esa-sceva/satcom-chunk-collection huggingface dataset:
# Precise Point Positioning Using Triple GNSS Constellations in Various Modes
## 3 BSSD Post-Processing PPP Models  
When combining the GPS, Galileo, and BeiDou observations in an un-differenced PPP model, the ambiguity parameters lose their integer nature as they are contaminated by the receiver and satellite hardware delays. It should be pointed out that the number of unknown parameters in the combined PPP solution equals the number of visible satellites from any system plus seven parameters, while the number of equations equals double the number of the visible satellites. This means that the redundancy equals \(n_{G}+n_{E}+n_{B}-7\). In other words, at least seven mixed satellites are needed for the solution to exist. In comparison with the GPS-only un-differenced scenario, which requires a minimum of five satellites for the solution to exist, the addition of Galileo or BeiDou satellites increases the redundancy by \(n_{E}+n_{B}-2\). In other words, we need a minimum of three satellites from both Galileo and BeiDou systems in order to contribute to the solution. As indicated earlier, the reference satellite can be selected from any of the three satellite constellations [37].
# Retrieval of Nearshore Bathymetry around Ganquan Island from LiDAR Waveform and QuickBird Image
## 2 Related Work
Multi-Gaussian function fitting is performed to derive the control points and validation points, and the bathymetric method of combining active and passive remote sensing is carried out in the sea area around islands far from the mainland.
# Interference Analysis for Vehicle-to-Vehicle Communications at 28 GHz
## Abbreviations
[CrossRef]
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* (28) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] PHY Abstraction Techniques for IEEE 802.11p and LTE-V2V: Applications and Analysis. In Proceedings of the 2018 IEEE Global Communications Conference, Abu Dhabi, UAE, 9-13 December 2018; pp. 1-7. * (29) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Throughput of Infrastructure-Based Cooperative Vehicular Networks. _IEEE Trans. Intell. Transp. Syst._**2017**, _18_, 2964-2979. [CrossRef]
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* (31) [PERSON]; [PERSON]; [PERSON] Coverage and capacity of millimeter-wave cellular networks. _IEEE Commun. Mag._**2014**, _52_, 70-77. [CrossRef]* [PERSON] et al. (2015) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] VANET Modeling and Clustering Design Under Practical Traffic, Channel and Mobility Conditions. _IEEE Trans. Commun._**2015**, 63, 870-881. [CrossRef]
* [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON]; [PERSON] On physical layer security of two way energy harvesting relays. In Proceedings of the 2017 International Conference on Frontiers of Information Technology (FIT), Islamabad, Pakistan, 18-20 December 2017; pp. 35-40. * [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON] Higher Order Statistics for the \(\alpha-\eta-\kappa-\mu\) Fading Model. _IEEE Trans. Antennas Propag._**2018**, 66, 3002-3016. [CrossRef]
* [PERSON] (2016) [PERSON], [PERSON] The \(\alpha-\eta-\kappa-\mu\) Fading Model. _IEEE Trans. Antennas Propag._**2016**, 64, 3597-3610. [CrossRef]
* [PERSON] (2014) [PERSON]; [PERSON] _Table of Integrals, Series, and Products_; Academic Press: New York, NY, USA, 2014. * [PERSON] et al. (1990) [PERSON]; [PERSON]; [PERSON] _Integrals and Series, Vol. 3: More Special Functions_; Gordon and Breach Science Publishers: Amsterdam, The Netherlands, 1990. * [PERSON] et al. (2014) [PERSON]; [PERSON]; [PERSON]; [PERSON] Outage performance analysis of underlay cognitive RF and FSO wireless channels. In Proceedings of the 2014 3 rd International Workshop in Optical Wireless Communications (IWOW), Funchal, Portugal, 17 September 2014; pp. 6-10.
## 5 Analysis of Results and Field Testing
### Field Tests
#### 5.5.3 Measurement Test without a Water Gauge  
The primary objective of the algorithm proposed in this paper is to address the limitations of current mainstream water level detection algorithms in field environments where water gauge information may be missing due to uncontrollable conditions such as corrosion, stains, strong reflection, and low brightness, leading to a loss of detection accuracy or complete failure. By establishing a mapping relationship, the algorithm is capable of water gauge-free measurement using a fixed-view camera after the initial calibration. To evaluate the effectiveness of the proposed algorithm in achieving water gauge-free measurement, we conducted simulation experiments as follows: first, we selected a test site with a constant water level; then, we captured images of the water gauge after setting it up and after removing it using a fixed-point camera; finally, we tested the captured images using the proposed algorithm, and compared the water level detection results obtained with and without the water gauge. The test results shown in Figure 12  
Figure 11: Results of water level measurement.  
demonstrate that the proposed algorithm can achieve water gauge-free measurements while maintaining a constant camera shooting angle.
