esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison of the Heights of Sporadic E Layers and Vertical Ion Convergence Parameters
## 2 Materials and Methods
### Data Analysis  
#### 2.2.1 Ion Vertical Velocity  
In our analysis, we employed hourly outputs of horizontal winds in the mesosphere and lower thermosphere. To facilitate subsequent calculations and analyses, wind data were interpolated into a 1 km altitude grid. The interpolated wind data were then utilized to compute the ion vertical velocity and gradient for Fe\({}^{+}\) ions. The vertical velocity \(W_{i}\) of ion (upward is positive) can be expressed as [18]:  
\[W_{i}=\frac{VcosIsinI+\left(\frac{v_{i}}{w_{i}}\right)UcosI}{1+\left(\frac{v_{ i}}{w_{i}}\right)^{2}} \tag{1}\]  
where \(v_{i}\) is the collision frequency of ions relative to neutral species, \(w_{i}\) is the ion gyro frequency, and \(v_{i}\) and \(w_{i}\) are given by Nygren [33]. \(U\) is the zonal wind (eastward is positive), \(V\) is the meridional wind (southward is positive), and \(I\) is the magnetic dip, which is derived from the International Geomagnetic Reference Field (IGRF). Then, the vertical ion convergence can be obtained by [19]:  
\[VIC=-\frac{\partial W_{i}}{\partial z} \tag{2}\]  
where \(z\) is the altitude. Ion will be convergent while \(VIC\) is positive.
# Fabric Sol-gel Phase Sorptive Extraction Technique: A Review
## 2 Sol-gel Technology in Developing Microextraction Sorbents  
Sol-gel technology is an interesting approach for the synthesis of inorganic polymers and organic-inorganic hybrid porous products of various sizes, shapes and formats like films, fibers, particles and monoliths, by employing mild reaction conditions [22]. By carefully modifying the synthesis process, the resulted materials are thermally and chemically stable with better homogeneity and purity, tunable porosity, and selectivity. The most important advantages of sol-gel technology for sorbent micro-extraction are the strong retention of the coating onto the substrate due to chemical bonding as well as the reduction of the extraction equilibrium time and the fast mass transfer thanks to the inherent porous structure [17].  
A sol-gel process involves the catalytic hydrolysis of sol-gel precursor(s) and subsequent polycondensation of the hydrolyzed precursor(s), resulting in the transition of a liquid colloidal (solid particles with diameter of 1-100 nm) suspension known as \"sol\" into a 3D network of solid matrix \"gel\" with pores of sub-micrometer dimensions and polymeric chains whose average length is greater than a micrometer [23].  
Initially, in the sol-gel process an inorganic or organically-modified inorganic precursor, for example methyltrimethoxysilane (MTMOS) is mixed with water and solvent in the presence of acid/base/fluoride catalyst. Afterwards, the hydrolyzed sol-gel precursor is condensed leading to the formation of a growing sol-gel network, into which a sol-gel active organic polymer, like hydroxy-terminated PDMS, can become chemically integrated. The reactions of the sol-gel process are schematically represented in Figure 2. As a result, a surface-bonded sol-gel hybrid organic-inorganic polymeric network is created and a new sol-gel hybrid material is synthesized.  
A wide variety of available organically modified sol-gel precursors with different polarities can be used in the sol solution design to complement the overall polarity of the extraction sorbent [23]. The characteristics and the chemical properties of sol-gel hybrid organic-inorganic sorbents are affected by several factors, including the nature and type of precursors, the precursor to water ratio, the type of catalyst and its concentration, the pH of sol-solution, the organic solvent, the temperature and humidity  
Figure 2: Schematic representation of chemical reactions involved in the synthesis of sol–gel hybrid organic–inorganic sorbents. Reproduced from [17] with permission of Elsevier.  
during reactions, as well as the post-gelation aging conditions. The chemical structure of the produced sol-gel matrix depends on the type of the catalyst, used in the sol solution. The most commonly used precursors as well as different types of catalysts employed in sol-gel technology are given in Table 2.
