esa-sceva/satcom-chunk-collection huggingface dataset:
# Scaling of Rain Attenuation Models: A Survey
## 3 Existing Scaling Techniques
### Frequency Scaling
#### 3.3.1 Frequency-Ratio-Based Scaling
However, the downside of the proposed method is that the applicable frequency range is short in the range of 20-30 GHz, where the scaling method is given by:  
\[A=(kR^{a})\left[1-exp(-\gamma^{\prime}ah_{r}\text{esc}\theta)/(\gamma^{ \prime}a)\right] \tag{23}\]  
where \(\gamma^{\prime}\) denotes the specific attenuation coefficient whose value can be determined with the help of the nonlinear least squares approach. The unique feature of this model is that it is a good candidate for real-time applications based on spatial variation through the specific attenuation index. The main challenge in frequency scaling is the nonuniform rain rate distribution and the nonuniformity of the DSD of the rain along its path. The distribution of the frequency scaling factors in the K and Q bands was examined with the rain rate, and the DSD measurement results in [14]. However, it was not easy to compare these two results due to the unavailability of the rain rate distribution and the DSD of the rain information along the entire path. To determine the short-term scaling, moving and block average and sampling techniques were used, and it was recommended to limit this instantaneous time within 5 min [40].
## 3 Construction of Mathematical Model
### CEI-Based Precision Measurements of Targets in Earth-Moon Space
#### 4.2.4 Tidal Effect  
The tidal effect mainly causes the displacement of the CEI observation array, which affects the pseudocode ranging quantity between the non-cooperative spacecraft and theground-based CEI receiving array, and the error correction equation for the tidal effect is shown as follows:  
\[\begin{split} c_{i(space\to CEL)}^{\text{Tidal}}=\sum\limits_{i=1}^{2} \frac{GM_{i}}{GM}\cdot\frac{R^{4}}{R_{i}^{3}}\cdot\{[3l_{2}\cdot(\overset{ \Lambda}{R}\cdot\overset{\Lambda}{R}_{i})]\cdot\overset{\Lambda}{R}_{i}\\ +[3(\frac{h_{2}}{2}-l_{2})\cdot(\overset{\Lambda}{R}\cdot \overset{\Lambda}{R}_{i})^{2}-\frac{h_{2}}{2}]\cdot\overset{\Lambda}{R}\}\end{split} \tag{28}\]  
where \(GM_{i}\) is the gravitational constant of the tide-generating object (Moon at \(i=1\) and Sun at \(i=2\)); \(R\) and \(R_{i}\) are the geocentric positions of the CEI observing array and the tide-generating object, respectively; \(\overset{\Lambda}{R}\) and \(\overset{\Lambda}{R}_{i}\) are the unit vectors corresponding to \(R\) and \(R_{i}\); \(h_{2}\) is the Love number; and \(l_{2}\) is the Shida number.
## 2 Structural Model and Analysis Methods
With the comparability of the operating principle of the two-dimensional (2D) mode and three-dimensional (3D) mode, a 2D geometric model with greatly reduced computational complexity was established, and the finite element method (FEM) was used to analyze the propagation characteristics. Then, hyperfine meshing was used to guarantee the accuracy of the emulation. In addition, the absorbing boundary condition was established by the perfect matched layer, which can absorb the outward reflected waves.
