esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Modeling of the Cost Function: A DER-CAM Case Study
### DER-CAM Cost Function  
DER-CAM is an optimization platform that provides information regarding the viability of various DER configurations from both an economic and environmental perspective. The latter is achieved by providing information on the DER mix that yields the least CO\({}_{2}\) emissions, while the former is done by obtaining the most cost-effective mix of generation and storage installations [28]. Therefore, DER-CAM is useful for both investment and planning decisions. DER-CAM does not perform simulations or power flow analyses, and can be utilized for both buildings and microgrids. The data being used in this paper is for a mid-rise building, and data points have been adapted from [PERSON] et al. [27]. This is because DER-CAM is a physically-based optimization model [28]. The microgrid to which the apartment is connected is shown in Figure 3, and the information about microgrid cable impedances and transformer specifications can be obtained from [PERSON] et al. [27].  
The cost function for DER-CAM is formulated as a mixed integer linear programming (MILP) problem in which objective functions and constraints are linear, while decision variables can be either integers or continuous [28]. The objective cost function to be minimized consists of:  
* Retail electricity charges (by the distribution network operator on a monthly basis)
* Energy charges per unit load/hour/day/month (for peak, week, and weekend days)
* Maximum power charges (for peak days)/hour/day/month
* Total generation cost/DER/hour/day/month (for each DER utilized)
* Total depreciation cost for each DER (over a 20-year period)
* Excluding the total energy exported to the grid by each DER.  
Figure 3: 14-bus microgrid connecting mid-rise apartments (all of the bus connection cable lengths are in meters) [27].
# Detection of Rock Mass Defects Using Seismic Tomography
###### Abstract
(2016) [15] conducted rock fracture tests, using granite under a confining pressure with fluid injection in a laboratory setting. Their study, which incorporated laboratory experiments and numerical simulations, indicates that such an approach is helpful for finding a better model in terms of both simulating experimental results and for upscaling purposes. The studies mentioned above qualitatively show that most rock physical properties can be tomographically imaged, including pore fluid content, pore pressure, rock composition, temperature, elastic moduli, and water saturation, which also demonstrates the broader applications of seismic tomography beyond mining engineering. For example, by analyzing the velocity and attenuation characteristics of rock mass, it is possible to identify groundwater flow in different strata, as well as changes in porosity and permeability within the rock layers, thereby enabling the detection and monitoring of potential water resources (e.g., [18; 19]). Additionally, during tunnel excavation and operation, imaging the velocity distribution around the tunnel can reveal potential geological weaknesses and fractures, aiding in the assessment of tunnel construction safety (e.g., [20]). The purpose of our study is to quantitatively delineate anomalous structures whose physical properties are different from the surrounding rock mass by using seismic tomography on a laboratory scale, and identify the boundaries of anomalous areas, finally achieving non-destructive detection of rock mass defects. The inversion program in our study involves four prerequisites:  
