esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Material and Methods
### Quantification of Monomeric and Polymeric Phenolic Compounds by HPLC-DAD  
Monomeric phenolic compounds were determined by using the Agilent 1100 liquid chromatograph equipped with a LiChrospher 100 RP-18 reversed-phase column (0.4 \(\times\) 25 cm, 5 \(\upmu\)m) as described [58]. DAD chromatographs were obtained at 360 nm (flavonols), 320 nm (hydroxybenzoic and hydroxycinnamic acids), and 280 nm (flavanols) with the calibration curves of their respective standards (r\({}^{2}\) > 0.999) or according to the calibration of the most similar compound standard. Each wine was analyzed in triplicate after one month (T1) and twelve months (T12) of bottling.  
The flavon-3-ols and condensed tannins of Viura wines were analyzed following a methodology previously described [59]. A multi-step analytical method with an initial fractionation of wine phenolics by gel permeation chromatography (GPC) with a TSK Toyorpeal gel HW-50F (Toshoas, Montgomery-ville, PA, USA) packed in a Millipore (Bedford, MA, USA) Vantage L column (120 mm \(\times\) 12 mm i.d.). Two milliliters (2 mL) of wine was directly applied to the column and the flow rate was regulated at 1 mL min\({}^{-1}\). The first fraction was eluted with 60 mL of a solution of ethanol/water/trifluoracetic acid (55:45:0.05, \(\
u/\
u/v\)), and the second fraction was recovered by elution with 50 mL of acetone/water (60:40, \(\
u/v\)); the second fractions were taken under dryness under vacuum conditions.  
The dried fractions of wine flavan-3-ols and condensed tannins were solved in 1.5 mL of MeOH and 0.5 mL of phloroglucinol solution made by 0.1 N HCl in MeOH, 50 g L\({}^{-1}\) phloroglucinol, and 10 g L\({}^{-1}\) ascorbic acid, were added, and the solution was at 50 \({}^{\circ}\)C for 20 min [59]. After the reaction time, 0.5 mL of sodium acetate (40 mM) was added to stop the reaction of the proanthocyanins and was analyzed by HPLC-DAD. Each fired wine was analyzed in triplicates after one month (T1) and twelve months (T12) of bottling.  
The flavan-3-ols and condensed tannins of Viura wine were quantitated by high-performance liquid chromatography-diode array detection (HPLC-DAD) in an Agilent modular 1100 liquid chromatograph (Waldbronn, Germany) using an ACE HPLC (5 C18-HL) column (5 \(\upmu\)m packing, 200 mm \(\times\) 46 mm i.d.). Phenolic compounds were eluted at a 1 mL min\({}^{-1}\) flow rate with solvent A: formic acid/water (2:98, \(\
u/v\)); solvent B: acetonitrile/water/formic acid (80:18:2, \(\
u/v/v\)). The chromatograms were acquired at 280 nm for the identification and quantitation of flavan-3-ols and condensed tannins. The total proanthocyanidin content (PA) was calculated as the sum of extension subunits (phloroglucinol adducts) and terminal subunits (catechin, epicatechin, and epicatechin gallate). The apparent mean degree of polymerization (mDP) was calculated as the sum of all subunits divided by the sum of the terminal subunits.
