esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Wetting Conditions on the In Situ Density of Soil Using the Sand-Cone Method
## 2 Density Tests Using Sand-Cone Method
### Test Sand and Calibration  
The sand used to fill the test hole has to adhere to the standards. ASTM D 1556-07 [3] recommends the use of sand with a Cu less than 2.0, a maximum particle size smaller than 2 mm, and that less than 3% of the weight pass through a 0.25 mm sieve. KS F 2311 [5] specifies the use of sand that can pass through a 2-mm sieve but is retained on a 0.075-mm sieve. Jumujin sand was used in this study, and the results of the sieve test and its particle-size distribution curve are shown in Table 1 and Figure 3, respectively. The physical properties of the sand are shown in Table 2. The specific gravity (Gs) was 2.65, and the diameters through which 10%, 30%, and 60% of the particles passed (D\({}_{10}\), D\({}_{30}\), and D\({}_{60}\)) were 0.31 mm, 0.45 mm, and 0.62 mm, respectively. The Cu was 1.98, and the coefficient of curvature (C\({}_{\rm c}\)) was 1.05. Based on the results, the sand was classified as poorly-graded (SP) according to the Unified Soil Classification System (USCS). Therefore, Jumujin sand satisfied the specifications of both ASTM and KS.  
Before the sand-cone test, the dry density of the test sand had to be determined. This determination process is known as calibration. The dry density of the test sand was calculated in accordance with ASTM D 1556-07 [3] by using a compaction mold with a diameter of 15 cm, a height of 17.5 cm, and a volume of 3093 cm\({}^{3}\). The dry unit weight (density) of the Jumujin sand used in this study was calculated to be 13.40 kN/m\({}^{3}\) (1.37 g/cm\({}^{3}\)).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Sieve number** & 4 & 10 & 20 & 40 & 60 & 100 & 200 \\ \hline
**Mesh size (mm)** & 4.75 & 2.00 & 0.85 & 0.425 & 0.25 & 0.15 & 0.075 \\ \hline
**Soil passing (\%)** & 100 & 100 & 99.80 & 25.60 & 1.00 & 0.60 & 0.00 \\ \hline \end{tabular}
\end{table}
Table 1: Sieve-analysis results of Jumujin sand.  
Figure 2: Cross-sections showing dry (**a,c**) and wet (**b,d**) compacted grounds filled with sand (**a,b**) and after removal of sand (**c,d**).
## 4 Simulation and Measurements Results
### Results for the 28 GHz Array
However, the lower gain values will lead to a reduced data rate, which is not suitable for various 5G automotive mobility applications at the Ka band. In [22] and dual-layer stacked patches for L- and S-band were utilized to realize dual-CP operation. In addition to a lower value of gain, 3-dB axial ratio bandwidth does not cover the operational bandwidth of \(\text{B}_{\text{op}}=100\) MHz. Moreover, the antenna structure operating at the K- and Ka-band in [23] is complex in addition to having reduced gain. The proposed dual-band UE antenna terminal is verified through measurements after em  
Figure 8: (**a**) Input reflection coefficients \(1\text{S}_{11}\,|^{2}\) for the bare simulated (red-colored curve), bare measured (blue), and embedded measured (green) antenna array; (**b**) axial ratio versus frequency along zenith, \(\Theta=0^{\circ}\); (**c**) axial ratio versus co-elevation at f = 28 GHz; (**d**) vertical cut of normalized realized gain at f = 28 GHz for \(\Phi=0^{\circ}\) and (**e**) for \(\Phi=90^{\circ}\) (same color coding as in panel a). bedding into the rear spoiler of a passenger car compared to the above antennas. High-gain dynamic beam tracking antennas were developed for 5G satellite communications [5,6] but they are not feasible for automotive mass-market applications due to their significant complexity, size, and power consumption. In summary, the proposed work realizes a very compact, low-profile, and easy-to-manufacture circularly polarized dual-band UE nested antenna array.
