esa-sceva/satcom-chunk-collection huggingface dataset:
# Unit Commitment with Ancillary Services in a Day-Ahead Power Market
## 4 Simulation Results  
Many study cases were conducted for the thermal units of TPC and IPP, including 14 coal-fired units, 23 gas-fired units, and 6 oil-fired units of TPC, and 5 coal-fired units and 9 gas-fired units of IPP. There are 57 units in total. The maximum generation output, minimum generation output, ramping rates, electric energy price, and startup costs are shown in [31]. The minimum operating time of a coal-fired unit is set as 8 h, the minimum operating time of a gas-fired unit is set as 4 h, and the minimum operating time of an oil-fired unit is set as 6 h. A typical day shown as an example is chosen from the middle ten days of March, and another single day example is taken from the first ten days of July in Taiwan in 2016. The single-day load demands represent the summer and non-summer seasons as shown in Figures 2 and 3. The daily load demand is from 1.2 MW to 2.35 MW for non-summer and from 1.9 MW to 2.6 MW for summer. The demand of AGC is assumed to be 2% of the load  
The electric energy and the demands on ancillary services for non-summer are shown in Tables 1 and 2. The coal-fired unit of TPC and IPP provided 52.02% of the electric energy, while the gas-fired unit of TPC and IPP provided 47.32%. The most ancillary services are provided by gas units: AGC (98.58%), RSR (100.00%), and SR (99.83%). The oil-fired unit has a higher cost and provides much less electric energy and ancillary services.
## 3 Results and Discussion
### Streamlines and Velocity Flow Fields  
To understand the flow patterns, it is helpful to demonstrate how the flow fields change with the Reynolds number. Figure 10a,b illustrate the flow field streamlines and the velocity fields in the region between the fifth and the seventh ribs at Reynolds numbers of 10,000 and 60,000, respectively. The maximum velocity value of each color legend is 2.5 times that of the inlet velocity. The high-velocity values occur just downstream of the opposite higher ribs, with a maximum value at the tube axis.  
Figure 8: Average Nusselt number between the ribs along the test section at \(\mathrm{e_{2}/e_{1}}\) values of 0.25 and 1.0, Re values of 10,000, 30,000, and 60,000, and a Pr number of 7.0.  
Figure 9: Friction factor (f) variation with the Reynolds number at different rib height ratios.  
A large circulation zone exists downstream of the taller ribs and occupies most of the length up to the shorter rib. A small circulation zone can be observed downstream of the shorter rib. Other smaller circulation zones exist upstream and downstream of each rib. The circulation enhances fluid mixing by bringing the cold fluid from the middle region to the hot fluid region near the tube wall.
# Seismic Performance of LRS-FRP-Concrete-Steel Tubular Double Coupling Beam
###### Abstract  
To improve the ductility and seismic performance of a double coupling beam, the authors applied a polyethylene terephthalate (PET) sheet and steel tube to form fiber-reinforced polymer (FRP)-concrete-steel double-skin tubular (DST) composite coupling beams. A low-cyclic reversed experimental program was carried out which factored in the member form, steel tube diameter, and construction methods. The results indicate that the ductility and energy dissipation performance of double coupling beams--whether wrapped with a PET-FRP sheet or surrounded by an FRP-concrete-steel DST composite system--is a substantial improvement over the traditional reinforced-concrete double coupling beam (RC-DCB). The ductility coefficient and accumulated energy dissipation of the DST-DCB members improved above 170% and 2300%, respectively. These percentages compare to the RC-DCB and are based on the rupture of a PET-FRP sheet. The results are similar to those of the large rupture strain double coupling beam (LRS-DCB). Meanwhile, the external wrapped PET-FRP sheet does not affect the initial stiffness and peak strength of the RC-DCB. Relatively, the inner steel tube will improve the initial stiffness, yielding strength, and peak strength. DST-DCB members still have considerable deformability after 85% of peak strength since the external PET-FRP sheet provided an effective constraint effect on the core concrete and the inner steel tube could bear excellent shear deformation.  
seismic performance; LRS-FRP; double coupling beam; double skin tubular double coupling beam (DST-DCB); energy dissipation; ductility
## 2 Results and Discussion
### Transcript Level of Key LOX-HPL Pathway Genes during Development
E-L 35 (early veraison), E-L 36 (mid-ripening stage), E-L 37 (end of veraison), and E-L 38 (harvest). “Zmgx” represents “Zaomeiguixiang” cultivar and “Alexandria” represents “Muscat of Alexandria” cultivar. Expression levels of each gene were expressed as a ratio relative to the E-L 35 stage of “Xiangfei” cultivar, which was set at 1.  
