esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Assessment of Available RT Orbit and Clock Products and RT ZTDs
### Assessment of Real-Time Orbit and Clock Corrections
Largest differences are observed for the orbits from IGS03, which might be attributed to a different outlier detection method applied when including GLONASS satellites. Time evolution of the orbit comparison for each product and specific component is shown in Figure 4. Coordinate differences greater than 30 cm are plotted at the top horizontal lines of each graph. Orbits from the IGS01 stream are less affected by the outliers compared to IGS02 and IGS03 products, as indicated by outliers mainly during March 2015. The switch from the IGS08 to the IGS14 PCO model (28 January 2017) can be observed in statistics of the radial component. It seems that the CNS91 product used the new IGS14 model as of 9 March 2017, while official IGS solutions are difficult to recognize due to most likely asynchronous switches by different contributing providers. Otherwise, the orbit accuracy for all products shows an overall good consistency over the period. Table 1 also summarizes RMSE and standard deviation (SDEV) of the real-time clock corrections. The former represents the accuracy relevant for the processing of code pseudoranges while the latter characterizes the precision important for the carrier-phase processing. It can be also interpreted from the PPP point of view combining both observation types as follows--the former have a positive impact on the PPP convergence time while the latter enable more precise positioning within already converged solution [38]. Obviously, this is the case of IGS01 and CNS91 products when the first is more accurate, but the second more precise for the PPP application. The IGS02 and IGS03 products performs slightly worse in terms of both RMSE and SDEV. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & **RMSE** & & & **SDEV** \\ \cline{2-7}  & **Radial** & **Along** & **Cross** & **3D** & **Clock** & **Clock** \\ \hline IGS01 & 1.84 & 2.83 & 2.38 & 4.34 & 5.72 & 2.95 \\ IGS02 & 2.35 & 3.71 & 3.04 & 5.63 & 10.08 & 3.52 \\ IGS03 & 2.41 & 3.82 & 3.10 & 5.70 & 10.28 & 3.03 \\ CNS91 & 2.68 & 3.07 & 2.47 & 5.01 & 11.16 & 2.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: RT clocks and orbits components compared to the IGS final products (units: cm). Figure 4: Daily RMS of real-time orbits with respect to IGS final orbit. Figure 5 finally shows time series of the clock SDEV and RMSE statistics. The former (top plot) indicates a comparable high quality over the period for IGS01 and CNS91, while more outliers are observed for IGS02 and IGS03 including the problematic period in 2015 identified in the orbit availability evaluation. The clock RMSE from IGS01 is the lowest and the most stable compared to the others during the period while, the RMSE of CNS91 clocks was more accurate during 2015 when compared to the other years.
## 3 Results and Discussion
### Desorption  
Various reagents were tested in the experiments to find the most effective eluate: 1 M NaOH, 1 M HCl, 1 M NaCl, 50% \(v/v\) CH\({}_{3}\)OH, 1 M NaOH + 50% \(v/v\) CH\({}_{3}\)OH, 1 M HCl + 50% \(v/v\) CH\({}_{3}\)OH and 1 M NaCl + 50% \(v/v\) CH\({}_{3}\)OH. Figure 12 shows the results of DY50 desorption from the weakly basic anion exchangers A21, A23 and A24. The highest yield of reaction was observed during DY50 desorption from A23 and A24 using 1 M NaOH + 50% \(v/v\) CH\({}_{3}\)OH (18.76% and 18.72% for A24 and A23, respectively). It is also acceptable to use  
Figure 10: ATR–FT-IR spectra of (**a**) A21 and (**b**) A24 before and after sorption of DY50.  
1 M NaOH for A23 (13.20%) or 1 M NaCl + 50% CH\({}_{3}\)OH for A24 (14.56%). Regenerating agents such as 1 M NaCl, 1 M HCl or 50% CH\({}_{3}\)OH turn out to be ineffective (Figure 12). Comparing the values of the desorption degree in aqueous and methanol solutions, it can be concluded that the use of methanol increases the desorption efficiency. The mechanism of DY50 sorption of the weakly basic resins occurs not only through ion exchange but also as a result of strong chemical adsorption. The same conclusions were drawn in case of C.I. Direct Red 75 removal from the weakly (Amberlite IRA67) and strongly basic (Amberlite IRA458) anion exchange resins as well as C.I. Reactive Black 5, Orange 16, Blue 15, Red 198 and Red 120 from weakly (MP62) and strongly (S6328a) basic resins [(37; 49; 78)].