1. All the receivers are used as active transducers, providing rays with known source positions;
2. The receivers can be located in three dimensions;
3. The real velocity structure demonstrates strong transverse or orthotropic anisotropy;
4. The velocity contrasts are strong and occur over very short distances (millimeters).
## 2 Results and Discussion
### Desorption
The compounds found in the dichloromethane soluble fraction are shown in Table 7, both for the liquid phase separated from the winery wastes and further diluted and for the product desorbed from selected polymeric resins under optimal conditions. Most components were not detected in the liquid phase separated from winery wastes further diluted, but were found in the concentrated desorbed product. The most abundant were the monoethyl succinate, propanoic acid and benzeneacetaldehyde, followed by two acids (acetic and lauric), two alcohols (2-methyl-2-butanol and 2-phenylethanol) and two esters (1,3-propanediol acetate and ethyl lactate). Most of these compounds are generated during the alcoholic fermentation at higher contents and therefore, they can be found in winery wastes. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**t (min)**} & \multirow{2}{*}{**Name**} & **Relative Area to 3-Octanol** \\ \cline{2-3}  & & **A** & **B** \\ \hline
8.57 & 2-methyl-2-butanol & 5.979 & 7.195 \\
10.14 & 1,1-diethoxy-3-methylbutane & ND & 0.326 \\
10.75 & 2-methylpropanol (isobutanol) & ND & 0.113 \\
16.73 & 3-octanone & 0.776 & 0.191 \\
18.09 & 3-hydroxy-2-butanone (acetoin) & ND & 1.015 \\
19.39 & methyl lactate & ND & 0.263 \\
20.40 & ethyl lactate & ND & 4.559 \\
21.20 & 2-hydroxy-2-methyl-4-pentanone & 0.272 & ND \\
21.69 & 3-ethoxy-1-propanol & ND & 0.232 \\
25.12 & acetic acid & 0.180 & 7.900 \\
28.07 & benzaldehyde & ND & 0.692 \\
28.58 & propanoic acid & ND & 15.648 \\
29.91 & 2,3-butanediol & ND & 0.689 \\
31.93 & methyl benzoate & ND & 1.299 \\
32.27 & dihydro-2(3H)-furanone (\(\gamma\)-butyrolactone) & ND & 1.453 \\
32.78 & benzeneacetaldehyde & ND & 13.022 \\
33.74 & 3-methylbutanoic acid (isovaleric acid) & ND & 0.206 \\
33.90 & diethyl succinate & ND & 0.212 \\
35.48 & (2,2 diethoxyethyl)benzene & ND & 0.294 \\
36.21 & 1,3-propanediol diacetate & ND & 5.869 \\
37.84 & 1,3-propanediol & ND & 0.203 \\
38.65 & ethyl propanoate & ND & 1.108 \\
40.18 & hexanoic acid & ND & 0.371 \\
40.63 & \(N\)-(3-methylbutyl)acetamide & ND & 0.844 \\
42.44 & phenylethanolamine & ND & 3.434 \\
54.81 & 2-ethylhexyl-2-hydroxybenzoate & ND & 0.543 \\
57.46 & monoethyl succinate & ND & 50.183 \\
60.22 & dodecanoic acid (lauric acid) & ND & 5.589 \\
62.32 & diethyl succinate & ND & 0.683 \\
72.51 & homovanillyl alcohol (4-hydroxy-3-methoxyphenylethyl alcohol) & ND & 0.804 \\ \hline \hline \end{tabular} ND, non detected.  
\end{table} 
Table 7: Composition of the DCM soluble fractions from the liquid phase separated from winery wastes further diluted (A) and from the product desorbed from Sepabeads SP207 with 96% ethanol at 50 \({}^{\circ}\)C (B), analysed by GC-MS.
## 3 Coherent \(\pi\)-Electron Angular Momentum and Current of a Molecule Having Two Aromatic Rings
### Quantum Localization of Coherent \(\pi\)-Electron Angular Momentum  
In this subsection, we briefly describe the fundamental concept of a quantum localization of coherent \(\pi\)-electron angular momentum (ring current) in aromatic ring molecules and demonstrate the quantum localization by numerical simulations [91]. As an example, we again take (\(P\))-2,2\({}^{\prime}\)-biphenol. The localization considered here consists of sequential two steps: the first step is to localize the \(\pi\)-electron angular momentum at a selected ring of the two benzene rings, and the other is to maintain the localization. Optimal control theory was used for obtaining the optimized electric fields of linearly polarized laser pulses to realize the localization. The optimal electric fields and the resultant coherent electronic dynamics were analyzed. The target molecule was assumed to be fixed on a surface by a (CH\({}_{2}\)) chain free from \(\pi\) electrons.
