esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparing GRACE-FO KBR and LRI Ranging Data with Focus on Carrier Frequency Variations
## 8 Conclusions
However, that figure excludes the 1/rev, 2/rev and 2/day frequencies as well as linear and quadratic drifts. Hence, the actual reduction might be larger. Moreover, applying the frequency correction to KBR and LRI should reduce the error at the 1/rev frequency by approx. \(0.9\,\mathrm{\SIUnitSymbolMicro m}\)-peak due to the relativistic proper time effect, as the current regular processing that generates v04 data is not accounting for this effect in LRI and KBR data. We expect that the micron-scale effects at low frequencies discussed here have a negligible impact on the current gravity field determination, as typical pre-fit and post-fit residuals of gravity field determination are much larger than direct KBR-LRI residuals. However, the corrections are of interest if one attempts to understand the low-frequency behavior of the instruments, e.g., the tone errors at 1/rev and 2/rev frequency. These corrections might also be important for future missions employing laser ranging that aim for higher precision at sub-mHz frequencies. Conceptualization, V.M. ; Funding acquisition, G.H. ; Investigation, M.H., M.M., L.M. and H.W. ; Project administration, G.H. ; Writing--original draft, V.M. and Y.Y. ; Writing--review & editing, M.H., M.M., L.M., H.W. and G.H. All authors have read and agreed to the published version of the manuscript. This work and some authors have been supported by: The Deutsche Forschungsgemeinschaft (DFG, German Research Foundation, Project-ID 434617780, SFB 1464). Clusters of Excellence \"QuantumFrontiers: Light and Matter at the Quantum Frontier: Foundations and Applications in Metrology\" (EXC-2123, project number: 390837967); the European Space Agency in the framework of Next Generation Gravity Mission development and ESA's third-party mission support for GRACE-FO; the Chinese Academy of Sciences (CAS) and the Max Planck Society (MPG) in the framework of the LEGACY cooperation on low-frequency gravitational-wave astronomy (M.IF.A.QOP18098) and Bundesministerium fur Bildung und Forschung (BMBF, project number: 03F0654B). Data can be made available upon reasonable request. The authors declare no conflict of interest.
## 3 Materials and Methods
### Materials
#### 3.1.3 Features under Analysis  
Besides \"Contact Reason\", previous studies considered other available information (age, time and day of the week of the call, and comments) for building classification models but no better results were obtained. Building upon those results, the classification models will consider only the \"Contact Reason\" attribute and the corresponding clinical pathway (selected by the nurse). \"Contact Reason\" is a medium length text attribute, written in Portuguese by the technician who answered the call, containing the most relevant information about the patient's condition; it is composed of a maximum of 25 words and the average number of words is 8.27. Table 1 presents a few examples of the \"Contact Reason\" attribute (the original Portuguese text is written in italics following the corresponding translation to English) and the corresponding clinical pathway (one for each of the 10 most common pathways).  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Contact Reason** & **Clinical Pathway** \\ \hline _Rinorreira transparente, tosse produtiva e febre hd 3d._ & \\ Transparent rhinorrhea, productive cough and fever 3d. & \\ \hline _Dor Abdominal e nausea hd 15 dies, aggraamento hoje_ & \\ Abdominal pain and nausea for 15 days, worse today & \\ \hline _Dor suprahumilical hd cerca de alguns dias_ & \\ Supraumbilical pain for about a few days & \\ \hline _Congestão nasal hd 24 h._ & \\ Nasal congestion for 24 h. & \\ \hline _Foliculite purulenta ffac, tronco, braso e pernas desde 3\({}^{a}\) feira_ & \\ Purulent follicilitits face, corpo since Tuesday & \\ \hline \hline \end{tabular}
\end{table}
Table 1: One example of the “Contact Reason” for each of the 10 most common clinical pathways.  
The following attributes were further considered to characterize the population using the SNS24 service: gender, age, date and time of the call, district of the call, and final referral.
