esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A  
**Example A1**.: _An RNS is given by a set of moduli \(m_{1}=3\), \(m_{2}=5\), and \(m_{3}=11\). It is required to find the remainder on dividing the number \(X=(1,4,6)\) in this RNS by the modulus \(m_{4}=7\). For ease of further consideration, note that \(X=94\) in the WNS._  
_Appendix A.1. Base Extension. Pre-Computation Stage_  
_We obtain the constants for the moduli set \(\{3,\ 5,\ 7\}\)._  
\(M_{K}=m_{1}m_{2}m_{3}=3\cdot 5\cdot 11=165\)_._  
_We calculate the constants \(B_{i}\) by Equation (15):_  
\[B_{1} =\frac{M_{K}}{m_{1}}\bigg{|}\frac{m_{1}}{M_{K}}\bigg{|}_{m_{1}}= \frac{165}{3}\bigg{|}\frac{3}{165}\bigg{|}_{3}=55\cdot 1=55,\] \[B_{2} =\frac{M_{K}}{m_{2}}\bigg{|}\frac{m_{2}}{M_{K}}\bigg{|}_{m_{2}}= \frac{165}{5}\bigg{|}\frac{5}{165}\bigg{|}_{5}=33\cdot 2=66,\] \[B_{3} =\frac{M_{K}}{m_{3}}\bigg{|}\frac{m_{3}}{M_{K}}\bigg{|}_{m_{3}}= \frac{165}{11}\bigg{|}\frac{11}{165}\bigg{|}_{11}=15\cdot 3=45.\]  
_Next, we calculate the constants for Equation (16):_  
\[\left|B_{1}\right|_{m_{4}}=\left|55\right|_{7}=6,\ \left|B_{2}\right|_{m_{4}}= \left|66\right|_{7}=3,\ \left|B_{3}\right|_{m_{4}}=\left|45\right|_{7}=3,\ \left|-M_{K}\right|_{m_{4}}= \left|-165\right|_{7}=3.\]  
_Finally, we calculate the constants necessary for finding the rank \(r_{X}\) of \(X\). According to (10),_  
\[k_{1}^{*}=\frac{\left|m_{1}/M_{K}\right|_{m_{1}}}{m_{1}}=\frac{\left|3/165 \right|_{3}}{3}=\frac{1}{3},\]  
\[k_{2}^{*}=\frac{\left|m_{2}/M_{K}\right|_{m_{2}}}{m_{2}}=\frac{\left|5/165 \right|_{5}}{5}=\frac{2}{5},\]  
\[k_{3}^{*}=\frac{\left|m_{3}/M_{K}\right|_{m_{3}}}{m_{3}}=\frac{\left|11/165 \right|_{11}}{11}=\frac{3}{11}.\]  
_Appendix A.2. Base Extension. Computation Stage_  
_Step 1. We obtain \(X_{m_{4}}^{(1)}\) by Equation (17):_  
\[X_{m_{4}}^{(1)}=\left|\sum_{i=1}^{3}\left|B_{i}\right|_{m_{4}}x_{i}\right|_{m_ {4}}=\left|6\cdot 1+3\cdot 4+3\cdot 6\right|_{7}=1.\]  
_Calculating the first term in Equation (16), we calculate the rank \(r_{X}\) in parallel. According to (10),_  
\[r_{X}=\sum_{i=1}^{3}k_{i}^{*}x_{i}=\frac{1}{3}\cdot 1+\frac{2}{5}\cdot 4+ \frac{3}{11}\cdot 6=3\frac{94}{165}=3.\]  
_Using Equation (18), we find the second term \(X_{m_{4}}^{(2)}\) in Equation (16):_  
\[X_{m_{4}}^{(2)}=\left|r_{X}\cdot\left|-M_{K}\right|_{m_{4}}\right|_{m_{4}}= \left|3\cdot 3\right|_{7}=2.\]  
_Step 2. We calculate the desired remainder by Equation (19):_  
\[\left|X\right|_{m_{4}}=\left|X_{m_{4}}^{(1)}+X_{m_{4}}^{(2)}\right|_{m_{4}}= \left|1+2\right|_{7}=3.\]_This value matches the remainder on dividing_ \(X=94\) _by the modulus_ \(m_{4}=7\)_._  
_End of example._
###### Abstract  
In this study, Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)@TiO\({}_{2}\) (FS-FT (0 g)) photocatalysts, featuring a magnetic core-shell structure, and Fe-doped Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)@Fe-TiO\({}_{2}\) (FS-FT (x g)) photocatalysts, were fabricated via the sol-gel method. Structural and compositional analyses of the processed samples were systematically conducted through X-ray diffraction (XRD), transmission electron microscopy (TEM) with selected area electron diffraction (SAED), surface-sensitive X-ray photoelectron spectroscopy (XPS), and optical property assessment via UV-Vis diffuse reflectance spectroscopy (UV-DRS). The results show that TiO\({}_{2}\) on the outer layer of FS-FT (0 g) and FS-FT (x g) has an anatase structure, and that Fe is doped into FS-FT (x g). The photodegradation of methyl orange (MO) using FS-FT (0 