esa-sceva/satcom-chunk-collection huggingface dataset:
# Property Analysis of Slag Composite Concrete Using a Kinetic-Thermodynamic Hydration Model
## 4 Discussion  
Compared with previous studies, the main innovations of this paper are as follows:  
First, using the results of the experimental study of the isothermal heat of hydration, we determined the reaction degree of slag. There are many experimental techniques for measuring the reaction extent of slag, such as the method of selective dissolution, the back-scattered electrons (BSE) image analysis method, and nuclear magnetic resonance testing [29; 30; 31]. These experimental methods are direct methods that directly demonstrate the reaction extent of slag. In contrast, isothermal hydration heat must be measured using the indirect method because the reactions of cement and slag are coupled together. The proposed model can separate the slag reaction from the hydration of Portland cement. Slag reactions are decoupled from the hydration of Portland cement. The contribution of slag to the hydration heat can be quantitatively determined. The results from using the indirect method in this study show agreement with the results of the direct method used in [7].  
Second, the reaction degree of slag is an essential input parameter for GEM-Selektor. The proposed kinetic hydration model can determine the reaction degree of slag, which  
Figure 10: CSH-based strength model.  
can be used as an input parameter for GEM-Selektor. The purpose of this study was not to replace GEM-Selektor but to provide convenient input parameters for GEM-Selektor. Moreover, because the coefficients of the proposed kinetic hydration model are constant for various mixtures, one set of experimental data on hydration heat is sufficient to determine the coefficients [32]. In other words, by using the proposed kinetic hydration model, the high time- and cost-consumption of other methods for the measurement of the reaction extent of slag can be eliminated.  
Third, the theoretical implication of the work is that the proposed kinetic-thermo dynamic hydration model can predict the phase assemblage and strength development of cement-slag composite. Moreover, because concrete's durability properties are closely related to phase assemblage, the proposed model has potential application in predicting the durability properties of concrete. In addition, the main practical implication of the work is the material design and construction management of slag composite concrete. Based on the proposed model, concrete manufacturers can select the suitable slag replacement percentages, and the construction managers can confirm the stages of the removal of formwork from concrete structures.
## 6 Design of the Proposed MPPT Technique
### Functioning of Proposed MPPT  
The flowchart of the proposed algorithm is shown Figure 7. The inputs to the algorithm are \(I_{pv}\) and \(V_{pv}\), based on which the instantaneous photovoltaic power \(P_{pv}\) is calculated. Following the conventional _P&O_ algorithm, the proposed algorithm then compares the stored photovoltaic power with the instantaneous PV power. Then, the proposed algorithm decides the positive or negative direction of a new voltage step in the same way as _P&O_ and sets the value of D. Before moving to a new iteration, the proposed algorithm estimates the \(V_{pv(CAL)}\) value and measures the \(V_{pv}\) value to identify whether the converter is in CCM or DCM, as explained in previous section. If the calculated \(V_{pv(CAL)}\) and measured \(V_{pv}\) values are less than a limit '\(e\)', then the converter is considered to be operating in CCM and the conventional _P&O_ is maintained. If it is not within this limit, the system is considered to be operating in discontinuous mode. When DCM operation is detected, the duty cycle is changed by adding an arbitrary value to ensure that it returns to continuous mode operation. In this work, this arbitrary value is set at 0.25 and, the positive step in D leads to a negative step in \(V_{pv}\).  
Figure 7: Flow Chart of the proposed MPPT technique.
# A Topology Map Generation Algorithm for Optimal Path Finding for Image-Based Maps
## 7 Conclusions  
Here, we propose using a CNA for generating a topology map through preprocessing to address the limitations of pathfinding algorithms encountered when processing high-resolution image maps. The performance of existing pathfinding algorithms, such as A*, improved significantly by first generating the topology map with the CNA. The CNA creates a topology map through two steps: node creation and connection. Since implementing the CNA is simple and uses a mathematical method to connect nodes, a path can be easily created. In addition, the topology map has the advantage that it can be reused as long as the map data do not change. For indoor situations, the CNA is highly useful because the internal structure of the building itself rarely changes. In this paper, we applied the CNA to an AR device, HoloLens2, and displayed the path using a topology map for indoor navigation. The user selects the destination location through the application, and a path from the user's location to the destination is created and given to the user as a guide.  
Since the generated path is displayed as a virtual object, in the real world and on the mini-map, the user does not need to understand the structure of the building because the application directly displays the path in the real world as a guideline through AR devices. We expect that the time required for pathfinding during activities such as rescue will be reduced.  
