esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Client Selection in Satellite Federated Learning
## 5 Simulation
### LEO Constellation  
We consider an LEO Walker constellation consisting of 120 satellites, distributed at 400 km, 500 km, and 600 km, respectively. Five orbital planes are evenly deployed at each altitude, with each orbital plane having eight satellites. The details of constellation simulation parameters are listed in Table 1, and the results of the Walker configuration constellation simulated using the STK based on these parameters are shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Height** & **Inclination** & \
\begin{tabular}{c} **Number of Orbital** \\ **Planes** \\ \end{tabular} &
\begin{tabular}{c} **Number of Satellites** \\ **in Each Plane** \\ \end{tabular} \\ \hline
400 km & 40\({}^{\circ}\) & 5 & 8 \\
500 km & 45\({}^{\circ}\) & 5 & 8 \\
600 km & 50\({}^{\circ}\) & 5 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The constellation simulation parameters.  
Figure 8: The simulated constellation with 120 satellites.  
In the simulation, we established a GS in Shanghai, situated at latitude 31.25\({}^{\circ}\) and longitude 121.48\({}^{\circ}\), to serve as the ground PS. Additionally, we selected a satellite orbiting at an altitude of 500 km to serve as the space PS.
# Flood Monitoring Based on the Study of Sentinel-1 SAR Images: The Ebro River Case Study
## 2 Study Area and Data Source
### Hydraulic Parameters Characteristic of the Flood  
To know the maximum flow dates during the flood and to confirm the adequacy of the dates of the Sentinel-1 SAR images, data were taken from some of the gauging stations that exist along the course of the Ebro River (Figure 2). The values at the stations of Castejon and Tudela, in the province of Navarra and upstream of the study area, were taken into account, despite not being within the delimited study area. These give us an idea of the development of the flood and how the maximum flood points have been moving along down along the Ebro axis with time.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Station** & **Annual** & **Annual** & **Annual** & & **Annual** & **Annual** \\
**Annual** & **Average** & **Average** & **Average** & **Away** & **Away** & **Maximum** \\
**Gauging** & **Level of** & **Ebro (m)** & **Ebro (m\({}^{3}\)/s)** & & **Ebro (m)** & **Ebro (m\({}^{3}\)/s)** \\ \hline \multirow{2}{*}{**Castejon**} & 2.31 & 226 & 6 February 2003 & 02:45 & 7.54 & 2847 \\ \cline{2-7}  & 2.63 & 261 & 27 February 2015 & 00:00 & 7.78 & 2691 \\ \cline{2-7}  & 2.82 & 265 & 13 April 2018 & 9:30 & 7.77 & 2682 \\ \hline \multirow{2}{*}{**Zaragoza**} & 1.41 & 258.78 & 9 February 2003 & 03:00 & 5.76 & 2237 \\ \cline{2-7}  & 1.38 & 268.95 & 2 March 2015 & 02:00 & 6.10 & 2448 \\ \cline{2-7}  & 1.5 & 288.52 & 15 April 2018 & 19:45 & 5.36 & 2037 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Representative values on the annual levels and flows of the Ebro river in two gauging stations, Castejón and Zaragoza, in its course for the years 2003, 2015, and 2018. Historical data taken from the SAIH of the CHE, 2019.  
Table 2, representative of the year 2018, shows the values collected at the stations. This reflects the extraordinary flood of the Ebro in the month of April when it reached the highest values of the year. These data show the evolution of the flood from 13 to 15 April, obtaining the maximum annual level of accumulation in the Pina Dam, below the village of El Burgo de Ebro, on 15 April at 9:30 pm at 6.03 m, having had an average annual level of 4.48 m [22].