## 1 Introduction
The cost of utilizing these types of solutions may be reduced as the obtention of images from satellites such as Sentinel are free of charge. Furthermore, the spatial resolution of 10 m pixel size provided by Sentinel is adequate to monitor soil moisture. Other satellites may present different resolutions. Therefore, the characteristics of each satellite should be studied before deciding on which resources to use. The contribution of this paper is the obtention of soil moisture indexes specific for the studied region to monitor soil moisture in a golf course and its surroundings by utilizing correlating sensors' data with satellite information. The currently available indexes are developed for other regions, and their accuracies are low. Nine sensors were deployed on three types of soil for approximately three months. The sensors were calibrated for each type of soil ranging from clay soil to soil with both clay and sand. Sentinel images were obtained for the same time period to assess its adequacy for soil moisture monitoring of golf courses. Furthermore, the need for the satellite to be calibrated as well according to each soil type was evaluated as well. The main objectives of this study are twofold. Firstly, we aim to obtain a training dataset with the data from both sensors and satellites and a validation dataset. Secondly, we determine the best form of sprinkler management according to the soil moisture results. The rest of the paper is organized as follows. Section 2 discusses the related works regarding soil moisture monitoring through satellite data. The description of the methodology of our study is presented in Section 3. The results of the comparison between soil moisture monitoring through sensors and satellite images are provided in Section 4. Section 5 discusses our results. Lastly, Section 6 presents the conclusion and future work.
## 4 Conclusions  
The electrical conductivity of a cement composite depends on curing age, CF content, CF length, electrode spacing, and probe configuration. The research findings are summarized below:  
1. The conductivity of the cement composite incorporating 0.1% CF was predominantly influenced by the electrolyte of the pore solution, rather than the CF content. Beyond 0.5% CF content, the influence of CF on the conductivity was greater than that of the electrolyte of the pore solution.
2. Only when the electrode spacing was relatively wide (40 mm), the resistivity decreased with increasing CF length. Further, for narrow electrode spacing (10 mm), the resistivity of the cement composite increased with an increase in the CF length. These results imply that the CF length should be considered along with the electrode spacing when composing the electrodes of the cement composite by incorporating CF.
3. For the four-probe configuration, there was no significant difference between the cement composites with 1.0% and 2.0% CF. Additionally, they showed low resistivity values of less than 100 \(\Omega\)-cm. This indicates that the percolation threshold of CF was 1.0% corresponding to the four-probe configuration. The cement composites evaluated via the two-probe configuration showed continuous decreases in resistivity until the 2.0% CF content value.
4. Regardless of the CF length, the measured resistivity differences between the two- and four-probe configurations were large at low CF contents. These gaps became marginal as the CF content increased.
5. As a major conclusion of this study, for the efficient conductivity of cement composites, CF length should be sufficiently short compared to electrode spacing. Since the conductive cement composite is basis to implement functional cement composite structure such as self-heating and self-sensing. This finding can help the methodology of design of a conductive cement composite containing CFs.  
Conceptualization, I.Y. and S.-J.L.; methodology, I.Y. and D.L.; validation, S.-J.L. and G.Z.; investigation, I.Y. and D.L.; writing--original draft preparation, I.Y.; writing--review and editing, S.-J.L. and G.Z.; visualization, I.Y. and D.L.; supervision, S.-J.L.; project administration, S.-J.L. and G.Z.; funding acquisition, S.-J.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2020R1 IIA1A01071824) and the Korea government (MIST) (No. 2019R1C1C1008190).  
The authors declare no conflict of interest.
# Modeling of NOMA-MIMO-Based Power Domain for 5G Network under Selective Rayleigh Fading Channels
## 3 System Model
### UL Scenario  
The power domain multiplexing for uplink NOMA is almost entirely different. In downlink NOMA, the BS employed superposition coding to offer power domain multiplexing; however, the user's transmit power is limited only by their battery capacity in the uplink. That is, both users can transmit at full strength. Changes in the users' channel gains cause variation in the power domain at the receiver side of BS.  
Let \(x_{1},\ x_{2},\ x_{3}\) and \(x_{4}\) represent the messages that will be sent by four UL NOMA users \(U\)1, \(U\)2, \(U\)3, and \(U\)4, accordingly. Suppose that both users' signals have the same strength and consider the \(64\times 64\) MIMO system and BW equal to 80 MHz on a wireless network (as depicted in Figure 2). Let \(d1>d2>d3>d4\) denote the various BS distances, with \(d1>d2>d3>d4\) being the preferred order. \(U\)1 from BS is the weak/far user, while \(U\)4 is the strong/near user, depending on the distance. Let \(h_{T1},\ h_{T2},\ h_{T3}\), and \(h_{T4}\) determine which selective Rayleigh fading coefficients they relate to \(|h_{T1}|^{2}<|h_{T2}|^{2}<|h_{T3}|^{2}<|h_{T4}|^{2}\).  