g) and FS-FT (x g) with various Fe doping levels was evaluated in both pure MO (C\({}_{0}\) = 10 mg/L) and MO-Na\({}_{2}\)SO\({}_{4}\)-blended solutions. Under irradiation with high-pressure mercury lamps, the removal rates of MO using FS-FT (0 g) and FS-FT (0.36 g) in pure MO solution reached 90.25% and 99% at 25 min, respectively, which indicates that FS-FT (0.36 g) can enhance photocatalytic performance. The removal rates of MO using FS-FT (0 g) and FS-FT (0.36 g) in MO-Na\({}_{2}\)SO\({}_{4}\)-blended solution (C\({}_{0}\) = 10 mg/L, C\({}_{\text{Na}}\)2 SO\({}_{4}\) = 12.5 g/L) reached 92.38% and 97.16% at 25 min, respectively. The removal rate of MO using FS-FT (0.36 g) decreased in MO-Na\({}_{2}\)SO\({}_{4}\)-blended solution in the previous 25 min, which indicates that Na\({}_{2}\)SO\({}_{4}\) can inhibit degradation using FS-FT (0.36 g). The degradation experiments of MO-Na\({}_{2}\)SO\({}_{4}\)-blended solutions with different concentrations of Na\({}_{2}\)SO\({}_{4}\) using FS-FT (0.36 g) showed that as the concentration of Na\({}_{2}\)SO\({}_{4}\) increases, the inhibitory effect becomes more pronounced. Recovery and recycling experiments confirmed that the photocatalyst exhibited robust degradation performance over multiple cycles. Kinetic analysis of the photocatalytic data, based on a first-order model, was conducted to explore the underlying degradation principles.  
photocatalytic degradation; Fe-doping; TiO\({}_{2}\); methyl orange; sodium sulfate 2025
## 3 Design of Functional Modules  
### Ethernet Communication  
The Ethernet communication model has two parts: send and receive, as illustrated in Figure 5.
## Abbreviations  
BNNs Boron nitride nano-sheets CNF Cellulose nanofibrils CNTs Carbon nanotubes DGE-BA Diglycidyl ether - Bisphenol-A MOSFETs Metal-oxide-semiconductor field-effect transistors MWCNTs Multi-walled carbon nanotubes OFETs Organic field-effect transistors PC Polycarbonate PDMS Polydimethylsiloxane PET Polyethyleneerephthalate PP Polypropylene PPC Polypropylene PRTFE Polytetrafluoroethylene PVC Polyvinyl chloride PVA Polyvinyl alcohol P(VDF-TrFE-CFE) Poly(vinylidene fluoride-trifluoroethylene chloroformoethylene) PVDF Polyvinylidene fluoride PVPy Polyvinylpyrrolidone RAFT Reversible addition-fragmentation chain transfer RF Radio frequency SMECH Sigma mixing, extrusion, calendering, followed by hot pressing TFTs Thin-film transistors
# A Combined Approach of Field Data and Earth Observation for Coastal Risk Assessment
## 2 Methodology
### Land-Sea Interface Extraction  
A radiometric approach has been used for the extraction of the shoreline: it exploits the amplitude of SAR images by assuming that, in the absence of wave and/or wind, surface water exhibits very low backscattering values with respect to onshore areas.  
Three different segmentation approaches have been implemented and tested on real data: (i) edge-detection methods, (ii) region-growing algorithms, (iii) active contour analyses. The first two approaches have been widely experimented for shoreline extraction from SAR images, with satisfactory results. The last one, known as Local Gaussian Distribution Fitting (LGDF) [57], is an innovative approach, originally developed for bio-medical applications, and never experimented before for the segmentation of SAR images. The LGDF approach has been tested because SAR images have a similar behavior as medical diagnostic images that are also affected by many noise sources and classical edge- or region-based segmentation approaches often fail.  