# Flexible Dispatch for Integrated Power and Gas Systems Considering Power-to-Gas and Demand Response
## 4 Solution Methodology
### Coordinated Optimization of Two Systems
#### 4.3.2 Slack Variable \(s_{\mathrm{P2G},p,t}\) for P2G Facilities  
The slack variable \(s_{\mathrm{P2G},p,t}\) is to ensure that the gas produced by the P2G facility can be fully used by the gas system, so it is calculated according to the following formula:  
\[s_{\mathrm{P2G},p,t}=f_{\mathrm{P2G},p,t,\mathrm{max}}-f_{\mathrm{P2G},p,t} \tag{55}\]  
when \(s_{\mathrm{P2G},p,t}\) is greater than zero, adjust the maximum output of P2G facility according to the Equation (56), so that the two systems can achieve coordinated optimization.  
\[P_{\mathrm{P2G},p,t,\mathrm{max}}=h_{2}^{-1}\left(f_{\mathrm{P2G},p,t, \mathrm{max}}-s_{\mathrm{P2G},p,t}\right) \tag{56}\]  
where \(h_{2}^{-1}(x)\) is the inverse of Equation (46).  
Figure 1 shows the flowchart of the proposed bi-level coordinated optimal dispatch of the IPGS. The main steps are as follows:  
(1) Solve the main problem of power system unit commitment and adjustable capacity allocation.  
(2) Solve the sub-problem of the adequacy test of adjustable capacity in power system.  
(3) Check if (47) is satisfied, if not, add Benders cut and return step 1.  
(4) According to the output of the gas-fired units and P2G facilities as well as the upward and downward adjustable capacity, the respective natural gas variation range are determined by (45) and (46).  
(5) By solving the natural gas system optimal dispatch model, the output of the gas-fired units and P2G facilities as well as the upward and downward adjustable capacity are verified. If both slack variables are zero, the solution is used as an effective solution to the optimization problem of the power system. Otherwise, the maximum output of the gas-fired units and P2G facilities is updated, and the upper power system dispatch model is resolved.  
Figure 1: Flowchart of the model solution.
## 2 Materials and Methods  
### Mine Spoils  
The geological background is described in [8]. The samples analyzed in this study were taken from a lignite mining area in the Iberian Range (Teruel, Spain). Sixty bulk samples of mine spoils were mineralogically and chemically analyzed, 40 of which came from the Oliete-Estercuel (OE) area, and 20 from the Arino-Andorra (AA) mine area (Teruel, Spain). Ten representative samples from each area were selected, in order to separate them into three grain size fractions and, subsequently, carry out their mineralogical analysis. These fractions were separated from the bulk mine spoils following the standard protocols from ceramic industries. These mine spoils were classified as ball clays, and are mined today [1, 3, 4]. A representative sample of each deposit was selected for firing tests.  
The mineralogical analysis was carried out using X-ray diffraction (XRD). The three fractions analyzed were: F1 (particle size \(<\)2 \(\upmu\)m); F2 (particle size between 2 and 50 \(\upmu\)m); and F3 (particle size \(>\)50 \(\upmu\)m). In the case of F1 and F2, oriented aggregates were prepared for the characterization of phyllosilicates, treated with ethylene glycol, and calcined at 550 \({}^{\circ}\)C for 2 h. XRD mineralogical quantitative approach was carried out (DIFFRACT.EVA and EDQ software v. 6.1.2, Bruker, Mannheim, Germany). The X-ray diffraction experiments were conducted in a Siemens D5000D powder diffractometer (Berlin, Germany) with the Bragg-Bentano \(\theta\):20 configuration, using Cu K\(\alpha\) radiation (40 kV, 30 mA), aperture slit = 2 mm, divergence slit = 2 mm, detector slit = 0.2 mm, anti-scatter slit = 0.6 mm, and a graphite secondary monochromator. Samples were analyzed within the range of 3-65\({}^{\circ}\). Measurements were taken at every 0.04\({}^{\circ}\) in a time step of 3.2 s. The X-ray diffraction plots were analyzed using Siemens automatic software for peak recognition, mineral identification, and peak intensity calculations.  
