esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Identification of TPS Family Members in _D._ officinal  
The recently released _D. officinal_ genome [(23)] was used in this study. Two specific TPS domains, PF03936 and PF01397, which respectively indicate the _C_-terminal and _N_-terminal domain of TPS from the Pfam database ([[http://pfam.xfam.org/](http://pfam.xfam.org/)]([http://pfam.xfam.org/](http://pfam.xfam.org/))), were used to build the corresponding Hidden Markov Model (HMM) file. HMMER v3.3 ([[http://www.hmmer.org/](http://www.hmmer.org/)]([http://www.hmmer.org/](http://www.hmmer.org/))) was used to search the _D. officinal_ protein database with the PF03936 and PF01397 domains model data as queries. Significant hits (e-value \(<10^{-3}\)) were retrieved as candidate _D. officinal_ TPS proteins. To verify the sequences, BLASTp ([[http://blast.ncbi.nlm.nih.gov](http://blast.ncbi.nlm.nih.gov)]([http://blast.ncbi.nlm.nih.gov](http://blast.ncbi.nlm.nih.gov))) and SMART ([[http://smart.embl-heidelberg.de/](http://smart.embl-heidelberg.de/)]([http://smart.embl-heidelberg.de/](http://smart.embl-heidelberg.de/))) searches of the retrieved TPS proteins were carried out. Non-redundant sequences that did not contain the terpene synthase \(C\)-terminal domain and terpene synthase \(N\)-terminal domain were removed. The grand average of hydrophobicity (GRAVY), molecular weight (Mw), and isoelectric points (pI) of the TPS proteins were predicted from the ExPASy database ([[http://expsy.org/](http://expsy.org/)]([http://expsy.org/](http://expsy.org/))). The aliphatic index (AI), and instability index (II) were calculated by EMBOSS Pepstats tool ([[https://www.ebi.ac.uk/Tools/](https://www.ebi.ac.uk/Tools/)]([https://www.ebi.ac.uk/Tools/](https://www.ebi.ac.uk/Tools/))). Plant-mPLoc (www.csbio.situ.edu.cn/bioinf/plant-multi/), AtSubP ([[http://bioinfo3.noble.org/AtSubP/](http://bioinfo3.noble.org/AtSubP/)]([http://bioinfo3.noble.org/AtSubP/](http://bioinfo3.noble.org/AtSubP/))), and pLoc-mPlant (www.jci-bioinfo.cn/pLoc-mPlant/) were used to predict the subcellular localization of TPS proteins. The secondary structure of TPS proteins was determined using the SOPMA program ([[http://npsa-pbil.ibcp.fr/](http://npsa-pbil.ibcp.fr/)]([http://npsa-pbil.ibcp.fr/](http://npsa-pbil.ibcp.fr/))). In addition, TPS proteins from _P. equestris_ and _A. shenzhenica_ were obtained from their reported genome database [25]. The other TPS proteins from _A. grandis_, _A. thaliana_, _O. sativa_, _P. trichocarpa_, _S. moellendorffii_, _S. lycopeneum_, and _S. bicolor_ were downloaded from the Phytozome version 12.1 database ([[https://www.phytozome.net](https://www.phytozome.net)]([https://www.phytozome.net](https://www.phytozome.net))).
# Occlusion-Robust Facial Expression Recognition Based on Multi-Angle Feature Extraction
###### Abstract  
Facial occlusion represents a significant challenge in the domain of facial expression recognition (FER). The absence of feature information due to occlusion has been demonstrated to result in a reduction in recognition accuracy and model robustness. To address this challenge, a multi-angle feature extraction (MAFE) method is proposed in this paper, aiming to enhance the recognition accuracy under occlusion conditions by employing multi-scale global features, local fine-grained features, and important regional features. The MAFE approach involves three core modules: multi-feature extraction, regional detail feature fusion, and consistent feature recognition. In the multi-feature extraction module, PTIR-50 and Swin Transformer are used to extract global features and fine-grained features, and at the same time, the five key points of the face are combined to crop out the important regions from the global features. The Regional Bias Loss (RB-Loss) is then utilized to guide the model to focus on the key information regions. The subsequent Regional Detail Feature Fusion module combines fine-grained features with those from the important regions. This process enhances the expressiveness of the features. The Consistent Feature Recognition module proposes consistent feature loss (con-feature Loss) to ensure that global features and fused features guide each other, forcing the model to focus on more discriminative expression features. The experimental results demonstrate that MAFE attains 89.42% and 86.94% accuracies on the Occlusion-RAFDB and Occlusion-FERPlus datasets, thereby surpassing the existing methods. Accuracies of 92.11% and 90.15% are also obtained on the original RAF-DB and FERPlus datasets.  
