esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Photocatalytic Reaction
#### 3.2.3 Effects of Anions  
Anionic species are very common in natural and industrial wastewater. In this section, the impact of anions on the photodegradation of ethiefencarb using SnIn4S8 photocatalyst was studied, and 0.05 M solutions of Na2 CO3, NaCl, NaNO3, and Na2 SO4 were used separately to study the influence of carbonate, chloride, nitrate, and sulfate ions on the reaction rate using an initial concentration of 10 mg L-1 of ethiefencarb with a 0.5 g L-1 of SnIn4S8. Figure 7 shows that ethiefencarb degradation was slightly inhibited by these anions, leading to prolonged reaction times. Similar inhibition effect has been reported before with anionic species such as NaCl, NaNO3, and Na2 SO4, where these anions might compete on the active sites of the photocatalyst surface or lead to its deactivation [45; 46]. Other reports showed that such anions might act as scavengers for the active species (positive holes (h+) and hydroxyl radicals (\({}^{\bullet}\)OH)), producing less reactive radicals such as Cl* or ClOH-\({}^{\bullet}\) (Equations (1) and (2)). The generated less active radicals slow down the rate of photocatalytic degradation [5].  
\[\text{Cl}^{-}+\text{h}^{+}\rightarrow\text{Cl}^{\bullet} \tag{1}\]  
\[\text{Cl}^{-}+{\text{\textbullet}}\text{OH}\rightarrow\text{ClOH}^{-\bullet} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c} \hline
**pH** & \(\mathbf{k_{\text{app}}}\) (h\({}^{-1}\)) & \(\mathbf{R^{2}}\) \\ \hline
3 & 0.1546 & 0.998 \\
5 & 0.0982 & 0.986 \\
7 & 0.0773 & 0.977 \\
9 & 0.0661 & 0.978 \\ \hline \end{tabular}
\end{table}
Table 1: Photodegradation kinetics parameters (rate constants and linear regression coefficients \(R^{2}\)) for ethiefencarb in the presence of SnIn4S8 (0.5 g L-1).  
Figure 6: (**a**) pH effect on the photocatalytic degradation rate of ethiefencarb. Experimental conditions: ethiefencarb concentration 10 mg L-1; SnIn4S8 concentration 0.5 g L-1. (**b**) Photodegradation kinetics of ethiefencarb in the SnIn4S8/visible system.
## 5 Results and Analysis
### Sufficiency Analysis  
The analysis of sufficiency, as described in Section 3.7, is visually represented in Figure 19, which plots IM sufficiency in relation to site conditions using \(\theta_{\text{max}}\) as the DM, incorporating both earthquake magnitude and fault distance. Figure 19 reveals that 78% of the selected IMs adequately meet the criteria for magnitude sufficiency. Notably, \(A_{\text{rms}}\), VI, \(I_{\text{c}}\), \(D_{\text{rms}}\), and CAI display enhanced magnitude sufficiency, whereas vector compound-type IMs lag behind in this regard. Figure 19 confirms that all IMs satisfy the fault distance sufficiency criteria.  
In terms of the sufficiency between IMs, values of \(I\) for each IM are calculated using Equation (7) and are depicted in Figure 20. The figure highlights IMs with excellent and poor sufficiency, including \(A_{\text{rs}}\), ASI, PGD, and DI. \(A_{\text{rs}}\) manifests better sufficiency, as evidenced by consistently positive \(I\) values, followed by ASI. Conversely, DI shows negative \(I\) values (except for self-comparison), indicating its limited sufficiency relative to other IMs. Similarly, PGD also exhibits reduced sufficiency. Overall, the analysis reveals that \(A_{\text{rs}}\), ASI, \(I_{\text{c}}\), PGA, and SMA possess greater sufficiency, whereas displacement-type IMs generally demonstrate inferior sufficiency.  
Figure 19: Sufficiency of site conditions.
## 4 Conclusions  
The study of air pollution by nutrients, PM\({}_{2.5}\), PM\({}_{10}\), and elemental carbon was carried out during 2020-2022.  