# Building Scalable and Secure Multicast Delivery Infrastructure in a Local Area Network
## 3 Pseudo-Identity Based Encryption Framework
Let \(X_{1},X_{2},\cdots,X_{n}\) be the non-forgeable IDs of all the nodes in the network and their public-keys are \(PK_{1}^{1},PK_{1}^{1},\cdots,PK_{n}^{1}\). The CA then computes the _public-key polynomial_\(P(x)=b_{0}+b_{1}x+b_{2}x^{2}+\ldots+b_{n-1}x^{n-1}\) of degree \(n-1\) whose \(b_{i}\)'s are given as follows:  
\[\left[ \begin{array}{cccc}1&X_{1}&X_{1}^{2}&\ldots&X_{1}^{n-1}\\ 1&X_{2}&X_{2}^{2}&\ldots&X_{2}^{n-1}\\ 1&X_{3}&X_{3}^{2}&\ldots&X_{3}^{n}\\ \vdots&\vdots&\vdots&\vdots&\vdots\\ 1&X_{n}&X_{n}^{2}&\ldots&X_{n}^{n-1}\\ \end{array} \right]\left[ \begin{array}{c}b_{0}\\ b_{1}\\ b_{2}\\ \vdots\\ b_{n-1}\\ \end{array} \right]=\left[ \begin{array}{c}PK_{1}^{1}\\ PK_{2}^{1}\\ PK_{3}^{1}\\ \vdots\\ PK_{n}^{1}\\ \end{array} \right].\]  
Since the identities of all the nodes are different, the above matrix equation will have a unique solution. It is easy to see that \(P(X_{i})=PK_{i}^{1}\). We call this polynomial as a hash-polynomial for our threshold cryptosystem. The CA will load this hash-polynomial onto every end-host and device in the network during initial configuration. This polynomial is used to generate the public-key of any end-host or a device in a network. If all of the coefficients of this hash-polynomial are known to an adversary, he will not be able to compromise the system. The main advantage in distributing thispolynomial is that, if a node knows the identity of any other node, it can easily compute its public-key without the presence of a CA. After this step, there is no need for the existence of a CA.
## 3 Materials and Methods
### Classification Stage
[PERSON] proposed the SVM based on computational learning theory [35]. In the field of engineering, SVM has found application in tasks such as classification, regression, and density estimation [36]. Even though SVM is primarily a classifier, it offers the flexibility to create linear classifiers by mapping input patterns into a higher-dimensional feature space. The main objective of SVM is to build hyper-planes that maximize the margin between classes while minimizing mis-classification errors [36]. In this research, the \(k\) value for the KNN classifier was chosen by varying \(k\) between 1 and 10 at intervals of 1. Maximum classification accuracy was achieved after training the classifier to determine the optimal value of \(k\), which was found to be \(k=5\). Each experiment was categorized using KNN, with the Euclidean distance serving as the similarity metric. The KNN algorithm is widely considered to be one of the non-parametric classification methods. The labels were assigned to samples in the training set by comparing their similarities. Specifically, it looks at the training observations in the significant features matrix to determine the classification [37; 38; 39; 40]. The object is allocated to the class that has the highest frequency of occurrence among its k-nearest neighbors, where k is always a positive integer. When k > 1, KNN is more robust, and a bigger k value can assist to lessen the effect of noisy points in the training set [41; 42; 43]. The properties of the datasets generally dictate the parameter k, and the nearest samples are expected to contribute more than the far samples. The unknown sample belongs to a class that is shared by every KNN [44]. The classifier was prepared to locate the best estimation of k, which was obtained at k = 5, and to maximize the classification accuracy. The Euclidean distance was utilized as a similarity measure to classify each trial using the KNN. The presentation of the proposed framework was assessed by utilizing average classification accuracy. The best results for the classifiers were obtained by carrying out a 5-fold cross-validation (CV). This procedure was carried out, and the average of the 5 folds was then reported. During this procedure, the entire dataset was split up into a (24 \(\times\) 4) training set and a (6 \(\times\) 4) testing set. This was done so that the accuracy of our overall results could be improved as a result. The performance of the proposed framework was evaluated by using the average classification precision, sensitivity, specificity, accuracy, and F1-score, which were reported as a percentage, and the confusion matrix made it possible to classify the BC severity. The confusion matrices were produced for each classifier while doing a 5-fold cross-validation (CV), in which the final confusion matrix was obtained by averaging the 5 folds.
