esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
This paper discusses the problem of continuous casting-hot rolling scheduling for steel enterprises by considering the cold and hot conversion mode of the reheating furnace. First, the slab conversion mode is divided into four categories: cold slab to hot slab (cold-hot), hot slab to cold slab (hot-cold), cold slab to cold slab (cold-cold), and hot slab to hot slab (hot-hot). Based on this, a scheduling model that considers the characteristics of the production process of continuous casting-hot rolling is established, considering the conversion mode of the reheating furnace. In the solution process, an ordered pair improved bat algorithm is proposed to achieve the mixed optimization objective of minimizing completion time and gas wastage, considering the expanded problem model and the inconsistent operating units of the continuous casting-hot rolling process.  
In actual production, the proposed model can find a feasible solution for steel mill scheduling in a short time, which makes it possible to reduce the gas consumption of the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Rated Gas** & **Minimize Time** & **Mixed Model** & **Energy** \\  & **Consumption (GJ)** & **Gas Waste (GJ)** & **Gas Waste (GJ)** & **Conservation** \\ \hline mixed batch & 32,522.6 & 1866.5 & 1066.9 & 2.46\% \\ hot-charge batch & 18,532.8 & 633.4 & 323.7 & 1.67\% \\ cold-charge batch & 49,678.2 & 578.0 & 194.2 & 0.77\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Energy-saving comparison of different types of batches.  
Figure 9: Scheduling Gantt chart in cold-charge mode.  
reheating furnace while completing the production within the delivery period. Among them, the mixed reheating furnace has the greatest energy-saving advantage, which can save more than 5000 GJ per month. If the gas saved here is used for electricity generation in CCPP units, it can significantly increase the enterprise's profit. In conclusion, the modeling and solution algorithm proposed in this paper is effective for solving the actual problem and can achieve the energy-saving and profit maximization of steel mills.  
Conceptualization, N.Z. and Z.L.; methodology, N.Z. and Z.L.; software, N.Z., T.J. and Z.L.; validation, N.Z. and T.J.; writing--original draft preparation, N.Z. and T.J.; writing--review and editing, N.Z., T.J. and Z.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.  
The authors declare no conflicts of interest.
# Collision Avoidance Adaptive Data Rate Algorithm for LoRaWAN
## 6 Results and Discussion
### Confirmed Packet Success Rate  
The confirmed packet success rate (CPSR) refers to the probability of successfully receiving both the uplink and downlink packets within the available transmission attempts. Figures 8 and 9 depict the performance of the proposed SSFIR-ADR protocol in respect of CPSR. The SSFIR-ADR algorithm demonstrates lower energy consumption in relation to the standard ADR algorithm while maintaining comparable or slightly improved CPSR. This is evidenced by the results presented in Figures 8 and 9. The reduction in interference achieved by SSFIR-ADR contributes to energy savings without compromising CPSR. It should be noted that as the node density increases, CPSR tends to decrease. Conversely,  
Figure 6: Data interval vs. UL-PDR.  
Figure 7: Number of EDs vs. UL-PDR.  
CPSR increases with longer application data intervals. These trends are observed due to the higher likelihood of interference and collisions in denser networks, as well as the potential for more retransmissions with shorter data intervals. While using confirmed traffic ensures a higher level of QoS by guaranteeing accurate data reception and processing, it comes with the drawback of consuming additional airtime, limiting the number of downlinks that can be transmitted. This limitation is especially relevant when utilising a single gateway in the network. In cases where the application can accommodate unconfirmed traffic, it is advisable to opt for that approach to maximise network capacity.
## 2 Results and Discussion
### Preparation of Antioxidant Peptides from SBP  
#### 2.2.1 Fractionation of SBP by Ultrafiltration  
Protein hydrolysate is composed of peptides with different molecular sizes and free amino acids, and their bioactivities are influenced by the molecular size of peptides. Therefore, ultrafiltration is a popular method for fractionation and enrichment concentration of peptides with specific molecular sizes from hydrolysates [17]. To obtain purified swim bladder peptide, SBP was fractionated by ultrafiltration with three molecular weight (MW) cut-off membranes of 10, 5, and 3 kDa in turn, respectively, and the resulting four fractions were prepared and named as SBP-I (MW < 3 kDa), SBP-II (3 kDa < MW < 5 kDa), SBP-III (5 kDa < MW < 10 kDa), and SBP-IV (MW > 10 kDa), respectively. The yields of SBP-I, SBP-II, SBP-III, and SBP-IV were 10.08, 17.30, 23.37, and 28.97 mg protein/g swim bladder, respectively.  
