esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Drone Swarms as Networked Control Systems
### Networked Control System Model  
A drone swarm can be modelled as a NCS encompassing a set of computational systems (i.e., the drones) connected via a communication network; a NCS is a computational system that is controlled in a closed loop through a communication network. Specifically, the control and feedback messages are exchanged among the system computational units, or agents, in the form of information packet transmitted through a network. The functionality of a typical NCS is established using two basic elements: (i) a computational system able to gather data via sensors, to reach decision and to perform commands via actuators; (ii) a network, based on communication modules, standards and protocols (e.g., medium access control and routing) to enable the exchange of information.  
Drones are equipped with on-board computers responsible for data processing, each of them can include several micro-services to perform specific tasks, such as sensing, decision-making (e.g., about the flight control), actuation on the environment or on the swarm itself [27]. Acting as a NCS the drone swarm can appraise the benefits derived from executions, by aiming at modifying its functioning, to realize the close-loop typical of NCS.  
By evaluating and reacting, the effectiveness of the action of each drone is leveraged allowing them to behave as a group of multiple agents that work together aiming to produce a common goal; A swarm system exhibits emergent behaviors, where simple behaviors are shared across the many agents giving rise to a collective behavior capable of solving complex problems [28]. The deployment of drone swarms largely depends on the wireless and networking technologies for transmitting specific data and control commands among drones or between drones and the ground control, leading the deployment of drone swarms based on two strategies: non-interactive and interactive.
## 3 Results and Discussion
### Continuous LBI Measurement
#### 3.2.2 Backscattering Images  
In Figure 6a, the temporal changes in backscattering images are illustrated. At the initial timepoint (0 h), the backscattering was characterized by a large scattering area with an overexposed center. As time progressed to 1 h, the scattering area notably reduced but the central region became even brighter. Subsequently, both the central brightness and the scattering area diminished, and at time 720 h, the overexposed center was no longer present. Figure 6b shows an \"L\" shape, where the backscattering shows a sharp change followed by  
Figure 5: Plot of the (a) interface height vs. time and (b) the sedimentation velocity vs. time.  
a gradual decrease over time. Notably, purplish hues were observed between the center and the surrounding scattering area, while in the discontinuous LBI measurement, it was yellow. [PERSON] et al. indicated that this was caused by the different sensitivities of the camera color channels [37]. Additionally, the discontinuous LBI measurement showed the Tyndall effect (Figure 3) but it was not observed in Figure 6. This could be due to the lid on top of the sedimentation tank, which caused a reduction in laser light intensity when reaching the oil.  
Figure 6: (**a**) Backscattering images during sedimentation in in situ experiment. (**b**) Plot of mean grey value of the segmented image vs. time.
## 3 Results and Discussion
### Nutritional Properties of Bread and Pomace
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Product** & **Probe** & \begin{tabular}{c} **Protein** \\ **(\%d. w.)** \\ \end{tabular} & \begin{tabular}{c} **Fat** \\ **(\%d. w.)** \\ \end{tabular} & \begin{tabular}{c} **Ash** \\ **(\%d. w.)** \\ \end{tabular} & \
