esa-sceva/satcom-chunk-collection huggingface dataset:
# The Performance and Fouling Control of Submerged Hollow Fiber (HF) Systems: A Review
## 3 Fouling and Concentration Polarization in Submerged HF Systems
### Fouling in Submerged Cross-Flow Filtration  
For filtration with cross-flow, particle back-transport can be caused by the mechanisms of Brownian motion, shear-induced diffusion and/or inertial lift depending on the foulant size and the tangential shear rate [95]. For a submerged HF module, the major hydrodynamic technique to mitigate particle deposition on the fibers is bubbling, which induces unsteady-state shear at the membrane surface through turbulent eddies, fiber oscillations, particle scouring, and the recirculation of the bioreactor liquid [14; 15; 22; 96; 97]. The critical flux phenomenon (the flux below which negligible fouling occurs) also is observed in bubbled submerged HFs [98; 99; 100]. Bubbling intensity can increase the critical flux [100; 101; 102; 103] and eventually reach a plateau beyond which it has little effect. [PERSON] [104] gave typical values of the bubbling intensity (specific aeration demand) in submerged MBRs, defined either as the air flowrate per unit membrane area (m\({}^{3}\)/h m\({}^{2}\)) or airflow per unit permeate (m\({}^{3}\)/m\({}^{3}\)). For complex feeds, such as those encountered in an MBR, the imposed flux is usually'subcritical' for the biofoc, but could be above critical for the supernatant colloids and macrosolutes. Under these conditions the submerged HF MBR typically initially shows a slow TMP rise that eventually becomes more rapid or displays a TMP 'jump' [105; 106]. The TMP jump is not specific to submerged MBRs and can have a number of possible causes [106]. Importantly, earlier TMP jumps occur with higher fluxes and/or inadequate bubbling in the submerged HF MBR. More details on the role of bubbles and the attendant hydrodynamics are in Section 5.2.
## 4 Experimental Evaluation  
In order to evaluate the proposed models, we carried out an experimental evaluation in three different scenarios. First scenario (_Scenario 1_) evaluates the reliability of a single region using different routing protocols (FLOODING, DIRECT and LEACH). Second scenario (_Scenario 2_) assesses the reliability of three different regions (1, 3 and 5 nodes) using the same protocol (LEACH). Finally, the last scenario (_Scenario 03_), evaluates the reliability of three different regions that use the same routing protocol (DIRECT), but whose distances to the sink node is variable. Using these scenarios, it is possible to observe that the reliability of a particular region is affected by the routing protocol (_Scenario 1_), by the number of nodes belonging to the region (_Scenario 2_) and by the distance from these regions to the sink node (_Scenario 3_).  
These scenarios were evaluated using a WSN including 20 sensor nodes (with energy equal to 1 mJ) and one sink node. The distance between the sink node and other nodes ranges from 10 to 50 m (_Scenario 1_ and _Scenario 2_) and from 10 to 100 m (_Scenario 3_). This setup was also used by [PERSON] _et al._[24], who evaluated the power consumption of different protocols. However, it is not interesting to assess the effect of distance on the WSN reliability because the sensor nodes were very close. For that reason, it was necessary to change the distance between the sensor nodes and the sink node in the _Scenario 3_.  
As shown Figure 18, four regions were created in different positions of the WSN: Regions 1, 2 and 3 were used in _Scenario 3_; and Region 4 was used in _Scenarios 1_ and _Scenario 2_. The reliability of the Basic Block elements (Link, App, OS, Middleware, Hardware and Radio) was set to \"1\", whilst the reliability of the battery was set after evaluating the power consumption models as presented in the next subsection.  
Figure 16: Step-by-step to evaluate the reliability of WSNs.
## 5 Numerical Results and Analysis
### The BER Performance versus \(E_{b}/N_{0}\) Comparison  
The BER _versus_\(E_{b}/N_{0}\) curves in the AWGN channel are depicted in Figure 6, when the number of users in this system is 10 and the energy per bit of all users is ensured to be equal by perfect power control, that is, \(E_{b1}=E_{b2}=...=E_{b10}\).  
It can be seen from Figure 6 that the BER performances of these MUD algorithms can be divided into two types: one is the algorithms that the decision-level fusion is not considered, involving CD, SIC, DEC, MMSE, and OMD; the other is the algorithms that the decision-level fusion is considered on the basis of SIC, DEC, and MMSE as Figure 1 shows, involving O-DFC, MV-DFC, CA-DFC, and VL-DFC.  