The chemical analysis of the main elements was carried out by X-ray fluorescence (XRF) using conventional techniques, using a PHILIPS PW2400 X-ray Spectrometer (Mahwah, NJ, USA). Statistical correlations and cluster analysis were established, following the methodology described in [9].
# Predicting Output Power for Nearshore Wave Energy Harvesting
## 3 Methods  
This section describes the methods used to obtain the data of the buoy to reach a final characterization of the wave. A wave's energy generating potential at a specific point on the wave can be roughly measured by the height, period and direction of the wave at the point. However, for example, waves of the same height may produce different behaviors in the buoy and in turn generate different amounts of electric power. Therefore, it is necessary to monitor the movement of the buoy and to associate the movement with the electric power that is generated. Assuming we can obtain clear relationships between them, we are able to predict the power generation from the movement of the buoy.
# Asymmetric Total Synthesis of Ieodomycin B  
[PERSON]  
1 School of Pharmacy, Jiangxi Science and Technology Normal University, Fenglin Road 605, Nanchang 330013, China; [EMAIL_ADDRESS] (S.L.); [EMAIL_ADDRESS] (J.Z.); 1 [EMAIL_ADDRESS] (Z.Z.); [EMAIL_ADDRESS] (T.X.)  
[PERSON]  
1 School of Pharmacy, Jiangxi Science and Technology Normal University, Fenglin Road 605, Nanchang 330013, China; [EMAIL_ADDRESS] (X.W.); Tel.: +86-791-8380-5358 (S.H.)  
[PERSON]  
1 School of Pharmacy, Jiangxi Science and Technology Normal University, Fenglin Road 605, Nanchang 330013, China; 1 [EMAIL_ADDRESS] (S.L.); [EMAIL_ADDRESS] (J.Z.); 1 [EMAIL_ADDRESS] (Z.Z.); [EMAIL_ADDRESS] (T.X.)  
[PERSON]  
stereoselective, eight-step synthesis of ieodomycin B from commercially available 4-pentyne-1-ol [8]. This route involves a Crimmins-modified Evans aldol reaction and a nucleophilic addition of the potassium salt of monomethyl malonate, forming the \(\delta\)-hydroxyl \(\beta\)-keto ester. It then ends in a 1,3-reduction, affording the desired chiral centers. [PERSON] and coworkers achieved ieodomycin B in nine linear steps starting from the same alcohol. In their route, they used the Crimmins-modified Evans aldol reaction twice to construct the two chiral hydroxyls [9]. Recently, our group reported a short total synthesis of ieodomycin B involving seven steps [10]. In this approach, we used Ti\({}^{4+}\)-catalyzed asymmetric Mukaiyama aldol reaction and the subsequent 1,3-induced reduction to construct the chiral centers. However, its enantioselectivity is only 88% ee. This unsatisfactory result prompted us to develop an alternative route to ieodomycin B.  
In continuation of our studies on developing a new approach toward the synthesis of lactones and on obtaining the natural product at a higher optical purity, we focused on the total synthesis of ieodomycin B. Although the chemical structure of ieodomycin B appears to be simple, a novel and effective protocol to its total synthesis is important. It is well-known that chelation-controlled  
Figure 1: Chemical structures of ieodomycins A–D.  
Mukaiyama aldol addition using alkoxy groups such as \(p\)-methoxybenzyl (PMB) ether or benzyl (Bn) ether is a very important methodology for the highly diastereoselective construction of the \(syn/anti\)-diol unit (for an example, see [11]). Using this methodology, we developed in the present study a total synthesis of ieodomycin B using a Crimmins-modified Evans aldol reaction. This reaction is followed by a Mukaiyama aldol reaction induced by PMB ether to selectively construct C-5 and C-3 chiral centers.