multi-angle feature extraction; occlusion facial recognition; con-feature loss Article  
20251539  
20251539
## 2 Results and Discussion
### Phytochemical Characterisation
#### 2.2.1.
The test revealed a difference in the content and profile of compounds between the two varieties, which may explain the differences observed in their biological activities [49; 58; 84]. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Compounds/Extracts**} & \multicolumn{6}{c}{_A.P_ var. _pyrethrum_} & \multicolumn{6}{c}{_A.P_ var. _depressus_} \\ \cline{2-9}  & **Capitula** & **Seeds** & **Roots** & **Leaves** & **Capitula** & **Seeds** & **Roots** & **Leaves** \\  & **(CPP)** & **(GPP)** & **(RPP)** & **(FPP)** & **(CPD)** & **(GPD)** & **(RPD)** & **(FPD)** \\ \hline Tannins & \(-\) & \(-\) & \(+\) & \(-\) & \(+\) & \(-\) & \(+\) & \(+\) \\ Catechic tannins & \(-\) & \(-\) & \(+\) & \(-\) & \(-\) & \(-\) & \(+\) & \(+\) \\ Gallic tannins & \(-\) & \(-\) & \(-\) & \(-\) & \(+\) & \(-\) & \(-\) & \(-\) \\ \hline Flavonoids & \(+\) & \(++\) & \(+\) & \(++\) & \(++\) & \(++\) & \(++\) & \(++\) \\ \hline Sterols & \(+\) & \(+\) & \(-\) & \(+\) & \(+\) & \(+\) & \(+\) & \(+\) \\ \hline Alkaloids & & & & & & & & \\ Dragondorf test & \(+\) & \(++\) & \(++\) & \(+\) & \(+\) & \(+\) & \(++\) & \(+\) \\ \multicolumn{2}{c}{Mayer’s test} & \(++\) & \(+\) & \(++\) & \(++\) & \(++\) & \(+\) & \(++\) \\ \hline Saponosides & \(+\) & \(+\) & \(-\) & \(-\) & \(++\) & \(+\) & \(+\) & \(-\) \\ \hline Cardiac glycosides & \(+\) & \(-\) & \(-\) & \(+\) & \(+\) & \(-\) & \(-\) & \(+\) \\ \hline Oses and holosides & \(+\) & \(-\) & \(-\) & \(+\) & \(+\) & \(-\) & \(-\) & \(+\) \\ \hline Mucilages & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) \\ \hline Free quinones & \(+\) & \(++\) & \(-\) & \(++\) & \(+\) & \(-\) & \(-\) & \(++\) \\ \hline Sterols and terpenes & \(+\) & \(++\) & \(++\) & \(-\) & \(++\) & \(++\) & \(++\) & \(--\) \\ \hline Steroidal heterosides & \(++\) & \(+\) & \(-\) & \(++\) & \(++\) & \(+\) & \(-\) & \(++\) \\ \hline Triterpenes heterosides & \(+\) & \(+\) & \(+\) & \(++\) & \(+\) & \(+\) & \(+\) & \(++\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Phytochemical screening of the hydroethanol extracts of the different parts of the two varieties _A.P_ var. _pyrethrum_ and _A.P_ var. _depressus_.