## 3 Experimental Characteristics of Multicolored Pulses
### Spectra and Wavelength Tuning of Multicolored Sidebands  
#### 3.2.1 Tuning the Wavelength of Sidebands by Changing Cross-Angle  
The Type-1 experimental setup was applied, and a short-pass filter with cut-off wavelength of 800 nm was used to eliminate the red components of Beam 1. As a result, the spectra of the two pump pulses were as in Figure 5. The input powers of Beam 1 and Beam 2 were 11 and 19 mW, respectively.  
Figure 4: Type-2 experimental setup. Focal lengths of concave mirrors, M1, M3, and M4 are 500 mm, while that of M2 is 250 mm. GDD: chirped mirrors. F1: longpass filter with a cut-off wavelength of 800 nm. F2: short-pass filter with a cut-off wavelength of 800 nm. BS: beamsplitter. VND: variable neutral-density filter. NL: nonlinear medium for multicolor pulse generation.  
without a gap between the neighboring order sidebands. The spectra of AS8-AS15 are shown in Figure 6d. The spectra of S1 and S2 are shown in Figure 6e. The whole wavelength range obtained by angle tuning of all sidebands covered the near UV-visible-near IR range from 360 nm to 1.2 \(\upmu\)m, corresponding to more than 1.8 octaves. These broadband spectra and large tunability are very attractive and useful from the viewpoints of application and creating simple tuning mechanisms.
## 3 Results  
Forty-one participants aged 18-26 were examined (females: \(n=22\); males: \(n=19\)). There was a statistically significant difference in body mass, height and body mass index (BMI) between males and females. BMI in men was higher than in women (25.41 vs. 21.72, \(p<0.001\)). There were no statistically significant differences in the level of PA between women and men in any aspect of activity. There were also no significant differences between women and men in sitting time and preferred walking speed. Both women and men more often declared that they carried a load in their right hand/on the right shoulder (75.61%). Detailed results are presented in Table 1.  
The MANOVA analysis revealed significant overall effects of incline and load position and interactions of those factors (Table 2). This significant result permitted additional univariate analysis to identify the significant dependent variables. There was no statistically significant difference between the loaded and unloaded sides.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{2}{c}{**Males**} & \multicolumn{2}{c}{**Females**} & \multicolumn{2}{c}{**Statistics**} \\ \cline{2-7}  & \(M\) & \(SD\) & \(M\) & \(SD\) & \(Z\) & \(p\) \\ \hline Age [years] & 22.58 & 1.46 & 22.50 & 1.50 & 0.29 & 0.77 \\ \hline Height [m] & 1.77 & 0.08 & 1.70 & 0.05 & 3.29 & 0.001 * \\ \hline Body mass [kg] & 80.26 & 14.23 & 62.55 & 12.73 & 3.69 & \textless{}0.001 * \\ \hline BMI [kg/m\({}^{2}\)] & 25.41 & 3.53 & 21.72 & 4.89 & 3.02 & \textless{}0.001 * \\ \hline Intensive PA [METmin/week] & 884.44 & 1042.88 & 308.57 & 526.60 & 1.76 & 0.08 \\ \hline Moderate PA [METmin/week] & 644.44 & 625.10 & 907.81 & 1372.30 & 0.06 & 0.95 \\ \hline Walking [METmin/week] & 1445.92 & 1548.41 & 1132.21 & 1123.07 & 0.89 & 0.37 \\ \hline Sitting [min/day] & 285.56 & 137.98 & 293.50 & 140.46 & \textless{}0.38 & 0.70 \\ \hline Preferred gait speed [km/h] & 3.78 & 0.79 & 3.35 & 0.89 & 1.38 & 0.17 \\ \hline \hline \end{tabular}
* Statistically significant difference (\(p<0.05\)).  
\end{table}
Table 1: Comparison of males and females in terms of age, height, weight, Body Mass Index (BMI) and physical activity (PA).
## 4 Conclusions  
The conclusions below were drawn from an experimental study on the microstructural changes and the strength formation mechanism during the curing process of epoxy asphalt.  