## 3 Results
### The Influence of the Parallel Bridge Construction on the Flood Level of the River Channel
#### 3.3.3 Analysis of Water Level Variations Along the Path  
By analyzing the measured water levels along the river section before and after the construction of the parallel bridge for 10-year (Q = 9600 m\({}^{3}\)/s) and 20-year (Q = 11,000 m\({}^{3}\)/s) floods, the additional water-blocking effect of the \"reconstruction and expansion\" project's piers combined with the old bridge piers was further examined. Near the proposed bridge site, the water surface line returns to the gradient of the surrounding water surface. Under the once-in-a-decade operating conditions, before the construction of the parallel bridge, the water level difference between the two measuring points upstream and downstream of the bridge site was 0.05 m. After construction, the difference at the measuring points became 0.09 m. Under the once-in-twenty-years operating conditions, the water level difference at the measuring points changed from 0.07 m before construction to 0.12 m after the parallel bridge was built. Overall, as the flow rate increases, the water level rises, and the piers located within the river channel occupy part of the flood discharge section, reducing the flow area and increasing the resistance to the water flow. This causes the water level to rise within a certain range upstream of the bridge site, and the distance of water accumulation increases upstream with the increase in flow rate. Under the design flood flow of Q = 9600 m\({}^{3}\)/s, the impact of water accumulation extends to about 3000 m upstream of the bridge site; under the design flood flow of Q = 11,000 m\({}^{3}\)/s, the impact extends to about 3500 m upstream. The water surface line along the river under the design flood conditions is shown in Figure 12. It was found that the construction of the parallel bridge causes the water level in front of the bridge piles to rise, and this effect is more pronounced under high-flow conditions.
# SVM-Based Sea Ice Extent Retrieval Using Multisource Scatterometer Measurements
## 2 Data Sources and Preprocessing
### Data Sources  
#### 2.1.1 Scatterometer data  
The HSCAT scatterometer uses two rotating pencil beams, and the normalized radar cross-section (\(\sigma^{0}\)) is alternately measured using the horizontally polarized (HH) inner beam and the vertically polarized (VV) outer beam at incidence angles of 41.5\({}^{\circ}\) and 48.5\({}^{\circ}\), respectively. Notably, the swath width on the ground of the VV-beam (1800 km) is larger than that of the HH-beam (1400 km). The HSCAT radar backscatter data contained in the level 1B (L1B) data products are used for the period of 1 January 2019-31 December 2021. The resolution of the \(\sigma^{0}\) data is equal to the size of the \"footprint\" on the ground, i.e., approximately 25 km \(\times\) 32 km.  
The China-France Oceanography Satellite (CFOSAT), carrying the Ku-band scatterometer (CSCAT), was launched in October 2018. The CSCAT scatterometer uses rotating fan-beams. The \(\sigma^{0}\) data are alternately measured by the HH inner beam and the VV outer beam at incidence angles ranging from 28\({}^{\circ}\) to 51\({}^{\circ}\)[27]. In this study, the Level 2A (L2A) data products, containing the \(\sigma^{0}\) data corresponding to each wind vector cell, with a grid size of 25 km \(\times\) 25 km, were used in the period of 1 January 2019-31 December 2021.  
The MetOp-A, MetOp-B, and MetOp-C satellites, carrying the C-band ASCAT scatterometer, were successfully launched in 2006, 2012, and 2018, respectively. The ASCAT scatterometer is equipped with three antennas on each side (corresponding to a 500 km swath width on the ground), all of which use VV polarization at incidence angles ranging from 25\({}^{\circ}\) to 65\({}^{\circ}\). In addition, the \(\sigma^{0}\) measurements are performed by antennas pointing  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scatterometer** & **HSCAT** & **CSCAT** & **ASCAT** \\ \hline Frequency (GHz) & 13.4 (Ku-Band) & 13.256 (Ku-Band) & 5.3 (C-Band) \\ \hline Polarization & HH, VV & HH, VV & VV \\ \hline Incidence Angle (\({}^{\circ}\)) & 41.5, 48.5 & 28–51 & 25–65 \\ \hline Swath Width (km) & 1400, 1800 & 1000 & 500 \(\times\) 2 (on each side) \\ \hline Spatial Resolution of \(\sigma^{0}\) (km) & 25 \(\times\) 32 & 25 \(\times\) 25 & 12.5 \(\times\) 12.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information regarding the three scatterometers used in this study.  
