esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this study, we at first assessed the accuracy of the multi-GNSS orbit and clock solutions from different IGS-RT services, comparing with GFZ final orbit and clock products. Then, we fully exploited the available multi-GNSS observations to validate the method of RT atmospheric parameter retrieving based on different IGS-RT orbit and clock services. Furthermore, we have conducted a detailed analysis of the initialization time and accuracy of the retrieved RT ZTD estimates by using the USNO products as a reference.  
In terms of the accuracy of RT orbits, the results show that for the GPS satellites, the RMS values of the differences between IGS-RT orbits and GFZ final orbits are generally better than 5 cm in radial and cross-track directions, and better than 10 cm in along-track direction. For the GLONASS satellites, the RMS values are generally better than 8 cm in radial and cross-track directions, and better than 12 cm in along-track direction which is slightly worse than the GPS orbit accuracy. For the BDS IGSO satellites, GFZD2 has the best orbit accuracy, for the BDS MEO satellites, CLK92 and GFZD2 have comparable orbit accuracies, better than that for GFZC2. Additionally, the results for the RT clocks show that the GPS clock has the highest accuracy comparing with the other system clocks. CLK92 has the highest GPS clock accuracy which is about 0.12 ns. CLK01 has the highest GLONASS clock accuracy which is about 0.22 ns. CLK92 has the highest Galileo clock accuracy which is about 0.22 ns. GFZD2 has the highest BDS clock accuracy which is about 0.44 ns.  
As for the RT ZTD estimates, the results reveal that the initialization process can be accelerated in the fixing coordinate mode comparing with that in the kinematic positioning mode. By fully exploiting multi-GNSS observables, the convergence time of the real-time estimated ZTDs can be decreased by about 8.1% compared to the GPS-only solution. In the combined GPS/GLONASS/Galileo/BDSsolution for different IGS-RT services, the shortest initialization time of around 480 s has been achieved for the retrieved RT ZTD estimates in the case of GFZC2. The averaged accuracy of the RT ZTD estimates for all available stations can be improved to about 5.06 mm in the multi-GNSS solution with fixing station coordinates. Owing to the addition of multi-GNSS observables, the improvement in the accuracy of the real-time estimated ZTDs can reach about 22.2% compared to the GPS-only solution. For the multi-GNSS real-time retrieving tropospheric delays, the ZTD accuracy in the fixing coordinate mode can be improved by a factor of approximately 2.7 in comparison to that in the kinematic mode.  
Thanks go to the International GNSS service (IGS) for providing multi-GNSS RT data and product streams.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper; [PERSON], [PERSON], [PERSON] and [PERSON] helped modify the paper.  
The authors declare no conflict of interest.
## 4 Simulation Experiment
### Results of the Simulation Experiment  
Figure 3 is the optical spectrum of point A in Figure 1, from which we can see that all the sidebands are suppressed except the \(\pm 8^{\text{th}}\)-order sidebands; the peak power of the \(+8^{\text{th}}\)-order sideband is lower than that of the \(-8^{\text{th}}\)-order; the data are only modulated into the \(+8^{\text{th}}\)-order sideband.  
Figure 4 is the optical spectrum of point B in Figure 1. In Figure 1, the \(0^{\text{th}}\)-order sideband is the inserted pilot signal.  
Figure 5 is the optical spectrum of point C in Figure 1, from which we can see that the uplink data were modulated onto the pilot signal.  
Figure 6 is the optical spectrum of point D in Figure 1, from which we can see that the uplink data were modulated onto the pilot signal.  
Figure 4: Optical spectrum at the output of the CS (Point B) in Figure 1.  
Figure 5: Optical spectrum at the output of the FBG (Point C) in Figure 1.  
Figure 3: Optical spectrum at the output of the \(8^{\text{th}}\)-order sideband generator (Point A) in Figure 1.  
Figure 7 is the spectrum of point E in Figure 1. From Figure 7, we can see that the output of the PD contains the frequency 16-tupling MMW with the downlink data.  