In order to assess the potentials of shoreline extraction algorithms based on high resolution SAR data acquired by the CSK constellation, tests have been performed on both sandy and rocky (or urbanized) shores. The LGDF algorithm has been proved to work well in all the examined situations and it has been implemented in a two-step shoreline extraction process. The co-registered dataset is initially segmented using a global thresholding algorithm [55], in order to extract a preliminary \"coarse\" shoreline. The land-sea interface detection is hence refined by exploiting the \"first-guess\" land-sea boundary as well as the coastal type information, that are both provided as input to the LGDF segmentation code.  
After the LGDF segmentation step, for noisy images, the end result might not be what is expected and a refinement step must be performed. In such cases, COSMO-Beach enables the operator to re-analyze SAR images and manually change input parameters of the segmentation algorithm. This change can be applied globally (i.e., to the whole image) or locally, i.e., only on those stretches that exhibit critical results, thus allowing a faster and more accurate refinement. In any case, a visual inspection of segmentation results by a skilled operator is recommended, although time-consuming, since land-sea interface from SAR images is not clear as shoreline from optical images. The visual inspection and the refinement step require operator interaction, whereas all the other processes are fully automated.
## 2 Materials and Methods
### Remote Sensing Data
#### 2.2.2 UAV Data  
Remote sensing data collection involved ultra-high-resolution UAV data captured via a DJI Phantom 4 Multispectral UAV from an altitude of 150 m with base RTK-station. The P4 Multispectral (Table 2) consolidates the process of capturing data that provides insight into crop health and vegetation management. A spectral sunlight sensor catches solar irradiance, ensuring maximization of the accuracy and consistency of data collection. When combined with post-processed data, this information helps obtain the most accurate calculation of the vegetation indices (VI) [57]. The camera and image calibrations were carried out during processing in Agisoft Metashape.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Satellite** & **Band Name** & **Wavelength (nm)** & **Spatial Resolution (m)** & **Citation** \\ \hline \multirow{8}{*}{**Planet Scope**} & Band 1—Coastal Blue & 431–452 & 3 \\ \cline{2-4}  & Band 2—Blue & 465–515 & 3 \\ \cline{2-4}  & Band 3—Green & 513–549 & 3 \\ \cline{2-4}  & Band 4—Green & 547–583 & 3 \\ \cline{2-4}  & Band 5—Yellow & 600–620 & 3 \\ \cline{2-4}  & Band 6—Red & 650–745 & 3 \\ \cline{2-4}  & Band 7—Red Edge & 780–680 & 3 \\ \cline{2-4}  & Band 8—Near Infrared & 845–885 & 3 \\ \hline \multirow{8}{*}{**Sentinel-2 L2A**} & Band 1—Coastal/Aerosol & 433–453 & 60 \\ \cline{2-4}  & Band 2—Blue & 458–523 & 10 \\ \cline{2-4}  & Band 3—Green & 542–578 & 10 \\ \cline{2-4}  & Band 4—Red & 650–680 & 10 \\ \cline{2-4}  & Band 5—Red Edge 1 & 698–713 & 20 \\ \cline{2-4}  & Band 6—Red Edge 2 & 733–748 & 20 \\ \cline{2-4}  & Band 7—Red Edge 3 & 773–793 & 20 \\ \cline{2-4}  & Band 8—Near Infrared (NIR) & 784–875 & 10 \\ \cline{2-4}  & Band 8—Near Infrared (NIR Narrow) & 865–885 & 20 \\ \cline{2-4}  & Band 9—Water Vapor & 935–955 & 60 \\ \cline{2-4}  & Band 10—SWIR—Cirrus & 1373–1390 & 60 \\ \cline{2-4}  & Band 11—SWIR 1 & 1565–1655 & 20 \\ \cline{2-4}  & Band 12—SWIR 2 & 2100–2280 & 20 \\ \hline \multirow{8}{*}{**Landsat 8 OLI**} & Band 1—Coastal/Aerosol & 433–453 & 30 \\ \cline{2-4}  & Band 2—Blue & 450–515 & 30 \\ \cline{2-4}  & Band 3—Green & 525–600 & 30 \\ \cline{2-4}  & Band 4—Red & 630–680 & 30 \\ \cline{2-4}  & Band 5—Near Infrared (NIR) & 845–885 & 30 \\ \cline{2-4}  & Band 6—Shortwave Infrared 1 (SWIR 1) & 1560–1660 & 30 \\ \cline{2-4}  & Band 7—Shortwave Infrared 2 (SWIR 2) & 2100–2300 & 30 \\ \cline{2-4}  & Band 8—Panchromatic & 500–680 & 15 \\ \cline{2-4}  & Band 9—Cirrus & 1360–1390 & 30 \\ \cline{2-4}  & Band 10—Thermal Infrared 1 (TIRS 1) & 10600–11190 & 100 \\ \cline{2-4}  & Band 11—Thermal Infrared 2 (TIRS 2) & 11500–12510 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The technical characteristics of applied RS space sources.