## 3 Results
### Response of Vegetation to Drought
Therefore, \(\text{SIF}_{\text{yield}}\) can be a better indicator to describe the developments of drought impacts of water stress on crop growth. The drought condition in the EBF region was moderate during the El Nino event (Figure 6b). The decline of EVI, SIF, \(\text{SIF}_{\text{yield}}\) in EBF regions were similar during the El Nino peak time. However, a sudden increase of \(\text{SIF}_{\text{yield}}\) appeared from September to October in 2015. The substantial decrease of solar radiation in EBF area was in September 2015 (Figure 3e), which coincided with the highest monthly aerosol optical depth (Figure 7) caused by fire in September in Indonesia [6]. Consequently, \(\text{SIF}_{\text{yield}}\) increased in October and November because of the fire in EBF area. But for the other months, \(\text{SIF}_{\text{yield}}\) was less than multiyear mean in late 2015. Both regions exhibited larger reduction of \(\text{SIF}_{\text{yield}}\) than that of SIF and EVI during the drought peak time. The \(\text{SIF}_{\text{yield}}\) quickly responded when the drought began, and anomalies of \(\text{SIF}_{\text{yield}}\) became larger as the drought aggravated. The analyses for the crop and EBF regions indicated that the \(\text{SIF}_{\text{yield}}\) not only described the spatial patterns of the drought event but also captured the temporal dynamics of their impacts on effective fluorescence yield. Figure 6: The monthly averages of (**a,b**) precipitation, (**c,d**) EVI, (**e,f**) SIF, (**g,h**) \(\text{SIF}_{\text{yield}}\) for the croplands (left column) and for the evergreen broadleaf forest area (right column) during the drought period (from May 2015 to April 2016) in Southeast Asia. The shaded area showed uncertainties of the multiyear mean from 2007 to 2014.
# Performance Analysis of the Korean Positioning System Using Observation Simulation
## 3 Simulations
### Observation Simulation
SPP is considered for positioning performance of single-frequency users and is referred to as single point positioning using pseudo-range measurements and navigation parameters. For KPS/GPS SPP simulation, we use only the simulated P1 code measurements, as presented in Equation (1). The error sources in SPP simulation include satellite ephemeris, satellite clock, atmospheric effects (ionosphere and troposphere), and observation noises. In this study, precise satellite orbit and clock products generated by orbit simulations are considered for KPS/GPS SPP. A kinematic PPP is also adopted to investigate the performance of precise positioning for the simulated KPS and GPS observations in the regional service area. In general, GNSS PPP uses dual-frequency measurements to obtain a precise position solution with a single station only [27]. To remove the first-order ionospheric delay, the linearized observation equations for the ionosphere-free (IF) code and phase linear combinations can be written as follows [27]:  
\[P_{IF}=\rho+c\left(dt_{r}-dt^{s}\right)+T+\varepsilon_{P_{IF}} \tag{5}\]  
\[\Phi_{IF}=\rho+c\left(dt_{r}-dt^{s}\right)+T+N_{IF}+\varepsilon_{\Phi_{IF}} \tag{6}\]  
where \(P_{IF}\) and \(\Phi_{IF}\) are the IF code observables and IF phase observables, respectively. \(\rho\) is the geometric range from the GNSS satellite to the ground receiver, and \(c\) is the speed of light. \(N_{IF}\) is the IF phase ambiguity. \(\varepsilon_{P_{IF}}\) and \(\varepsilon_{\Phi_{IF}}\) represent the code and phase measurement noises by IF combinations, respectively.
## 3 Results and Discussions
### Accuracy Verification of CORDIC Algorithm
#### 3.2.2 Quadrature Demodulation  
To specifically test the performance of the simulation results, some sampling points can be taken on the simulation curve and then compared with the theoretical values to calculate the absolute error values. The absolute error distribution of the MATLAB square root simulation based on the partitioned-hybrid algorithm is shown in Figure 10a. The square root operation based on the partitioned hybrid CORDIC algorithm can achieve absolute error values below \(2.5\times 10^{-4}\). To further verify the reliability of the algorithm's accuracy, the same sampling points on the Model Sim simulation plot are selected for data reading and then analyzed for absolute error value magnitude. The ModelSim square root simulation data based on the partitioned hybrid CORDIC algorithm is shown in Table 2. The absolute error value of the Model Sim square root simulation results is in the order of \(10^{-3}\), which is significantly larger than the MATLAB simulation results. The two main reasons for this phenomenon can be explained as follows:  
1. During the FPGA simulation, 16-bit wide data is used that can be accurately represented in MATLAB. This process introduces quantization errors, especially in the quantization of the calibration factor, which can have a large impact on the final results.
