esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Cross-linked thin films
In contrast, the ellipsometry data on the spin coated films are more accurate. For instance increasing the concentration from 0.1 wt % to 5 wt % resulted in films with thickness of 10 nm and 500 nm, respectively. The spin coating process is certainly not appropriate to coat large surfaces, however, the accuracy of the results demonstrate that the copolymer spreads evenly on the silicone oxide/air interface leading to a thin homogenous layer down to the nanometer scale. We anticipate that the copolymer behaves in a similar way on SCG since adsorption is known to take place by hydrogen bonding on the titanium dioxide providing a homogenous coating with good adhesion to the support [22,23]. \begin{table}
\begin{tabular}{|c|c|c|c|c|} \hline
**Copolymer** & \multirow{2}{*}{Photoinitiator a **acylic acid/** ally acrylat**} & \multirow{2}{*}{Accelerator in wt \%} & Irradiation time & Optical Microscopy \\ \cline{3-4}  & & & in h & observation \\ \hline
**P(acrylic acid-_co_-AA)** & 2 & 2 b & 2 & soluble \\ \cline{2-4}
**95/5** & 3 & 3 b & 4 & partial soluble \\ \hline
**P(acrylic acid-_co_-AA)** & 3 & 2 b & 2 & partial soluble \\ \cline{2-4}
**90/10** & 2 & 3 b & 4 & insoluble \\ \cline{2-4}  & 2 & 5 c & 2 & insoluble \\ \hline \end{tabular}  
* 2,2 dimetoxy-2-phenylacetophenone; b Di-(ethylglycol)-dimethacrylate; c urethane dimethacrylate: 7,7,9-trimethyl-4,13-diox-3,14-diox-5,12-diazhexadecane-1,16-diyl bis(2-methylacrylate).  
\end{table} 
Table 1: Water solubility of the coating: Effect of the ratio of repeating units, the photoinitiator, the accelerator and the irradiation time.
# Animal Movement Prediction Based on Predictive Recurrent Neural Network
## 3 Methods
### Movement Density Sequence Generation  
After movement interpolation, we generate the movement density sequence that eventually indicates the movement range of animals. The movement density gives information which represents the spatial distribution of the moving animals. First, we construct a grid map of \(I\times J\) considering the region of interest. \(I\) and \(J\) indicate the image width and height, respectively. Figure 4a shows an example of \(I\times J\) grid map.  
To describe animal movements effectively, we use the KDE method. This method has been widely used in movement ecology and detection of animal habitat [35; 36; 37]. It is one of non-parametric density estimation methods and improves the non-zero probability problem of the histogram method by using the kernel function. A kernel function \(K\) is controlled by the bandwidth parameter \(h\). According to the  
Figure 4: Example of movement density image construction—(**a**) grid generation; (**b**) kernel density generation.  
Figure 3: Movement interpolation using the random forest method.  
kernel definition, we calculate the probability density function \(f\) for the input geo records using the following equation.  
\[\hat{f_{h}}(y)=\ \frac{1}{nh}\sum_{i=1}^{n}K\Big{(}\frac{y-x_{i}}{h}\Big{)} \tag{1}\]  
here, \(n\) and \(x_{i}\) indicate the number of geo records and geo record at time \(i\), respectively, and \(y\) indicates estimated density value. In particular, to reduce the complexity of computation, we use the Gaussian kernel for generating density map. In addition, we use the bandwidth parameter of 5 and normalize the kernel values to a range of 0 to 1. Figure 4b shows an example of animal movement density map using KDE.
## 2 Why the Electroencephalogram (EEG)?
### qEEG Stability, Reliability, and Specificity  
Studies have reportedly demonstrated that the majority of qEEG characteristics have high (up to 90%) within-subject stability (internal consistency measured by Cronbach's alpha) within an EEG recording session, high (up to 90%) reproducibility (test-retest reliability) over a period of hours, weeks, months and years, and high (up to 99%) intra-subject specificity, meaning that qEEG can accurately identify subjects from a large group [60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78].  
These results suggest that qEEG characteristics possess trait-like qualities (stability over time). In this context, intrinsic properties of brain activity measured by resting qEEG constitute a neural counterpart of personality traits (Section 4.2) and can be regarded as the _statistical neuro-signature_ of a person. Such high stability, reliability, and specificity of qEEG characteristics suggest that genetic factors have a strong influence on qEEG variation.
