esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Simulated Results  
This section focuses on demonstrating by the use of simulations the virtual resistance contribution to a fast and seamless transference between operation modes, the power over-load supervisor operation, and the fault current limitation strategy for an ideal (<1 m\(\Omega\) impedance) short-circuit occurrence and clearance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Parameter** & **Value** & **Units** \\ \hline \multirow{3}{*}{Adaptive DC-link PI controller} & \(k_{p}\) & 0.043 & \\  & \(k_{i}\) & 1.43 & \\ \hline \multirow{3}{*}{Adaptive 100 Hz filter [50]} & Adaptive coefficient \(\mu\) & 0.05 & \\  & Attenuate \(B\) coefficient of cut-off frequency & 4 & \\ \hline \multirow{3}{*}{PI Voltage controller} & \(k_{p}\) & 3.5 & \\  & \(k_{i}\) & 70 & \\ \hline \multirow{3}{*}{PI Current controller} & \(k_{p}\) & 0.16 & \\  & \(k_{i}\) & 33.75 & \\ \hline \multirow{3}{*}{DC–DC converter} & Switching \& control frequency & 8 & kHz \\  & \(L_{bat}\) (each interleaved inductance) & 400 & \(\mu\)H \\  & \(C_{bat}\) & 420 & \(\mu\)F \\  & \(C_{\text{DC}-\text{link}}\) & 7.2 & mF \\ \hline \hline \end{tabular}
\end{table}
Table 1: Interleaved DC–DC (direct current–direct current) converter parameters. PI (Proportional Integral).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameter** & **Value** & **Units** \\ \hline \multirow{3}{*}{Droop controller} & \(m\) for active power loop & 0.00003 & \\  & \(n\) for reactive power loop & 0.00004 & s \\  & \(k_{i}\) for reactive power loop & 0.0009 & \\  & \(t_{f}\) Low-pass filter (LPF) constant & 0.1 & \\ \hline \multirow{3}{*}{PRHC Voltage controller} & \(k_{p}\) & 0.27 & \\  & \(k_{i_{0}}\) & 0.26 & \\  & \(k_{i_{3}}\) & 0.001 & \\  & \(k_{i_{5}}\) & 0.001 & \\ \hline \multirow{3}{*}{PRHC Current controller} & \(k_{p}\) & 0.7468 & \\  & \(k_{i_{0}}\) & 3.93 & \\  & \(k_{i_{5}}\) & 0.1 & \\  & \(k_{i_{6}}\) & 0.04 & \\ \hline \multirow{3}{*}{Fault current limiters} & \(K\) & 0.9 & \\  & \(I\) & 130 & A \\  & \(\alpha\) (over-load filter parameter) & 0.01 & \\ \hline \multirow{3}{*}{Virtual impedances} & \(R_{v}\) (initial-state grid-connected) & 1.0 & \(\Omega\) \\  & \(R_{v}\) (steady-state grid-connected) & 0.2 & \(\Omega\) \\  & \(R_{v}\) (steady-state grid-disconnected) & 0 & \(\Omega\) \\  & \(R_{v}\) change ratio & \(-0.16\) & \(\Omega/s\) \\ \hline \multirow{3}{*}{DC–AC converter} & Switching \& control frequency & 8 & kHz \\  & \(L_{1}\) (active phases \& neutral wire) & 250 & \(\mu\)H \\ \cline{1-1}  & \(C\) (star connected) & 350 & \(\mu\)F \\ \cline{1-1}  & \(L_{2}\) (leakage transformer inductance) & 70 & \(\mu\)H \\ \hline \hline \end{tabular}
\end{table}
Table 2: Four-wire DC–AC (direct current–alternating current) converter parameters. (Proportional Resonant with Harmonic Compensator).