## 3 Results and Discussion
### Single-Factor Investigation of Extraction Conditions
#### 3.2.2 Effects of Extraction Temperature on Extraction Yield
This continuous increase in the TPC may have been the main factor contributing to the rise in the DPPH antioxidant activity (Figure 4C) when the solvent-to-material ratio was elevated to 80/1 (mL/g). A recent study by [PERSON] (2023) [29] concluded that a solvent-to-material ratio of 40/1 (mL/g) was the optimal ratio for the recovery of TSC from _Polyscias fruticosa_ roots because no significant increase in recovery efficiency was observed when the solvent volume exceeded this ratio. Practically, the extraction yields of compounds cannot proportionally rise with higher solvent-to-material ratios and reach the maximum value at a certain ratio at which the concentration of solutes in the material is limited and an excessive amount of solvent may hinder the diffusion rate [8]. This result indicates that using excessive solvent to extract bioactive compounds may not yield significant recovery efficiency and instead increases the costs associated with the used solvent and the energy required to remove the solvent from the extract in the subsequent stages during the production of products from the plant extracts.
# Cold Boot Attacks on the Supersingular Isogeny Key Encapsulation (SIKE) Mechanism
## 3 Supersingular Isogeny Key Encapsulation Mechanism
### Key Encapsulation Mechanism
#### 3.2.1 Public Parameters  
We start off by describing the public parameters that SIKE makes use of:  
* A prime number of the form \(p=2^{\mathbf{e}_{2}}3^{\mathbf{e}_{3}}-1\), where \(\mathbf{e}_{2}\) and \(\mathbf{e}_{3}\) are two positive integers.
* A starting supersingular elliptic curve defined as \(E_{0}/\mathbb{F}_{p^{2}}:y^{2}=x^{3}+6x^{2}+x\), such that \(\#E_{0}\Big{(}\mathbb{F}_{p^{2}}\Big{)}=\left(2^{\mathbf{e}_{2}}3^{\mathbf{e}_ {3}}\right)^{2}\) and \(j(E_{0})=287\),496.
* Two points \(P_{2}\) and \(Q_{2}\) in \(E_{0}[2^{\mathbf{e}_{2}}]\), such that both points have exact order \(2^{\mathbf{e}_{2}}\) and \(\{P_{2},Q_{2}\}\) forms a basis for \(E_{0}\Big{(}\mathbb{F}_{p^{2}}\Big{)}[2^{\mathbf{e}_{2}}]\).
* Two points \(P_{3}\) and \(Q_{3}\) in \(E_{0}[3^{\mathbf{e}_{3}}]\), such that both points have exact order \(3^{\mathbf{e}_{3}}\) and \(\{P_{3},Q_{3}\}\) forms a basis for \(E_{0}\Big{(}\mathbb{F}_{p^{2}}\Big{)}[3^{\mathbf{e}_{3}}]\).  
These points are normally encoded for efficiency. Particularly, the points \(P_{2}\) and \(Q_{2}\) are encoded as a byte array from the three x-coordinates \(x_{P_{2}}\), \(x_{Q_{2}}\) and \(x_{R_{2}}\), where \(R_{2}=P_{2}-Q_{2}\). Similarly, the points \(P_{3}\) and \(Q_{3}\) are encoded as a byte array from the three x-coordinates \(x_{P_{3}}\), \(x_{Q_{3}}\) and \(x_{R_{3}}\), where \(R_{3}=P_{3}-Q_{3}\)[1].
## 5 Structure Measured Temperature and Deformation Analysis  
### Structural Temperature and Deformation System  
The temperature and deformation monitoring system of the Chongqing Nanjimen track bridge structure adopts a temperature and humidity sensor to measure the ambient temperature change in the bridge and adopts a GNSS deformation system to monitor the deformation of the main beam, and obtains the transverse, longitudinal and vertical spatial deformation data of the main beam, which provide an important basis for the state assessment of the bridge structure. The ambient temperature measuring point and the three-way deformation monitoring point of the main beam are arranged at the closing section of the main span of the main bridge (the Chongqing Nanjimen track bridge structural temperature and deformation system overview see Supplementary Document Figure S2).
# A Space-Interconnection Algorithm for Satellite Constellation Based on Spatial Grid Model
## 1 Introduction
More importantly, by dividing the spatial range into discrete grids, visibility between the grids will turn into an inherent property, which can improve the calculation efficiency. In our study, GeoSOT-3D was selected as the discrete grid framework and extended to the aerospace region [31]. A general method for inter-satellite grid computing is then proposed. The remainder of the paper is organized as follows: in Section 2, the grid inter-connection calculation method for satellite constellations based on GeoSOT-3D is proposed, and the key algorithms including satellite grid visibility and grid network topology are presented. Section 3 presents the results and analysis. The advantages and disadvantages of the work are discussed in Section 4. Finally, Section 5 concludes the paper.