# Analysis of Random Dynamics of Cell Segmented by a Modified Active Contour Method
## 3 Results & Discussion
### Difference of Trajectory between cPMS Particles and MCF-7 Cells
The VACF  
Figure 2: Representative trajectories of random motion of (**a**) cPMS and (**b**) MCF-7 cells calculated using the radial symmetry method. The ensemble-averaged MSDs of (**c**) cPMS and (**d**) MCF-7 cells calculated from the trajectories and the VACFs of (**e**) cPMS and (**f**) MCF-7 cells, \(\delta\) from 0.04 to 2 s (black to white). of cPMS does not show a peak value for any \(\delta\), which indicates that the particle follows random dynamics. On the other hand, the VACF of the MCF-7 cell shows a peak distribution according to \(\delta\), which corresponds to the peak distribution with localization error, that is, a sharp leak over the short time scale and peaks decay as \(\delta\) increases. Following the error analysis in single-particle tracking [41], the MSD can be fitted using Equation (3) so that the mean localization error, \(\sigma=\sqrt{\left(\xi^{2}(t)\right)}\), can be estimated as a parameter of the degree of noise in position measurement. As shown in Figure 2c,d, \(\sigma_{cPMS}\approx 0.0566\) and \(\sigma_{MCF-7}\approx 0.2265\), which can be interpreted to signify that the analyzed trajectory of MCF-7 possesses approximately 5.4 times more localization error than that of cPMS. It also confirms that the VACF of cPMS follows random dynamics, whereas the VACF of MCF-7 shows a peculiar peak distribution depending on \(\delta\), possible evidence of localization error in the trajectory data. According to the results shown in Figure 2, localization of the cell requires a different approach to minimize localization error. Thus, in this study, a modified active contour method is introduced for analysis of cellular trajectory and random dynamics and is compared to other methods as follows.
# A High Signal-Noise Ratio UWB Radar for Buried Pipe Location Using Golay Complementary Sequences
###### Abstract  
A Golay-based ultra wideband ground penetrating for underground pipes location is proposed and experimentally demonstrated. Golay complementary codes with the code length of 1024 and frequency of 1 GHz are used as the probe signals. The two-dimensional image of the buried pipes is achieved by a correlation method and a back-projection algorithm. The experimental results show that both the plastic pipe and metallic pipe can be located with a range resolution of 10 cm. Furthermore, as the Golay complementary sequences are a pair of complementary sequences, the sum of their correlation function yields twice the value of the peak at the target position and zero elsewhere. Thus, compared with the stepped frequency signal radar or chaotic signal radar, the Golay-based radar can significantly improve the signal-noise ratio and has the capability of deep detection.  
G 2019 9 5090; doi:10.3390/appr9235090
# GSFedSec: Group Signature-Based Secure Aggregation for Privacy Preservation in Federated Learning
## 5 Performance Analysis
### Signaling Overhead  
Signals indicate the number of transmissions required to complete the communication. Suppose that the client wants to send updates to the server. For this, it must communicate with the admin and server, collect the necessary keys, and wait for its turn. Figure 4 shows the number of signals required for one iteration. Assume that \(m\) is the total number of devices in the network, and \(n\) is the total number of devices chosen for the FL. In our algorithm, clients first send their FL requests to the server, which receives \(m\) signals. The server then selects \(n\) clients and replies to them. It takes \(n\) signals. The server also sends a list of participants to the admin, which receives one signal. Similarly, sending GSReq to the admin requires \(m\) signals, and sending GSRep to clients also requires \(m\) signals. \(n\) clients send updates to the server using \(n\) signals. The server and admin communicate with each other using two signals. The total number of signals transmitted for one iteration of the FL is \(m+n+1+m+m+n+1+1\).  
For example, if we assume 2000 devices (of which 1000 are chosen for FL), the signaling overhead would be \(2000+1000+1+2000+2000+1000+1+1=8001\) for our proposed algorithm. The signaling overhead in [5] is the highest of all compared algorithms. Other steps would be the same, but instead of having the admin message the client, we would have \(n\times(n-1)\) messages for client-to-client communication. Thus, for 2000 clients with 1000 participants, the signaling overhead would be \(2000+2000+1000+1000(1000-1)+1000+1+2000=1,007,001\) signals. Similarly, we can compute the signaling overhead of the other algorithms; the output is given in Table 9, and the associated graph is shown in Figure 9. The comparison graph shows that our algorithm outperforms the other existing algorithms, even when the number of clients increases in the network. Although other algorithms perform better for fewer clients or iterations, our algorithm is highly scalable and yields good results even for larger networks.  
Figure 8: Comparison of communication cost of algorithms.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Average Cost (Bytes)** \\ \hline [PERSON] et al. [24] & 135,000 \\ [PERSON] et al. [1] & 40,500 \\ [PERSON] et al. [5] & 135,000 \\ [PERSON] et al. [23] & 1350 \\ [PERSON] et al. [22] & 2700 \\ Proposed & 473 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Communication cost and packet size.  
