esa-sceva/satcom-chunk-collection huggingface dataset:
# Stability Improvement Method for Embankment Dam with Respect to Conduit Cracks
## 3 Results and Discussion
### Investigation of Erosion of Current and Improvement Models
In the crack condition  
Figure 4: Reinforcement condition of current model (Case 1—Reinforcement): (**a**) no loss of soil particles inside conduit, (**b**) formation of wide wet area outside the conduit, (**c**) reinforced crack area using membrane. Figure 3: Crack conditions in current model (Case 1—Crack): (**a**) soil particle loss inside conduit, (**b**) surface conditions of downstream slope, (**c**) erosion in near-conduit-filling zone. (Case 2--Crack), the amount of soil loss inside the conduit was markedly smaller than that in the current model (Case 1--Crack), as shown in Figure 5a. For the erosion area at the bottom of the conduit, the improvement model was found to have approximately 1.7 times less erosion than the current one; this proved it to be effective in reducing internal erosion. Meanwhile, locally wet areas were observed in the downstream slope, as shown in Figure 5b. This shows that the seepage water concentrated in the crack area of the conduit was partially dispersed in the sand layer. In contrast to Case 1--Crack, no cavitation was observed in the vicinity of the conduit in Case 2--Crack, despite the erosion inside the conduit, as shown in Figure 5c. These results are associated with the layers of sand and geotextiles in the cracked area of the conduit suppressing the erosion rates. Under the condition of geomembrane reinforcement (Case 2--Reinforcement), there was no loss of soil inside the conduit, and the area around it was stable against erosion, as shown in Figure 6a,c. In addition, on the downstream slope, the wet area decreased considerably, as the seepage water in the embankment was dispersed throughout the sand layer, as shown in Figure 6b. The improvement model analyzed in this study used the combined effect of the sand and geotextile layers in the downstream slope to suppress cavitation, even in the presence of other structural defects (such as cracks in the piping). Therefore, it can be concluded that the improvement model is a reliable method to improve the safety of an embankment compared to the current method. Furthermore, applying the improvement model to the downstream slope is simple and does not require drilling, which can be a great advantage in terms of constructability.
## 2 Basic Principle of SAR Imaging Simulation
### SAR Echo Signal Simulation
#### 2.3.2 Calculation of the Ocean Surface Echo Signals  
The algorithm for simulating echo signals is divided into a time-domain algorithm and frequency-domain algorithm. Among these algorithms, the calculation of the frequency-domain algorithm is relatively small, but the velocity bunching effect cannot be introduced in the process of generating echo signals. The time-domain algorithm simulates the real working process of the SAR receiver and can acquire the original echo signals. Although the calculation amount is large, the generated ocean surface echo signals are the most accurate. Based on the comparison, this paper selected the time-domain algorithm and assumed that the SAR working mode was side-looking to simulate the ocean surface echo signals.  
Combined with the SAR operating parameter set in the simulation process, according to the SAR working principle, the 1D echo signal is written in the 2D form with respect to the azimuth and range. After the carrier is removed by quadrature demodulation, the echo of the single-point target can be written as follows [30]:  
\[s_{r}(s,\;t;\;r)= \sigma\cdot rect(\frac{t-2R(s;\;r)/C}{Tr})\exp[j\pi K_{r}(t-2R(s; \;r)/C)^{2}]\cdot \tag{10}\] \[rect(\frac{r}{Tsar})\exp[-j\frac{4\pi}{\lambda}R(s;\;r)]\]  
where PRF is the pulse repetition time, rect represents a rectangular function, C is the speed of light, and \(\sigma\) is the backscattering cross-section of the point target. The echoes of K ideal point targets can be obtained by superposition, as shown in the following Equation [30]:  
\[\begin{array}{rl}Sr(n,\;m)=&\sum\limits_{k=1}^{K}\sigma\cdot\exp\{j\pi[t(m) -\frac{2R(n;\;k)}{C}]\}\cdot\exp[-j\frac{4\pi}{\lambda}R(n;\;k)]\\ &0<[t(m)-\frac{2R(n\lambda k)}{C}]<Tr;\;\Big{|}R(n;\;k)-x(k)\Big{|}<Tsar\\ &\qquad\qquad n=1,\;2,\;3\cdots N;\;m=1,\;2,\;3\cdots M\end{array} \tag{11}\]  
where R(n; k) is the slant distance between the SAR and imaging points, Tsar is the SAR integration time, and Tr is the pulse duration. The idea of generating ocean surface echo signals was to calculate the echo signals of all of the points that make up the ocean surface separately and superimpose the echo signals of the points to obtain the echo signals of the whole ocean surface at a certain azimuth, traverse all of the azimuth moments, and finally obtain the echo signals of the entire ocean surface.