# Non-Destructive Testing Applications for Steel Bridges
## 3 NDT Methods with Potential Application to Steel Bridges
### Test Methods Applicable to Internal/Subsurface Defects
#### 3.2.3 Infrared Thermography Testing (IT)  
The IT method is based on the relationship between thermal radiation and temperature, where the heteromorphic structure of an object is described by the difference of the surface temperature distribution. In this technique, an infrared camera is implemented to measure the emitted infrared radiation from the test elements (Figure 17). Thus, by using different forms of active thermal excitation, the defect can be effectively detected. Figure 18 presents the principle of IT technology, where a heating source is applied to both a non-defected and defected object. If the material is uniform, i.e., without any defects, the thermal signal propagates smoothly through the object, while, for the defected object, the thermal signal results in sudden changes in the surface temperature distribution [43].  
Figure 16: Application of AE for steel bridges [10,40,41].  
Figure 17: Infrared camera used for IT method [44].  
Figure 18: Principle of infrared thermography for uniform and nonuniform elements and their thermal wave propagation [43].  
Much research has been carried out on the application of IT for steel elements [45; 46; 47]. [PERSON] et al. [46] conducted an IT test on the steel deck of a steel bridge, with two types of cracks: Crack A (weld-bead-penetrant-type) and Crack B (through-deck-type), as shown in Figure 19[46]. They found this technique practical for identifying the cracks. [PERSON] (2006) employed IT for the non-destructive evaluation of cables in cable-stayed bridges with an ambient heat source [44].  
There are two types of IT technology, which include a traditional approach known as pulse thermography (PT) and the more advanced lock-in thermography (LT) [48]. The traditional PT uses a short-duration energy pulse and records the thermal response. In contrast, the LT uses mono-frequency sinusoidal thermal excitation to find the phase and magnitude of the reflected thermal wave from the recorded thermal images. The LT method provides better accuracy and efficiency for noise rejection, but it takes a considerably longer time to measure frequencies [48; 49]. The PT method is known for its fast detection speed, large detection area, and ease of data access. This method can be implemented to detect cracks, rust, fatigue damage, and debonding in steel bridges [48; 49; 50].  
According to the Non-destructive Evaluation Center of the Federal Highway Administration, compared to other NDT techniques, IT covers more areas for inspection, therefore it is a cost-effective method for inspection of steel bridges with large areas. Moreover, this method can indicate the percentage of deteriorated areas. However, there are some disadvantages to this technique, including the high cost of test equipment, being dependent on the proper temperature of the ambient, and the need for special training for the operators [51].
## 6 Performance Evaluation
### Performance Evaluation Metrics  
In this subsection, we provide a brief explanation of the performance evaluation metrics used in this research paper. We evaluate the three routing protocols in terms of the sensor network density test and transient and permanent node failure rate tests. In order to have consistent and reliable routing performance evaluation, the five evaluation metrics listed below are used. Such metrics are widely accepted by the research community and are used for wireless sensor network performance evaluations [14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59]:  
1. **Average End-to-End Data Packet Delivery (Success Rate):** This is the fraction or percentage of success among a number of attempts to transmit a packet.
2. **Average Packet End-to-End Delay:** This refers to the time required for a packet to be transmitted across a network from source to destination.
3. **Energy Consumption:** Defined as the ratio between the number of packets transmitted to the amount of energy consumed, where the amount of energy consumed is the difference between the initial energy before simulation to the energy used after the simulation. The unit is expressed in bits/joule.
4. **Average Number of Transmitted MAC Packets:** Total number of packets received at the MAC layer.