Figures 7-9 show that our algorithm performs more efficiently than others. This demonstrates the high efficiency of our approach without affecting the accuracy of the updates.
# Friction Factor Correlation for Regenerator Working
## 3 Experimental Setup  
The schematic diagram of the test rig is shown in Figure 2. In essence, it is a travelling-wave cooler in which a sample of regenerator material can be placed and the resulting pressure drop is measured. The rig consists of a linear motor, resonator, hot and cold heat exchanger, regenerator (mesh numbers #30, #94, #180, and #200 have been used) and a set of components known as resistance, \(R\), inertance, \(L\), and compliance, \(C\) (forming an RLC network). The network is a combination of suitable valves, a 1.6 m long tube with a diameter of 8 mm and a buffer volume of 2.5 \(\times\) 10\({}^{-3}\) m\({}^{3}\). The frequency of the acoustic wave is set using a linear motor. The linear motor is enclosed in a specially designed high-pressure cylindrical casing with a glass window to allow laser displacement measurement of the motor piston. The laser displacement sensor (Keyence LK-G152, Milton Keynes, UK) senses the piston displacement, \(\delta\). The amplitude of the velocity at that location, defined as \(V_{1}\), is then calculated as \(V_{1}=\omega\delta\).  
The resonator has the inner diameter of 55 mm. The hot heat exchanger, regenerator and cold heat exchanger are made to fit into the resonator. The lengths of the resonator, hot heat exchanger, regenerator and cold heat exchanger are 185 mm, 25 mm, 50 mm and 27 mm, respectively. The regenerator is built using stainless steel mesh screens stacked on top of one another to fill up the 50 mm length. The supplied acoustic wave creates a temperature gradient along the regenerator. The development of the temperature gradient is controlled to concentrate only on the hydrodynamic condition of the regenerator. This is achieved through the cold heat exchanger located at the bottom end of the regenerator. The cold heat exchanger is connected to a reservoir that supplies water at room temperature so as it is kept at room temperature. The temperatures at locations T1, T2, T3 and T4 are measured by type-K thermocouples. The rig typically operates with helium at mean pressure up to 60 bar.  
Figure 2: Schematic representation of the experimental rig and the selected computational domain.
# A Comparative Study on the Laser Welding of Ti6 Al4V Alloy Sheets in Flat and Horizontal Positions
## 4 Discussion  
### Effects of Gravity on Weld Profile  
Results on weld profile defects show that using the flat welding position leads to deeper undercuts on the top side than the bottom side of the weld, while using horizontal welding position leads to deeper undercuts on the left side (on the top side during welding) than on the right side (on the bottom side during welding) of the weld.  
These differences can be attributed to enhanced fluid flow towards the root of the weld under the action of gravity for the flat welding position (as shown in Figure 9a); for the horizontal welding position, gravity drives the molten metal flow from the top side of the weld toward the bottom side, leading to necking on the top and thickening on the bottom (as shown in Figure 9b). Such a lateral flow also contributes to horizontal welds being wider than flat welds for a given set of welding parameters. The notable downward movement of the weld pool metal when the flat position welding also accounts for the smaller amount of excess weld metal and larger amount of excess penetration, when compared with horizontal welds.  
Figure 8: Static tensile properties of the laser welds made using four welding conditions. (**a**) Yield strength; (**b**) Specific elongation.
## 3 Results and Discussion
### Extraction of Permanent Offset and Fault Slip Inversion  
In addition to P-wave arrival time, the important information, provided by the integrated position series, is the permanent offset. We use the real-time algorithm proposed by [PERSON] and [PERSON] [1] to remove dynamic oscillations and extract these offsets. The permanent offsets derived from integrated solution (about 1 min after the arrival of the earthquake wave) are compared with the ones from the post-processed daily solution in Figure 7. The RMS of the differences between them is about 3.7 mm.  
