esa-sceva/satcom-chunk-collection huggingface dataset:
# Present and Future Marine Microwave Satellite Missions in China
## 4 History of Progress in China's Marine Surveillance Satellite Remote Sensing Technology
### Oceanographic Information Retrieval Technology  
From the perspective of wave information extractions, [PERSON] et al. [70] developed an algorithm, which included a wave parameter retrieval method based on co-polarized SAR data. Based on the acquisition of the full-polarization backscattering coefficient of the sea surface, the wave parameters are calculated. Regarding sea surface wind field information extraction processes, [PERSON] [71] introduced the polarizability model XPR2, that included both radar incidence angles and sea surface wind speeds. The XPR2 model was fitted with 56 dual-polarized TS-X and TD-X images and matched ECMWF wind field data. The validity of the XPR2 model was verified by 38 HH polarized Terra SAR-X images and corresponding buoy measurements. The verification results showed that the root mean square error of the XPR2 model retrieval results was 1.79 m/s, and the deviation was 0.68 m/s. [PERSON] et al. [72] proposed typhoon identification technology in combination with HY-2A scatterometer and GF-3 C-band SAR wind fields and obtained fine typhoon observations using China's independently developed marine satellite.  
In another related study, [PERSON] et al. [73] adopted the advanced synthetic aperture radar large-amplitude model data of ESA (European Space Agency) ENVISAT, removed the frequency shifts caused by the relative motions of the Earth and the satellite based on the theoretical model of Doppler frequency shifts, and eliminated the influencing effects of the sea surface wind fields and Bragg scattering using the C-band empirical model CDOP (C-Band Doppler Frequency) and a dual-scale convergent scattering model, respectively, thereby obtaining the retrieval of high-resolution sea surface flow fields.  
[PERSON] et al. [74; 75] carried out analyses of oil spill monitoring parameters from the aspects of bands, polarization modes, incidence angles, and so on, using SAR data based on the calculations of backscattering coefficients. In the this study, the weights of each type of index were obtained by an analytic hierarchy process (AHP) in combination with such main indexes as oil spill shape parameters, texture characteristic indexes, and physical characteristic indexes. An oil spill remote sensing monitoring method was used to analyse the data, and a multi-index oil spill information extraction method based on SAR data was proposed.
# Evaluation of Low-Cost GNSS Receiver under Demanding Conditions in RTK Network Mode
## 2 Materials and Methods
### Accuracy Assessment  
The block-chart of all tests and measurements is presented in Figure 5.  
Figure 4: U-blox C099-F9P application board in self-made case with powerbank, Getac T800G2 rugged tablet with custom survey software, Leica AS10 antenna and cables.  
Figure 3: Leica GS18T receiver with Leica CS35 controller.  
The collected observations in one 30-s measurement series were averaged (Equation (1)). After measuring with each antenna in at least 5 series (Equation (2)), the results were taken for further analysis. The differences from the reference coordinates (Equation (3)) and standard deviations of the measured coordinates (Equation (4)) were calculated for each station and each hardware configuration.  
\[OMS=\frac{\sum_{i=1}^{n-30s}\ a_{i}}{n} \tag{1}\]  
where:  
\(OMS\)--one measurement series;  
\(n\)--number of sub-measurements (observations) until the time reaches 30 s;  
\(a_{i}\)--sub-measurement (observation) of X, Y and H coordinates.  
\[MMS=\frac{\sum_{i=1}^{m}OMS_{i}}{m} \tag{2}\]  
where:  
\(MMS\)--mean measurement series;  
\(m\)--number of measurement series (mostly 5).  
\[RD=a_{R}-MMS \tag{3}\]  
where:  
\(RD\)--difference between reference and mean measured coordinates (Ref.);  
\(a_{R}\)--reference X, Y or H coordinate.  
Figure 5: Block-chart of the experiment.  
\[SD=\sqrt{\frac{\sum_{t=1}^{m}\left|OMS_{i}-MMS\right|^{2}}{m}} \tag{4}\]  
where:  
\(SD\)--standard deviation of X, Y or H coordinate (St. deviation);  
\(m\)--number of measurement series (mostly 5).