decrease in _VvADH1_ expression [40]. In the present study, a higher transcript level on the _VvADH2_ was found in the \"Xiangfei\" cultivar, and this gene expression exhibited a decrease after the veraison and then an increase at the harvest (Figure 3). However, the rest of the grape cultivars showed a constant decrease in the expression of the _VvADH2_ throughout the berry development stages. Additionally, higher expression of _VvADH1_ was observed in the \"Xiangfei\" and \"Zaomeiguixiang\" cultivars. The berry maturation process of these two cultivars resulted in a decrease in its expression. A previous study has reported that _VvADH1_ expression was enhanced before veraison and then inhibited when wine-making grapes approached ripeness [26]. The reactions between alcohols and acetyl coenzyme A have been confirmed to be catalyzed by alcohol acetyltransferase (AAT), and such reactions could result in the formation of esters [18]. A high transcription level of _VvAAT_ was found in the \"Xiangfei\" cultivar. During the berry development period, the expression of this gene exhibited a significant increase and then a dramatic decrease under the 2014 vintage. However, the transcription level of this gene was low in the other cultivars, and an expression decrease was found from the veraison to the harvest stage. This evolutionary pattern of _VvAAT_ was not in accordance with that in Cabernet Sauvignon [27]. It should be noted that only the evolutionary pattern of the _VvAAT_ in the \"Xiangfei\" cultivar matched the evolutionary pattern of the C6 esters (Figure 2).
# Analyses of GLONASS and GPS+GLONASS Precise Positioning Performance in Different Latitude Regions
## 2 Evaluation Methods
### Constellation Visibility Analysis  
The visibility of GPS, GLONASS, and GPS+GLONASS in different latitude networks was analyzed and evaluated using the following criteria:  
1. The number of visible satellites. The mean number of visible satellites in each network at each epoch is computed, and the observed probability of different positioning combinations is also analyzed.
2. The elevation angle of visible satellites. The mean elevation angle of visible satellites in each network at each epoch is computed, and the occurrence probability corresponding to different degrees is evaluated.
3. The Position Dilution of Precision (PDOP). The mean PDOP of each network at each epoch is calculated.  
As the GLONASS constellation geometry repeats about every 8 sidereal days, we used observation data from 28 March 2021 (day of year (DOY) 087) to 4 April 2021 (DOY 094) to evaluate the observation quality. The elevation cutoff angle was set to 3\({}^{\circ}\). The study employed the same data set as the data quality check for visibility assessment.
# Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers
## 3 Theory and Methods
### Sea Ice Classification Method
#### 3.2.1 Random Forest (RF)  
The RF is one of the ensemble methods that have received increasing interest since they are more robust and accurate than single classifiers [52; 53]. RF is composed of a set of classifiers where each classifier casts a vote for the allocation of the most frequent type to the input vectors. The principle of ensemble classifiers is based on the basic precondition that a variety of classifiers usually outperform an individual classifier. Some of the advantages of RF for remote sensing applications include (1) It has high efficiency on large datasets; (2) It can deal with a large number of input variables without variable deletion; (3) The variable importance can be estimated in the classification; (4) Relatively strong robustness to noise and outliers.  
The design of a decision tree needs to choose an attribute selection measure and a pruning approach. The Gini index [54] is usually used to measure the impurity of training samples in RF. For a given training dataset \(T\), the Gini index (\(G_{index}\)) can be expressed as,  
\[G_{index}(T)=1-\sum_{i=1}^{m}p_{i}^{2}, \tag{8}\]  
Figure 7: The distribution of (**a**) RESC (Right Edge Slope of CDW), (**b**) RESI (Right Edge Slope of IDW), (**c**) RESD (Right Edge Slope of DDW), (**d**) REWC (Right Edge Waveform of CDW), (**e**) REWI (Right Edge Waveform of IDW), and (**f**) REWD (Right Edge Waveform of DDW) in 5 days (from 11 to 15 February 2018) versus the OSISAF SIT maps. The open water (OW) is described by the light blue color. The white and gray represent the first-year ice (FYI) and multi-year ice (MYI).  
where \(m\) is the number of categories and \(p_{i}\) is the proportion of samples \(T\) that belongs to class \(i\). In the case of binary classification problems, the most appropriate characteristics at each node can be identified by the Gini index, given by:  
\[G_{index}(T)=2p(1-p). \tag{9}\]  
The RF can also evaluate the relative importance of different features in the classification process. It is helpful to select the best features and know-how each feature influences the classification results [55; 56].
