esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 TIES Method
### Determine System Feasibility and Viability
#### 3.5.2 Parameter Optimization  
A desirability function is typically used for optimizing the model created by the RSM. The desirability function approach is based on the concept of optimizing multiple response processes.  
Figure 7: Feasible design space for target design variables.  
The desirability functions are categorized as response maximization, response minimization, and assigning a target value, as shown in Equations (5)-(7) as follows:  
\[d_{i}=\begin{cases}\begin{aligned} 0&if\ \hat{y}_{i}<y_{i}^{L}\\ \left(\frac{\hat{y}_{i}-y_{i}^{L}}{T_{i}-y_{i}^{L}}\right)^{s}&if\ y_{i}^{L}<\hat{y}_{i}<T_{i}\\ 1&if\ \hat{y}_{i}>T_{i}\end{aligned}\end{cases} \tag{5}\]  
\[d_{i}=\begin{cases}\begin{aligned} 1&if\ \hat{y}_{i}<T_{i}\\ \left(\frac{\hat{y}_{i}-y_{i}^{L}}{T_{i}-y_{i}^{L}}\right)^{s}&if\ T_{i}\leq\hat{y}_{i}\leq y_{i}^{L}\\ 0&if\ \hat{y}_{i}>y_{i}^{L}\end{aligned}\end{cases} \tag{6}\]  
\[d_{i}=\begin{cases}\begin{aligned} \left(\frac{\hat{y}_{i}-y_{i}^{L}}{T_{i}-y_{i}^{L}} \right)^{s}&if\ y_{i}^{L}\leq\hat{y}_{i}\leq T_{i}\\ \left(\frac{\hat{y}_{i}-y_{i}^{L}}{T_{i}-y_{i}^{L}}\right)^{t}&if\ T_{i}<\hat{y}_{i}\leq y _{i}^{L}\\ 0&if\ \hat{y}_{i}>y_{i}^{L}\end{aligned}\end{cases} \tag{7}\]  
where \(T_{i}\) is the target value, \(y_{i}\) is the desired response, \(y_{i}^{L}\) is the lower value, and \(y_{i}^{L}\) is the upper value.  
The goal of the desirability function approach is to find the combination of design variables by which the geometric mean is maximized for each desirability function. The geometric mean is a mean or average indicating the central tendency or a typical value of a set of numbers using the product of their values. The geometric mean is defined as the nth root of the product of n numbers, as shown in Equation (8). The function shape can be determined by changing the variables. Once these variables are defined as greater than 1, they approach the target values [23]:  
\[D=(d_{1}\times d_{2}\times\cdots\times d_{n})^{\frac{1}{n}}. \tag{8}\]  
JMP software provides the capability of determining a \"desirability function\". In this study, the authors used the capability for six responses (MTOW, wing span, wing root chord, W/S, P/W, and fuel efficiency) and performed optimization with response maximization.  
In Figure 8, the \(x\)- and \(y\)-axes represent the design variables and responses, respectively. In addition, the characteristic of desirability is depicted in Figure 8. The optimized design variables and target values are summarized in Tables 8 and 9, respectively. It should be noted that the optimized target values were evaluated by the PAV sizing tool, which means that the values were not estimated by the RSE. As depicted in Table 9, it was found that all design variables except for the wing root chord reach the target values. The wing root chord design variable was investigated further in the technology identification section below.
## 5 Sensitivity Analysis of Thermal Conductivity
### Effects of Coarse Aggregate Content  
Obviously, coarse aggregate and FAM in asphalt concrete have a great difference in thermal conductivity. Therefore, when the content of coarse aggregate in asphalt concrete changes, it will alter the contact between the coarse aggregate and the coarse aggregate in asphalt concrete, thus altering the thermal conductivity. The virtual modeling method can control the change of experimental parameters more accurately. In this section, by changing the gradation of virtual specimens, the effects of coarse aggregate content were researched.  
