esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Characterization
#### 3.1.3 Thermal Analysis  
Thermogravimetric analysis of the ligand occurred in two stages as shown in Figure 6A. The first stage which was because of the loss of one molecule each of NH\({}_{3}\) and CO covered the range 60-200 \({}^{\circ}\)C. Complete decomposition of ligand occurred throughout the second stage at 200-600 \({}^{\circ}\)C. No residue was observed. Scheme 2 shows the proposed path of ligand thermolysis. Thermogravimetric analysis of Cu(II)-AMT involved four stages as shown in Figure 6B and Scheme 3. The loss of one molecule of uncoordinated water was in the first stage and occurred at 27-104 \({}^{\circ}\)C with a 4.45% weight loss. Two molecules of coordinated water were lost in the second stage at 120-200 \({}^{\circ}\)C with a weight loss of 8.25%. The third stage at 200-528 \({}^{\circ}\)C involved partial fragmentation of the ligand moiety and the release of nitrogen oxides amounting to a 63.02% weight loss. Complete decomposition of the complex leaving a residue of copper oxide occurred in the final stage at 528-1000 \({}^{\circ}\)C (24.64% weight loss).  
.1.4 Electron Spin Resonance of [Cu(L)(NO\({}_{3}\))(H\({}_{2}\)O)\({}_{2}\)]\(\bullet\)H\({}_{2}\)O  
The powdered ESR spectrum of the Cu-AMT complex measured at 300 K showed two bands at 330 and 310 mT with g\({}_{\perp}\) = 2.08306 and g\({}_{\rm{u}}\) = 2.2175, respectively (Figure 7). The g-values confirmed that the unpaired electron of Cu(II) occupies the orbital of d\({}_{\rm{x2-y2}}\), where g\({}_{\rm{u}}\) is greater than both g\({}_{\perp}\) and g of free electron (2.0023) [32; 33]. The shape of the ESR spectrum suggests a distorted octahedral symmetry for the Cu-ATM complex [32] in excellent agreement with \(\mu_{\rm{eff}}\) value. The g\({}_{\rm{av}}\) value calculated from Equation (2) was equal to 2.1279. Deviation of g\({}_{\rm{av}}\) from g of free electron was because of the covalent property of bonding in Cu-ATM [33].  
\[{\rm{g}}_{\rm{av}}=1/3({\rm{g}}_{\rm{u}}+2\;{\rm{g}}_{\perp}) \tag{2}\]  
A molar conductivity of \(\Lambda_{\rm{m}}\) = 8.2 \(\Omega^{-1}\) cm\({}^{2}\) mol\({}^{-1}\) measured at 1.00 \(\times\) 10\({}^{-3}\) M in DMF solvent at room temperature indicated that the Cu-ATM complex is a non-electrolyte [34]. Full characterization of the  
Figure 7: Electron spin resonance of [Cu(L)(NO\({}_{3}\))(H\({}_{2}\)O)\({}_{2}\)]\(\bullet\)H\({}_{2}\)O. Two arrows indicate the significant peaks in spectrum.  
complex formed between Cu\({}^{2+}\) and AMT suggests the chemical formula to be [Cu(L)(NO\({}_{3}\))(H\({}_{2}\)O)\({}_{2}\)]\(\bullet\)H\({}_{2}\)O. Figure 8 shows the proposed structure of the complex.