For acquiring the fraction with high antioxidant activity, HO\(\bullet\) scavenging activities of four prepared fractions were measured, and the results indicated that HO\(\bullet\) scavenging activities of SBP, SBP-I, SBP-II, SBP-III, and SBP-IV were 50.32% \(\pm\) 0.90%, 34.89% \(\pm\) 2.01%, 40.63% \(\pm\) 1.59%, 58.36% \(\pm\) 1.72%, and 52.57% \(\pm\) 2.20%, respectively, at the concentration of 5 mg/mL. SBP-III showed significantly higher antioxidant activity than SBP and the other three fractions at the tested concentrations (\(p\) < 0.05). In addition, the EC\({}_{50}\) of SBP-III was 3.579 mg/mL and significantly less than that of SBP (8.85 mg/mL). The result was in line with the previous report that that samples with lower average molecular weights possibly contained more substrates, which were electron donors and could react with free radicals to convert them to more stable products and terminate the radical chain reactions [9]. From the data, it could be concluded that SBP-III contained more effective antioxidant peptides and could be chosen for subsequent separation.
## 4 Simulation Analysis
### Economic Analysis of Different Planning Schemes  
The preceding five schemes are employed to conduct the simulation, during which the lower model determines the operating cost of ADN in pursuit of optimal daily flexibility. This encompasses the cost of power purchase and network losses from ADN. In contrast, the upper model calculates the annual investment and operating cost of the SOP and the ESS in pursuit of economic optimality. A comparison of the economic indicators of different schemes is presented in Table 4.  
A comparison of Scheme 1 with the other four schemes demonstrates that the joint installation of SOP and ESS in ADN can effectively enhance the economic benefits of ADN, thereby corroborating the advantages of the coordinated planning of SOP and ESS. Table 4 illustrates that the economic benefits of Schemes 2-5 are 4.54, 6.6, 10.5, and 11.91. Among these schemes, Scheme 4 is observed to have the greatest impact on economic benefits. Although the installation of SOP and ESS does result in a certain amount of investment costs, the network loss is reduced after the installation of SOP and the peak shaving and valley filling reduces the cost of power purchasing after the installation of ESS. This, in turn, reduces the operation and maintenance cost of ADN and improves ADN benefits accordingly. The revenue of ADN is also improved accordingly.
# XOR Binary Gravitational Search Algorithm with Repository: Industry 4.0 Applications
## 6 Industry 4.0 Applications
### Motion Planning of Industrial Robots Inside a Digital Twin Created by V-REP Software
#### 6.1.5 Simulation Results  
The proposed XOR BGSA with repository is iterated for 30 times in Matlab software connected to V-REP through API using 30 particles. The evolution of cost function during optimization is depicted in Figure 11 on a logarithmic scale. As can be seen from this figure, the best cost function value among the 30 particles starts from a high value of 3446 due to collisions and is reduced during optimization to 6.77, which is a collision free, time optimal solution to the problem which hits the desired point. This indicates that for the final collision free solution, the total time is equal to 6.77 s.  
The resulting movements are depicted with a red line in Figure 12, which can be inspected by human expert before final implementation on a real robot.  
The 3D environment provided by simulation software provides complete visual demonstration of movements, which makes it is easy to perform the inspection and ensure suitability of the solution to real-world implementation before that happens. This can be an important safety feature within any working environment and such linking between the human and automated elements of production is a key factor in Industry 4.0 practice.  
Figure 11: Cost function values associated with UR5 during path planning application.  
Figure 12: Optimal path obtained after performing optimization.