Figure 4: The computational complexity of VL-DFC and OMD.  
Figure 5: The average \(L\)_versus_\(E_{b}/N_{0}\) curves when \(K=5\), 10, 15, and 20 in AWGN channel.  
In the first type, the BER performance of CD is the worst while that of OMD is the best, which has been demonstrated in the literature. However, due to the assumption of statistical independence between SIC, DEC, and MMSE is not satisfied, the BER performances of O-DFC and MV-DFC are even not better than that of MMSE. On the other hand, CA-DFC and VL-DFC both have much better BER performances than MMSE, O-DFC, and MV-DFC. Besides, compared with CA-DFC, VL-DFC is still a little better, which we have anticipated in Section 4.3.  
Note that there is still a performance gap between VL-DFC (also CA-DFC) and OMD. To our knowledge, the reason is that in both our proposed methods, the situation when the preliminary detection results of these sub-optimal detectors (here, SIC, DEC, and MMSE) are all wrong is not considered. Since the assumption of statistical independence is not established, the estimation of this probability is out of this paper.
## 4 Discussion  
Non-alcoholic fatty liver disease (NAFLD) is a common cause of liver disorder worldwide. Many studies investigating the natural history of NAFLD have verified its progression from chronic non-alcoholic steatohepatitis (NASH) to end-stage cirrhosis and hepatocellular carcinoma (HCC) [24]. Because a multitude of complications impede their accurate identification and treatment, their prevalence has been evaluated with a variety of diagnostic methods. Quantitative assessment through digital histological imaging has been established as the gold standard in clinical trials, with liver biopsies being the mean for the detection and staging of NASH and NAFLD. However, it is an invasive patient procedure and for this reason, it can be applied in cases that do not allow subjective evaluations.  
The current study is an extension of an earlier project [25], the results of which were presented at 42 nd International Conference on Telecommunications and Signal Processing (TSP) held in Budapest in July 2019. It focuses on resolving the aforementioned diagnostic barrier by fully automating the supervised classification process using deep learning systems. In particular, a CNN architecture is defined for fast training and accurate classifications on four liver tissue structures from biopsy images. Objects of interest relate to two liver disease structures including, (a) ballooned hepatocytes and (b) fat droplets, as well as two non-disease related objects including, (c) sinusoids and (d) veins. Then, the performance of this new deep topology is compared with that produced by well-known pre-trained CNN models, as well as with a conventional MLP-ANN.  
The forthcoming subsections aim to comment on techniques previously applied to a 4-class recognition problem, eventually producing a 95% classification accuracy. The following steps include an overview of research efforts on histopathological liver specimens. The main goal is the obtained results to be qualitative compared with those coming from different diagnostic applications and different liver tissue examinations in recent years. Then, a brief description of the possibilities of extending the present methodology is given, continuing on the motif of fully automated object recognition and how they can offer effective solutions to medical diagnostic centers.
## 3 Vehicular Communications: Core Concepts and Challenges
### SCMS: Pseudonym Certificates  
Vehicular Public Key Infrastructure (V-PKI) networks are deployed globally for secure vehicle communication, with initiatives such as ETSI, C2C-CC, SCMS, and SCME leading the way [30; 31; 32]. Among these, SCMS stands out as a standardized solution for securing V2V communication. The system ensures trust among vehicles by exchanging anonymized data and utilizing Pseudonym Certificates PCs with short durations. Within the SCMS framework, the Pseudonym Certificate Authority (PCA) collaborates with the Misbehavior Authority (MA), Linkage Authorities (LA1 and LA2), and Registration Authority (RA) to identify linkage values for adding vehicle information in the Certificate Revocation List CRL in case misbehavior is detected [23].  