The total Rayleigh fading channel for each user is given by:  
\[h_{jT}=\sum_{j=1}^{N}h_{jT} \tag{8}\]  
Figure 2: Illustrates the wireless network consisting of 4 users \(64\times 64\) MIMO-UL-NOMA (power domain).  
where \(j=1,2,3,4\) is the number of users and \(N=64\) is the number of channels.  
The signal was received at the BS.  
\[y=\ \sqrt{P_{x1}}h_{1T}+\ \sqrt{P_{x2}}h_{2T}+\sqrt{P_{x3}}h_{3T}+\sqrt{P_{x4}}h_{4T}+\ w \tag{9}\]  
where \(w\) is the noise power.
# Solar Radiation Parameters for Assessing Temperature Distributions on Bridge Cross-Sections
## 2 State of Art of Estimation Models for Solar Radiation
### Estimation Models for Global Solar Radiation
#### 2.1.2 Estimation Model for Hourly Global Solar Radiation  
Hourly global solar radiation data are needed for research on temperature distribution on bridge girder cross-sections. Research on hourly global solar radiation is less frequent than its daily counterpart. A linear relationship between hourly and daily global solar radiation was proposed by [PERSON] and [PERSON] in 1960 using the following Equation [38]:  
\[r_{\rm T}=I/H=(\pi(\mathrm{cos}\omega-\mathrm{cos}\omega_{\mathrm{s}}))/(24( \mathrm{sin}\omega_{\mathrm{s}}-\omega_{\mathrm{s}}\mathrm{cos}\omega_{\mathrm{ s}})) \tag{3}\]  
where \(r_{T}\) is the proportionality coefficient, \(I\) is the hourly global solar radiation, \(H\) is the daily global solar radiation, \(\omega\) is the solar hour angle, and \(\omega_{s}\) is the sunset hour angle. However, some researchers have reported that a linear relationship only exists in a clear day [39]. The equation proposed by [PERSON] and [PERSON] was modified by [PERSON] and [PERSON] as follows [40]:  
\[r_{\rm T}=I/H=((a+b\mathrm{cos}\omega)(\pi(\mathrm{cos}\omega-\mathrm{cos} \omega_{\mathrm{s}})))/(24(\mathrm{sin}\omega_{\mathrm{s}}-\omega_{\mathrm{s}} \mathrm{cos}\omega_{\mathrm{s}})) \tag{4}\]  
where \(a=0.409+0.5016\mathrm{sin}(\omega_{\mathrm{s}}-60)\) and \(b=0.6609-0.4767\mathrm{sin}(\omega_{\mathrm{s}}-60)\). The validity of this equation was verified by measured data [41; 42]. Although some modifications were proposed by other researchers [42; 43; 44], the [PERSON] and [PERSON] equation is the most frequently used model for estimating the hourly global solar radiation.  
The Gaussian function was considered by some researchers to estimate the hourly global solar radiation, which assumes that the meteorological parameters are all stochastic and the variation of hourly global solar radiation follows a normal distribution. However, this approach can only be used for a clear day [45; 46].
# Vibration Suppression of Collaborative Robot Servo with Dual Encoders
## 8 Experiment
### Speed Response During Steady-State Phase  
When the speed measured by the encoder on the reducer side is not included in the control system, the steady-state speed waveform is as shown in Figure 11a.  
As observed in Figure 11a, the speed fluctuations on the reducer side are significantly greater than those on the motor side. Figure 11b illustrates the waveform after incorporating the speed measured by the reducer encoder into the control system. This can be clearly seen in Figure 11b. Once the speed measured by the encoder on the reducer side is incorporated into the control, the speed fluctuation on the reducer side will be significantly reduced, while the speed fluctuation on the motor side remains similar. This indicates a reduction in the fluctuations of the load and link speed. This means that in the steady phase, the dual encoder vibration suppression scheme is also very effective. In Figure 11b the control parameters are k1 = 0.5, k2 = 0.5, V\({}_{\text{kp}}\) = 20, V\({}_{\text{ki}}\) = 0.001, C\({}_{\text{kp}}\) = 0.42, C\({}_{\text{ki}}\) = 0.2.
## 5 Capacity Trends Analysis
### Relationships Between the Capacity Model and Major Constraints  
In order to conclude the relationships between the network capacity model and major constraints, we now introduce a four-layered network capacity model as shown in Figure 7. With every additional model layer, different classes of constraints are progressively considered to get a more realistic representation. That is, each layer describes successively higher model fidelity, and network capacity generally results in a reduction.  