## 2 Experimental Details  
### Deposition and Characterization  
The Co-cemented tungsten carbide (WC-6 wt.% Co) drills (4 mm in diameter) are used as substrates for diamond deposition. The two-step chemical pretreatments are carried out before deposition. Firstly, the WC-Co drills are dipped in the Murakami reagent (KOH + K\({}_{3}\)Fe(CN)\({}_{6}\) + H\({}_{2}\)O = 1 g:1 g:10 mL) in an ultrasonic vessel for 30 min. The second etching step is performed using acid solution (HCI + H\({}_{2}\)O\({}_{2}\) = 1 mL:4 mL) for 1 min. Subsequently, the drills are abraded using the diamond powders with the size of 5 \(\upmu\)m. The deposition of diamond coatings are completed in the hot filament CVD apparatus, in which the tantalum wires are used as hot filaments. The acetone is introduced as a carbon source using the bubbling method as descripted in [13]. During the deposition, the temperatures of hot filaments and the substrate surface are 2200 \(\pm\) 100 \({}^{\circ}\)C and 900 \(\pm\) 100 \({}^{\circ}\)C. And the negative bias voltage has been added with 40 V through the electrode poles. Three types of diamond coatings are deposited on the WC-Co drills using the hot filament CVD (HFCVD) method: (I) MCD, (II) NCD, and (III) MCD/NCD. The deposition time of MCD coatings is 6 h. And the NCD coatings are deposited with argon for the same time as that of the MCD. As for dual-layer composite diamond coatings, the MCD coatings are deposited on the drill surfaces for 4 h, then the NCD coatings are deposited in situ for 2 h by adjusting the deposition parameters. The detailed deposition parameters are all listed in Table 1. The SEM (Zeiss ULTRA55) is adopted to investigate the morphology of as-fabricated diamond films. The Raman spectroscopy (SPEX1403) equipped with a He-Ne laser (\(\lambda\) = 632.8 nm) is used to access the film quality.
## 4 Analytical Procedures Using Microextraction Techniques for the Stationary Phase
\begin{table}
\begin{tabular}{|c|c|c|c|c|} \hline
**Fibre Cover** & **Acronym** & \begin{tabular}{c} **Thickness** \\ **of the Film** \\ **(nm)** \\ \end{tabular} & \begin{tabular}{c} **Final** \\ **Determination** \\ \end{tabular} & **Application** \\ \hline \hline \multicolumn{4}{|c|}{**of Fibre with Non-polar Cover**} \\ \hline Polydimethylsiloxane & PDMS & \begin{tabular}{c} 100 \\ 30 \\ 7 \\ \end{tabular} & GC, HPLC & \begin{tabular}{c} Non-polar organic compounds (Hg\({}^{0}\), \\ MetHg, MBT, DBT, TBT, MPhT, \\ DPhT, TPhT), VOCs, PAHs, BTEX \\ \end{tabular} \\ \hline \hline \multicolumn{4}{|c|}{**Fibre with Polar Cover**} \\ \hline Polyacrylate & PA & 85 & GC, HPLC & \begin{tabular}{c} Polar organic compounds, \\ triazine, phosphor organic, pesticides \\ and phenols \\ \end{tabular} \\ \hline \multicolumn{4}{|c|}{**Fibres with Mixed-Properties Cover**} \\ \hline Polydimethylsiloxane– & PDMS-DVB & \begin{tabular}{c} 65 \\ 60 \\ GC \\ \end{tabular} & HPLC & \begin{tabular}{c} Aromatic hydrocarbons \\ aromatic amines, VOCs, TMT, \\ DMT, MMT, MBT, DBT, TBT \\ \end{tabular} \\ \hline Polydimethylsiloxane– & PDMS-CAR & 75 & GC & \begin{tabular}{c} Gaseous/volatile analytes (Hg\({}^{0}\), \\ MeHg), VOCs, hydrocarbons \\ \end{tabular} \\ \hline Carbowax– & CW-DVB & 65 & GC & \begin{tabular}{c} Polar organic compounds, alcohols, \\ ketones, nitroaromatic compounds \\ \end{tabular} \\ \hline Carbowax—resin with & CW-TPR & 50 & HPLC & \begin{tabular}{c} Anion surfactants, aromatic amines \\ \end{tabular} \\ \hline Polydimethylsiloxane– & PDMS/ & \
\begin{tabular}{c} GC \\ DVB/CAR \\ \end{tabular} & 50/30 & GC &
\begin{tabular}{c} Hg\({}^{0}\), MeHg, DBT, TBT \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 3: Characteristics of extraction fibres for commercially available SPME set [27, 29, 52, 57]. The derivatisation process is usually used at the sample collection stage while determining polar or thermally unstable compounds in the SPME technique. The transformation of these compounds into more volatile derivatives allows for easier/faster/more effective extraction and enables the reduction of the limits of detection by as much as three orders of magnitude [29, 70]. The combination of SPME and alkylborate reagents was used for the derivatisation of:  
* inorganic and organic forms of lead [30, 34, 71, 72, 73, 74, 75, 76];
* organic forms of mercury [34, 71, 74, 77];
* organic forms of tin [32, 34, 40, 71, 74, 78, 77]. In SPME, derivatisation can be conducted in three different modes:  
* simultaneously with SPME sampling;
* after the analyte is already in the fibre;
* when the analyte is desorbed to GC (in the dispenser) [29, 79].