## 3 Results and Discussion
### Samples Characterization
#### 3.1.1 TXRF Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{11}{c}{**Stabilized Samples**} \\ \hline
**Time** & \multicolumn{2}{c}{**1 M**} & \multicolumn{2}{c}{**2 M**} & \multicolumn{2}{c}{**3 M**} & \multicolumn{2}{c}{**11 M**} \\ \hline
**pH** & \multicolumn{2}{c}{**12.1**} & \multicolumn{2}{c}{**10.2**} & \multicolumn{2}{c}{**9.87**} & \multicolumn{2}{c}{**9.7**} \\ \hline
**P \({}^{*}\)** & 62 & \(\pm\) & 8 & 24 & \(\pm\) & 2 & 35 & \(\pm\) & 4 & 33 & \(\pm\) & 14 \\
**S \({}^{*}\)** & 297 & \(\pm\) & 23 & 512 & \(\pm\) & 58 & 597 & \(\pm\) & 56 & 491 & \(\pm\) & 104 \\
**Cl** & 3793 & \(\pm\) & 714 & 2117 & \(\pm\) & 128 & 2865 & \(\pm\) & 341 & 1825 & \(\pm\) & 185 \\
**K** & 522 & \(\pm\) & 195 & 284 & \(\pm\) & 34 & 303 & \(\pm\) & 21 & 195 & \(\pm\) & 39 \\
**Ca** & 3117 & \(\pm\) & 643 & 1834 & \(\pm\) & 102 & 2203 & \(\pm\) & 47 & 1432 & \(\pm\) & 278 \\
**Fe** & 0.13 & \(\pm\) & 0.08 & 0.11 & \(\pm\) & 0.002 & 0.51 & \(\pm\) & 0.14 & 0.21 & \(\pm\) & 0.06 \\
**Cu** & 0.04 & \(\pm\) & 0.01 & 0.05 & \(\pm\) & 0.03 & 0.16 & \(\pm\) & 0.03 & 0.29 & \(\pm\) & 0.11 \\
**Zn** & 0.47 & \(\pm\) & 0.22 & 0.06 & \(\pm\) & 0.01 & 0.1 & \(\pm\) & 0.06 & 0.11 & \(\pm\) & 0.05 \\
**Br** & 71 & \(\pm\) & 29 & 88 & \(\pm\) & 13 & 98 & \(\pm\) & 15 & 87 & \(\pm\) & 4 \\
**Rb** & 2.5 & \(\pm\) & 1.2 & 3.5 & \(\pm\) & 0.9 & 4.2 & \(\pm\) & 0.5 & 3.6 & \(\pm\) & 1.1 \\
**Sr** & 12 & \(\pm\) & 3 & 11 & \(\pm\) & 1 & 12 & \(\pm\) & 1 & 13 & \(\pm\) & 2 \\
**Ba** & 1.22 & \(\pm\) & 0.85 & 0.36 & \(\pm\) & 0.05 & 0.72 & \(\pm\) & 0.35 & 0.6 & \(\pm\) & 0.36 \\
**Pb** & 2.6 & \(\pm\) & 0.1 & \multicolumn{2}{c}{\(<\)LOD} & \multicolumn{2}{c}{\(<\)LOD} \\ \hline \hline \end{tabular} \({}^{*}\)Corrected values based on calibration curves; LOD—lower detection limit. The material naturally aged at room temperature for 11 months was chosen to realize the composites. This sample results to be chemically stable with a pH 9.7.  
\end{table} 
Table 2: Results of the TXRF analysis and pH values of leachate stabilized sample after 1, 2, 3 and 11 months. Values are expressed as the average \(\pm\) standard deviation of three TXRF measurements. Relative sensitivities for elements with \({}^{*}\) were calculated based on a calibration curve (see the experimental section). LOD—lower detection limit.
## 3 Results
### Regional Case Studies  
The potential utility of the SMAP FT products was evaluated from two regional case studies highlighting selected science applications indicated in prior studies (e.g., [4; 58; 59]). The first case study applies the SMAP NPR algorithm portion of the FT record to determine the primary spring thaw timing over Alaska and its relationship to the growing season onset and seasonal increase in vegetation gross primary production (GPP), represented by the SMAP Level 4 Carbon product [37]. The second case study highlights the FT-SCV portion of the domain for detecting transient frost events in Australia, which can be damaging to cropland and natural vegetation [59; 60].
###### Abstract  
Multi-satellite imaging mission planning (MSIMP) is an important focus in the field of satellite application. MSIMP involves a variety of coupled constraints and optimization objectives, which often require extensive simulation and evaluation when solving, leading to high computational costs and slow response times for traditional algorithms. Surrogate model expensive multi-objective evolutionary algorithms (SM-EMOEAs), which are computationally efficient and converge quickly, are effective methods for the solution of MSIMP. However, the recent advances in this field have not been comprehensively summarized; therefore, this work provides a comprehensive overview of this subject. Firstly, the basic classification of MSIMP and its different fields of application are introduced, and the constraints of MSIMP are comprehensively analyzed. Secondly, the MSIMP problem is described to clarify the application scenarios of traditional optimization algorithms in MSIMP and their properties. Thirdly, the process of MSIMP and the classical expensive multi-objective evolutionary algorithms are reviewed to explore the surrogate model and the expensive multi-objective evolutionary algorithms based on MSIMP. Fourthly, improved SM-EMOEAs for MSIMP are analyzed in depth in terms of improved surrogate models, adaptive strategies, and diversity maintenance and quality assessment of the solutions. Finally, SM-EMOEAs and SM-EMOEA-based MSIMP are analyzed in terms of the existing literature, and future trends and directions are summarized.  