## 3 Results and Discussion
### Methylene Blue Adsorption
#### 3.2.5 Hydrochar Regeneration  
Five cycles of methylene blue adsorption were performed to evaluate the reusability of H-180. Figure 5 presents the removal percentage of methylene blue for each cycle. It is worth noting that after five cycles the removal percentage remained around 80%; it decreased from 92.5% (first cycle) to 79.2% (fifth cycle). This represents a reduction of only 14.4% in relation to the first cycle. For comparison purposes, [PERSON] and colleagues reported a removal percentage reduction from 85% (first cycle) to 77% (fourth cycle) in methylene blue adsorption by hydrochar from liquorice-root pulp. According to the authors, the changes to the adsorption sites on the adsorbent surface reduce the removal after each cycle. This occurs because some methylene blue molecules attach permanently to the surface, blocking the vacant pores and reducing dye adsorption efficiency [53].  
Figure 4: Linearization of Equation (7) to obtain the thermodynamic parameters of the methylene blue adsorption by hydrochar (H-180) from co-hydrothermal carbonization (Co-HTC) of sawdust and non-dewatered sewage sludge.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\Delta\)G (J\(\cdot\)mol\({}^{-1}\)) & \(\Delta\)S (J\(\cdot\)mol\({}^{-1}\)K\({}^{-1}\)) & \(\Delta\)H (J\(\cdot\)mol\({}^{-1}\)) \\ \hline \(-3845.12\) (30 \({}^{\circ}\)C) & & \\ \(-4128.52\) (40 \({}^{\circ}\)C) & & \\ \(-4411.92\) (50 \({}^{\circ}\)C) & & \\ \(-4695.32\) (60 \({}^{\circ}\)C) & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Thermodynamic parameters of the methylene blue adsorption by hydrochar (H-180) from co-hydrothermal carbonization (Co-HTC) of sawdust and non-dewatered sewage sludge.
## 3 Results
### Water Conveyance Efficiency  
Water conveyance efficiency (_CE_) varied greatly across the Agri-Parks with _CE_ values hovering between 81 and 99% (Table 3). Rooiwal had _CE_ values varying between 92 and 97% for the Filter unit-Manifold and Main pump-Tank, respectively. Soshanguve values ranged from 81% for the Filter unit-Manifold to 99% for the Tank-Booster pump. Tarlton Agri-Park exhibited the lowest _CE_ value of 83% for the Main pump-Tank and the highest value of 94% for the Booster pump-Filter unit. The Booster pump-Filter unit performed the poorest at Westonaria with a _CE_ value of 73%. However, its Tank-Booster pump component performed impressively at 98% _CE_. Overall, the performance of the Agri-Parks in terms of water conveyance efficiency declined in the order Rooiwal > Soshanguve > Tarlton > Westonaria (Table 3).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Irrigation System Component**} & \multicolumn{4}{c}{**CI**} \\ \cline{2-5}  & **Rooiwal** & **Soshanguve** & **Tarlton** & **Westonaria** \\ \hline Pump–Tank & 5.99 & 6.29 & 6.25 & 5.61 \\ Tank–Booster pump & 4.71 & 4.10 & 4.90 & 4.43 \\ Booster pump–Filter unit & 4.90 & 2.73 & 5.54 & 2.12 \\ Filter unit–Manifold & 5.52 & 5.48 & 5.65 & 5.60 \\ Infield & 5.04 & 5.00 & 5.05 & 5.02 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Condition indices (CIs) for irrigation infrastructure components at the Agri-Parks, Gauteng province (February–March 2021).
# Deep Fake Image Detection Based on Pairwise Learning
## 2 Fake Face Image Detection  
The most serious challenge in the image and video forgery detection field is the fake face image detection. Fake face images can be used to create fake identities on social media networks, thus stealing personal information illegally. For instance, the fake image generator can be used to produce images of celebrities with inappropriate content, which has hazardous consequences. In this section, the proposed deep learning framework with the pairwise learning strategy is introduced in detail.  
The proposed two-step learning method that combines the CFF based on pairwise learning strategy and the classifier learning is presented in Figure 1. Introducing the supervised learning strategy in the fake face image detection the problems related to both difficult collection of training samples generated by all possible GANs and the need to retrain the fake face detector to obtain an effective model for the fake face images generated by a new GAN, are addressed. Specifically, to overcome these problems, the fake and real images are paired and follow by using the pairwise information to construct the contrastive loss to learn the discriminative common fake feature (CFF) by the proposed CFFN. Once the discriminative CFF is learned, the classification network captures the discriminative CFF to identify whether the image is real or fake. The details of the proposed method are described in the following.  
