esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental
### Honey Samples Measurements  
#### 2.3.1 Isotope Determinations  
The stable isotope values were expressed in delta (8) notation: \(\delta\)X = (R\({}_{\text{sample}}\)/R\({}_{\text{reference}}\) - 1) \(\times\) 1000, where X is the heavy isotope (\({}^{2}\)H/\({}^{1}\)H, \({}^{13}\)C/\({}^{12}\)C, \({}^{18}\)O/\({}^{16}\)O), \(\delta\) is in parts per thousand (%) deviation relative to a reference gas, and R\({}_{\text{sample}}\) and R\({}_{\text{reference}}\) are the ratios of the heavy to the light isotopes for the sample and the reference, respectively. The isotopic compositions were expressed relative to international standards: V-PDB (Vienna-Pee Dee Blemnite) for \({}^{13}\)C/\({}^{12}\)C measurements and V-SMOW (Vienna-Standard Mean Ocean Water) for \({}^{18}\)O/\({}^{16}\)O and \({}^{2}\)H/\({}^{1}\)H, respectively.  
For \({}^{13}\)C fingerprint determination of honey samples and corresponding honey protein, an isotope ratio mass spectrometer (Delta V Advantage, Thermo Scientific, Waltham, MA, USA) connected with a dual inlet system was employed. Daily, before honey samples analysis, a working standard was measured. This standard was calibrated against an NBS-22 oil (IAEA-International Atomic Energy Agency) certified reference material (\(\delta^{13}\)C\({}_{\text{VPDB}}\) = \(-\)30.03%). All samples were measured in duplicate. The limit of uncertainty was \(\pm\)0.3% for \(\delta^{13}\)C from bulk and extracted honey protein samples.  
Regarding \({}^{18}\)O and \({}^{2}\)H isotopic compositions measurements, a liquid-water isotope analyzer (DLT-100, Los Gatos Research, San Jose, CA, USA) was used. The liquid water isotope analyzer (LWIA) from Los Gatos Research (LGR) is based on absorption laser spectroscopy using as a principle of operation Beer's Law. For this purpose, the isotopic values were calibrated against laboratory-used references (working reference 1, with \(\delta^{18}\)O = \(-\)19.57 \(\pm\) 0.1% and \(\delta^{2}\)H = \(-\)154.1 \(\pm\) 1%; reference 2, with \(\delta^{18}\)O = \(-\)15.55 \(\pm\) 0.1% and \(\delta^{2}\)H = \(-\)117.0 \(\pm\) 1%; reference 3, with \(\delta^{18}\)O = \(-\)11.54 \(\pm\) 0.1% and \(\delta^{2}\)H = \(-\)79.0 \(\pm\) 1%; reference 4, with \(\delta^{18}\)O = \(-\)7.14 \(\pm\) 0.1% and \(\delta^{2}\)H = \(-\)43.6 \(\pm\) 1%; reference 5, with \(\delta^{18}\)O = \(-\)2.96 \(\pm\) 0.1% and \(\delta^{2}\)H = \(-\)9.8 \(\pm\) 1%). All these values are reported against the international standard VSMOV (Vienna Standard Mean Ocean Water). The limits of uncertainty of the water isotopes analysis were \(\pm\)0.2% for \(\delta^{18}\)O and \(\pm\)1.0% for \(\delta^{2}\)H.
