esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods and Results
### Sar
#### 3.2.3 Height from Shadow  
In addition to the interferometric technique, there are also techniques that make it possible to estimate elevation parameters by analyzing a single SAR image. The freeboards of icebergs and ice islands can be reconstructed using height from shadow.  
Discontinuous objects, such as icebergs, produce shadows in a SAR image (Figure 14). In this case, we can use shadows to determine the iceberg height from the shadow dimensions using Equation (3).  
\[h=s\,\cos(\theta) \tag{3}\]  
where \(s\) is length of the shadow in the slant range direction, and \(\theta\) is the incidence angle.  
Figure 13: Color composites of dual-polarized RS-2 imagery of standard beam mode S8 acquired on 3 March 2014: HH (**a**), HV (**b**), RGB color composite image (HH—red, HV—green, HH—blue) (**c**), and Landsat 8 image (**d**); (**e**) is the enlarged color composite, and (**f**) is Landsat 8 of ice island. Landsat 8 images courtesy of the U.S. Geological Survey. RADARSAT-2 data and products © MDA Ltd. (2014)—all rights reserved.  
Figure 15 shows an iceberg shadow and the extracted elevation profile. The DEM generated using single-pass TDM InSAR can be used for verification of the extracted elevation. We can see that extracted elevation values range from 5 to 18 m, which corresponds to the InSAR DEM values.  
Figure 14: Geometry of SAR data acquisition (\(\theta\)—incidence angle) over ice island with freeboard (h).  
Figure 15: Shadow of iceberg highlighted in red rectangle in TerraSAR-X image (**a**); extracted elevation profile using Equation (3) (**b**); DEM generated using single-pass TDM InSAR pair (**c**). TerraSAR-X/TanDEM-X data © DLR.
# Wind-Wave-Current Coupled Modeling of the Effect of Artificial Island on the Coastal Environment
## 2 Materials and Methods
### Data Source and Model Setup
#### 2.2.2 Sediment Transport and Tidal Boundary Conditions  
The open boundary included the water level boundary at the entrance of Wanquan River and the tidal driving boundary of the ocean. The 2020 daily recorded water level and suspended sediment concentration data from Jiaji Hydrographic Station at the entrance of Wanquan River were used, as shown in Figure 4. Except for flood season (concentrated from August to November), the flow and sediment transport rates of Wanquan River were very low.  
Figure 3: (**a**) Bathymetric survey line in the study area; (**b**) distribution of water depth around the artificial island; (**c**) bathymetric maps for ETOPO1 data collection; (**d**) three-dimensional topographic and geomorphic features of the study area for fusion of infill bathymetric data [The \(x\)-axis is the east longitude axis and the \(y\)-axis is the north latitude axis].  
The tide boundary drive data from the Gangbei Tidal Station near the study area were adopted, as shown in Figure 5a. The T_TIDE program in MATLAB language was used to conduct harmonic analysis on the recorded tide data [20], and the tide value calculated by harmonic analysis was in good agreement with the measured value when applied to the ocean tidal boundary (Figure 5b).
## 4 Discussion
Although the spatial extent of the error was rather small, alternative predictors need to be evaluated to better address the cropland-settlement confusion, particularly for study areas with a larger urban presence or higher density of villages. There are no major limitations of this approach as far as data availability. Temporal data requirements are minimal and images acquired during the growing season are not requisite. Both recent and historical Landsat data is available through the USGS Global Visualization viewer ([[http://glovis.usgs.gov/](http://glovis.usgs.gov/)]([http://glovis.usgs.gov/](http://glovis.usgs.gov/))), Earth Explorer ([[http://earthexplorer.usgs.gov/](http://earthexplorer.usgs.gov/)]([http://earthexplorer.usgs.gov/](http://earthexplorer.usgs.gov/))), and a number of other online sources. Continuity missions such as Landsat 8 Operational Land Imager (OLI), and the planned Sentinel-2 satellite constellation, will allow the methods to be extended and enhanced with improved instrumentation. The logit models are rather unrestrictive with the primary requirements being independent data points and categorical outcomes. However, with the use of logit models, care should be taken to avoid over-fitting by using a minimal number of predictors with an adequate number of observations. The method is heavily dependent on repetitive sampling that requires high-resolution imagery for land cover coding. The difficulty and expense of obtaining this imagery, particularly for Africa, makes Google Earth the most viable alternative currently. There are, however, other approaches being proposed, such _as DIYlandcover/Mapping Africa_[72, 73]. In this approach, crowdsources generate land cover data and geometric details of fields. DIYlandcover has the potential to be coupled with computer algorithms adapted for land cover mapping, iteratively training the algorithm and identifying the areas of high error through each iteration. The method we propose in this paper could be usefully paired with a crowdsourcing platform such as this where active learning takes place and iterations continue until the desired accuracy is achieved.
