esa-sceva/satcom-chunk-collection huggingface dataset:
# Tuning of Graphene-Based Optical Devices Operating in the Near-Infrared
## 2 Graphene Optical Properties
### Graphene Conductivity
#### 2.1.1 Dependence of Graphene Complex Permittivity
However, an increase in the temperature causes a slight decrease in the real and an increase in the imaginary parts of the permittivity at a chemical potential from 0.3 to 0.6 eV, which is confirmed by the curves in Figure 4.  
It should be borne in mind that the levels are usually filled in accordance with the Fermi-Dirac distribution function. At zero temperature, it is a step function, from which it can be concluded that, in this case, the energy levels (which are below the chemical potential) are completely filled. The situation changes at room temperature: the distribution function expands by \(k_{B}T\approx 25\) meV. The previous explanation also makes it clear that in the equilibrium state, photons at a wavelength of \(1.55\) um will not be absorbed if the chemical potentials of the upper and lower graphene electrodes are equal to 0.425 eV. Therefore, when there is no applied voltage (\(V_{G}=0\)), the graphene layers behave as transparent thin sheets [15]. The thermal-optical effect is not large enough to provide an equivalent change in the refractive index. Thus, the heat generated in graphene is usually transferred to the carrier waveguide or substrate. This leads to an increase in temperature and a change in the refractive index of the entire structure [62]. The work reported in Reference [18] describes a minor temperature dependence of the performance characteristics that remain practically unchanged for temperatures up to \(49\)\({}^{\circ}\)C. It should also be noted that ring or disk modulators (in contrast to the Mach  
Figure 4: **(a)** Real (blue curves) and imaginary (red curves) parts of the permittivity, (**b**) refractive index and (**c**) extinction coefficient as a function of the temperature from various publications (\(\lambda=1.55\) μm, \(t_{G}=0.34\) nm, \(\mu_{c}=0+1\) eV, \(\Gamma=8\)e\(13\) s\({}^{-1}\), \(\Gamma=0\div 100\)\({}^{\circ}\)C). Zehnder modulator [9, 18] or planar photonic crystal nanocavity-based modulator [20]) are sensitive to temperature fluctuations [63]. In References [14, 64], reliable athermal operation of the modulator without a noticeable change in the speed characteristics of the device at high temperatures up to 145 \({}^{\circ}\)C is presented, which does not close the question of the effect of temperature on the wavelength tuning. This point is related to the effect of heat transfer--graphene electrodes are connected to the dielectric materials, which includes other dielectric materials such as Si\({}_{3}\)N\({}_{4}\) or Si. The parameters of these materials have a higher thermal sensitivity, which can affect the stability of the tuning mechanism.
## 4 The Proposed Group Authentication Framework
### Phase 1: Group Formation  
The first phase deals with preparing the IoT devices and Blockchain for group formation as shown in Figure 4. In part A of phase 1, all participating TAs will register their IoT devices on the Blockchain. In this part, a TA will generate the Chebyshev attributes, such as the required degree of the Chebyshev polynomial and the value of variable \(x\), and compute the public key \((x,Cheby_{Alice})\) as explained in the previous section. The public key of each IoT device is stored by each TA on Blockchain in the form of a digital certificate named _blockCert_dev_, along with other essential metadata, such as \(ID_{puf}\), TA name, device name, device type, etc. A PUF is used to create a unique identifier (known as \(ID_{puf}\)) for an IoT device. In part B of phase 1, one TA (known as patron TA) will initiate the group formation process. The patron TA will create a group name and identifier. The patron TA then identifies the \(ID_{puf}\) of the participating IoT devices and binds these IDs with the group ID using a function _bindGroup_ of the smart contract. After this, the patron TA will share the group name and group ID with all the participating TAs on a secure channel. All of the remaining participating TAs will also use _bindGroup_ of the smart contract to bind their IoT devices with the supplied group ID. After the successful execution of the first phase, the smart contract will have information, such as group name, group ID, and list of \(ID_{puf}\) all participating IoT devices. At the end of this phase, the public keys of all participating IoT devices are also stored in Blockchain.
# Electrophysical Method for Identifying the Causes of Excessive Hydro Generator Vibration
## 1 Introduction  
Renewable hydro energy power generation is one of the priorities of power engineering development. Hydroelectric power plants (HPPs) constitute a significant part of global power generation. HPPs are operated in strict compliance with legal requirements and regulatory standards [1; 2; 3; 4; 5; 6; 7]. A hydraulic turbine and a hydro generator are considered to be the most important and high-cost equipment of a hydroelectric power station. The service life of a hydro generator is 20-30 years [8].  