To compare performance when using the A* algorithm alone with the image map and using it with the topology map generated through the CNA, we analysed the node compression rate, number of node visits in the topology map, and processing time. The CNA reduced the number of visited nodes by (at least) 99.82 to 99.95%. A comparison of the path search times with and without the topology map showed that the processing time was reduced by at least 90.77% and up to 99.71%.  
The CNA showed excellent results in most cases, especially with fewer corners and more obstacles to the destination. However, it showed poor performance when preprocessing in a diagonal space, which should be addressed in future work.  
Conceptualization, S.-H.K., K.-O.L. and Y.-J.-C.; Formal analysis, S.-H.K., K.-O.L., S.-H.K. and Y.-Y.P.; Investigation, S.-H.K., K.-O.L. and Y.-Y.P.; Project administration, S.-H.K. and H.-W.L.; Resources, Y.-J.C. and H.-W.L.; Software, S.-H.K.; Supervision, K.-O.L., Y.-J.C. and Y.-Y.P.; Validation, S.-H.K., K.-O.L., H.-W.L. and Y.-Y.P.; Writing--Original draft, S.-H.K.; Writing--Review & editing, S.-H.K., K.-O.L. and H.-W.L.; All authors have read and agreed to the published version of the manuscript.  
This work was supported by a National Research Foundation of Korea grant funded by the Korean Government (NRF2021R1A2C1004651).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Effect of Coatings on the Content of Polyphenols of Fresh-Cut Parsley during Storage
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Storage Time** **(Days)**} & \multicolumn{4}{c}{**Polyphenols (mg GAE/g d.m.)**} \\ \cline{2-5}  & \multicolumn{4}{c}{**Type of Material**} \\ \cline{2-5}  & **Control** & **WPI** & **WPI\_JO\_1\%** & **WPI\_JO\_2\%** \\ \hline
0 & 4.32 \(\pm\) 0.26 \({}^{\text{a,B}}\) & 3.61 \(\pm\) 0.21 \({}^{\text{a,A}}\) & 3.13 \(\pm\) 0.37 \({}^{\text{a,A}}\) & 5.97 \(\pm\) 0.13 \({}^{\text{a,C}}\) \\
7 & 4.02 \(\pm\) 0.57 \({}^{\text{a,A}}\) & 5.11 \(\pm\) 0.32 \({}^{\text{b,A}}\) & 7.21 \(\pm\) 0.22 \({}^{\text{b,B}}\) & 7.56 \(\pm\) 1.17 \({}^{\text{ab,B}}\) \\
14 & 3.72 \(\pm\) 0.17 \({}^{\text{a,A}}\) & 6.03 \(\pm\) 0.35 \({}^{\text{c,B}}\) & 6.95 \(\pm\) 0.05 \({}^{\text{b,C}}\) & 6.72 \(\pm\) 0.13 \({}^{\text{ab,C}}\) \\
21 & 8.60 \(\pm\) 0.13 \({}^{\text{c,B}}\) & 7.55 \(\pm\) 0.37 \({}^{\text{d,A}}\) & 9.38 \(\pm\) 0.47 \({}^{\text{c,B}}\) & 7.41 \(\pm\) 0.39 \({}^{\text{ab,A}}\) \\
28 & 5.59 \(\pm\) 0.05 \({}^{\text{b,A}}\) & 5.81 \(\pm\) 0.09 \({}^{\text{bc,A}}\) & 9.82 \(\pm\) 0.09 \({}^{\text{c,C}}\) & 7.77 \(\pm\) 0.51 \({}^{\text{b,B}}\) \\ \hline \hline \end{tabular} Mean values with standard deviations in brackets. Different superscript letters (*–d) within the same column or (*–C) within the lines indicate significant differences between the samples (\(p<0.05\)).  