multi-satellite imaging mission planning; expensive multi-objective evolutionary algorithm; surrogate model; adaptive strategy +
Footnote †: journal: Aerospace  
_Aerospace_
## 2 Materials and Methods
### Method
#### 2.3.4 Evaluation Metrics and Materials  
Confusion matrix analysis is the most commonly used method in the evaluation of image-classification accuracy. Statistical analysis of the matrix with the ground-truth type and the classification result can obtain an important classification accuracy evaluation index. True positive (TP), true negative (TP), false positive (FP), and false negative (FN) are four basic elements of the confusion matrix. They represent states corresponding to the classification results and ground truth. Overall accuracy (OA), recall, precision, F1-score, and Kappa coefficient are derived from the above four basic elements, and they are selected to evaluate the performance of the classification methodology [46; 47]. Their specific formula is as follows:  
\[\text{OA}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{FN}} \tag{1}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{2}\]  
Figure 4: Comparison between manually edited samples and MLC results of three grounds.  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{3}\]  
\[\text{F1}-\text{score}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{4}\]  
\[\text{Kappa}=\frac{\text{OA}-\text{P}_{\text{e}}}{1-\text{P}_{\text{e}}} \tag{5}\]  
\[\text{P}_{\text{e}}=\frac{\text{a}_{1}\times\text{b}_{1}+\text{a}_{2}\times \text{b}_{2}+\ldots+\text{a}_{n}\times\text{b}_{n}}{\text{N}*\text{N}} \tag{6}\]  
In the above equation, a\({}_{1}\), a\({}_{2}\), and a\({}_{n}\) represent the pixel number of real samples of each type. b\({}_{1}\), b\({}_{2}\), and b\({}_{n}\) represent the pixel number of samples of each type predicted. n represents the total number of land-cover types. N represents the total number of pixels in each sample.  
Two forms of validation samples were selected for confusion matrix analysis. One was a ground-truth classification raster used to check five land covers without sample plots of changed land, and the other was ground truth points of interest that included changed land. The first sample data are 15 areas of 612 \(\times\) 580 pixels. The area proportions of agricultural land, vegetation, water, buildings, and tidal flats are 43.5%, 16.9%, 12.8%, 26.6%, and 0.2%, respectively, in these data (Figure 5). These data were selected by referring to the satellite images and NDVI images, mainly used for robustness test.  
The other validation samples are 902 typical points that were selected according to the field survey. The number of sample points for agricultural land, vegetation, water, buildings, tidal flats, and changed land are 230, 137, 196, 161, 53, and 125, respectively.
# AI-Based Classification of Pediatric Breath Sounds: Toward a Tool for Early Respiratory Screening
## 4 Feature Extraction
### Mel-Frequency Cepstral Coefficients  
Mel-Frequency Cepstral Coefficients (MFCCs) are widely used in sound processing to capture the short-term power spectrum of audio signals. This is achieved by applying a linear cosine transform to the logarithm of the power spectrum, which is scaled according to the nonlinear Mel-frequency scale [47].  
The speech signal is pre-emphasized to enhance high frequencies:  
\[y(n)=x(n)-0.97x(n-1) \tag{19}\]  
The signal is segmented into frames and windowed with a Hamming window:  
\[s_{w}(n,m)=y(n+m\cdot H)\cdot w(n) \tag{20}\]  
where \(w(n)=0.54-0.46\cos\left(\frac{2m}{N-1}\right),\quad 0\leq n<N\)  
The power spectrum is calculated for each frame:  
\[S[k,m]=\left|\sum_{n=0}^{N-1}s_{w}[n,m]e^{-j\frac{2m-k}{N}}\right|^{2} \tag{21}\]  
The Mel scale converts frequency to a perceptual scale, followed by filter bank application:  
\[H_{m}[k]=\begin{cases}0,&k<f[m-1]\\ \frac{k-f[m-1]}{f[m]-f[m-1]},&f[m-1]\leq k<f[m]\\ \frac{f[m+1]-k}{f[m+1]-f[m]},&f[m]\leq k<f[m+1]\\ 0,&k\geq f[m+1]\end{cases} \tag{22}\]  
\[E[m]=\sum_{k=0}^{N/2}S[k,m]H_{m}[k] \tag{23}\]The filter bank energies are logged, and the Discrete Cosine Transform (DCT) yields MFCCs:  
\[L[m]=\log(E[m]) \tag{24}\]  
\[c[l]=\sum\limits_{m=0}^{M-1}L[m]\cos\biggl{(}\frac{\pi l(2m+1)}{2M}\biggr{)}, \quad l=0,1,\ldots,12 \tag{25}\]
## 6 Conclusions  
Power optimization is a valuable technique for extending the lifetime and improving the connection quality of wireless sensor networks. Optimized transmission power benefits network performance. In addition, an optimal network draws on the adjustment of transmission power. Therefore, how to tradeoff between prolonging network lifetime and maintaining connection quality is a challenge. In this study, an optimization approach for wireless sensor networks based on a hierarchical network is developed with a focus on topology control of the transmission range. Considering network lifetime and connection quality simultaneously, the transmission range with a minimum node degree is optimally adjusted according to node degree and node density to establish an optimal network with energy efficiency and full reachability. This approach facilitates the selection of a transmission range in the deployment of a wireless sensor network. Although the continuous transmission range is hardly available in real applications due to only discrete levels of transmission power commonly affordable in industrial product such as the cc2530 Zigbee chip from Texas Instrument, the proposed algorithm is applicable to determine the transmission power level.  
