esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Evaluation of ECHERP
A more analytical description of the effects of the use of the two protocols in comparison may be derived by focusing on the first node depletion time, the last node depletion time and the average energy consumption. Table 1 summarizes the results, concerning these metrics which are illustrated in Figures 4 and 5 in the case of uniform energy distribution. Figure 6: Last node depletion time in rounds _versus_\(N_{\mathrm{r}}\) as base station (BS0 is located 100 m, 150 m, 200 m, 300 m and 400 m away from the center of the network field. Figure 7: First node depletion time in rounds _versus_\(N_{\mathrm{r}}\) as BS is located 100 m, 150 m, 200 m, 300 m and 400 m away from the center of the network field. In Tables 2 and 3, the simulation results, which are illustrated in Figures 6 and 7, concerning the base station located at a distance of 150 m and 300 m from the center of the sensor field are presented. In both tables, it is shown that the proposed protocol has better performance when the node ratio \(N_{\text{r}}\) of the nodes of the upper level to nodes of the lower level is 1.1 for the network. \begin{table}
\begin{tabular}{c c c c c} \hline
**Distance between the Base** & **First Node** & **Last Node** & **Mean Energy** & **Compared** \\
**Station and the Center of** & **Depletion Time** & **Depletion** & **Consumption** & **Protocol** \\
**the WSN Field (m)** & **(\%)** & **Time (\%)** & **(\%)** & **(\%)** \\ \hline
150 & \(+98\) & \(+7.5\) & \(-25.05\) & LEACH \\
150 & \(+5\) & \(+30\) & \(-19.12\) & PEGASIS \\
150 & \(-10\) & \(+16\) & \(-17.25\) & BCDCP \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of ECHERP to LEACH, PEGASIS and BCDCP as a function of the distance of the base station from the center of the sensor field when the initial node energy is set to 2 J. \begin{table}
\begin{tabular}{c c c c} \hline
**Node Ratio \(N_{\text{r}}\) of Nodes** & **First Node** & **Last Node** & **Mean Energy** \\
**on upper _versus_ Nodes** & **Depletion Time** & **Depletion Time** & **Consumption** \\
**on Lower Level** & **(\%)** & **(\%)** & **(\%)** \\ \hline
1 & - & - & - \\
1.07 & \(+2\) & \(+3.5\) & \(-2.5\) \\
1.1 & \(+10\) & \(+10\) & \(-5.7\) \\
1.15 & \(+8.2\) & \(+8\) & \(-4.5\) \\
1.2 & \(+4\) & \(+4.8\) & \(-3.2\) \\
1.25 & \(+2\) & \(+2.7\) & \(-2.1\) \\
1.5 & \(+2\) & \(+2\) & \(-1.9\) \\ \hline \end{tabular}
\end{table} 
Table 2: Percentage of change in WSN performance by the use of ECHERP as a function of the node ratio \(N_{\text{r}}\) when the base station is located 150 m away the center of the sensor field. \begin{table}
\begin{tabular}{c c c c} \hline
**Node ratio \(N_{\text{r}}\) of Nodes** & **First Node** & **Last Node** & **Mean Energy** \\
**on Upper _versus_ Nodes** & **Depletion Time** & **Depletion Time** & **Consumption** \\
**on Lower Level** & **(\%)** & **(\%)** & **(\%)** \\ \hline
1 & - & - & - \\
1.07 & \(+2.1\) & \(+3.1\) & \(-2.2\) \\
1.1 & \(+10\) & \(+10\) & \(-5.7\) \\
1.15 & \(+6.8\) & \(+7\) & \(-3.5\) \\
1.2
# An Efficient SMOTE-Based Deep Learning Model for Voice Pathology Detection
## 3 Results  
### Experimental Setup  
The 20-dimensional MFCC and LPC parameters are obtained from each speech file of the SVD. The modules used to extract the MFCC and LPC are set to a frame size with 40 ms and a frame overlapping rate of 50%. The SMOTE, ADASYN, and Borderline-SMOTE algorithms with the four nearest neighbors are used (k = 4), with a total of 42 random states. We experimented SMOTE, ADASYN, and Borderline-SMOTE algorithms with k = 3, 4, and 5. When k was equal to 4, it was confirmed that the best performance was shown. This study focuses on the effectiveness of using the proposed method to generate minority class samples rather than controlling the hyperparameters of the deep learning