# Hybrid Deep Learning-Based Intrusion Detection System for RPL IoT Networks
## 4 Proposed Hybrid Deep Learning-Based IDS (DL-IDS)  
The proposed IDS model applies semi-supervised and supervised deep-learning models to detect normal and abnormal (attacks) behaviors in the IoT network. The concept is to use the semi-supervised DAE to verify whether a given traffic behavior has been seen before or not by comparing its reconstructing error with known normal and attack reconstruction errors. If the reconstruction error is within predefined boundaries, it will pass it to the supervised DANN, which will classify the traffic more accurately. Otherwise, the traffic is considered a new attack type, and, therefore, a new class and reconstruction error is added to the system. The proposed model's overall architecture is presented in Figure 2, and the following subsection describes it in detail.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Dataset** & **Normal Traffic** & **Attack** & **Total** \\ \hline Normal & 122,594 & 0 & 122,594 \\ DIS & 60,422 & 7838 & 68,260 \\ Rank & 55,851 & 16,581 & 72,432 \\ Wormhole & 98,641 & 18,805 & 117,446 \\ Combined & 337,508 & 43,224 & 380,732 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Number of Samples in IoTR Subsets.  
Figure 2: Hybrid DL-IDS Architecture.
## 2 Literature Review
### Types of Review and Product Type  
Different types of reviews are known to have different effect on users depending on the type of product users are purchasing. Types of review that users seek for can be different depending on the types of products they are shopping for [8; 10; 37]. For example, users looking for search products seek for attribute-based reviews, while users looking for experience products search for experience-based reviews [38]. Additionally, negative reviews have a more significant effect for experience goods than for search goods, due to the higher accessibility of search-attributes information compared to that of experience-attributes [39].  
Many studies explain such phenomena with various matching theories. One of theories is a cognitive fit theory. Cognitive fit theory defines 'cognitive fit' as the match between problem representation and task, and that the match between the two variables can lead to efficient problem solving [40]. Several studies tested cognitive fit theory in e-commerce context. Particularly, video formatted reviews were more effective than text reviews for experience goods compared to search goods [37]. The content of the review (i.e., attribute-centric and benefit-centric) also had different effect on the users depending on their expertise-novice or expert [10].  
Another example is media richness theory, which defines media richness as a fixed property of a medium that can support the communication occurring in media [41]. When the richness of the medium is not appropriate for the complexity of the task, it is more likely that messages will be misunderstood [42]. Simple tasks, when given too much information, will very likely lead to higher complexity of decision process [43]. These findings were also found to be true in e-commerce context- complex products such as computer and automobiles showed better fit with websites that offered richer information while simple products did not show such conclusive results [41].  
Review-product congruity was proposed as well. Under review-product matching conditions consumers not only showed deeper-level comprehension, but also spent more time doing so [44]. Following prior studies, we divide reviews depending on their contents (feature and ratings) and also pose research questions on the interaction effect of review types and product types.  
**RQ 1:** Do certain matches between the review types and product types show higher purchase intention and trust?
## 3 Proposed TFR Approach
### Operations
#### 3.2.4 Repairing the Fault Recovery Mechanism Failure  
When the fault recovery is complete, the nodes in the PTP network start the standard PTP synchronization mechanism between the MC and the SCs. In the case of a fault that occurs in the SC network after the fault recovery, the SC enables the fault recovery mechanism repetitively until the failure in the network is repaired.  
The extra feature of the TFR fault recovery mechanism is that it can repair the recovery mechanism if the failure occurs during the fault recovery operation; several possible scenarios are covered in the TFR, as follows:  
* Fault occurrence after broadcasting a Link-Alive message: If a failure occurs after broadcasting a Link-Alive message, the SC starts an Announce timer. If the Announce timer times out, the slave port broadcasts the Link-Alive message once again toward the master and starts waiting for the Announce message from the MC.
* Fault occurrence after broadcasting a Link-Alive message for the second time: If a failure occurs after broadcasting a Link-Alive message for the second time, the SC starts an Announce timer. Consequently, the slave port starts waiting for the Announce message from the MC; if the SC does not receive an Announce message after this duration, it converts the slave port state to an MC state and distributes its local clock in the same network segment.
