esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion  
### Structural Characterization of the Monomer  
The N,N-(diisopropylcarbamoyloxy)ethyl methacrylate (N-MA) monomer was obtained by the reaction of N,N-diisopropylclaramoyll chloride with 2-hydroxyethyl methacrylate (HEMA), its structure being fully confirmed by FTIR, \({}^{1}\)H, and \({}^{13}\)C NMR analyses. The \({}^{1}\)H and \({}^{13}\)C NMR spectra of N-MA monomer are illustrated in Figure 1a,b, respectively. The \({}^{1}\)H NMR spectra highlighted the presence of characteristic unsaturated monomer protons at 6.15 and 5.58 ppm due to the trans/cis configuration of methylene protons at 4.37-4.26 ppm, methine protons at 3.85 ppm, and methyl protons at 1.21 ppm. Supplementary confirmation of the correct monomer structure was brought by \({}^{13}\)C NMR analysis. In Figure 1b, the peaks at 166.16 and 154.38 ppm are assigned to the ester carbon, while the peaks at 135.08 ppm and 124.88 ppm are attributed to the carbon atoms engaged in the formation of the double bond. The carbon atoms from the methylene group were found at 61.96 ppm and 59.68 ppm; the ones from the methine group arose in the interval between 46.41 and 44.87 ppm, while the methyl carbons appeared at 19.57 ppm and 17.35 ppm.  
In the FTIR spectrum shown in Figure S1, Supporting Information (SI), the monomer N-MA displayed absorption bands characteristic for C-H stretching vibrations at 2971-2877 cm\({}^{-1}\), carbonyl groups at 1723 cm\({}^{-1}\), C=C bonds at 1638 and 815 cm\({}^{-1}\), and C-O-C unit at 1291 cm\({}^{-1}\). This analysis brought additional proof for the right structure of the synthesized monomer.
## 2 Materials and Methods
### HPTLC Method and Instruments  
#### 2.4.1 Application of Standards and Samples  
A semi-automated HPTLC applicator (Linomat 5, CAMAG, Muttenz, Switzerland) was employed to apply the standard solutions onto the HPTLC plates. Application volumes ranged from 1-3 \(\mu\)L for vitamins D\({}_{2}\), D\({}_{3}\), and E and 2-6 \(\mu\)L for vitamin K\({}_{1}\). The standard solutions were applied at a rate of 30 nL/s with the application points located 20 mm from the side edges and 8.0 mm from the lower edges of the HPTLC plates, resulting in 8.0 mm long bands spaced 11.4 mm apart. Calibration curves were constructed over a concentration range of 100-300 ng/\(\mu\)L for vitamins D\({}_{2}\), D\({}_{3}\), and E and 20-60 ng/\(\mu\)L for vitamin K\({}_{1}\).
## 3 Results and Discussion
### Nuclear Magnetic Resonance (NMR) Analysis)  
Polyethylene glycol (PEG), cellulose nanocrystals (CNCs), and cellulose solution in the aqueous PEG/NaOH solution (CNCs-PEG/NaOH) were analyzed using carbon nuclear magnetic resonances (\({}^{13}\)C-NMR). Figure 11 shows the results of the analysis. (Figure 11) displays the \({}^{13}\)C-NMR spectrum of (CNCs-PEG/NaOH) (400 MHz, D\({}_{2}\)O, \(\delta\) (ppm)).  
Figure 10: The zeta potential (\(\zeta\)) of CNCs suspension with reference of standard MCC.  
Figure 11b represents the NMR spectrum of CNCs, and it shows the characteristic peaks of cellulose. The peak at a chemical shift of 62-66 ppm is associated with carbon C\({}^{6}\) and is indicative of amorphous cellulose. The peak at a chemical shift of 104-107 ppm corresponds to carbon C\({}^{1}\). The chemical shift in the range of 82-90 ppm is attributed to carbon C\({}^{4}\), with a value of 84 ppm for amorphous cellulose chains and 89 ppm for crystalline chains. The carbons C\({}^{2}\), C\({}^{3}\), and C\({}^{5}\) are represented by the region between 71-77 ppm. These carbon atoms are not involved in the \(\beta\) (1-4) linkage and could not be distinguished. These signals are consistent with previous studies on cellulose [59; 60; 61].  