The results of our study are as follows:  
* The maximum phosphates concentration for the samples from an open sampler was more than 26 times higher than the volume-weighted mean concentration; the concentrations of inorganic nitrogen and silicates exceeded the VWM value for an open sampler by 5 times. These concentration values were determined in precipitations after dust air masses transported above the Crimean coast;
* In case of dust transport from the deserts of Central Asia, Sahara, and Syria, the daily-averaged concentrations of PM\({}_{10}\) and PM\({}_{2.5}\) particles in the atmosphere of Sevastopol reach and even exceed the corresponding maximum permissible concentrations. The values of the daily-averaged concentrations of microparticles compared with the norms adopted in Russia, exceeded the MPC values in seven cases for PM\({}_{2.5}\) and in three cases for PM\({}_{10}\). The European MPC values were exceeded in 17 cases for PM\({}_{2.5}\) particles and in 6 cases for PM\({}_{10}\). The average annual concentrations of suspended particles are approximately half of the MPC for PM\({}_{2.5}\) particles, according to Russian standards. However, according to the norms of European countries, the average annual concentration of PM\({}_{2.5}\) even exceeds the established level, and the concentration of PM\({}_{10}\) corresponds to the maximum permissible concentration;
* The duration of the influence of both local sources and long-range atmospheric transport depends on weather conditions: wind speed and direction and atmospheric pressure as well as precipitation, which cleanses the atmosphere of impurities;
* The concentration of soot in the air samples collected at the meteorological station did not exceed the MPC level. However, elemental carbon concentration in the air samples collected near highways exceeds the concentration at the background station by 30-50 times.  
This work was carried out within the framework of the state assignments of Marine Hydrophysical Institute of RAS No. 0555-2021-0005 and the Russian Foundation for Basic Research (RFBR) according to research project No. 19-05-50023.  
Not applicable.  
**Data Availability Statement:** All the data and additional information supporting the findings of this study are available from the corresponding author upon reasonable request.  
The author declares no conflict of interest.
# Experimental Study on the Workability and Stability of Steel Slag Self-Compacting Concrete
###### Abstract  
**Featured Application: Steel slag self-compacting concrete (SSCC) with relatively ideal workability was prepared by substituting steel slag for natural fine aggregate to realize solid waste resource utilization. It is beneficial to solve the problems of steel slag abandonment, land occupation, environment pollution and low utilization rate, and realize utilization of a large amount with low-energy consumption for traditional material saving and sustainable development. SSCC has very high application value and economic significance.**  
**Keywords: steel slag; self-compacting concrete; workability; dynamic stability; static stability** +
Footnote †: journal: Applied Sciences
## 2 Background  
Initially, a survey of the state of the art [1] concerning the problem at hand was conducted, directed at machine learning in EOL testing or the industrial environment.  
The mentioned paper [1] followed a design science research methodology that revised the necessary technologies to develop the prototype.  
Considering the growing demands connected with quality and reliability standards and the vast quantity of data generated by the massive realization of EOL tests, these data can be treated through artificial intelligence/machine learning algorithms [2] to improve industrialization significantly. The survey focused on reviewing the most common machine learning algorithms, which may help develop the system. Out of all the algorithms that were reviewed, long-short term memory networks (LSTM) were shown to be the most suitable based on the generated data.  
One application which may be useful to consider can be seen in [3] where [PERSON] et al. used an LSTM model to compensate the negative effects of imperfect channel state information (CSI) in the practical radiofrequency systems. This CSI imperfection is usually caused by the channel estimation error and the transmission and processing delay, knowing that imperfect CSI severely reduces the system secrecy capacity. An LSTM-based predictor was designed to alleviate negative effects effectively.  
Analyzing the results obtained from log files (which store the data from testing processes) allows for a better understanding of the evolution of the data, which can indicate patterns and trends based on data sequences (which can be processed by LSTM). Data-driven approaches using machine learning techniques are found to offer a promising potential for improved quality control in manufacturing [4].  
Detecting such patterns and trends can help in the preventive detection of system disruptions before a fault occurs in the system or corrective maintenance after detecting a fault in the system.  
LSTM is also used for anomaly detection seen in [5]. The cited paper presents a novel detection and prediction procedure based on a LSTM architecture to cooperatively predict process outputs and anomalies by using two separate but interacting models. This interesting application of LSTM allows a solution for short-term as well as long-term anomalies.  
Although some applications show the use of LSTM to benefit and optimize the production of certain products, the type of product under study is yet to be seen discussed in other papers.
## 2 Methodology
### First Step: New Target Locations in Case of Intersections  
The communication ranges of some nodes can intersect. In this case, the UAV can be advantageously positioned at the intersections to communicate with several nodes simultaneously. The first step consists then to change the target locations by the points located at the center of the intersection areas, see Figure 4. That enables to reduce the number of target locations that will lead to reduce the tour length. For simplicity reasons, the communication ranges of these new points are defined by circles centered on the new point and whose radius is defined with respect to the boundaries of the previous circles.