* Fault occurrence after unicasting a Sync-Request message: If a failure occurs after unicasting the Sync-Request message, the SC starts a Link-Alive timer. If the Link-Alive timer times out, the slave port starts the fault recovery mechanism once again to detect and recover the failure occurrence in the network.  
Here, two extreme detection scenarios can be considered. The first is the worst-case detection scenario for a fault recovery occurrence after unicasting the Sync-Request message. In this case, the timing cost is (6 \(\times\) Announce-Interval + 6 \(\times\) Sync-Interval); specifically, the timing costs are (3 \(\times\) Announce-Interval) for an Announce timer for the first broadcast duration timer and (3 \(\times\) Announce-Interval) for an Announce timer of the second broadcast duration timer that was noted in the first two points. Additionally, the timing cost is (3 \(\times\) Sync-Interval) for a Link-Alive timer multiplied by 2 for the main duration of detecting the failure and the detection duration after unicasting the Sync-Request message that was noted in the last point. In the second case, the best-case detection scenario is that the node detects the failure after a Link-Alive timer duration and receives the standard synchronization messages before the Announce timer times out. In this case, the timing cost is (3 \(\times\) Sync-Interval), which is only caused by a Link-Alive timer.
# Fast Stepwise Inertial Control Scheme of a DFIG for Reducing Second Frequency Drop
## 3 Frequency Support Schemes of a DFIG
### Conventional Stepwise Inertial Control Scheme  
Figure 2 illustrates the structure of the conventional stepwise inertial control scheme, which consists of two phases demonstrated by segment A-B-C-C-D: a frequency support phase (FSP, operating point A to operating point C) and rotor speed recovery phase (RSRP, operating point C to operating point D). When a frequency disturbance occurs, the DFIG rapidly increases its output power (\(P_{set}\)) to \(P_{\text{Tlim}}(\omega_{0})\) (the torque limit at \(\omega_{0}\)) to reduce the maximum frequency deviation. This process corresponds to segment A-B in Figure 2. To prevent the rotor speed from stalling, \(P_{set}\) decreases with the rotor speed, which is defined in (15).  
\[P_{set}=P_{MPPT}(\omega_{\text{min}})+\frac{P_{\text{Tlim}}(\omega_{0})-P_{MPPT }(\omega_{\text{min}})}{\omega_{0}-\omega_{\text{min}}}(\omega_{r}-\omega_{ \text{min}}) \tag{15}\]  
Based on the swing equation, the rotor speed increases since the output power of the DFIG is more than the mechanical power, and \(\omega_{r}\) converges to operating point C, which represents the intersection of (15) and the \(P_{m}\) curve. Thus,  
Figure 2: Operational features of the conventional scheme.  
inertial control scheme prevents the stalling of the wind turbine. However, during the rotor speed convergence stage, considerable energy is released into the grid.  
In the RSRP, to regain the rotor speed, \(P_{\textit{ref}}\) should decrease to lower than the mechanical input power. To this end, the conventional stepwise inertial control scheme instantly reduces the reference to \(P_{\textit{set}}(\omega_{\text{C}})-\Delta P_{r}\), which is maintained until \(P_{\textit{set}}\) meets the \(P_{MPPT}\) curve. The power reference for segment C-C'-D-A is represented in (16) and (17).  
\[P_{\textit{set}}=P_{\textit{set}}(\omega_{\text{C}})-\Delta P_{r} \tag{16}\]  
\[P_{\textit{set}}(\omega_{r})=P_{MPPT}(\omega_{r}) \tag{17}\]  
As indicated in (14), the rapid and large change in \(\Delta P_{\textit{DFIG},\textit{pu}}\) significantly affects the system frequency; this signifies that using large \(\Delta P_{r}\) produces a severe depth of the SFD with rapid rotor speed recovery and vice versa. Hence, the control strategy of the RSRP cannot counterbalance the performance of the recovered rotor speed and depth of the SFD.