# A Comprehensive Analysis of Smartphone GNSS Range Errors in Realistic Environments
## 5 Conclusions and Future Work
In addition, there are new and interesting findings for the range error characteristics under different environments and the correlations between range error and pre-fit residuals, which can potentially benefit smartphone positioning algorithm development. e.g., different code precision and different weight may be applied to different constellations and different frequencies based on the analysis, and the SNR mask and elevation cut-off angle can be adjusted with different datasets. In addition, regarding different range error behavior under different environments, the weighting scheme considering different environments is worth investigating. Furthermore, the prefit residuals can partly reflect the range errors, different thresholds for prefit outlier rejection may be helpful according to this paper. Finally, different code precisions can also be applied when the smartphone is mounted in different places. **Author Contributions:** Conceptualization, J.H. and S.B. ; Data curation, D.Y. ; Formal analysis, J.H. ; Funding acquisition, S.B. ; Investigation, J.H. ; Methodology, J.H. and D.Y. ; Software, J.H. and D.Y. ; Supervision, S.B. ; Validation, J.H. and D.Y. ; Writing--original draft, J.H. and D.Y. ; Writing--review and editing, J.H., D.Y. and S.B. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data showed in this study are available from the corresponding author on reasonable request. **Acknowledgments:** The authors would like to thank their colleagues at the GNSS Laboratory for their support in collecting field data and discussing technical aspects of this work. In addition, the authors would like to acknowledge the data contribution from the International GNSS Service (IGS), German Research Centre for Geosciences (GFZ), and Centre National d'Etudes Spatiales (CNES). **Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Conclusions  
Currently, the RPC parameters provided with some spaceborne SAR images do not account for atmospheric correction, while atmospheric delay errors can reach several meters.  
Therefore, eliminating the impact of atmospheric delay errors on geometric positioning is a critical task for achieving high-precision mapping. To address the atmospheric correction issue in the RPC model for spaceborne SAR imagery lacking ephemeris data, this study proposes a novel vector-autonomous intersection method for inverting the SAR satellite position. The atmospheric delay errors, calculated using the ionospheric and neutral atmospheric delay models, are converted into RPC parameters, upon which an advanced RPC model reconstruction method is introduced to achieve the atmospheric correction of SAR data. The feasibility of the proposed method was validated through atmospheric correction and uncontrolled positioning experiments using GF-3 imagery data. The vector-autonomous intersection method can accurately extract SAR atmospheric delay values under ephemeris-free conditions and effectively replace the traditional ephemeris data interpolation method for atmospheric correction, with both methods yielding nearly identical atmospheric delay values. After atmospheric correction, the new RPC parameters significantly improve the uncontrolled positioning accuracy of GF-3 imagery, with image-space accuracy increasing by 18% to 44% and object-space accuracy improving by 19% to 32%. Therefore, the proposed method effectively performs atmospheric correction on spaceborne SAR imagery without ephemeris data, significantly enhancing the uncontrolled positioning accuracy of the RPC model, and thereby meeting the requirements for high-precision mapping applications. Additionally, it provides a solid technical foundation for high-precision geometric processing, such as uncontrolled positioning and sparse control point positioning for SAR imagery, and holds great potential for practical applications.
## 4 Results and Discussion
### Study A: Forward Stroke Analysis through Segmentation Technique
Since the healthy segments all stayed below the 10% limit (green line), only the segments where the introduced misalignment was above 0.25 mm consistently showed a 20% error. * Kurtosis also showed an interesting result as the signal was further segmented. As the segment levels increased, the segment where the misalignment is present goes from a 20% error at the half stroke to approximately 10% at the quarter stroke level and ultimately to no difference when compared to other healthy segments. This phenomenon is due to the nature of the feature, since it analyzes the distribution of a signal, and as the signal is further segmented, the closer to a normal distribution each segment becomes. Study A was concluded by applying the segmentation technique and evaluating the repair state dataset. When looking at the full stroke analysis the results of the repair state for the evaluated features are similar to the baseline state. However, the RMS and standard deviation features showed an interesting result when the segmentation technique is applied on the dataset. It was observed that at the quarter and eighth level segment analysis, each of their last segments where the misalignment was more pronounced, the features exhibited a percentage error of 11% and 15%, respectively, as illustrated in Figure 12.  
This information is useful, since it can be utilized to update thresholds at different segment levels to avoid false alarms. Additionally, the results also demonstrated that even after a repair the system exhibited a slight change to its baseline operation, and should be further investigated. The other statistical features used in the analysis showed a decrease from the faulty state, and were once again stabilized within the limits of the baseline state, as illustrated in Figure 13. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Segment Level** & \multicolumn{8}{c}{**Segment Level Characteristics**} \\ \hline Quarter Stroke & Segment \# & 1 & 2 & 3 & 4 & 4 & \\  & Distance & & & 175–350 & & 350–525 & & 525–700 \\  & Travelled (mm) & 0–175 & & 175–350 & & 350–525 & & 525–700 \\ \hline  & Segment \# & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 \\  & Distance & 0–87.5 & 87.5–175 & 175–262.5 & 265.5–350 & 350–437.5 & 437.5–525 & 525–612.5 & 612.5–700 \\  & Travelled (mm) & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Segment level analysis characteristics. Figure 11: Forward stroke faulty signal segmentation analysis. (**a**) Peak-to-Peak, (**b**) Kurtosis. Figure 12: Forward stroke repair signal segmentation analysis. (**a**) RMS, (**b**) Standard Deviation.