## 1 Introduction
[24] improved the PEARL method to detect crop row centerlines and generate navigation paths in weeding operations under poor GPS signal conditions, thereby enhancing operational efficiency. LiDAR technology has also demonstrated remarkable effectiveness in field-based crop recognition. [PERSON] et al. [25] and [PERSON] et al. [26] conducted in-depth studies on autonomous navigation and crop row centerline detection in maize fields. By applying threshold-based filtering methods, they accurately extracted crop row feature points and efficiently derived the centerlines using the least squares method. The experimental results showed that the centerline extraction rates for maize crop rows reached 95.1% and 87.3%, respectively, with both the accuracy and processing speed outperforming those of traditional machine vision-based approaches. In summary, although LiDAR technology has achieved significant progress in crop row detection, most existing studies have focused on single-crop systems or regular plant ing patterns. Research remains limited for more complex configurations, such as the soybean-maize strip intercropping system characterized by spatial heterogeneity and irregular canopy distribution. To address this gap, this study proposes a LiDAR-based algorithm for crop row detection and navigation line extraction tailored to intercropping systems. The algorithm is evaluated using navigation angle, lateral deviation, and processing time as performance metrics. Field experiments under three representative scenarios--broken rows with missing plants, low occlusion, and high occlusion--were conducted to assess its adaptability and robustness. This work aims to enable accurate crop row identification and stable navigation path generation in structurally complex fields, providing a reliable technical foundation for intelligent and autonomous agricultural operations.
## 4 Prediction Model of the Leakage Current and Example Analysis
### Verification Analysis and Result  
Many factors, such as humidity, wind speed, temperature, and atmospheric pressure, influence the leakage current on the surface of the insulator. Changes in weather parameters can result in uncertain and irregular behavior of the leakage current. Therefore, to produce more accurate predictions, it is crucial to employ advanced and suitable models.  
In order to verify and analyze the predicted results of leakage current, this study applied measurement data collected in 2019 from the test station for the simulation. The input data, which can be divided into three case studies based on different time segments, are as follows:  
Case study 1: 1 day (1 February 2019)  
Case study 2: 5 days (1 March 2019-5 March 2019)  
Case study 3: 6 days (16 June 2019-22 June 2019)  
This study proposes three prediction models (SVR, GBR, and LSTM). The input data for each case study include temperature, humidity, wind speed, air pressure, and ultraviolet  
Figure 10: The flow chart of analysis and algorithm modeling.  
The proposed LSTM demonstrates better predictive performance for case study 1, with a mean absolute error (_MAE_) of 0.0780, a mean squared error (_MSE_) of 0.0065, and an explained variance score (_EVS_) of 0.8349, as compared to SVR and GBR. The analysis results show that when using mean squared error (_MSE_) and mean absolute error (_MAE_) to evaluate the performance of LSTM, the model's prediction errors are very small. An _EVS_ of 0.8349 is considered a good result, indicating that the model can explain a significant portion of the variance in the target variable, suggesting that the model's predictions are quite accurate.  
Overall, based on the results of _MSE_, _MAE_, and _EVS_, it can be concluded that the LSTM model performs well in predictions, with very small errors between predicted and actual values, and it exhibits good explanatory capabilities.
# Breath Analysis: A Promising Tool for Disease Diagnosis--The Role of Sensors
## 2 The Role of Nanomaterials
### Carbon Nanotubes (CNTs)  
CNTs are investigated as gas sensing materials for their interesting electrical, mechanical, optical and thermal properties [13; 61], as well as for their compatibility with other nanomaterials for enhanced performance [7]. CNTs are divided into single wall CNTs (SWCNTs) with a diameter in the range of 1-5 nm [50] and multi-wall CNTs (MWCNTs) with a diameter in the range of 5-100 nm [7] and an interlayer spacing of 3.4 A [50]. Concerning their electrical properties, which are most commonly exploited, SWCNTs act as metallic conductors or semiconductors (depending on the chiral angle between hexagons and tube axis), while MWCNTs behave as metallic conductors, with current density up to \(10^{9}\) A/cm\({}^{2}\)[7].  