\end{table} 
Table 4: Polyphenol content of samples uncoated (control) and coated with whey protein isolate (WPI) coatings incorporated with jojoba oil (JO) of fresh-cut root parsley during storage. 2% jojoba oil, and no significant differences (\(p<0.05\)) were observed between the control sample and the coating with the addition of 1% jojoba oil. [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) coated fresh-cut parsley roots of 0.5 cm-thick rings with coatings based on 0.1% chitosan and 1% citric acid. The coated samples were stored for 4 weeks at 4 \({}^{\circ}\)C. The authors showed similar decreasing and increasing trends in the total content of polyphenols during storage. The increase in the total content of polyphenols occurred after 1 week of storage from 23.4 mg/g d.m. up to 32.8 mg/g d.m. in the control sample, 35.1 mg/g d.m. for controls containing citric acid, and 28.9 mg/g d.m. in trials of chitosan-coated parsley. The content of polyphenols decreased after 2 weeks and increased again after 3 and 4 weeks of storage under refrigerated conditions. However, [PERSON] and [PERSON] and [PERSON] (2018), analyzing the effect of a chitosan coating on chestnut fruit during a storage period of 2 weeks, noticed an increase in the content of polyphenols after a week of storage and a decrease in their content after 2 weeks. The delayed increase in the polyphenol content was probably related to the inhibition of the enzyme that influences the regulation of the phenolic compound biosynthetic pathway. Also in these studies, the authors did not observe any significant effect of the chitosan coating on the total phenol content during storage.
# Drone SAR Imaging for Monitoring an Active Landslide Adjacent to the M25 at Flint Hall Farm
## 2 Study Area
### Hydrological Regime  
In situ permeability tests have demonstrated significant fissure flow in the Gault Clay, with high permeability rates between 5 \(\times\) 10\({}^{-6}\) and 7 \(\times\) 10\({}^{-7}\) m/s [1]. Whilst the groundwater flows towards the bedrock dip direction at a regional scale, the local groundwater flows towards the slope dip direction. The Gault Clay is an impermeable barrier, thus creating a perched aquifer at the top surface, and spring lines at the Upper Greensand and Gault Clay boundary. The springs saturate the underlying Gault Clay and create instabilities in slopes exceeding a 7\({}^{\circ}\) gradient [25].  
Groundwater monitoring data from 2012 indicates a relatively high groundwater table surrounding Flint Hall Farm, which may be a contributing factor to any ground displacements [27]. The groundwater fluctuates seasonally, generally between 1 m and 2 m, with flow orientated towards the slope direction. To the north of the M25, groundwater levels vary from 0.6 m below ground level (bgl) during wet winters to 3.5 m bgl during dry months. To the south of the M25, groundwater levels vary from 1-2 m bgl to ground level, with saturated ground visible during site visits [27]. In an attempt to reduce the water level and stabilise the slope, 48 siphon wells across 310 m of the southern embankment were installed in 2005. Despite this, records from 2008 to 2012 indicate these measures were ineffective due to maintenance and biofouling issues [25].
### [PERSON] and [PERSON] *  
Department of Atmospheric Science, Kongju National University, 182 Shinkwan-dong,  
Gongju 314-701, Korea; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +82-41-850-8533; Fax: +82-41-856-8527.  
_Received: 21 June 2013; in revised form: 30 July 2013 / Accepted: 1 August 2013 / Published: 9 August 2013_  
**Abstract:** We evaluated the precision of land surface temperature (LST) operationally retrieved from the Korean multipurpose geostationary satellite, Communication, Ocean and Meteorological Satellite (COMS). The split-window (SW)-type retrieval algorithm was developed through radiative transfer model simulations under various atmospheric profiles, satellite zenith angles, surface emissivity values and surface lapse rate conditions using Moderate Resolution Atmospheric Transmission version 4 (MODTRAN4). The estimation capabilities of the COMS SW (CSW) LST algorithm were evaluated for various impacting factors, and the retrieval accuracy of COMS LST data was evaluated with collocated Moderate Resolution Imaging Spectroradiometer (MODIS) LST data. The surface emissivity values for two SW channels were generated using a vegetation cover method. The CSW algorithm estimated the LST distribution reasonably well (averaged bias = 0.00 K, Root Mean Square Error (RMSE) = 1.41 K, correlation coefficient = 0.99); however, the estimation capabilities of the CSW algorithm were significantly impacted by large brightness temperature differences and surface lapse rates. The CSW algorithm reproduced spatiotemporal variations of LST comparing well to MODIS LST data, irrespective of what month or time of day the data were collected from. The one-year evaluation results with MODIS LST data showed that the annual mean bias, RMSE and correlation coefficient for the CSW algorithm were \(-\)1.009 K, 2.613 K and 0.988, respectively.  