classifier to achieve optimal classification performance. Table 2 lists the main parameters of each model. First, we study the classification between normal and pathological voices using an FNN with two hidden layers. A rectified linear unit (ReLU) is activated after the first layer, and the activation of softmax function occurs after the last layer [38]. The main parameter values are shown in Table 2. This study also uses a CNN with four consecutively convolutional layers, in which the convolutional mask has a kernel of size 3 \(\times\) 3 and ReLU activation functions with 64, 64, 32, and 32 convolutional masks for each layer. The CNN also has four max pooling layers with sizes of 2 \(\times\) 2, one dense layer with 512 nodes where each node has an ReLU activation function, and one softmax output layer with four neurons. The details are presented in Table 2. Experimental results are obtained through 10-fold cross-validation to ensure that each fold of the training (70%) and testing (30%) data contains at least one sample from the minority class. The models are implemented using Python 3.7 with the scikit-learn, imbalanced-learn, and PyTorch libraries.
## 5 Proposed Solution
### Data Recovery  
The sink receives \(\beta\) and \(\alpha\) coefficients, or the correlated packet for data recovery by means of prediction. It distinguishes this based on packet size. Thereafter, the predictor calculates the values of the missing readings based on \(\beta\) and \(\alpha\) coefficients of the multiple linear regression function [Equation (6)]. However, if the correlated packet arrives at the sink instead of coefficients, it uses \(\beta\) and \(\alpha\) coefficients of the correlated sensor node stored in the coefficients table of the sink.  
In our approach, we decided to adopt a statistical technique to be the predictor due to two main reasons: (1) we are initially developing studies to assess the effects of multivariate correlation and its advantages over univariate correlation; and (2) we intend to adopt computational intelligence techniques to identify its benefits over statistical techniques in further works.  
The prediction of variables using multiple linear regression is calculated according to Equation (7):  
\[Y_{q_{ij}}=\beta_{0}+\ \beta_{1}*X_{p_{i}\alpha}+,...,+\beta_{j}*X_{p_{ij}} \tag{7}\]  
where \(Y_{q_{ij}}\) represents one one-dimensional vector, which contains the values of predictions made for one dependent variable \(q\) and \(X_{p_{ij}}\) represents the multidimensional vector, which contains values history of the samples from more than one independent variable \(p\). \(Y_{q_{ij}}=y_{q_{1}\alpha},...,y_{q_{ij}}\) and \(X_{p_{ij}}=x_{p_{i}\alpha},...,x_{p_{ij}}\), with \(i=1,...,n\), where\(n\) is the number of samples, and \(j=1,...,k\), where \(k\) is the dimension of the vector \(X_{p_{ij}}\). \(\beta\) and \(\alpha\) respectively represent the coefficients calculated using Equation (6). As a reminder, \(\beta_{0}=\alpha\) due to compatibility with the notation of \(\beta\) and \(\alpha\) coefficients used in this paper.  
The prediction by simple linear regression is calculated by Equation (4), but our proposed solution uses a multivariate correlation, instead of a univariate one. Then, our solution uses Equation (7) to perform predictions of the values of the variables.
## 5 Conclusions  
Laboratory experiment procedures were designed to quantify the influence of loading stress and velocity on the deformation and particle breakage behaviour of the gangue backfill materials. Factors that lead to these influences were also analysed. The following conclusions can be drawn:  
1. Loading stress and strain have a logarithmic relationship. Increasing the loading velocity destroys the contact structures among the gangue particles and leads to a larger deformation of gangue filling materials.