towards azimuth angles of 45\({}^{\circ}\), 90\({}^{\circ}\), and 135\({}^{\circ}\) with respect to the satellite track. The ASCAT data are taken from the L2B products in BUFR (Binary Universal Form for the Representation) format, with resolutions of 12.5 km. In addition, since the differences in \(\sigma^{0}\) among ASCAT-A, ASCAT-B, and ASCAT-C are negligible (i.e., within 0.2 dB), all ASCAT data will be treated as a single data source hereafter.
## 3 Data Collection
### Weather Data  
Previous studies have shown that weather conditions have a significant impact on traffic flow characteristics such as road capacity and operating speed [64,65]. This work uses this evidence to assess weather impacts in prediction modelling and achieves this  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Location** & **Total Data Set** & \begin{tabular}{c} **Training Data Set** \\ **(60\%)** \\ \end{tabular} & \begin{tabular}{c} **Test and Validation** \\ **Data Set (40\%)** \\ \end{tabular} \\ \hline \begin{tabular}{c} Hoddle Street, \\ Southbound \\ \end{tabular} & 35,036 observations & 21,022 observations & 14,014 observations \\ \hline \
\begin{tabular}{c} Hoddle Street, \\ Northbound \\ \end{tabular} & 35,036 observations & 21,022 observations & 14,014 observations \\ \hline
\begin{tabular}{c} Total \\ \end{tabular} & 70,072 observations & 42,044 observations & 28,028 observations \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data used for model development.  
Figure 2: Sections of Hoddle Street along this corridor selected for this study. Note: The sections included in this study are marked and numbered.  
by retrieving historical weather observations from the Bureau of Meteorology from the nearest station to Hoddle Street in Melbourne, Australia. Weather data were collected every minute from the Melbourne Olympic Park--86,338 station, which is the closest station to Hoddle Street. The collection time of weather data corresponds to traffic flow and speed data described in the data collection section. The environmental factors included in this analysis are:  
* Rainfall observation in mm.
* Air temperature in degrees Celsius.
* Relative humidity in percentage %.
* Wind (1 min) speed in km/h.  
The weather data were aggregated into 15-min intervals to match the speed and flow input field observations. Figures 3 and 4 show speed and flow data under rainfall conditions. The data were incorporated alongside traffic flow and speed to evaluate the model's effectiveness under weather conditions. The \(x\)-axis shows the time between the observations, while the \(y\)-axis shows the speed/volume conditions for the selected arterial road. The blue line represents the speed/volume values during rainy days, while the grey line refers to the speed/volume values during sunny weather.
## 3 Results and Discussion
### The Behavior of VOCs in the Food Samples After Irradiation
#### 3.2.4 Other Compounds  
Sulfur compounds, particularly methanethiol, are formed as a result of the breakdown of the amino acids cysteine and methionine, which are part of the amino acid sequence of proteins present in meat and poultry [50]. A 20% and 60-80% decrease in methanethiol concentration in beef samples and chicken, respectively, with the irradiation dose ranging from 0.25 kGy to 5 kGy may be a sign of the breakdown of amino acids after irradiation and the decomposition of methanethiol due to irradiation. The absence of sulfur compounds in salmon samples may be due to the fact that fish contains a large amount of tocopherols (Vitamin E) and selenium, which act as antioxidants, preventing the formation of sulfur compounds [51].  