Let the set of the collected training images generated by \(M\) GANs be defined as: \(\textbf{X}_{fake}=[\textbf{x}_{i=1}^{k=1},\textbf{x}_{i=2}^{k=1},...,\textbf{x }_{i=N_{t}}^{k=M},\textbf{x}_{i=N_{t}}^{k=M}]\), where each GAN generates \(N_{k}\) training images. Let the training set consisted of real images be denoted as \(\textbf{X}_{real}=[\textbf{x}_{i=1},\textbf{x}_{i=2},...,\textbf{x}_{i=N_{t}}]\), containing \(N_{r}\) training images. Therefore, the total number of training images, including both real and fake images, will be \(N_{T}=N_{r}+N_{f}=N_{r}+\sum_{k=1}^{M}N_{k}\). The label information set denoted as \(\textbf{Y}=[\textbf{y}_{1},\textbf{y}_{2},...,\textbf{y}_{N_{t}}]\) indicates whether an image is fake \((y=0)\) or real \((y=1)\). As stated previously, the pairwise information is necessary for the training stage so that the CFFN can learn the discriminative CFFs well. Toward this end, the pairwise information can be generated from the training set **X** and its corresponding label set **Y** by the permutation combination. Therefore, there are \(C(N_{T},2)\) pairs \(P=[p_{i=0,j=0},p_{i=0,j=1},...,p_{i=0,j=N_{t}},...,p_{i=N_{f},j=N_{r}}]\) generated from the training samples. In this paper, we set the total number of pairwise samples to \(N_{r}=2,000,000\).
# Radiometric Cross-Calibration of GF-1 PMS Sensor with a New BRDF Model
## 5 Discussion
### Results and Discussion of Time Series Cross-Calibration
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Site**} & \multirow{2}{*}{**Date**} & \multicolumn{2}{c}{**Blue**} & \multicolumn{2}{c}{**Green**} & \multicolumn{2}{c}{**Red**} & \multicolumn{2}{c}{**NIR**} \\ \cline{3-8}  & 7 August 2014 & \(5.82\%\) & \(8.55\%\) & \(6.21\%\) & \(5.86\%\) \\ Dunhuang & 6 August 2016 & \(4.57\%\) & \(6.94\%\) & \(6.96\%\) & \(7.45\%\) \\  & 9 July 2015 & \(-8.77\%\) & \(-5.44\%\) & \(-6.85\%\) & \(-6.15\%\) \\ \hline \multirow{3}{*}{Golmud} & 24 February 2014 & \(-1.74\%\) & \(-4.90\%\) & \(-2.86\%\) & \(0.55\%\) \\  & 12 December 2014 & \(0.91\%\) & \(-0.40\%\) & \(1.47\%\) & \(2.98\%\) \\ \cline{1-1}  & 25 September 2015 & \(2.25\%\) & \(-2.63\%\) & \(-1.50\%\) & \(3.72\%\) \\ \cline{1-1}  & 16 December 2015 & \(3.46\%\) & \(-5.36\%\) & \(-0.20\%\) & \(4.02\%\) \\ \hline \hline \end{tabular}  
* \({}^{1}\) Lab’ represents the calibration coefficients obtained in laboratory conditions. 2 \({}^{2}\) ‘CRESDA’ represents the official calibration coefficients by site calibration method.  
\end{table} 
Table 12: The relative errors of cross-calibration coefficients between the two reference sensors. As shown in Table 13, the absolute values of relative errors between average cross-calibration coefficients and laboratory calibration coefficients were between 5% and 18%, which means that PMS1 was attenuated obviously after satellite launching, and the laboratory calibration coefficients was no longer available and post-launch calibration of PMS1 sensor was necessary. However, the absolute values of relative errors between average cross-calibration coefficients and CRESDA site calibration coefficients were less than 3%, except for individual bands. In summary, the results of this method are in good agreement with those of site calibration and can be used to validate the results of site calibration, with higher calibration frequency and lower cost.