## 4 Experimental Verification and Results Analysis  
### Experimental Platform  
To validate the measurement performance of the attitude measurement system in practical applications, experiments were conducted using a precision turntable for dynamic attitude measurement verification. As shown in Figure 11, the attitude measurement system consists of a total station, camera, cooperative target, and upper computer. The precision two-dimensional turntable has a measurement accuracy of 2 arcseconds and a  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Net No.**} & \multirow{2}{*}{**Input**} & \multirow{2}{*}{**Output**} & \multirow{2}{*}{**Number of** **Neurons**} & \multicolumn{2}{c}{**The Source of the Theoretical Value**} & \multicolumn{2}{c}{**Training**} \\ \cline{5-8}  & & & & **True Value** & **IMU** &
\begin{tabular}{c} **Neighborhood** \\ **Search** \\ \end{tabular} & **MSE** & **R** \\ \hline
1 & 6 & 3 & 11 & \(\surd\) & & & 0.039 & 0.976 \\
2 & 6 & 3 & 11 & \(\surd\) & & & 0.296 & 0.901 \\
3 & 6 & 3 & 11 & & & \(\surd\) & 0.196 & 0.956 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the results of different outputs of the neural network training.  
Figure 10: Comparison of neighborhood search effects.  
displayed angular velocity of 5\({}^{\circ}\)/s. The azimuth angle can rotate from 50\({}^{\circ}\) to 330\({}^{\circ}\), and the elevation angle can rotate from 0\({}^{\circ}\) to 360\({}^{\circ}\). However, the precision two-dimensional turntable can rotate only in azimuth and elevation directions, and the roll angle accuracy is higher than that of the azimuth and elevation angles based on the literature. Therefore, this study only focuses on validating the azimuth and elevation angles experimentally. The IMU frequency is 100 Hz, and the visual measurement frequency is 1 Hz.
# Insider Threat Detection Based on User Behavior Modeling and Anomaly Detection Algorithms
## 4 Results
### Insider-Threat Detection with Daily Activity Summaries
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Anomaly Rank** & **Gauss** & **Parzen** & **PCA** & **KMC (K = 3)** & **KMC (K = 5)** & **KMC (K = 10)** & **Parzen + PCA** \\ \hline
1\% & 0.5000 & 0.4000 & 0.4933 & 0.5233 & 0.5333 & **0.5367** & 0.4833 \\
5\% & 0.6000 & 0.5000 & 0.6667 & 0.6400 & 0.6300 & 0.6333 & **0.7633** \\
10\% & 0.6167 & **0.7933** & 0.7467 & 0.7033 & 0.6467 & 0.6933 & **0.7933** \\
15\% & 0.7000 & **0.9000** & 0.7800 & 0.7167 & 0.6767 & 0.7333 & 0.8000 \\
20\% & 0.7000 & **0.9000** & 0.7900 & 0.7500 & 0.6967 & 0.7600 & 0.8167 \\
25\% & 0.7000 & **0.9000** & 0.8000 & 0.7767 & 0.7433 & 0.7767 & 0.8233 \\
30\% & 0.7000 & **0.9000** & 0.8033 & 0.7767 & 0.7700 & 0.7933 & 0.8500 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: True detection rate of each anomaly detection algorithm based on the daily activity summary dataset (Electrical Engineer). Best performance is shown in bold and underlined (number of normal behaviors in the test set: 14,120, number of malicious behaviors in the test set: 10). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Anomaly Rank** & **Gauss** & **Parzen** & **PCA** & **KMC (K = 3)** & **KMC (K = 5)** & **KMC (K = 10)** & **Parzen + PCA** \\ \hline
1\% & 0.0435 & 0.0478 & 0.0739 & 0.0580 & 0.0521 & 0.0522 & **0.0971** \\
5\% & 0.0435 & 0.1739 & 0.2130 & 0.0841 & 0.0739 & 0.0768 & **0.2174** \\
10\% & 0.0435 & **0.3015** & 0.2304 & 0.1246 & 0.1087 & 0.1174 & 0.2580 \\
15\% & 0.0971 & **0.3043** & 0.2884 & 0.1391 & 0.1275 & 0.1362 & 0.2913 \\
20\% & 0.1594 & 0.3043 & **0.3348** & 0.2333 & 0.2000 & 0.2043 & 0.3246 \\
25\% & 0.1739 & 0.3043 & **0.3681** & 0.3029 & 0.2681 & 0.2797 & 0.3551 \\
30\% & 0.2609 & 0.3043 & **0.4087** & 0.3493 & 0.3304 & 0.3406 & 0.3928 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: True detection rate of each anomaly detection algorithm based on the daily activity summary dataset (IT Admin). Best performance is shown in bold and underlined (number of normal behaviors in the test set: 3424, number of malicious behaviors in the test set: 23). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Anomaly Rank** & **Gauss** & **Parzen** & **PCA** & **KMC (K = 3)** & **KMC (K = 5)** & **KMC (K = 10)** & **Parzen + PCA** \\ \hline