\begin{tabular}{c} **C** \\ **(\%d. w.)** \\ \end{tabular} &
\begin{tabular}{c} **EV** \\ **(\%d. w.)** \\ \end{tabular} \\ \hline \multirow{4}{*}{Bread} & 0 & 8.43 \(\pm\) 0.01 \({}^{\text{a}}\) & 5.74 \(\pm\) 0.06 \({}^{\text{b}}\) & 2.83 \(\pm\) 0.04 \({}^{\text{a}}\) & 65.7 \(\pm\) 0.15 \({}^{\text{a}}\) & 171 \(\pm\) 0.59 \({}^{\text{a}}\) \\  & 5 FD & 8.40 \(\pm\) 0.03 \({}^{\text{a}}\) & 7.05 \(\pm\) 0.26 \({}^{\text{a}}\) & 2.98 \(\pm\) 0.02 \({}^{\text{b}}\) & 63.7 \(\pm\) 0.14 \({}^{\text{b}}\) & 173 \(\pm\) 0.82 \({}^{\text{a}}\) \\  & 10 FD & 8.51 \(\pm\) 0.02 \({}^{\text{a}}\) & 7.18 \(\pm\) 0.02 \({}^{\text{a}}\) & 3.12 \(\pm\) 0.06 \({}^{\text{c}}\) & 63.9 \(\pm\) 0.04 \({}^{\text{c}}\) & 171 \(\pm\) 0.17 \({}^{\text{a}}\) \\  & 5 CD & 8.44 \(\pm\) 0.01 \({}^{\text{a}}\) & 6.81 \(\pm\) 0.1 \({}^{\text{a}}\) & 2.95 \(\pm\) 0.00 \({}^{\text{a}}\) & 64.8 \(\pm\) 0.83 \({}^{\text{b}}\) & 173 \(\pm\) 1.58 \({}^{\text{a}}\) \\  & 10 CD & 8.48 \(\pm\) 0.04 \({}^{\text{a}}\) & 6.92 \(\pm\) 0.06 \({}^{\text{a}}\) & 3.02 \(\pm\) 0.03 \({}^{\text{c}}\) & 63.9 \(\pm\) 0.16 \({}^{\text{b}}\) & 172 \(\pm\) 0.01 \({}^{\text{a}}\) \\ \hline \multirow{4}{*}{Pomace} & FD & 9.73 \(\pm\) 0.02 \({}^{\text{a}}\) & 5.97 \(\pm\) 0.05 \({}^{\text{a}}\) & 2.39 \(\pm\) 0.02 \({}^{\text{a}}\) & 38.6 \(\pm\) 0.11 \({}^{\text{a}}\) & 235 \(\pm\) 0.69 \({}^{\text{a}}\) \\  & CD & 9.59 \(\pm\) 0.08 \({}^{\text{a}}\) & 5.99 \(\pm\) 0.00 \({}^{\text{a}}\) & 2.32 \(\pm\) 0.02 \({}^{\text{a}}\) & 40.3 \(\pm\) 0.02 \({}^{\text{a}}\) & 231 \(\pm\) 0.15 \({}^{\text{b}}\) \\ \hline \hline \end{tabular} \({}^{\text{a}}\)–control probe; 5 FD—gluten-free bread with the addition of 5% freeze-dried raspberry pomace; 10 FD—gluten-free bread with the addition of 10% freeze-dried raspberry pomace; 10 CD—gluten-free bread with the addition of 10% force-dried raspberry pomace; 10 CD—gluten-free bread with the addition of 10% convection-dried raspberry pomace; 10 CD—gluten-free bread with the addition of 10% convection-dried raspberry pomace; 10 CD—gluten-free bread with the addition of 10% convection-dried raspberry pomace. FD—freeze-dried raspberry pomace; CD—convection-dried raspberry pomace; 10 CD—convection-dried raspberry pomace; 10 CD—convection-dried raspberry pomace; 10 CD—carbohydrate content; EV—energy value. The values of each parameter with different superscript letters in columns are significantly different (Tukey’s test, \(p\leq 0.05\)).  
\end{table} 
Table 4: Nutritional properties of bread and pomace.
# Control of _Francisella tulareis_ Virulence at Gene Level: Network of Transcription Factors
## 7 Response Regulators Involved in Two-Component Systems Work as Transcription Factors
### QseB/PmrA Response Regulator  
QseB, or its homolog PmrA, can be found in all _Francisella_ species. Inactivation of _pmrA_ gene in _F. novicida_ led to reduced survival and growth within human and murine macrophages, and the mutant strain was able to elicit a protective immune response against _F. novicida_ wild-type challenge. The first analyses showed that PmrA regulates up to 148 genes, including FPI genes [132; 133]. The importance of PmrA in biofilm formation was also described [60]. PmrA is a DNA-binding protein and following phosphorylation, it recognizes its own promoter and also the promoter of the FPI protein PdpD. The histidine kinase that is primarily responsible for phosphorylation of PmrA in _F. novicida_ is KdpD [61]. The orphan sensor kinase QseC was also demonstrated to be important for PmrA phosphorylation and Francisella virulence [61].  