# Adversarial Sample Generation Method Based on Frequency Domain Transformation and Channel Awareness
## 7 Simulation Result
### Adversarial Sample Generation Based on Frequency Conversion and Channel Awareness
It can be seen from the results of Tables 6 and 7 that the accuracy of channel estimation has a decisive influence on the performance of the FTHA method, which directly reflects the key role of precise channel information in adversarial attacks. In CNN1D and CNN2D models, the performance of FTHA (SDRNet) has always been superior to other methods, especially in terms of the misclassification rate and the average confidence of misclass prediction. This is mainly because SDRNet can estimate the channel state more accurately, thereby generating more targeted adversarial samples. In contrast, FTHA (MMSE) has a significantly weaker attack effect than other methods due to its lower channel estimation accuracy. Especially in the CNN2D model, its MR Is only 77.50%, which is significantly lower than 80.50% of FTHA (SDRNet). This gap indicates that the error of channel estimation will directly affect the generation quality of adversarial samples, resulting in a decline in the attack effect. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Attack Method** & **MR (\%)** & **ACAC** & **ACTC** & \(\mathbf{L_{2}}\) \\ \hline FTA (No Channel) & 68.70 & 0.900 & 0.025 & 1.250 \\ FTHA (MMSE) & 76.70 & 0.858 & 0.014 & 1.200 \\ FTHA (OMP) & 78.80 & 0.935 & 0.008 & 1.250 \\ FTHA (LS) & 78.50 & 0.868 & 0.005 & 1.300 \\ FTHA (SDRNet) & 79.90 & 0.935 & 0.005 & 1.350 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance of FTHA of different channel estimation methods on CNN1D. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Attack Method** & **MR (\%)** & **ACAC** & **ACTC** & \(\mathbf{L_{2}}\) \\ \hline PGD & 65.00 & 0.820 & 0.090 & 1.412 \\ FGSM & 28.00 & 0.850 & 0.060 & 1.734 \\ BIM & 64.00 & 0.820 & 0.090 & 1.425 \\ AA & 75.00 & 0.850 & 0.070 & 1.511 \\ MIFGSM & 65.50 & 0.810 & 0.085 & 1.669 \\ FTA & 67.58 & 0.830 & 0.075 & 1.458 \\ FTHA & 80.50 & 0.912 & 0.008 & 1.318 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance of different methods on CNN2D under gaussian channel conditions.
## 3 Experimental Results
### Evaluation of Vehicle Detection Accuracy According to Hyperparameters and Training Options
To address this issue, the proposed method focuses on detecting the rear area of the moving vehicle instead of the front area, which improves the accuracy of vehicle detection. Figure 10b shows that the vehicle was incorrectly detected because the front area of the vehicle was not detected or overlapped, resulting in a relatively low vehicle detection accuracy. Figure 10a shows the real-time detection of the rear areas of a moving vehicle from various road images. This information can be used as a basis for optimal lane change in autonomous vehicles. The accuracy of vehicle detection in driving images from different roads was compared, and it was found that applying the pre-processing step before vehicle detection improved accuracy by at least 0.7%. Applying the pre-processing step increased the additional processing time increased by an average of 0.3 s. However, this did not affect real-time processing. The pre-processing step improved vehicle detection accuracy when images were acquired on roads with limited forward visibility due to environmental factors such as fog or cloudy weather. Therefore, the time-consuming processing can be offset by the increase in hardware processing speed, improving the safety of autonomous vehicles.
## 3 Analysis of Control Strategies
### NPC Three-Level Inverter Output Stage Control Strategy
#### 3.2.2 Dual Closed-Loop Control  
The mathematical model of the three-level NPC inverter in the \(dq\) rotating reference frame is represented by the following equations:  
\[\left\{\begin{array}{l}C\frac{du_{d}}{dt}=i_{Ld}-i_{Old}+\omega Cu_{q}\\ C\frac{du_{q}}{dt}=i_{Lq}-i_{Old}-\omega Cu_{d}\end{array}\right. \tag{8}\]  
\[\left\{\begin{array}{l}L\frac{d_{l1}}{dt}+ri_{Ld}=u_{Ld}-u_{Old}+\omega Li_{Lq }\\ L\frac{d_{l1}}{dt}+ri_{Lq}=u_{Lq}-u_{Old}-\omega Li_{Ld}\end{array}\right. \tag{9}\]  
In the formula, \(u_{d}\) and \(u_{q}\), \(i_{Ld}\) and \(i_{Lq}\), \(i_{Old}\) and \(i_{Old}\), \(u_{Ld}\) and \(u_{Lq}\), and \(u_{Old}\) and \(u_{Old}\) and \(u_{Old}\) respectively denote the voltage across filtering capacitors, inductance phase currents, load phase currents, output phase voltage, and the components of load phase voltage on the \(dq\)-axis.  
To obtain a high-quality output voltage waveform and minimize harmonic distortion, a dual closed-loop control scheme with a voltage outer loop and current inner loop was applied prior to SVPWM modulation. The control system is depicted in Figure 8.  