The reliability and feasibility of the proposed approach are shown by the simulation results and performance indices used to evaluate the energy efficiency and connection quality of a network. The relationship between these performance indices is analyzed to generalize characteristics for low and high node densities. These characteristics analyses inform the deployment strategy of a wireless sensor network. In addition, the practicability of network connectivity and network reachability are compared. As a result, network reachability is a better indicator of connection quality than is network connectivity. These results are a helpful contribution to wireless sensor network design. However, in this study, the power model is incomplete because it disregards the data model, environmental impact, and transmission frequency, among other factors, although transmission power is employed to calculate energy efficiency. In the future, we will consider the effect of factors such as data model, environmental impact on transmission to design a power consumption model for nodes with which network energy efficiency can be evaluated. Appropriate transmission strategies will be suggested for different network topologies.
# DeepST-Net: Leveraging Spatiotemporal Data Refinement for Enhanced Travel Time Prediction
## 6 Results and Discussions
### Ablation Study Findings
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Model** & **MAE (s)** & \multicolumn{3}{c}{**MAPE (\%)**} & \multicolumn{3}{c}{**RMSE (s)**} \\ \hline  & **Mean SD** & **Best** & **Mean SD** & **Best** & **Mean SD** & **Best** \\ \hline Raw & \(392.38\pm 5.34\) & \(378.58\) & \(56.02\pm 1.23\) & \(55.12\) & \(538\pm 6.98\) & \(532.22\) \\ Clean & \(145.43\pm 3.55\) & \(142.19\) & \(21.91\pm 0.58\) & \(20.72\) & \(276.84\pm 3.73\) & \(270.16\) \\ Temporal & \(93.33\pm 3.73\) & \(90.86\) & \(13.20\pm 0.61\) & \(12.90\) & \(148.06\pm 2.49\) & \(145.95\) \\
**DeepST-Net** & \(\mathbf{59.27\pm 2.97}\) & \(\mathbf{57.42}\) & \(\mathbf{9.73\pm 0.53}\) & \(\mathbf{9.40}\) & \(\mathbf{89.63\pm 2.34}\) & \(\mathbf{86.67}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Ablation study results. Impact of data refinement and feature engineering on model performance. Bold text highlights the optimal values in each evaluated metric. tance of temporal features in augmenting the model's ability to generalize across different time scales. Finally, the DeepST-Net model achieved the strongest performance among all configurations, with an average MAE of \(59.27\pm 2.97\) s, a MAPE of \(9.73\pm 0.53\%\), and a RMSE of \(89.63\pm 2.34\) s. The best-performing run achieved a MAE of 57.42 s, indicating that the integration of temporal and pattern-based features significantly enhances the model's predictive capabilities. The consistently low standard deviations across all metrics reflect the robustness of DeepST-Net, which demonstrates an exceptional ability to generalize from training data and maintain performance across diverse testing scenarios. By leveraging advanced spatiotemporal data refinement and feature integration, DeepST-Net comprehensively addresses the challenges posed by urban travel environments, such as variability, noise, and non-linear relationships between input features and travel time. The ablation study underscores the transformative role of advanced preprocessing techniques and feature engineering in enhancing model performance. The progressive improvements observed from the raw data model to the DeepST-Net configuration demonstrate that the integration of high-quality data refinement processes is critical for achieving both accuracy and stability. Moreover, the consistently low variability in DeepST-Net's results highlights its reliability and adaptability, key attributes for real-world deployment in intelligent transportation systems. These findings not only validate the proposed framework but also provide a solid foundation for future advancements in travel time prediction, with broader implications for optimizing urban mobility and improving transportation efficiency.