In addition, the \({}^{13}\)C NMR spectra of the cellulose solution at room temperature revealed similar signals to the CNCs sample but with slight variations in intensity, as shown in (Figure 11c). Notably, new peaks were observed at 52 ppm, 63 ppm, 69.9 ppm, and 89 ppm, which are characteristic of PEG chains, as shown in (Figure 11a). These values were consistent with those reported in previous studies [62; 63]. The change in the position of C\({}^{4}\) suggested that the hydrogen bonds within the cellulose molecule were disrupted, similar to the case of wood pulp dissolved in LiCl/DMAc (as reported by [64]. Additionally, the peak at 167 ppm was attributed to C\({}^{*}\), suggesting that one hydroxyl group converted into a carboxyl group, which was consistent with the results reported by [PERSON] et al. [65].
# How 5G Wireless (and Concomitant Technologies) Will Revolutionize Healthcare?
## 6 Technology for Healthcare: Challenges/Pitfalls
### Security and Privacy for 5G-Enabled Healthcare  
The 5G network will link almost every aspect of human life to the Internet by using billions of devices and sensors. This will probably result in various threats to the security and privacy of consumer data. 5G security threats are more serious to healthcare systems as cyber attacks on them can be detrimental at a society level. In particular, IoT devices will be more exposed to vulnerabilities as most of the small sensors and tiny devices with low computational power are unable to handle complex encryption algorithms. Consequently, the data in transit will have to be sent without any encryption. Therefore, strong mechanisms are required to secure or encrypt such bare communications. Similarly, cloud-based IoT platforms used for outsourced storage and computation, due to the resource constraints of IoT sensors and devices, will also bring a series of privacy and security issues [91]. 5G networks must deal with the cybersecurity risks and privacy concerns of users, governments and organizations. The vision of 5G will not become a reality without robust security measures that can preserve the ethical and privacy concerns. Therefore, the level of end-to-end integrated security and confidentiality for 5G networks should be more comprehensive than previous generations of mobile networks.
# MODIS Retrieval of Aerosol Optical Depth over Turboid Coastal Water
## 2 Data
### Data Extraction Procedure  
MxD04 [4] AOD and the AOD retrieval from the new algorithm are evaluated against AERONET measurements. The spatio-temporal matching approach by [PERSON] et al. [16] is applied to collocate AERONET AOD measurements and MODIS retrievals (MxD04 and/or new algorithm) for comparison. AERONET measurements within \(\pm\)30 min of the MODIS overpass time are averaged and compared against MODIS retrievals averaged within a 50-km diameter circular region centered over the AERONET sites. MxD04 products include DT, DB, and DT/DB merged retrievals, corresponding to 0.55 \(\upmu\)m AOD from Dark Target algorithms (DT-land and DT-ocean algorithms), Deep Blue algorithm, and combination of the two retrievals, respectively [4]. DT retrievals cover both vegetated land and ocean while DB is limited to land. As the DT-land algorithm is not designed to retrieve AOD over bright desert, there are significant retrieval gaps over land. The DB algorithm was originally designed to complement the gaps, based on the principal that desert pixels are relatively darker in deep-blue  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Site** & **Location \({}^{\ast}\)** & **Period** & \
\begin{tabular}{c} **Data** \\ **Level** \\ \end{tabular} &
\begin{tabular}{c} **Monthly Mean 0.44–0.87 \(\upmu\)m** \\ **Angstrom Exponent** \\ \end{tabular} \\ \hline MVCO, New England & 41.3\({}^{\circ}\)N 70.6\({}^{\circ}\)W & August 2015 & 2.0 & 1.842 \\ Bhola, Bangladesh & 22.2\({}^{\circ}\)N 90.8\({}^{\circ}\)E & December 2015 & 1.5 & 1.206 \\ Anmyon, S. Korea & 36.5\({}^{\circ}\)N 126.3\({}^{\circ}\)E & May 2016 & 1.5 & 1.076 \\ Dalma, UAE & 24.5\({}^{\circ}\)N 52.3\({}^{\circ}\)E & August 2004 & 2.0 & 0.711 \\ Karachi, Pakistan & 24.9\({}^{\circ}\)N 67.0\({}^{\circ}\)E & March 2014 & 2.0 & 0.701 \\ MAARCO, UAE & 24.7\({}^{\circ}\)N 54.7\({}^{\circ}\)E & September 2004 & 2.0 & 0.597 \\ \hline \hline \end{tabular} \({}^{\ast}\) All the locations are shown as lime points in Figure 1f.  
\end{table}
Table 1: Information of AERONET sites used for validation.  
bands [5]. However, now DB has been extended to vegetated land surface as well. Thus, for this study, we use the DB/DT merged product (Quality flag = 1, 2, 3 over ocean, and Quality flag = 3 over land) from MxD04. Specifically, DB/DT merged product over ocean is indeed DT.