## 3 Experiment
### Experimental Details  
The input of Driving Behavior Information Network (DBIN) is the video frame of DBNet. We change the original size to make the input size \(224\times 224\times 3\). Epoch is set to 100. The labels of training data are driving behaviors (speed and steering wheel angle), in which the steering wheel angle indicates turning right and turning left with positive and negative values. The loss function is the Mean Square Error (MSE), which can evaluate the degree of data change. The smaller the MSE value, the better the accuracy of the experimental data described by the prediction model. The MSE mathematical expression is shown in Equation (3), where \(k\) represents the dimension of the data, \(y_{t}\) represents the label of the training data (driving behavior), and \(y_{p}\) represents the predicted value of the driving behavior information network (DBIN).  
\[MSE=\frac{1}{k}\sum_{k}{(y_{t}-y_{p})^{2}} \tag{3}\]  
In order to prevent the value of the loss function from being too large and increase the effect of data fitting, we conduct some processing on the driving behavior data in DBNet. The mathematical expression of the processing process is shown in Equation (4), where \(v\) and \(Ang\) represent the actual collected speed and steering wheel angle, and \(V_{r}\) and \(Ang_{r}\) represent the processed speed and steering wheel angle. The hardware configuration of the experimental environment is NVIDIA GTX1080 video card and 16 GB of memory; the programming environment is Tensorflow.  
\[V_{r}=\frac{v-20}{v}\]  
\[Ang_{r}=Ang\times\frac{\pi}{180} \tag{4}\]
# Smart Ocean: A New Fast Deconvolved Beamforming Algorithm for Multibeam Sonar
## 3 Experimental Results
### Simulation Data Experiment  
In this section, the performances of deconvolved beamforming and CBF are evaluated with the simulation data. The essential parameters of the simulation were as follows: the number of array elements M = 100, array element spacing \(d=\frac{\lambda}{2}\), the half of wavelength, the signal frequency was 200 kHz.  
Figure 3(a) shows the CBF beam power (solid line) and deconvolved beam power (dotted line) with the simulated data. All the beam plots were normalized with the peak set at 0 dB to show the same dynamic range. The SNR for each receiving channel was 40 dB. For CBF, we can see that the main lobe is fat and the leakage of side lobe energy is serious. For deconvolved beamforming, the main lobe was narrow and there was no side lobe leakage, which is almost an impulse function, and this is consistent with the theoretical analysis.  
Figure 4: The CBF beam power (solid line) and deconvolved beam power (dashed line) for a simulated target located at a bearing of 0\({}^{\circ}\) with (**a**) SNR = 40 dB and (**b**) SNR = –10 dB.  
Next, the SNR was turned to -10 dB without changing the other parameters and the performance in a case of low SNR was studied. As shown in Figure 4b, deconvolved beamforming could still form a narrow beam at the bearing of 0\({}^{\circ}\) with an SNR = -10 dB and the noise power was about 4-5 dB lower than that of CBF. The explanation for this is that the noise power of CBF is composed of the equivalent noise introduced by the channel noise and the energy leakage of the main lobe, but the noise power of deconvolved beamforming is made up of only the equivalent impulse noise introduced by the channel noise. Therefore, the noise power of the reduced 4-5 dB is exactly due to the elimination of the side lobe leakage.  
Based on the case shown in Figure 4a, another same strength target was added at the bearing 0.7\({}^{\circ}\) to the simulation and Figure 5a was obtained. For CBF, these two targets were indistinguishable and they were regarded as one target. However, because deconvolved beamforming could easily distinguish them, this implies that the resolution of deconvolved beamforming is higher than that of CBF. Figure 5b shows the details of the main lobe.  
Up to now, the performance of deconvolved beamforming considering both the width of the main lobe and the side lobe level was greatly improved compared to CBF. Then, we generated the multibeam echo data that reflects a flat seabed terrain, the transmit pulse width was 0.5 ms, the receiving channel SNR was 10 dB white noise, and the rough procedure of signal processing of multibeam sonar system is shown in Figure 6.
## 1 Introduction
### Contributions  
The study presented a novel EDO algorithm that enhances the derivation of PV parameter values for various PV models using DE and a bound-constraint modification procedure. The primary contributions of this work can be described as follows:ISSAEDO is a novel approach proposed to effectively identify the appropriate values for PV model parameters by enhancing the SSA and EDO algorithms using a DE technique and a bound-constraint adjustment procedure, which is defined accurately in Sections 3.3.1 and 3.3.2.
