esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Stability of EUD3A, EUD3A_DIC4 and EUD3A_DIEP3 in Simulated Nasal Fluid  
NP interactions with the cellular compartment is a complex phenomenon governed by different critical parameters such as particles properties (i.e., shape, size, charge, etc.) and phenomena such as particle interaction with cellular microenvironment [45]. In order to investigate the NPs behavior after IN administration is of outmost importance to evaluate the particle stability in the nasal environment. Accordingly, stability study was performed after incubation of EUD3A, EUD3A_DIC4, and EUD3A_DIEP3 in SNF containing mucin (0.1%, \(w/v\)) at 37 \({}^{\circ}\)C after 1 and 24 h investigating the effect of protein and ions on the stability of NPs intended for nasal delivery.  
Experimental results (Figure 3) revealed a variation in particle size after incubation with a mucin dispersion in SNF compared with the particle suspensions alone (EUD3A, 290.9 \(\pm\) 0.22 nm; EUD3A_DIC4, 244.2 \(\pm\) 0.13; EUD3A_DIEP3, 1434.3 \(\pm\) 0.34). This trend was particularly evident for EUD3A and EUD3A_DIC4 after 1 and 24 h of incubation, while EUD3A_DIEP3 remained almost unchanged. Similarly, PdI values were influenced by mucin presence, since all formulations appeared heterogeneous with the following trend: EUD3A > EUD3A_DIC4 > EUD3A_DIEP3 compared with the native systems after both time interval considered.  
These findings corroborate the mucoadhesive evaluation confirming that the interaction of EUD_NPs with mucin is stronger for EUD3A and EUD3A_DIC4, compared to EUD3A_DIEP3.  
Moreover, the increased particle size up to 1-2 \(\upmu\)m for all systems once in contact with the mucin dispersion could assure their absorption on the nasal mucosa avoiding possible particle transport through nerve pathways located in the mucosal epithelia [31].  
The application of particles with \"bioadhesive\" properties would permit to enhance their residence time once delivered in the nasal mucosa, control the rate of clearance from  
Figure 3: EUD3A, EUD3A_DIC4, and EUD3A_DIEP3 mean size (Z-Ave, bars) and PdI (symbols) after 1 and 24 h of incubation with mucin at 37 \({}^{\circ}\)C (EUD3A-MUC, EUD3A_DIC4-MUC, and EUD3A_DIEP3-MUC).  
the nasal cavity, thereby giving drugs a longer time to be available at the absorptive surface and facilitate drug fit with mucous pores.
## 2 Related Works
Authors in References [35; 36; 37] focused on achieving secure UAV-ground (U2G) communications in the presence of terrestrial eavesdroppers/jammers, they did not consider UAV-UAV (U2U) communications in the air. Reference [12] discussed how an active eavesdropper can attack the training phase in wireless communication to improve its eavesdropping performance, however, Reference [12] did not consider the mobility of UAVs in their communications, and Reference [12] just considered the case of eavesdropping and jamming. In general, there is a lack of researches on power consumption controlling, legitimately eavesdropping and selection policy towards suspicious UAV communications.
## 4 On-Site Monitoring of Disaster Prevention and Response Measures for Water-Rich Loess Tunnels
### Result Analysis
#### 4.3.3 Analysis of Contact Stress Between Surrounding Rock and Initial Support of the Tunnel
The stacked bar chart showing the percentage of contact pressure at each measurement point relative to the total pressure is presented in Figure 14.  