In the implementation of SCMS in disconnected vehicular networks, two primary challenges arise as depicted in Figure 3. Firstly, maintaining and synchronizing the CRL is crucial for identifying the misbehaving vehicles. The CRL must be constantly updated and shared with all vehicles, a process that requires regular access to network infrastructure, typically via RSUs. This becomes problematic in areas with limited connectivity as the CRL grows with the number of misbehaving vehicles, necessitating frequent online updates. Secondly, SCMS demands the use of multiple PCs for each vehicle to ensure message integrity and privacy. These PCs require regular updates--as often as every five minutes--to prevent message linkability, posing a significant challenge in disconnected areas. Vehicles must either preload a long-term supply of PCs or obtain them on demand, which requires substantial storage capacity or consistent online access, respectively [31]. This dual challenge of managing the CRL and PCs effectively underscores the complexity of deploying SCMS in environments with limited network connectivity.
## 4 Results
### Validation
#### 4.1.2 Channel Flow  
The flow through a channel can be solved analytically and is often used to validate the correctness of CFD solvers. In the channel flow case, the fluid flow is analysed for a  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Re** & **Mesh Resolution** & **Number of Time Steps** & **Computation Time** \\ \hline
100 & 128 \(\times\) 128 & 12,000 & 29 s \\
1000 & 256 \(\times\) 256 & 100,000 & 56 s \\
3200 & 1024 \(\times\) 1024 & 1,000,000 & 4 min \\
5000 & 2048 \(\times\) 2048 & 3,000,000 & 44 min \\
10,000 & 4096 \(\times\) 4096 & 10,000,000 & 12.5 h \\ \hline \hline \end{tabular}
\end{table}
Table 1: Validated cavity simulations for different Reynolds numbers Re. Time of computations is given for GTX Titan Xp GPU and includes the compilation of kernels (about 20 s) and a few dozens of saves of the simulation state to the disk.  
Figure 2: Lid-driven cavity results for Re = 5000 and D2Q9 lattice with \(2048\times 2048\) resolution. The picture on the **left** was generated from simulation results with ParaView [29] and shows the magnitude of velocity and streamlines (denoted with white lines) after \(3\times 10^{6}\) time steps. The plot on the **right** contains a comparison of velocity profiles (lines) with the reference solution (symbols) adapted from [28]. The complete simulation took about 44 min on a GPU.  
long channel with a radius \(R\) (for the 2D case the radius equals half of the channel height), and the initial conditions force the flow along the channel. The stabilised flow should form a parabolic velocity profile  
\[v_{x}=v_{max}\cdot\left(1-\left(\frac{r}{R}\right)^{2}\right) \tag{7}\]  
with maximum velocity \(v_{max}\) at the centre of the channel (\(r\) denotes the distance from the centre).  
We tested two versions of channel flow differing with inlet boundary conditions. Simulation parameters were set to get similar \(v_{max}\) for both cases. The first case, denoted as chan_v, used the constant velocity \(v_{x}=0.1\) boundary condition at inlet. For fluid nodes, initial density was set to \(\rho(t_{0})=1.0\). In the second case, chan_p, the inlet boundary condition was set to constant pressure with \(\rho=1.016\), and the initial density for fluid nodes was set to \(\rho(t_{0})=1.008\). Both versions used channels containing \(4096\times 512\) nodes with bounce-back boundaries on the top and bottom walls. Outlet condition was set to constant pressure with \(\rho=1.0\), fluid viscosity was \(\
u=0.25\), initial velocity for fluid nodes was set to \(\mathbf{v}(t_{0})=[0,0]\). We calculated \(10^{6}\) time steps which on the GPU took about 10 min per case, including saves of the simulation state every \(10^{4}\) time steps. The achieved computational kernel performance was 5.38 GLUPS, 387 GB/s for the single-precision version.  
As can be seen in Figure 3, the obtained velocity profiles were close to parabolic. The maximum values of velocities were slightly different (\(v_{max}=0.158\) for chan_v and 0.160 for chan_p). Since we were using the quasi-compressible fluid model, then we were not able to achieve a complete agreement with theoretical models.
