esa-sceva/satcom-chunk-collection huggingface dataset:
# State-of-the-Art DC-DC Converters for Satellite Applications: A Comprehensive Review
## 3 Review of DC-DC Converters for Satellites
### Isolated Topologies
#### 3.2.5 Resonant Converters  
Resonant converters, like isolated LLC and LCC resonant DC-DC converters, as well as series resonant converters that use resonant networks to achieve zero-voltage or zero-current switching, have been proposed for satellite applications [96; 98]. In terms of efficiency, the resonant LLC converter is superior, and the LCC converter is superior in the input or output voltage range. In [99], a two-stage factorized power architecture was proposed for power distribution in satellites, in which the second stage adopts an interleaved LLC resonant converter. The converter has high efficiency and power density over a wide voltage range. Recently, a SiC-based T-type three-level LLC resonant converter was proposed in [100] and experimentally demonstrated for a 500 W, 48 V/11 A satellite application. A novel modulation scheme was used to achieve a wide output voltage range and high efficiency in the converter. Although resonant converters are highly efficient and have reduced mass, they require complex control circuitry [96]. Figure 14b shows the circuit of a basic resonant LLC converter.
# Enhancing Automatic Modulation Recognition for IoT Applications Using Transformers
## 4 Experimental Results and Discussion
### Ablation Study
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Methods** & **Tokenization** & **F1 Score** & **Number of Parameters** \\ \hline \multirow{4}{*}{TransDirect} & 8 samples & 53.15 & 17.2 K \\  & 16 samples & **56.29** & 44.1 K \\  & 32 samples & 56.20 & 128 K \\  & 64 samples & 52.24 & 420 K \\ \hline \multirow{4}{*}{TransDirect-Overlapping} & 8 samples & 53.98 & 17.2 K \\  & 16 samples & 59.43 & 44.1 K \\  & 32 samples & **60.08** & 128 K \\  & 64 samples & 57.67 & 420 K \\ \hline \multirow{4}{*}{TransIQ} & 8 samples with 8 output channels (head = 2, layer = 4) & 63.69 & 128 K \\  & 8 samples with 16 output channels (head = 2, layer = 4) & 63.25 & 420 K \\  & 16 samples with 8 output channels (head = 2, layer = 4) & 63.72 & 420 K \\  & 32 samples with 8 output channels (head = 2, layer = 4) & 62.68 & 1.5 M \\  & 8 samples with 8 output channels (head = 4, layer = 8) & **65.80** & 229 K \\  & 8 samples with 8 output channels (head = 2, layer = 6) & 65.39 & 179 K \\ \hline \multirow{4}{*}{TransIQ-Complex} & 8 samples with 8 output channels (head = 2, layer = 4) & 61.19 & 420 K \\  & 16 samples with 8 output channels (head = 2, layer = 4) & **62.79** & 1.5 M \\  & 32 samples with 8 output channels (head = 2, layer = 4) & 59.33 & 5.6 M \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A comparative analysis of different methods of tokenization in terms of F1 score and number of parameters on CSPB.ML.2018+ dataset. The best result for each tokenization strategy is in bold. minimal increase in accuracy, a 0.83% improvement, is observed when comparing TransDirect to TransDirect-Overlapping for a token size of eight, but there is a more significant improvement, nearly 10%, from TransDirect-Overlapping to the TransIQ model. For the token size of 16, both the TransDirect and TransDirect-Overlapping architectures have the same number of parameters of 44.1 K, but TransDirect-Overlapping achieves an approximately 3% higher accuracy due to its overlapping tokenization technique. Furthermore, adopting the TransIQ model with the same token size, and with a convolutional layer with eight output channels, increases the parameter count to 420 K. This change results in a 4% accuracy improvement over TransDirect-Overlapping and an impressive 7% improvement compared to TransDirect with the same token size. Conversely, the TransIQ-Complex architecture, despite increasing its parameters to 1.5 M with the token size of 16, experiences a roughly 1% drop in accuracy compared to the TransIQ with the same token size. This indicates that, while token size plays a crucial role in achieving higher accuracy, simply increasing model complexity does not ensure improved performance. Identifying the optimal balance requires thorough experimentation, as evidenced by the data presented in Table 2.