As an added benefit, irradiation with accelerated electrons at doses ranging from 1 kGy to 5 kGy ensured a 30-40% decrease in the concentration of trimethylamine--a toxic compound formed as a result of the degradation of proteins that causes significant changes in the color and taste of seafood [52]. It should be noted that the doses 0.25 kGy and 0.5 kGy increased the trimethylamine content up to 30-40%, which is a sign of protein degradation due to irradiation.  
The analysis of dose dependencies of VOCs in food samples shows that the concentration of volatile compounds is determined by the accumulation of compounds due to the decomposition of macronutrients after irradiation, as well as the decomposition of volatile compounds under the action of radiation. The behavior of compounds in different dose ranges is influenced by the dose thresholds at which the macronutrients from which these compounds are formed decompose, the initial concentration of volatiles, and the rate of their decay with a higher irradiation dose.
# Phononic Coupled-Resonator Waveguide Micro-Cavities
## 3 Discussion  
In Ref. [15], we introduced a dynamical matrix model to predict the resonant frequencies of a phononic polymer, or a generalized chain of coupled resonators that is not necessarily organized along the principal axes of the supporting phononic crystal. Here we extend the model to the more general case of unequal coupling coefficients. In short, the model assumes that the state of the phononic defect structure is described by one macroscopic degree of freedom per resonator, \(U_{n}\). All resonators are identical except for a spatial shift in the crystal and have the same isolated resonance frequency \(\omega_{0}\). Then, the dynamical equation for coupled resonators is written  
\[-\tilde{U}_{m}=\sum_{n=1}^{N}D(m,n)U_{n}, \tag{1}\]  
Figure 4: Experimental (**a**) and numerical (**b**) out-of-plane displacement maps at frequencies 2.26 MHz, 3 MHz, and 3.56 MHz.  
with a symmetric dynamical matrix \(D(m,n)\) such that \(D(m,m)=\omega_{0}^{2}\). The eigenvalues of the dynamical matrix give the observable resonant frequencies of the finite structure. Their number equals the number of resonators for a non-degenerate band.  
Within each of the three CREW cavities in this paper, the dominant coupling coefficient is between nearest neighbors, with a lattice separation \(2a\). For every waveguide band, the coupling coefficient can be estimated from the frequency extent of the dispersion relation. Hence a coupling coefficient \(\gamma\) can be estimated for each band, or each single defect mode. Furthermore, the CREW cavities are separated by a distance of at least \(4a\) (vertically, the separation between defects numbers 2 and 5 is \(4a\); similarly the vertical separation between defects numbers 3 and 9 is also \(4a\)). This leads us to define a coupling coefficient \(\delta\) that is much smaller than \(\gamma\) to describe the evanescent coupling between CREW cavities 1 and 2, and 1 and 3 as well. Neglecting all other coupling coefficients, we thus can write the dynamical matrix as  
\[D=\left(\begin{array}{cccc|cccc}\omega_{0}^{2}&\gamma&0&0&0&0&0&0&0&0&0&0&0&0 \\ \gamma&\omega_{0}^{2}&\gamma&0&\delta&0&0&0&0&0&0&0&0&0\\ 0&\gamma&\omega_{0}^{2}&\gamma&
# A Backwards-Tracking Lagrangian-Eulerian Method for Viscoelastic Two-Fluid Flows
## Appendix A Oldroyd-B Fluid Fully Developed Channel Flow  
Assuming steady two-dimensional, fully developed channel flow, the constitutive equation reduces do  
\[\tau_{xy}=\eta\frac{\partial u}{\partial y}, \tag{1}\]  
\[\tau_{xx}-2\lambda\tau_{xy}\frac{\partial u}{\partial y}=0, \tag{2}\]  
\[\tau_{yy}=0. \tag{3}\]  
Insertion of \(\tau_{xy}\) from (1) into (2) gives  
\[\tau_{xx}=2\lambda\eta\left(\frac{\partial u}{\partial y}\right)^{2}, \tag{4}\]  