2. The number of pipeline stages is limited. Considering the complexity of programming and the consumption of internal resources of the FPGA, the number of pipeline stages is 7, which affects the accuracy to some extent.  
For the 8-bit gray-scale image of the radial artery ultrasound system, the error is extended to the 8-bit range, i.e., \(10^{-3}\times 2^{8}=0.256<1\). Since it is less than 1 bit, the error is undetectable in this digital resolution system, so the output accuracy of the orthogonal demodulation module can meet the requirements of the ultrasound imaging system.  
Figure 9: Test results of the dynamic FIR filtering performance based on the radix\(-\)4 CORDIC algorithm. (**a**) FPGA simulation of time-domain waveform before and after filtering of synthetic single\(-\)frequency signals; (**b**) FPGA simulation of time\(-\)domain waveform before and after filtering of white noise signal; (**c**) FPGA simulation of the spectrum before and after filtering synthetic single\(-\)frequency signals; (**d**) FPGA simulation of the spectrum before and after filtering white noise signal.
## 5 Simulation and Discussion  
### The Design of the Experimental Prototype  
#### 5.1.1 Wind Turbine and Variable Pump  
A Micron 600 kW wind turbine is chosen and will be turned into the experimental platform of hydraulic wind turbine, as shown in Figure 7. The rotor blades of the wind turbine remain unchanged for absorbing wind energy. The variable pump will be installed in the nacelle by replacing the original gearbox and generator, which transmits the wind energy captured by turbine blades into hydraulic energy. To provide the hydraulic system with high reliability and long life at the same time, the maximum working pressure of this hydraulic system is limited at 210 bars. The parameters of wind turbine and variable pump are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Symbol** & **Value** & **Unit** \\ \hline Rotor diameter & \(D\) & 43 & m \\ Swept area & \(A\) & 1453 & m\({}^{2}\) \\ Hub height & \(H\) & 46 & m \\ Cut-in wind speed & \(V_{in}\) & 3.5 & m/s \\ Cut-out wind speed & \(V_{out}\) & 25 & m/s \\ Rated wind speed & \(V_{ntal}\) & 15.5 & m/s \\ Rated power & \(P_{ntal}\) & 600 & kW \\ Maximum rotor speed & \(R_{p}\) & 27 & r/min \\ Pump maximum displacement & \(V_{p}\) & 67,000 & cm\({}^{3}\)/rev \\ Moment of inertia & \(J_{p}\) & 20,000 & kg\(\cdot\)m\({}^{2}\) \\ Coefficient of viscosity & \(B_{p}\) & 50 & Nm/(r/min) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main parameters of wind turbine and hydraulic pump.  
Figure 7: The Micron 600 kW wind turbine experimental platform.
# A New Zenith Tropospheric Delay Grid Product for Real-Time PPP Applications over China
## 2 Data and Processing Strategy
### GPS Data and the Processing Strategy
Taking ZTD estimated in post-processing mode (i.e., IGS final orbits and clocks were used and parameters were estimated by least squares method) as references, the bias and RMS of real-time ZTD errors at all stations in CMONOC for four months in different seasons are summarized in Table 3 where the mean bias and RMS are about 0.25 cm and 1.18 cm, respectively.
## 2 Materials and Methods
### Statistical Analysis  
The ability to detect ERD in stroke survivors was determined using the activation of the DiC. In the MSM, the sliding window of the EEG-signaled DiC activation occurred when the probability of ERD occurrence was \(<\)60% and the probability of the resting state was \(<\)40% during a moving average of 1 s. In RP, the RMS of the resting EEG was used as the reference value, and a signal to activate the DiC was sent when the RMS exceeded 1.5 times the reference value for \(>\)0.2 s. If the elbow joint of the arm held by the DiC moved with each signal, ERD was considered detected. The time (t) between the EEG playback and ERD detection was measured using the MSM and RP, and the reaction times of both systems were compared using [PERSON]'s signed rank sum test. The statistical significance level was set at \(<\)5%.  
Methods for Exclusion of Missing Data and Outliers for the Reaction Time from the EEG Playback to ERD Detection  
The difference between the third and first quartiles of the total reaction time from the EEG playback to ERD detection was defined as the interquartile range (IQR). 'Mean + IQR' and'mean \(-\) IQR' were defined as the upper and lower inner boundary points, respectively; values beyond the upper and lower inner boundary points were excluded from the statistical analysis as outliers.