CNTs-analyte interaction may include Van der Waals or donor-acceptor interactions. However, gas adsorption-provoked charge-transfer is the main sensing mechanism since SWCNTs specifically can function as p-type semiconductors [7; 13; 50]. Oxidizing gas-adsorption, such as NO\({}_{2}\), decreases sensing-layer resistance, due to electron withdrawing by the gas. In contrast, reducing analyte-adsorption, such as NH\({}_{3}\), increases resistance [13]. Remarkably, in those cases SWCNTs act as both the sensing element and the transducer [50].  
Both categories of CNTs, however, are characterized by lack of chemical selectivity, high H\({}_{2}\)O affinity, low sensitivity for nonpolar compounds [38], and slow recovery [7]. For this reason, functionalization is imperative. Decoration with MNPs for enhanced selectivity is discussed in the literature [38]. More importantly, CNTs are commonly functionalized with analyte specific entities, covalently (esterification, amidation of carboxyl groups added to CNTs) or non-covalently (supramolecularly, via Van der Waals, and \(\pi\)-\(\pi\) interactions) [50]. Modification of CNTs with non-polymeric organic layers [38] (e.g., polycyclic aromatic hydrocarbons [62]) or polymers [50] have been used for the development of effective cross-reactive gas sensors as diagnostic tools via breath analysis.
# Resource-Aware ECG Classification with Heterogeneous Models in Federated Learning
## 4 Experimental Settings
### Performance Metrics  
To evaluate the effectiveness of our ECG classification model within the heterogeneous Federated Learning (HeteroFL) [12] framework, we used two primary performance metrics: the accuracy and F1 score. These metrics were crucial for assessing the model's ability to correctly classify ECG signals, particularly in the context of imbalanced datasets, where certain classes may be underrepresented.  
The accuracy is the proportion of correctly classified instances out of the total number of instances. It provides an overall measure of the model's ability to correctly classify both the majority and minority classes. However, in the case of imbalanced datasets, the accuracy alone may not fully capture the model's performance, as it can be biased towards the majority class. To address this limitation, we also report the F1 score, which is the harmonic mean of precision and recall.  
\[\text{F1 score}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{5}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Key** & **Value** \\ \hline \multirow{5}{*}{Control} & data\_split\_mode & i.i.d \\  & model\_split\_mode & two-model/multi-model \\  & fixed\_control\_norm\_bn & 1 \\  & control\_scale\_1 & 1 \\  & num\_clients & 10 \\  & control\_frac & 0.1 \\ \hline \multirow{5}{*}{Data} & data\_name & MITBIHECG \\  & test\_ratio & 0.2 \\  & num\_epochs & 25 \\  & batch\_size & 64 \\  & lr & 0.001 \\  & num\_classes & 5 \\  & num\_rounds & 20 \\  & seed & 42 \\  & server\_learning\_rate & 0.001 \\ \hline \multirow{5}{*}{Optimizer} & optimizer\_name & Adam \\  & weight\_decay & 3.0 \(\times\) 10\({}^{-4}\) \\ \cline{1-1}  & momentum & 0.9 \\ \hline Scheduler & Reduce Relu \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experiment hyperparameters.  
where precision and recall are defined as  
\[\text{Precision}=\frac{TP}{TP+FP}\quad\text{and}\quad\text{Recall}=\frac{TP}{TP+ FN}\]  
Here, \(TP\) denotes the number of true positives, \(FP\) denotes the number of false positives, and \(FN\) denotes the number of false negatives.  
The F1 score is particularly useful when dealing with imbalanced classes, as it provides a more balanced measure of the model's performance by considering both false positives and false negatives. The F1 score is especially important in medical applications like ECG classification, where misclassifying certain arrhythmia types could have serious implications [40; 41].  