## 2 Experiments and Methods
### Model Parameter Identification
#### 2.3.3 Polarization Resistance and Polarization Capacitance  
As discussed in Section 2.3.2, for the polarization process the terminal voltage completes the transient process in an exponential way approximately when the current sees a sudden change. According to the Kirchhoff laws, for the first-order RC model the variation law of the terminal voltage can be ruled by the following formula:  
\[\mathrm{U(t)=V_{OC}-IR_{1}e^{-t/\tau}} \tag{2}\]  
where \(\tau=R_{1}C_{1}\) represents the time constant.  
Replacing the coefficients of Equation (2) and rewriting the form as:  
\[\mathrm{U(t)=V_{OC}-ae^{-bt}} \tag{3}\]  
Compared to the two formulas above, the parameter values can be obtained as follows:  
\[R_{1}=a/I \tag{4}\]  
\[C_{1}=1/(R_{1}b) \tag{5}\]  
According to the method described above, the polarization resistance and polarization capacitance of the first-order RC model can be obtained by the least square method as shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline
**SOC** & **\(\tau_{1}\)/s** & **R\({}_{1}\)/\(\Omega\)** & **C\({}_{1}\)/10\({}^{3}\) F** \\ \hline
0 & 56.2430 & 0.0382 & 1.4743 \\
0.05 & 42.8082 & 0.0263 & 1.6265 \\
0.1 & 51.3875 & 0.0226 & 2.2758 \\
0.15 & 47.5964 & 0.0244 & 1.9491 \\
0.2 & 55.9597 & 0.0237 & 2.3622 \\
0.3 & 34.7826 & 0.0203 & 1.7126 \\
0.4 & 35.8938 & 0.0204 & 1.7612 \\
0.5 & 41.9287 & 0.0211 & 1.9919 \\
0.6 & 37.5657 & 0.0267 & 1.4049 \\
0.7 & 40.6504 & 0.0242 & 1.6798 \\
0.8 & 38.5654 & 0.0272 & 1.4178 \\
0.9 & 37.3134 & 0.0235 & 1.5878 \\
1 & 46.2321 & 0.0240 & 1.9287 \\ \hline \end{tabular}
\end{table}
Table 2: Polarization resistance and polarization capacitance values of lithium-ion batteries for the first-order RC model.  
Figure 6: The varying curve of ohmic resistance with SOC.  
Similarly, for the second-order RC model, the variation law of the terminal voltage can be ruled by the following equation:  
\[\mathrm{U(t)}=\mathrm{V_{OC}}-\mathrm{IR_{1}e^{-t/\tau_{1}}-\mathrm{IR_{2}e^{-t/ \tau_{2}}}} \tag{6}\]  
where \(\tau_{1}=\mathrm{R_{1}C_{1}},\tau_{2}=\mathrm{R_{2}C_{2}}\) represent the time constant.  
Replacing the coefficients of Equation (6) and rewriting the equation as:  
\[\mathrm{U(t)}=\mathrm{V_{OC}}-\mathrm{ae^{-bt}-ce^{-dt}} \tag{7}\]  
Compared to coefficients of the two equations above, the parameter values can be obtained as follows:  
\[\mathrm{R_{1}}=\mathrm{a/I} \tag{8}\]  
\[\mathrm{R_{2}}=\mathrm{c/I} \tag{9}\]  
\[\mathrm{C_{1}}=1/(\mathrm{R_{1}b}) \tag{10}\]  
\[\mathrm{C_{2}}=1/(\mathrm{R_{2}d}) \tag{11}\]  
According to the method described above, then the polarization resistance and polarization capacitance of the second-order RC model can be obtained by the least square method as shown in Table 3.
# A Prototype of a Portable Gas Analyzer for Exhaled Acetone Detection
## 2 Development of the Portable Gas Analyzer  
This section presents and discusses the development and construction of the portable breath analyzer. The device is constructed upon an electrochemical gas sensor array placed in a dedicated measurement chamber. When the array is exposed to the exhaled breath sample, the resistance of each sensor is dependent upon three factors: gas concentration, humidity, and temperature. In addition to acetone, which is the most important biomarker, other gases, including ethanol, can also be found in exhaled breath. The use of many sensors with different sensitivities to gases in the exhaled air provides excessive information that, after some processing, enables the system to precisely determine the concentration of acetone despite other gases being present. The prototype analyzer was assembled using a custom 3D-printed housing and dedicated PCBs (printed circuit boards) with control electronics. Detailed descriptions of the individual components are presented in the following subsections.