The maximum model assumes ideal constant availability between communication nodes, such that the elements of \(\Phi_{i}\left(t\right)^{M\times Q}\) are one. The first level is used to characterize the overall maximum throughput rate. Then, we assume the link availability as a function of the nodes' dynamics. This layer falls into the framework of the topological model. Network scheduling constraints are introduced in the higher fidelity scheduled model. The number of nodes and conflicting satellite schedules are key parameters in the scheduled level. Link efficiency is considered in the final actualized model and includes many actualized parameters.  
Figure 7: Relationships between the network capacity model and major constraints. This is a schematic diagram with an increasingly higher fidelity network capacity model within smaller ellipses, where the ellipse area represents network capacity.
## 2 Materials and Methods
### Neural Network Data Model
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rotation** & **Within \(\pm 5^{\circ}\)** & **Front-Back Confusions** & **Max Error** \\ \hline \multicolumn{4}{c}{**KEMAR Reflections**} \\ \hline \(\pm 15^{\circ}\) & 29.86\% & 15.28\% & 173 \\ \(\pm 30^{\circ}\) & 34.03\% & 6.25\% & 54 \\ \(\pm 60^{\circ}\) & 29.17\% & 9.72\% & 50 \\ \(\pm 90^{\circ}\) & 32.64\% & 9.03\% & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Direction of arrival accuracy comparison for the reflected component measured with the KEMAR 45 BC for different fixed receiver rotation angles.
# Degradation of Trace Organic Contaminants by a Membrane Distillation--Enzymatic Bioreactor
###### Abstract  
A high retention enzymatic bioreactor was developed by coupling membrane distillation with an enzymatic bioreactor (MD-EMBR) to investigate the degradation of 13 phenolic and 17 non-phenolic trace organic contaminants (TrOCs). TrOCs were effectively retained (90-99%) by the MD membrane. Furthermore, significant lacase-catalyzed degradation (80-99%) was achieved for 10 phenolic and 3 non-phenolic TrOCs that contain strong electron donating functional groups. For the remaining TrOCs, enzymatic degradation ranged from 40 to 65%. This is still higher than those reported for enzymatic bioreactors equipped with ultrafiltration membranes, which retained lacase but not the TrOCs. Addition of three redox-mediators, namely syringaldehyde (SA), violuric acid (VA) and 1-hydroxybenzotriazole (HBT), in the MD-EMBR significantly broadened the spectrum of efficiently degraded TrOCs. Among the tested redox-mediators, VA (0.5 mM) was the most efficient and versatile mediator for enhanced TrOC degradation. The final effluent (i.e., membrane permeate) toxicity was below the detection limit, although there was a mediator-specific increase in toxicity of the bioreactor media.  
enzymatic membrane bioreactor (EMBR); lacase; membrane distillation; redox-mediators; trace organic contaminants (TrOCs) +
Footnote †: journal: Applied Sciences
## 3 Results  
### Spatiotemporal Distribution and Variation of GPP  
Figure 1(a) reveals an obvious increasing trend of multi-year average \(GPP_{GS}\) from north to south and from west to east in the NEC, which is consistence with the spatial distribution of vegetation types and climate. The productivity of forests ranges from 700 to 1500 g C/m\({}^{2}\). yr\({}^{-1}\), which is obvious larger than that of crops (50-700 g C/m\({}^{2}\). yr\({}^{-1}\)), and the productivity of forests in warm-humid lower latitude regions is about 1000-1500 g C/m\({}^{2}\). yr\({}^{-1}\), larger than that of forests in cold-dry regions (700-1000 g C/m\({}^{2}\). yr\({}^{-1}\)).  
Spatial distribution of inter-annual variation of \(GPP_{GS}\) in the NEC is shown in Figure 1(b) which owns an overall increasing trend, though great spatial heterogeneity exists. Specifically, the western NEC growing with crops has the largest \(GPP_{GS}\) increment (over 11 g C/m\({}^{2}\). yr\({}^{-2}\)), followed by a slight increment of \(GPP_{GS}\) in the GKM and LKM (around 3-7 g C/m\({}^{2}\). yr\({}^{-2}\)) covered with forests, while the variation of \(GPP_{GS}\) is not significant for the majority regions of CBM with sporadic pixels showing increases. Statistically, around 66.4% of the regions in the NEC has an increasing trend in \(GPP_{GS}\), among which the area of croplands accounts for 67.3%.  