## 3 Urban Water System Issues and Potential Smart Solutions
### Disasters  
Disasters that affect urban water systems include water main breaks, weather and geologic events, terrorism, and accidents. Pipes normally break down as they age. Their breakdown can be accelerated, however, due to corrosive elements in the water or surrounding the pipe, high water pressure, pressure transients, vibrations, and traffic loads [3]. Eventually, the stress in the pipes may reach a point that causes a water main break.  
Weather events, geologic events, terrorism, and accidents can directly cause damage to water infrastructure or damage water infrastructure through a domino effect [41; 42]. For example, the terrorist attack on the World Trade Center caused significant damage to water mains and caused flooding that damaged communication infrastructure [42]. Disasters can also shut down water treatment facilities and prevent the distribution of drinking water to taps. Flooding events can also overwhelm sewer systems and prevent the proper disposal of wastes.  
Smart pressure management could manage and prevent premature pipe deterioration that leads to water main breaks. Flood sensors could also detect a pipe that is about to break and alert authorities. Smart flood management could detect flooding and respond to reduce the amount of damaged caused using flood sensors, smart valves, and smart pumps. Flood sensors could detect a pipe break, which would trigger a smart valve to shut off the water supply. Then, a smart pump could pump water away from the immediate area.
###### Abstract  
The main scientific result of this paper is the demonstration of the fact that tidal effects induced by the Moon affect the Schumann resonance amplitudes measured at magnetometers located at different geographical locations of the Global Coherence Monitoring System. Each magnetometer is paired with the closest monitoring station of the global tidal wave measurement network. This paper introduces the Schumann Resonance Complexity Index (SRCI), computed by using the calibrated H-rank algorithm on the local magnetic field data recorded by each magnetometer of the Global Coherence Monitoring System. Tidal wave data recorded at each monitoring station are also used to compute the Tidal Wave Complexity Index (TWCI). Eliminating diel cycles from the SRCI data yields significant statistical correlations between the SRCI and TWCI data, proving the hypothesis of tidal effects on the global network of magnetometers.  
Schumann resonance; magnetic field; Tidal Wave Complexity Index; correlation Article
## 3 Artificial Intervention in the Degradation of Antibiotics
### New Type of Catalyst
#### 3.5.3 Carbon-Based Single-Atom Photocatalysts  
By anchoring metal atoms in a monodisperse form on carbon carriers, distinct active sites are formed. The embedding of metal single atoms can effectively adjust the energy band structure, electronic structure, and surface structure of the light-capturing carrier, thereby modifying the material's light absorption behavior, charge carrier conversion, and surface adsorption. Therefore, utilizing the characteristics of single-atom catalysts for the photocatalytic removal of organic pollutants from water is an ideal concept [124; 125; 126]. [PERSON] et al. loaded single-atom barium (Ba) onto g-C3N4 as a carrier and verified its photocatalytic degradation of carbamazepine (CBZ) and diclofenac (DCF), demonstrating that the addition of single-atom Ba significantly enhanced the photocatalytic activity, with the reaction's first-order kinetic constant being over 70 times that of g-C3N4 [127]. [PERSON] et al. synthesized a hybrid of mesoporous graphitic carbon nitride (mpg-C3N4) modified with single-atom dispersed Ag (Ag/mpg-C3N4) using a co-condensation method, which exhibited a good photocatalytic performance for the degradation of bisphenol A (BPA) as a visible light photocatalyst. The performance improvement may be attributed to the synergistic effect between single-atom Ag and mpg-C3N4 [128]. [PERSON] et al. developed a simple in situ growth strategy to cultivate single-atom cobalt on polymeric carbon nitride, achieving an efficient photocatalytic degradation of tetracycline [129].  