The response regulator PmrA was observed to be important for survival and replication within the macrophages for both _F. tularensis_ and _F. novicida_. PmrA works as both a positive and negative regulator. As already mentioned, PmrA participates in _F. novicida_ biofilm formation due to the induction of diguanylate cyclase and phosphodiesterase expression. These enzymes are key regulatory components of biofilm formation [60; 62]. PmrA is able to bind to the _pdpD_ promoter in _F. novicida_, and interaction between PmrA and the MgIA/SspA complex was described, suggesting the involvement of PmrA in FPI gene regulation [61]. According to a recent study, PmrA could be a nucleoid-associated protein. It was found that PmrA is able to bind to 252 regions on chromosomal DNA in the _F. tularensis_ LVS strain but directly influences only a few of them, mainly as a repressor. PmrA is able to repress the expression of the _priM_ gene by direct binding to its promoter, which leads to the ability of _F. tularensis_to grow intracellularly. PriM is referred to as PmrA-repressed inhibitor of intramacrophage growth and it was described as an anti-virulence factor, the expression of which leads to downregulation of intracellular growth [63]. However, the latter was very recently revised [64]. Although, in the first study, PriM was detected as an anti-virulence determinant, because the deletion of the _priM_ gene restored an _F. tulareis_ subsp. _holarctica_ LVS ability to survive within macrophages [63], the second study reported that double deletion mutant strain of _pmrA_ (repressor of PriM) and _priM_ did not restore the virulence phenotype, suggesting that loss of anti-virulence effect of PriM is caused by an unknown mechanism [64].
## 6 The Extension Strain Criterion as a Macroscopic Rock Sample Failure Condition
### Discussion of the Results
In this case, [PERSON]'s extension strain criterion was used to evaluate the macroscopic strength of the rock sample. The state of stress and strain in the sample in the simple shear test is complex, and its experimental evaluation is practically possible only with the aid of DIC. Figure 11 shows strain maps \(\varepsilon_{\text{xx}}\), (Figure 11a) \(\varepsilon_{\text{yy}}\) (Figure 11b) and \(\gamma_{\text{xy}}\) (Figure 11c) on the front surface of a 50 mm \(\times\) 50 mm \(\times\) 50 mm cubic specimen, corresponding to a loading level of 0.7F\({}_{\text{crit}}\) (Figure 12). In the complex deformation state, the maximum values of the extension strain should be determined as principal strains (Equation (9)). \[\varepsilon_{1,2}=\frac{\varepsilon_{xx}+\varepsilon_{yy}}{2}\pm\sqrt{\left( \frac{\varepsilon_{xx}-\varepsilon_{yy}}{2}\right)^{2}+\left(\frac{\gamma_{ xy}}{2}\right)^{2}} \tag{9}\]  
Figure 12: Characteristics of vertical displacement of the piston of the testing machine in the straight shear test of sandstone at load level of 0.7F\({}_{\text{crit}}\). (red circle). Based on the values of principal strains and laboratory-determined extension strains, \(\varepsilon_{\text{TC}}\) a simple strength condition can be defined based on [PERSON]'s criterion, using Equation (10). A value of \(W_{F}>1\) indicates the failure of the rock. \[W_{F}=\frac{\varepsilon_{2}}{\varepsilon_{\text{TC}}} \tag{10}\]  
This condition enables determining the degree of rock failure in the selected section. As can be seen in Figure 11, due to the inaccurate placement of the specimen in the machine's jaws, there is a concentration of deformations, locally in the right corner of the specimen. Despite the relatively low-loading level of \(0.7\text{F}_{\text{crit}}\) locally, the values of deformation reached high levels, accompanied by local failure (Figure 13), before reaching the critical destruction force of the whole sample. It should be noted that the destruction is local and does not affect the entire specimen.
# Low-Cost Underwater Communication System: A Pilot Study
## 2 System Design  
### Initial Assumption  
Using cheap off-the-shelf elements in the design is the base assumption in this project. According to this, the entire system should be built on the basis of a popular microcontroller, possibly a simple ultrasonic signal amplifier and a readily available piezoelectric transducer. It was also initially assumed that it would be necessary to use a floating point co-processor in the calculations. Therefore, it was decided to implement the modulation and demodulation algorithms in the software on the Cortex M4 microcontroller.  
The assumed simplicity of the designed communication system limits the choice of modulation techniques because it was decided to use hardware timers embedded in the microcontroller to avoid the generation of cosine functions in the software. For this reason, binary phase shift keying (BPSK) and QPSK modulation were selected in the design. Other types of modulation, for example, those using chaotic functions [20], were also taken into consideration, but they require high computing power, and without hardware support, they are not possible to be implemented on popular microcontrollers.  