2. The DE technique is used to support diversity in populations and provide a comprehensive examination of the search space, thereby reducing the possibility of the ISSAEDO algorithm parallel to a local optimum.
3. In order to establish the adequacy of the proposed ISSAEDO, the appropriate parameter values for two separate PV models, namely the SDM and the DDM, are specified.
4. The ISSAEDO algorithm is assessed using recognized and competitive methodologies for the estimation of unknown parameters in PV systems. The results illustrate the strength and accuracy of the ISSAEDO in acquiring the coefficients of PV models.
## 4 Results
### Results of Numerical Simulation
#### 4.2.2 FOS by Numerical Simulation  
FOS calculated by SRM is shown in Tables 7 and 8 (the percentage of FOS decreased from W0 is marked in the brackets). The FOS of the two models shows a very consistent trend. When the slope is individually affected by three basic factors (W1-W3), the drop of the FOS is in the range of 0.30-19.54% (Model 1, Table 7) and 0.58-18.60% (Model 2, Table 8). When subjected to double factors (W4-W6), the scope of the FOS decrease is 8.14-25.85% (Model 1, Table 7) and 7.78-25.36% (Model 2, Table 8). When all factors appear (W7), the FOS decreases in the range of 25.23-26.15% (Model 1, Table 7) and 25.65-26.03% (Model 2, Table 8).  
Section 4.2.1 shows sliding surfaces in different positions lead to slope failure in two modes. The results of this section indicate the FOS has a consistent variation trend, which is an unexpected outcome. Therefore, EFA is introduced in the next section to analyze the features of the FOS data further.  
Figure 11: **(a)** Maximum shear strain increment cloud maps of sliding surfaces in Model 1, showing a banded shaped area (inset with the location of the screenshot). **(b)** Maximum shear strain increment cloud maps of sliding surfaces in Model 2 (inset with the location of the screenshot). **(c)** Tensile plastic zone of Model 1. The deepest area of the tensile plastic zone has touched the sliding surface. **(d)** Tensile plastic zone of Model 2.  
In our analysis of Model 1 (Table 9), we identified two common factors, F1 and F2, from the data. The cumulative contribution rate of variance reached 98.981%, which exceeds the recommended valid result of 70% for EFA [70]. This demonstrates high efficiency, as each commonality is greater than 0.73. Given that the contribution rate of F1 is higher than that of F2, we consider the factor determined in F1 is the primary factor, while the factor in F2 is considered is the secondary. The factors of F1 and F2 were sorted based on their factor loads. Before sorting, the researchers categorized the factors as single and double. Table 10 summarizes the sorting results of F1 (Model 1). Among the single factors, W2 represents the influence of rainfall, with the highest factor load of 0.859. In the double factors, the factors containing rainfall (W4 and W6) have greater factor loads than the factor without rainfall (W5). Table 10 indicates that the primary factor of Model 1 is rainfall. Other results were sorted and analyzed in the same manner (see Appendix B, Tables A1-A4). Continuing the analysis, for Model 1, the primary factor is rainfall, and the secondary factor is prestress loss. For Model 2, the primary factor is earthquake loading, and the secondary factor is prestress loss.
# Optimization of Machining Parameters Using Fuzzy Taguchi Method for Reducing Tool Wear
## 3 Research Design
### Fuzzy Rules Establishment  
According to the membership function in each rule, three input factors were compared to select the fuzzy membership function. The minimum membership function was derived from the intersection of the two selected input factors. For the fuzzy output, fuzzy values were determined by the maximum area of the centroid value calculated from the maximum value of the union of the fuzzy sets. In this paper, the triangle membership function was selected, and the center of gravity method was used for defuzzification.  