# Road Event Detection and Classification Algorithm Using Vibration and Acceleration Data
## 6 Impact of the Butterworth Filter on Classification Performance
### Algorithmic Complexity Analysis
#### 6.1.2 Feature Extraction  
* Sliding Window: Using a sliding window approach with a size of \(w\) and overlap \(o\), the number of windows processed is approximately \((n-w)/(w-o)\). Extracting the features for each window involves the following:
* Statistical Features (mean, variance, skewness, kurtosis): \(O(w)\) each.
* Frequency features (FFT, spectral entropy): FFT has a complexity of \(O(w\log w)\), and the entropy calculation is \(O(w)\). Overall Complexity: \(O\big{(}\frac{n-w}{w-o}\cdot w\log w\big{)}\), dominated by the FFT computation.
# Multi-Sensor Precipitation Estimation from Space: Data Sources, Methods and Validation
## 1 Introduction
Presently, the primary SPPs encompass TRMM Multi-Satellite Precipitation (TMPA) [25], Climate Prediction Center (CPC) morphing (CMORPH) [26], Global Satellite Mapping of Precipitation (GSMaP) [27], Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network (PERSIANN) [28; 29], and the Integrated Multi-satellite Retrievals for GPM (IMERG) [30]. Numerous reviews on SPE have been published from diverse perspectives, as shown in Table 1. These reviews have predominantly focused on the visible/infrared (VIS/IR)-based or PMW-based approaches during the 1990s, with an increased emphasis on global coverage after the 2000s. Several reviews have focused on global SPPs from the aspects of algorithms, datasets, validation, and applications. Others have addressed SPP applications and programs. Additionally, several books related to SPE have been published. Despite extensive efforts, there has been a limited number of reviews specifically addressing MPE. Given that MPE serves as the primary approach for obtaining global SPPs, it is imperative to provide a comprehensive review of its data sources, physical foundations, methodologies, and uncertainties. Such a review will (1) stimulate the development of novel MPE approaches, (2) facilitate the expansion of global precipitation data in terms of both temporal coverage and spatial resolution, and (3) enhance our understanding of MPE. The review is organized as follows: Section 2 introduces the satellites and sensors employed for precipitation estimation, followed by a comprehensive discussion of the underlying physical principles in Section 3. Section 4 provides an overview of the primary methodologies utilized for MPE. The subsequent section, Section 5, elucidates the development of SPPs and is succeeded by a thorough validation in Section 6. Finally, Section 7 concludes this discourse on MPE while proposing future prospects. \begin{table}
\begin{tabular}{l l} \hline \hline
**Theme** & **References** \\ \hline VIS/IR & [31] \\ \hline PMW & [18; 32; 33] \\ \hline VIS/IR, PMW & [8; 34; 35; 36; 37] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of existing review papers on SPE.
### Appendix A.1  
Table 14 presents the acquisition parameters for the Galileo E1b/c signals when operating in high-sensitivity mode. The 'Doppler max' represents the maximum Doppler frequency, and the 'Doppler step' is the frequency step in the search grid. Doppler prediction is enabled to narrow down the Doppler frequency search space. The proposed two-step acquisition scheme is used. In the first step, a large Doppler search space of \(\pm\)50 Hz is performed. In the second step, a very small Doppler search space of \(\pm\)10 Hz is used around the Doppler frequency detected in the first step.  
Additionally, a downsampling filter is specifically applied in the L1/E1 band to reduce the acquisition latency. This filter is exclusively utilized during the acquisition process and reduces the sampling frequency used to acquire the Galileo E1b/c signals from 15.728640 Msps to 3.932160 Msps, hence facilitating the computation of long FFTs in the acquisition.  
Table 15 displays the receiver's tracking configuration parameters for the Galileo E1b/c signals when operating in high-sensitivity mode. The CI time is specifically set to 40 ms to enhance the apparent signal-to-noise ratio. In high-sensitivity mode, upon initiating tracking for the detected signals, the receiver is already synchronized with the secondary code of the received signals, enabling for the utilization of a long CI time right from the start.  