The momentum equation in the flow direction reduces to \[\frac{\partial p}{\partial x}=\mu\frac{\partial^{2}u}{\partial y^{2}}+\frac{ \partial\tau_{xy}}{\partial y}, \tag{100}\]  
which by insertion of \(\tau_{xy}\) from (101) becomes  
\[\frac{\partial p}{\partial x}=\mu\frac{\partial^{2}u}{\partial y^{2}}+\frac{ \partial}{\partial y}\bigg{(}\eta\frac{\partial u}{\partial y}\bigg{)}=(\mu+ \eta)\frac{\partial^{2}u}{\partial y^{2}}, \tag{101}\]  
Integration of (101) with respect to \(y\) once yields  
\[y\frac{\partial p}{\partial x}=(\mu+\eta)\frac{\partial u}{\partial y}+C_{1}, \tag{102}\]  
where \(C_{1}\) is an integration constant. Due to the symmetry condition \(\frac{\partial u}{\partial y}|_{y=0}=0\), which by insertion of \(y=0\) into (102) leads to \(C_{1}=0\). Inserting \(C_{1}=0\) to (102) and integrating in \(y\) again yields  
\[\frac{y^{2}}{2}\frac{\partial p}{\partial x}=(\mu+\eta)u+C_{2} \tag{103}\]  
where \(C_{2}\) is an integration constant. Using the no-slip condition \(u_{y=h}=0\) gives  
\[C_{2}=\frac{h^{2}}{2}\frac{\partial p}{\partial x}, \tag{104}\]  
which inserted to (103) after some rearrangement gives  
\[u(y)=-\frac{1}{2(\mu+\eta)}\frac{\partial p}{\partial x}\Big{(}h^{2}-y^{2} \Big{)}, \tag{105}\]  
or, simply,  
\[u(y)=A(h^{2}-y^{2}), \tag{106}\]  
where a is a constant.  
Now, the mean velocity \(U\) can be calculated as  
\[U=\frac{1}{h}\int_{0}^{h}u(y)dy=\frac{A}{h}\int_{0}^{h}(h^{2}-y^{2})dy=\ldots= \frac{2}{3}Ah^{2}, \tag{107}\]  
such that \(A=3U/(2h^{2})\). The velocity can then be expressed in terms of the mean velocity as  
\[u(y)=\frac{3U}{2h^{2}}(h^{2}-y^{2})=\frac{3U}{2}\bigg{(}1-\frac{y^{2}}{h^{2}} \bigg{)}. \tag{108}\]  
Hence, the velocity gradient reads  
\[\frac{\partial u}{\partial y}=-\frac{3 Uy}{h^{2}}, \tag{109}\]  
which gives the final expressions for \(\tau_{xy}\) and \(\tau_{xx}\).
## 3 System Design
### System Design Overview
#### 3.4.3 Web-GIS  
The system uses Mapbox as the digital map of Web-GIS. In Web-GIS, information concerning the name and its longitude and latitude of the spots and facilities from the database are loaded, and the pictograms that represent these in the appropriate areas are displayed on the screens of the users' mobile information terminals. By selecting a pictogram on the digital map, the name and distance to that spot and facility will be displayed on the screen.  
Additionally, users can search for sightseeing spots and tourism-related facilities using Web-GIS. When users are searching for a destination, once a category from the pictograms is selected, information concerning the sightseeing spots and tourism-related facilities within that category is loaded from the database, generating a list to be displayed. Users can select a destination from this list. Additionally, the system is implemented with a function for users to submit information concerning new sightseeing spots and tourism-related facilities, which is explained later in Section 4.1.3. Users can submit such information by selecting a location on the digital map concerning the information they want to submit, entering its name, and selecting the pictogram in the applicable category. The process mentioned above is shown in Figure 3.  
Disaster support facilities can also be displayed on the digital map, where pictograms are shown in the location of the facilities on the digital map once users select the pictogram images of the facilities they want to display. Information concerning disaster support facilities is managed by csv files which are integrated into the application. Therefore, the locations of such facilities can be displayed even when offline, enabling users to receive support from the system, even if they are in a situation where the Internet is not available during disasters.