# Fault Parameter Estimation Using Adaptive Fuzzy Fading Kalman Filter
## 2 Parameter Estimation with Kalman Filter
### Existing Adaptive Fading Kalman Filter  
The innovation covariance of the Kalman filter is given by  
\[C_{k}=E[\eta_{k}\eta_{k}^{T}]=HP_{k}^{-}H^{T}+R_{k} \tag{13}\]where \(\eta_{k}=z_{k}-h(\hat{x}_{k}^{-})\), \(P_{k}^{-}\) and \(R_{k}\) are innovations, the predicted error covariance and the measurement noise covariance, respectively. \(C_{k}\) is referred to as the calculated innovation covariance [7,8]. The innovation covariance shows the effect of any unaccounted errors, as it is directly involved in the computations of the innovation. When the exact dynamic equation of a nonlinear stochastic system is not available, the estimation error and predicted error covariance may increase due to the effect of incomplete information. Therefore, the change of innovation covariance can be used for an adaptive filter. The increased innovation covariance is estimated by  
\[\overline{C}_{k}=\frac{1}{M-1}\sum_{i=k-M+1}^{k}\eta_{i}{\eta_{i}}^{T} \tag{14}\]  
where, \(M\) is window size. \(\overline{C}_{k}\) is called as the estimated innovation covariance. The relationship between \(C_{k}\) and \(\overline{C}_{k}\) is defined as \(\overline{C}_{k}=\alpha_{k}C_{k}\). Then, the scalar value can be estimated by  
\[\alpha_{k}=\max\left\{1,\frac{1}{p}tr(\overline{C}_{k}C_{k}^{-1})\right\}\quad \text{or}\quad\alpha_{k}=\max\left\{1,\frac{tr(\overline{C}_{k})}{tr(C_{k})}\right\} \tag{15}\]  
where, \(p\) is the dimension of \(z_{k}\) and \(tr()\) denotes the matrix trace. The estimated innovation covariance shows the estimated filter-computed innovation covariance.  
Generally, the convergence property worsened by insufficient _a priori_ information can be improved by introducing a forgetting factor to the predicted error covariance \(P_{k}\). Thus, the predicted error covariance must be increased to compensate for the effect of incomplete information on the dynamic system as follows:  
\[\overline{P}_{k}^{-}=\lambda_{k}P_{k}^{-},\quad\lambda_{k}\geq 1 \tag{16}\]  
where, \(\lambda_{k}\) is the _forgetting factor_ which is given by:  
\[\lambda_{k}=\frac{tr(\alpha_{k}HP_{k}^{-}H^{T}+(\alpha_{k}-1)R_{k})}{tr(HP_{k} ^{-}H^{T})}. \tag{17}\]  
This indicates that the ratio of the innovation covariance \(\alpha_{k}\) is mainly generated by \(\lambda_{k}\). Therefore, \(\alpha_{k}\) is almost equal to \(\lambda_{k}\) (i.e., \(\alpha_{k}=\lambda_{k}\)). The predicted error covariance given as \(\overline{P}_{k}^{-}=\alpha_{k}P_{k}^{-}\). This is referred to as \"AFKF with rescaling-Pk.\"
# Tropical Cyclone Winds from WindSat, AMSR2, and SMAP: Comparison with the HWRF Model
## 2 Data Sets
### AMSR2 and WindSat TC-Winds  
The TC-winds products are the result of a new algorithm that allows sensors with C- and X- band channels to retrieve winds in tropical cyclones, even in the presence of rain [4]. As with SMAP, the TC-winds from AMSR2 and WindSat consist of winds in tropical cyclones in the form of daily gridded 0.25 \(\times\) 0.25 degree maps for both ascending and descending satellite passes and are freely available to download ([[http://www.remss.com/tropical-cyclones/tc-winds/](http://www.remss.com/tropical-cyclones/tc-winds/)]([http://www.remss.com/tropical-cyclones/tc-winds/](http://www.remss.com/tropical-cyclones/tc-winds/)); accessed on 10 May 2021). The AMSR2 data extend from 2012 to the present day, while the WindSat data extend from 2003 to October 2020, when WindSat ceased operation. The spatial resolution of the AMSR2 and WindSat TC-winds is about 50 km, which is close to the spatial resolution of the SMAP winds. Along with wind speed, the WindSat TC-winds files contain values for wind direction, rain rate, and ancillary sea surface temperature (SST). Similarly, the AMSR2 TC-winds files contain rain rate and ancillary SST, as well as columnar water vapor. It should be noted that, since the AMSR2 and WindSat algorithms are specifically trained in tropical cyclone conditions, they may be less accurate in areas where SST < 25 \({}^{\circ}\)C and wind speed is <10 m/s. Therefore, no AMSR2 and WindSat TC-wind measurements are included in their respective files when either of those conditions are present. Like SMAP, the AMSR2 TC-winds are also produced in near-real time.  