Together, the accuracy and the F1 score provide a comprehensive view of the model's performance, ensuring that it is both reliable and robust in handling imbalanced ECG data.
# Evaluation of Aging Resistance of Graphene Oxide Modified Asphalt
## 3 Results and Discussion
### UV Aging Resistance Performance
#### 3.2.2 Physical Performance
and GO-modified 90 A are shown in Figure 7, and the VAI results of SBS MA and the GO-modified SBS MA are shown in Figure 8.  
The results shown in Figure 7 indicate that the VAI values of all temperatures increase with the increase of the UV-aging time, which shows that the UV-aging degree is increased. For example, the VAI of the GO-modified 90 A decreased as the content of the GO increase. Therefore, for the 90 A binder tested, one can conclude that the GO can improve the UV aging resistance performance and, at the same UV-aging condition, the VAI of the 1% GO-modified 90 A has no significant difference with 3% GO-modified 90 A. However, the VAI value after adding 3% GO was slightly lower than that of 1% GO. The VAI results of SBS MA in Figure 8 show the same trend as of 90 A, the VAI is increasing with an increase in UV-aging time. However, the VAI of the GO-modified SBS MA is much smaller than that of SBS MA. It is clear that the GO can improve the UV aging resistance performance of 90 A and  
Figure 8: VAI of SBS MA and GO modified SBS MA after UV aging. \[\mathrm{SPI}=\mathrm{SP_{UV}}-\mathrm{SP_{TOT}} \tag{4}\]0 VAI of 90 A and GO modified 90 A after UV aging. SBS MA, and the UV aging resistance performance will be enhanced by increasing the GO content from 1% to 3%.
# A Cognitive Environment Modeling Approach for Autonomous Vehicles: A Chinese Experience
## 3 Spatial-Time Environment Model Statement  
### Definition  
The spatial-time environment model is a 3D spatial-time occupancy grid map consisting of a series of 2D occupancy grid maps at different times \(t\). In this work, we model the environment with a resolution \(D_{M}=0.1\) per grid. An example spatial-time environment model \(\mathbf{M}_{st}\) is shown in Figure 2, which contains three coordinate axes: the X axis, Y axis, and time axis. The X axis and the Y axis form a space plane (capable both in Gaussian coordinates and vehicle coordinates) at time \(t\). The time axis represents the time during which the plane is located. The green-line plane in Figure 2 represents the 2D occupancy grid map at \(t=0\) s, which represents the occupancy in the region of interest on the road at the current moment \(t=0\) s (the plane where the autonomous vehicle is located), and the blue-line plane represents the 2D occupancy map at the time \(t\prime\). If the ego vehicle is at the origin of the coordinate system, \(\mathbf{p}(x,y,t)\) is an occupied point located at \(X=0.5\) m, \(Y=0.5\) m that will appear after \(0.5\) s. The point \(\mathbf{p}\) at time \(t=0.5\) s in the spatial-time domain is no longer a feasible driving region, as it is occupied, \(T(\mathbf{p})=1\) (\(0\) for free). The ego vehicle should avoid this point at time \(t=0.5\) s.  
Figure 1: Strategy architecture.  
Figure 2: Spatial–time environment model.  
An example \(M_{st}\) is shown in Figure 3, which contains one dynamic time-varying obstacle. The moving speed of the obstacle in the spatial-time domain can be obtained by derivation. Assuming that \(\mathbf{p}_{1}(x_{1},y_{1},t_{1})\) and \(\mathbf{p}_{2}(x_{2},y_{2},t+\Delta t)\) are two points at different times on a dynamic obstacle trajectory, where \(\Delta t\geq D_{M}\), the average moving speed between them can be calculated by  
\[v_{(p_{1},p_{2})}=\lim_{\Delta t\to D_{M}}\frac{\sqrt{\left(y_{2}-y_{1} \right)^{2}+\left(x_{2}-x_{1}\right)^{2}}}{\Delta t} \tag{1}\]
## 2 Contact Pressure Estimation from Laryngeal High-Speed Video  
Herein, we propose a method for estimating the contact pressure experienced by colliding vocal folds based purely on kinematic and geometric information extracted from laryngeal a high-speed video. We refer to this method as Contact Pressure Analysis (CPA). Briefly, the motion of each observed VF edge is extracted from HSV during the open phase of the glottal cycle. The trajectories of the two folds are tracked until collision at which point a simple spring-mass-damper VF model is employed to predict the fictitious overlap of the colliding folds such that the trajectory of each edge matches the observations in the bookending opening phases. It is assumed that the rebounding force due to collision can be estimated using a Hertz contact model, wherein the fictitious overlap is related to the penetration depth for contact between elastic bodies. A schematic of the procedure is illustrated in Figure 1, which shows the VF edges in the open phase and the fictitious penetration depth \(\delta_{c}\) and contact length \(L_{c}\) during collision.  