Using the latest GNSS processing models and methodology, the IG2 products come out of the reanalysis of the full history of GNSS data [11]. It is favorable to elaborate the consistency of the seasonal non-tidal VD with the newest solutions. Using the latest products is supposed to reduce the GNSS/GRACE or GNSS/GMs discrepancies, especially to improve the correlation between GNSS and GRACE or GMs modeled seasonal variations. Furthermore, although many studies have investigated the relationships between GNSS and GRACE or GNSS and GMs over the globe, the noise characteristics, another main variation reflected in the GNSS coordinate time series, have not been carefully studied and there are few noise analysis results regarding the consistency differences between the GNSS corrected by GRACE solutions and GNSS corrected by GMs for the global stations. It is beneficial to further investigate the relationships of the non-tidal crustal deformation estimated by different geodetic techniques in terms of noise amplitudes variations. Therefore, in this paper, we provide a detailed discussion of the results about GRACE and GM solutions correcting GNSS vertical deformations. Our main objective is to investigate the consistency of the non-tidal seasonal VD obtained by GNSS, GRACE and GMs in the global scale, and also we attempt to provide new noise solutions to identify the sources of the comparison differences. Firstly, GNSS observations from IG2 products, the GRACE inversion model and GM solutions are described in detail. Then, the IG2 reprocessed height coordinate time series are compared with the VD derived from both GRACE and GM solutions. Secondly, we introduce one further aspect that reflects the total effect of the loading corrections errors, that is the noise in the different types of deformations time series. Then, we perform detailed analysis about the comparisons between the GRACE-derived loading deformations with the IG2 station position time series. Finally, we try to perform comparisons with the previous research and detail the thermal expansion effects and leakage errors analysis.
## 2 Materials and Methods
### Microplasma Treatment  
The applied microplasma irradiation conditions were based on our published literature [12]. IEC6 cells cultured in dishes (60 mm \(\times\) 15 mm) were irradiated with microplasma discharge for a duration of 2 min. A thin-film electrode was used to generate a dielectric barrier discharge under atmospheric conditions. The microplasma irradiation did not use any gas flow. The distance between the surface of the cell medium and the thin-film electrode was approximately 2 mm. Two voltage parameters (i.e., 4 kV or 4.5 kV), at a frequency of 5 kHz, were used for the treatment. A function generator (AFG3102, Tektronix, Beaverton, OR, USA) was used to set a triangle (positive and negative) waveform, which was subsequently amplified by a high-voltage amplifier (5/80, Trek, New York, NY, USA). The time-point of microplasma irradiation to different group samples is shown in Figure 1.  
Figure 1: Workflow of cell culture and microplasma treatment for LC/MS and flow cytometry analyses. (**a**) Set A and (**b**) Set B samples were used for LC/MS analysis. In both cases, cells were initially cultured in flasks for 2-3 days followed by transfer to culture dishes. In Set A, samples were divided into three groups: (i) no irradiation (control), (ii) microplasma irradiation at 4.0 kV followed by 0 h incubation (A4K0H), and (iii) microplasma irradiation at 4.0 kV followed by 1 h incubation (A4K1H). In Set B, samples were divided into four groups: (i) no irradiation (control), (ii) microplasma irradiation at 4.0 kV followed by 0 h incubation (B4K0H), (iii) microplasma irradiation at 4.0 kV followed by 12 h incubation (B4K12H), and (iv) microplasma irradiation at 4.5 kV followed by 0 h incubation (B4.5K0H). The shaded boxes indicate the timing of microplasma irradiation for the respective samples. Samples were prepared to ensure that the total incubation time for each sample within a particular set remained consistent (with variations of \(\pm\)0.25 h). Cells were washed and snap-frozen within 10 min after microplasma treatment or after an additional incubation period (if any) after irradiation. (**c**) Set C samples were prepared similarly to Set B samples and were utilized for flow cytometry analysis. Sample preparation (from cell culture to LC/MS analysis/flow cytometry analysis) of individual sets were carried out independently.
## 3 Results and Discussion
### Analysis of Aromatic Characteristics  
The wines were analyzed for their aromatic characteristics (Figure 7).  
Figure 6: PCA analysis of electronic nose data.  