2. With the increase in stress, the particles of 16-20 mm size were the first to be crushed in large quantities, and their CPPAC reached \(-65.29\%\). At this time, the large-size gangue particles above 20 mm showed a stronger bearing capacity, and were mainly crushed after a loading stress greater than 10 MPa.
3. Further increasing the loading velocity increases the breakage of the gangue filling materials. The particle breakage ratio has a logarithmic functional relationship with the loading velocity.
4. The compression deformation of gangue backfill materials and particle breakage has a high correlation. When ground stress is lower than 5 MPa, the content of coarse particles should be increased to enhance the bearing capacity of the backfill body. When the ground stress is higher than 10 MPa, the content of fine particles should be increased to reduce the particle fragmentation rate.  
Conceptualization, Y.G. and J.Z.; methodology, M.L.; validation, Y.G. and L.S.; investigation, P.L.; resources, L.S.; data curation, Y.G.; writing--original draft preparation, Y.G.; writing--review and editing, Y.G., W.T. and P.L.; supervision, J.Z. and W.T.; funding acquisition, J.Z., M.L. and W.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science Fund for Distinguished Young Scholars [grant number 51725403]; the National Natural Science Foundation of China [grant number 52004271]; the China Postdoctoral Science Foundation [grant number 2021M693417]; the Jiangsu Postdoctoral Research Funding Program [grant number 2021K039A]; the Postgraduate Research & Practice Innovation Program of Jiangsu Province [KYCX22_2611]; and the Graduate Innovation Program of China University of Mining and Technology [grant number 2022 WLKXJ005].  
Figure 8: The fitting relationship between the breakage ratio and the strain.  
Data Availability Statement: The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors would like to acknowledge the support of the Xinjulong Coal Mine for the specimen collection.  
The authors declare no conflict of interest.
# Minimum Frequency and Voltage Stability Constrained Unit Commitment for AC/DC Transmission Systems
## 4 Model Decomposition and Solution
### Security Constraints Sub-Model
Set \(iter=1\), then, initialize bus real and reactive power injection and buses voltage;
* Calculate coefficient matrix and right-end unbalance value in Formulas (31)-(33) and the upper and lower limits of the variables in Formulas (34)-(39);
* Solve the above models with the linear programming method and obtain \(\Delta P\), \(\Delta Q\), \(\Delta\theta\), \(\Delta V\), \(\Delta P_{\mathrm{dc}}\), \(\Delta Q_{\mathrm{dc}}\), \(\Delta X_{\mathrm{ac}}\), \(\Delta X_{\mathrm{dc}}\), and all relaxation variables;
* Update all variables; if min (\(\Delta P\), \(\Delta Q\), \(\Delta\theta\), \(\Delta V\), \(\Delta P_{\mathrm{dc}}\), \(\Delta Q_{\mathrm{dc}}\)) \(\leq\)\(\varepsilon\) or \(iter\geq IT_{\max}\), the calculation is over; or \(iter=iter+1\), return to step 2. After several calculations of the linear programming problem, if the objective function \(w^{t}=0\) at the time interval \(t\), the generator ON/OFF and real power output could satisfy operating constraints; otherwise Benders cut constraints, as shown in constraint (40), would be formed and supplied to the master model for recalculation. \[\mathrm{w}^{t}+\sum_{i\in N_{G}}\lambda_{i}\times\left(P_{G,i}^{t}-\left(P_{G, i}^{t}\right)^{t}\right)+\sum_{i\in N_{G}}\overline{\psi}\times Q_{G,j,max} \times\left(u_{i}^{t}-\left(u_{i}^{t}\right)^{t}\right)+\sum_{i\in N_{G}} \underline{\psi}\times Q_{G,i,min}\times\left(u_{i}^{t}-\left(u_{i}^{t} \right)\right)\leq 0 \tag{40}\]  
Because of Benders cuts, new solution of generators' ON/OFF and real power output would be forced to eliminate \(w^{t}\) as much as possible. By repeating this process in such way, the optimal generators' ON/OFF and active power are finally obtained.