Figure 8: Dual closed-loop control system diagram.  
Figure 7: Schematic diagram of seven-level SVPWM modulation.  
The three-phase output side currents and voltages were sampled and transformed into _dq_ quantities to obtain \(u_{dq}\) and \(i_{dq}\) in the _dq_ coordinate system. \(u_{dq}\) was compared with the given value \(u_{dq}^{*}\) and the current reference value \(i_{dq}^{*}\) was obtained by voltage PI regulation. After comparing \(i_{dq}^{*}\) and \(i_{dq}\), the disturbance component \(i_{\Box q}\) was compensated, and \(u_{dq}\) was added after adjustment by the current PI. Through the over-modulation judgment and processing procedure, the DC component was transformed into an _abc_ coordinate, and the SVPWM algorithm generated trigger pulses to control the main switches of the inverter circuit.
## 3 Materials and Production
### Industrial Products and By-Products Suitable as Bed Material
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Material** & **Component** & **References** \\ \hline Iron ore sinter fines (MAF) & Fe(71) SiO2(0.9) MgO(0.5) TiO2(0.5) CaO(0.2) & LKAB \\ \hline \multirow{2}{*}{Iron ore concentrator tailings} & SiO2(44) CaO(10) Al2O3(9.9) Fe(9.1) MgO(8.3) & \multirow{2}{*}{LKAB} \\  & K2O(2.3) P(1.6) TiO2(1.6) & \\ \hline \multirow{2}{*}{LD-slag} & CaO(43) FeO(22) SiO2(10) MgO(9) MnO(3) V(1.5) & \\  & TiO2(1.3) Al2O3(1.1) & \\ \hline Foundry slag & Mn(18) Mg(14) Si(9.0) Ca(8.9) Fe(5.7) Al(2.7) Ti(1) & Elemental analysis Sandvik SRP [53] \\ \hline \multirow{2}{*}{Iron mill scales} & Fe(102) Mn(0.45) Si(0.31) Al(0.16) & \multirow{2}{*}{\
\begin{tabular}{c} Elemental oxygen free analysis \\ Glodskal B [69] \\ \end{tabular} } \\ \hline \multirow{2}{*}{Copper smelter slag} & FeO(43) SiO2(36) Al2O3(2–5) CaO(2–5) MgO(1) & \multirow{2}{*}{
\begin{tabular}{c} Boliden, product specification \\ sheet Järnsand \\ \end{tabular} } \\  & Zn(1) S(0.6) & \\ \hline Rock ilmenite & Fe(34) Ti(28) Mn(0.5) Mg(0.4) Al(0.2) Si(0.2) & Norwegian (Titania A/S) [40] \\ \hline Sand ilmenite & Fe(33) Ti(24) Mg(1.8) Si(0.9) Al(0.3) Ca(0.3) Mn(0.1) & Australian (Sibelco) [40] \\ \hline \multirow{2}{*}{Olivine} & MgO(50) SiO2(42) Fe2O3(7.4) Al2O3(0.46) & \multirow{2}{*}{[66]} \\  & NiO(0.32) Cr2O3(0.31) & \\ \hline \multirow{2}{*}{ Blast furnace slag} & SiO2(34) CaO(31) MgO(17) Al2O3(13) TiO2(2.3) S(1.3) & \\  & & sheet Hyttsand \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Composition of representative samples of the materials. Note that some entries present composition as oxides, some as elements. Balance is mainly oxygen. Figure 4: Estimated amounts of sand consumed annually as bed material in BFB and CFB boilers in Sweden for three different assumptions about biomass combustion. information about production and bed material properties of ilmenite can be found in provided references. The focus in this study is on the larger industries, where the production is distributed in few locations and is often handled by one company. Less focus will be on smaller, decentralized, producers of potential oxygen carriers such as foundries, rolling mills, and other downstream processes.
# Optimization of Truck-Drone Parcel Delivery Using Metaheuristics
## 1 Introduction  
Over the last few years, drones have been adopted in many sectors [1, 2], especially the commercial sector [3, 4]. Drones are now deployed to support parcel delivery, an area that has traditionally been handled by trucks. Moreover, the COVID-19 pandemic has taken a significant toll on people all over the world [5]. Drones have helped people to comply with social distancing rules, as they provide contact-free delivery services. In addition, drones help with transportation logistics in many ways, such as avoiding traffic congestion, allowing for faster deliveries, and providing lower transportation costs [6]. However, there is an upper limit for the parcel weight, as drones cannot carry heavy parcels [7]. Additionally, drones are battery-powered, thereby limiting their delivery ranges. In contrast, truck delivery can work over a longer range, as it is fuel-based, and trucks can carry heavy and large parcels. Nonetheless, traditional truck delivery is slow and has high transportation costs [6]. Combining both truck and drone delivery could minimize the total operational costs and time, thereby enhancing the quality of service. This variant of the traveling salesman problem (TSP) [8, 9] is known as the traveling salesman problem with drone (TSP-D) [6, 10].