Figure 14 indicates that, influenced by the excavation space and time effect, before the closure of the inverted arch (from the excavation of the upper bench to the closure), the contact pressure on the surrounding rock at the measurement points of the arch crown, arch waist, and sidewall accounts for 61-81% of the total contact pressure; the contact pressure at the measurement points of the arch foot and inverted arch accounts for 34-47% of the total. From the closure of the inverted arch to before the construction of the secondary lining, the  
Figure 12: Time history curve of contact pressure changes between the surrounding rock and the initial support in the monitoring section. Figure 13: Schematic diagram of contact pressure distribution between surrounding rock and initial support in the monitoring section. contact pressure at the measurement points of the arch crown, arch waist, and sidewall accounts for 11-28% of the total; the contact pressure at the measurement points of the arch foot and inverted arch accounts for 37-73% of the total. After the construction of the secondary lining, the contact pressure at the measurement points of the arch crown, arch waist, and sidewall accounts for 7-10% of the total; the contact pressure at the measurement points of the arch foot and inverted arch accounts for 11-29% of the total. After the construction of the secondary lining, except for the inverted arch, the contact pressure at other measurement points accounts for 7-19% of the total contact pressure, while the contact pressure at the inverted arch measurement points accounts for 29% of the total. The monitoring results are generally consistent with the findings in reference [47], which obtained a load-sharing ratio for the secondary lining between 8 and 13% using a reasonable ratio calculation method for vertical and horizontal rock pressures. Compared with other measurement points, the contact pressure at the inverted arch accounts for a relatively larger proportion after the construction of the secondary lining. This is because after the closure of the inverted arch, with the improvement of the bearing capacity of the base reinforced by the rotary jet pile, the resistance to deformation of the primary support is enhanced, and the contact and extrusion between them are evident. After the secondary lining is closed to form a ring, the contact pressure of the surrounding rock is readjusted. Coupled with the self-weight load of the secondary lining acting on the inverted arch, the contact pressure at this stage accounts for a relatively large proportion, which also verifies that the design of the secondary lining for loess tunnels needs to bear part of the load.
# Generation of Synthetic Rat Brain MRI Scans with a 3D Enhanced Alpha Generative Adversarial Network
## 2 Materials and Methods  
### Sigma Dataset of Rat Brains  
An MRI dataset of the Wistar rat brain from the Sigma project was used to test the ability of GANs to produce synthetic scans. A total of 210 scanning sessions were performed using a Bruker (Bruker Corporation, Etlingen, Germany) preclinical ultra-high field scanner 11.7 Tesla and a 4 \(\times\) 4 surface coil dedicated to the rat head. A T2-weighted Echo Planar Imaging sequence was implemented to acquire resting-state data, with a spatial resolution of 0.375 mm \(\times\) 0.375 mm \(\times\) 0.5 mm over a matrix of 64 \(\times\) 64 \(\times\) 40, a TR of 2000 ms, a TE of 17.5 ms, and nine averages. Figure 2 shows slices from three different planes--coronal, sagittal and axial--of a rat brain. The dataset consists of 210 scans with a resolution of 64 \(\times\) 64 \(\times\) 40, which have been preprocessed to avoid complex values. For more information on the rat brain sigma dataset, see [PERSON] et al. (2019) and [PERSON] et al. (2018) [3; 5].  
In this work, a different number of scans and sources--real or synthetic--were used in some steps of the experiment. Therefore, each dataset is formally defined as  
Figure 1: Representation of the basic Generative Adversarial Networks (GANs) architecture with noise vector (z), generator (G), synthetic data (\(x^{\prime}\)), real data (\(x\)) and discriminator (D).  
Figure 2: Example of one Magnetic Resonance Imaging (MRI) scan with resolution of 64 \(\times\) 64 \(\times\) 40 from the Sigma rat brain dataset displayed in all three planes—coronal, sagittal and axial planes, respectively.  
\(\text{D}_{\text{s}}\text{N}=(\text{x}_{i},\,\text{y}_{i})_{i=1}\text{N}\), where s indicates whether the scans are synthetic (s) or real (r), x indicates the scans, y indicates the respective labels--WM, GM and CSF masks--and N indicates the number of scans. The original sigma dataset of rat brains is formally defined as \(\text{D}_{\text{r}}\text{2}\text{1}\text{0}\) = \((\text{x}_{i},\,\text{y}_{i})_{i=1}\text{2}\text{1}\text{0}\).