Implementation of the procedure consists of five stages: (1) _video pre-processing_ to remove camera motion effects and to create a uniform viewing orientation; (2) VF _edge detection_ to locate the projected medial surface of each vocal fold; (3) VF _curve fitting_ to convert the extracted edge location data into a polynomial function; (4) VF _edge tracking_ during the open and collision phases; and (5) _contact pressure estimation_ through an adapted Hertzian contact model. This section is devoted to expanding on the details of each step in the proposed CPA method.
# Characterization of Two Hydrogen Peroxide Resistant Peroxidases from _Rhodococcus apacus_ 1 Cp
## 3 Results
### DypA and DypB Activities against Different Types  
Seven dyes were tested to check the dye-decolorizing capability of Dyps. The dyes were comprised of two anthraquinone dyes (Reactive Blue 5 and Reactive Blue 19), diazo dye (Reactive Black 5), copper complex azo dye (Reactive Red 23), single azo dyes (Reactive Orange 16 and Reactive Yellow 37), and the polyanthraquinone dye with poly(vinylamine) sulfonate backbone (Poly R-478).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Enzyme**} & \multirow{2}{*}{**Substrate**} & **Km** & **Vmax** & **Kcat** & **Kcat/Km** \\  & & **(\(\mu\)M)** & **(U/mg)** & **(s\({}^{-1}\))** & **(\(\mu\)M/s\({}^{-1}\))** \\ \hline \multirow{2}{*}{DypA} & H\({}_{2}\)O\({}_{2}\) & 196.89 \(\pm\) 16.95 & 282.39 \(\pm\) 9.52 & 228.17 \(\pm\) 7.69 & 1.16 \(\pm\) 0.45 \\  & RB5 & 33.34 \(\pm\) 2.37 & 254.6 \(\pm\) 6.69 & 205.72 \(\pm\) 5.41 & 6.17 \(\pm\) 2.28 \\ \hline \multirow{2}{*}{DypB} & H\({}_{2}\)O\({}_{2}\) & 1011.8 \(\pm\) 78.15 & 28.74 \(\pm\) 1.11 & 19.07 \(\pm\) 0.89 & 0.018 \(\pm\) 0.011 \\  & RB5 & 12.78 \(\pm\) 1.15 & 23.21 \(\pm\) 0.96 & 15.47 \(\pm\) 0.78 & 1.21 \(\pm\) 0.68 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Steady-state kinetic parameters of DypA and DypB towards H\({}_{2}\)O\({}_{2}\) and Reactive Blue 5. Calculation of these parameters were based from the Michaelis-Menten fit from Figures 8 and 9.  
Figure 8: Michaelis-Menten kinetics of (**A**) DypA and (**B**) DypB with varying concentrations of H\({}_{2}\)O\({}_{2}\) (20 \(\mu\)M to 1000 \(\mu\)M) in Reactive Blue 5 assay.  
Figure 9: Michaelis-Menten kinetics of (**A**) DypA and (**B**) DypB with varying concentrations of Reactive Blue 5 (15 \(\mu\)M to 144 \(\mu\)M for DypA; 5 \(\mu\)M to 57 \(\mu\)M for DypB) in Reactive Blue 5 assay.  