# A Dynamic Service Placement Based on Deep Reinforcement Learning in Mobile Edge Computing
## 3 Model and Problem Formulation
### System Model  
First, we are given a substrate distribution of MEC nodes \(M=\{m_{j}\}\) that are supported by the network operator. Each MEC node is attached to a base station with limited computing and storage capacities, where \(R^{c}_{m_{j}}\) denotes the computing capacity of \(m_{j}\), and \(R^{s}_{m_{j}}\) denotes the storage capacity of \(m_{j}\). We use a set \(U=\{u_{i}\}\) to denote the users with mobilities that are served by the MEC nodes. The users that subscribe to the services from the MEC operators are distributed over the coverage region of the base station. To better capture the users' mobilities, the system is assumed to operate in a slotted structure, and its timeline is discretized into time frame \(t\in\mathbf{T}=\{0,1,2,...,T\}\)[14]. At all discrete time slots, each mobile user sends a service request to the MEC node that can be accessed. We use \(V\) to denote the set of services that are supported by the operators, where \(V=\{v_{h}\}\). We assume that the services are deployed on the virtual machines, and each user can only be served by one service on the MEC. To simplify the description, we use color squares to represent the placed services. Each MEC has a service range shown in Figure 1. Here, we suppose that the capacities of MECs are heterogeneous, and their service ranges are different. We use light orange color circles with different sizes to represent the coverage ranges of each MEC. Let \(x^{j}_{\hat{m}}(t)=1\) denote user \(u_{i}\) using the service \(v_{h}\) which is placed on edge server \(m_{j}\) at time slot \(t\); otherwise, \(x^{j}_{\hat{m}}(t)=0\). For each MEC node, we use \(\mathbb{V}_{m_{j}}\) to denote the set of services that are placed on edge server \(m_{j}\), where \(\mathbb{V}_{m_{j}}=\{v_{h}|_{m_{j}<v_{h}}\}\). We suppose that each service only serves one user at a time, and we use \(\mathbb{U}(\mathbb{V}_{m_{j}})\) to denote the set of users that is served by the services in set \(\mathbb{V}_{m_{j}}\). For the convenience of reference, we summarize the main notations throughout this paper in Table 1.
## 3 Development of the Proposed Agricultural Drone Battery Cell Balancing Algorithm
### SOC Estimation Algorithm with Improved Current Integration  
The proposed system includes an improved SOC estimation algorithm that addresses the shortcomings in existing current integration methods used for battery cell balancing in agricultural drones. In existing current integration methods, sensor errors and noise are allowed to accumulate [14]; however, in the proposed system, the accumulated error is periodically reset through the battery model [15]. This model is based on an improved shepherd battery model (ISBM). The existing shepherd battery model has a major drawback, i.e., it cannot be used to simulate the transient characteristics of the battery, because it incorporates the internal resistance of the battery. Instead, in the proposed algorithm, the open-circuit voltage (OCV) of the battery [13] was calculated by applying an ISBM that includes a parallel resistor-capacitor (RC) circuit like that in a Thevenin battery model (TBM) [16]. Here, the OCV refers to the potential difference between both terminals of a cell or battery when the voltage circuit is open in a no-load state. The OCV-SOC table was calculated by measuring the cumulative error for each 10% change in the SOC [16; 17]. Then, SOC estimation was performed based on the accumulated error. A flowchart illustrating the SOC estimation algorithm is shown in Figure 6.
## 4 Flow Field Characteristics behind the WGB
### Mean Wind Characteristics
#### 4.1.2 Transverse Velocity (\(v\)) Distributions  
Figure 14 shows the effect of various WGB cases on the transverse wind velocity on the bridge deck. It can be observed that as the distance from the WGB increases, the transverse wind velocity gradually decreases. Compared with a vertical wind barrier, the flow-deflecting effect of WGB is significantly enhanced within the train height range. However, high transverse wind velocity still appears near the windward side of the bridge deck due to the influence of jet flow. Large-scale transverse airflow deflection also appears above the top of the WGB and below the shear layer, with a max transverse wind velocity of up to \(-0.25\). Since there is no abrupt change in wind velocity in the external flow field of the shear layer, and the transverse wind velocity contour lines exhibit a trend of decreasing speed with increasing distance from the wind barrier in the external flow field, the difference in transverse wind velocity may be caused by changes in the pressure gradient.  