# Implementation of MS Circle Map in Digital Image Encryption
## 3 Results and Discussion
### Practical Applications  
The application of the MS Circle Map-based encryption algorithm extends beyond controlled experimental settings and is highly relevant for real-world scenarios. In the healthcare sector, medical image data, which contain sensitive patient information, must be securely stored and protected against unauthorized access. By encrypting these images before storage, hospitals and healthcare institutions can ensure compliance with data privacy regulations and prevent potential data breaches.  
Beyond medical applications, this encryption technique can be utilized in the financial sector to secure sensitive customer data, such as identity documents, bank account details, and transaction records. Encrypting such data ensures that only authorized personnel have access, mitigating risks associated with identity theft and financial fraud.  
Additionally, military and governmental agencies require robust encryption for securing classified digital imagery related to national security, surveillance, and protected communications. The MS Circle Map encryption method provides strong security for digital image confidentiality, ensuring privacy protection and safeguarding sensitive information against cyber threats.  
With its strong resistance to brute force attacks, statistical analysis, and differential cryptanalysis, the MS Circle Map encryption algorithm presents a viable solution for real-world applications where data security is paramount.
## 5 The Proposed Protocol
### Password and Biometrics Update Phase  
In this phase, \(P_{i}\) updates their password and biometrics stored in \(SC_{i}\) to enhance the security. As depicted in Figure 4, the procedure is performed as follows.  
_Step UI1:_\(P_{i}\) enters \(ID_{i}^{\prime},PW_{i}^{\prime},B_{i}^{\prime}\) into \(SC_{i}\). \(SC_{i}\) computes \(\sigma_{i}=\varepsilon_{i}\oplus h\big{(}ID_{i}^{\prime}||PW_{i}^{\prime}||h_{bio }(B_{i}^{\prime})\big{)}\). Next, \(SC_{i}\) verifies whether \(V_{i}\stackrel{{?}}{{=}}h(h(ID_{i}^{\prime}||PW_{i}^{\prime}|| \sigma_{i})||h_{bio}(B_{i}^{\prime}))\). If there is a match, \(SC_{i}\) requests \(P_{i}\) to enter new credentials \(PW_{i}^{new},B_{i}^{new}\).  
_Step UI2:_ Upon receiving \(PW_{i}^{new},B_{i}^{new}\), \(SC_{i}\) computes \(\varepsilon_{i}^{new}=\sigma_{i}\oplus h\big{(}ID_{i}^{\prime}||PW_{i}^{new} ||h_{bio}(B_{i}^{new})\big{)}\) and \(V_{i}^{new}=h(h(ID_{i}^{\prime}||PW_{i}^{new}||\sigma_{i})||h_{bio}(B_{i}^{new }))\). Finally, \(SC_{i}\) replaces \(\varepsilon_{i},V_{i}\) with \(\varepsilon_{i}^{new},V_{i}^{new}\). The new password and biometrics are provided to the smart card.
## 5 Simulation Results and Discussion
### Results and Discussion under Different Scenarios  
#### 5.2.1 Case Study under Different EV Penetration Levels  
This case study analyzes the performance of different methods for different penetration levels. At 30% penetration, we can observe from Figure 7(a) that the proposed method activates the V2G mode during the peak period and starts charging during the off-peak period. Similarly, the reactive power discharge with the proposed method can be seen in Figure 7(b). Figure 7(a) shows that there is a severe undervoltage problem with uncoordinated charging during peak load conditions, whereas the system voltage is close to the desired limit with the proposed method. We can observe from Figure 7(b) that the VUF lies under 2% for all methods except Method 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(c_{1}\) & 1.4962 \\ \(c_{2}\) & 1.4962 \\ Iteration & 50 \\ Population size & 25 \\ Weight for VUF in Stage 1 (\(\theta_{1}\)) & 0.5 \\ Weight for CC in Stage 1 (\(\theta_{2}\)) & 0.25 \\ Weight for BC in Stage 1 (\(\theta_{3}\)) & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation parameters for PSO.  
Figure 8: Schedule of (**a**) active power for EVs for different methods; (**b**) reactive power for EVs for different methods under 30% EV penetration.  
Figure 9: Bus 846 (**a**) voltage and (**b**) VUF under 30% EV penetration.  
Figure 10a shows the result for 50% penetration; it can be seen that Methods 1 and 3 are unable to maintain the voltage level near the prescribed limit (\(\pm\)5%) for this case study. The other methods, however, can keep the bus voltage at the prescribed limits. The plot of VUF, as shown by Figure 10b, shows that the VUF also lies within the prescribed limit of the proposed method for this case study.  