# An Extensible Automated Failure Localization Framework Using NetKAT, Felix, and SDN Traceroute
## 2 Background
### Trace Logging with SDN Traceroute  
Since SDN uses different concepts than traditional networks, we can have a better success in tracing paths a packet takes when using a more adapt tool than the standard traceroute for route discovery. SDN traceroute [17] is a controller application that can install non-invasive rules into each connected network switch. These filter-marked packets, and thus the controller, can analyze gathered data and recreate the path the packets took. At first, SDN traceroute maps the network and discovers connections among switches. Using this information, it assigns a color to every switch so that neighboring switches get different colors. A field in the packet header--that is large enough and otherwise unused--is used for storing the color tag. After this, OpenFlow rules, with the highest priority, are installed to every switch that match every color in the network except for the color assigned to the switch at hand. At this point, the algorithm illustrated in Figure 6 kicks in. The figure shows our previous sample network with a possible coloring. Assuming we want to trace the path from _host 1_ to _host 3_, we have to specify the switch that connects to the first host. Based on our packet specifications and the given switch, the controller creates a packet tagged with the color of this switch and sends it to the switch, in this case, _switch 1_ (see step 1). Since the switch has no high priority rules that match the tag, it uses its forwarding rules to send out the packet in accordance with the network policy (step 2). Here the packet color matches one of the rules set by SDN traceroute, and thus, the switch sends the packet to the network controller (3), which registers the switch as the next element in the packet's route. Following this, SDN traceroute replaces the color in the packet to match that of the second switch and sends it back to the switch (4). After this, the cycle starts anew, e.g., step 4 corresponds to step 1 and step 5 to step 2. By the time the packet traverses the network, the controller has registered the complete path the packet had taken and reports it to the user.  
The above operation clearly shows that although SDN traceroute can log the trace of a packet, it lacks automatic configuration options in a sense that the starting switch and packet parameters should be supplied manually. It can also be seen that the tool is unable to give any information about links connecting hosts and switches. We argue that the combination of the SDN traceroute and Felix tools can be a strong option for localizing failures in networks, especially when aided with a logic that can collect information about network paths from Felix and then use it to assemble test cases, execute the tools based on them, and evaluate results in an automatic manner. Thus, the next section gives the details of our framework that integrate the above tools and realize automatic failure detection by applying them.
# Towards Moving Objects Behavior Analysis: Region Speed Limit Rate Measure
###### Abstract  
In this paper, a measure is proposed that, based on the trajectories of moving objects, computes the _speed limit rate_ in each of the cells in which a region is segmented (the space where the objects move). The time is also segmented into intervals. In this way, the behavior of moving objects can be analyzed with regard to their speed in a cell for a given time interval. An implementation of the corresponding algorithm for this measure and several experiments were conducted with the trajectories of taxis in Porto (Portugal). The results showed that the _speed limit rate_ measure can be helpful for detecting patterns of movement, e.g., in a day (morning hours vs. night hours) or on different days of the week (weekdays vs. weekends). This measure might also serve as a rough estimate for congestion in a (sub)region. This may be useful for traffic analysis, including traffic prediction.  
trajectory; moving object; speed; speed limit rate; congestion +
Footnote †: journal: _Informatics_
# Satellite-Derived Bathymetry for Selected Shallow Maltee Coastal Zones
## 2 DataSets  
### Satellite Data  
In this study, the twin polar-orbiting satellites Sentinel-2A and Sentinel-2B were used [5]. These satellites were developed by the European Space Agency for the Copernicus programme, with each satellite having a revisit period of 10-days. Due to the availability and revisit time of the two satellites, each area is regularly covered every 5-days, with the onboard instruments having the same viewing conditions [28]. The twin satellites carry the Multi-Spectral Instrument (MSI), with a radiometric resolution of 12-bit, giving the satellites a higher capability of detecting differences between reflectance or intensity of the different structures [28]. When compared to Landsat 8, the Sentinel-2 satellites produce an  
Figure 1: **(a)** Topo-bathymetric map of the Maltese archipelago showing the main structural lineaments. In the inset, Malta’s location in the Mediterranean is highlighted with a star. The red rectangle indicates the position of the studied bays and image “**b**”. **(b)** orthomosaic of the study area generated by UAV photogrammetry.  
overall higher accuracy, better results, and a spatial resolution of 10 m [29]. The Sentinel-2 constellation can also measure more spectral bands relative to Landsat 8, producing a clearer, distinct image classification [30].  