## 2 Theory and Methods
### MOSkin Characterisation
**Bottom**: Imaging of the scintillator to align targets with the centre of the PEER beam. The central axis of the scintillation spot is marked and locked in thesoftware, then the targets are moved into the centre of the field and irradiated. The scale markers on the scintillator are in millimetres. The value of \(\Delta\)V for each MOS_kin_ irradiation was measured and compared to the dose recorded on the EBT-XD film. The average central-axis-dose within a circular area of 0.91 mm\({}^{2}\) (chosen due to allowable pixel selection determined by the scanning resolution) was extracted from the film. The sensitive volume of the MOS_kin_ is represented by a ring with a diameter of 250 \(\upmu\)m and width of 20 \(\upmu\)m, and will experience a slightly higher point dose if positioned with perfect accuracy, centrally within the beam. However, attempting to extract information from a small region of the film, corresponding to the MOS_kin_ sensitive volume may lead to biased data, as this scale is in the order of single pixels when film is scanned at typical clinical settings of 150 DPI. Instead, the peak dose can be calculated using a fitted Gaussian, if required. The MOS_kin_ results were plotted against the total absorbed dose measured with the EBT-XD and the sensitivity within the irradiation field at PEER determined as the slope of a linear fit. Due to the inherent difficulties performing dosimetry of a novel field, the same batch of MOS_kin_s were calibrated within a clinical 20 MeV electron field at St George Hospital, Sydney, Australia using a Varian Truebeam. This allowed the assessment of the validity of the determined sensitivity in the 100 MeV field at PEER. If there were drastic, unexpected changes relative to clinical energies, it could suggest an error in the measurements at PEER. Three MOSkins were mounted at a depth of 2 cm in Solid Water RMI457 (Gammex-RMI, Middleton, WI, USA) and irradiated with a 10 cm \(\times\) 10 cm field size and 10 cm \(\times\) 10 cm electron applicator. A dose of 2 Gy was repeatedly delivered. Again, the MOS_kin_ results were plotted against the total dose, and the sensitivity determined using a linear fit. The estimated pair separation distance along the track for 20 MeV electrons is 42.10 nm. As this is larger than for 100 MeV electrons, the amount of initial recombination will reduce, so the MOS_kin_ sensitivity to 20 MeV electrons should increase, in the absence of other effects. The ratio of spacings, 100 Mev / 20 Mev is proportional to the ratio of stopping powers, about 8%, so the ratio of sensitivities is expected to be within a similar range.
# Spinel Magnetic Iron Oxide Nanoparticles: Properties, Synthesis and Washing Methods
## 2 Crystal Structure and Magnetic Properties of Spinel Iron Oxide
### Crystal Structures
#### 2.1.2. Magnemite Crystal Structure  
Maghemite, \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\), is obtained by the complete oxidation of magnetite. Divalent cations Fe\({}^{2+}\) are replaced by trivalent cations F\({}^{3+}\) and the electronic neutrality is assured by the apparition of cationic gaps.  
Three distributions of cationic gaps have been identified for this compound [1] depending on the synthesis conditions (Figure 2):  
* A random distribution of gaps in the octahedral sites of the mesh without deformation (in yellow on the Figure 2A) with the same probability of presence of the gap and an occupation rate of 5/6. In this case, the crystallographic structure of maghemite stays cubic and describes the space group Fd\(\overline{3}\)m. Its lattice parameter of 0.8354 nm at room temperature (JCPDS card 04-013-7114) is slightly reduced (\(\Delta=-0.0039\) nm) compared to that of magnetite. This reduction translates the slight contraction of the structure due to the appearance of gaps.
* A partially organized division of gaps in the defined octahedral sites of the mesh (in yellow on the Figure 2B) always without deformation with an occupation rate of these octahedra of 2/3. The gaps are situated preferentially in the defined octahedral of the mesh with a probability of presence of the gap of 1/3. The cubic system is always preserved but the space group P4\({}_{1}\)32 translates a lowering of symmetry. Its lattice parameter is 0.8346 nm at room temperature (JCPDS card 04-016-4344).
* A totally organized distribution of gaps. The symmetry drops from cubic to tetragonal (P4\({}_{3}\)2\({}_{1}\)2) and the ordering of the gaps is carried out in a superstructure built on three superimposed meshes (Figure 2C). In some works [2], the space group P4\({}_{3}\)2\({}_{1}\)2 is indicated instead of P4\({}_{1}\)2\({}_{1}\)2. This difference translates the direction of rotation chosen for the helical axis 4\({}_{3}\) or 4\({}_{1}\) but the structure is identical. The lattice parameters of this structure are a = b = 0.83296 nm and c = 0.83221 nm at room temperature (JCPDS card 04-007-2135).  
