the pure water permeability of the as-prepared support, single-layered MF membrane, and double-layered MF membrane were 614, 388, and 311 L m\({}^{-2}\) h\({}^{-1}\) bar\({}^{-1}\), respectively. The main reasons for a slightly lower permeance of the double-layered MF membrane compared with the single-layered MF membrane were the increase in the thickness and decrease in the pore size. The pure water permeability curve of the double-layered MF membrane was more stable over time. This confirmed the presence of a crack-free membrane. If the top layer was defective, the permeability would not stabilise over time [32].  
After the pure water permeability test, the cross-sectional areas of the multichannel cylindrical tubes were observed, as shown in Figure 8a,b. The deposition of contaminants was detected on the outer channels of the multichannel support layer and microfiltration membrane, while the inner channels were unaffected. This revealed that only the outer channels contributed to the water permeation flux, while the inner channels were not active. These unaffected channels were called dead zones [33].  
This behaviour may be explained by the multichannel configuration of the support. The flow distance of the channels located far from the circumferential boundary, that is, the permeate side [9], was significantly larger than that of the channels near the boundary. Therefore, the pressure accumulated in the centre, and the feed tended to flow only from the outermost channels. Thus, the flow resistance of the inner channels became greater than that of the outer channels, which led to the generation of a dead zone in the inner channels. Figure 8 also shows that the microfiltration membrane layer had a smaller dead zone area than the as-prepared support. This was due to the small pore size of the microfiltration membrane, which allowed a uniform distribution of feed pressure across the channel rings. As a result, the area of the dead zone decreased, increasing the efficiency of the microfiltration membrane. Thus, a decrease in pore size decreased the formation of dead zones owing to the uniform distribution of feed pressure. This illustrated the high permeation stability of the double-layered microfiltration membrane compared with that of the support and single-layered microfiltration membrane.  
Figure 7: Pure water permeability of extruded support layer and alumina microfiltration (MF) membranes.
# Improved PBFT Algorithm Based on Comprehensive Evaluation Model
## 4 Evaluation
### Fault Tolerance Analysis
#### 4.2.1 The Maximum Number of Byzantine Fault Tolerances Analysis  
The TB-PBFT algorithm improves the network topology of the original PBFT algorithm to the grouped blockchain architecture. Similarly, for convenience of description, it is assumed that \(n\) nodes are divided into \(n/m\) groups on average, and each group has \(m\) nodes. To calculate the maximum number of Byzantine fault tolerances that the system can tolerate, we assume that the Byzantine nodes are divided into optimal locations. We know that the maximum number of Byzantine groups that the system can tolerate is \(B_{1}=(n/m-1)/3\) (where the Byzantine group means that the primary node is a Byzantine node or the number of Byzantine nodes is more than \((m-1)/3\) in the group), and when all nodes are Byzantine nodes in the Byzantine group, the maximum number of Byzantine nodes is \(m(n/m-1)/3\). However, in other groups, the primary node must be honest, and the maximum number of Byzantine nodes that can be tolerated is \((m-1)/3\) in each group, and the total number of Byzantine nodes that can be tolerated is \(B_{2}=(n/m-(n/m-1)/3)(m-1)/3\). Therefore, the maximum number of Byzantine nodes that the TB-PBFT algorithm can tolerate is:  
\[B=B_{1}+B_{2}=\frac{5 mn-2n-2m^{2}-m}{9m} \tag{20}\]  
Figure 6: Ratio of the communication complexity of the TB-PBFT to the PBFT.  
We take \(m=4\), 10, and 16 to conduct simulation experiments when the total number of network nodes is less than 500 and compare the maximum number of Byzantine nodes of the TB-PBFT algorithm to the PBFT algorithm for different network nodes. The experimental results are shown in Figure 7. The figure shows that the fault tolerance of the TB-PBFT algorithm is better than that of the original PBFT.  
In addition, the TB-PBFT algorithm proposed a comprehensive evaluation model to select the excellent primary nodes. The primary node is selected by combining the scores of all the nodes in the group. The probability that the primary node is the Byzantine node is very small, so the comprehensive evaluation model can effectively improve the fault tolerance and create conditions for the system to reach the maximum number of Byzantine nodes under certain circumstances.