Figure 8 is the spectrum of point F in Figure 1, which is the baseband signal of the uplink data.  
In Figure 9, the relation curve of the Q factor with the length of the transmission fiber is shown. The eye diagrams of the data signal at particular points of the curve at 0 km, 20 km, 40 km, and 60 km are inserted in Figure 9. From Figure 9, we can see that the distance of the transmission fiber can reach up to 60 km for a Q greater than 6.  
Figure 8: Spectrum at the output of the PD2 (Point F) in Figure 1.  
Figure 6: Optical spectrum at the output of the OA (Point D) in Figure 1.  
Figure 7: Spectrum at the output of the PD1 (Point E) in Figure 1.  
Figure 10a,b show the relationship between the BER and the received power of the PD for the uplink and downlink, respectively. From Figure 10a, we can see that the power penalty in the downlink is 0.49 dB and 1.16 dB for transmission distances of 10 km and 20 km, respectively. From Figure 10b, we can see that the power penalties in the uplink are 0.48 dB and 0.99 dB for transmission distances of 10 km and 20 km, respectively.
## 3 Results and Discussion
### Mechanism of Adsorption  
The adsorption mechanism of Cd and Pb by the different NZ tested adsorbents followed surface sorption initially. In general, electrostatic interaction, ion exchange, and surface complexation are dominant mechanisms responsible for cationic metal adsorption on zeolite adsorbents [69; 70]. Usually, these mechanisms act together, leading to enhanced sorption of cationic metal contaminants on negatively charged adsorbents. However, under our experimental conditions, the better crystallinity and abundance of additional surface functional groups, including NO\({}_{3}\)\({}^{-}\) and metal oxides and hydroxides (Si-O, Al-O, Si-OH, and Al-OH), particularly with the LDH-N, enhanced the overall surface negativity and induced electrostatic interactions between cationic metal ions and adsorbents, suggesting that electrostatic interactions were the main mechanism for the sorption of Cd and Pb on the surfaces of the different adsorbents. The higher affinity and sorption of Cd and Pb by the LDH-N adsorbent could be due to the higher surface area, which provides more active surface sites to fix more quantity. Additionally, ion exchange and surface complexation played an important role in increasing the sorption efficiency due to the presence of metal oxides and hydroxides, which facilitates the formation of surface complexation with Cd and Pb (Cd(OH)\({}_{2}\), Pb(OH)\({}_{2}\)), leading to higher sorption affinity.  
Our findings showed that tested heavy metal sorption was influenced by initial pH, and increasing pH to a neutral pH value of 7 enhanced metal sorption, which was then decreased with further pH increase. Increasing pH resulted in deprotonation of surface functional groups of zeolite adsorbents, which generated net negative surface charge. Cationic metal ions (e.g., Cd\({}^{+2}\), Pb\({}^{+2}\)) are attached to negatively charged adsorbent surfaces by developing electrostatic interactions between cationic metal ions and negatively charged adsorbent surfaces. This was also confirmed by the first-order kinetics model, which described the physical adsorption of metal ions [71], and by the abundance surface functional groups present on the adsorbent's surface. Owing to the chemical nature of zeolites, these surface-adsorbed heavy metals induced chemical interactions by ion exchange mechanism with exchangeable cations on the zeolite's surface [72]. In pH-mediated reactions, the predominant mechanism for cationic metals (Cd\({}^{+2}\), Pb\({}^{+2}\)) adsorption on natural zeolites is ion exchange, which is generally facilitated at pH values above 5 and below 8 [73]. [PERSON] et al. [74] and [PERSON] [75] reported ion exchange with exchangeable cations on natural zeolites and surface complexation with surface functional groups as principal mechanisms for Pb, Cd, and mercury (Hg) adsorption on zeolites. Also, the chemical interaction involved in Cd\({}^{+2}\) and Pb\({}^{+2}\) adsorption on natural and modified zeolite adsorbents was confirmed by the pseudo-second-order kinetics model.