## 3 Results and Discussion
### Compression Properties
#### 3.3.1 Machine Learning Prediction Models of Compression Distribution  
Figures 13-15 display the results that were obtained from modeling the tensile properties of AMC. These results include the compressive strength, yield strength, and fracture strain. The results show that there was a significant correlation between the dataset that was predicted and the actual dataset that was collected from the studies. The RMSE and R\({}^{2}\) scores shown in Table 7 demonstrate that the tuned ANN with two hidden layers and an activation function of ReLu produced the best results. The ideal RMSE and R\({}^{2}\) values for the \(\varepsilon_{\rm{f}}\) (%) parameter were 0.2 and 0.98, respectively, for the training set; for the testing set, these values were 0.2 and 1. Moreover, the ideal RMSE and R\({}^{2}\) values for the \(\sigma_{\rm{c}}\) parameter were 3.58 and 0.98, respectively, for the training set; for the testing set, these values were 4.55 and 0.98. Additionally, the ideal RMSE and R\({}^{2}\) values for the \(\sigma_{\rm{y}}\) parameter were 3.17 and 0.98, respectively, for the training set; for the testing set, these values were 5.19 and 0.99. The vast majority of the applied algorithms demonstrated successful performance throughout the data training and testing processes. The experimental and anticipated results for the mechanical properties of AMC are compared in Table 8, which shows the differences in error that existed between the two sets of data.  
Figure 14: (**a**) Evaluation of predicted versus experimental \(\sigma_{y}\) for the training and testing groups. (**b**) Predicted train, predicted test, and experimental datasets for the anticipated \(\sigma_{y}\) as a function of the sample index.  
Figure 13: (**a**) Evaluation of predicted versus experimental \(\sigma_{\text{uc}}\) for the training and testing groups. (**b**) Predicted train, predicted test, and experimental datasets for the anticipated \(\sigma_{\text{uc}}\) as a function of the sample index.
# MAOA: A Swift and Effective Optimization Algorithm for Linear Antenna Array Design
## 4 Results and Discussion
### Antenna Position Optimization
#### 4.1.3 Design 3: Side Lobe Average Power Minimization Without FNBW Constraint of a 20-Element LAA  
The total side lobe average power minimization of an LAA with no constraints on FNBW is focused here by optimizing the position parameter using the MAOA algorithm. The fitness function for this design is provided in Equation (29), which requires an integration of the array factor. In this equation, \(\theta_{\text{ii}}\) and \(\theta_{\text{ui}}\) are the lower and upper bounds of the region in which the SLL is required to be suppressed, while \(\Delta\theta_{\text{i}}=[\theta_{\text{ui}}-\theta_{\text{ii}}]\) is the difference between them. The values of the SLL region focused on for minimization are \([0^{\circ},82^{\circ}]\) and \([98^{\circ},180^{\circ}]\).  
\[\text{fitness function}=\sum_{i}\frac{1}{\Delta\theta_{\text{i}}}\int_{ \theta_{\text{ii}}}^{\theta_{\text{ui}}}|\text{AF}(\theta)|^{2}\text{d}\theta \tag{29}\]  
The fitness function is simulated by using three optimization algorithms, and the obtained values of the positions are listed in Table 3. The array pattern using these optimized position values is shown in Figure 8, and Table 4 indicates the SLL of the 20-element LAA  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{5}{c}{**Optimized Element Positions (\(\times\lambda\))**} & \multirow{2}{*}{**CSLL (dB)**} \\ \cline{2-2} \cline{4-7}  & **I** & **II** & **III** & **IV** & **V** & \\ \hline ULA & 0.2500 & 0.7500 & 1.2500 & 1.7500 & 2.2500 & \(-\)12.97 \\ \hline ALO [28] & 0.1792 & 0.5407 & 0.9452 & 1.5097 & 2.2500 & \(-\)30.24 \\ \hline PSO [4] & 0.1685 & 0.5461 & 0.9364 & 1.5107 & 2.2500 & \(-\)31.00 \\ \hline GWO [22] & 0.1792 & 0.5410 & 0.9354 & 1.5096 & 2.2500 & \(-\)31.30 \\ \hline MAOA & 0.1669 & 0.5443 & 0.8321 & 1.4739 & 2.1409 & \(-\)34.50 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optimized positions for positive half elements and CSLL of design 2.  
Figure 7: Array pattern of a 10-element LAA for design 2.  
along with the FNBW obtained by ULA, ALO, AOA, and MAOA. The average power for one-half SLL of ULA is \(-21.68\) dB, which is now reduced to \(-25.75\) dB by MAOA, offering an improvement of \(4.07\) dB of average SLL power in this design by using the proposed algorithm.