At 75% penetration, the bus voltage profile is improved for most methods, but lies closest to the desired voltage with the proposed method, as shown by Figure 11a. The plot of VUF as given by Figure 11b shows that the VUF exceeds the 2% limit with other methods but it lies within the limits for the proposed method.  
Figure 12a,b shows the plots of the bus voltage and VUF at a 100% penetration level. It can be seen from the figures that both the bus voltage and the VUF also lie within the prescribed limits using the proposed method compared to other methods.  
Figure 13 shows that the final SOC lies within the desired limits for customers with the application of the proposed method.  
Figure 11: Bus 846 (**a**) voltage and (**b**) VUF under 75% EV penetration.  
Figure 10: Bus 846 (**a**) voltage and (**b**) VUF under 50% EV penetration.
## 1 Introduction
These questions are investigated using a time series of TSX and CSK images acquired in HH and HV polarizations and a radar incidence angle near 30\({}^{\circ}\) over an agricultural region in southeastern France between April and October 2013. The study site and the database of satellite images and experimental measurements are described in Section 2. The results concerning the correlation between the X-band SAR signals and the soil and vegetation characteristics are presented and discussed in Section 3. In Section 3, the discussion will focus on analyzing (a) the correlation between the X-band radar signal and soil moisture; (b) the potential of the radar data to track irrigation; and (c) the correlation between the X-band radar signal and the biophysical parameters of the vegetation. Finally, conclusions and perspectives are presented in Section 4.
## 4 Simulation Results and Analysis
### Nominal NDI Controller Simulation  
The normal overload \(N\!y\!s\) of HFV is expressed as  
\[N\!y\!s=\frac{V}{g}(q-\dot{\alpha})+\frac{I_{\!\!\!\!\!\!\!\!\!\!\!\!  
where \(I_{xs}\) is the distance from the center of gravity of the aircraft to the normal overload sensor. The track inclination of the aircraft is expressed as  
\[\mu=\theta-\alpha \tag{60}\]  
The small disturbance linearization processing method must be used near a certain system equilibrium point, and the equilibrium state is selected as the angle of attack \(\alpha_{0}=3.25\) deg.  
Set the reference signals to  
\[\alpha_{c}=4\times\frac{\pi}{180}rad,q_{c}=0 rad/s,\theta_{c}=3.27\times\frac{ \pi}{180}rad\]  
the simulation initial conditions are \(\alpha=0\) rad, \(q=0\) rad/s, \(\theta=0\) rad.  
The simulation results of the nominal controller are shown in Figure 6.  
The simulation results show that the system can quickly recover to a stable state, and the system state \(x=[\alpha,\,q,\,\theta]^{T}\) can track the given reference command \(x_{c}=[\alpha_{c},\,q_{c},\,\theta_{c}]^{T}\), the derivative of the state variable can eventually tend to 0, and the normal overload _Nys_ tends to 0, which satisfies the control objective. When the system moves longitudinally, the default sideslip angle \(\beta=0\), and the selected equilibrium state should ensure that the track inclination angle \(\mu=0\). The simulation results show that the track inclination angle \(\mu\) is finally stabilized at \(-0.72\) deg, which is slightly different from the ideal state. There is still room for improvement in compensating for parameter perturbation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter Meaning** & **Symbol** & **Parameter Value** \\ \hline HFV mass & \(m\) & 9295.44 kg \\ Pitch moment of inertia & \(I_{y}\) & 75,673.8 kg-m\({}^{2}\) \\ Reference wing area & \(S\) & 28.87 m\({}^{2}\) \\ Span & \(b\) & 9.144 m\({}^{2}\) \\ Air density & \(\rho\) & 1.22 kg/m\({}^{3}\) \\ Flight speed & \(V\) & 5104.41 m/s \\ Track inclination angle & \(\mu\) & 0 deg (\({}^{\circ}\)) \\ Acceleration due to gravity & \(g\) & 9.806 m/s\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: HFV simulation control parameters.  
Figure 6: _Cont._
## 2 Materials and Methods
### Deriving TEC from GPS Measurements  
The ionospheric model is established in the polar regions, which requires high-precision ionosphere TEC observations. The said observations obtained from the geometry-free combination of dual-frequency pseudo-range measurement suffer from noise and multipath effects [31]. In terms of the carrier phase measurements, it is difficult to quantify the unknown integer phase ambiguities. Therefore, a carrier-to-code leveling (CCL) approach was used to calculate the ionosphere TEC observations [14], which, derived from CCL method, have the expression shown in Equation (1).  
\[\hat{I}_{r}^{s}=\text{STEC}_{r}^{s}-\delta(\text{DCB}_{r}+\text{DCB}^{s})+ \varepsilon_{I}, \tag{1}\]  
where \(\hat{I}_{r}^{s}\) is the ionospheric observation in TEC unit (TECU); \(s\) and \(r\) denote the pseudo-random noise (PRN) of the given satellite and receiver, respectively; \(\delta=\frac{c}{\alpha(f_{i}^{-2}-f_{j}^{-2})}\) is a constant used to convert the time unit (second) to TECU, where \(\alpha=4.028\times 10^{17}\) ms\({}^{-2}\)TECU\({}^{-1}\), \(c\) is the speed of light, \(f\) is the frequency; DCB\({}_{r}\) and DCB\({}^{s}\) are receiver and satellite differential code biases (DCBs).  