Due to the pilot nature of the work, the communication protocol was not established. It was assumed that all tests would be performed with the use of two fixed nodes: the transmitter and the receiver. This simplification allows to avoid many problems related to access conflicts in the communication channel, typical for a multinode case.
## 3 Results
### Crop Classification
#### 3.2.6 Normalized Variable Importance for Crop Classification  
Based on the unique ability of the RF algorithm, the variable importance for crop classification with all SAR images and all polarimetric observables was quantified across input features, as well as over all dates. Among the 189 variables (a total of seven images and 27 features for each image) used in the RF classification, the most important 50 variables are listed in ascending order in Figure 16. The variables from LP, Pauli, and Span exhibited the greatest importance, and the variables from polarimetric decompositions (FD, CP, ND) presented moderate importance in this study. Across time, generally the summer and autumn acquisitions (from June to September) occupied greater importance positions than the spring acquisitions (April and May).  
Figure 15: The RF overall accuracies for the optimal combination SAR image produced by a forward image selection procedure using all polarimetric observables after exchanging the training and testing samples in previous tests. Note that numbers in the figure denote the best accuracy in each step (at the bottom), and combinations of SAR images (at the top), for example “6, 9(1), 5, 8, 4” represents the combination of images dated 23 June, 3 September, 6 May, 10 August, and 12 April (see Table 1).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Acquisition Date** & **OA (\%)** & **Kappa** \\ \hline
12 April 2015 & 55.25 & 0.45 \\
6 May 2015 & 56.85 & 0.47 \\
23 June 2015 & 68.12 & 0.61 \\
17 July 2015 & 59.78 & 0.51 \\
10 August 2015 & 55.30 & 0.45 \\
3 September 2015 & 63.74 & 0.56 \\
27 September 2015 & 67.49 & 0.60 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Accuracy assessment of RF classifications with all polarimetric observables using single SAR image on each date after exchanging the training and testing samples in previous tests.
## 3 Numerical Model Definition
### Cross Section and Boundary Conditions Modelling  
A numerical model was established to simulate the four-point bending behaviour of sandwich beams with TRC faces, in order to compare it with experimental results of the same sandwich beams. In this way, more confidence in the numerical model was gained.  
Both the faces and the core were defined as solid homogeneous sections in the numerical model and, were discretised by eight-node linear brick elements (C3D8R). The elements size was 35.7 mm \(\times\) 3.6 mm \(\times\) 200 mm (w \(\times\) h \(\times\) d) for the faces and 35.7 mm \(\times\) 13.3 mm \(\times\) 200 mm (w \(\times\) h \(\times\) d) for the core. Six elements were stacked over the thickness of the faces, and twelve over the thickness of the core of the sandwich beam. The mesh size was the result of a convergence study on the force-displacement behaviour of the sandwich beam, as well as on the stress and strain output. Multiple elements were necessary throughout the thickness of the faces to evaluate the stress/strain distribution over the thickness. Only one element was assumed over the width of the beam, since the load distribution, and therefore also the beam response, was assumed to be uniform. The mesh is shown in Figure 6.  
The contact between the rigid bodies (loading beams and supports) and the sandwich panels was established by a frictionless and hard contact interaction. The bond between the core and faces, however, was considered perfect, since no debonding was encountered during the experiments. Hence, the surfaces of the core and faces were modelled by a cohesive surface interaction, without defining damage interaction, defining a perfect bond. The default contact enforcement method was implemented, meaning that the elastic properties of the bond are based on the underlying element stiffness [32].  
To simulate the bending behaviour of the sandwich beams, two rigid bodies and symmetry planes were used. In order to limit the number of elements, and therefore also the calculation time, symmetry boundary conditions were used in the \(XY\) and \(YZ\) planes. The results, however, can be plotted for the whole beam. One of the rigid bodies represents the support, while the other represents one of the loading beams. The support was restricted in the \(X\), \(Y\) and \(Z\) directions, and could only rotate around the \(Z\)-axis. The loading beam was restricted in the \(X\) and \(Z\) directions, and could rotate only around the \(Z\)-axis.  
For convergence reasons, the imposed displacement was performed with a smooth amplitude, so that the increments were smaller.