## 3 Results and Discussion
Figure 2: Anticancer properties of the beverages. (**A**) AGS (Human Caucasian gastric adenocarcinoma); (**B**) HT 29 (Human Caucasian colon adenocarcinoma). 1x-D, 3x-D (lyophilized digests applied at concentrations being the equivalents of 0.7% and 2.1% beverage, respectively); 1x-P, 3x-P (potentially bioaccessible phenolics applied at concertation being the equivalents of 0.7% and 2.1% beverage, respectively). C—beverages based on lentil sprouts; P—beverages based on lentil proteins; V—lyophilized parsley and broccoli sprouts; F—lyophilized strawberry and raspberry; 0–15—amounts of flaxseeds gum (%). Means (\(\pm\)SD) followed by different letters are significantly different (\(n=9;p\leq 0.05\)). Compared with the adequate controls (the beverages enriched with lyophilized vegetables, fruit and without additives, respectively), in the beverages based on AGF and containing 15% of FSG, an increase in stability of 56, 70 and 42% was recorded. What is important, the introduction of lyophilized green-leafy vegetables significantly deteriorated the stability both in the case of the beverages based on the sprouted lentil flour and AGF. The positive effect of the FSG was the highest during the analysis of the T\({}_{0.8}\) parameter. The time required for a reduction of 20% of the initial optical density of the beverages without additives or enriched with lyophilized vegetables and fruit was extended 16-, 28- and 7-times when compared to the appropriate control, respectively. The swelling potential is mainly described as swelling capacity or swelling index and flours and powders with high swelling potential are good tools for combating hunger as they provide satiety [62]. The swelling potential of the beverages ranged from 25.6 (the beverages based on the AGS containing 5-10% of FSG) to c.a. 32.5 (the beverages based on AGS containing 15% of FSG and those based on the sprouted lentil flour enriched with lyophilized fruit CF). Porous particles, such as freeze-dried powders have a high swelling and water holding capacity which promote the formation of viscous solutions and play an important role in reconstitution properties [63]. Additionally, powdered fruits and vegetables are a good thickening agent and thus may play a vital role in the food industry. [PERSON] et al. studied cacao beverages enriched with apple and orange pectin and recorded a swelling capacity of 7.42 and 10.45, respectively [41]. The swelling values obtained in our study were slightly higher, which may be caused by AGS exhibiting high emulsifying properties [64]. More, [PERSON] et al. [65] proved that electrostatic interaction between FSG and proteins stabilizes the emulsion, thus, this polysaccharide may be used as a functional additive. More, the results obtained for FSG are comparable with those for maltodextrin used in the instant beverage powder from red beetroot, quinte fruit, and cinnamon extracts [7].
## 2 Problem Formulation and Model  
Consider a fixed remote base station which uses \(N\) homogeneous UAVs for tracking a moving ground target, as shown in Figure 1. Each UAV is identified by a unique identity number \(A_{i}\), \(i\) = 1, 2,..., \(N\), and is equipped with a moving target indicator radar (MTIR) to localize the position of the target by measuring the relative distance and azimuth with respect to the UAV's own position. With the MTIR measurement,each UAV executes the filter algorithm and obtains its local estimation of the target state (position and velocity). However, since a single local estimate is suboptimal, each UAV sends its local estimate to the remote base station for fusion which enhances the tracking accuracy. After the base station receives all the UAVs' local estimates via the communication links, a global optimal estimate of the target state is obtained with the fusion algorithm. The overall goal is for the station to constantly have a good global estimate of the target state. However, the communication links between the UAVs and the base station are affected by path loss, shadowing, fading, and noise, which can drastically degrade the quality of the received information.  