We further identified the inter-annual variation of zonal \(GPP_{GS}\) at five regions: the whole NEC, plain, GKM, LKM, and CBM. There is an overall increasing trend of the whole NEC (the change ratio (denoted as CR, hereafter) = 4.95 g C/m\({}^{2}\). yr\({}^{-2}\); \(p<0.01\)) from 1982 to 2015 as shown in Figure 2(a), which is mostly attributed to the growth of crops in the plain (Figure 2(b)). Crops growing in the NEC have a stable increase with the largest amplitude (CR = 7.47 g C/m\({}^{2}\). yr\({}^{-2}\); \(p<0.01\)), while, in the other three forested regions, \(GPP_{GS}\) shows a much smaller increasing trend (LKM: CR = 5.35 g C/m\({}^{2}\). yr\({}^{-2}\), \(p<0.01\); GKM: CR = 3.28 g C/m\({}^{2}\). yr\({}^{-2}\), \(p<0.05\); CBM: CR = 0.75, \(p>0.05\)) with quite a bit of inter-annual fluctuation over the past three decades.  
Figure 3: Trend of regional GPPGS anomalies (true value of GPPGS minus multi-average GPPGS) from 1982 to 2015 in five zones (the Greater Khingan Mountain (GKM), Lesser Khingan Mountain (LKM), the ChangBai Mountain (CBM), and Plain and the whole NEC) of the NEC (Here, the base value (the blue line) is the multi-year average GPPGS from 1982 to 2015; CR refers to the change ratio of GPPGS (g C/m\({}^{2}\). yr\({}^{-2}\)), and P refers to the significance testing. The red line is a trend line calculated by the MK algorithm.).  
Figure 2: Spatial distribution of **(a)** multi-year average of accumulated gross primary productivity (GPPGS) (unit: g C/m\({}^{2}\). yr\({}^{-1}\)) and **(b)** inter-annual GPPGS trend (unit: g C/m\({}^{2}\). yr\({}^{-2}\)) across the NEC during 1982–2015.
# Time Aware F-Score for Cybersecurity Early Detection Evaluation
## 4 Results  
Results for eRisk dataset over Random Forest, Extra Tree, Ada Boost and Logistic Regression models are presented on Figures 3 and 4. Figure 3 shows the results obtained with Time aware F-score \((TaF)\) metric, when compared with Figure 4, which shows the results when \(F\)-_latency_ metric is used, some differences must be noticed.  
In this case, as the problem analysed is specific of an OS Scan Attack and defined flows present distinct characteristics, models trained display high values of TaF metric regardless the combination of models and selectors. Even though that is the case for this particular, it allows to observe how higher values of the metric seem less influenced by differences in selectors, and small differences can be observed in the model (Logistic Regression) where those values are lower.  
Results on Kitsune dataset display also particularities when the amount of items processed is lower, and this situation is enhanced by the fact that this dataset presents a high number of entities with just two items. Also, it must be remembered that those small entities are in their vast majority attack entities.  
Although no direct comparison with previous results referenced is possible due to the use of a new metric presented in this paper, the analysis of both _F-latency_ and TaF results for eRisk dataset (Figures 3 and 4) provides a base to connect it with previous experiments. Besides, the use of one of the selection methods for the early detection systems that was already applied for the same Machine Learning models supplies the needed base for the required results interpretation.  
In view of the conducted experiments and the results obtained, TaF metric is shown as an improved alternative to the early detection metrics. This means that this metric could be applied to evaluate any problem from the domain overcoming the limitations present on other time aware metrics. In terms of improvements of the early detection by machine learning models, it can be seen that a further exploration of selection methods could be applied. By expanding the ways the final decision is taken, performance might be improved.  
Figure 3: Results for combinations of selectors and models (Random Forest (RF), Extra Tree (ET), AdaBoost (AB) and Logistic Regression (LR)) for each number of items analyzed with eRisk dataset using Time aware F-score (TaF) as metric.
## 3 Results and Discussion
### Chemical Compatibility  
#### 3.4.1 Chemical Compatibility between Different Components  
The chemical compatibility of different components was studied by XRD. Figure 6 shows the XRD patterns of the eutectic salt (K\({}_{2}\)CO\({}_{3}\)-Na\({}_{2}\)CO\({}_{3}\)), MgO, SiC, and the sintered composite PCM (Sample S8). It can be found that all the peaks of the composite PCM correspond exactly to the peaks of the components. No new peaks were observed after sintering, indicating no chemical reactions among these components, meaning they are chemically compatible.  
Figure 5: SEM images of Sample S8 after 100 thermal cycles: (**a**) 100\(\times\); (**b**) 500\(\times\); (**c**) EDS mapping, 500\(\times\).