## 2 Materials and Methods
### Surface Properties Analysis  
The superficial and cross-sectional characteristics of the modified samples were observed using a JEOL JSM-6500F scanning electron microscope (SEM; Tokyo, Japan) with an operating voltage of 15 kV and a JEOL JSM-2100 transmission electron microscope (TEM; Tokyo, Japan) with an operating voltage of 200 kV, respectively. The Vecco Nanoscope III D5000 atomic force microscope (AFM, Plainview, NY, USA) with a scanning probe of Si was also employed to examine the surface characteristics. The sample was scanned with a high-resolution of 512 scans at 1 Hz scanning rate under the tapping mode. The elemental compositions of the surfaces were detected by secondary ion mass spectroscopy (SIMS; CAMECA IMS-4f, Gennevilliers Cedex, France). In the SIMS experiment, the primary Ar ion beam was pulsed to obtain good mass resolution. The impact energy of 3 keV and ion etching rate of 0.05 nm\(\cdot\)s\({}^{-1}\) were used to gain the depth profiling information in the etching area of 2 mm \(\times\) 2 mm. The wettability of the investigated samples with different parameters (Tables 1 and 2) was simulated and analyzed through the RSM. Input the vital factors (power, time, and pressure) for characterizing the interactions, and then visualize the response surface from all angles with 2D graphs and rotatable 3D plots. Finally, the optimal operating parameter can be gained from the RSM analysis results.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} EBM & Electronic Beehive Monitoring \\ FFT & Fast Fourier Transform \\ DL & Deep Learning \\ ConvNet & Convolutional Neural Network \\ PIV & Particle Image Velocimetry \\ DPIV & Digital Particle Image Velocimetry \\ DVR & Dynamic Velocity Range \\ DTW & Dynamic Time Warping \\ NT & No Traffic \\ LT & Low Traffic \\ MT & Medium Traffic \\ HT & High Traffic \\ \end{tabular}
Figure 13: 3D plots of correlation matrices of the vectors in Figure 14. Each row in the figure corresponds to each row in the vector plot of Figure 14.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**DTW vs. Video** & **HT\_Vid.mp4** & **MT\_Vid.mp4** & **LT\_Vid.mp4** & **NT\_Vid.mp4** \\ \hline \(DTW(C_{1}^{*},C_{3}^{*})\) & 20.75 & 21.71 & 15.36 & 10.53 \\ \(DTW(C_{2}^{*},C_{3}^{*})\) & 18.23 & 18.78 & 19.26 & 6.47 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Omnidirectional Traffic Curve Comparison: \(C_{1}^{*}\) is DPIV; \(C_{2}^{*}\) is MOG2/ConvGS3; \(C_{3}^{*}\) is Human Count (ground truth); the video frame size was \(640\times 480\), the interrogation window size was \(90\); and the interrogation window overlap was \(60\%\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Img. Size** & **Bee Size** & **Inter. Win. Size** & **\% Overlap** & **Pix. Overlap** & **DTW(\(C_{1}^{*}\),\(C_{3}^{*}\))** & **DTW(\(C_{2}^{*}\),\(C_{3}^{*}\))** \\ \hline
640 \(\times\) 480 & 32 \(\times\) 32 & 28 & 10 & 13 & 22.5870 & 18.1374 \\ \hline  & & 28 & 30 & 8 & 16.6490 & 18.1374 \\ \hline  & & 28 & 50 & 14 & 19.7898 & 18.1374 \\ \hline  & & 32 & 10 & 3 & 19.9289 & 18.1374 \\ \hline  & & 32 & 30 & 10 & 22.1706 & 18.1374 \\ \hline  & & 32 & 50 & 16 & 21.5204 & 18.1374 \\ \hline  & & 36 & 10 & 4 & 19.9186 & 18.1374 \\ \hline  & & 36 & 30 & 11 & 20.2431 & 18.1374 \\ \hline  & & 36 & 50 & 18 & 19.0067 & 18.1374 \\ \hline  & & 64 & 10 & 6 & 18.7126 & 18.1374 \\ \hline  & & 64 & 30 & 19 & 17.3478 & 18.1374 \\ \hline  & & 64 & 50 & 32 & 17.7892 & 18.1374 \\ \hline  & & 90 & 10 & 9 & 3.4591 & 18.1374 \\ \hline  & & 30 & 27 & 14.7361 & 18.1374 \\ \hline  & & 90 & 50 & 45 & 14.7819 & 18.1374 \\ \hline \end{tabular}
\end{table}
Table 6: DTW values for \(LT\_Vid.mp4\) with image size of 640 \(\times\) 480; \(C_{1}^{*}\) is DPIV; \(C_{2}^{*}\) is MOG2/VGG16; and \(C_{3}^{*}\) is Human Count (ground truth).  