## 2 Fracture Mechanics-Based Fatigue Assessment of Homogeneous Materials
### Determination of the Initial Crack Size  
According to Equation (2), an important element of performing fatigue assessment is the choice of an initial crack size \(a_{0}\). For an actual component, \(a_{0}\) can be observed through inspection. However, at the design stage, it is necessary to set an initial crack size \(a_{0}\) by a certain theoretical method or technical means.  
At present, the setting of \(a_{0}\) can be summarized as academic method and engineering method. Academic methods such as non-propagation cracking method, in which the stress intensity factor threshold value \(\Delta K_{th}\) was used as a basis for the division of the crack initiation and propagation stage. It is considered that when the stress intensity factor at the crack tip is lower than the threshold value, the crack does not expand. Thus, \(a_{0}\) can be obtained as the intersection point of the fatigue limit of a smooth specimen and the long crack propagation threshold in the well-known Kitagawa-Takahashi diagram [24].  
Engineering methods usually use the minimum detectable crack size or an initial crack size recommended in standards and codes, which ranges from 0.1 to 0.5 mm [25]. At present, the smallest crack size detectable by X-ray inspection is 0.1 mm [26], which is consistent with the initial crack size recommended by IIW [7]. Thus, this paper selects 0.1 mm as the initial crack size \(a_{0}\).  
Figure 2: “Universal” curve of fatigue crack propagation rate for several materials [23].
# Deep Reinforcement Learning for Energy-Efficient 6G V2X Networks
## 5 Conclusions  
We investigated energy efficiency in 6G V2X communication systems and formulated an ILP-based optimization problem to reduce the energy consumption of the system. We obtained the optimal solutions using the CPLEX solver and implemented a DRL framework using the PPO algorithm. Under various simulated conditions, our research evaluated the DRL-PPO-derived allocation decisions against optimal centralized solutions.  
The energy consumption linearly increased with respect to the problem scale in both ILP-based and DRL-based solutions. The proposed DRL approach ran efficiently in the range of scenarios studied and produced dynamic decisions that approximated the optimal ones at acceptable energy consumption levels. An important aspect of the DRL-based solution is that even when different latency levels were studied, the calculated energy consumption per RSU remained stable in different scenarios.  
Our numerical results illustrated that the proposed approach is appropriate for energy-saving operation in 6G V2X networks. The system model under study allowed us to  
Figure 6: Number of vehicles vs. energy consumption per active RSU.  
Figure 7: Number of vehicles vs. proportion of active RSUs.  
minimize the energy consumption while satisfying the allowed latency levels to ensure 6G V2X communications.  
For further research and future work, we plan to consider additional constraints (e.g., vehicle mobility), formulate multi-objective optimization models, and investigate the impact of different factors on the performance of the proposed model. We also plan to perform a comparative analysis between our PPO-based algorithm and alternative DRL approaches. In addition, we plan to address security and reliability concerns by validating the proposed model through real testbed experiments to assess its robustness and resilience.  
Conceptualization, A.V. and F.M.; methodology, F.M.; software, F.M. and A.V.; validation, F.M., A.V. and P.C.; formal analysis, F.M. and A.V.; investigation, A.V. and F.M.; data curation, A.V.; writing--original draft preparation, F.M.; writing--review and editing, A.V. and P.C.; visualization, A.V.; supervision, P.C.; project administration, P.C. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the National Research Institute, grant number POIR.04.02.00-00-D008/20-01, on \"National Laboratory for Advanced 5G Research\" (acronym PL-5G), as part of the Measure 4.2 Development of modern research infrastructure of the science sector 2014-2020 financed by the European Regional Development Fund.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.