# Estimation of Compressible Channel Impulse Response for OFDM Modulated Transmissions
## 3 DFT-Based LS Channel Estimation Methods
### Proposed Modification
Owing to the subchannel orthogonality, its value can be determined as follows:  
\[\epsilon_{E}=U\sigma_{W}^{2}, \tag{24}\]  
where \(\sigma_{W}^{2}\) is the noise power in the time domain. The approximation error defined as:  
\[\epsilon_{A}=\sum_{n\in\mathbb{U}}|\mathbf{h}_{\mathbb{C}}(n)|^{2}, \tag{25}\]  
expresses how much the actual impulse response \(\mathbf{h}_{\mathbb{C}}\) differs from its sparse representation \(\mathbf{h}_{U}\). The set \(\mathbb{U}^{\prime}\) consists of \(M-U\) indices of the CIR that are not recovered. Both components of MSE (23) are monotonic but in the opposite direction. If a longer and longer sparse approximation of \(\mathbf{h}_{\mathbb{C}}\) is selected, the approximation error decreases but the estimation one increases. The length \(U\) that balances the contribution of both errors (\(\epsilon_{E}=\epsilon_{A}\)) is the target condition adopted in the proposed modification. Unfortunately, the actual values of the \(\mathbf{h}_{\mathbb{C}}\) are unknown. Hence, the estimated energy of every coefficient in \(\hat{\mathbf{h}}_{\mathbb{C}}\) minus the noise variance is used instead. If the pilot symbols have unit amplitude, the LS estimation variance of the individual coefficient equals the noise variance [22] represented in this study by \(\sigma_{W}^{2}\) (14). Using this, the approximation error can be estimated as follows:  
\[\epsilon_{A}\approx\sum_{n\in\mathbb{U}}|\hat{\mathbf{h}}_{\mathbb{C}}(n)|^{2 }-(M-U)\sigma_{W}^{2} \tag{26}\]Consequently, the following condition is proposed to find the suitable length \(U\) of the moving average filter:  
\[U=\min_{u\in\mathcal{N}}(u\ \mid\ Mo^{2}_{W}\geq\sum_{i=1}^{M-u}|\hat{\mathbf{h}}_{C} (m(i))|^{2}\wedge m\in\mathbb{U}^{\prime}) \tag{27}\]  
where \(\mathcal{N}\) stands for the set of natural numbers and \(\mathbb{U}^{\prime}\) is the set of \((M-u)\) indices pointing to the smallest amplitude of nonzero coefficients in the vector \(\hat{\mathbf{h}}_{C}\). Expression \(m(i)\) represents the indices of \(\hat{\mathbf{h}}_{C}\), arranged in the order corresponding to the ascending amplitudes of the CIR coefficients, i.e., for \(i=1\), the estimated channel coefficient \(\hat{\mathbf{h}}_{C}(m(1))\) has the smallest amplitude, and vice versa, for \(i=M-u\), the amplitude is the largest. An explanation for the above condition is as follows: if the inequality in (27) is not satisfied, it means that the sum on its right-hand side may include some coefficients which are significant enough in the impulse response reconstruction. The proof of inequality (27) is presented in Appendix A.