Hydro generators are affected by nearby conversion units [9; 10]. These can be a source of disturbances that can cause unfavorable resonance processes in turbine shaft trains. Because of the effect of control devices of a conversion unit, torsional vibration excitation occurs, leading to vibration problems, increase in damage frequency, and decrease in the service life of hydraulic turbines [11; 12; 13]. The authors discuss the causes of damage to hydraulic machines, turbines and the cost of such damage resulted from dynamic impacts at the conferences [14; 15; 16; 17; 18; 19; 20; 21]. The Sayano-Shushenskaya HPP accident, the largest one in Russia, occurred in August 2009 and drew even more attention to this problem.  
To investigate these phenomena, a cascade of hydropower plants located in the Arctic zone of Russia was considered.
## 4 Conclusions  
In summary, four new compounds, including one novel (**1**) and one new (**2**) monoterpneoids and two new polykeptides (**3** and **4**), were obtained from the EtOAc extract of the deep-sea-derived fungus _Aspergillus yadowii_ MCCC 3A00324, together with seven known compounds (**5**-**11**). The structures of metabolites were determined by comprehensive analyses of the NMR and HRESIMS spectra, in association with quantum chemical calculations of the ECD, \({}^{13}\)C NMR, and specific rotation data for their configurational assignment. Compound **1** possessed a novel monoterenoid skeleton,biogenetically probably derived from the osmane-type monoperpenoid after the cyclopentane ring cleavage and oxidation reactions. Additionally, **2** was the first osmane-type monoterpenoid representative discovered from the fungi, while **3** was the first example of the \(\alpha\)-pyrone derivatives bearing two phenyl units at C-3 and C-5, respectively, indicating that the deep-sea-derived fungi are a unique source of the structurally novel compounds. Compound **6** exhibited significant inhibitory effects against NO secretion in LPS-activated BV-2 microglia cells (94.4% inhibition rate, 10 uM), suggesting the potential application for the anti-inflammatory agents.  
The following are available online at [[http://www.mdpi.com/1660-3397/18/11/561/s1](http://www.mdpi.com/1660-3397/18/11/561/s1)]([http://www.mdpi.com/1660-3397/18/11/561/s1](http://www.mdpi.com/1660-3397/18/11/561/s1)), Figure S1: The HPLC and TLC metabolic profile of the study fungus, Figures S1-S4-7: HRESIMs, \(\mathrm{I}\), \(\mathrm{I}\), \(\mathrm{I}\)\(\mathrm{{}^{3}C}\), HSQC, COSY, and HMBC spectra of new compounds **1-4**, and NOESY spectra of **1** and **2**.  
[PERSON], [PERSON], and [PERSON] isolated and identified the fungus. [PERSON] isolated and elucidated the structures. [PERSON] and [PERSON] performed the anti-inflammatory assay. [PERSON] and [PERSON] designed and coordinated the study. [PERSON], [PERSON], and [PERSON] wrote and revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the Scientific Research Foundation of the Third Institute of Oceanography (2018018), the National Natural Science Foundation of China (81901133), and the COMRA program (DY135-B2-08).  
The authors declare no conflict of interest.