Sentinel-2 data for the Golden Bay and Ghajn Tuffieha coastal areas was downloaded through a code implemented within Google Earth Engine (GEE). The downloaded GeoTIFF files corresponded to Level-1C (L1C) products with each radiometric measurement being corrected to Top of Atmosphere (TOA) reflectance. GEE is a cloud-based geographical information analysis system [31], which makes use of a cloud storage system containing a vast catalogue of satellite imagery and several other geospatial data produced in an analysis-ready format [32]. This made GEE optimal for this study as it allowed data to be extracted over the required domain rather than having to download an entire scene as provided by the Copernicus data hub. The Id GEE script allowed the fusion of data collected over seven consecutive days to obtain a gap-free product. Three files representing measurements from 7 July 2020 to 13 July 2020, from 17 August 2020 to 24 August 2020, and from 19 February 2021 to 25 February 2021, were downloaded. A buffer of three days from the date in which the in situ measurements were captured was applied in order to generate averaged raster sets with reduced cloud coverage, surface reflectance, and any other variable that might have hindered the satellite image quality.
## 3 Results and Discussion
### Impact of Temperature to Urease Activity  
Urease activity with various temperature conditions is shown in Figure 5 for the G1, G2, and G3 species. High urease activity was observed at a temperature above 30 \({}^{\circ}\)C (G1 species) and then gradually decreased and showed an almost bell-shaped profile because of cell denaturation at a higher temperature, except in the case of G2 and G3 species. The divergent tendency of urease activity (nearly 40-60 \({}^{\circ}\)C) was also observed by [PERSON] et al. [26]. However, there are few data about the urease  
Figure 4: Impact of pH for the urease activity of the reaction solution at 30 \({}^{\circ}\)C.  
activity of marine ureolytic bacteria in Greece. Even though the urease activity of G1, G2, and G3 species are lower than that of _Pararhodobacter_ sp., the results obtained in this study would be innovative and adventitious information for the sand solidification method through microbial carbonate precipitation because the use of native ureolytic species (isolated from marine area). In future, it may be possible to use G1, G2, and G3 species for sand solidification, which could help to manufacture artificial beachrock following the MICP technique. This result could also be adventitious in Greece for coastal erosion control purposes by increasing bacterial population either by centrifugation and/or urease enzyme extraction by ultrasonication and/or adding bacterial populations several times during the sand solidification process before urease activity completely declined. Similar results were also found for the strains such as _Deleya venusta_ and _Pararhodobacter_ sp. [26; 27; 28]. Previously, it was also found that urease activity influenced to CaCO\({}_{3}\) precipitation at temperatures from 10 to 60 \({}^{\circ}\)C, which is similar to the findings of this study. However, the effectiveness of environmental factors on urease activity of the negative species with variation temperatures was not examined. Therefore, on the basis of these experimental findings, the G1, G2, and G3 species showed high urease activity in the different range of temperatures that could reflect the importance of isolating the ureolytic bacterial species from the local environment and their subsequent application for sand solidification and soil improvement.  
These results could be very useful for the sand solidification by the MICP method in Mediterranean coastal regions considering the local environmental conditions because the isolated ureolytic bacterial species are well adapted to the local environmental conditions. These results could also be very advantageous if we want to focus on soil or ground improvement techniques by bio-stimulation or bio-augmentation method [29], and we must consider the native ureolytic bacterial species which are well adapted with the local environment and could ensure long term sustainability.