## 2 Requirements and Challenges in the Operationalization of the Cloud Continuum
### Benchmark of Resources and Application Classification  
Given the heterogeneity of the cloud resources and the types and complexity of the applications it is not trivial to select the optimal resources (or combination of resources) for each application (or application component) in order to maximize its performance [13], or to choose the best performance/cost trade-off [14]. That is why benchmarking is a widely used technique in cloud computing. Moreover, when shifting from the cloud to the edge computing, benchmarking techniques are seen as even more valuable because of the high diversity and variability of edge nodes in terms of hardware, software stacks, energy consumption or connectivity capabilities. A common practice used in the cloud [15] is to rely on specialized benchmarking tools [16; 17; 18] to simulate how an application would behave when deployed on different cloud options. The extension of these techniques to the edge, however, still poses some new challenges [19] such as the limited capabilities of edge nodes needing more lightweight benchmarking tools, specific workloads running in the edge (e.g., virtual reality, image processing) being different from the workloads in the cloud (e.g., data analysis, searching, video streaming), and benchmarking composition  
Figure 1: Challenges in the Operationalization (Ops) of applications in the Cloud Continuum. (cloud + edge nodes) in order to provide information on complex deployment options. These challenges are currently being tackled by different initiatives through different ongoing works: characterization of the most common edge use cases and their workloads [20], selection of metrics to define their performance [21] and the implementation of new suites of benchmarking tools designed to run in the edge for different use cases such as speech decoding and face recognition [22], surveillance cameras and autonomous vehicles [23], etc. A big challenge today in existing service catalogues is that, in particular, the information about security attributes, such as certificates or compliance to standards, is not available. In [24], a solution is proposed that allows the discovery and monitoring of security-related aspects on what it is called the Legal Level. This Legal Level assesses the regulation and legislation supported by the different Cloud Services through the verification against evidences included in CSPs contract forms. Nevertheless, new metrics and aspects related to privacy and security and other infrastructural elements (i.e., edge nodes, IoT agents) need to be addressed.
# Ionospheric Weather at Two Starlink Launches during Two-Phase Geomagnetic Storms
###### Abstract
The W index has four levels for the positive logarithmic deviation from the quiet state (W = 1) to an intense positive storm (W = 4) in steps of 1 and four levels for the negative deviation from the quiet state (W = \(-\)1) to the intense negative storm (W = \(-\)4). Thresholds for the different grades of the W index and values of the logarithmic deviations for the positive and negative W indices are given in Table 3 of [11]. Forecasts of GIM-TEC and GIM-W maps for 1 and 2 days in advance (denoted further as d1 and d2) are produced for the current day and the next day by IZMIRAN, from the JPLR GIM-TEC provided daily for the preceding day [11]. Independent 1- and 2-day GIMs forecasts provided by the Center for Orbit Determination in Europe (CODE, CODE1 and CODE2) are also used for the analysis [12]. The third class of 1- and 2-day GIMs forecast (B1 PG and B2 PG) are provided by the Beijing University of Aeronautics and Astronautics model [13]. The 1- and 2-day GIMs forecasts with the data set CASG provided by the Chinese Academy of Sciences are the fourth data set under consideration [14]. The Universitat Politecnica de Catalunya (UPC-ionSAT) GIMs forecasts for 6, 12, 18, 24 and 48 h in advance present another family of the Nearest-Neighbor (NN) model [15] used for the comparisons. There are other methods of GIMs forecast published in literature which are not used in the present analysis [16; 17; 18; 19; 20; 21; 22; 23; 24]. As a measure of the instant ionosphere state, we use the Global Electron Content (\(GEC\)) [25; 26]. The global electron content (\(GEC\)) is equal to the total number of electrons in the near-Earth space environment within the GPS orbital altitude of approximately 20,200 km. It presents unique metrics of the instant ionosphere state providing one magnitude, \(GEC\) (in GECU units, 1 GECU = 10\({}^{32}\) electrons) derived from the instant Global Ionospheric Map of Total Electron Content (GIM-TEC) [9; 10; 27; 28; 29]. While evaluations of the different GNSS global ionospheric mapping techniques for the observed GIMs have been made by different authors [27; 28; 29], the global view forecast of GIMs is evaluated for the first time in the present study. The results of comparisons of the different GIMs forecast techniques are provided in the following sections.
# Recent Progress on Vegetation Remote Sensing Using Spaceborne GNSS-Reflectometry
## 2 Research Status
### Theoretical Model Research
However, actual measured data are still needed to verify the model in the near future.