## 2 RISs and Their Integration with NTNs
### Recent Advances in RIS-Integrated NTNs: Unveiling Progress
* **Physical-Layer Security and Reliability:** HAP communication often suffers from LoS blockage and vulnerability to eavesdropping. An RIS can mitigate external interference, enhancing physical-layer security and reliability. For instance, passive beamforming through an RIS directs beams away from eavesdroppers while strengthening signals for intended users. This approach improves security and reliability in HAP communication, primarily when airborne RISs (ARISs) support performance enhancement after optimizing HAP trajectory or location [13; 59; 62; 63]. * **Enhanced Data Rate and SNR:** RIS-assisted HAP communication aims to boost the data rate and SNR. Factors such as the number of RIS-reflecting elements, RIS location, and HAP-hovering position are considered. Optimization approaches are adopted to enhance the received signal power at the HAP [16; 60; 64]. * **Mitigating Interference and Spectrum Reuse:** Coexisting with TNs brings forth the challenge of co-channel interference in NTNs. An RIS emerges as a strategic tool for interference mitigation, creating nulls or constructive interference in specific directions [65; 66]. This proactive interference management minimizes disruptions, ensuring efficient spectrum reuse and safeguarding the integrity of both TNs and NTNs. * **Energy Efficiency and Resilience:** Energy efficiency takes center stage in NTNs, impacting mission duration, costs, and sustainability. Operating passively without active transmissions, RISs become a key player in enhancing energy efficiency and fostering green communications [65; 66]. Moreover, RISs contribute to NTN resilience by providing an additional layer of redundancy. RISs adeptly redirect signals in the face of obstacles, atmospheric conditions, or disruptions to terrestrial networks, ensuring continuous and reliable communications. This comprehensive examination illuminates the multifaceted contributions of RISs in shaping the future of non-terrestrial networks and their pivotal role in next-generation wireless communication systems [67; 68].
## 4 Statistical Optimization and Model Validation
### Errors and Uncertainties  
Before proceeding, it is worthwhile to define the terms error and uncertainty. An error is the difference between the true value and the predicted value, and has both a sign and a magnitude. An uncertainty is defined as an imprecision in a value [55]. In the system under consideration in this study, some model inputs, such as model parameters, boundary conditions are considered to be uncertain. According to Reference [55], these uncertain quantities may be random in nature (i.e., aleatory) or due to a lack of knowledge on the part of the analyst performing the simulation (i.e., epistemic). The uncertain model inputs are treated by propagation through the model to determine their effects on the system response quantities (SRQs) of interest.  
As per errors, we acknowledge the errors related to the measurement, numerical approximation and surrogate modeling.  
It is assumed that all these errors follow Gaussian distributions and are independent in space and time, conditioned upon the true process values. By doing so, one can pack them in the (co)variance matrix \(\Sigma\) in Equation (16).
# Modular Bus Unit Scheduling for an Autonomous Transit System under Range and Charging Constraints
## 4 Computational Results
### Algorithm Efficiency  
To test the effectiveness of the proposed algorithm, we extract four instances of progressively increasing size from the test scenario described in Section 4.1. Table 2 lists the results obtained by the commercial solver GUROBI and the CGBH algorithm on four instances. The node number is the total number of elements in the set \(N\). Whether under the label \"GUROBI\" or \"CGBH\", we report the lower bound provided by the linear relaxation problem (column LB (\(\$\))), the best-known objective value obtained for a certain time (column Obj (\(\$\))), the solution time (column Time(s)) and the percentage gap (column Gap (\(\%\))), calculated by (Obj-LB)/Obj.  
From Table 2, it is apparent that GUROBI is able to easily obtain optimal solutions that are identical to those obtained by the CGBH algorithm, but only in small-scale instances (1). However, as the size of the instances increases, GUROBI exhibits a significant discrepancy between the lower bound and best-known objective value, even consuming a longer time of more than 7200 s. In contrast, the column-generation-based heuristic algorithm consistently demonstrates favorable performance in terms of both solution time and gap, regardless of the instance size. Consequently, the algorithm proposed in this study shows remarkable efficiency and precision in solving the MB scheduling model.