## 2 Theory
### Identification Procedures of the Two Algorithms  
The identification procedures used in SDA and TODA to estimate the rotor unbalances (\(m_{uj}\cdot e_{j}\), \(a_{i}\)) of all discs are defined in Figure 2.  
Firstly, each disc's unbalance response and the other \(n+1\) unbalance responses should be measured and changed to dimensionless unbalance responses in the frequency domain according to Equation (23). Meanwhile, the rotating speed should also be measured. The inherent parameters, which are the length of the shaft, the mass per unit length of the rotor shaft, the elastic modulus of the shaft, and the diameter of the shaft, should be known, as prior knowledge and the location of the selected measured points on the shaft should also be used as inputs.  
Secondly, the matrix \(H_{1}\) and \(H_{3}\) could be calculated according to Equations (4) and (12), respectively. Then, \(H_{2}\) and \(H_{4}\) can be calculated according to Equations (5) and Equation (13), respectively.  
Thirdly, using \(H_{2}\) and the dimensionless unbalance responses in the \(y\) direction in frequency domain obtained in the first step, each disc's unbalance can be calculated according to Equations (8) and (10). Or, using \(H_{4}\) and the dimensionless unbalance responses in the \(x\) direction in the frequency domain obtained in the first step, each disc's unbalance can be calculated according to Equations (16) and (17). This is the identification procedure used in SDA. While for TODA, \(C_{1d}\),......, \(C_{md}\) should be calculated using the dimensionless unbalance responses in the frequency domain in both the \(x\) and \(y\) directions according to  
Figure 2: Identification procedures of SDA and TODA.  
Equation (18). Then, using \(H_{2}\) and \(H_{4}\), all rotor unbalances can be calculated based on Equations (20) and (21).  
\[UD=\frac{A}{\pi L}[\cos(\alpha)+i\cdot\sin(\alpha)] \tag{23}\]  
where \(UD\) is the dimensionless unbalance response in the frequency domain; and \(A\) and \(\alpha\) are the amplitude and phase of the unbalance responses in the time domain, respectively.
# The Performance and Fouling Control of Submerged Hollow Fiber (HF) Systems: A Review
## 5 Parameters Affecting the Performance of a Submerged Hollow Fiber System
### Hydrodynamics in Submerged HF Membranes
#### 5.2.1 Role of Air Bubbles  
The use of bubbly flow has been reviewed by [PERSON] et al. [14] and more recently by [PERSON] et al. [82]. The major benefit of rising bubbles is the unsteady or transient shear stress at the membrane surface that causes particle back-transport away from this surface [14; 15; 22]. Figure 2, based on [PERSON] et al. [14], illustrates the possible interactions of bubbles with the surface of hollow fibers and shows three different effects on the submerged hollow fibers: (i) a shear stress on the surface of the hollow fiber induced by the wake generated by the rising bubbles, (ii) fluctuating liquid flows transverse to the fibers induced by the bubbles, and (iii) lateral fiber movement induced by the bubbles that depends on the looseness of the fiber. [PERSON] et al. [82] indicated that aeration intensity not only enhances the hydrodynamics, but also can affect the biomass properties in aerobic membrane bioreactors. [PERSON] et al. [202] ([PERSON], 2017 #321) showed that aeration also influenced the biofilm growth. In addition, [PERSON] et al. [203] reported that bubbles seem to alter the structure of the cake or fouling layer such that the specific resistance is reduced. They based this conclusion on the observation that gas sparging applied to the MF of particles increased the fluxes significantly after a period of flux decline at the higher feed concentrations. [PERSON] et al. [204] correlated the bubble hydrodynamics with the critical flux and found that bubble momentum and the bubble-membrane contact area had the most positive correlation with the local critical flux. However, [PERSON] et al. [201] found that the shear stress associated with the bubbles was insufficient to mitigate the deposition of fine (1.75 \(\upmu\)m) particles.