1. The epoxy group content was quantitatively determined using FTIR and combined with the results for the tensile mechanical properties of epoxy asphalt. The conversion rate of epoxy groups is significantly related to the tensile strength of epoxy asphalt. The curing strength of epoxy asphalt increases via the development of a 3D network of crosslinked epoxy resin. The curing degree of epoxy asphalt can be evaluated using the conversion rate of epoxy groups.  
Figure 9: Schematic of the epoxy asphalt curing process.  
2. A morphological analysis shows that during the curing process of epoxy asphalt, the asphalt PDI first increases, then decreases, and eventually stabilizes at approximately 1.15. The polymerization of epoxy resin into a spatial network results in a gradual two-phase separation, whereby asphalt is evenly distributed in a 3D epoxy resin network. The elongation at break of the cured epoxy asphalt is approximately 80% higher than that of the pure epoxy resin. The 3D network of epoxy asphalt increases the deformation of the pure epoxy resin.
3. In the curing of epoxy asphalt, epoxy resin is crosslinked into a stable 3D network structure in three stages: an initial stage (\(G_{\alpha}\) less than approximately 27%), an intermediate stage (\(G_{\alpha}\) between approximately 27% and 65%), and a late stage (\(G_{\alpha}\) greater than approximately 65%).
4. In engineering practice, during the mixing, transporting, paving, and rolling process, the epoxy asphalt mixture is in the initial stage of curing and has a high reactivity. In the intermediate and late stages of curing, epoxy asphalt concrete should not be disturbed to avoid affecting the strength increase and long-term pavement performance. Attention should also be given to shortening the construction interval between the upper and lower pavement layers, completing the overall construction to the greatest extent possible in the initial stage of curing, and reducing disturbances to the concrete in the lower pavement layer.  
Conceptualization, W.X. and G.Z.; methodology, W.X. and G.Z.; software, G.Z.; investigation, G.Z. and Z.C.; data curation, J.W.; writing--original draft preparation, G.Z.; writing--review and editing, W.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflicts of interest.
# Analysis, Design, and Experimental Research of a Novel Wheelchair-Stretcher Assistive Robot
## 2 Mechanical Structure Design of the Wheelchair-Stretcher Assistive Robot
### Kinematics, Dynamics Analysis and Optimization of the Folding Mechanism
#### 2.2.3 Driving Device Computation of the Folding Mechanism  
According to the above analysis, the degree of freedom for the folding mechanism is 1, so only one driving motor is needed to complete the mechanical motion. It is proposed that 24 V DC gear motor is adopted as the main driving power, and the torsion spring group as the auxiliary driving power for the motion of the folding mechanism. Since the maximum torque of the gear motor is about 30 N\(\cdot\)m, the torsion spring group needs to overcome all the remaining torques; therefore, the designed torsional spring group includes 6 torsional springs, each of which can withstand a maximum torque of 22 N-m and a maximum torsional angle \(\varphi_{m}=90^{\circ}\).  
According to the character of the load, the spring material is determined to be 60 Si2 MnA, elastic modulus \(E\) is 206 GPa, ultimate tensile strength \(\sigma_{\text{b}}\) is 1667 MPa, allowable bending stress [\(\sigma_{\text{b}}\)] is 1334 MPa.  
Based on these parameters, the required spring steel diameter \(d\) can be calculated as,  
\[d=\sqrt[3]{\frac{32T_{n}K_{1}}{\pi[\sigma_{b}]}}\approx 5.7\text{mm} \tag{23}\]  
where \(K_{1}=1.15\) is coefficient of curvature, \(T_{n}=22\) N-m is maximum working torque. According to the design requirements, the value of the steel wire diameter \(d\) should be rounded, so 6 mm should be taken.  