New types of catalysts have significant advantages in improving the degradation efficiency of antibiotics, but their application still faces some challenges. For example, there are issues with the recovery and stability of nanocatalysts, the water stability limitations of MOFs, and the high production costs of SACs. Future research needs to further optimize the design and preparation processes of catalysts, as well as assess their long-term performance and ecological safety in real water environments.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 2 Technologies for LV Smart Metering--Background Information
### Wireless Technologies
#### 2.2.3 WiMAX  
WiMAX is a wireless communication standard that can provide with a data rate up to 75 Mbps. It can be considered as an alternative technology for delivering fixed wireless broadband access [4]. It refers to the family of IEEE 802.16 standards for wireless-networks and it can be used as a backup technology for cellular phone technologies like GSM and CDMA [32]. With respect to the smart grid applications, it can be a suitable candidate for connecting the data concentrator with the utility's control center. This technology can be a suitable solution for utilities, since it implies low implementation costs, a lower degree of complexity and with an effective control over possible applications [33]. In [6] some smart metering projects are listed, where WiMAX is used for connecting the back-haul utility system with data concentrators. Despite the advantages that it has to offer, the fact that the bandwidth of WiMAX needs to be shared among users remains an issue [30].
# Ext-LOUDS: A Space Efficient Extended LOUDS Index for Superset Query
## 3 Ext-LOUDS
### Algorithm Complexity Analysis  
Space complexity: the core structure of Ext-LOUDS is an integer of length n, three bit vectors of length n, and a binary vector of length m. If the integer occupies four bytes, then the space overhead of Ext-LOUDS is (4 + 3/8) * n + 8 m bytes.  
Time complexity: The time complexity of the algorithm is related to the number of visited nodes. In the worst case, the algorithm needs to visit \(\frac{|Q|*(|Q|+1)}{2}\) nodes in total, so the time complexity of the algorithm is \(O(\left|Q|_{2}\right).\)
# Cotton Yield Estimate Using Sentinel-2 Data and an Ecosystem Model over the Southern US
###### Abstract  
High-resolution data with nearly global coverage from Sentinel-2 mission open a new era for crop growth monitoring and yield estimation from remote sensing. The objective of this study is to demonstrate the potential of using Sentinel-2 biophysical data combined with an ecosystem modeling approach for estimation of cotton yield in the southern United States (US). The Boreal Ecosystems Productivity Simulator (BEPS) ecosystem model was used to simulate the cotton gross primary production (GPP) over three Sentinel-2 tiles located in Mississippi, Georgia, and Texas in 2017. Leaf area index (LAI) derived from Sentinel-2 measurements and hourly meteorological data from Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) reanalysis were used to drive the ecosystem model. The simulated GPP values at 20-m grid spacing were aggregated to the county level (17 counties in total) and compared to the cotton limit yield estimates at the county level which are available from National Agricultural Statistics Service in the United States Department of Agriculture. The results of the comparison show that the BEPS-simulated cotton GPP explains 85% of variation in cotton yield. Our study suggests that the integration of Sentinel-2 LAI time series into the ecosystem model results in reliable estimates of cotton yield.  