## Abbreviations
  
Appendix A Estimation of \(\mathrm{PAR_{diff}}\), \(\mathrm{PAR_{dir}}\), \(\mathrm{APAR_{sun}}\) and \(\mathrm{APAR_{sh}}\):APAR on per unit leaf area is calculated separately for sunlit (\(\mathrm{APAR_{sun}}\)) and shaded leaves (\(\mathrm{APAR_{sh}}\)):  
\[APAR_{sun}=(1-\alpha)\times\left(\frac{PAR_{dir}\times\cos\beta}{\cos\theta}+APAR _{sh}\right) \tag{1}\]  
\[APAR_{sun}=(1-\alpha)\times(1-\delta)\times\left(\left(PAR_{dir}-PAR_{diff,u} \right)/LAI+C\right) \tag{2}\]  
\[C=0.07\Omega\times PAR_{dir}\times(1.1-0.1 LAI)e^{-\cos\theta} \tag{3}\]  
\[PAR_{diff,u}=PAR_{dir}\times e^{\frac{-0.5\Omega\times Lt}{(3.5\Omega+0.025 Lt)}} \tag{4}\]  
\[\delta=0.2\times(1-e^{\frac{-0.5\Omega\times Lt}{(3.5\Omega+0.025 Lt)}}) \tag{5}\]  
where \(\alpha\) is the albedo related to vegetation types setting as 0.15 for confierous forest; \(\mathrm{PAR_{diff}}\) and \(\mathrm{PAR_{dir}}\) are the diffuse and direct components of incoming PAR, respectively, and they are calculated using equation (6); \(\mathrm{PAR_{diff,u}}\) is the diffuse PAR under the canopy; \((\mathrm{PAR_{diff}}-\mathrm{PAR_{diff,u}})\)/LAI represents the diffuse PAR on per unit leaf area within the canopy; \(C\) quantifies the contribution of multiple scattering of the total PAR to the diffuse irradiance per unit leaf area within the canopy; \(\beta\) is mean leaf-sun angle and set as \(60^{o}\) for a canopy with a spherical leaf angle distribution; \(\delta\) is a correction for nonlinear response of leaf photosynthesis to the vertical variation of diffuse radiation within the canopy that makes \(\mathrm{APAR_{sh}}\) represents absorbed radiation times light response of photosynthesis; and \(\theta\) is the solar zenith angle. Diffuse and direct PAR are partitioned [15; 23; 24] with parameters calibrated using daily clearness index (CI) and total incoming radiation as:  
\[PAR_{diff}=PAR\times\left(0.7702+3.6895 CI-15.4527 CI^{2}+16.9828 CI^{3}-5.7773 CI^ {4}\right) \tag{6}\]  
CI is a ratio of the global solar radiation on the surface of the earth (\(\mathrm{R_{g}}\)) and the extraterrestrial radiation at the top of the atmosphere (\(\mathrm{R_{0}}\)). \(\mathrm{R_{0}}\) for any day and any moment can be calculated using solar zenith (\(\theta\)) at a given place as:  
\[R_{0}=S_{0}\times(1+0.033\cos(360 DOY/365))\times\cos\theta \tag{7}\]  
where \(\mathrm{S_{0}}\) is the solar constant (1367 W/m\({}^{2}\)); DOY denotes day of the year. With observed half-hourly total incident radiation (\(\mathrm{R_{g}}\)), CI is calculated as \(\mathrm{R_{g}}/\mathrm{R_{0}}\).
## 5 Packet Length Control for Sparse Signal Transmission
### Relationship between Communication Parameter and Mutual Coherence
#### 5.2.1 Combined Effects of BER and Packet Length on PRR  
Given bit error rate \(P_{e}\), the packet reception rate \(P_{prr}\) is obtained as follows:  
\[P_{prr}=(1-P_{e})^{L} \tag{15}\]  
where \(L=L_{overhead}+L_{payload}\) denotes the whole packet length including overhead. \(P_{e}\) depends on the modulation scheme. For binary phase shift keying (BPSK) or quadrature phase shift keying (QPSK) adopted by the IEEE 802.15.4 standard at 2.4 GHz, the bit error rate is the same and equals:  
\[P_{e}=Q(\sqrt{\frac{2E_{b}}{N_{0}}})=Q(\sqrt{2\beta}) \tag{16}\]  
where \(\beta\) is the \(\frac{E_{b}}{N_{0}}\) ration. The \(Q\)-function is defined as \(Q(x)=\int\limits_{x}^{\infty}\frac{1}{\sqrt{2\pi}}\exp(-x^{2}/2)\mathrm{d}x= \frac{1}{2}[1-erf(\frac{x}{\sqrt{2}})]\). Hence, the packet reception rate \(P_{prr}\) is defined as:  
\[P_{prr}=(1-Q(\sqrt{2\beta}))^{L} \tag{17}\]  
We now conjecture that the successfully-received packet number by the FC denoted as \(M\) has a binomial distribution with parameter \(n=\frac{N}{L_{payload}}\) and probability \(P_{prr}\), _i.e._,  
\[P(M^{\prime}=m)=B(n,P_{prr})=C_{n}^{m}P_{prr}{}^{m}(1-P_{prr})^{n-m} \tag{18}\]  
Figure 8: Relationship between measurement number, mutual coherence and reconstruction error.  
where \(n\) is the total transmitted packet number. The measurement number (\(M\)) is:  
\[M=n\times P_{prr}\times L \tag{19}\]  
where \(N\) denotes the number of sampling data. In this paper, we use \(N=1024\). In this case, given a deterministic channel condition and packet length, we can acquire the quality of signal/information transmission.