In this paper, we employ a packet-erasure channel model to describe the realistic wireless communication links. That is to say, the base station will drop all the packets with a received signal-to-noise ratio (SNR) below a predefined threshold. Please note that in practice, more distance between two communication nodes results in a lower received SNR. In this case, these two communication nodes might be regarded as disconnected. Thus, for a UAV that is far away from the base station, it fails to communicate with the base station directly. To ensure that the UAV can send its local estimation results to the remote base station, some other UAVs act as communication relays to maintain connectivity. The quality of the links should be considered in motion-planning such that the UAVs intelligently move to locations that maximize both the information gain and the successful transmission probabilities in communication links with the base station.
# Machine Learning Method for Prediction of Hearing Improvement After Stapedotomy
## 2 Materials and Methods
### Feature Engineering  
The preoperative hearing intensities at neighboring frequencies do not differ significantly. This results in the value similarity of many features. In machine learning, we aim to have many independent features that each correlate well with the target [11]. The situation where one has mutually interdependent variables is called collinearity, which reduces the interpretability and accuracy of the model [12].  
To discover the potential relevance of interactions between features, we combined hearing intensity features in various ways by using aggregations and arithmetic operations. Similar features were combined to reduce the overall similarity of the feature space, as the combined predictor contains the summarized information of the individual features [13]. The constructed features are shown in Table 3. We averaged the pairs of hearing intensities at neighboring frequencies (subtable 'Hearing intensity means' in Table 3). These pairwise averages are labeled \(frequency_{1}\_frequency_{2}\_frequency_{1}\) and \(frequency_{2}\) denote the frequencies at which the hearing intensities were averaged. Air mean and bone mean values are averages of all AC and BC preoperative hearing intensities, respectively, for an individual. Bone/Air and Bone \(\times\) Air are the product and ratio of the above averages. ABG stands for air-bone gap, which is the difference between preoperative air PTA and preoperative bone PTA [10]. ABG is commonly used to determine the need for stapedotomy and its closure to measure the success rate of it [10].  
Figure 1: Air and bone conduction hearing intensities—before and after procedure. In this paper, the after-procedure intensities (green) are predicted.
## 2 Materials and Methods
### Partial Correlation Analysis  
Partial correlation analysis is a method for measuring the intrinsic relationship between only two variables, with cross-correlation among three or more variables [31; 32]. In this study, the partial correlation analysis was the process of analyzing the partial correlation of two variables using NN-based reasoning [33; 34]. The previously trained \(NN_{k,x_{i}}\) model was used to analyze the partial correlation for each variable. When the noise values of the other variables are substituted in \(NN_{k,x_{i}}\), a larger deviation indicates greater correlation. That is, when the data of \((\texttt{X},(\texttt{X},\ x_{n})\) excluding \((\texttt{X},\ x_{i})\) are input into a specific \(NN_{k,x_{i}}\) model, a larger influence increases the partial correlation. Two procedures were performed for the partialcorrelation analysis. The first procedure calculated the sensitivity of each variable for the \(NN_{k,x_{i}}\) models for a specific variable. The sensitivity calculation is shown in Equation (3):  
\[\text{Sensitivity}(X,x_{i})_{z}=NN_{k,x_{i}}\left(X,x_{i\eta}\right) \tag{3}\]  
where z represents the noisy data for each variable. Thus, z Sensitivity(X,\(x_{i}\)) was constructed. Next, to construct a partial correlation with a different variable according to the sensitivity, the threshold value of the corresponding variable was determined. The threshold was the same as that in Equation (4).  
\[\text{Treshold}(X,x_{i})=\frac{1}{z}\sum\text{Sensitivity}(X,\ x_{i})_{z} \tag{4}\]  
The threshold is the average of all sensitivity values that are associated with the variable. When the threshold is lower than the mean, the two variables exhibit a low partial correlation. As indicated in Equation (5), if the sensitive data are higher than the threshold, they become a candidate for partial correlation.  