Figure 2: (**A**) Projection [111] of maghemite Fd\(\overline{3}\)m. (**B**) Projection [111] of maghemite P4\({}_{1}\)32. (**C**) Projection [113] of maghemite P 4\({}_{1}\)2\({}_{1}\)2.  
The chemical formula of maghemite becomes [4]:  
* For the space group \(\mathrm{Fd}\overline{3}\mathrm{m}\): \(\mathrm{(Fe^{3+})^{tetra}[Fe^{3+}{}_{5/3}\square_{1/3}]^{octa}(O^{2-})_{4}}\)
* For the space group \(\mathrm{P4}_{1}3\mathrm{2}\): \(\mathrm{(Fe^{3+}{}_{5})^{tetra}[Fe^{3+}{}_{4/3}\square_{8/3}Fe^{3+}{}_{12}]^{ octa}(O^{2-})_{32}}\)
* For the space group \(\mathrm{P4}_{1}2_{1}2\): \(\mathrm{(Fe^{3+}{}_{24})^{tetra}[Fe^{3+}{}_{40}\square_{8}]^{octa}(O^{2-})_{96}}\).
## 2 Materials and Methods
### Energy Dispersive X-ray Spectroscopy (EDX)  
To detect possible drill alterations, the drills were examined before and after sterilization with a scanning electron microscope/energy dispersion X-ray microanalysis (SEM/EDX) using scanning electron microscopy (SEM, JSM-6480 LV; Jeol, Tokyo, Japan) with the EDS QUANTAX-200 probe (Bruker Nano GmbH, Berlin, Germany).
## 4 Security Analysis of the Proposed Protocol
### Formal Security Proof
#### 4.1.1 Basis for Security Proof
Otherwise, the oracle flips an unbiased coin \(b\). If \(b=1\), test (\(\Pi^{\prime}\)) provides the real session key to \(\mathcal{A}\); if \(b=0\), test (\(\Pi^{\prime}\)) yields a random string with the same length as the real session key and sends it to \(\mathcal{A}\). Semantic Security: Given a protocol \(\mathbf{P}\), probabilistic polynomial time (PPT) adversary \(\mathcal{A}\) requests new instances for a series of queries, including execute query, send query, corrupt query, and test query. \(\mathcal{A}\) endeavors to compromise protocol \(\mathbf{P}\) by guessing the value of \(b\) in the test query and returns a guessed value \(b^{*}\). Let \(\text{Succ}(\mathcal{A})\) denote \(\mathcal{A}\)'s successful guess of \(b^{*}\) as \(b\), i.e., \(b^{*}=b\). Then, the advantage of \(\mathcal{A}\) successfully breaking the semantic security of protocol \(\mathbf{P}\) concerning the session key is defined as \(Adv_{\mathcal{A}}^{\mathbf{P}}=2\text{Pr}[\text{Succ}(\mathcal{A})]-1\).
# GIS and BIM as Integrated Digital Environments for Modeling and Monitoring of Historic Buildings
## 2 Materials and Methods
### 3D Metric Survey  
In order to create geometric products and embed them within a GIS as well as an HBIM environment, the objective of the survey needs to be the realization of a high accuracy 3D point cloud of the building under study, while also obtaining high resolution texture.  
For this purpose, a survey operation was carried out by using terrestrial laser scanner (TLS) and image-based approach techniques. Terrestrial and UAV (unmanned aerial vehicle) images of the exterior of the building and of the rooms with significant artistic details were obtained. A control network was established by using GNSS (global navigation satellite systems) and total station survey, while the control points necessary for the image orientation and the laser scanner clouds registration were measured using total station [43]. The geometry of the building interior was complex, since there were many objects scattered in the rooms, causing occlusions. The exterior upper part of the facades and the roof were acquired by the DJI Phantom 3 Professional UAV, which captured oblique and nadir images. Additionally, close range image acquisition of the roof was obtained. The close-range images were recorded using a Canon Eos 1 Ds Mark III camera with lenses of 24 mm and 50 mm focal length. The data were processed using Metashape Agisoft(r) software(Metashape Agisoft software, Agisoft LLC, Petersburg, Russia), which combined all the datasets using a common reference system in accordance to the topographic survey measurements. The accuracy of various control points both in the interior and exterior of the building succeeded the prerequisite accuracy of 1/50 scale. Furthermore, in the developed model, the exterior walls and the roof incorporated texture, material differentiation and alterations. All the exterior facades of the building, as well as most of the rooms in the interior, were recorded using the Leica Scan Station 2 terrestrial time-of-flight laser scanner. Registration was performed using target-to-target, as well as cloud-to-cloud procedures, with an average spatial resolution of \(\pm\) 7-9 mm. The field survey measurements were accomplished with the Topcon 3003 LN geodetic total station and the geo-referencing with the GNSS NR2 Altus GPS.