## Nomenclature  
### Acronyms  
EDRP Emergency Demand Response Program  
DG Distributed Generation  
MILP Mixed Integer Linear Programming  
DSO Distribution System Operator  
MGs Micro Grids  
RRCs Routing Repair Crews  
**Set and Indexes**  
\(\sigma\) Index of each depot  
\(m/n\) Index of an outage and damaged component  
\(b\) Index of bus  
\(t\) Index of time  
\(N_{b}\) Number of buses  
\(N_{t}\) Number of times  
**Parameters**  
\(RES^{p}(\sigma)\) Existing equipment in each depot  
\(Res^{N}(m)\) Required component for repairing outage \(m\)  
\(a_{1},a_{2},a_{3},a_{4}\) Cost function coefficients  
\(d(dep_{\sigma},m)\) The distance between outage and depot  
\(P_{d}(b,t)\) Load of bus b in time t  
\(Pen(b)\) Penalty factor (USD/kW)  
\(H_{m}\) Public hazard index  
\(TD(\sigma,m)\) The binary parameter that shows the ability of each crew to repair the outage \(m\)  
\(sus(b,bp)\) Susceptance of lines  
\(P_{Lize}^{\max}\) Maximum power flow in lines  
\(P_{DG}^{\max}\) Maximum power of each DG  
\(nc(\sigma)\) Number of crews in depot \(\sigma\)  
\(CAP(c)\) The crew vehicle capacity  
\(r(m,c)\) Repair time for outage \(m\)  
\(tr(m,n,c)\) Traveling time between outage \(m\) and \(n\)  
**Variables**  
\(P_{DG}(i,t)\) Power of DG \(i\) in time \(t\)  
\(s(\sigma,m)\) A binary variable that shows the clustering of the outages  
\(\delta(b,t)\) The angle of the bus  
\(u(m,t)\) Status of lines and DGs  
\(Z(m,t)\) Availability of damaged component \(m\)  
\(x(m,n,c,\sigma)\) The binary variable showing when crew \(c\) moves from outage \(m\) to \(n\)  
\(y(m,c,\sigma)\) The binary variable showing when the crew \(c\) visits the outage \(m\)  
\(REC^{c}(c)\) Number of pieces of equipment assigned to crew c  
\(AT(m,c)\) Arrival time of crew \(c\) at outage \(m\)  
\(q(b,t)\) A binary variable that shows the status of each load bus  
\(f(m,t)\) Indicates the time that outage m is repaired
## 10 Conclusions  
This article primarily discusses the advantages, applications, and relevant parameters of an advanced initiation chemical vapor deposition (iCVD) process known as the chemical deposition polymerization (CDP) synthesis technique. The core principles of the CDP synthesis technique are twofold. Firstly, under conditions where the monomer vapor pressure is lower than the saturation vapor pressure, a hydrophobic polymer film with a smooth and uniform surface (such as pPFDA) is synthesized as the substrate. Secondly, a two-step method is employed to synthesize polymer nanodomes (PNDs). In the initial step, with the monomer vapor pressure higher than the saturation vapor pressure, the monomer vapor condenses, forming nanodome-shaped liquid droplets. In the subsequent step, by introducing an initiator and activating it using a filament, free radicals are generated. These free radicals then react with the previously formed nanodome liquid droplets, resulting in polymerization and solidification. The volume of the PNDs is significantly reduced due to the subsequent removal of unreacted monomers and initiators.  
We primarily discussed an improved iCVD process, focusing on a novel route for synthesizing polymer nanoparticles--the chemical deposition polymerization (CDP) forming technique. We also summarized that the key aspects of this forming technique involve a two-step process (condensation followed by polymerization) and control of monomer vapor  
Figure 11: Schematic illustration of the strategy for superhydrophobic film preparation. **(i)** schematic of SU-8 molds (SU-8 photosensitive resin, a negative-tone, epoxy-based, near-UV photoresist, and they act as micro-nanostructure templates). **(ii,iii)** schematic of PDMS (poly(dimethylsiloxane)) arrays obtained by spin-coating, curing, and peeling off processes. **(iv)** schematic of the iCVD process [44].  
p8 ressive, which lead to the formation of drop-like or dome-shaped polymer nanodomes (PNDs). Furthermore, we compared and analyzed the two-step processes for porous polymer films and non-spherical polymer nanodomes. This comparison further confirmed the superiority of the two-step (condensation followed by polymerization) technique, which can inspire and influence the synthesis processes of various polymer forms using iCVD. This is particularly relevant in meeting the requirements for diverse polymers in areas such as biomedicine and drug delivery.  
The author gratefully acknowledges funding support from the China Scholarship Council with post-doctoral fellow project. The funding number is 202108220128.  
Data Availability Statement: Data openly available in a public repository.  
I would like to express my gratitude to [PERSON] in the Smith School of Chemical and Biomolecular Engineering at Cornell University for providing me with the postdoctoral work environment and academic guidance.  
Author [PERSON] was employed by the company Jilin Dongcheng Sumika Polymer Compounds Co., Ltd., the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.