## 3 Results
### Intercomparison between UTOPIA and NOAH Simulations Initialized with GLDAS  
The experiments considered in this intercomparison are EXP2b, EXP3b for UTOPIA and EXP4, EXP5 for NOAH. In EXP2b and EXP4 the boundary conditions used are retrieved from GLDAS2.1 and in EXP3b and EXP5 from GLDAS2.0. Since vegetation type for NOAH was \"irrigated crops\", only UTOPIA simulations with the same vegetation type were considered, to avoid any problem of result interpretation. In any case, previous subsection already investigated the role of vegetation type in UTOPIA simulations. Finally, as in this section the intercomparison involve only model simulations and not observations, the dataset includes all days in the considered three-year period, and, as a result, the values are different from those in Tables 4-11.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **Bias** & **Mean** & **Standard Deviation** & **Median** & **Min** & **Max** & **Range** \\ \hline EXP1a & 2.0 & 18.2 & 11.9 & 21.3 & \(-\)3.0 & 34.9 & 37.9 \\ EXP2a & \(-\)1.1 & 15.1 & 9.9 & 17.3 & \(-\)2.6 & 30.2 & 32.8 \\ EXP3a & \(-\)0.1 & 16.1 & 8.4 & 18.4 & \(-\)0.6 & 27.8 & 28.4 \\
**OBS** & **0.0** & **16.2** & **8.8** & **19.1** & **1.2** & **28.3** & **27.1** \\ EXP1b & 1.3 & 17.5 & 11.3 & 20.0 & \(-\)3.1 & 32.8 & 35.9 \\ EXP2b & \(-\)1.2 & 15.0 & 9.7 & 17.6 & \(-\)2.6 & 29.8 & 32.4 \\ EXP3b & \(-\)0.1 & 16.1 & 8.2 & 18.5 & \(-\)0.6 & 27.7 & 28.3 \\ \hline \end{tabular}
\end{table}
Table 10: TS (\({}^{\circ}\)C) (0–10 cm) simulated and observed at the FB station (data number 400).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **Bias** & **Mean** & **Standard Deviation** & **Median** & **Min** & **Max** & **Range** \\ \hline EXP1a & 0.09 & 0.29 & 0.06 & 0.31 & 0.16 & 0.44 & 0.28 \\ EXP2a & 0.11 & 0.31 & 0.06 & 0.33 & 0.18 & 0.41 & 0.23 \\ EXP3a & 0.13 & 0.33 & 0.04 & 0.35 & 0.23 & 0.42 & 0.19 \\
**OBS** & **0.0** & **0.2** & **0.06** & **0.22** & **0.09** & **0.42** & **0.33** \\ EXP1b & 0.09 & 0.29 & 0.06 & 0.31 & 0.21 & 0.42 & 0.21 \\ EXP2b & 0.12 & 0.32 & 0.05 & 0.33 & 0.23 & 0.41 & 0.18 \\ EXP3b & 0.13 & 0.33 & 0.04 & 0.35 & 0.25 & 0.41 & 0.16 \\ \hline \end{tabular}
\end{table}
Table 11: Volumetric water content (VWC) (\(\mathrm{m^{3}}\)\(\mathrm{m^{-3}}\)) (0–10 cm) simulated and observed at the CO station (data number 427).
# In-Field Quantum-Protected Control-Based Key Distribution with a Lossy Urban Fiber Link
## 7 Methods
### [PERSON]'s Information  
Let us derive an analytical expression for the mutual information between [PERSON] and [PERSON] after postselection and advantage distillation, \(I_{M}(A,E)\), in the scenario where [PERSON] intercepts a fraction \(r_{\text{E}}\) of the signal intensity, and [PERSON] and [PERSON] apply advantage distillation with a block size \(M\).  
To encode the bit \(a\in\{0,1\}\), [PERSON] prepares a phase-randomized coherent state with intensity \(\mu_{a}=|\gamma_{a}|^{2}\), the density matrix of which is given by  
\[\rho_{\text{A}}^{(a)}=\frac{1}{2\pi}\int\limits_{0}^{2\pi}d\varphi|e^{i\varphi }\sqrt{r_{\text{E}}}|\gamma_{a}|\rangle\langle e^{i\varphi}\sqrt{r_{\text{E }}}|\gamma_{a}||=e^{-\mu_{a}}\sum\limits_{n=0}^{+\infty}\frac{\mu_{n}^{n}}{n!} |n\rangle\langle n|. \tag{4}\]Diverting the fraction \(r_{\rm E}\) of the signal, [PERSON] obtains the corresponding state  
\[\rho_{\rm E}^{(a)}=e^{-r_{\rm E}\mu_{a}}\sum_{n=0}^{+\infty}\frac{(r_{\rm E}\mu_{ a})^{n}}{n!}|n\rangle\langle n|. \tag{5}\]  
Note that the postselection procedure employed by [PERSON] and [PERSON] does not change the overall probability distribution of the bits, and--as long as the transmission line contains no elements (e.g., optical amplifiers) that introduce additional correlations between [PERSON]'s and [PERSON]'s states--the state \(\rho_{\rm E}^{(a)}\) remains independent of postselection [19].  