The effective coil number \(n\) of spring can be obtained by the following equation:  
\[n=\frac{Ed^{4}\varphi_{m}}{3667D(T_{n}-T_{1})}\approx 7.4 \tag{24}\]  
where \(T_{1}=0\), \(D\) is the mean diameter of coil and \(D=40\) mm. According to the actual working situation, the effective winding number of spring is selected as 7.  
The stiff of the spring is expressed as:  
\[T\cdot=\frac{Ed^{4}}{3667D\cdot n}=0.26\text{N}\cdot\text{m}. \tag{25}\]  
Similarly, the free length of the spring can be computed as  
\[H_{0}=n\cdot t+d=51.5\text{mm} \tag{26}\]  
where \(t=6.5\) mm.  
According to the force analysis of the folding mechanism and the design analysis of the torsional spring, the torque required by the motor can be calculated as,  
\[T_{m}=M-T \tag{27}\]  
where \(T_{m}\) is the torque of the motor, \(T\) is the torque of the spring and \(T=T^{\prime}\varphi\), \(\theta_{1}=90-\varphi\).  
According to the Equations (22), (25), and (27), the torque variation curve can be plotted, as shown in Figure 11. It is found that the torque of the motor is maximum in \(\varphi=57^{\circ}\) place and its value is \(T_{m}\)= 46 N-m. According to this parameter, the performance parameters of the worm gear reducer motor can be determined in shown in Table 1.  
Figure 11: Torque change of folding mechanism.
## 3 Results and Discussion
### Gain and Radiation Efficiency Characteristics  
The simulated gain (G) and radiation efficiency (\(\eta\)) of a SWB monopole antenna are shown in Figures 9 and 10. The proposed design has a peak-realized gain of 18.01 dB and a radiation efficiency of 62-95% in the super-wideband frequency range of 3.71-337.88 GHz. As shown in Figure 9, there is a minor discrepancy between realized gain and total gain up to 255.56 GHz, and an exponential drop at higher operating frequencies due to mismatch losses and reflection losses at the input port. Similarly, as shown in Figure 10, these losses affect the radiation efficiency in the same manner. This high gain is achieved by etching a flower-shaped slot into a radiator and employing a partial ground and tapered feed technique.  
Figure 8: The surface current distribution of the designed antenna for (**a**) 24.1 GHz, (**b**) 67.1 GHz, (**c**) 129.1 GHz, (**d**) 249.1 GHz, (**e**) 287.1 GHz.
# Full Three-Dimensional Inverse Design Method for S-Ducts Using a New Dimensionless Flow Parameter
## 3 Upgrading and Validating the 3-D BSA for Unique Solution
### Validation of 3-D Upgraded Inverse Design for 3D S-Duct with Specified Centerline Curvature  
Initially, it was assumed that the duct centerline curvature is specified, and the proposed inverse design method is used to correct the cross-sections. The initial and target geometries shown in Figure 6 were used for the validation to show the capability and convergence of the method using the dimensionless pressure and eliminating the effects of area.  
With a specified centerline curvature, the cross-sections perpendicular to the centerline are corrected according to Equation (8). The balls are placed along the upper and lower walls of the 2-D planar ducts. The difference between the current and target dimensionless pressure is applied to each upper and lower lines of the planar ducts separately to correct the height distribution of the longitudinal sections. Figure 11 shows the convergence process of the initial geometry to the target geometry after only 40 geometry corrections. Figure 11a,b shows how the cross-sections and pressure distributions along the upper and lines of the symmetry plane converge. Figure 11c compares the dimensionless pressure distributions on cross-section \"C\" for the initial, target, and two intermediate modified geometries. Since the curvature of the centerline for the initial geometry is known and equal to that for the target geometry, the dimensionless pressures at points \"A\" and \"B\" in Figure 11 for the initial and target geometry are equal. Thus, these points do not shift during the inverse design process. However, according to Figure 11b, the amount of pressure at these points varies due to the effects of area. For this reason, the use of the pressure parameter causes divergence (Figure 6), while the use of the dimensionless pressure parameter causes convergence for the geometry correction in the inverse design of a 3-D duct.