## 2 Applied Sensor Engineering in Logistic and Shipping Activities
This work provides valuable insights to maritime safety authorities regarding adjustment of ship speeds to avoid heavy traffic congestion in narrow waterways and minimize the associated maritime risks. [PERSON] et al. [69] also applied AIS data to propose a time efficiency assessment that evaluates the amount of time each ship spends in the different areas within a port (i.e., berth, anchorage, and fairway) based on the space-time trajectories of ship movements in Shanghai Yangshan Port and Xiamen Port in China. In this study, the space-time path concept from time geography is used to generate space-time trajectories from collected AIS data for vessel traffic services (VTS). The basic idea in this proposed work assumes that a ship is considered a moving object whose location changes over time in a port. The AIS is integrated with a GPS receiver and gyrocompass, and reports specific information, including Maritime Mobile Service Identity, navigation status, turn rate, speed over ground, position coordinate, etc. The AIS trajectory-based time efficiency assessment framework is divided into five different steps: trajectory construction, geographic zone identification, status classification, time efficiency statistics, and time efficiency assessment, as illustrated in Figure 7. This study provides useful information to shipping lines and port authorities for efficient scheduling and logistic support during operation. The challenge for future research is the integration of AIS data with other relevant ship information (e.g., cargo volume and cargo type) and port operation data (e.g., operation schedule and loading/unloading capacity) that can further enhance the proposed framework to conduct more comprehensive efficiency assessments. Figure 7: AIS-based time efficiency assessment framework [69].
## 3 Results and Discussion
### Genesis of Low As Bioavailability in Paddy Soils in Karst Areas
Previous studies have demonstrated that Mn oxide minerals play a primary role in oxidizing As(III) to As(V), which is typically accompanied by the reduction of Mn(IV) to Mn(III) and Mn(II) [77; 78]. It was found that the released Mn(II) was easily re-adsorbed at the vacancy sites of Mn oxides until they were completely occupied, which led to the surface passivation of Mn oxides, thereby reducing As adsorption [79]. Iron oxide minerals are recognized as effective adsorbents of As, exhibiting a notably stronger adsorption capacity for As than Mn oxide minerals under isothermal conditions [80]. The X-ray absorption spectroscopy results of the partial environment of As in goethite show that, in addition to the usual bidentate binuclear complex, there is a bidentate mononuclear complex in the absorption, which suggests that As is adsorbed onto the surface of the particles or blocked in the voids and pores [81]. [PERSON] et al. [82] observed a stronger As(III) adsorption on hematite than on goethite over a broad pH range from 0 to 14. Furthermore, a similar adsorption of As was observed in clay minerals. [PERSON] and [PERSON] [83] investigated six clay minerals, demonstrating that As adsorbed onto clay surfaces can likely infiltrate into the inner pores of clay aggregates, and the dehydration of As residing on clay surfaces could enhance the adherence of As to clays. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Groups** & **Diameter (mm)** & **Statistics** & **As (mg/kg)** & **TFe\({}_{\text{2}}\)O\({}_{\text{3}}\) (\%)** & **Mn (mg/kg)** & **Al\({}_{\text{2}}\)O\({}_{\text{3}}\) (\%)** & **SiO\({}_{\text{2}}\) (\%)** \\ \hline \multirow{4}{*}{A} & \multirow{4}{*}{0.3–0.5} & Min & 128.00 & 25.78 & 764.00 & 18.48 & 14.14 \\  & & Max & 187.00 & 30.01 & 11,640.00 & 22.04 & 27.92 \\  & & Mid & 143.50 & 26.69 & 3830.00 & 20.47 & 22.00 \\  & & Mean & 151.33 & 27.10 & 5108.00 & 20.35 & 21.04 \\ \hline \multirow{4}{*}{B} & \multirow{4}{*}{0.5–1} & Min & 158.00 & 27.67 & 832.00 & 18.34 & 11.55 \\  & & Max & 253.00 & 32.55 & 15,451.00 & 20.28 & 20.01 \\  & & Mid & 175.50 & 30.03 & 3296.50 & 19.23 & 15.86 \\  & & Mean & 188.50 & 30.09 & 5458.67 & 19.37 & 15.81 \\ \hline \multirow{4}{*}{C} & \multirow{4}{*}{1–2} & Min & 153.00 & 27.96 & 803.00 & 17.62 & 10.63 \\  & & Max & 288.00 & 32.33 & 26,331.00 & 20.51 & 20.88 \\ \cline{1-1}  & & Mid & 196.00 & 31.27 & 5194.00 & 19.35 & 14.77 \\ \cline{1-1}  & & Mean & 201.50 & 30.54 & 9512.00 & 19.20 & 15.24 \\ \hline \end{tabular}