## 3 Results and Discussion
### Synthesis of NODAGA-Glu-CFM Conjugate
The presence of the four-membered ring (lactam) adjacent to a six-membered ring in the synthetized conjugate resulted in the broadening of the signals, leading to a poor resolution of the multiplets. Moreover, the obtained \({}^{1}\)H NMR spectrum was complicated by overlapping signals arising from many protons and was difficult to  
Figure 5: HPLC chromatogram (at 270 nm) of the reaction mixture of Boc-Glu-CFM derivative synthesis: Boc-Glu-CFM(\(\alpha\)) (R\({}_{\rm T}\) = 18.28 min) and Boc-Glu-CFM(\(\gamma\)) (R\({}_{\rm T}\) = 18.79 min) (gradient 2). analyze. For this reason, the NMR spectrum was not essential to confirm that the conjugate was successfully obtained, and the NMR data are not shown. IR (KBr) \(\gamma_{\rm max}/\)cm\({}^{-1}\): 3435 s, br (OH, NH), 2942 w, br (CH) 2853 sh (CH), 1777 w (C=O), 1677 vs. (C=O), 1629 sh (C=O), 1562 sh (C=O), 1462 w, 1393 w, br, 1352 w, sh, 1286 w, 1204 m, 1183 sh, 1135 w, 1072 ww sh, 1043 w, 923 vvw, 836 vw, 801 vw, 722 vw, 594 vw, 518 vw. MS for C\({}_{39}\)H\({}_{55}\)N\({}_{10}\)O\({}_{15}\)S\({}_{2}\)\({}^{+}\), TOF MS ES\({}^{+}\)\(m/z\): (calculated: 967.33); found: 967.36. Synthesis of [\({}^{68}\)Ga]Ga-NODAGA-Glu-CFM Radioconjugate and Ga-NODAGA-Glu-CFM 'Cold' Reference Compound  
For the radiolabeling reaction, NODAGA-Glu-CFM conjugate was selected. The conditions have been described previously in Section 2.1.2. Under these conditions, [\({}^{68}\)Ga]Ga-NODAGA-Glu-CFM was synthesized with a radiochemical yield (RCY) higher than 90%, as  
Figure 6: HPLC chromatogram recorded at 270 nm of the deprotection reaction of Boc-Glu-CFM in 2.5% TFA solution (gradient 2). Figure 7: HPLC chromatogram at 270 nm of the reaction mixture of NODAGA-Glu-CFM conjugate (R\({}_{\rm t}\) = 14.77 min) synthesis (gradient 2). is shown in Figure 8. The radiochemical purity (RPC) of the [\({}^{68}\)Ga]Ga-NODAGA-Glu-CFM radioconjugate was checked using the ITLC method, where free \({}^{68}\)Ga\({}^{3+}\) cations moved with the solvent front (R\({}_{\rm f}\approx\) 1), while the [\({}^{68}\)Ga]Ga-NODAGA-Glu-CFM radioconjugate remained at the origin (R\({}_{\rm f}\approx\) 0.0-0.1) (Figure 9). The HPLC chromatogram of the 'cold' reference compound Ga-NODAGA-Glu-CFM (Figure 8), obtained with a high yield (95%) and final purity over 98%, showed a peak with R\({}_{\rm T}\) = 13.76. The slight shift of the peak position compared to the R\({}_{\rm T}\) of the [\({}^{68}\)Ga]Ga-NODAGA-Glu-CFM radioconjugate (R\({}_{\rm T}\) = 13.94 min) results from the arrangement in the series of the gamma detector after the DAD detector. The peak position of the 'cold' reference compound (characterized by MS) confirmed that the corresponding radioconjugate was obtained in the labeling reactions. MS for C\({}_{39}\)H\({}_{52}\)GaN\({}_{10}\)O\({}_{15}\)S\({}_{2}\)\({}^{+}\), TOF MS ES\({}^{+}\)\(m/z\): (calculated: 1033.23); found: 1033.22. The \({}^{1}\)H NMR data for the reference compound Ga-NODAGA-Glu-CFM are not shown for the same reason as the NODAGA-Glu-CFM conjugate.