The TEC derived from GPS measurements contains the satellite and receiver DCBs [32]. In this paper, the satellite DCBs of P1-C1 were provided by the Center for Orbit Determination in Europe (CODE). Since P1-P2 DCB products provided by IGS only have receiver DCBs of IGS tracking stations, the GPS satellite and receiver DCBs of P1-P2 were computed employing the IGGDCB (IGG, Institute of Geodesy and Geophysics, Wuhan, China) method based on all GPS stations shown in Figure 1. Figure 2 shows the bias, upper panel, and root mean square (RMS) errors, bottom panel, of the differences between the satellite DCBs estimated by the IGGDCB method and the monthly P1-P2 DCBs products provided by the CODE. Compared with the DCB products of CODE, the bias of IGGDCB-based satellite P1-P2 DCB estimates was determined at the level of \(\pm 0.1\); the maximum RMS error was 0.34 ns, while the average appeared to be less than 0.2 ns. The DCBs of satellite and receiver were taken into Equation (1) to obtain the pure TEC value.  
Figure 2: Bias (**top**) and root mean square (RMS) errors (**bottom**) of the differences between the satellite differential code biases (DCBs) estimated by the IGGDCB method (Institute of Geodesy and Geophysics) and the monthly P1-P2 DCBs products provided by the Center for Orbit Determination in Europe (CODE) at the individual GPS satellites as a function of the pseudo-random noise (PRN).  
For this study, in order to mitigate the influence of spatial gradients on ionospheric mapping [33] and multipath effect, we only adopted the slant TEC data corresponding to the line-of-sight (LOS) with a cutoff angle of 20\({}^{\circ}\) at the observation point. Instead of modeling the ionosphere as being distributed in altitude, the thin shell model was used for mapping the polar ionospheric TEC at the height of 425 km.
## 4 Results and Interpretations
### Facies Analysis and Depositional Environment
#### 4.2.6 H Facies: Hybrid Event Beds  
Hybrid beds are characterized by intermediate flow behavior comprised of two arrangements of lower mud-deficient sand overlain by a mud-rich sand interval, and various terms were assigned like slurry flows [18], transitional flow deposit [9], linked debrite [86], hybrid event [87] and matrix-rich sand [88]. The hybrid beds are often termed as bi- or tripartite beds, depending on the characteristics of the underlying and overlying units, and vary significantly from the downslope of the channelized to the unconfined area. Lateral lobe fringes are predominantly low-density turbidites and have hybrid events [23; 45].  
Figure 4: Sedimentary facies. (**a**) Amalgamated massive sand with floating mud clasts in the Jalan UMS road section, interpreted to be S1 lithofacies. (**b**) Massive, coarse-grained sandstone at the Jalan UMS road section, which belongs to the S2 facies. (**c**) Parallel lamination S3 facies and cross-lamination S4 facies in the Jalan UMS road section. (**d**) Parallel laminated S4 facies at the Jalan UMS road section. (**e**) Flame structure S5 facies. (**f**) Laminated siltstone facies Si1 at the Jalan UMS KFC section. (**g**) Laminated muddy siltstone Si2 facies at the Jalan UMS KFC section. (**h**) Massive dark shale or mudstone M facies at the University of Prima Condo road section.  
A great variety of lithofacies can be prevalent within the hybrid units and are also variable within the individual beds over a scale of centimeters to meters. These hybrid beds contain both the characteristics of turbidite and debrite within the same depositional event. The scales of thickness of hybrid beds vary considerably from tens of centimeters to more than a meter, which is associated with the influx of sediments deposited within the single event of hybrid flow. The hybrid beds in the study area (Universiti Prima road section) consisted of only three divisions (H1, H3 and H5) of hybrid event deposition [43]. The basal structureless graded sandstone (H1), overlain by a banded sand unit (H2), was composed of both sand and shale (irregular) bands. The third division was more chaotic (H3), having patches of sand with more mud, with the fourth subdivision having a laminated sand mud unit (H4) capped by a clayy shale unit (H5).