## 2 Materials and Methods
### Quarry's Study Case with Elevation Profile from DEM Generation
#### 2.3.1 Pre-Processing  
The first point to do is to capture all the useful data: Sentinel data imagine and perimeter regional extractive sites in the shapefile format. On 3 April 2014 was launched Sentinel-1A and on 25 April 2016 Sentinel-1B, the first Copernicus Sentinel series. They represent two identical radar imaging satellites in the same orbit and provide a reserve of images of the Earth's surface in any weather, day, or night. This Sentinel-1 mission develops in C-band synthetic aperture radar imaging with different resolutions and coverage. Thismission establishes a new period of available spaceborne radar imagery. This characteristic makes the Sentinel-1 image suitable for our study case for the monitoring and mapping of the land surface. The Sentinel-1A images can be downloaded for free from several platforms such as Copernicus Open Access Center, Copernicus Dedicated Access Center, The NASA Vertex platform or Alaska Satellite Facility (ASF), Copernicus Access Center mirrors by the National Center of Space Studies (CNES), EO platform [35]. A Digital Elevation Model (DEM) based on data from Sentinel-1 data was used to create a longitudinal profile of the limestone quarry and to analyse the elevation divergence. Based on the data made available by the region land registry, the correlation of the DEM with the orthophoto map highlights illegal environmental development. For the analysis, with C-band synthetic aperture radar instrument, was chosen the Interferometric Wide swath (IW) as the acquisition mode in Level 1 product for Sentinel-1. The Interferometric Wide swath using terrain observation with the TOPSAR technique. This technique provides homogeneous image quality throughout the 250 km of the swath. The level 1 products are characterised by 29-46 incidence angle, 5 \(\times\) 20 m resolution, 250 km swath width, HH + HV \(-\) VH + VV \(-\) HH or VV (horizontal and vertical) polarisation. In single-look complex products, in IW mode marked by 2.7 \(\times\) 22 m to 3.5 \(\times\) 22 m Resolution, 2.3 \(\times\) 14.1 mm Pixel spacing, 1 \(\times\) 1 number of looks and 1 equivalent number of independent looks (ENL).  
The extraction sites were received from data provided by the regional _Puglia_ authorities in 2022 and updated by us.
# Design for Additive Manufacturing and for Machining in the Automotive Field
## 2 Materials and Methods
### Topology Optimization and Finite Element Analysis  
Altair HyperMeshTM (Altair, Troy, MI, USA) was used for the TO and for the finite element analysis (FEA) of the bracket.  
As a first step, the DS was defined based on the computer-aided design (CAD) model of the engine and of the surrounding components in such a way as to avoid any contact between the bracket and the close-by parts and mechanisms. Then the DS was discretized with a mesh composed of 638,000 tetrahedral elements, corresponding to 120,000 nodes.  
The TO of the bracket accounted for two superimposed static load cases, each of which combined flexural and torsional components, as shown in Figure 1a.  
The material properties considered for the TO were measured via tensile tests on parts produced with ALSI10 Mg in an SLM250 machine and heat-treated at 190 \({}^{\circ}\)C for 2 h: elastic modulus of 62 GPa, yield strength of 235 MPa, tensile strength of 290 MPa and elongation at break of 8%, hardness 95 HB and residual porosity of 0.6%.  
The basic objective of the TO was set as stiffness enhancement. All the surfaces close to bolts and joints were excluded from the TO in order to respect the prior requirement of keeping them perfectly planar and smooth. The final overall computational time was 1 h 25 min.  
The TO output from Altair HyperMeshTM was an STL file that was redesigned to a completely parametric CAD model with PTC CREO (Parametric Technologies Corp., Needham, MA, USA). This model was validated for potential structural problems by means of finite element analysis (FEA) in Altair HyperMeshTM. The outcomes of this verification were used to adjust the TO, and an iterative process was followed until all the topological and structural constraints were met. The part was then critically evaluated under a Design for Finishing perspective, and modifications were performed in order to ease its clamping in the final machining step.
# Improvement of Global Ionospheric TEC Derivation with Multi-Source Data in Modip Latitude
## 2 Data and Analysis Method
### Analysis Method
#### 2.2.4 Error Analysis  
Several important evaluation metrics, such as mean absolute error (MAE), mean error (ME), and root-mean-square error (RMSE), can be used for quantifying the performance. Here we choose RMSE, which is used to represent the accuracy of the model value relative to the measured value, to specify the precision [29]. The expression of RMSE is defined as  
\[\text{RMSE}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(y_{f}-y_{r})^{2}}, \tag{10}\]  
where \(N\) is the number of derived values, \(y_{f}\) and \(y_{r}\) are the fitting and reference values, respectively, and \((y_{f}-y_{r})\) represents the fitting residual.  
Figure 1: Comparison of modip (red) and geomagnetic (blue) latitude in September 2014.