\end{table} 
Table 8: The contents of As, \(\text{TF}_{\text{e}}\text{2}\text{O}_{3}\), Mn, \(\text{Al}_{\text{2}}\text{O}_{3}\), and \(\text{SiO}_{\text{2}}\) in the three groups of the Fe–Mn nodules (\(N=6\)). than 0.01%. This suggests that, in karst areas, generous As, tightly adsorbed into Fe-Mn nodules, is barely bioavailable, making it difficult for rice to assimilate it, which may be the ultimate cause of the high content but low bioavailability of As in the paddy soil.
## 4 Experiments and Results
### Evaluation Metrics  
Five different metrics were used to evaluate the performance of the proposed scheme: Mean Absolute Error (MAE), accuracy, precision, recall, and F1-score. F1-score is the harmonic mean of precision and recall.  
The selected metrics can be calculated as follows:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{8}\]  
where \(TP\) represents true positives, \(TN\) represents true negatives, \(FP\) represents false positives, and \(FN\) represents false negatives.  
\[MAE=\frac{1}{K}\sum_{k=1}^{K}\lvert g_{k}-g^{\prime}_{k}\rvert \tag{9}\]  
where \(g_{k}\) and \(g\prime_{k}\) represent the real and predicted number of beach visitors, respectively, and \(K\) denotes the total number of testing samples.  
\[Precision=\frac{TP}{TP+FP} \tag{10}\]  
\[Recall=\frac{TP}{TP+FN} \tag{11}\]  
\[\text{F1}-\text{score}=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{12}\]
## 4 Proposed ECC-GA-PASR Approach
### Genetic Algorithm (GA)-Based Optimized RSU Distribution
#### 4.1.2 Optimal RSU Distribution
\[M_{f}=\alpha P_{1}+\beta P_{2}+\gamma P_{3} \tag{6}\]  
\[\alpha+\beta+\gamma=1 \tag{7}\]  
In Equation (6), \(M_{f}\) indicated the minimizing function for RSUs distribution. \(\alpha,\;\beta,\;\text{and}\;\gamma\) are the weight factors used for the deployment process. \(P_{1},\;P_{2},\;\text{and}\;P_{3}\) are decision-making parameters for RSU distribution. In GA, Phenotype and Genotype are used, which helps to represent the genetic code (binary in nature) congruent to the number of RSUs. This code represents the localization of RSUs in the given coverage area. The calculation of those parameters helps in finding the finest road segments and the required number of RSUs to cover the entire network. In Table 5, the genetic code illustration is shown. In the table, road division 2 and 4 need two RSUs those decisions are made by the reflection of the considered parameters according to the need of the road division. This is the process behind the distribution of RSUs in the VANETs-based network. Hence in VANETs, providing security becomes essential due to the intervention of DDoS attacks. In the upcoming section, ECC-based authentication is used to protect the network from the Sybil and black hole attack.
## 3 Samples and Analytical Methods
### Analytical Methods
#### 3.2.1 Carbon-Sulfur Content Analysis  
Rock sample powders were pre-treated. First, we soaked the sample in excess HCl: H\({}_{2}\)O = 1:7 dilute hydrochloric acid for the full reaction time to remove inorganic carbon. Then, we washed the sample with ultrapure water and dried it. After pretreatment, we put the samples into the CS-230 analyzer of LECO Company to analyze the total sulfur and total carbon content of the rock sample powder. The analysis standard of carbon and sulfur content was GB/T 19145-2003.