Figure 13: Effect of various WGB cases on streamwise wind velocity for the bridge deck: (**a**) Case 2; (**b**) Case 3; (**c**) Case 4; (**d**) Case 5; (**e**) Case 6; (**f**) Case 7.  
Regarding the effect of \(\theta_{1}\), the test results of Cases 3 and 4 show that as \(\theta_{1}\) increases, the transverse wind velocity in the root of the WGB increases, and the absolute value of the transverse wind velocity at 1.65 m increases from \(-\)0.10 to \(-\)0.19. At the same time, the transverse wind velocity in the backflow area near the top of the barrier slightly decreases, ultimately resulting in a decrease in the flow-deflecting effect. Regarding the effect of \(h_{1}\), the test results of Cases 3 and 5 show that, compared with \(\theta_{1}\), changing \(h_{1}\) leads to greater differences in transverse wind velocity, indicating that \(h_{1}\) is crucial for the airflow deflection effect of the WGB. Compared with \(h_{1}\) = 350 mm, when \(h_{1}\) = 500 mm, the max transverse wind velocity on the windward side decreases by more than 0.1, and the max value appears at a height of 1.65 m above the windward sidetrack. Regarding the effect of \(B_{1}\), the test results of Cases 4, 6, and 7 show that as \(B_{1}\) increases, the airflow deflection effect of the WGB gradually strengthens, and the transverse wind velocity on the upstream side of the WGB increases within the height range of the WGB, enhancing the flow-deflecting effect. However, the trend of the transverse wind velocity on the downstream side is opposite to that on the upstream side, which may be due to the influence of the downstream barrier.
# Finger-Vein Verification Based on LSTM Recurrent Neural Networks
## 3 Experiments and Results
### Verification Results Based on Image Dataset from Two Sessions  
This experiment aims at estimating the effectiveness and robustness of various algorithms on the finger-vein image data from both sessions. In the testing dataset, there are 1260 (105 fingers \(\times\) 6 images \(\times\) 2 sessions) images, acquired at two sessions. For each finger, we select the 6 images captured at the first session as enrollment samples and the remaining 6 images captured at the second session as testing samples. The genuine matching scores are produced by matching samples from same finger, while the impostor scores are produced by matching samples from different fingers. This results in a total of 630 (105 \(\times\) 6) genuine scores and (105\(\times\) 104 \(\times\) 6/2) impostor scores, based on which we the compute FRR and FAR. In addition, we computed the sensitive index(\(d^{\prime}\)) [48] by \(d^{\prime}=Z(hit\quad rate)-Z(false\quad alarm\quad rate)\) to estimate the performance of various approaches.  
The experimental results from various approaches are summarized in Table 2. The ROC curves for the corresponding performances are illustrated in Figure 11. The experimental results summarized in Table 2 show consistent trends with the those from experiments in each session. The proposed SCNN-LSTM-based approaches (e.g., SCNN-LSTM + Unsupervised encoding and SCNN-LSTM + Supervised encoding) get the best results, especially at the lower FAR. The lowest EER of 2.38% is achieved using the supervised encoding approach. Similarly, the proposed method achieves higher \(d^{\prime}\) (e.g., 3.89 and 3.95) compared to existing approaches, which implies that the lowest verification error is achieved using our SCNN-LSTM model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **First Session** & **Second Session** & **Two Sessions** \\ \hline Repeated line tracking [24] & 4.76 & 5.67 & 5.21 \\ Maximum curvature point [15] & 3.91 & 3.27 & 3.59 \\ Region growth [27] & 2.32 & 1.24 & 1.75 \\ Wide line detector [13] & 3.68 & 3.11 & 3.39 \\ Gabor filters [3] & 2.10 & 1.84 & 1.95 \\ Mean curvature [14] & 2.06 & 1.50 & 1.73 \\ Difference curvature [43] & 3.61 & 3.64 & 3.64 \\ CNN [33] & 1.21 & 0.86 & 1.12 \\ SCNN-LSTM + Unsupervised encoding & 1.12 & 0.62 & 1.01 \\ SCNN-LSTM + Supervised encoding & 1.01 & 0.58 & 0.95 \\ \hline \hline \end{tabular}
\end{table}
Table 1: EER of various approaches on image dataset from one session.  