In Table 15, 'early-late narrow space chips' refers to the spacing between the Early and Prompt correlators, and between the Prompt and Late correlators. Similarly,'very early late space narrow chips' indicates the spacing between the Very-Early and Prompt correlators, and between the Prompt and Very-Late correlators. Both spacings are normalized by the chip period. The 'PLL filter bandwidth' with a narrow correlator configuration represents the bandwidth of the PLL low-pass filter. Finally, the 'DLL filter bandwidth' with a narrow correlator configuration denotes the bandwidth of the DLL low-pass filter.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Signal** & **Parameter** & **Value** \\ \hline \hline \multirow{6}{*}{Tracking Galileo E1b/c} & Coherent integration time & 40 ms \\  & Early–Late space narrow chips & 0.15 chips \\ \cline{1-1}  & Very Early–Late space narrow chips & 0.5 chips \\ \cline{1-1}  & PLL filter bandwidth (narrow correlator) & 5 Hz \\ \cline{1-1}  & DLL filter bandwidth (narrow correlator) & 1.0 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 14: Receiver HS acquisition configuration.
## 3 OLSR Attacks
### False Control Information Generation
#### 3.1.1 Hello Message Scope  
Hello messages are essential for neighbor discovery, link sensing, topology control, and network stability in the OLSR protocol. They facilitate the establishment of communication, monitor link quality, construct network topologies, and enable timely adaptation to network changes, ultimately ensuring efficient and reliable routing within the network. Both Identity Spoofing and Link Spoofing are security threats that can undermine the trust and reliability of the protocol.  
* Identity Spoofing: A node sends HELLO messages while pretending to be another node [11]. For example, Node X sends HELLO messages with Node A's original address. This could result in the network having conflicting routes to Node A. Specifically, Node X would choose MPRs from its neighbors, and while signaling this selection, it pretends to have the identity of Node A. The MPRs would then notify them that they could provide the \"last hop\" to Node A in their TC messages.
* A node sends HELLO messages to an incorrect set of neighbors. It may take either of two forms. If the set is incomplete and certain neighbors are ignored, it can result in the network losing connectivity with those ignored neighbors. On the other hand, a compromised node might falsely advertise a neighbor relationship with non-existent nodes, leading to inaccurate MPR selection. This will lead to a situation where some nodes are not reachable [12].
## 4 Case Study  
### Background  
The lead-zinc TSF (Figure 4) located in Hunan Province, China, is a third grade TSF, according to China's national standard [38] \"Safety regulation for tailings pond (GB 39496-2020)\". The total dam height of the TSF is 51.5 m, with a total storage capacity of 10.66 million cubic meters. The major structures of the TSF include the main dam, auxiliary dams 1#-3#, and drainage facilities. The starter dam is built as the homogeneous earth dam. The accumulation dam has been constructed by the upstream way. As the height of the tailings dam increased, auxiliary dams 1#-3# have been built. The structure of the flood discharge system is a frame-type drainage well combined with a concrete drainage pipe.  
Auxiliary dam 1# is located on the west side of the TSF. For its starter dam and accumulation dam, the height of the dam is about 12.4 m and 22.7 m, respectively. The outer slope ratio is 1:2 and 1:4.5, respectively. Auxiliary dam 3# is located on the north side of the TSF, with a total dam height of 15 m and a slope ratio of 1:4.5.  
The accumulation dams for auxiliary dams 1# and 3# are connected as a whole at an elevation of 310.5 m. At the starter dam crest (at an elevation of about 300 m), there is a horizontal seepage pipe with a leakage phenomenon in each of auxiliary dams 1# and 3#.  
Figure 4: Huangshaping tailings storage facility used in the case study.  
The starter dams are all homogeneous earth dams with poor permeability, and most of the original drainage facilities fail to allow seepage. Consequently, the seepage water inside the accumulation dam cannot be discharged, resulting in a high burial depth of the seepage line within the accumulation dam. A large amount of the seepage line has escaped at the toe of the slope of the accumulation dam, and swamping has been further generated within some local areas.  
During the operation of the TSF, there are phenomena such as the high seepage line of the dam and local swamping of the dam slope, which need to be handled with great care. To reduce the seepage line of the dam and ensure the normal operation of the TSF, it is necessary to carry out drainage treatment at auxiliary dams 1# and 3#.  
The four proposed risk reduction schemes are shown in Table 3.