Tool wear is a vital factor affecting cutting quality in precision machining. Changing the cutting tool before tool life results in higher production cost, lower production efficiency, and much disposal of tool inserts, which cause environmental pollution. Therefore, this study established fuzzy rules using cutting speed, cutting depth, and feed rate to minimize the tool wear. The rules are as follows:  
* IF cutting speed is \"low\" and cutting depth is \"low\" and feed rate is \"low\", THEN tool wear is \"large.\"
* IF cutting speed is \"low\" and cutting depth is \"low\" and feed rate is \"medium\", THEN tool wear is \"greatest.\"
* IF cutting speed is \"low\" and cutting depth is \"low\" and feed rate is \"high\", THEN tool wear is \"large.\"
* IF cutting speed is \"low\" and cutting depth is \"medium\" and feed rate is \"low\", THEN tool wear is \"moderate.\"
* IF cutting speed is \"low\" and cutting depth is \"medium\" and feed rate is \"medium\", THEN tool wear is \"large.\"
* IF cutting speed is \"low\" and cutting depth is \"medium\" and feed rate is \"high\", THEN tool wear is \"large.\"
* IF cutting speed is \"low\" and cutting depth is \"high\" and feed rate is \"low\", THEN tool wear is \"minimal.\"
## 3 Interference Mitigation Schemes
### MAC Approach
#### 3.2.12 Cooperative Scheduling with Graph Coloring  
Cooperative scheduling (CS) with graph coloring [36] is the hybrid interference mitigation of the scheduling scheme and graph coloring. The scheduling scheme is similar to DRA [28], in which the sensors within a region affected by interference will transmit orthogonally. In CS, two nearby interfering WBASNs form one cluster. In each cluster, the sensor nodes will transmit orthogonally. The coloring method is applied to assign colors to every cluster so that every cluster has a different color transmitting simultaneously. Interference avoidance is modeled as a graph in which the vertices represent the wireless nodes and the edges represent the resource collision. The color set comprises the time slots that will be assigned to the vertices.
## 3 Observations and Discussions
### Comparisons to [PERSON]'s Analog Spring Model  
Figure 9 presents the data from Table 3 for coefficients of subgrade reactions at the center of foundations with the soil spring constants calculated from [PERSON]'s analog model, i.e., \(k_{\mathrm{s}}=4 Gr_{0}/(1-v)\), where \(G\) is the shear modulus of the soil and \(r_{0}\) is the equivalent radius of the foundation. Note that [PERSON]'s model was suggested for total soil spring under a rigid foundation. The Units of \(k_{\mathrm{s}}\) in [PERSON]'s model are \(F\)/\(L\). In order to make the comparisons, the value of \(k_{\mathrm{s}}\) from [PERSON]'s model was divided by \((n+1)^{2}\) to obtain the value of \(k_{\mathrm{sc}}\) for a series of micro springs, where n is the integer number of the foundation width in meters. Each spring was located at the grid point occupying the area of 1 m\({}^{2}\). It can be learnt that when [PERSON]'s model was used for raft foundations in clay, \(k_{\mathrm{sc}}\) would be significantly overestimated except for the case where raft dimension was 36 m and \(V_{\mathrm{s}}\) was at 180 m/s. Note that the deviations became significant for small rafts (Figure 9a). If [PERSON]'s model was used for raft foundations in sand, \(k_{\mathrm{sc}}\) would be significantly underestimated in the cases where raft dimension was kept at 26 m or 36 m (Figure 9b). The underestimations were especially noted for very stiff sand.  
Figure 8: Normalized function for coefficients of subgrade reactions, \(k_{\mathrm{s}}\) of raft foundations with different dimensions: (**a**) 16 m, (**b**) 26 m, (**c**) 36 m in clays; (**d**) 16 m, (**e**) 26 m, (**f**) 36 m in sands.  
In Figure 10, the values of \(k_{sc}\) are plotted against the corresponding values of foundation rigidity \(K_{r}\) obtained from Equation (3). It can be seen that the influence of \(K_{r}\) on \(k_{sc}\) was relatively small for those obtained from this study in comparison to those from [PERSON]'s model. For the foundation models studied with \(K_{r}\) in the range of 0.005-0.1, the value of \(k_{sc}\) was found to be mainly affected by soil stiffness. Similarly, the values of \(k_{sc}\) predicted from [PERSON]'s model in clay were overestimated compared to those obtained from this study. The values of \(k_{sc}\) from [PERSON]'s model in sand would be underestimated compared to those from this study. The deviations of \(k_{sc}\) in sand became trivial as the foundation rigidity increased, especially for low stiffness sand.  
Figure 10: Comparisons of the coefficient of subgrade reaction \(k_{sc}\) at the foundation center from this study and those from [PERSON]’s model varying with foundation rigidity: (**a**) raft in clays, (**b**) raft in sands.  
Figure 9: Comparisons of the coefficient of subgrade reaction \(k_{sc}\) at the foundation center from this study and those from [PERSON]’s model varying with foundation dimensions: (**a**) rafts in clays, (**b**) rafts in sands.