## 3 Proposed Methodology
### Evaluation Metrics  
Different evaluation metrics like Accuracy, precision, recall, and F1-score were applied to evaluate each ESC model. For each prediction, a confusion matrix was used to detect True-positive (TP), False-positive (FP), True-negative (TN), and False-negative (FN).  
**Accuracy:** this metric is used to calculate the frequency of sound classes that can be accurately determined from the entire speech signal. The following equation is used to calculate the accuracy of results:  
\[\text{Accuracy}=\frac{1}{N}\ \sum_{i=1}^{N}\frac{(TP+TN)i}{(TP+TN+FP+ FN)i} \tag{3}\]  
**Recall:** to check the number of positive instances that are accurately detected by the proposed model is checked by recall using the following equation.  
\[\text{Recall}=\frac{1}{N}\sum_{i=1}^{N}\frac{(TP)i}{(TP+FN)i} \tag{4}\]  
**Precision:** to check the correctly detected actual utterances precision method is used using the following equation.  
\[\text{Precision}=\frac{1}{N}\sum_{i=1}^{N}\frac{(TP)i}{(TP+FP)i} \tag{5}\]  
The evaluation matrices used to evaluate the proposed transformer model are widely used to measure the performance of models used for detection, classification, and prediction systems.
# Weighted \(z\)-Distance-Based Clustering and Its Application to Time-Series Data
## 5 Conclusions  
SCC is an exclusive clustering method, performing only one training cycle on the training instances. Clusters are created incrementally. However, the clusters produced may depend on the feeding order in which the instances are considered, and assignment errors are more likely to occur. Also, all dimensions are equally weighted in the clustering process, which may not be suitable in certain applications, e.g., time-series clustering. We have presented two improvements, SCC-I and SCC-IW. SCC-I performs two or more training cycles iteratively, and allows instances to be re-assigned afterwards. In this way, the clusters produced are less likely to be affected by the feeding order of the instances. On the other hand, SCC-IW allows each dimension to be weighted differently in the clustering process. The values of the weights are adaptively learned from the data. Experiments have shown that SCC-IW performs effectively in clustering time-series data.  
SCC-I and SCC-IW take more training time due to: (1) they have to compute \(z\)-distances and Gaussian values, which is computationally expensive; (2) in order to do re-assignment, the operation of removing instances from clusters is done during the clustering process. We will investigate these issues to reduce the training time in the future. Spectral clustering [74] and multidimensional scaling [75] deal with extraction of features from original ones. SCC-IW has probably some relationship with these methods since SCC-IW is also somehow able to extract some new \"axis or principal components\" by the adaptation of the weights. It will be interesting to explore such a relationship in the future.  
Conceptualization, Z.-Y.W. and S.-J.L.; methodology, Z.-Y.W. and S.-J.L.; software, Z.-Y.W., C.-Y.W., and Y.-T.L.; validation, Z.-Y.W., C.-Y.W. and Y.-T.L.; formal analysis, Z.-Y.W. and S.-J.L.; investigation, S.-J.L.; resources, S.-J.L.; data curation, Z.-Y.W., C.-Y.W., and Y.-T.L.; writing--original draft preparation, Z.-Y.W.,  
Figure 1: Performance of SCC-IW vs. \(\alpha\).  
C.-Y.W., and Y.-T.L.; writing--review and editing, S.-J.L.; visualization, C.-Y.W. and Y.-T.L.; supervision, S.-J.L.; project administration, Y.-T.L.; funding acquisition, S.-J.L.  
This research was funded by the Ministry of Science and Technology under grants MOST-103-2221-E-110-047-MY2, MOST-104-2622-E-110-014-CC3, and MOST-106-2221-E-110-080. The APC was funded by National Sun Yat-Sen University.  
The authors declare no conflict of interest.