5. **Packet Drop Rate/Error Rate:** The Packet Error Rate (PER) is the number of incorrectly received data packets divided by the total number of received packets. A packet is declared incorrect if at least one bit is erroneous.  
\begin{table}
\begin{tabular}{l l} \hline
**Parameter(s)** & **Value** \\ \hline Network Size Terrain (m\({}^{2}\)) & 2000 \(\times\)2000 \\ \hline Number of Nodes & 100–1000 varied \\  & 600 nodes fixed \\ \hline Number of Source Nodes & 10, 20, 40 varied \\ \hline Data Packet size (byte) & 1000 \\ \hline Active Percentage & 100\%, 20\% fixed \\  & 10\%–60\% varied \\ \hline Active Cycle & 100\%, 20\% fixed \\  & 10\%–60\% varied \\ \hline Transmission Time (s) & 0.002 \\ \hline Transmission Power (Tx) (Watt) & 0.0290 \\ \hline Channel Model & Radio \\ \hline Broadcast data packet & CBR \\ \hline Slot Width (s) & 0.0001 \\ \hline Simulation Time (s) & 200 \\ \hline Scale Factor (s) & 0.001 \\ \hline Traffic Direction & Bi-directional \\ \hline Route Repair & SHR, ASAART (True) and SSR (False) \\ \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.
# Enhanced Dual Reversible Data Hiding Using Combined Approaches
## 2 Preliminaries
### Arithmetic Coding
\begin{bmatrix}p_{1}&p_{2}&p_{3}&\cdots&p_{16}\\ p_{17}&p_{18}&p_{19}&\cdots&p_{32}\\ p_{33}&p_{34}&p_{35}&\cdots&p_{48}\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ p_{241}&p_{242}&p_{243}&\cdots&p_{256}\end{bmatrix}  \tag{9}\]
**Step 5:**: After restoring \(M\), decompress it using the AC (Algorithm 2) function \(A_{D}(\cdot)\); that is, \(M_{decompressed}=A_{D}(M)\). **Step 6:**: Two pixels (\(x_{j}^{1}=(b_{1}^{1}b_{2}^{1}\ldots b_{8}^{1})\) and \(x_{j}^{2}=(b_{1}^{2}b_{2}^{2}\ldots b_{8}^{2})\)) are read from each of the blocks \(B_{i}^{1}\) and \(B_{i}^{2}\) and assigned to the variables \(x_{j}^{1}\) and \(x_{j}^{2}\), respectively. The codeword \(P_{j}=\{b_{j,7}^{1},b_{j,6}^{1},b_{j,5}^{1},b_{j,4}^{1},b_{j,27}^{2},b_{j,6}^{ 2},b_{j,5}^{2}\}\) is constructed by extracting the top 4 bits of \(x_{j}^{1}\) and the top 3 bits of \(x_{j}^{2}\), as described in Equation (3). The value of \(M_{ck+2}\) is first converted to a decimal number and stored in the variable \(l\), which identifies the error position in the codeword \(P_{j}\). The corresponding bit in \(P_{j}\) is then flipped to correct the error, and Equation (11) is applied to compute the syndrome. Here, \(b2d(\cdot)\) denotes a function that converts binary numbers to a decimal format. \[ \begin{cases}l=b2d(M_{j;j+2})=M_{j+2}\cdot 2^{2}+M_{j+1}\cdot 2^{1}+M_{j}\cdot 2 ^{0}\\ p^{(flipped)}=P\oplus e_{j},\text{ where }e_{l}=[0,0,\ldots 1_{l},\ldots,0]\\ m_{kk+2}=H(P\oplus e_{j})\end{cases}  \tag{11}\]
**Step 7:**: Repeat step 6 for the length of \(L/2\). Afterwards, if there is another block to process, return to step 2. The process terminates when there are no more blocks left to process. **Step 8:**: The recovered cover images \(I_{1}\) and \(I_{2}\) were restored to be identical to the original image \(O\), and the message \(m\) was successfully recovered. Algorithm 4 presents the process described above in the form of a simplified pseudocode algorithm. This algorithm extracts the hidden data from the two marked images \(I_{1}^{\prime}\) and \(I_{2}^{\prime}\) and restores the original image. The images are divided into \(16\times 16\) blocks, and the length of the hidden data \(L\) is extracted from specific pixel positions. The pixel values are used to recover the hidden data, and arithmetic decoding is applied to decompress the extracted data. The recovered data are then used to reconstruct codewords by combining the upper bits of the pixels, and the error positions are corrected to retrieve the hidden secret data. Finally, all the blocks are combined to restore the original image, and the extracted data are the output.