In the advantage distillation step, [PERSON] groups her bits into blocks of length \(M\) and for each block \(a_{1}\ldots a_{M}\) publicly discloses that its value is either \(a_{1}\ldots a_{M}\) or its bitwise complement \(\overline{a}_{1}\ldots\overline{a}_{M}\). Therefore, [PERSON]'s task is to distinguish two equiprobable product states: \(\rho_{\rm E}^{a_{1}\ldots a_{M}}=\rho_{\rm E}^{(a_{1})}\otimes\cdots\otimes \rho_{\rm E}^{(a_{M})}\) and \(\rho_{\rm E}^{\overline{a}_{1}\ldots\overline{a}_{M}}=\rho_{\rm E}^{( \overline{a}_{1})}\otimes\cdots\otimes\rho_{\rm E}^{(\overline{a}_{M})}\). To quantify the maximum information that [PERSON] can extract, we apply the Holevo theorem [36] and maximize over all pairs of opposing blocks. Thus, [PERSON]'s information is expressed as  
\[I_{M}(\text{A,E})=\max_{\alpha_{1}\ldots\ldots\alpha_{M}}\bigg{\{}S\bigg{(} \frac{1}{2}\rho_{\rm E}^{(a_{1}\ldots a_{M})}+\frac{1}{2}\rho_{\rm E}^{( \overline{a}_{1}\ldots\overline{a}_{M})}\bigg{)}-\frac{1}{2}S\bigg{(}\rho_{ \rm E}^{(\overline{a}_{1}\ldots\overline{a}_{M})}\bigg{)}-\frac{1}{2}S\bigg{(} \rho_{\rm E}^{(\overline{a}_{1}\ldots\overline{a}_{M})}\bigg{)}\bigg{\}}, \tag{6}\]  
where \(S(\cdot)\) is the von Neumann entropy. By substituting Equation (5) into Equation (6), one obtains an explicit form of [PERSON]'s information. This expression is then incorporated into Equation (1) to calculate the secret key rate.
# A Survey on Intrusion Detection Systems for Fog and Cloud Computing
## 8 Case Studies
### Adopting Content for Healthcare Service Providers
#### 8.2.2 Polish e-Health Center  
Work on medical ICT systems is treated as a priority in Poland. The most developed system is the Internet Patient Account (IPA), whose aim is to facilitate patients' convenient use of digital services and organize medical information about the patient's health in one place. Every person with a personal ID number has a free IPA account.  
The Internet Patient Account provides information about past, current, or planned treatment and allows to arrange a number of issues without the need to visit the clinic or hospital. The IPA system also provides information about issued and filled e-prescriptions, the dosage of the medicine prescribed by the doctor, history of visits to the doctor, vaccinations, and many others [55]. In addition, using the IPA system, a patient can consent to certain health services such as invasive surgeries, share e-prescriptions and e-referrals with a medical facility or a medical professional, or check the medical results. These are sensitive data that require special security.  
Therefore, the e-Health Center, which is responsible in Poland for monitoring the development of e-Health and responsible for medical ICT systems created at the national and regional level, uses national cloud resources to implement medical systems [56]. The unlimited computing power available in the public cloud allows operating a scalable, flexible, and fully efficient system. Moreover, it provides advanced security mechanisms in the sphere of digital security, operating procedures, as well as physical security in data processing centers. The applied security mechanisms are based on recognized norms and the highest standards: ISO 27001, ISO 22301, ISO 27017, ISO 27018, and CSA STAR.  
For example, the e-Registration system integrated with the IPA system has so far enabled more than 32 million appointments for covid vaccination. In the days of the system's peak popularity, 880,000 visits per day and 122,000 in just one hour were registered with its help, and users conducted 350,000 searches for free appointments per hour and 950 searches per second. The solution has been integrated with many existing public IT systems (including the e-Health System (P1) and the Vaccine Distribution System (SDS)) and it was designed to allow for easy integration with other healthcare systems in the future and, through the security mechanisms used, allows for intrusion detection.
# Orbit Determination Method for BDS-3 MEO Satellites Based on Multi-Source Observation Links
## 3 Test Analysis
### Choosing of Constellations and Ground Monitoring Stations
#### 3.1.2 Selection of Monitoring Stations  
In tests with simulated data, the BJFS, CHAN, HKSL, LHAZ, URUM and WUH2 stations of the international GNSS service (IGS) in China were selected as the ground motoring stations, with consideration of the dispersibility and actual distribution of BDS-3 monitoring stations. The distribution of the chosen stations is shown in Figure 6.  
In tests with real data, the URUM, WUH2, JFNG, URUM, IISC and SGOC stations of the IGS around China were selected, according to quality of observed data from 2019. The distribution of the chosen stations is shown in Figure 7.
## 5 Experimental Evaluation
### Test Setup  
The test setup is composed of two experimental OBUs, two Edge Geoserver applications, one TMS, and two Edge MQTT brokers that are based on the open-source Eclipse Mosquito implementation. Each OBU is based on a laptop running Ubuntu 18.04 and connected to an LTE AirPrime EM7565 modem from Sierra Wireless which provides cellular connectivity for V2N communications. The OBUs are connected to the 4G cellular network of Vodafone-Spain.  
No real vehicles are involved in this test, as it was performed in a lab environment; therefore, the OBUs are configured to operate as virtual vehicles, where their movements and the detection of hazards are emulated. In this mode, vehicles move based on pre-defined traffic trajectories that are provided from a file containing a sequence of GPS coordinates and associated time-stamps. The Web Interface of the OBUs allows to choose a traffic trajectory file and to start and stop the movements of the virtual vehicle. In each OBU, the CAM messages are periodically transmitted at every 100 ms following the ETSI ITS standard. The DENM messages are also periodically triggered to collect application-level latency measurements for the events that are exchanged between two OBUs and between two OBUs and the TMS. The periodicity is explained in the corresponding test cases.