Figure 10: Receiver operating characteristics from image data collected at (**a**) first session, (**b**) second session, and (**c**) two mixed sessions.
## 3 Implementation Strategy of Dynamic Routing Protocol for Awareness Combat Networks  
There are two routing mechanism modes for awareness combat networks: node state routing and mobile cellular routing. The switching of the two routing mechanisms is controlled by the transmission power of the node. For each node, power switching is performed only when it cannot find the next-hopnode according to the current routing mechanism, thereby avoiding the end-to-end delay [13] caused by frequent power switching. In general, when the transceiver parameters and the channel conditions are fixed, the transmission power of the node determines the communication distance of the node. The transmission power parameter of a node is contained in its data packet transmitted, and the node receiving the data packet sets the transmission power parameter to be the same when replying to the above node. In this way, unidirectional links can be avoided. The routing protocol designed in this paper supports QoS and automatically selects the corresponding routing algorithm based on the priority according to the service type transmitted by the node (i.e., the packet header identifier added on the network layer).
## 3 Test Results and Analysis
### Analysis of Orthogonal Test Results
#### 3.1.1 Range Analysis  
From the results of the orthogonal test polarity in Figure 3, we can observe that the larger the extreme value of the factor R, the more significant the effect on the compressive strength of the cured soil at that age. The factors affecting the 3d and 28d unconfined compressive strength of the cured soil were in the following orders of priority: cement dosage > slag dosage > phosphogypsum dosage > fly ash dosage and cement dosage > slag dosage > phosphogypsum dosage > fly ash dosage. Additionally, the main relationship of the factors affecting 7d was as follows: cement dosing > slag dosing > fly ash dosing > phosphogypsum dosing. It can be seen that cement dosage and slag dosage were the main factors, and fly ash and phosphogypsum were the secondary factors; the dosage of phosphogypsum had a positive effect on the excitation of the strength of the fluid-cured soil.  
According to the results of the analysis graph of the mean value of the unconfined compressive strength in Figure 4, Factor A (cement admixture, K\({}_{4}\)), Factor C (slag admixture, K\({}_{4}\)), and Factor D (phosphogypsum admixture, K\({}_{2}\)) were greater than the other mean values, K, at 3d, 7d, and 28d. Factor B (fly ash admixture, K4) was at its maximum at 3d and 7d, and K2 was greater than the other mean values, K, at 28d. The optimal ratios at 3d and 7d derived from the above analysis were A\({}_{4}\)B\({}_{4}\)C\({}_{4}\)D\({}_{1}\) (18% cement, 8% fly ash, 8% slag, 1% phosphogypsum), while the optimum ratio at 28d was A\({}_{4}\)B\({}_{1}\)C\({}_{4}\)D\({}_{1}\) (18% cement, 2% fly ash, 8% slag, 1% phosphogypsum).  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline
**Serial** & & & & & **3d** & **7d** & **28d** \\
**Number** & **A** & **B** & **C** & **D** & **Compressive** & **Compressive** & **Compressive** \\  & & & & & & **Strength / kPa** & **Strength / kPa** \\ \hline F\({}_{8}\) & 12 & 8 & 6 & 1 & 47.07 & 95.74 & 182.78 \\ F\({}_{9}\) & 15 & 2 & 6 & 2 & 42.83 & 72.80 & 168.62 \\ F\({}_{10}\) & 15 & 4 & 8 & 1.5 & 61.97 & 118.37 & 239.72 \\ F\({}_{11}\) & 15 & 6 & 2 & 1 & 34.88 & 58.60 & 130.09 \\ F\({}_{12}\) & 15 & 8 & 4 & 0.5 & 37.02 & 61.05 & 159.23 \\ F\({}_{13}\) & 18 & 2 & 8 & 1 & 79.40 & 125.63 & 356.51 \\ F\({}_{14}\) & 18 & 4 & 6 & 0.5 & 59.39 & 139.70 & 279.56 \\ F\({}_{15}\) & 18 & 6 & 4 & 2 & 53.23 & 103.83 & 230.49 \\ F\({}_{16}\) & 18 & 8 & 2 & 1.5 & 46.94 & 77.96 & 159.45 \\ \hline \end{tabular}  
* A: cement content. B: fly ash content. C: blast furnace slag content. D: phosphogypsum content.  
\end{table}
Table 3: _Cont._  
Figure 3: Analysis graph of the range of unconfined compressive strength.