## 3 Results and Discussion
### Chemical Composition
#### 3.2.1 Functional Groups Analysis  
FTIR analyses were performed on all samples calcined at 950 \({}^{\circ}\)C to identify the functional groups. Figure 5 shows the FTIR spectra from all the samples analysed, exhibiting narrow bands that suggest a high crystallinity degree. The presence of phosphate groups (PO\({}_{4}\))\({}^{-3}\) can be identified for the three species. These groups show bands at -960 cm\({}^{-1}\) and between 1000 and 1200 cm\({}^{-1}\) due to the stretching modes of P-O bonds, as well as bands between 540 and 600 cm\({}^{-1}\) corresponding to the bending mode of the phosphate groups. Hydroxyl groups (OH)\({}^{-}\) can be observed in two different regions, with low intensity bands between 3500 and 3600 cm\({}^{-1}\), corresponding to its stretching mode, and at -630 cm\({}^{-1}\), corresponding to its bending mode. Carbonate groups (CO\({}_{3}\))\({}^{-2}\) are also present with bands around 714 cm\({}^{-1}\), 875 cm\({}^{-1}\), and between 1400 and 1460 cm\({}^{-1}\)[33]. In particular, peaks between 1400 and 1460 cm\({}^{-1}\) are associated with the bending mode in B-type hydroxyapatite, due to the substitution of (PO\({}_{4}\))\({}^{-3}\) by the (CO\({}_{3}\))\({}^{-2}\) group [15; 34; 35], in accordance to the high Ca/P ratio obtained from ICP-OES analysis. Peaks located in these bands, associated with the carbonate groups, present a very low intensity, especially for the Salmo sample, which can probably be due to the carbonate decomposition into carbon dioxide that takes place at temperatures between 750 and 1100 \({}^{\circ}\)C [36].  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Element**} & **Maximum Concentration in** & **Maximum Legal** \\  & **Samples (ppm)** & **Concentration (ppm)** \\ \hline Cd & 0.68 & 5 \\ \hline Pb & 1.6 & 30 \\ \hline Hg & 0.04 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Concentration of heavy metals in samples.
Our recent studies showed that HSP47 is present on the platelet surface and is involved in the recognition and response to collagen [16]. It is plausible that aberrant expression of HSP47 may contribute to the onset of various cardiovascular diseases. As HSP47 is the only collagen-specific molecular chaperone, targeting its binding interactions with collagen could be an effective therapeutic approach in managing these HSP47-overexpression-related diseases. Several types of small molecules have been developed as HSP47 inhibitors [17; 18; 19; 20]. Recently, one of these compounds (COM IV) was reported to effectively prevent collagen synthesis, cell viability, and migration of lung fibroblasts [21]. There have been extensive biochemical and structural studies on the binding interactions between HSP47 and collagen using short synthetic collagen-mimetic peptides [7; 22; 23]. These showed that HSP47 binds to triple-helical peptides containing a key Arg residue at the Y position of collagenous repeats (Gly-Xaa-Yaa). These results were confirmed by X-ray crystal structures of collagen model peptides (designated CMPs) complexed with canine HSP47 [24]; however, the exact binding sites of HSP47 on native collagens are not fully defined. For example, there are more than 40 arginine residues at similar Y positions in collagen II or III, and it is unclear if HSP47 could bind them all. To clarify this, here we have mapped the HSP47 binding sites using collagen toolkits and identified HSP47 binding sites on collagens II and III. This provides novel information on the binding mode of HSP47 on collagen and sheds light on the development of novel HSP47 inhibitors.