**Keywords:** land surface temperature; split-window algorithm; COMS; MODIS; evaluation
# A Stable Backup Routing Protocol for Wireless Ad Hoc Networks
## 3 Simulation and Analysis
On the contrary, both AODV-SBR and DYMO-SBR select stable backup routes with lower packet error rate, and hence their performances improve a lot. Figure 6b shows that the packet delivery ratio of AODV-SLR is also lower than that of AODV-SBR and DYMO-SBR and the performance gap becomes more evident as the CBR data rate increases. The key reason is that when the network load becomes heavier, the probability of interference and collision suffered becomes higher and, hence, link breaking occurs more frequently. In SBR, the stability of backup routes is considered. Therefore, SBR guarantees transmission with better reliability and the packet loss decreases. The results of average packet delay are demonstrated in Figure 7. As we can see from Figure 7a, the delay under our AODV-SBR and DYMO-SBR remains lower than that under AODV-SLR. The reason is that link condition is considered in SBR, which leads to better reliability. As we know, link breaking may force the routing protocol to initiate the path rebuilding process, which inevitably incurs extra transmission delay. Therefore, a more stable route means lower delay. Figure 7b reflects that AODV-SLR improves delay performance a little bit since it does not need to wait a period of \(\mathrm{T_{th}}\) for selecting backup routes. However, as network load becomes heavier, the AODV-SBR and DYMO-SBR decrease  
Figure 6: Packet delivery ratio versus (a) pause time and (b) CBR data rate. transmission delay significantly. That is because the backup routes in SBR are more stable. Moreover, DBR is given highest priority to be selected to deliver packets, and thus the backup route in SBR may be shorter than that constructed by SLR. Control overhead performance is shown in Figure 8, where we can observe that the proposed SBR has better performance with lower overhead. This is because SBR builds more stable backup routes. In fact, when link breaking happens, the processes of both backup route construction and original route rediscovery would bring additional routing cost. Therefore, improving route stability plays a key role in decreasing control overhead. In addition, from Figure 8, DYMO-SBR achieves the lowest control overhead, which benefits from its path accumulation scheme.
## 2 Proposed Method
### Appliance States Identification and Load Disaggregation
#### 2.2.2 Power Retrieve  
We received several total power consumption trajectories when the appliance with the largest power contribution was operating. Then, we used SVR to retrieve the power of the highest contribution appliance in the time window and then adjusted the result.  
Support vector machines (SVM) were first introduced in 1992 [36]. As one of two categories of support vector machines, a version of SVM for regression (SVR) was proposed by [PERSON] [37]. The parameters \(c\) and \(g\) have a large impact on the predicted results of the SVR model. Parameter \(c\) is the penalty factor. The higher the \(c\), the lower the tolerance of error is, and overfitting is easy to happen. The smaller \(c\) is, the easier underfitting is. This means that the generalization ability is poor if \(c\) is too big or too small. Parameter \(g\) comes with an RBF (Radial Basis Function) as the kernel. The larger the \(g\), the fewer the support vectors, and the smaller the \(g\), the more the support vectors. The number of support vectors affects the speed of training and prediction. Genetic algorithms [38] are used for parameter \(c\) and \(g\) selection to optimize the SVR to estimate the power trajectory of the appliance.  
In our paper, the power values of signature segments split in Section 2.1 were numbered from 1 min, and each signature segment represented the duration of an appliance. For example, the power values of signature segments of a refrigerator in an operating cycle are as follows:  
[148,135,129,127,127,125,124,124,123,122].  
After being numbered, the signature segment was turned to:  
[(1,148),(2,135),(3,129),(4,127),(5,127),(6,125),(7,124),(8,124),(9,123),(10,122)].  
As each appliance has its own consumption pattern, the SVR model can use it to realize power estimation. We adopted time number in the above example to train the model. In order to improve the performance of the SVR model, the genetic algorithm was used to select \(c\) and \(g\). The flowchart of optimized SVR (OSVR) is shown in Figure 2.  
The newest total power time series were obtained by subtracting the retrieved power subsequence by SVR from the total power time series. Repeat the process in Section 2.2 until all the appliances are retrieved.