c 27 June 201921 August 201924 August 201927 June 201921 August 201924 August 201927 June 201921 August 201924 August 2019
## 3 System Model and Problem Formulation
### Antenna Model  
The antenna radiation pattern is crucial in interference analysis and is a key input for system simulation experiments. To facilitate frequency coordination and interference assessment in different frequency bands, the ITU has issued some radiation pattern reference recommendations for earth station and satellite antennas such as ITU-R S.465-6 [32], ITU-R S.672-4 [33], ITU-R S.1428-1 [34], and ITU-R S.1528 [35]. In this paper, in order to simplify the problem, we adopt a unified antenna model on both the satellite and the ground terminals without loss of generality, in which the beam gain can be calculated by the following equation [36; 37]:  
\[G(\theta)=G_{0}[\frac{J_{1}(u(\theta))}{2u(\theta)}+36\frac{J_{3}(u(\theta))}{ u(\theta)^{3}}]^{2} \tag{6}\]  
where \(\theta\) represents the off-axis angle, which means angular position of the terminal user from the beam center with respect to the satellite. \(G_{0}\) is the maximum antenna gain defined as \(G_{0}=\eta N^{2}\pi^{2}/\theta_{3 dB}{}^{2}\), in which \(\eta\) is the antenna efficiency, \(N\) is a constant related to the field distribution of the antenna radiation pattern, \(\theta_{3 dB}\) is the antenna 3 dB gain angle. \(J_{1}(\cdot)\) and \(J_{1}3(\cdot)\) are the first-order and third-order Bessel functions of the first kind, respectively. \(u(\theta)=2.07123\sin\theta/\sin\theta_{3 dB}\). The 3 dB gain angle of the satellite transmitting antenna can be calculated as follows:  
\[\theta_{3 dB}=\frac{180^{\circ}}{\pi}\arctan(\frac{R}{H}) \tag{7}\]  
in which \(H\) represents the satellite orbit height, \(R\) denotes the single beam radius. Once the 3 dB angles are obtained, we can determine the antenna radiation patterns of the satellite and terrestrial terminal through Equation (6). In this paper, the parameters related to the antenna radiation pattern are set as shown in Table 1:Substituting the above parameters into the model for the simulation, we can obtain the antenna radiation pattern of the satellite and the terrestrial terminal.
## 4 Discussion
### Practical Implications  
To experience a realistic rowing training scenario in a simulator, expert rowers demand well-designed and synchronized rendering of visual, auditory, and haptic aspects of the task. In our simulator, forces rendered by the implemented rowing model and the synchronous rendering of visual and auditory displays were generally found to be realistic by the expert rowers. However, simplifications during modeling and technical limitations of the hardware setup might have led to perception of unnatural forces or unrealistic auditory and visual features, which may have confounded the overall realism of rowing scenario in the simulator. Therefore, in cases where residual technical problems cannot be readily resolved due to practical or safety reasons, individualization of the model parameters might be an appropriate approach to maintain or increase the perceived realism of the simulator.  
Indeed, in our study, individualization of the model parameters was observed to yield a significant improvement in the acceptance of training on the simulator. Thus, expert rowers were also asked which other parameters of the rowing model and features to enhance the indoor training they would prefer to have on the simulator.  
The majority of the experts proposed the parametrization of the inboard and outboard length of the oar, which could be virtually configured based on the rigging preferences of the rower. In real rowing, the preferred inboard and outboard oar lengths are usually found after a trial and testing process in the trainings. The required length of the oar also needs to be adjusted for the weather conditions. Thus, our rowing simulator can provide an easy and fast rigging within a training session.  
In addition, expert rowers were interested in visualizing the errors related to the kinematic and dynamic outcomes of their rowing strokes in the simulator. Although commercial products, which can provide propulsion forces and oar kinematics while rowing on a boat, are available, these devices are not yet capable of providing feedback about the resulting errors, i.e., difference between desired and actual performance [49; 50]. Therefore, as previously studied with beginner rowers, three modalities rendered on the rowing simulator could also be used to present feedback to the expert rowers for performance enhancement [26; 51; 52].  
Some expert rowers were also interested in the visual display of an 'opponent boat', against which the rower in the simulator can train or race to simulate a regatta scenario. According to this idea, the 'opponent boat' could be set to propel with a desired speed setting, and the rower in the simulator could train to match the set goals for the training. Therefore, realistic rendering of the rowing forces can further be employed to augment the simulator to train professional athletes [53].  
Finally, thanks to the achieved haptic realism of a sweep rowing application, potential sources of injuries associated with the asymmetries unique to the sweep rowing can be systematically assessed on our virtual reality simulator [54; 55; 56].
## 4 Experimental Results and Security Analysis
### Security Analysis
#### 4.2.1 Brute-Force Attack  
A brute-force attack is a common attack that can be used to attempt to decrypt any encrypted data [1]. Such an attack might be used when it is not possible to take advantage of other weaknesses that will make the task easier. To resist brute-force attack, the key space must be large. It is generally considered that a key space of size smaller than \(2^{128}\) is not secure. For our proposed improved CML-based keystream generators, all of the system parameters and initial values were produced by the input 128-bit key. Its key space was \(2^{128}\). Therefore, the security strength of the proposed scheme can avoid brute-force attacks.