We derived the spatial distribution of the fault slip using the coseismic displacements obtained from both the real-time tightly-integrated solution and the post-processed daily solution. In the same way as done by [PERSON] _et al_. [34], the fault geometric parameters (strike 312\({}^{\circ}\)/dip 88\({}^{\circ}\)) are adopted from the Global Centroid Moment Tensor (GCMT) solution of the earthquake. The rake angle (slip direction relative to the strike) is allowed to vary \(\pm 20^{\circ}\) around the GCMT solution of 186\({}^{\circ}\). The fault size is given to be 130 km along the strike and 20 km along the dip, which is then divided into \(26\times 4=104\) sub-faults. In the inversion, the data is weighted twice as much for the two horizontal components as for the vertical component.  
The inversion results are shown in Figure 8. The two inversions result in scalar seismic moments of \(7.27\times 10^{19}\) Nm and \(7.18\times 10^{19}\) Nm respectively, equivalent to moment magnitude of Mw7.18 for both. Although there are some differences existing on the maximum slip values which may be caused by the inconsistency in the vertical component between the two datasets, the two inversion results are quite similar not only in the moment magnitude, but also in the slip distribution pattern. The major slip area occurred at a very shallow depth (near the surface) at about 90 km along the strike direction on the fault plane. The rake variation shows that there is a purely right lateral strike slip at the northwest of the fault, and a minor normal fault component occurs at the south east of the fault. Considering the hypocentral location, we can confirm that this earthquake is an asymmetric bilateral rupture event: the rupture mainly propagates northwestward from the hypocenter during the source process. Overall, the  
Figure 7: Comparison of the permanent (static) offsets from the tightly-integrated solution and the post-processed daily solution. The blue rectangle shows static offsets derived from the static PPP solution with daily observations (the difference between daily solutions of the day before the earthquake and the day after the earthquake). The red rectangles show the static offsets derived from the real-time tightly-integrated solution.  
comparison of the two inversion results shows that the integrated solution can provide a reliable estimation of earthquake magnitude and even of the fault slip distribution in real time.
## 4 Implementation
### Proposed Modulation Scheme
#### 4.3.4 Implementation Results  
Taking advantage of the 2D-MIMO scheme, we utilized commercial cameras to evaluate the proposed scheme. We conducted this experiment using a Microsoft global-shutter camera and a Point Gray rolling-shutter camera to evaluate system performance under frame-per-second fluctuations. On the receiver side, using a combination of deep-learning methods and image-processing techniques, data decoding is performed sequentially in steps from LED tracking and detection to missing data packet detection and data packet merging based on the SN value of each data packet. The quantized intensity profiles of the LED array images captured at a distance of 4 m and an exposure time of 300 s are shown in Figure 7. Depending on the conditions of the application, for example, the distance communication or mobility environment, these parameters can be changed. The setup of the proposed implementation is shown in Figure 8, and the webcam experiment results are shown in Figure 9. The transmitter side will be implemented with an LED matrix and controlled by Arduino with a 2D-MIMO encoder. In the receiver side, the Python 3.6 version was proposed to decode and for tracking the LED matrix. Table 1 displays the results of the implementation. Based on Table 1, we can see that with a 16 \(\times\) 16 LED matrix, the data rate can achieve 15.360 kbps when it is 3.840 kbps with an 8 \(\times\) 8 LED matrix with a velocity of 3 m/s. Then, to increase the data rate, we can change the version of the LED matrix.  
We experimented with differing exposure times and communication distances using a Point Gray rolling shutter camera to determine the BER value of the OCC system. We measured the BER of these two approaches under the same distance and environmental conditions to have the best comparison between the deep-learning method and the conventional decoder. The comparative outcomes are shown in Figure 10 with an exposure time of 100 us. At a distance of 10 m, we could achieve a BER of 10\({}^{-3}\) with the deep-learning approach; by contrast, the BER value was only 10\({}^{-2}\) with the conventional approach. Thus, with the deep-learning approach, we could enhance the performance of the OCC system under long distance and the mobility effect. Besides, channel coding plays an important role in implementing an OCC system when there is a lot of noise from the external environment, which directly affects the quality of data transmission between the receiver and transmitter. Applying channel coding to the system will help to reduce the number of bit errors and improve the transmission and reception distance at the same time. However, the trade-off between exposure time and signal noise needs to be carefully considered because of the close relationship between the communication bandwidth and exposure time. Table 1
# Anti-Multipath Performance Improvement of an M-ary Position Phase Shift Keying Modulation System
## 2 System Description
### High Frequency Multipath Channel  
HF channels are nonstationary in both frequency and time, but if consideration is restricted to band-limited channels (for example, 10 kHz) and sufficiently short times (for example, 10 min), most channels are nearly stationary and can be adequately represented by a stationary model [20]. The channel model is shown in Figure 5.  