The floral aromatic characteristic was most intense in wines made from group C grapes, followed by wines made from group B grapes, and then by those made from group A grapes. The citrus/lemon aromatic characteristic was most intense in group A wines, followed by group C wines, and then group B wines. The pineapple/banana characteristic was most intense in group C wines, followed by group B and then group A wines. The honey characteristic was most intense in group C wines, followed by group B and then group A wines; however, the difference in honey intensity between group B and A wines was small. The plant-based flavor was most intense in group B, followed by group C and then group A wines. The overall fragrance characteristic was most intense in group C, followed by group B, and then group A. The floral, pineapple/banana, honey, and overall fragrance aromatic characteristics were most intense in the wines made from group C grapes. To further clarify sensory differences, direct score comparisons across groups reveal trends linked to Botrytis cinerea (noble rot) infection levels. Group C consistently excellent in key aroma dimensions. For \"Flowers,\" Group C scored 7.2 (vs. B: 6.1; A: 4.8), driven by noble rot's role in concentrating sugars and volatile terpenids--amplifying floral and honeyed notes. In the \"Honey\" (Group C: 6.5; Group B: 3.2; Group A: 2.1) and \"Pineapple/Banana\" (Group C: 5.8; Group B: 2.9; Group A: 1.7) aroma dimensions, the superior performance of Group C aligns with the principal component analysis (PCA) results derived from GC-MS data. Specifically, wines fermented from Group C grapes exhibited a strong association with acetate ester-derived aroma profiles, which corresponded to their prominent fruity characteristics. Collectively, these sensory and instrumental analytical findings corroborate a positive correlation between the severity of _Botrytis cinerea_ (noble rot) infestation and the richness of aromatic attributes.
## 3 Results and Discussion  
### Physico-Chemical Water Parameters, Dissolved Major Components  
The physico-chemical water parameters from all sampling sites, as well as the results of the dissolved major and trace element analyses, are presented in Table 2. The mean EC and TDS content were significantly higher at site 1. This site is directly fed by the MIW from the treatment plant. Neutralization methods in the treatment--including the addition of limestone, aeration, settling, and coagulation--substantially increase the EC and TDS of the water at site 1. Especially the addition of limestone (CaCO\({}_{3}\)) can explain the high EC. Following the course of the river system, major dilution effects were observed during the whole observation period, resulting in significantly lower mean values for EC. A pre-treatment EC measurement at site 1 by [PERSON] et al. (2019) of 3.6 mS/cm is well comparable to our mean results. They credited the high mineralization of the water even before treatment at site 1 to AMD and the subsequent dissolution of elements from the host rock including Al, Mn, Ca, and Mg as well as high quantities of sulfates. The Mg and Ca concentrations detected in this study were much higher than reported in pre-treatment times ([PERSON], 2020). They can be attributed to AMD treatment and were exceeded by far by the contributions from aquifers in the area rich in Ca\({}^{2+}\) and Mg\({}^{2+}\), as described by [PERSON] (2018). Importantly, the pH of 3.14 measured by [PERSON] et al. (2019) at the same site was much lower than any of our measured pH values, which can easily be explained by the alkalinization during AMD treatment. Interestingly, during the winter months a significant drop in pH was observed at site 1, which was not recorded at any other sampling site. This raised the question if the AMD treatment was fully functional at this time. Irregularities in the treatment potentially causing the temporary increase were also reported by the Trans-Caledon Tunnel Authority (Stein  
Similar dilution effects were also observed for Na\({}^{+}\), with significantly higher levels in site 1 and continuously decreasing concentrations downstream. However, there were no significant differences in the concentrations of K\({}^{+}\) in the water phase for any sampling sites. This indicates that AMD and water treatment did not influence the concentration of this metal.
# Router Activation Heuristics for Energy-Saving ECMP and Valiant Routing in Data Center Networks
## 2 Communication Management in a Data Center
### Equal Cost Multipath Routing  
Routing in data center networks (DCNs) can be implemented in the open systems interconnection (OSI) model layer 2 or 3. It significantly depends on the network devices. Static routing uses paths entered by the system administrator to the routing table. In contrast, dynamic routing populates the routing table during network operation, which allows path changes to eliminate failures or balance links load. In data center networks, efficiency and reliability are crucial. Typically, these networks contain many redundant links to take over tasks of the infrastructure that has failed. The routing used in this case should allow automatic minimization of the effects of failures and overloads in the network by selecting alternative paths to handle flows at risk.  
Equal-cost multipath (ECMP) routing is used extensively in DCNs as it solves two problems: high availability and bandwidth scaling. It makes it possible to increase the flow capacity. Furthermore, the distribution of traffic between multiple paths reduces the sensitivity of the quality of service to potential failures of individual switches. However, it should be noted that the differentiation of link bandwidth and length of paths may lead to delivering packets out of sequence.  
The typical protocol stack in the DCN is Link Aggregation Control Protocol (LACP) in layer 2 and ECMP in layer 3 of the OSI reference model. Examples of commonly used routing protocols utilizing the concept of ECMP routing are the Open Shortest Path First (OSPF), Border Gateway Protocol (BGP), or the relatively new Routing in Fat Trees (RIFT) [17].