Using the gradation in Table 1, four groups of asphalt concrete models were generated in this section, and were marked as C1\(\sim\)C4, respectively. In these four groups of asphalt concrete models, the proportion of aggregate, asphalt, and air voids remained consistent. The ratio of the coarse aggregate to fine aggregate of model C1 is consistent with Table 1 (as the same as A1). In sample C2, the content of coarse aggregates was decreased in the order of the particle size gradation from large to small. Compared with A1, the content of coarse aggregates was decreased by 10% in total. The reduced coarse aggregate was converted into equal volumes of 2.36 mm fine aggregate and was added to the fine aggregate of the sample C2. In the sample C3, the content of the coarse aggregates was decreased in the order of the particle size gradation from large to small. Compared with A1, the content of the coarse aggregates was decreased by 20% in total. The reduced coarse aggregate was converted into equal volumes of 2.36 mm fine aggregate and added to the fine aggregate of the sample C4. The effective thermal conductivities of specimens C1\(\sim\)C4 were calculated using the method in this paper. The result are shown in Figure 10.  
As shown in Figure 10, replacing coarse particles with 2.36 mm fine aggregates reduces the thermal conductivity, which was similar to previous literature [26]. This illustrates that increasing the content of coarse aggregate can improve the contact between the aggregate in asphalt concrete and increase the thermal conductivity. However, Figure 10 demonstrates that substituting fine aggregate for coarse aggregate had a negligible impact on effective thermal conductivity.
## 3 Results and Discussion
### Enhanced Microfluidic Immunoassay by Applying MET
#### 3.2.3 Cross Influence of Da Number and Voltage Amplitude on Binding Rate  
After confirming the best frequency condition for MET-mediated immune reaction at the micrometer dimension, the effect of Da number on the enhancement of immunization efficiency is then investigated. The controllable adjustment of the dimensionless number Da is realized by adjusting the magnitude of the k\({}_{\text{on}}\) parameter, which helps distinguish the different antigen-antibody pairs used in practical experiments. Subsequently, we fix the Da number to 1000 and adjust the magnitude of Vsw and Vtw, respectively, to clarify the importance of dual-frequency voltage amplitude in the desired binding reaction.  
Figure 8A,C shows the effect of Da number on the resulting Be at t = 600 s. As shown in Figure 8A, the Vsw is a fixed value of 6 V. When Vtw > 0, with the increase in the Da number, the Be value under different Vtw rises, and when Da value is larger than 10\({}^{5}\), the state of immune reaction saturation is attained, and Be tends to reach a stable plateau. As in Figure 8C, the Vtw is fixed at 6 V. When Da increases, the Be value under different Vsw grows and then remains stable.  
Figure 8B,D shows the effect of voltage amplitude on Be. As in Figure 8B, when Vtw is a constant value of 0, there is no significant increase in Be with Vsw, because there is no horizontal TWET pump motion of the electrolyte solution. When Vtw > 0 V, then with the enhancement of Vsw, the overall Be enhancement is obvious, but there is a decline in individual data points. As shown in Figure 8D, as Vtw rises, there is a clear propensity for Be to enhance under any Vsw, but there exist some trivial individual points of decline.  
Accordingly, our numerical simulations indicate that the optimal combination of SW and TW voltage signals achieves near-saturated binding efficiency at excessively large Da numbers. In addition, the enhancement of microfluidic immunoassay by MET is much more stable when both Vsw and Vtw are no more than 6 V, so we prefer to employ moderate voltage amplitudes in practical experiments.  