# Dynamic Path Planning for Vehicles Based on Causal State-Masking Deep Reinforcement Learning
## 3 Problem Formulation
### Dynamic Path-Planning Model
We define the path length cost \(C_{\text{pat}}(t)\) as the Euclidean distance between the current position and the target position:\[C_{\text{pat}}(t)=\sqrt{(x(t)-x_{\text{g}})^{2}+(y(t)-y_{\text{g}})^{2}} \tag{6}\]  
where \(x(t)\) and \(y(t)\) represent the vehicle's longitudinal and lateral positions in the global coordinate system at the current time step, respectively, and \(x_{\text{g}}\) and \(y_{\text{g}}\) denote the longitudinal and lateral positions of the endpoint in the global coordinate system, respectively. Because the variations in control variables at adjacent time steps reflect the variation in the vehicle's state at those times, we define the path smoothness cost \(C_{\text{smo}}(t)\) as related to the variations in control variables:  
\[C_{\text{smo}}(t)=\left(F_{x}(t)-F_{x}(t-1)\right)^{2}+\left(F_{y}(t)-F_{y}(t -1)\right)^{2}+\left(M_{z}(t)-M_{z}(t-1)\right)^{2} \tag{7}\]  
where \(F_{x}(t)\), \(F_{y}(t)\), and \(M_{z}(t)\) represent the vehicle's current longitudinal force, lateral force, and yaw moment, respectively. The collision risk cost is a penalty for the vehicle's proximity to dynamic obstacles in the environment. To quantify this risk, we employ the following formula:  
\[C_{\text{col}}(t)=\frac{1}{d_{\text{min}}(t)+\epsilon} \tag{8}\]  
where \(\epsilon\) is a small constant to avoid division by 0 and \(d_{\text{min}}(t)=\sqrt{(x(t)-x_{\text{min}})^{2}+(y(t)-y_{\text{min}})^{2}}\) represents the Euclidean distance between the vehicle's current position and the nearest obstacle's position, where \(x_{\text{min}}\) and \(y_{\text{min}}\) denote the longitudinal and lateral positions of the nearest obstacle in the global coordinate system, respectively. When the vehicle approaches an obstacle, the value of this cost function increases significantly, driving the vehicle away from obstacles to establish a safer path. Figure 1: Vehicle dynamic model and path-planning environment.
## 2 Materials and Methods
### Characterization Techniques
#### 2.3.2 Acidity Analysis  
The acidity of the catalysts was evaluated using temperature-programmed desorption of ammonia (NH\({}_{3}\)-TPD) using a BELCAT II Chemisorption analyzer. About 50 mg of a sample was placed in a quartz tube and initially treated at 500 \({}^{\circ}\)C for one hour under helium flow. The sample was then ramped to 100 \({}^{\circ}\)C and exposed to ammonia blended with helium (10% He) at a 50 mL/min flow rate for 30 min. The sample was then ramped to 600 \({}^{\circ}\)C at a ramping rate of 10 \({}^{\circ}\)C per minute. The desorbed ammonia was detected by a DTC detector and quantified as a function of temperature.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**S/N**} & **Form of Parent** & \multirow{2}{*}{**Name**} & \multirow{2}{*}{**Conc. (M)**} & **Temperature** & **Crystallinity** & **Crystallinity** \\  & & & & (\({}^{\circ}\)C) & **Relative to** & **Relative to** \\  & & & & & **NH\({}_{4}\)-Form (\%)** & **K-Form (\%)** \\ \hline
1 & NH\({}_{4}\)-Form & 0.05 Cu-H-CHA & 0.05 & 80 & 124.96 & 185.11 \\
2 & NH\({}_{4}\)-Form & 0.1 Cu-H-CHA & 0.1 & 80 & 108.47 & 160.68 \\
3 & NH\({}_{4}\)-Form & 0.1 Cu-H-CHA-RT & 0.1 & 25 & 98.25 & 145.53 \\
4 & K-Form & 0.05 Cu-K-CHA & 0.05 & 80 & 76.03 & 112.63 \\
5 & K-Form & 0.1 Cu-K-CHA & 0.1 & 80 & 79.03 & 118.37 \\
6 & K-Form & 0.1 Cu-K-CHA-RT & 0.1 & 25 & 79.6 & 117.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental conditions of the modified Cu-CHA catalysts and relative crystallinity.