# Novel ZIF-67@Bentonite (ZIF-67@BNT) Nanocomposite for Aqueous Methyl Orange Removal
## 4 Conclusions  
This study examined the removal of MO dye from polluted water through adsorption using a ZIF-67@Bentonite nanocomposite (ZIF-67@BNT). The synthesized ZIF-67@BNT was characterized using Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and thermogravimetric analysis (TGA). Furthermore, the batch adsorption studies demonstrated ZIF-67@BNT has a high MO adsorption capacity of 187 mg/g at an initial concentration of 30 mg/L, 5 mg dosage, and a 5 h duration. The response surface methodology (RSM)-based modeling exercise was used to optimize the adsorption process. While assessing the impact of various operational factors, the initial MO concentration, followed by ZIF-67@BNT dose, was noted to be important. Adsorption kinetics and isotherm studies were also completed. The adsorption kinetics results were best described by the Avrami model, whereas the MO adsorption isotherm data fitted well to the Redlich-Peterson (R-P) model. The ZIF-67@BNT nanocomposite 'after adsorption analysis' indicated multiple mechanisms facilitating MO uptake onto ZIF-67@BNT, including electrostatic and \(\pi\)-\(\pi\) interactions. Additionally, various machine learning (ML) models, such ANN, SVR, RF, and GPR, were also utilized to predict MO adsorption onto ZIF-67@BNT nanocomposite under a varying set of conditions. In conclusion, the novel ZIF-67@BNT adsorbent can effectively treat MO-contaminated water, and both the RSM- and ML-based modeling approaches yielded good optimization results.  
Conceptualization, S.A.O. and M.S.V.; Methodology, K.F.; Software, K.F., S.A.O. and M.S.V.; Validation, K.F., S.A.O. and M.S.V.; Formal analysis, K.F., S.A.O. and M.S.V.; Investigation, K.F., S.A.O. and M.S.V.; Resources, S.A.O. and M.S.V.; Data curation, K.F., S.A.O. and M.S.V.; Writing--original draft, K.F., S.A.O. and M.S.V.; Writing--review & editing, K.F., S.A.O. and M.S.V.; Visualization, K.F., S.A.O. and M.S.V.; Supervision, S.A.O. and M.S.V.; Project administration, M.S.V.; Funding acquisition, M.S.V. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Interdisciplinary Research Center for Construction and Building Materials (IRC-CBM) at the King Fahd University of Petroleum & Minerals (KFUPM) under Research Grant # INCB2420.  
Figure 16: Taylor diagram for both testing and training.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Acknowledgments:** The authors would like to thank the Civil and Environmental Engineering Department and the Chemical Engineering Department at King Fahd University of Petroleum & Minerals (KFUPM) for providing the lab facilities.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Reducing Video Coding Complexity Based on CNN-CBAM in HEVC
## 3 Experimental Results
### Analyzing Experimental Outcomes  
#### 3.3.1 Performance Comparison with HM16.5 Encoder  
To evaluate the proposed CNN-CBAM algorithm, we performed it and the original one in HM16.5. It has already been mentioned how the four indicators were obtained (i.e., BD-BR, BD-PSNR, \(\Delta T\), and \(\Delta T_{avg}\)).  
Table 4 indicates that the computational complexity of the suggested method was significantly lower than that of the method in HM16.5. On the one hand, the proposed approach saved an average 64.05% of time. The complexity reduction was especially evident for class B with gentle motion, reaching 74.24%, and even for class C sequences with vigorous motion, reaching 53.21%. For sequences such as Kimono, which has a background with high texture complexity and slow-moving foreground targets, the average time saving even reached 87.32%. The proposed CNN-CBAM method for the CU partition's accurate prediction accounted for the proposed algorithm's low complexity, thereby avoiding unnecessary redundant iterations. On the other hand, although there was a decrease in RD performance per sequence, it was not significant. The average loss of video quality  
Figure 7: Comparison results between CNN-CBAM and CNN in average of the A-E classes. (**a**) comparison of \(\Delta T_{avg}\) (**%**); (**b**) comparison of BD-BR (**%**); (**c**) comparison of BD-PSNR (dB).  
was only 0.09. There was only a 1% average bit rate increase. Undeniably, the proposed algorithm, in general, maintained a good rate-distortion performance.  
To display the proposed algorithm's RD performance, we took the two sequences with the greatest difference in performance loss among the test sequences as an example. Figure 8 depicts the RD curves of the Basketball Drive and BQ Square sequences. The figure indicates that the suggested algorithm's RD performance lines up with the original approach in HM16.5. The experimental results showed that various sequences with varied video properties had comparable characteristics. In addition, the average loss of the video quality was only 0.09 dB, with a maximum drop of 0.15 dB (for the Four People sequence) and a minimum drop of 0.02 dB (for the BQ Square sequence), which was almost negligible. Undeniably, the network structure we devised efficiently decreased the coding complexity while maintaining the coding quality.