Figure 7: Influence of the imposed AC signal frequency on the index of Be at t = 600 s with respect to the zero voltage condition. (**A**) The SW field frequency. (**B**) The TW field frequency.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Species** & **Exposure Period** & **Toxicity Values** & **Particle Size and Type** & **Exposure Media** & **pH** & **Reference** \\ \hline  & & EC\({}_{06}\) 2 a/g/L & Ion (ZnSO\({}_{0}\)) & & & \\  & & EC\({}_{06}\) 2 a/g/L & 50–70 nm & OECD test medium & 8.0 \(\pm\) 0.4 & [PERSON] et al., 2009 [16] \\ _P. subcapitata_ & 72 h & EC\({}_{06}\) 37 g/g/L & Bulk & & & \\  & & EC\({}_{06}\) 49 g/L & 30 nm (Disparent) & & & \\  & & EC\({}_{06}\) 68 g/g/L & Bulk & & & \\ \hline _Skeletonous contouratum_ & 96 h & EC\({}_{02}\) 2.36 mg/L & 20 nm & Artificial seawater & 8.0 \(\pm\) 0.1 & [PERSON] et al., 2010 [9] \\ \hline _Dundial trittichots_ & 96 h & EC\({}_{02}\) 2.42 mg/L & 100 nm & Artificial seawater & - & [PERSON] et al., 2013 [32] \\ \hline  & & LC\({}_{06}\) 3.2 mg/L & 50–70 nm & Synthetic freshwater & 7.3-7.8 & [PERSON] et al., 2008 [21] \\ \hline  & & LC\({}_{06}\) 7.5 mg/L & \(<\)200 nm & Erendt M4 medium & 7.7-8.4 & [PERSON] et al., 2009 [22] \\ \cline{2-6}  & & EC\({}_{06}\) 0.62 mg/L & 20 nm & ISO test medium & - & [PERSON] et al., 2009 [23] \\ \cline{2-6}  & & EC\({}_{06}\) 2.6 mg/L & 50–70 nm & Natural water & 7.5–8.2 & [PERSON] et al., 2010 [24] \\ \cline{2-6}  & & EC\({}_{06}\) 1.9 mg/L & \(<\)50 nm & Commercial mineral water & & \\  & & EC\({}_{06}\) 3.1 mg/L & \(<\)100 nm & (San Benedetto\({}^{8}\)) & 8.81 & [PERSON] et al., 2014 [18] \\ \cline{2-6}  & & LC\({}_{06}\) 0.99 mg/L & 43 nm & & & \\  & & LC\({}_{06}\) 1.10 mg/L & 43 nm & ISO test medium & 7.8 \(\pm\) 0.2 & [PERSON] et al., 2015 [25] \\ \cline{2-6}  & & LC\({}_{06}\) 1.01 mg/L & Ion (Zn(NO\({}_{3}\))\({}_{2}\)) & & & \\  & & LC\({}_{06}\) 0.76 mg/L & Ion (Zn(Cl\({}_{3}\)) & & & \\  & & LC\({}_{06}\) 1.02 mg/L & 30 nm & ASTM hard water & 7.9 \(\pm\) 0.3 & [PERSON] et al., 2013 [19] \\  & & LC\({}_{06}\) 1.09 mg/L & 80–100 nm & & & \\  & & LC\({}_{06}\) 0.89 mg/L & \(>\)200 nm & & & \\  & & EC\({}_{06}\) 4.7 mg/L & 151 nm & M? medium & 8.2 \(\pm\) 8.5 & [PERSON] et al., 2015 [20] \\  & & EC\({}_{06}\) 2.2 mg/L & 151 nm & + NOM & & \\  & & EC\({}_{06}\) 0.07 mg/L & 151 nm & N FE medium & 7 & [PERSON] et al., 2016 [26] \\  & & EC\({}_{06}\) 4.9 mg/L & 151 nm & M? medium & 8.6 & \\ \hline \multirow{2}{*}{_Dario [PERSON]_} & \multirow{2}{*}{96 h} & LC\({}_{06}\) 3.97 mg/L & 30 nm & \multirow{2}{*}{36 nm} & \multirow{2}{*}{69-7.3} & [PERSON] et al., 2011 [27] \\  & & LC\({}_{06}\) 2.52 mg/L & \(<\)500 nm & Distilled water & & \\ \cline{1-1} \cline{2-6}  & & LC\({}_{06}\) 1.79 mg/L & 20 nm & & & \\ \cline{1-1} \cline{2-6}  & & LC\({}_{06}\) 1.55 mg/L & 1000 nm & Distilled water & - & [PERSON] et al., 2008 [28] \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of ecotoxicity of ZnO NPs in the previous studies.
## 2 Literature Review
### Reduction in Travel Time  
The impact of shortened travel time that has occurred from rapid technical improvements has been carefully researched. The benefits of HSR services exceed the drawbacks of competing modes of transportation. HSR considerably lowers travel times across cities when compared to conventional rail services. The fundamental advantage of HSR is the reduction in travel time, since large time savings encourage travellers to use HSR services [6; 12].  