1\% & 0.0093 & **0.1177** & 0.0781 & 0.0375 & 0.0396 & 0.0281 & 0.1021 \\
5\% & 0.0313 & 0.3271 & 0.3375 & 0.1083 & 0.0843 & 0.0802 & **0.3406** \\
10\% & 0.0313 & 0.5677 & 0.5458 & 0.1396 & 0.1125 & 0.1135 & **0.6156** \\
15\% & 0.6
## 3 Procedures of InSAR Image Co-registration
### Determination of Interval of Tie Points for Co-registration  
Conventionally, tie points are usually selected in a grid form, i.e. at grid nodes. Tie points grid sizes of 2\(\times\)10, 9\(\times\)9, 6\(\times\)6 and 8\(\times\)8 have been adopted by [PERSON] _et al_. [1], [PERSON] [65], [PERSON] and [PERSON] [66] and [PERSON] _et al_. [67], respectively. At present, the selection of tie points is determined mainly by experience.  
The interval of tie points greatly influences the quality of image co-registration and the accuracy of final product, DEM. The optimum interval of tie points for image co-registration is systematically conducted [44, 68]. In the study, only the interval of tie points is considered while other factors are kept unchanged. Four pairs of SAR images of 1,760\(\times\)400 pixel size of the Hong Kong area were used for testing. The effects of interval of tie points are assessed by a relative measure for the quality of the resultant interferogram and an absolute measure for the accuracy of the resultant DEM. Results show that the effect of tie point interval on the accuracy of the final DEM is not linear. When the tie point interval is smaller than 273\(\times\)44 pixels (273 pixels in row and 44 pixels in column), the variation in the resultant DEM accuracy is not significant. It is also noticeable that an interval of 205\(\times\)34 pixels always results in the best or very good results. An interval of around 200\(\times\)30 pixels (200 pixels in row and 30 pixels in column) is an appropriate choice for the selection of tie points for image co-registration in hilly areas.
## 3 Methods
### Extraction of Paired Training Patches  
The procedure for extracting the SAR-optical training patches is illustrated in Figure 5. Prior to patch extraction, all of the images with different data value ranges were normalized to have a value between 0 and 1 to be used as inputs for the CGAN. In the first processing step, the training patches were extracted from the paired Sentinel-1 (29 July) and Sentinel-2 (30 July) images to train the generation model. As the Sentinel-2 image on 30 July was assumed to be unavailable in the pilot study area, the training patches were extracted from the cloud-free regions outside of the pilot study area within the full scenes of Sentinel-1 and -2 images. After the clouds and shadows were masked out by visual inspection, the Sentinel-1 and -2 training patches were manually extracted from the cloud-free regions. The size of the training patches was experimentally set to 512 by 512 pixels.  
As the generated virtual Sentinel-2 image will be used for crop classification, the training patches should include crop areas and be extracted from the regions with similar spatial characteristics to the pilot study area. To this end, regions containing more than 70% of crops within the patch window were extracted using the CDL and then selected as training patches. To avoid over-fitting and find the optimal hyperparameters of the generation model, the extracted 1120 training patches were divided into two disjoint groups, including training (1000) and validation (120) patches for the model training and the selection of optimal parameters of the generation model, respectively. Furthermore, data augmentation (DA), which artificially enlarges the number of training data [42; 61], was applied to improve the generalization ability of the trained model by increasing the diversity of the training patches. Four DA schemes, including rotations and flips in the vertical and horizontal directions, were applied to expand the number of training patches intentionally. Finally, 4000 pieces of training data were considered for the model training.