Figure 17: Seven consecutive frames from a high bee traffic video; the frames are numbered row by row from top left down as Frame 1-7; a fast moving bee is delineated in each frame by a green polygon; the bee is moving right to left; the x, y coordinates of the green dot in each polygon in the corresponding frames are: x = 507.0, y = 1782.0 in Frame 1; x = 448.5, y = 1569.0 in Frame 2; x = 427.5, y = 1351.5 in Frame 3; x = 433.5, y = 1104.0 in Frame 4; x = 486.0, y = 778.5 in Frame 5; x = 580.5, y = 444.0 in Frame 6; x = 660.0, y = 151.5 in Frame 7.
# Effect of Venturi Structures on the Cleaning Performance of a Pulse Jet Baghouse
## 3 Results and Discussion
### Effect of the New Venturi-type on the Baghouse Cleaning Performance  
Experimental results for the cleaning cycle time and dust emission concentration of baghouses adapted with the typical or the new venturi tubes are shown in Figure 15. In these experiments, the pulsing air pressure was set to 0.4 MPa and the pulsing air injection time to 100 ms. According to the data, the average cleaning cycle time of the baghouse with the new venturi (321 s) was significantly longer than that of the baghouse with the typical one (257 s). The test conditions of the two groups were identical, which indicated that the baghouse adapted with the new venturi displayed better dust cleaning performance. In this new tube, the cleaning airflow was more efficient in removing the dust cake deposited on the filter bag. Therefore, after dust cleaning, the baghouse installed with the new venturi displayed lower resistance, and the cleaning cycle time increased. Thus, compared to the baghouse with the typical venturi, the one with the new venturi tube showed longer cleaning cycle time, less compressed air consumption, and lower dust cleaning energy consumption during the same filtration time. In addition, the average dust emission concentration was lower in the new venturi baghouse (4.38 mg/m\({}^{3}\)), compared to the typical one (6.82 mg/m\({}^{3}\)). These results may indicate that the new venturi baghouse displayed a better control effect on dust emission. A potential explanation for this phenomenon is as follows: the cleaning airflow might have destroyed the dust cake deposited on the filter bag. In consequence, the filter bag was exposed to the dust-laden airflow. Shortly after the cleaning and before the dust cake was redeposited again, an instantaneous peak of the dust emission concentration occurred [23; 24; 25]. During the 1-h test period, the baghouse with the typical venturi showed a shorter cleaning cycle and more bag cleaning times (as shown in Figure 16). Additionaly, the number of peak values for the dust emission concentrations, the dust leakage quantity, and the dust emission concentration were higher. Additionally, in the new venturi-type design, the dust cleaning pressure in the area near the mouth of the filter bag was changed into a positive one. This allowed a more effective removal of the dust cake deposited near the mouth of the filter bag (Figure 13b). Thus, compared to the baghouse adapted with the typical venturi-type (as shown in Figure 9b), the former displayed a larger filtration area. This was one of the reasons for the low dust emission concentration of the baghouse installed with the new venturi.  
Figure 15: Average cleaning cycle time and average dust emission concentration of baghouses adapted with a typical venturi tube or a new venturi design.
# A Collaborative UAV-WSN Network for Monitoring Large Areas
###### Abstract  
Large-scale monitoring systems have seen rapid development in recent years. Wireless sensor networks (WSN), composed of thousands of sensing, computing and communication nodes, form the backbone of such systems. Integration with unmanned aerial vehicles (UAVs) leads to increased monitoring area and to better overall performance. This paper presents a hybrid UAV-WSN network which is self-configured to improve the acquisition of environmental data across large areas. A prime objective and novelty of the heterogeneous multi-agent scheme proposed here is the optimal generation of reference trajectories, parameterized after inter- and intra-line distances. The main contribution is the trajectory design, optimized to avoid interdicted regions, to pass near predefined way-points, with guaranteed communication time, and to minimize total path length. Mixed-integer description is employed into the associated constrained optimization problem. The second novelty is the sensor localization and clustering method for optimal ground coverage taking into account the communication information between UAV and a subset of ground sensors (i.e., the cluster heads). Results show improvements in both network and data collection efficiency metrics by implementing the proposed algorithms. These are initially evaluated by means of simulation and then validated on a realistic WSN-UAV test-bed, thus bringing significant practical value.  