## 2 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \# & **Retention Time (min)** & **Compound Name, IUPAC** & **Retention Coefficient, KI** & **Retention Coefficient** & **CAS Number** \\  & & & & **Experimental (MS Database)** & **(Kovats C7-40 Index)** & **CAS Number** \\ \hline
1 & 1.87 & acetic acid & - & 593 & 123-72-8 \\
2 & 2.45 & propanoic acid & 700 & 700 & 79-49-4 \\
3 & 2.93 & pyrimidine & 740 & 736 & 289-95-2 \\
4 & 3.10 & pyridine & 753 & 746 & 110-86-1 \\
5 & 3.29 & pentan-l-ol & 768 & 765 & 71-41-0 \\
6 & 3.36 & toluene & 774 & 769 & 108-88-3 \\
7 & 3.45 & 2-methylpropanoic acid & 781 & 775 & 79-31-2 \\
8 & 3.78 & heanal & 804 & 800 & 66-25-1 \\
9 & 4.23 & 2-methylpyrazine & 826 & 831 & 109-08-0 \\
10 & 4.41 & furan-2-carbaldehyde & 835 & 833 & 98-01-1 \\
11 & 5.01 & 1,3-xylene & 864 & 866 & 108-38-3 \\
12 & 5.06 & 2-oxopropyl acetate & 866 & 870 & 992-20-1 \\
13 & 5.18 & 1,4-xylene & 872 & 866 & 106-42-3 \\
14 & 5.35 & pentanacid & 881 & 902 & 109-52-4 \\
15 & 5.49 & unknown compound & 887 & - & \\
16 & 5.63 & heptan-2 one & 893 & 891 & 110-43-0 \\
17 & 5.68 & styrene & 896 & 893 & 100-42-5 \\
18 & 5.78 & 1,2-xylene & 900 & 887 & 95-47-6 \\
19 & 5.88 & heptan & 904 & 902 & 111-71-7 \\
20 & 6.03 & hexa-2,4-diene, (_E,E_)- & 909 & 911 & 592-46-1 \\
21 & 6.15 & 1-(furan-2-yl)ethanoneone & 914 & 912 & 1192-62-7 \\
22 & 6.24 & 2-ethylpyrazine & 917 & 921 & 13925-00-3 \\
23 & 6.34 & 2-5 dimethylpyrazine & 920 & 925 & 123-32-0 \\
24 & 6.55 & cumene & 927 & 926 & 98-82-8 \\
25 & 6.64 & 1,4-dimethylpyridine & 931 & 930 & 108-47-4 \\
26 & 6.81 & 4,6,6-trimethylbleyl(3,1.1)hept-3-ene & 936 & 937 & 80-56-8 \\
27 & 6.97 & 3-methylbutanoic acid & 942 & 947 & 503-74-2 \\
28 & 7.02 & 4-ethylpyridine & 944 & 956 & 536-75-4 \\
29 & 7.34 & _n_-propylbenzene & 955 & 953 & 103-65-1 \\
30 & 7.53 & benzaldehyde & 962 & 963 & 100-52-7 \\
31 & 7.59 & 5-methylfuran-2-carbaldehyde & 964 & 965 & 620-02-0 \\
32 & 7.77 & 1,3,5-trimethylbenzene & 970 & 972 & 108-67-8 \\
33 & 8.14 & phenol & 980 & 983 & 108-95-2 \\
34 & 8.47 & 4-methyl-1-propan-2-ylcyclohexene & 993 & 988 & 500-00-5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: VOCs emitted from torrefied carbonized refuse-derived fuel (CRDF).