Since HSR services have become a travel choice, they have gained market share from competing for transportation modes, especially airlines. This is because it can offer high-frequency train services, providing shorter travel times, connecting major cities, and providing convenient services. Moreover, shorter travel times are the biggest factor in fulfilling the demands of consumers [12]. Therefore, the efficient range of HSR services has been covered in many publications; however, the exact distance has not been revealed as it depends on uncontrolled factors. The most suitable and effective distance is between 483 and 692 km [13; 14], whereas some scholars has suggested the range 500-700 km as appropriate for services [15; 16].  
Travel times are shortened owing to HSR's operating speeds and station accessibility. Nowadays, most HSR services have been compared to airline services in the worthiness of travel time and cost. For example, the California HSR intends to link both of the state's economic hubs (Los Angeles and San Francisco). According to the US High-Speed Rail Association's findings, the minimum journey time is 190 min for HSR services, while airplane and car trips take 320 and 440 min, respectively [17; 18]. Another example, European HSR services appeared to be more competitive with medium-haul airlines because they are related to travel duration [19; 20]. For example, Eurostar had gained a major portion of the market share in the London-Paris service [21; 22]. To summarise, HSR expedites check-in, immigration, and queuing up. Additionally, due to convenient station access, HSR services shorten journey times between cities and airports.
# Cotton Terry Textiles with Photo- and Bio-Activity in a Model Study and Real Conditions
## 3 Results
The results indicate that 15% of all VOCs were removed from the air over the modified towels, and that the concentration of 58% of all compounds was lower in the air as a consequence of the photocatalytic activity of the modified towels. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Volatile Organic Compounds (VOCs)**} & \multicolumn{2}{c}{**Control**} & \multicolumn{2}{c}{**Modified**} & \multicolumn{1}{c}{**Area Reduction in**} \\ \cline{3-6}  & & **Area** & **\% of Total** & **Area** & **\% of Total** & **Modified** \\  & & & **Area** & **Area** & **Area** & **Sample/VOCs Origin * \\ \hline
1 & Ethanol & 64,209 & 0.37 & 623,788 & 4.42 & \(-\)/M, C \\
2 & Isopropanol & 329,104 & 1.24 & 58,626 & 0.43 & \(+\)/M, C \\
3 & 2-Butanone & 25,785 & 0.10 & 87,122 & 0.64 & \(-\)/M, C \\
4 & Trichloromethane (syn. Chloroform) & 131,788 & 0.76 & 35,410 & 0.25 & \(+\)/T \\
5 & 1-Butanol & nd & nd & 134,561 & 0.99 & \(-\)/M, C, T \\
6 & 3-Methyl-1-butanol & 18,525 & 0.11 & 43,211 & 0.31 & \(-\)/M, C \\
7 & 2-Methyl-1-butanol & 6066 & 0.04 & 14,627 & 0.10 & \(-\)/M, C, T \\
8 & 1-Pentanol & 12,684 & 0.09 & 12,400 & 0.09 & \(+\)/–M, C, T \\
9 & Toluene & 85,973 & 0.32 & 107,123 & 0.78 & \(-\)/M, C, T \\
10 & Hexanal & 165,412 & 0.63 & 133,358 & 0.98 & \(-\)/M, C, T \\
11 & Acetic acid, butyl ester & 19,278 & 0.11 & 15,233 & 0.11 & \(+\)/–M, C \\
12 & 1-hexanol & 57,097 & 0.33 & 5508 & 0.04 & \(+\)/M, C \\
13 & Butanoic acid, ethyl ester & 18,687 & 0.07 & 2058 & 0.07 & \(+\)/–M, C \\
14 & 2-Pentanone, 4-hydroxy-4-methyl- (syn. & & & & \\ Diacetone/Acetonyldimethylxquinol) & nd & nd & 16,505 & 0.12 & \(-\)/C \\
15 & 2-Heptanone & 34,209 & 0.13 & 5301 & 0.04 & \(+\)/M \\
16 & Heptanal & 31,581 & 0.18 & 17,058 & 0.12 & \(+\)/M \\
17 & Benzaldehyde & 38,333 & 0.23 & 31,922 & 0.23 & \(+\)/–M, C, T \\
18 & 2-Ethylcaproaldehyde & 14,812 & 0.06 & 9650 & 0.06 & \(+\)/–C \\
19 & 2-Ethyltoluene & 14,720 & 0.09 & 8750 & 0.06 & \(+\)/T \\
20 & 1-Heptanol & 30,417 & 0.18 & 5704 & 0.04 & \(+\)/C, T \\
21 & 6-Methyl-5-heptene-2-one & 58,622 & 0.22 & 36,606 & 0.27 & \(-\)/C \\
22 & 2-Octanone & 66,411 & 0.39 & nd & nd & \(+\)/M, C, T \\
23 & Octanal & 60,464 & 0.35 & 65,850 & 0.47 & \(-\)/M, C, T \\ Hexanoic acid, ethyl ester (syn. Ethyl) & 31,829 & 0.18 & 7939 & 0.06 & \(+\)/M, C \\ caproate) & & & & & \\