# LoRaWAN Communication Protocols: A Comprehensive Survey under an Energy Efficiency Perspective
## 2 Background Knowledge  
This section describes in detail the LoRaWAN protocol, as well as its main functionalities. Specifically, we present protocol architecture, LoRa physical layer, packet format, frequency bands and duty cycle restriction, channel activity detection mechanism, LoRaWAN MAC layer, supported classes of LoRaWAN end-devices, as well as constraints of this technology in terms of collisions, scalability, and robustness.
# A General Euler Angle Error Model of Strapdown Inertial Navigation Systems
###### Abstract  
Attitude error models play an important role in analyzing the characteristics of navigation error propagation for the design and operation of strapdown inertial navigation systems (SINS). However, the majority of existing attitude error models focus on misalignment, rather than Euler angle errors. Misalignment cannot directly describe attitude error propagation, which is an indirect measurement. To solve the problem, a general Euler angle error model of SINS is proposed. Based on Euler angle error propagation analysis, relative Euler angle errors, and convected Euler angle errors are introduced to compose the general Euler angle error model. Simulation experiments are carried out to verify the proposed model.  
Keywords: attitude error model; navigation error propagation; relative Euler angle error; convected Euler angle error +
Footnote †: journal: Applied Sciences
# Relief Aircraft Dispatch Strategies Based on Different Levels of Information Sharing Systems
## 4 Dispatch Strategy Based on the Information Sharing System
### Level Two and Corresponding Transportation Method  
In Level two, there is communication between the shelters and airports. In this case, the shelters could report the number of injuries to the airport operation center, as shown in Figure 5. Then, dispatchers could assign the aircraft based on the reported request from the rescue shelters.  
In this case, the dispatchment center has information on the number and location of injuries. The dispatchment center can design a flight route before take-off. Hence, we propose two dispatch methods: \"one-destination\" and \"multiple-destinations\".
# Communication Transport Protocol Strategies for Rail Applications
## 7 Conclusions  
In order to counteract the GSM-R disposal in the coming years as a communication technology adopted by rail operators in traffic train management, the European Shift2 Rail Joint Undertaking (S2R-JU) proposed the adoption of the Adaptable Communication System (ACS) based on the Over-The-Top (OTT) approach, which implements bearer independence and supports connections over multiple networks. In this work, a testbed was implemented aiming at emulating the ACS behavior in order to evaluate ACS performance. In particular, we considered the duplication of messages in managing critical rail applications for train traffic management and investigated the best solutions in terms of transport protocol solutions for implementing tunnels through the on-board gateway and the network gateway. Two main rail applications were analyzed: the position report/movement authority for ETCS/ERTMS signaling and the critical data transfer (i.e., file transmission), considering both UDP- and TCP-based tunnel transport protocols.  
Results showed that for the first rail application, one UDP bearer can be selected only if the end-to-end channel delay is lower than 100 ms and the experienced packet loss is lower than 4% in the whole crossed networks. Two UDP bearers or one TCP bearer or two mixed UDP/TCP bearers should be selected in case the end-to-end channel delay is greater than 300 ms and the experienced overall packet loss is greater than 15%. Considering the critical file transfer in the rail scenario, TCP should be selected with two bearers to have a throughput greater than 50 Mbit/s, even for a packet loss of 1%.  
Conceptualization, F.M., R.G. and A.V.; methodology, R.G. and A.V.; software, A.C.; validation, R.G. and F.M.; formal analysis, R.G. and F.M.; investigation, R.G.; resources, F.M. and R.G.; data curation, A.C.; writing--original draft preparation, R.G., A.V. and A.C.; writing--review and editing, R.G. and A.C.; visualization, A.V.; supervision, R.G.; project administration, F.M.; funding acquisition, R.G. and F.M. All authors have read and agreed to the published version of the manuscript.  
This research was partially developed within the AB4 Rail project (www.ab4 rail.eu, (accessed on 1 January 2021) No. 101014517) funded by Shift2 Rail Joint Undertaking and European Union's Horizon 2020 research and innovation programmes.  
Not applicable.  
Figure 17: Data Transfer Throughput vs. Increasing Latency with fixed Packet Loss equal to 1% on Bearer Interfaces.  
**Acknowledgments:** The authors would like to thank the anonymous reviewers for their suggestions to improve the readability and content of this document.  
**Conflicts of Interest:** The authors declare no conflicts of interest.