# Energy-Efficient BWP Configuration for Multi-Slice Users
## 6 Performance Evaluation
It is clear that both proposed solutions achieve higher throughput values than the legacy scheme thanks to the load balancing of users between the BWP configurations and thanks to the devised cost function that aims to increase energy efficiency, which leads to an increase of the users' throughput. Figure 5 shows the users' sojourn time CDF for the centralized, distributed and legacy schemes for 20 users. The three schemes realize very close performances, which means that the proposed solutions aim to reduce the network's sojourn time to a minimum. Figure 2: User distribution among strategies with an exhaustive research algorithm for 20 users. Figure 4: Total throughput CDF for 20 users. Figure 5: Sojourn time CDF for 20 users. Figure 3: Energy-efficiency CDF for 20 users. As the number of users increases, the number of users with the MN BWP configuration is also increased to ensure a balance between both options with an inclination toward the SN BWP configuration. This can be seen in Figure 6, which represents the user distribution among strategies for 30 users. As for the energy-efficiency CDF, Figure 7 displays it for 30 users where we can see that both solutions still provide better energy efficiency than the legacy scheme. However, the performance of these solutions becomes closer to the legacy scheme performance as the number of users increases, since the network becomes saturated. For the sojourn time CDF for 30 users, the same results are observed as the scenario of 20 users. Figure 8 represents the users' total throughput CDF for the centralized, distributed and legacy schemes for 30 users. Similar to the energy-efficiency results, we have lower throughput values with the increasing number of users. Nonetheless, higher throughput values are observed w  
Figure 6: User distribution among strategies with centralized solution algorithm for 30 users. Figure 7: Energy-efficiency CDF for 30 users. Therefore, we deduce that the proposed solutions (centralized and distributed) achieve better energy efficiency, which in turn helps with improving the users' battery lifespan while ensuring the same sojourn time as the legacy scheme.
## 4 Proposed Framework  
The EdgeGuard framework in Figure 1 enables secure federated learning across distributed IoMTs by allowing the edge devices to perform local model training over the sensitive health data, and the encrypted model updates are transmitted over the blockchain layer safeguarding the data integrity and privacy. With the direction of continuous security analysis and resource management, the central server then aggregates these changes into a global model via adaptive aggregation. This very sophisticated technique enables collaborative learning with raw patient data remaining localized, therefore balancing usefulness and privacy against system efficiency in challenging medical situations. EdgeGuard will, therefore, address particular challenges when processing remote medical data and aid in providing more insight into healthcare by combining various modules without compromising individual patient privacy or system security.
## 5 Sensitivity Analysis of AV to Situations and Events for the Generation of Concrete Scenarios
### Analyze the Sensitivity to Events and Define the Sensitivity Distribution Function
#### 5.2.2 Sensitivity Distribution Function on Events  
To obtain the sensitivity distribution function, the same principles as those used in the case of logic apply. The sensitivity distribution function is obtained by normalizing the sensitivities or sensitivity gradients of events. As already noted, the considered logical situation makes it possible to identify the events that can be associated with it. Thus, for each logical situation \(Y_{j}\), there is a list \(E_{j}\) of events \(e_{ji}\). The sensitivity value of each event \(e_{ji}\) belonging to this list will be normalized to obtain a sensitivity distribution function \(q\left(e_{ji}\right)\).  
The probability distribution of \(q\left(e_{ji}\right)\) is defined as follows:  
\[\left\{\begin{array}{l}\text{q}\left(e_{ji}\right)\,\epsilon\,\left[0,1 \right]\\ \sum_{Y_{j}}q\left(e_{ji}\right)=1\end{array}\right.\]  
Figure 2: The three impact levels of an event.  
Let \(S\big{(}e_{ji}\big{)}\) be the sensitivity value of an event \(e_{ji}\) and let \(W_{E_{j}}=\sum_{e_{ji}}S\big{(}e_{ji}\big{)}\) be the normalization factor for the list \(E_{j}\) of events \(e_{ji}\). Thus, the sensitivity distribution function can be defined as follows:  
\[q\big{(}e_{ji}\big{)}=\frac{S\big{(}e_{ji}\big{)}}{W_{Ej}}\ \forall\ e_{ji}\ e\ E_{j}\]  
We can check that: \(\sum_{e_{ji}}q\ \big{(}e_{ji}\big{)}=\sum_{e_{ji}}\frac{S\big{(}e_{ji}\big{)}}{W_{Ej}}=1\).  
The proposals made in this section on the sensitivity of the AV to logical situations and events aim to provide a layer of risk analysis when declining the scenarios according to the three levels of abstraction: functional, logical and concrete. Such a consideration helps to guide the generation of scenarios by prioritizing the situations to which the AV has a high sensitivity value. In Section 6, we illustrate this principle of sensitivity analysis of the AV to logical situations and events.