## 2 Methodology
### Fluxnet2015 Dataset  
The FLUXNET dataset, based on the eddy covariance method, provides direct measurements of water and carbon fluxes (e.g., NEE, GPP, and ET) covering over 10 ecosystem types, including forests, grasslands, and wetlands. Early versions (e.g., FLUXNET2000) primarily consisted of single-site data with inconsistent quality control standards and significant energy balance closure errors [27]. FLUXNET2015 offers site-level 'gold standard' observations suitable for detailed validation of model diurnal cycles and seasonal dynamics. Its half-hourly data support the analysis of diurnal variations and short-term dynamics, while annual data are used for long-term trend assessments. The site data used in this study were obtained from the FLUXNET website ([[https://fluxnet.org/data/fluxnet2015-dataset](https://fluxnet.org/data/fluxnet2015-dataset)]([https://fluxnet.org/data/fluxnet2015-dataset](https://fluxnet.org/data/fluxnet2015-dataset)) (accessed on 3 December 2024)) and Zenodo ([[https://zenodo.org/records/12596218](https://zenodo.org/records/12596218)]([https://zenodo.org/records/12596218](https://zenodo.org/records/12596218)) (accessed on 3 December 2024)).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Physical Process** & **Code** & **Parameterization Option** \\ \hline Dynamic Vegetation Model (DVEG) & 2 & Dynamic Vegetation Process Model \\ Canopy Stomatal Resistance (CRS) & 1 & Ball–Berry \\ Surface Layer Drag Coefficient (SFC) & 1 & M-O \\ Canopy Radiation Transfer (RAD) & 1 & Three-Dimensional Canopy Morphology \\ Soil Water Potential Factor (BTR) & 1 & Noah \\ Frozen Soil Permeability (INF) & 1 & NY06 \\ Soil Bottom Temperature (TBOT) & 2 & Noah \\ Runoff and Groundwater (RUN) & 1 & SIMGM \\ Supercooled Water Process (FRZ) & 1 & NY06 \\ Snow Albedo (ALB) & 2 & CLASS \\ Rain and Snow Separation (SNF) & 1 & Jordan91 \\ Snow/Soil Temperature Integration (STC) & 1 & Semi-Implicit \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameterization schemes in Noah-MP.  
This study analyzes data from 105 global sites, encompassing 12 different vegetation types classified according to the International Geosphere-Biosphere Programme (IGBP) [28], including evergreen needleleaf forest (ENF), evergreen broadleaf forest (EBF), deciduous needleleaf forest (DNF), deciduous broadleaf forest (DBF), mixed forest (MF), closed shrublands (CSH), open shrublands (OSH), woody savannas (WSA), savannas (SAV), grasslands (GRA), permanent wetlands (WET), and croplands (CRO). The selection of these 105 sites was based on data availability and the aim to cover a wide range of IGBP vegetation types from the high-quality FLUXNET2015 dataset. Specifically, sites were chosen if they provided continuous, multi-year records of both the required meteorological forcing data and the target flux variables (ET and GPP) necessary for a robust evaluation. The site information is provided in Table S1. The geographical distribution of these sites, classified by IGBP type, is shown in Figure 1. Sites are primarily concentrated in Western Europe and North America, with fewer sites in China and Australia.
# Efficient Implementation of Polymer Microwave Fiber Links Employing Non-Galvanic Coupling Mechanism
## 4 AiP Implementation and Measurements
### PMF Measurements  
For measurement purposes, we used a solid-core cylindrical PMF with a 2.1-mm-diameter PE core and 6.4-mm-diameter PE foam cladding. In Figure 20, the insertion losses and group delays of three PMF samples are illustrated. The extrapolated insertion loss for the 1 m PMF is illustrated in Figure 21 with the adaptor losses de-embedded. The deviation between the measurements and simulation is due to additional factors that influence the measurement results, such as the excitation of the propagating modes. More specifically, in the measurement setup, the wave was excited directly in the core and the cladding of the PMF, due to the adaptors' shielding, thus neglecting the percentage of power propagating outside the PMF, which was considered in the simulations. In terms of the group delay, a GVD of approximately 2.1 ps per meter per GHz was calculated from all three measurements, which demonstrates the consistency of the measurements.  