# Two-Dimensional Histogram Shifting-Based Reversible Data Hiding for H.264/AVC Video
## 1 Introduction
The experimental results demonstrate the effectiveness of our embedding strategies. Compared with the related schemes, the marked videos of our schemes have better visual quality in most cases, and the increase in file size of them is smaller. The remainder of this paper is organized as follows. Firstly, the 2D HS-based RDH technique is briefly reviewed in Section 2. Then, based on the analysis of several 2D HS schemes for compressed media, the proposed two 2D HS-based RDH schemes are described in detail in Section 3. The experimental results and analysis are then presented in Section 4. Finally, the conclusions are given in Section 5.
# Improving Streaming Video with Deep Learning-Based Network Throughput Prediction
###### Abstract  
Video streaming represents a significant part of Internet traffic. During the playback, a video player monitors network throughput and dynamically selects the best video quality in given network conditions. Therefore, the video quality depends heavily on the player's estimation of network throughput, which is challenging in the volatile environment of mobile networks. In this work, we improved the throughput estimation using prediction produced by LSTM artificial neural networks (ANNs). Hence, we acquired data traces from 4G and 5G mobile networks and supplied them to two deep LSTM ANNs, obtaining a throughput prediction for the next four seconds. Our analysis showed that the ANNs achieved better prediction accuracy compared to a naive predictor based on a moving average. Next, we replaced the video player's default throughput estimation based on the naive predictor with the LSTM output. The experiment revealed that the traffic prediction improved video quality between 5% and 25% compared to the default estimation.  
traffic prediction; artificial neural networks; adaptive video +
Footnote †: journal: 2022, 12, 10274. [[https://doi.org/10.3390/app122010274](https://doi.org/10.3390/app122010274)]([https://doi.org/10.3390/app122010274](https://doi.org/10.3390/app122010274))
# Cable Temperature Prediction Based on RF-GPR for Digital Twin Applications
###### Abstract  
With the wide application of power cables in the field of transmission and distribution and the increasing emphasis of power departments on the reliability, safety and stability of power cable operation, how to more accurately and quickly analyze the temperature distribution of power cables and how to evaluate the running state of power cables have become research hotspots. Through the combination of finite element calculation and the artificial intelligence method, an innovative method of digital twin cable temperature prediction based on RF-GPR is proposed in this paper. Firstly, the finite element method is used to calculate the coupling of the electromagnetic field and temperature field of a 10 kV AC cable laid in the cable trench, and a certain amount of basic data are provided through the finite element calculation results. Then, using the basic principle of the random forest (RF) variable importance score, the RF-GPR cable temperature prediction model is constructed using the series hybrid model and Gaussian process regression (GPR), the model prediction results are compared and analyzed, and the calculation time is improved by about 1500 times. Finally, a digital twinning platform for cable temperature calculation based on RF-GPR is designed, which provides technical support for the application of digital twinning.  
digital twin; power cable; machine learning; temperature prediction 2023
# Energy-Balanced Routing Algorithm Based on Ant Colony Optimization for Mobile Ad Hoc Networks
## 3 IEMACO Routing Algorithm
### Generation of Pheromone
#### 3.2.2 Remaining Lifetime of Links  
The calculation method of the remaining lifetime of links in this paper is as follows: assuming that the moving speed, moving direction and geographical position of each node of the network are known, the remaining lifetime of the link between node \(i\) and node \(j\) can be calculated as \[\mathrm{RLT}l_{ij}=\frac{-(ab+cd)+\sqrt{(a^{2}+c^{2})r^{2}-\left(ad-cb\right)^{2}} }{a^{2}+c^{2}}, \tag{11}\]  
where \(a=v_{i}\cos\theta_{i}-v_{j}\cos\theta_{j}\), \(b=x_{i}-x_{j}\), \(c=v_{i}\sin\theta_{i}-v_{j}\sin\theta_{j}\), \(d=y_{i}-y_{j}\). \(r\) represents the maximum communication distance of nodes in the network. A longer remaining lifetime indicates a more stable link. In the dynamic network, it requires a stable link to avoid data retransmission, improve transmission efficiency, save energy consumption and reduce route discovery frequency.