In order to ensure consistency among sensors, TC-winds from each of the satellites were extensively compared to one another. An example of one of these comparisons is shown in Figure 1, which displays SMAP and WindSat TC-winds for their passes over TC Amphan off the East Coast of India on 19 May 2020 at 00:25 UTC and 00:46 UTC, respectively. The WindSat rain rates for this overpass also are included in this figure. It can be seen that SMAP and WindSat showed good agreement in terms of both storm structure and intensity, even in areas of heavy precipitation. This agreement was consistent across all three satellite TC-winds for storms of varying sizes and intensities.
## 1 Introduction
The impact of the research is verified through experimental testing and comparing the proposed tracking strategy with a conventional algorithm in identical tracking systems under the same weather conditions. The results are promising, so this research initiates and motivates the development of more complex models to increase the useful life of the tracking systems and their profitability and environmental impact concurrently. Finally, the main contributions of this work are listed below:  
* A novel tracking strategy based on real-time monitoring to increase the lifetime of dual-axis solar tracking systems without reducing the energy productivity of PV systems due to the considerable reduction of operating time, and consequently, the reduction of maintenance requirements, less wear and fatigue in the tracking system, and increasing the profit and reliability during long periods. * An approach to estimating the tracking system lifetime based on wear and tear and possible failures in the axes due to usage time of the tracking action. * The energy consumption of tracking systems is reduced due to the decision-making algorithm considering the tracking accuracy and the gain of energy production in an integrated manner. * An experimental analysis of two tracking strategies was performed by comparing the tracking errors, energy generation, and consumption of two solar tracking systems subject to the same climatic conditions. The paper is organized as follows. The proposed tracking strategy for the decision-making process in real-time to increase the lifetime of solar tracking systems is presented in Section 2. The verification of the approach is developed through a case study in Section 3, where the proposed and conventional algorithms are tested under the same weather conditions, and the analysis of the results is presented. Finally, Section 4 presents some concluding remarks and research gaps for future work.
# A Low Complexity Persistent Reconnaissance Algorithm for FANET
## 2 System Model and Problem Statement
### Problem Formulation
#### 2.2.2 FANET Threat Metric  
For the sake of practical application, we define the FANET threat metric, \(f^{T}\), to quantify and uniformly express various threats (military anti-reconnaissance threat, terrain features, weather conditions, communication interference, etc.) that each relay UAV faces in a scene. However, our main concern in this paper is not how to define or measure those threats posed to the UAVs by different factors but how each UAV can stay away from areas with high threat values while ensuring its reconnaissance performance and network connectivity. Therefore, in this paper, we do not discuss the modeling and quantification of the threat metric model. Instead, we give a predefined time-varying threat density distribution for the scenario.  
To help readers obtain a more intuitive impression, Figure 2 is used as an example to show the threat density distribution in the reconnaissance area. The threat density may come from anti-drone devices, communication jamming, etc. In the image, the darker the red, the greater the threat is. For each UAV in the simulated area, the threat value is the integral of the threat density in its associated area.  