Figure 19: PMF measurement setup.  
Figure 20: Measured \(|\)S21\(|\) and group delay of the PMF for different sample lengths.
# A New Stochastic Model for the _Aedes aegypti_ Life Cycle and the Dengue Virus Transmission
###### Abstract  
Dengue is a viral infection transmitted mainly by the _Aedes aegypti_ mosquito and to a lesser extent by the _Aedes albopictus_. This infectious disease generally causes flu-like symptoms, but it can also lead to life-threatening symptoms. Unfortunately, the number of cases increases every year and about a third part of the world's population is at risk of contracting this disease. To generate tools capable of containing dengue transmission, we present a novel stochastic model for the _Aedes aegypti_ life cycle and the dengue virus transmission, taking into account all the mechanisms of transmission and parameters estimated experimentally to date. This new model describes in detail all the interactions in the stages of the life cycle of the mosquito. It also considers the environmental conditions, i.e., the breeding sites and the temperature, which are very important factors for the mosquito survival. The results show that the contagion by bite only does not provoke an epidemic outbreak when five infected, pregnant, and fed females, looking for lay eggs, arrive to a susceptible human population. However, if in addition to the bite transmission, the virus is also transmitted in vertical transmission and sexual ways, then an outbreak arises. Altogether, the transmission mechanisms and the adequate environmental conditions could explain the virus persistence in the population. Under these conditions and by considering fumigation as a way to control the mosquito population, in this new model the outbreak and the virus propagation could be avoided--but only if the control is implemented within the first two weeks of the presence of the virus.  
Aedes aegypti; stochastic model; dengue virus transmission; population dynamics
## 3 Energy and Information Beamforming  
In this section, we consider the energy and information beamforming design with statistical CSI. When large numbers of RF chains are available at AP, the energy and information beamformers are designed jointly to maximize the average received SINR at AP, which we call the statistical max-SINR beamforming. Although good performance is expected, the scheme causes high computational and hardware complexity when the number of AP's antennas becomes very large. To deal with this problem, we then propose a heuristic strongest-path energy and information beamforming scheme, which requires only virtual AoD information of sensors and can be implemented in the analog-domain with low hardware complexity. Moreover, with strongest-path beamforming, a simple analytical expression for average received SINR at AP, can be obtained. With this property, an efficient parameter optimization scheme can be developed to further improve the system performance, as will be seen in the next section. The analysis of the relation between two beamforming schemes reveals that, with proper sensor scheduling, the strongest-path beamforming is equivalent to the statistical max-SINR beamforming when the number of AP antennas tends to infinity.  
In this section, for information beamforming design, we will assume that the harvested energy is enough, and as a result, the transmit power of sensor given by (10) is positive. When this assumption is not satisfied, the information beamforming problem becomes trivial since the received SINR at AP is always zero.
# Removal of Phosphate from Aqueous Solution by
## 3 Results and Discussion
### Adsorption Experiments  
#### 3.2.1 Effects of Dosage on Phosphate Adsorption  
The dosage that influenced phosphate adsorption is shown in Figure 4. An increased dosage of biochar and PZC 7:3 enhanced the removal rate of phosphate. In addition, the phosphorus removal rate rapidly increased when the amount of biochar and PZC 7:3 increased from 0.2 to 0.8 g, but when the amount exceeded 0.8 g, the increase in the phosphorus removal rate gradually decreased. The dosage of the adsorbent significantly influenced the adsorption efficiency. It is critical to seek out the optimum dosage of adsorbent for contaminants' removal in order to enhance its cost effectiveness [52; 53]. In this experiment, when the dosage was raised from 0.2 to 1.2 g, the removal rate of PZC 7:3 rose from 14.62% to 93.50%. This result showed that the adsorbent provided more adsorption sites due to the increased dosage. Then, phosphate was easier to bind to the adsorption sites, resulting in a greater removal rate. However, when the adsorption reached saturation, the total amount of adsorbed phosphate did not change [52; 54]. Therefore, according to the dosage-influenced phosphate removal rate (Figure 4), the dosage of 0.8 g was used for subsequent experiments.  
Figure 4: The influence of dosage on phosphate removal rate.  
Figure 3: FTIR spectra of zeolite, biochar, and PZC 7:3 before phosphate adsorption.