## 2 Materials and Methods
### The Self-Attention Mechanism
As shown, the channel sub-module employs both max-pooling and average-pooling outputs via a common network, while the spatial sub-module employs two outputs that are pooled along the channel axis and routed to a convolution layer. In CBAM, the channel attention [25] weights obtained from the channel attention module are multiplied by the original feature map. This adjustment highlights the important channels and suppresses the less significant ones. Subsequently, the spatial attention weights derived from the spatial attention module are applied to the feature map refined by the channel attention mechanism, resulting in the final output.
# Turbulence: A Significant Role in Clear-Air Echoes of CINRAD/SA at Night
## 2 Concepts and Theory  
### Dual-Polarization Radar Products  
A dual-polarization weather radar is an advanced sensor with a high observation accuracy and many products. It can provide multiple products at the same time. The reflectivity factor (denoted by \(Z\) in dBZ) is the most conventional product in radar meteorology, which is a more meteorologically meaningful way of expressing the radar reflectivity \(\eta\). The radar's property is independent of the radar's wavelength in the case of small scatterers such as precipitation. The reflectivity factor \(Z\) is expressed as  
\[Z=\frac{\eta\lambda^{4}}{\pi^{5}K^{2}} \tag{1}\]  
where \(\lambda\) is the wavelength; \(K^{2}\) is a dielectric constant and is often taken to be 0.9 for water and 0.2 for ice, respectively.  
Another conventional product of the weather radar is the Doppler velocity (or called the radial velocity) which is the velocity vector of a scattering object along the radial direction as observed by the Doppler radar. In general, the Doppler velocity is positive for object motion away from the radar.  
Prior to the dual-polarization upgrade, the weather radar transmitted and received in a purely horizontal polarization and could not measure any polarization-dependent attributes of a target. When the upgrade was finished, the radar could transmit and receiving two orthogonal signals. The ratio of the received wave powers between horizontal and vertical polarization is called the differential reflectivity (\(Z_{DR}\) in units of dB), which helps to infer the shape of the scatterer. \(Z_{DR}\) can be defined as the difference between the measured radar reflectivity factor in the horizontal polarization (\(Z_{H}\) in dBZ) and the vertical polarization (\(Z_{V}\) in dBZ):  
\[Z_{DR}=Z_{H}-Z_{V} \tag{2}\]  
The correlation coefficient is another useful polarimetric product which depends on the similarity of the received signals at the horizontal and vertical polarizations across multiple pulses. It is a measure of the variety of hydrometeor shapes in a pulse volume.  
There are also some other radar products that have not been introduced, and their definitions can be found in the Glossary of Meteorology, American Meteorological Society ([[http://glossary.ametsoc.org/wiki/climatology](http://glossary.ametsoc.org/wiki/climatology)]([http://glossary.ametsoc.org/wiki/climatology](http://glossary.ametsoc.org/wiki/climatology)), accessed on 27 January 2023).
## 2 State of the Art and Theoretical Framework
### Description of the Procedure for Filtration Testing and Sample Change  
For the utilization of the filtration device, the compressed gas supply (a) and liquid inlet (b) are connected to the control panel (c), ensuring that both connections are properly sealed to prevent leaks. Once the system is connected, the compressed gas supply valve (d) is opened, ensuring setting the initial pressure to a higher level than required for the test. Using the regulator valve (e), the pressure is then adjusted to the specified test level, being monitored by the value on the pressure gauge (f) until the desired pressure is reached.  
Subsequently, the liquid inlet hose is inserted into the container holding the sample (h). The filter holder (i), which contains the filtering medium, is connected to the control panel, ensuring proper installation. With the system ready, the valve (k) is opened to start the test, and the time is recorded from the first drop of filtrate, a key parameter for evaluating filtration efficiency under the established conditions.  
If a sample change is required, the residual volume of wine in the system, which is less than 80 mL, must be removed. This procedure is performed by opening the ball valve  
Figure 3: Diagram of components of the proposed system based on the use of a pneumatic pumping system with constant pressure.  
(k), which automatically activates the pump, allowing air to enter by suction and emptying the system in approximately 2 s.  
To clean the system, water at room temperature is passed through for 5 s, followed by repeating the air suction process (o). This cycle ensures that the system is free of residues, leaving it ready to process the next sample without risk of contamination.