The \(f^{T}\) is defined by  
\[f^{T}(X_{R},\varphi(x),r_{thr})=\frac{1}{|R|}\sum_{i=1}^{|R|}\oint\limits_{x\in \langle x_{i}|r_{thr}\rangle}\varphi(x)dx\ \ \ x_{i}\in X_{R}, \tag{3}\]  
where \(\varphi(x)\) is the known scenario's threat Probability Density Function (PDF), and \(r_{thr}\) is the threatened radius of drones. Note that \(\langle x_{i}|r_{thr}\rangle\) is the circular region with center \(x_{i}\) and radius \(r_{thr}\). The threat value of each UAV can be obtained by integrating the threat PDF in the corresponding area. When location \(x_{o}\) is outside the defined area \(D\), we define \(\varphi(x_{o})\) equals the average value of threat PDF in \(D\), i.e., \(\varphi(x_{o})=\frac{\sum\varphi(x)}{|D|},\forall x\in D,x_{o}\
otin D\).
# EOR: An Enhanced Object Registration Method for Visual Images and High-Definition Maps
## 3 Materials and Methods
### Spatial Distribution Factors
#### 3.3.2 Feature GDOP  
The feature GDOP, derived from GDOP [26], quantifies the level of geometric uncertainty associated with the positions of these features within the visual images and HD map. A lower feature GDOP indicates a higher level of confidence in the accuracy of the  
Figure 4: Diagram of Feature Occupancy and GDOP. (**a**) Occupancy 2D; (**b**) GDOP 2D; (**c**) GDOP 3D.  
feature localization, while a higher feature GDOP suggests a higher degree of uncertainty. GDOP 2D is calculated in the image 2D coordinate system. The principal point \(P(u,v)\) of the photography is taken as the center of feature 2D-DOP shown in Figure 4b. GDOP 3D is calculated in the camera 3D coordinate system shown in Figure 4c. The \(GDOP_{2d}\) and \(GDOP_{3d}\) are obtained using (4):  
\[\left\{\begin{aligned} GDOP_{2d}&=\sqrt{tr\Big{(}H_{2 d}{}^{T}H_{2d}\Big{)}^{-1}}\\ GDOP_{3d}&=\sqrt{tr\Big{(}H_{3d}{}^{T}H_{3d}\Big{)}^{-1} }\end{aligned}\right. \tag{4}\]  
where \(tr()\) represents the trace of a matrix referring to the sum of the elements on the main diagonal. \(H_{2d}\) and \(H_{3d}\) are given by (5):  
\[\left\{\begin{aligned} H_{2d}&=\left(\begin{array}{ cccc}\frac{x_{1}-u}{r_{1}}&\frac{y_{1}-v}{r_{1}}\\ \frac{x_{2}-u}{r_{2}}&\frac{y_{2}-v}{r_{2}}\\ \vdots&\vdots\\ \frac{x_{m}-u}{r_{m}}&\frac{y_{m}-v}{r_{m}}\\ \end{array}\right)\\ H_{3d}&=\left(\begin{array}{ccc}\frac{x_{1}-x_{c}}{r_{1}}&\frac{y_{1}-y_ {c}}{r_{1}}&\frac{z_{1}-z_{c}}{r_{1}}\\ \frac{x_{2}-x_{c}}{r_{2}}&\frac{y_{2}-y_{c}}{r_{2}}&\frac{z_{2}-z_{c}}{r_{2}}\\ \vdots&\vdots&\vdots\\ \frac{x_{m}-x_{c}}{r_{m}}&\frac{y_{m}-y_{c}}{r_{m}}&\frac{z_{m}-z_{c}}{r_{m}} \\ \end{array}\right)\end{aligned}\right. \tag{5}\]  
where \(r\) for \(H_{2d}\) is the distance between the pixel \((x,y)\) and the principal point \((u,v)\). \(r\) for \(H_{3d}\) is the distance between the point \((x,y,z)\) and the camera position \((x_{c},y_{c},z_{c})\). \(m\) is the total number of the key points.