## 6 Experiments and Discussion
### Discussion
Each axis represents the total number of product units of each product type. Figure 16: Average reduction in PAR-10 observed when using the configurations obtained with the CBR portfolio method instead of Gurobi’s default configuration. Results are presented per case base type and per case base size. The doted lines connect the mean values. Figure 17: Coefficient of variation of the average reduction in PAR-10 obtained when using the configurations obtained with the CBR portfolio method instead of [PERSON]’s default configuration. Results are presented per case base type and per case base size. Additionally, the CBR portfolio method seems to outperform the CBR method, albeit slightly, when using Case Base 1 (Figure 18). The difference observed is only statistically significant for a case base with 400 or more unique problem instances, but it is only 2.43% \(\pm\) 1.96% at best. For Case Base 2, the marginal performance improvement when using the CBR portfolio method instead of the CBR method is only statistically significant for a case base with 800 unique problem instances and corresponds to 8.25% \(\pm\) 5.31%. As such, even though the CBR portfolio method may have the potential for better performance (than the CBR method), the lack of robustness of the portfolio methods highlights issues regarding the consistency of performance estimates, and the difficulty of augmenting the model (since new samples may in some cases completely redefine the portfolio, evaluating new configurations implies evaluating them in all available instances). All of these issues are critical barriers to the implementation of portfolio methods in production environments that may tip the scale in favor of the CBR method. The CBR method, after all, showcases significant improvements over the default settings, high flexibility that facilitates the augmentation of the model even after deployment, and much more robust performance, which makes it more predictable, thus facilitating the assessment of sampling efforts to achieve a given performance.
## 6 Discussion
### Ground-Based Validations
Thus, it would be of great interest not only to seek a standardized measuring protocol in future experimental projects, but also to standardize common data processing strategies that deal with the compensation of O\({}_{2}\) transmittance, the ISRF convolution effects, and the changing environmental conditions related to thermal and surface pressure dynamics, \(T\) and \(p\). In addition, to avoid inter-calibration issues between different instruments acquiring down-welling and up-welling radiances, the use of a single spectrometer is also recommended. In line with the use of a single spectrometer, in the recent years dual fields of view instruments have been developed that guarantee quasi-simultaneous measurements of down-welling and up-welling radiation [4].
## 2 Materials and Methods  
### Synthesis of Fe\({}_{3}\)O\({}_{4}\)-Ag Decorated Nanoparticles  
The synthesis of Fe\({}_{3}\)O\({}_{4}\) nanostructures commenced by utilizing FeCl\({}_{3}\)-6H\({}_{2}\)O as a precursor at a concentration of 0.01 M. Reduction of Fe\({}^{3+}\) ions was achieved using a solution of NaBH\({}_{4}\) (0.02 M), with polyvinylpyrrolidone (PVP) serving as a stabilizing surfactant at a concentration of 0.1 M. Deionized water was employed as the colloidal medium. The PVP/FeCl\({}_{3}\)-6H\({}_{2}\)O ratio was maintained at 50, a parameter known to promote the formation of magnetite nanostructures [20]. Following the reduction of Fe\({}^{3+}\) ions, an oxidative etching process was employed to facilitate the formation of magnetite, a mechanism established in prior studies for Fe\({}_{3}\)O\({}_{4}\) nanostructure synthesis [20]. Importantly, all experiments were conducted at room temperature under a constant hydrogen potential (pH = 11). The proposed reduction process involves the initial decomposition of NaBH\({}_{4}\), generating hydrogen, which in turn facilitates the reduction of Fe\({}^{3+}\) ions, leading to the formation of iron nanoparticles.  
Subsequently, to the preformed Fe\({}_{3}\)O\({}_{4}\) nanoparticles, a solution of AgNO\({}_{3}\) (50 mM, 20 mL) was added, and the mixture was agitated at room temperature. Following this, a solution of NaBH\({}_{4}\) (2 mM, 5 mL) was promptly added, resulting in the formation of silver nuclei on the Fe\({}_{3}\)O\({}_{4}\) particles. This process was visually discernible by a change in coloration from black to dark gray, culminating in the synthesis of Fe\({}_{3}\)O\({}_{4}\)-Ag decorated nanoparticles.