## 2 Modeling Approach
### Continuity and Momentum Equations
#### Lift Force  
A lift force is experienced by the particle when the liquid is flowing at a shear rate or rotates at a rotational rate [29]. The lift force is the primary cause of the void fraction'suneven radial distribution in the dispersed phase, which is necessary to take into account when simulating the effect. The general expression is written as [30]:  
\[\overrightarrow{F_{lift}}=-C_{L}\alpha_{G}\rho_{L}\left(\overrightarrow{\text{u }_{\text{G}}}-\overrightarrow{\text{u}_{\text{L}}}\right)\times\left(\
abla \times\overrightarrow{\text{u}_{\text{L}}}\right) \tag{11}\]  
where \(C_{L}\) is the lift force coefficient. [PERSON] et al. [31] carried out the experiment in an air-water system and found a correlation between the coefficient of lift and a modified Eo number when the change in bubble shape was taken into account. The coefficient of the lift force is defined as:  
\[C_{L} = \left\{\begin{array}{ll}\min[\ 0.288\text{tanh}(0.121\ Re_{b}),\ f( Eo^{\prime})]&Eo^{\prime}\leq 4\\ f(Eo^{\prime})&4<Eo^{\prime}\leq 10\\ -0.27&10<Eo^{\prime}\end{array}\right. \tag{12}\]  
where \(Re_{b}\) is defined as \(\rho_{L}d_{b}\bigg{|}\overrightarrow{u_{\text{G}}}-\overrightarrow{u_{L}} \bigg{|}/\mu_{L}\).  
\[f\big{(}Eo^{\prime}\big{)}=0.00105 Eo^{\prime 3}-0.0159 Eo^{\prime 2}-0.0204 Eo^{ \prime}+0.474 \tag{13}\]  
\[Eo^{\prime}=\frac{g(\rho_{L}-\rho_{G}){d_{h}}^{2}}{\sigma} \tag{14}\]  
where \(Eo^{\prime}\) is the modified \(Eo\) number, \(\sigma\) is the coefficient of surface tension, and \(d_{h}\) is the maximum horizontal dimension of the bubble, which can be derived using following equation [32]:  
\[d_{h}=d_{b}\Big{(}1+0.163\ Eo^{0.757}\Big{)}^{1/3} \tag{15}\]  
where \(Eo\) is the Eotvos number. The magnitude of the lift coefficient is proportional to \(Eo\). When the \(Eo\) is small, the lift coefficient is greater than zero, and the bubbles retain their spherical form and migrate to the pipe wall. At high \(Eo\), on the other hand, the lift coefficient is smaller than zero, causing the bubbles to deform and flow toward the pipe's center.
# Person Re-Identification Based on Attention Mechanism and Context Information Fusion
## 2 Methods
### Loss Function  
#### 2.3.1 Margin Sample Mining Loss  
In deep metric learning, the triple loss function is usually used. The triple loss makes the distance of the same type of pedestrians smaller than that of different types to achieve the purpose of clustering the same type of pedestrian images in the feature space. But, the triple loss only measures the relative distance, because the triple loss may not provide a global optimal constraint, so the distance between classes may be smaller than that within the same class. Therefore, the model in this paper adopts the Margin Sample Mining Loss function proposed in [19]. The margin sample mining loss not only considers the absolute distance between positive and negative sample pairs, but also introduces the idea of difficult sample mining. In training, the most dissimilar positive sample pair and the most similar negative sample pair in the whole batch are selected.  
\[L_{eml}=(\underset{A,A^{\prime}}{\text{max}}(||f_{A}-f_{A^{\prime}}||_{2})- \underset{C,B}{\text{min}}(||f_{C}-f_{B}||_{2})+\alpha)_{+}, \tag{5}\]  
In Formula (5), \(\left(x\right)_{+}=\max(x,0)\). \(||f_{A}-f_{A^{\prime}}||_{2}\) represents the Euclidean distance between \(A\) and \(A^{\prime}\), and \(\alpha\) is a value of the margin used to distinguish positive samples from negative ones. \(A\) and \(A^{\prime}\) are the most dissimilar positive sample pairs in each batch of training, and \(C\) and \(B\) are the most similar negative sample pairs. If \(A\) and \(C\) are pedestrians in the same category, it considers the relative distance between the positive and negative samples. If \(A\) and \(C\) are not the same category, it considers the absolute distance between positive and negative samples. For the detailed derivation of this formula, please refer to [19].  
Figure 5: Context Information Fusion Module, where D represents dilation ratio and BN represents batch normalization. The input feature map is operated by using the convolution whose kernel size is 3 \(\times\) 3 and the dilation ratios are 1, 2, 3, and 4, respectively.