## 2 Materials and Methods  
### Study Location  
The Mukhrino Field Station (MFS) is located in the central part of West Siberia in the middle taiga bioclimatic zone (boreal climate), 20 km south-west of Khanty-Mansiysk (N 60.892135, E 68.682330), on the second terrace of the left bank of the Irtysh River (near the confluence with Ob River). The MFS research area is in the northeast part of the Mukhrino pristine mire complex, which covers a total area of \(\sim\)75 km\({}^{2}\) (Figure 1). The extensive area to the southwest is represented by the peatland and lake landscapes of the Kondinskaya lowland, interspersed with forests along the rivers. The MFS area covers \(\sim\)1 km\({}^{2}\) and has a 2-km-long system of boardwalks, an energy supply complex (solar panels and wind generator) and permanent hydrometeorological and biodiversity monitoring plots for different elements of peatland ecosystems (microtopes). The mean annual amount of precipitation (for the period from 1991 to 2021) and mean annual air temperature are calculated based on the data from three weather stations (\"Khanty-Mansiysk\", \"Ivdel\" and \"Ugut\") and were 549 mm and \(-\)0.3 \({}^{\circ}\)C, respectively.  
The mean air temperature from 1991 to 2021 was \(-\)20 \({}^{\circ}\)C in winter (January-March, November-December) and 20 \({}^{\circ}\)C in summer (May-September). The annual precipitation varied greatly in individual years during three periods: (February-April) from 24 mm to 33 mm, (May-September) from 53 mm to 82 mm, (October-December) from 61 mm to 35 mm from the maximum to the minimum values, respectively.  
The Mukhrino mire complex, according to [38], is an oligotrophic raised _Sphagnum_ bog. It occupies a local watershed between two small streams, the Mukhrina and Bolshaya rechka, and water discharges to both (see Figure 1). On the eastern side, the Mukhrino bog margin is formed by a terrace scarp that rises 2-6 m above the Mukhrina stream valley. The shape of the scarp is undulated due to active backward erosion by several source brooks of the Mukhrina stream [6].  
Figure 1: Location of research objects: the inset shows the boreal zone [39] on the left and pictures of the studied biotopes on the right.
# Research on Wellbore Trajectory Prediction Based on a Pi-GRU Model
## 3 Experiments and Analysis
### Experimental Design  
The results of the orthogonal experiment are shown in Figure 8. Among the nine experimental cases, Case 6 yielded outstanding predictive performance. At this time, the hyperparameter configuration was as follows: learning rate = 0.005, GRU neurons = 128, batch size = 16, and dropout rate = 0.3. Compared with the default learning rate (0.001), the learning rate of 0.005 accelerated the convergence speed of the model in the initial stage, and through the dynamic adjustment of the adaptive optimizer (adaptive moment estimation (Adam)), the problem of gradient oscillation was avoided. The first hidden layer, which was composed of 128 GRU neurons, significantly improved the ability of the model to capture the temporal features of the drilling trajectory. To suppress the overfitting  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Depth (m)** & **Azimuth (\({}^{\circ}\))** & **Dip Angle (\({}^{\circ}\))** & **Tool Face (\({}^{\circ}\))** & **Formation Types** \\ \hline
18 & 73.46 & 11.13 & 25.00 & limestone \\... &... &... &... &... \\
297 & 77.86 & 4.61 & 172.00 & limestone \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Depth (m)** & **Azimuth (\({}^{\circ}\))** & **Dip Angle (\({}^{\circ}\))** & **Tool Face (\({}^{\circ}\))** & **Formation Types** \\ \hline
300 & 78.00 & 5.01 & 242.00 & limestone \\
303 & 79.08 & 5.42 & 312.00 & limestone \\
306 & 78.69 & 5.63 & 22.00 & limestone \\
309 & 79.06 & 6.22 & 92.00 & limestone \\
312 & 78.39 & 6.52 & 162.00 & limestone \\
315 & 78.74 & 6.06 & 232.00 & limestone \\
318 & 78.32 & 5.61 & 302.00 & limestone \\... &... &... &... &... \\
450 & 77.69 & 6.22 & 142.00 & limestone \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test dataset.  
phenomenon during the model training process, the dropout mechanism was adopted, and a dropout rate of 0.3 was set, which could effectively alleviate the risk of overfitting. During the model optimization process, the randomness of the minibatch gradient update and the random dropout strategy formed a dual random regularization effect, further enhancing the generalization ability of the model. The architecture and training process of the Pi-GRU model proposed in this paper used the TensorFlow framework, and all the codes were run in Python 3.8.