\[\text{Candidateset}(X,\ x_{i})=\begin{cases}0:\text{Treshold}(X,x_{i})>\left( \text{Sensitivity}(X,\ x_{i})_{z}\right.\\ 1:\text{Treshold}(X,x_{i})<\left(\text{Sensitivity}(X,\ x_{i})_{z}\right.\end{cases} \tag{5}\]  
where 0 is not a candidate, and 1 is a candidate. Equation (6) establishes a partial correlation relationship.  
\[PCset_{i}=\left(Candidateset(X,x_{i})\supseteq Candidateset(X,x_{i+h})\right) \cap\left(Candidateset(X,x_{i+h})\supseteq Candidateset(X,x_{i})\right) \tag{6}\]  
Two partial correlation candidates must exist for a specific variable. Thus, when learning is performed in the NN, variables with partial correlations can be defined.
# Studies of General Precipitation Features with TRMM PR Data: An Extensive Overview
## 5 Rainfall Observation of PR Data
### Climate Characteristics of Rainfall
#### 5.3.4 Mechanisms of Regional Rainfall  
Formation mechanisms of regional rainfall were investigated when characteristics of rainfall structures were exhibited. [PERSON] et al. [145] analyzed the rainfall characteristics of Amazonian and subtropical South American convection for three separate wet seasons. They found that the variations of vertical structures, rainfall rates, and lightning activity were all associated with the low level easterly or westerly zonal wind at 850 hPa. [PERSON] and [PERSON] [146] analyzed the climatological features of mesoscale rainfall over the Mediterranean region. The analysis showed that, during the winter rainy season, the rainfall was larger over the western Mediterranean Sea than the eastern part and the mesoscale rainfall generally moved from west to east and from north to south, which indicated that the precipitation was associated with the mesoscale disturbances from mid-latitudes. Similarly, [PERSON] et al. [147] analyzed the extreme precipitation over the Mediterranean basin. They found that drought was mainly in the eastern regions of the Mediterranean basin while extreme precipitation was mainly on the western coasts of the continental regions. This phenomenon could be explained by the eastwards passing depression over the Mediterranean. [PERSON] et al. [148] studied the spatial-temporal distribution of rainfall in the Eastern Mediterranean. Results showed that the low level convergence of land breeze and the synoptic wind near the sea-land interface had a significant effect on precipitation formation. [PERSON] et al. [149] analyzed the rainfall structure and the environment of extreme precipitation. The study proved the importance of dynamic conditions and high water vapor content for the occurrence of extreme precipitation. [PERSON] et al. [150] analyzed the precipitation characteristics in Costa Rica. They found that the stratiform rainfall was associated with the low level jet and the ITCZ (Intertropical Convergence Zone), and the convective rainfall was associated with the sea breeze. [PERSON] et al. [151] compared precipitation properties between southern and northern sides of the Baiu front. The results showed that convective rainfall ratios, rainfall intensity and rainfall height in the south were larger than those in the north. These differences might be related to the greater atmospheric stratification instability and the more tropical air in the southern region of the Baiu front. [PERSON] et al. [152] made statistics on the bright band height and the rainfall height in South Africa.  
They found that, in 2015, the bright band heights were higher than those of the other years and they attributed this phenomenon to the occurrence of El Nino in the southern part of Africa transiting over the South Africa.