large area monitoring; wireless sensor network; unmanned aerial vehicle; optimal trajectory design; clustering +
Footnote †: journal: Sensors
## Materials and Methods
### Rheological Properties  
To measure the rheological properties, 2.0% (\(w/v\)) water dispersions of extracted RBPs were prepared by carefully dispersing the dispersions in pure water at 500 rpm and 80 \({}^{\circ}\)C for 2 h. The rheological properties were measured by a rheometer (HAAKE MARS II, Thermo Scientific, Germany). The measurement module had cone-plate geometry (cone diameter 60 mm, angle 1\({}^{\circ}\)). The gap between the plate and the cone was 0.052 mm and set automatically. All measurements were carried out at 25.0 \(\pm\) 0.05 \({}^{\circ}\)C. Each measurement was repeated three times. Samples were sheared continuously at a rate ranging from 0.01 to 200 s\({}^{-1}\) in 120 seconds. The shear stress (\(\tau\)) versus shear rate (\(\dot{\gamma}\)) data were fitted by using the Ostwald-de Waele model (Equation (3)) [16] or the non-Newtonian law of viscosity (Equation (4)):  
\[\tau=k\cdot\dot{\gamma}^{n} \tag{3}\]  
\[\tau=\eta\cdot\dot{\gamma} \tag{4}\]  
where \(k\) is the consistency coefficient (mPa \(s^{n}\)) and \(n\) is the flow behavior index (dimensionless).
# Characterization of Nanoparticle Batch-To-Batch Variability
## Materials and Methods
### Physicochemical Characterisation of NM Batches
#### 2.6.3 Radical Formation Potential (DCF Test)  
The radical formation potential of the NMs has been determined by their ability to oxidize the non-fluorescent 2\({}^{\prime}\),7\({}^{\prime}\)-dichlorohydrofluorescein (DCFH\({}_{2}\)) to the fluorescent 2\({}^{\prime}\),7\({}^{\prime}\)-dichlorof fluorescein (DCF) according to [PERSON] et al. [33], [PERSON] et al. [32] and [PERSON] et al. [35]. In a first step the commercial 2\({}^{\prime}\),7\({}^{\prime}\)-dichlorofhydrogenase-diacetate (DCFH\({}_{2}\)-DA, Invitrogen, Karlsruhe, Germany) was deacetylated with NaOH. 0.1 mL of 5 mM DCFH\({}_{2}\)-DA in ethanol has been mixed with 2.4 mL of 0.01 N NaOH and incubated at room temperature (24 \({}^{\circ}\)C) for 30 min. For neutralization, 10 mL phosphate buffered saline without Ca\({}^{2+}\) and Mg\({}^{2+}\) (PBS) has been added and kept on ice in the dark until use. Just prior to use, horseradish peroxidase (HRP, Sigma, Taufkirchen, Germany) has been added as a catalyst (2.2 U/mL). The DCFH\({}_{2}\) concentration in the working solution was 40 \(\upmu\)M. Suspensions of test particles have been sonified for 10 min and different concentrations were prepared in PBS. H\({}_{2}\)O\({}_{2}\) standard preparations (0.04 \(\upmu\)M to 10 \(\upmu\)M) were prepared as well. The test solutions have been mixed 1:1 (\(v/v\)) with the prepared DCFH\({}_{2}\) working solution and incubated at 37 \({}^{\circ}\)C for 15 min in the dark. The solutions were then centrifuged (20,000 \(\times\)\(g\) for 15 min) to remove the particles. Solutions containing polystyrene nanoparticles were centrifuged for 1 h. The fluorescence of the supernatant was monitored at 485 nm excitation and 530 nm emission using a fluorescence microplate reader (BIO-TEK FL600 from MWG-Biotech AG, Ebersberg, Germany). Results of fluorescence intensity were expressed as fold changes of the particle free sample.  
The detailed protocols for synthesis and characterization of the different NMs and the complete datasets for each NM batch are provided as Supplementary information (S1 to S6) to the paper, allowing subsequent data mining and modelling.