## 2 Results  
### Vapor Phase Chemical Composition of EOs  
SPME-GC-MS analysis was performed to delineate the vapor phase profile of the EOs. In total, 55 compounds were identified, with 15 in _A. alba_, 18 in _P. abies_, 21 in _P. cembra_ and 16 in _P. mugo_ (Table 1). \(\alpha\)-Pinence was the main compound in _A. alba_ (48.5%) and _P. cembra_ (52.9%), together with \(\beta\)-myrcene in _P. abies_ (24.7% and 24.8%, respectively) and the second-most abundant component in _P. mugo_ (28.0%), where limonene (33.1%) prevailed. Limonene was also detected at the same percentage (33.1%) in _A. alba_ as well as in a slightly smaller amount in _P. cembra_ (29.3%). \(\beta\)-myrcene was missing in _A. alba_, but significantly present in _P. cembra_ (9.2%) and _P. mugo_ (27.8%). The only ubiquitous sesquiterpene compound was \(\beta\)-caryophyllene which ranged from 0.1% to 0.4%. A series of compounds belonging to this chemical class were detected but with relative percentages lower than 0.1%. The exception was \(\delta\)-cadinene which in _P. mugo_ reached 0.3%.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(\mathbf{N}^{\circ}\) & **Component 1** & **Symbol 2 Class** & **LRI 3** & **LRI 4** & _A. alba (\%) 5** & _P. abies (\%) 6_ & _P. cembra (\%) 7_ & _P. mugo (\%) 8_ \\ \hline
1 & santere & other & 880 & 887 & 2.9 \(\pm\) 0.02 & 1.0 \(\pm\) 0.01 & tr & - \\
2 & cyclofenchene & m & 892 & 896 & tr & 2.8 \(\pm\) 0.03 & tr & 2.3 \(\pm\) 0.02 \\
3 & tricylene & m & 913 & 920 & 2.1 \(\pm\) 0.02 & 0.7 \(\pm\) 0.02 & 1.0 \(\pm\) 0.01 & 1.2 \(\pm\) 0.01 \\
4 & \(\alpha\)-pinene & m & 938 & 943 & 48.5 \(\pm\) 0.04 & 24.7 \(\pm\) 0.06 & 52.9 \(\pm\) 0.07 & 28.0 \(\pm\) 0.04 \\
5 & camphene & m & 940 & 946 & 1.8 \(\pm\) 0.01 & 8.6 \(\pm\) 0.02 & 2.0 \(\pm\) 0.02 & 2.9 \(\pm\) 0.02 \\
6 & sabinene & m & 975 & 972 & - & - & 0.1 \(\pm\) 0.01 & 0.1 \(\pm\) 0.01 \\
7 & \(\beta\)-pinene & m & 981 & 978 & 9.0 \(\pm\) 0.03 & 16.5 \(\pm\) 0.03 & 3.0 \(\pm\) 0.02 & - \\
8 & \(\beta\)-myrcene & m & 983 & 987 & - & 24.8 \(\pm\) 0.05 & 9.2 \(\pm\) 0.03 & 27.8 \(\pm\) 0.05 \\
9 & \(\alpha\)-phelandrene & m & 1000 & 1005 & - & - & 0.2 \(\pm\) 0.02 & 0.5 \(\pm\) 0.01 \\
10 & limonene & m & 1025 & 1023 & 33.1 \(\pm\) 0.05 & 17.2 \(\pm\) 0.04 & 29.3 \(\pm\) 0.04 & 33.1 \(\pm\) 0.06 \\
11 & trans-sabinene & m & 1045 & 1052 & - & - & tr & - \\
12 & \(\gamma\)-terpinine & m & 1057 & 1054 & - & 0.1 \(\pm\) 0.01 & tr & 0.3 \(\pm\) 0.02 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vapor phase chemical composition (%) of _A. alba, P. abies, P. cembra_ and _P. mugo_ EOs, as determined by SPME-GC-MS.