# Assessment of Two Streamline Curvature Correction Methods for an Elliptic Blending Turbulence Model
## 3 Results
### 2D U-Turn Duct Flow
At the locations \(s/H=\pi/2\) and \(s/H=\pi\), both the original STT \(k\)-\(\omega\)-\(\varphi\)-\(\alpha\) model and the STT \(k\)-\(\omega\)-CC model suppressed the turbulent kinetic energy near the outer concave wall, while the two proposed models predicted augmentation to varying degrees. Among them, the prediction of the STT \(k\)-\(\omega\)-\(\varphi\)-\(\alpha\)-CC2 model was the best. However, although the STT \(k\)-\(\omega\)-\(\varphi\)-\(\alpha\)-CC2 model predicted better turbulent kinetic energy, the improvement in velocity prediction was limited. The reason is that this model also predicts higher turbulent dissipation. Figure 7: Comparison of velocity profiles along different transverse lines for the 2D U-turn duct flow: (**a**) \(s/H=0\); (**b**) \(s/H=\pi/2\); (**c**) \(s/H=\pi\); and (**d**) \(s/H=\pi+2\). The distributions of the friction coefficients (\(C_{f}=2\tau_{w}/\left(\rho U_{b}^{2}\right)\)) along the inner and outer walls are depicted in Figure 9. The \(C_{f}\) along the inner wall, predicted by the different turbulence models, varied little. The recovery of \(C_{f}\) downstream in the re-circulation region is significantly delayed. Along the outer wall, the different turbulence models predicted \(C_{f}\) differently. The original STT \(k\)-\(\omega\)-\(\varphi\)-\(\alpha\) model underpredicted while the STT \(k\)-\(\omega\)-CC model overpredicted the peak of \(C_{f}\). The peaks predicted by the two proposed models were similar and agreed well with the experimental data. Overall, for the 2D U-turn duct flow, the three models with streamline curvature correction provided some improvements. However, it is difficult to determine which performed the best. For example, considering the velocity profile \(s/H=\pi\), the result of the STT \(k\)-\(\omega\)-CC model was the best; meanwhile, for the velocity profile \(s/H=\pi+2\), the result of the STT \(k\)-\(\omega\)-CC model was not good as those of the two proposed models. It should be noted that the results yielded by the STT \(k\)-\(\omega\)-CC model in this paper were very different from those reported in [PERSON] and [PERSON] [3]. The reasons for this phenomenon may be multi-faceted. For example, the different calculation methods for the second derivative, the different treatment methods for wall boundary conditions, and the different computational platforms. The specific reason should be determined in future research. Figure 8: Comparison of turbulent kinetics along different transverse lines for the 2D U-turn duct flow: (**a**) \(s/H=\pi/2\), and (**b**) \(s/H=\pi\). Figure 9: Comparison of friction coefficient distribution along the (**a**) inner and (**b**) outer walls.
# Neural Network Based Quality Control of CYGNSS Wind Retrieval
## 4 Results
### Wind Retrieval Performance  
The CYGNSS wind retrieval is evaluated based on 3 error statistics, namely, the Mean Difference (MD), RMS difference (RMSD) and variance in the data. The MD and RMSD are evaluated with respect to the 1:1 line thus is a superposition of both variance in the data and the intrinsic bias in the GMF. Whereas the variance is a measure of only the degree of scatter in the data. The error statistics are presented in Figures 12 and 13. In Figure 12 the MD and RMSD of the original dataset is shown by solid lines and the filtered dataset is shown by dashed lines. An increase in bias can be observed in the filtered dataset as compared to the original dataset; this is because, after filtering the samples that are identified as good by the filter are aligned closer to the GMF line rather than the 1:1 line. The increase in bias is more dominant above 10 m/s as the GMF line begins to deviate away from the 1:1 line above this wind speed. Figure 13 shows the variance in the data at different wind speed bins. Variance represents the degree of scatter in the data and after applying the filter there is a sharp drop in the scatter. The standard deviation in the filtered dataset is \(<\)=2 m/s for a wide range of wind speeds. These error statistics show a significant improvement in the nature of retrieval after the QC filter.  
Figure 11: Dominant diagnostic variables in identifying outliers. Variable definitions are provided in Table 1.