## 5 EMD-Resilient Reed-Solomon Codes
### Overall Performance of the Proposed Layer  
Figures 9 and 10 represent the categorized results of the proposed multi-layer structure on the overall performance of the channel. As can be seen, when \(k=1\) (i.e., Figures 9a,b and 10a,b), the rate of CFN dropped to an absolute zero under the considered electromagnetic variables, as was the objective of this paper. In addition, the rate of CTP dropped significantly in these cases. Furthermore, a large drop in CTP and CFN ratios is noticeable when \(k=3\) (i.e., Figures 9c,d and 10c,d). In this case, the majority of CFN and CTP caused by one-symbol-value code words at the frequencies of \(f_{\text{CFN}}\) are eliminated and turned into a detectable category (i.e., CTN) for the inversion layer. The remaining CTP and CFN at other frequencies are caused by multi-symbol-value code words. Nevertheless, although due to the inversion mechanism, more multi-symbol value CTPs and CFNs appear in Figure 9c compared to Figure 2c, and the overall resilience against CTP and CFN actually increased as all major spikes caused by one-symbol-value code words are eliminated. Figure 10 show that the proposed inversion mechanism has provided a better EM-resiliency for the symbol size of 4. This reflects the fact that the greater Hamming distance requires more bit-flips to generate a one-symbol-value code word, which in this case provides a better resiliency against single-frequency EMDs. In other words, using the proposed mechanism, symbol sizes greater than 3 would provide more resilient codes as they have greater Hamming distances (remember the relation which has been shown in Figure 6). In addition, Table 3 provides a comparison between the original RS Codes and the proposed multi-layer structure. For each category, all correspondent data are averaged out over the considered frequency range and are presented in percentage (%). As evidenced, except the three categories of CTN, CTP, and CFN, the difference among other categories of a same setup is not substantial. This is exactly in line with the objective of this paper. CTP and CFN are the only two categories that are determinantal to the overall safety of the system. The proposed approach was able to catch most of the CTP and CFN instances and flagged them as CTN while imposing minimal impacts on the other categories. Note that these values are indicated in bold in Table 3.  
Figure 8: Conceptual overview of the proposed multi-layer communication structure. Figure 9: The impact of the inversion layer on the EM-resiliency of RSCodes for a symbol size of 3.
# Perpendicular Electrical Conductivity in the Topside Ionosphere Derived from Swarm Measurements
## 2 Data and Methods
### Electrical Conductivity Perpendicular to the Geomagnetic Field in the Topside Ionosphere
All data collected within each bin were filtered out with a standard median filter to remove spikes. The mean of filtered data was set as the value representative of each bin. Statistical uncertainties on electrical conductivity were estimated as the standard error on the mean values within each bin.
# Autonomous Active Tag Using Energy Harvesting Strategies
###### Abstract  
In this work, we present autonomous active tags. The power sources of these active tags employ energy harvesting techniques, specifically, solar and mechanical techniques. The integration of these techniques, and the storage of the energy obtained with a supercapacitor, converts the active tag into an autonomous device. These tags work in a low power mode in which they dynamically adjust their radio communication capabilities. Such a configuration depends on the application. We tested the tags in a real environment with testing parameters to check the modules, meaning more wake-ups over a longer time. Under these conditions, the tags gather enough energy to autonomously maintain standby operation on a sunny day for ten hours. In conclusion, this autonomous active tag is a demonstration that the integration of energy harvesting techniques, supercapacitor storage and the management of low power modes for transceivers, microcontrollers, and memories creates a device without energy dependencies that only depends on the requirements, and can be used in many applications related to, for instance, smart homes, smart cities, smart cars, and connected forests.  
autonomous; energy harvesting; active tag; supercapacitor; solar energy; mechanical energy; low-power consumption Article
# Modified Echo State Network Enabled Dynamic Duty Cycle for Optimal Opportunistic Routing in EH-WSNs
## 5 Results and Discussion  
In this section, the MESN based dynamic DC with OOR for EH-WSNs is analyzed using network simulator (NS2), the performance analysis uses two simulation scenarios. In the first simulation scenario, the sensor nodes vary from 100 to 500 in the network region 1000 m \(\times\) 1000 m. In the second simulation scenario, the average energy harvesting rate ranges from 20, 40, 60, 80, and 100 mW with a fixed number of nodes as 500. Each sensor node is having initial energy of 0.5 J in the region. For sending a single packet of size 512 bytes per node, the transmission energy per node is \(2.9\times 10^{-3}\) J, also receiving energy per node is \(0.20\times 10^{-3}\) J. The transmission range of each sensor node is 150 m.  
Here, every node uses the AODV routing protocol for packet transmission. This scenario also uses MAC protocol 802_11. Each sensor node uses an omnidirectional antenna. Also, this scenario uses the two-ray ground radio propagation model. The simulation time is 100 s, and the transmission rate is 500 kbps. Also, this section consists of a comparison of the proposed scheme with the existing state-of-art techniques such as ESN-based, AOR and Max-SNR in terms of parameters such as delay, packet delivery ratio and throughput.