25 & 1-(2-Methoxypropoxy)-2-propanol & 66,640 & 0.39 & 7530 & 0.05 & \(+\)/C \\
26 & Benzyl alcohol & 16,420.08 & 0.06 & 22,993 & 0.17 & \(-\)/M, C, T \\
27 & Nitrohexane & 24,880 & 0.09 & nd & nd & \(+\)/T \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Volatile organic compounds identified in the air above towels (control and modified) after seven days of use in different light conditions. _Materials_**2020**, _13_, 3334
###### Abstract  
Conventional electricity generation methods are under the major revolution, and microgrids established on renewable energy sources are playing a vital role in this power generation transformation. This study proposes a hybrid AC/DC microgrid with a barrier function-based adaptive sliding mode controller, in which 8 kW wind energy system and 4.5 kW photovoltaic energy system perform as the hybrid RESs, and 33 Ah of battery works as the energy storage system. Barrier function-based adaptive sliding mode controller ensures the convergence of the system's output variable independent of the knowledge of the upper bound of the disturbances. Firstly, global mathematical modeling of the suggested system is ensured. Then, the control laws are defined, providing the DC bus voltage regulation during islanding mode and AC/DC link bus voltage regulation during the grid-connected mode. The proposed barrier function-based adaptive sliding mode controller technique is analyzed through 20 s simulations on MATLAB/Simulink, which validates the controller's robustness and effectiveness. Furthermore, a comparison of the proposed controller is made with the proportional integral derivative controller, Lyapunov controller, and sliding mode controller. In the end, hardware-in-loop tests are performed using C2000 Delfino MCU F28379D LaunchPad, showing the proposed structure's real-time performance.  
AC/DC microgrid; DC-DC converters; energy storage system; renewable energy sources; non-linear controller +
Footnote †: journal: Applied Sciences
# Mission Planning of UAVs and UGV for Building Inspection in Rural Area
## 3 Design of Planning Algorithm for Cooperative Inspection
### Mission Planning Algorithm Based on ACO-GA  
The mission planning problem addressed in this study is a large-scale NP-Hard optimization problem involving multiple vehicles and objectives, making it challenging to solve using traditional mathematical methods. In recent years, swarm intelligence algorithms based on natural laws or biological behaviors have received considerable attention and research. These algorithms have shown excellent performance in solving large-scale combinatorial optimization problems, offering advantages such as fast computation, high precision, and strong applicability.  
Both ACO and GA have demonstrated good performance in solving such problems, but they also have their limitations. ACO can easily fall into local optima and stagnate during the solution process due to the influence of transition probabilities and pheromones. GA, on the other hand, may suffer from slow convergence and low efficiency as individuals with high fitness values are heavily replicated during the selection process. Therefore, this study utilizes the method by which each ant in ACO obtains its path to replace the selection operation in GA. The obtained results are then subjected to crossover and mutation operations. Additionally, appropriate improvements are made to GA to enhance its global search capabilities. The specific process is outlined as follows:
# Comparative Analysis of Audio Features for Unsupervised Speaker Change Detection
## 3 Methodology
### Change Point Detection  
In order to examine various features in a different settings, in this study, two methods are used for speaker change detection: Bayesian information criterion with Gaussian mixture model (BIC-GMM) and KL divergence with Gaussian mixture model (KL-GMM) method. BIC-GMM, which is based on model selection, and KL-GMM, which is metric-based.