# Low Cost Circularly Polarized Antenna for IoT Space Applications
## 1 Introduction
In the next sections, by using Ansoft HFSS EM solver, a 3-element structure is designed and a new compact feeding circuit is proposed. The structure also includes a large ground plane over the entire bottom of the substrate to ease the integration of electronic components and batteries. In terms of the frequency band, 868 MHz is targeted with an 8 MHz bandwidth. Considering a Low-Earth-Orbit satellite constellation, a wide angle radiation pattern is desired to enable communication with the satellite even at a low-elevation angle.
# The Application of Wood Species in Enology: Chemical Wood Composition and Effect on Wine Quality
## 2 Chemical Composition of Wood Species
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Toasting Level**} \\ \hline
**Volatile Compounds** & **Without** **Toasting** & **Light Toasting** & **Medium** & **Strong** & **Oak Species** & **References** \\ \hline  & 1.6–2.5 & nd & 10.5–23.4 & 22.0–33.6 & _Q. pyrenaa_ & \\  & 6.8–7.5 & nd & 24.1–34.5 & 7.5–8.8 & _Q. alba_ & [11]\({}^{(1)}\) \\  & 2.0–3.4 & nd & 48.5–60.0 & 3.0–6.3 & _Q. petraea_ & \\ \cline{2-7} Vanillin & nd & 27.4 & 120.0 & 244.0 & _Q. alba_ & [16]\({}^{(2)}\) \\  & nd & 120.0 & 172.0 & 262.0 & _Q. petraea_ & [16]\({}^{(2)}\) \\ \cline{2-7}  & nd & nd & 14.59 & nd & _Q. alba_ & \\  & n.d. & 12.86 & 16.77 & 24.76 & _Q. petraea_ & [90]\({}^{(3)}\) \\ \hline  & 14.9–16.5 & nd & 82.0–88.4 & 69.0–88.8 & _Q. pyrenaaa_ & \\  & 16.3–20.2 & nd & 24.1–34.5 & 20.5–31.5 & _Q. alba_ & [11]\({}^{(1)}\) \\  & 12.5–14.8 & nd & 48.5–60.0 & 85.0–118.0 & _Q. petraea_ & \\ \cline{2-7} Springaldehyde & nd & 57.4 & 343.0 & 768.0 & _Q. alba_ & [16]\({}^{(2)}\) \\  & nd & 196.0 & 443.0 & 721.0 & _Q. petraea_ & [16]\({}^{(2)}\) \\ \cline{2-7}  & nd & nd & 40.0 & nd & _Q. alba_ & \\  & 50.5 & 72.2 & 63.4 & 75.2 & _Q. petraea_ & [90]\({}^{(3)}\) \\ \hline  & 3.9–4.5 & nd & 2176–2670 & 1635–2155 & _Q. pyrenaa_ & \\  & 1.2–1.8 & nd & 357.5–960.0 & 353.5–787.5 & _Q. alba_ & [11]\({}^{(1)}\) \\  & 3.4–7.0 & nd & 723.0–772.5 & 118.0–613.0 & _Q. petraea_ & \\ \cline{2-7} Furfural & nd & 41.0 & 681.0 & 61.0 & _Q. alba_ & \\  & nd & 78.0 & 357.0 & 170.0 & _Q. petraea_ & [16]\({}^{(2)}\) \\ \cline{2-7}  & nd & nd & 2.61 & nd & _Q. alba_ & \\  & 7.1 & 14.3 & 4.1 & 28.6 & _Q. petraea_ & [90]\({}^{(3)}\) \\ \hline  & 0.0–1.3 & nd & 3344–5078 & 2306–2976 & _Q. pyrenaa_ & \\  & 0.4–0.7 & nd & 1678–3221 & 781.9–922.3 & _Q. alba_ & [11]\({}^{(1)}\) \\  & 0.3–0.5 & nd & 1203–1722 & 654.2–980.6 & _Q. petraea_ & \\ \cline{2-7}  & nd & 14.6 & 74.5 & 30.2 & _Q. alba_ & \\  & nd & 37.2 & 58.3 & 44.2 & _Q. petraea_ & [16]\({}^{(2)}\) \\ \cline{2-7}  & nd & nd & 10.7 & nd & _Q. alba_ & \\  & 5.73 & 7.9 & 6.13 & 12.66 & _Q. petraea_ & [90]\({}^{(3)}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Influence of toasting level on volatile composition of different oak wood species.