# Survey and Perspectives of Vehicular Wi-Fi versus Sidelink Cellular-V2X in the 5G Era
## 3 Simulation Tool and Settings
### Main Settings
#### 3.2.4 Useful CAMs and Significant Neighbours  
Normally, when the cooperative awareness service is addressed, the performance of IEEE 802.11p and/or LTE-V2X is calculated for all possible nodes at some distance or within some given range. This might include also vehicles that are on parallel roads with respect to the transmitter or are moving away in the opposite direction, which are cases where the correct/wrong reception of the packet has indeed no particular relevance.  
To cope with this situation, here we only consider for our metrics those nodes that are possibly interested in the sent CAM, selected with the procedure detailed in [42]. Such procedure exploits the moving direction of each vehicle to verify if their trajectories can somehow intersect or are completely disjoint. We call the identified nodes _significant neighbours_. In the example shown in [42], referring to both technologies in an urban scenario, the reliability is significantly higher if we limit the attention to the significant neighbours and the improvement is larger for IEEE 802.11p.
## 3 Results and Discussion
### Energy Content  
The energy content obtained for samples following the fractionation ranged between 19 and 22 MJ/kg higher heating value (HHV) (Table 4). Crude olive pomace showed energy content equivalent to previous works [12]. The wet fractionation (pulp + stones fraction) did not reveal significant differences in HHV (\(p<0.05\)) in comparison to crude olive pomace. However, the separation of the pulp fraction from the stones fraction significantly influenced the energy content values (\(p<0.05\)). As expected, the pulp fraction exhibited the higher HHV (\(\sim\)22 MJ/kg), due to its substantial oil content. However, the pulp fraction's high composition in oil and low weight could generate uncontrolled combustions and emissions disturbing the combustion and its performance as fuel [12]. The lowest HHV was shown in the stones fraction (\(\sim\)19 MJ/kg). Nonetheless, these values were similar to those achieved in previous works focused on the stones fraction (18.80 MJ/kg) [66]. Besides, the HHV obtained for the stones fractions were identical to other biomass sources, namely oak wood (18.70 MJ/kg) and pine bark (18.30 MJ/kg), and to most agricultural residues (e.g., vine shoots (18.30 MJ/kg), palm kernel (18.67 MJ/kg)) [67]. The moisture content of stones fraction samples showed a moisture content of 6% (OM 1: \(5.48\pm 0.01\) g/100 g DW, OM 2: \(5.39\pm 0.08\) g/100 g DW) and a pH value of 4.3. The moisture values obtained are in agreement with results reported by [PERSON] et al. [66] for olive stones (6.1 g/100 g DW). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{8}{c}{**High Calorific Value (MJ/Kg Dry Weight)**} \\ \hline \multicolumn{2}{c}{C-OP} & \multicolumn{2}{c}{P + S-OP} & \multicolumn{2}{c}{P-OP} & \multicolumn{2}{c}{S-OP} \\ \hline OM 1 & OM 2 & OM 1 & OM 2 & OM 1 & OM 2 & OM 1 & OM 2 \\ \hline
20.57 \(\pm\) 0.03\({}^{\mathrm{c.d}}\) & 21.67 \(\pm\) 0.20\({}^{\mathrm{b}}\) & 20.21 \(\pm\) 0.05\({}^{\mathrm{d}}\) & 20.86 \(\pm\) 0.11\({}^{\mathrm{c}}\) & 21.52 \(\pm\) 0.12\({}^{\mathrm{b}}\) & 22.21 \(\pm\) 0.01\({}^{\mathrm{a}}\) & 18.94 \(\pm\) 0.00\({}^{\mathrm{d}}\) & 18.65 \(\pm\) 0.00\({}^{\mathrm{d}}\) \\ \hline \end{tabular}
\end{table} 
Table 4: Higher heating value (HHV) of crude olive pomace (C-OP) and olive pomace fractions produced after the wet and dry fractionation process (MJ/Kg DW).