# A Study on the Normalized Delineation of Airspace Sectors Based on Flight Conflict Dynamics
## 5 Validation and Analysis of Sectorization Algorithms
### Sectorization Analysis  
According to the busy/idle time segments divided above, the total number of aircraft nodes in the daily busy and idle time segments were recorded at 20 min intervals in the acquired 7-day radar data of Xiamen control area as shown in Table 4.  
The EM algorithm is used to cluster the daily busy/idle time period aircraft nodes to find the distribution of daily aircraft regional centers, respectively, and calculate the square of error (SSE) for all the clusters. The case of the 13 th busy time period is shown in Figure 8, which can be clearly seen. The time is the optimal time, then all the aircraft nodes of the 13 th busy time period will be divided into five categories.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Date** & **13 th** & **14 th** & **15 th** & **16 th** & **17 th** & **18 th** & **19 th** \\ \hline Number of nodes & & & & & & \\ during busy hours & 846 & 795 & 833 & 912 & 954 & 1120 & 1071 \\ \hline Number of nodes in & & & & & & \\ idle segment & 783 & 741 & 810 & 762 & 829 & 897 & 865 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Total number of aircraft nodes on 7 days.  
Figure 8: Error squared graph.  
Figure 7: Changes in consolidated network indicators.  
Similarly, the EM algorithm was utilized to cluster the aircraft nodes for the other six date busy/idle time slots to determine the K-values as shown in Table 5.  
The final clustering centers are obtained by clustering the 12,218 aircraft nodes of the original data, and the locations of the clustering centers during busy hours in the coordinate system of Figure 3 are shown in Table 6, with a total of 35 clustering centers during busy hours and 33 clustering centers during idle hours. From the clustering results, it can be observed that the distribution of clustering centers in some busy hours and idle hours are relatively similar, which is due to the influence of approach and departure modes and the location of the navigation station, and most of the flights will be concentrated in the core area of the control sector.  
For the 35 nodes in the busy time period, the aircraft node-point strength contained in each node is calculated according to Equation (29), and the weight value aa of each node is obtained, and the 35 cluster centers are reduced to 4 using the improved mean shift clustering algorithm described above, and the final cluster center coordinates obtained by clustering are {(113.2, 191.3), (118.4, 64.3), (308.9, 63.1), (288.5, 206.4)}. Based on the MEAN SHIFT segmentation delineation of the sector, the 33 cluster centers of the idle time segments of the same method are clustered similarly to obtain the final cluster center coordinates, {(92.3, 120.4), (209.1, 57.2), (218.0, 214.2), (346.5, 105.8)}, and the cluster centers are used as the generating elements of Voronoi diagrams to obtain the new sector divisions for busy and idle time periods shown in Figure 9.
# Video Temporal Grounding with Multi-Model Collaborative Learning
## 2 Related Work  
### Video Temporal Grounding  
Video Temporal Grounding (VTG) involves aligning temporal video segments with natural language queries, and existing methods predominantly fall into two categories: Span-based Models and 2D-MAP Models. Span-based Models, such as VSLNet [22], frame the VTG task as a span-based question-answering problem, where video segments serve as context, the target temporal fragment as the answer span, and the linguistic query as the question. These models excel at accurately detecting temporal boundaries and efficiently localizing target segments through boundary regression techniques. However, their focus on explicit boundary prediction often limits their ability to capture broader semantic contexts and handle long-duration interactions, making them less effective in complex video scenarios [10; 11]. In contrast, 2D-MAP Models encode all potential start-end intervals as elements of a two-dimensional matrix, refining high-scoring regions to locate the desired temporal segment [9]. The 2D-TAN model [23], for example, constructs a temporal map to model contextual interactions among video segments, offering robust capabilities in modeling context and segment dependencies [24]. Despite their strengths, these models may struggle with boundary prediction accuracy in densely overlapping temporal segments, particularly under intricate temporal distributions [12; 13; 14]. While both approaches offer distinct advantages, their independent optimization limits the potential of leveraging their complementary strengths, highlighting the need for a collaborative learning framework like MMCL to integrate these methodologies effectively.