# A Spatial-Temporal-Semantic Neural Network Algorithm for Location Prediction on Moving Objects
## 3 Methodology
### Long Short-Term Memory Network Model for Location Prediction
#### 3.2.2 Model Training and Predicting  
The training process. After the STS feature extraction, trajectories are transformed into fixed and discrete sequences with road IDs, expressed as \(LocSeq=\{roadID_{i}|i=1,\,2,\,3,\dots\}\). However, the sequences cannot be directly put into the LSTM model. Each sequence should be divided into segments with fixed length. The length of each segment is determined by specific applications. For example, in the bus system, traces of the past 15 min are used to predict the next location. There are 30 locations collected in 15 min so the length is 30. The length of location segment is defined as \(maxLength\). Each location sequence is scanned by a sliding window with fixed width equals to \(maxLength\). The window moves forward by one location until it reaches the end of each sequence. Locations in the window are gathered as training features. The next location outside the window is used as label. The process is called feature engineering and is illustrated in Figure 12. Then, each location sequence with features and a label is used to train the LSTM model.  
The predicting process. When a new track point is collected, a prediction is not made until all points in a time bin are gathered. Then a representative point is selected from each time bin by the time matching process. Each representative point is mapped to the nearest road in the city road networks. The mapping point is then classified to the nearest fixed road point along the road. The fixed road points are predefined with the distance threshold, which depends on different applications. Each fixed road point has a unique ID. Thus, the new trajectories is transformed into a location sequence represented by road IDs. Then, those IDs are used as the import of the LSTM model for prediction. The predicting process is shown in Figure 13. The model will be re-trained each day during the free time with the trajectories collected in that day to learn the new movement patterns.  
Figure 12: The process of feature engineering.
## 3 Results
### Compressive Strength--CS  
A CS ANOVA with 95% confidence can be seen in Table 3. This shows that CDW granulometry has a significant influence upon CS by a contribution of 15%. Studies performed by [PERSON] et al. [49] also found a small influence of granulometry on CS; however, natural sand was used as aggregate. FS also significantly influenced CS by a contribution of 71%. For the interaction between factors, FA and CDW granulometry did not influence each other; herein, no interaction is considered between them.  
Figure 4 shows CS with respect to FA for the different granulometries. It can be seen that increasing FA reduces CS due to the incorporation of air into the mortar, which reduces its density. The specimens with coarse CDW showed higher values of CS than those with medium CDW, and those higher than fine CDW. This phenomenon can be explained; the fine-grained residue absorbs more water than coarse. The WA allows the coalescence of foam bubbles inside the mortar, and consequently, an increase in the size of the bubbles and air content, which lessens density and CS. Results are in agreement with former studies by [PERSON] and [PERSON] [50] and [PERSON] et al. [49].  
Figure 4: CS as a function of foam amount (FA) for different CDW granulometries.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Factor**} & **Degrees of** & **Sum of** & **Mean of** & **F-Value** & **\(p\)-Value** &
\begin{tabular}{c} **Percent** \\ **Contribution** \\ \end{tabular} \\ \hline CDW granulometry (CDWG) & 2 & 4.0905 & 2.0453 & 14.873 & 0.0001 & 15 \\ Foam amount (FA) & 3 & 19.3912 & 6.4637 & 47.004 & 0.0000 & 71 \\ Interaction between CDWA and FA & 6 & 0.6304 & 0.1051 & 0.764 & 0.6053 & 2 \\ Error & 24 & 3.3003 & 0.1375 & & 12 \\ Total & 35 & 27.4124 & & & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Compressive strength (CS) An analysis of variance (ANOVA).  
The average values for CS are above the minimum values established by the Brazilian standard NBR 13438 [51] for autoclaved cellular concrete (values of at least 1.2-1.5 MPa at densities between 400-600 kg/m\({}^{3}\)). The specimen produced with 8.1% of FA related to total mass and fine CDW was an exception, with an average CS value of 0.6 MPa.