## 4 Discussion
That is, the souring date differs from the emergence date, where only the latter will change the VI values. Hence, although no method explicitly accounts for it, when using VIs to identify souring and harvest dates, the difference between souring and crop emergence needs to be accounted for. Note that setting a fixed number of days between souring and emergence needs to be inferred based on crop- and region-specific assumptions [72]. Furthermore, different philosophies--not based on Vis--were explored. For example, [17] used ultra-high spatial and temporal resolution satellites and obtained an RMSE of two days. However, this method is more computationally intensive than VIs-based methods and was evaluated on a small scale. In addition, other works also performed a re-scaling of SOS to obtain souring dates by subtracting a value in days and showed the potential of this methodology [73; 74]. Nevertheless, the limitations and uncertainties could potentially be reduced with methods and data sources that were not tested in this study. For example, calibrating each algorithm for a specific region with a similar growing pattern could improve the accuracy of the estimates. Bias removal methods, such as those proposed by [75] and [76], could be used to increase the reliability of the data and their use to guide management practices. Another opportunity that could improve this type of study is the synthetic aperture radar (SAR) data [77], which does not suffer from cloud interference [55]. Also, data sources with a finer spatial and temporal resolution, such as the Harmonized Landsat and Sentinel-2 (HLS) project [78] and the cube-satellite constellations (e.g., PlanetScope) [79], can provide more accurate time series. We have not tested these different data sources in this study; we know about their potential, but more research needs to be conducted since radar backscattering has presented a low correlation with the observed data so far [55]. Furthermore, PlanetScope images tend to suffer from inter-sensor inconsistencies that introduce noise into the time series of observations [80], and by the time we concluded this study, the HLS was not available in GEE. Nonetheless, this study provides insights when using well-documented methods to estimate phonology using vegetation indices from diverse sources. Furthermore, works that had higher correspondence with field data used small areas and small validation datasets. For example, 50 fields for [17] and 100 fields for [55] when compared with this study, where more than 900 fields were used for validation, distributed across large production areas of Brazil (with fields more than 2000 km apart). Thus, this work highlights that after twenty years of investigation, new approaches must consider the variation in different ecosystems and the easy application over large areas. This would support phenology investigations in moving from examining past phenology to predicting future phenology [37; 81].
# Adapting the Segment Anything Model for Volumetric X-ray Data-Sets of Arbitrary Sizes
## 1 Introduction
### Combination with Tile-Based FFN  
This work aims to evaluate the applicability of SAM for segmenting volumetric NDT data-sets and to examine its potential enhancement through the integration of Flood Filling Networks (FFN), initially proposed by [PERSON] et al. [20]. FFNs are instance segmentation methods originally based on convolutional networks [21; 22], which are able to segment arbitrarily large data-sets based on tiles. Originally, FFN was developed for the segmentation of organic objects but in the past, was extended to other applications, including the delineation of large-scale XXL-CT data [4].  
The FFN approach maintains the current state of segmentation within an accumulator volume, which is sized to match the dimensions of the input volume. During each segmentation step, a sub-volume or tile of the input volume and the corresponding partially computed tile of the accumulator is passed to the model (in our case, a volumetric variant of SAM). The segmentation proposal of the tile is then updated and written back to the corresponding tile position within the accumulator.  
Candidates for neighbouring tile positions with significant overlap, which could extend the current segment, are determined using the updated accumulator state and added to a queue of tiles pending processing. In the subsequent iteration, the next unprocessed tile is removed from the front of the queue for processing. Starting from a seed point, the FFN then processes all of the tiles that potentially belong to the current segment. The processing of the current segment is completed when the queue of potentially belonging tiles is depleted. The algorithm then proceeds with the next segment starting from another seed point.  
The seed points of the segments can be manually specified or computed automatically by a reasonable algorithm.