# Optimization of Airflow Organization in Bidirectional Air Supply Data Centers in China
## 2 Materials and Methods  
### Field Experiment  
#### 2.1.1 Overview of Data Centers  
The data center is located in Guangzhou, Guangdong Province, China (23\({}^{\circ}\)10\({}^{\prime}\) N, 113\({}^{\circ}\)45\({}^{\prime}\) E). The room dimensions are 20.95 m (length) \(\times\) 23.40 m (width) \(\times\) 5.2 m (height), with a raised floor height of 0.7 m and a floor-to-ceiling clearance of 4.5 m. The IT equipment room covers an area of 502.3 m\({}^{2}\), with a ceiling height of 4.5 m and a raised floor height of 0.7 m. The large air-conditioning room occupies 94.3 m\({}^{2}\), while the small air-conditioning room measures 64.8 m\({}^{2}\). The IT equipment room and the two adjacent air-conditioning rooms are separated by interior walls.  
A total of nine Computer Room Air Conditioning (CRAC) units are installed on both sides of the data center, with seven units operating continuously and two units kept on standby. The airflow organization follows an underfloor supply and side-return configuration, where cold air is delivered into the raised-floor plenum of the IT equipment room through curved air ducts at the bottom of the CRAC units. Hot air is returned to the top of the CRAC units via return air openings in the interior walls. Each CRAC unit has a rated power of 6.4 kW and a rated airflow of 34,800 m\({}^{3}\)/h.  
The data center houses 142 racks, arranged in eight rows. The servers inside the racks include 1 U, 2 U, 10 U, and 15 U configurations. A Power Distribution Unit (PDU)is installed at the end of each rack row. The racks are arranged in a cold aisle/hot aisle containment layout, with both cold and hot aisles having a width of 1.2 m. The perforated floor tiles in the cold aisles measure 0.6 m \(\times\) 0.6 m, with a porosity of 36%. Figure 1 shows the overall layout of the data center.
## 3 Results
### Phase I: Defining PC On-site Assembly Optimization Principles via BIM-GA Hybrid System
#### Constraints of Optimizing Assembly Sequence  
To improve productivity in construction, effective control of influencing factors such as labor and equipment is essential [35]. In practical construction settings, assembly plans are developed based on specific considerations to enhance operational efficiency. To gain insight into the specific considerations of real sites, interviews were conducted with stakeholders at a PC construction site. The interviewees, including three construction engineers, one administrative team leader, and one site management director, provided insights into the considerations during component assembly and identified the factors contributing to delays. Focus group interviews were conducted both on site in February 2023 and through subsequent email exchanges in June, unveiling the critical factors influencing assembly sequences:  
1. Time-Efficient Sequencing: Sequential installation of similar components, typically in the order of columns, beams, and slabs within specific zones, optimizing workflow dependencies.
2. Minimized delays and movement: Adjacent components within the work area are prioritized to save waiting times and unnecessary equipment and labor movements, ensuring efficient installation. The validation of component adjacency within a feasible radius is pivotal.
3. External Factors and Coordination: Weather fluctuations and other external elements can disrupt project schedules. Daily coordination in precast concrete assemblies, particularly during crane activity, is critical. Factors such as component damage, delivery delays, and on-site hindrances must be considered.  
Figure 2: System framework. (Source: own elaboration).  
Based on the findings from a Focus Group Interview (FGI), this study aimed to derive an assembly sequence considering the following factors influencing the on-site workflow: 1, Complexity of tasks; 2, Adjacency within the working radius; and 3, Interference with other construction activities. Typically, planning is conducted by floor levels, so as a result, this study restricts the scope to creating assembly plans within the same floor while considering these three constraints. The generated assembly sequence verifies the compliance of each item for consecutive components. If violations occur, penalty scores are assigned to minimize these scores and derive the most efficient assembly sequence (Figure 3). Further details on each item are outlined below.