# A Novel RPL Algorithm Based on Chaotic Genetic Algorithm
## 3 RPL-Cga
### Calculating Rank of Nodes  
The rank value of the node is calculated depending on OF. It represents the position of the node relative to root in DODAG. To prevent loops, rank values of nodes monotonically increase in the Down  
Figure 2: \(\alpha\) curve.  
direction (the direction from roots towards leaf nodes) and monotonically decrease in the Up direction (the direction from leaf nodes towards roots). In Ref. [27], authors proposed a method for calculating the rank values of non-root node. Suppose that the rank value of root is 1.0; then, the rank value of c (non-root) can be calculated based on Equation (17) (the used routing metric is ETX).  
\[R(c)=128\times ETX(c)+R(parent) \tag{17}\]  
Here, \(R(parent)\) represents parent's rank. \(ETX(c)\), calculated according to Equation (18), indicates the ETX of link from \(c\) to this parent.  
\[ETX(c)=ETX(c)_{old}\times 0.9+ETX(c)_{new}\times 0.1 \tag{18}\]  
In Equation (18), \(ETX(c)_{old}\) indicates the former value, and \(ETX(c)_{new}\) is computed through \(ETX=1/D_{f}*D_{r}\). Here, \(D_{f}\) is the probability that a packet received by neighbor. \(D_{r}\) is the probability that an acknowledgment packet is successfully received.  
Moreover, in the set candidate parents, the candidate parent with the lowest value of Equation (17) should be chosen as the preferred parent.  
In RPL-CGA, we proposed a new method to calculate the rank values of non-root nodes. Suppose that the rank value of root is 1.0. Then the rank value of c (non-root) can be calculated according to Equation (19).  
\[R(c)=R(parent)+(F(i)+1) \tag{19}\]  
In Equation (19), \(R(parent)\) represents the rank of parent, and \(F(i)\) indicates the comprehensive evaluation function value of the link from c to this parent.  
Then, in the candidate parents set, the one with the lowest value of Equation (19) should be chosen as the preferred parent.
# Compression of Multibeam Echosounders Bathymetry and Water Column Data
## 6 Test Results
### Pre-Processing Stage Performance  
This section is focused on evaluating the goodness of compressible data after the decorrelation stage. Our first approach is to plot a histogram of the original samples together with a histogram of the prediction errors (i.e., the values that will be sent to the FAPEC entropy coder). For a better visualization, the bin width is calculated from the number of samples, and it is the same for both histograms. In addition, the prediction errors histogram also includes three vertical lines at percentiles 5, 50 and 95 in order to clearly show the range where 90% of the data is contained.  
Making use of the histograms, the cumulative histograms of the original samples and the prediction errors are calculated and plotted together. Although these graphics are just the integral of the histograms described above, they provide a clear way to observe sample concentrations in intervals.  
Figures 16 and 17 show the histograms and cumulative histograms of one file from the EM2040 dataset (D2-EM2040-22) and one file with 8-bit phase samples from the EM304 dataset (D1-EM304-11).  
From the histograms, we see that original data are distributed among the whole dynamic range, which would result in a poor compression ratio. After the pre-processing stage, the dynamic range is heavily reduced. Clearly, Figure 16 shows better results than Figure 17. The reason is that D1-EM304 contains 8-bit phase samples, and as we mentioned in Section 4.2, they are too difficult to process.
## 3 Distributed Optical Fibre Sensing Applied to Geo-Hydrological Applications
### Slopes and Landslides Monitoring
#### 3.2.2 Other Distributed Sensing Approaches for Slopes and Landslides Monitoring  
Strain is not the only parameter that has been addressed by optical fibre sensing for slope and landslide monitoring: among others, several optical fibre geophones and accelerometers have been proposed so far for the monitoring of ground vibration induced or correlated by landslides. Nonetheless, all of them are based on fibre Bragg grating (FBG) technology [215] or interferometric measurements [216].  
To the best of our knowledge, distributed fibre-optic sensors have been proposed only recently for vibration-based landslide monitoring: the advantage of this approach consists of having a fibre cable acting like a concatenation of thousands of coherent geophones, located at a distance of a few meters from each other over distances of some kilometres. [PERSON] et al. [217] confirmed the feasibility of the approach by testing the technology in a small-scale flume with coil-like deployment. Real-time handling of a large amount of data produced was recognised as one of the main challenges to be addressed before the technology can be used for early warning purposes. The development of fibre optic distributed vibration sensors for debris flows has also been proposed by a recently funded European project [218].