## 3 Results
### LOC Fractions
#### 3.2.3 Changes in POC  
The effects of different land use patterns on the POC content in different soil layers are illustrated in Figure 6. Land use patterns exhibit distinct responses regarding soil POC. In terms of vertical distribution, POC in each treatment decreased with increasing soil depth, with the most significant decrease observed in the MSR treatment (31.91%) and the least in the SC treatment (16.67%). Notable differences exist among the treatments at the same soil depth. The POC content in the 0-20 cm soil layer for each treatment is ranked as FB > GB > MSR > MC \(\approx\) SC. Significant differences were found between treatments, with FB exhibiting a 3.58% higher POC content than GB. While no significant differences were noted between SC and MC, both treatments had POC content that was significantly higher than the other three treatments. SC, MC, and MSR treatments resulted in reductions in soil POC content of 60.14%, 56.73%, and 32.2%, respectively. The POC content in the MSR treatment was significantly higher than that in the two consecutive cultivation treatments, SC and MC, which did not show significant differences between them. SC demonstrated the lowest POC content among all treatments and was also the lowest in the field across the three treatments. All treatments exhibited substantial decreases in the 20-40 cm soil layer, with a trend similar to that observed in the surface layers; however, MC showed slightly lower POC content than SC in the 40-60 cm soil layer.  
Figure 5: Easily oxidized carbon (EOC) concentration in soil humus layers of mollisols under different land use patterns. Note: The experiment treatments included continuous soybean cultivation (SC), continuous maize cultivation (MC), maize-soybean rotation (MSR), grass belt (GB), and forest belt (FB). Different letters mean significant differences among different land use patterns in the same soil layer at 0.05 level. \(n\) = 3. The same is below.
# A Chest Strap-Based System for Electrocardiogram Monitoring
## 4 Results and Discussion
### System Evaluation
#### 4.2.2 Comparison Between the CEMS and a Commercial ECG Monitoring Device  
To further validate the accuracy of ECG signals acquired by the CEMS, we conducted a comparative experiment using the PM12B multi-parameter patient monitor (CHIETAIN, Changsha Innovo Medical Technology Co., Ltd., Changsha, China) as the reference device.  
The subject simultaneously wore both the CEMS and the PM12B (Figure 15a), enabling synchronous ECG data acquisition under identical physiological conditions. Correlation analysis and waveform feature comparison between the two signal sets indicated that (Figure 15b), although slight morphological differences were observed, primarily due to variations in lead configurations, the positions of key characteristic waves remained highly consistent. The Pearson correlation coefficient of 0.78 confirmed a strong correlation between the two devices. In addition, the mean RR interval difference of 0.015 s and PP interval difference of 0.012 s further supported the high accuracy of the CEMS in ECG monitoring.  
Figure 14: Results of a 600 s stability test of the CEMS on the subject.  
Figure 13: The smartphone display of the subject’s ECG signal.
## 6 Conclusions  
In this study, the ground deformation results of the Erhai region from 2015 to 2018 were obtained by performing SBAS-InSAR technology on a total of 44 ascending and descending Sentinel-1 images. Based on the four variables of temperature, precipitation, and water level of the lake and building area, the causal factors for the ground deformation were analyzed, which indicated that urban construction is the main cause of ground deformation disaster in the region of Erhai. The back-propagation (BP) algorithm was used to predict and verify the ground deformation of the study area with high precision, and some reasonable suggestions were made accordingly. The point of this research was that the introduction of the back-propagation algorithm into the prediction of regional ground deformation was used for the first time, which could provide a reference for future correlation research.  
Conceptualization, Y.W. and Y.G.; methodology, Y.W. and S.H.; software, Y.W. and Y.G.; validation, Y.W., Y.G. and Y.L.; formal analysis, Y.W. and Y.G.; investigation, Y.W.; resources, Y.W. and X.L.; data curation, Y.W., X.L. and L.W.; writing--original draft preparation, Y.W.; writing--review and editing, Y.W. and L.W.; visualization, Y.W.; supervision, Y.W., Y.L., and J.W.; project administration, Y.W. and Y.G.; funding acquisition, Y.W. The study was supported by the project of National Key R&D Program of China (2017 YFA0603004).  
The authors are grateful to [PERSON] and [PERSON].  
The authors declare no conflict of interest.