Where \(n\) is the number of paths, \(\tau_{n}\) is the delay of each path, and \(a_{n}\) is the gain of each path. The input signal is subjected to a finite order delay. Each order of the signal has an appropriate order gain to amplitude and phase modulate it. The modulated signal is summed at the output [20]. In this paper, when simulating the multipath effect, the multipath is regarded as the waveform of the main path delay. The received signal can be represented by superimposing the main path signal and the main path signal delayed for a certain time. It is assumed that the received expression of the transmitted MMPPSK modulated signal is \(ag(t)\). After transmission over a multipath channel, the received signal is given by  
\[r(t)=\sum_{i=1}^{n}a_{i}g(t-\tau_{i}) \tag{3}\]  
where \(a_{i}\) is the amplitude fading caused by the gain of each path channel, and \(\tau_{i}\) is the delay of each path relative to the main path.
## 3 Study Area and Data
### Data Sets and Preprocessing  
The Suomi National Polar Partnership satellite SNPP/VIIRS global night-time light DNB data is used in this study as the daily data, and the images were obtained from the official website of the National Oceanic and Atmospheric Administration ([[https://ngdc.noaa.gov/](https://ngdc.noaa.gov/)]([https://ngdc.noaa.gov/](https://ngdc.noaa.gov/))(accessed on 2 April 2022)). The imaging period of the daily night-time light images was selected from the conflict eruption between 24 February to 1 March, a total of 36 days. The monthly night-time light imaging period was selected as December 2021. The night-time light data for 2021, as released by the Payne Institute for Public Policy ([[https://www.mines.edu/](https://www.mines.edu/)]([https://www.mines.edu/](https://www.mines.edu/))), was used to calculate the built-up area. The night-time light imaging area is 75N/060W, using the coordinate system Select WGS_1984_UTM_Zone_50N. The administrative boundary data were downloaded from the Global Administrative Division Database ([[https://gadm.org/](https://gadm.org/)]([https://gadm.org/](https://gadm.org/))). The road network data was obtained from the OpenStreetMap ([[https://www.openstreetmap.org/](https://www.openstreetmap.org/)]([https://www.openstreetmap.org/](https://www.openstreetmap.org/)) (accessed on 27 November 2022)).  
Daily NPP VIIRS DNB data has been applied to assess the impact of natural or anthropogenic phenomena on human social activities [41]. In the process of constructing multi-time-series VIIRS images, it is necessary to ensure the continuity of image time and space [42]. Within the time-frame studied in this paper, on some days the NPP VIIRS data was partially missing, such as 19 March and 31 March. According to the idea of averaging, the missing night-time light data on a given day is obtained by interpolating the night-time lights of the nearest two days, the formula for which is as follows:  
\[\textbf{2}\;\textbf{D}_{i}^{t}=\textbf{D}_{i}^{t+1}+\textbf{D}_{i}^{t-1} \tag{1}\]  
In Formula (1), \(\textbf{D}_{i}^{t}\), \(\textbf{D}_{i}^{t+1}\) and \(\textbf{D}_{i}^{t-1}\) represent the radiance value of the \(i\)-th pixel on days \(t\), (\(t+1\)), and (\(t-1\)), respectively.  
To infer the changing trends of night-time light in a region affected by war, the minimum night-time light value of the research region in the study period should be extracted and compared to the minimum value of night-time light on a day prior to the outbreak of war. If the minimum value during a conflict period is smaller than the minimum value before the conflict period, this means that the area is affected by the conflict and has suffered from the loss of night-time lights.