DypA showed strong preferences for the two anthraquinone representatives as it exhibited about 114 \(\pm\) 2.94 U/mg and 5.91 \(\pm\) 0.6 U/mg for Reactive Blue 5 and Reactive Blue 19, respectively. Reactive Black 5 and Reactive Red 23 were also moderately accepted but not as efficient compared to the two anthraquinone dyes. DypB, on the other hand, only showed activity against the two anthraquinone dyes. Interestingly, both dyes did not have any activity on single azo dye structures used in this study. Poly R-478, an anthraquinone-based polymeric dye, was somewhat acted on by DypA. However, it only showed a low activity of 1.15 \(\pm\) 0.4 mU/mg at 0.4 m concentration of Poly R-478.  
Overall, DypA was a better dye converter than DypB as it acted on about 4 dyes out of the 7 that were tested. However, the substrate scope of the DypA and DypB from _Rhodococcus opacus_ 1 CP was still not up to par compared to known dye-decolorizing peroxididases.
## 5 Applications
### Reduction Strategy for Uncertainty  
The hourly historical data of wind speed, solar irradiance, and ambient temperature of Cairo, Egypt, for one year (2016) are considered in this paper. Three different reduction cases use the proposed criterion to obtain the appropriate accuracy and speed.
## 2 Materials and Methods
### Optimization of the SASs' Performance  
There are many methods how to quantify the performance of a sound-absorbing structure. For example, in recent papers, the mean value of the reflection coefficient spectrum [34], power absorbed in a given frequency range [35], frequency-weighted reflection coefficient integral [36], or a causally guided approach [37] have been proposed.  
Within this work, the SAS's performance is appraised by the mean value of the absorption coefficient  
\[\langle\mathcal{A}\rangle\ =\ \frac{1}{f_{\text{cut-on}}}\int_{0}^{f_{\text{cut-on }}}\mathcal{A}(f)\mathrm{d}f, \tag{7}\]  
whose value goes from \(\langle\mathcal{A}\rangle\ =\ 0\) (all the acoustic energy is reflected) to \(\langle\mathcal{A}\rangle\ =\ 1\) (all the acoustic energy is absorbed).  
Here, absorption coefficients at individual frequencies are calculated numerically by employing Equations (1), (4), and (5); the value of the integral (7) is then approximated using the trapezoidal rule.  
In both variants of the SAS, the absorbing performance depends on the following geometrical parameters: \(A\), \(A_{\text{min}}\), \(L\), \(n\), and \(\xi\). The structure profile also plays an important role (see, e.g., [17; 34; 36]). Within this work, the SAS's profile \(a(x)\) is introduced using a dimensionless shape function \(G(X)\) as  
\[a(x)\ =\ (A-A_{\text{min}})G(X)+A_{\text{min}},\]where \(X\ =\ x/L\) is the dimensionless distance, \(G(X_{0})\in\langle 0,1\rangle\) for any \(X_{0}\in\langle 0,1\rangle\), and \(G(0)\ =\ 0\). For example, linear and quadratic profile are introduced as \(G(X)=X\) and \(G(X)=X^{2}\), respectively.  
To allow for greater variability in the SASs' profile definition, the shape function \(G(X)\) is introduced using three control points interconnected with cubic splines--see Figure 3. This means that the SAS's shape is uniquely described using a 5-dimensional parameter vector \(\mathbf{Z}\ =\ [\Gamma_{1},X_{2},G_{2},G_{3},\Gamma_{3}]\), where \(\Gamma_{1}\) and \(\Gamma_{3}\) are the slopes of the shape function at its extremities.  
In the case of the MPP-SBH, the parameters of MPP (\(t,d,\phi\)) also strongly influence its absorbing performance.  
In the following section, the SAS's absorbing performance is optimized by maximizing the mean value of the absorption coefficient \(\langle\mathcal{A}\rangle\) introduced by Equation (7). The optimal values of the geometrical and physical parameters of the SASs are searched for by employing evolution strategies (see, e.g., [38]).