## 3 Discussion
Therefore, it was essential to assess apoptotic changes after treatment on the protein level using cytochrome-C level [76]. There are many techniques for assessment of cytochrome-C release, such as western blotting and immunocyt. However, western blotting had some limitations, including difficulty accurately determining the exact quantities of cytoplasmic cytochrome-C on X-ray film and whether those quantities exceeded the amount normally present within cytoplasm. Hence, immunocyt was used in this study to assess the expression of cytochrome-C in both cytosolic and mitochondrial fractions. This was done confocal microscopy coupled with the sensitivity of an Alexa Fluor 488 for quantitative analysis [77]. Cytosolic cytochrome-C activated further proteolytic activation of procosparse-9 to caspase-9 which, in turn, further activated caspase-3 with formation of apoptosome complex [78]. The cytosolic cytochrome-C can be linked also with previously observed gene expression levels of apoptotic genes, such as Fas and caspase-8, and extrinsic apoptosis, as described previously by others [79; 80]. Thus, our ICC observed results could explain the potent ability of abiotic acid to induce cell apoptosis by overexpression of the cytochrome-C gene through intrinsic dependent pathways. In summary, all the mechanistic actions of both pine extract and abiotic acid are shown in Figure 4.  
Cellular oxidative stress is important factor that reflects the inability of cells to remove intracellular reactive oxygen species (ROS) [81; 82]. Although some ROS are beneficial for redox signaling pathways [83], certain levels of ROS are toxic to cells [84; 85] and can react with all cellular components, e.g., proteins, lipids, and DNA, causing oxidative damage [86; 87]. Moreover, ROS have been demonstrated to activate oncogenes, mutagenesis, and genomic variability in cancer cells and to stimulate cancer progression [88; 89]. Recent studies have shown that cancer cells that have low ROS levels have augmented expression of ROS-scavenging signaling proteins and better responses to chemotherapy [84; 88]. Abietic acid was able to induce overexpression of total antioxidants in MCF-7 in both resting state and under exogenous oxidative stress induced by H2O2.
# A Proposal of a Troposphere Model in a GNSS Simulator for VANET Applications
## 2 Related Work
### GNSS in VANET  
Many works about GNSS in vehicular networks exist in the literature. Many researchers deal with the use of navigation system in applications typical of automative environment with the aim of perform navigation tests in a secure and repeatable way. In [7] the authors deal with the timing issue in VANET networks and then, on a GNSS-based time synchronization. They have conducted different experiments in order to test the accuracy of the time synchronization in a consumer-grade GNSS receiver. In [20] a novel unified Cooperative Positioning (CP) solution which enhances positioning accuracy and availability in urban canyons is presented. The proposal, called Angle Approximation (AA) does not require infrastructure or other aiding sensors and distributes and addresses two core challenges (limited positioning accuracy and availability) in a unified solution using a Dedicated Short Range Communication between vehicles. In [21] a vehicle localization enhancement solution inside VANET environment is addressed. Fusion of the GNSS and odometer measurement is augmented by ranging distance. A Dedicated Short Range Communication (DSRC) is used between vehicles. Experimental results show the performance of the proposed algorithm when GPS data is not temporarily available. In [22] the authors propose a novel local integrity concept for GNSS receivers in VANET environment. Their aim is to consider also the environment around the receiver in its nominal conditions and exploit the VANET infrastructure capability. In [23] the authors propose a new method based on a cooperative positioning in VANET in order to improve the relative positioning between two vehicles fusing the data of a GPS system.