esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of Existing Bond Models for Externally Bonded SRP Composites
## 5 Conclusions  
Although all evaluated models were developed for composites reinforced with organic (mainly carbon and glass) fibers, the obtained results of comparisons showed surprisingly good compatibility for ultra-high-strength steel textile composites.  
Most of the assessed models should be considered moderately conservative, with the ratio of predicted to experimental bond strength in the range of 0.5-0.85. As shown in Table 7, this trend also applied to CFRP composites, and in most comparisons, the results obtained for SRP can be assessed as more accurate.  
The best accuracy was guaranteed by [PERSON] et al.'s proposal, [21] [PERSON] and [PERSON]'s proposal [28], and technical guidelines recommendations (except TR55/2000 [33]). This proved the predominance of models calibrated on a very large number of different samples.  
Among all the analyzed models, there were proposals whose results differed significantly from the test results. In a few cases, the predicted to experimental bond strength ratio exceeded the risky limit of 1.15. The worst and most unsafe were the proposals of [PERSON] et al. [22], [PERSON] and [PERSON] [26], [PERSON] et al. [27], and [PERSON] et al. [29]. These models should not be used to determine the bond strength of SRP composites.  
The highest compliance was obtained in the analysis of SRP composites bonded to concrete. This was because most of the tested models were developed for epoxy-bonded concrete strengthening systems. In the case of bonding to masonry, the predicted to experimental bond strength ratios were several percentage points higher. Nevertheless, most of the analyzed models could still be considered safe. Apart from the previously mentioned, the [PERSON] et al. [16], [PERSON] [23], and [PERSON] and [PERSON] [31] models should not be used in this case.  
The classification of technical guidelines with the use of the DPC scale as the most universal, distinguished the Swiss standard SIA166/2004 [36] and the latest fib bulletin 90/2019 [39]. The TR55/2000 [33] guideline seemed too conservative. The Italian standard CNR-DT200R1/13 [37], in the case of masonry substrate, estimated too many results classified as dangerous. Perhaps this was the effect of taking the overestimated value of the \(k_{G}\) coefficient in the analysis.  
The last conclusion concerned SRG composites. The results obtained from the analysis were inconclusive. They should be extended to a larger experimental database.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **\textgreater{}2** & **1.15–2** & **0.85–1.15** & **0.5–0.85** & **\textless{}0.5** & **Total DPC** \\ \hline TR55/2000 [33] & - & 1 & 11 & 13 & 5 & 28 \\ Fib Bulletin 14/2001 [34] & 3 & 18 & 6 & 2 & 1 & 124 \\ JCI 2003 [35] & - & 15 & 9 & 5 & 1 & 82 \\ SIA 166/2004 [36] & - & 14 & 10 & 4 & 2 & 78 \\ CNR-DT200R1/13 [37] & 8 & 16 & 4 & 1 & 1 & 163 \\ _Fib_ Bulletin 90/2019 [38] & - & 20 & 7 & 2 & 1 & 104 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Classification of technical guidelines according to Collins’ DPC scale; SRP composites bonded to masonry.
# Finite-Time Consensus Tracking Control for Speed Sensorless Multi-Motor Systems
## 4 Finite-Time Consensus Tracking Protocol Design
\[\dot{V}\leq-(q_{2}-\frac{p2^{1-1/p}}{1+p}-\frac{\alpha+n\gamma}{1+p} \sum\limits_{i=1}^{n}|\sigma_{i}|^{1+1/p}+ \tag{28}\] \[(\frac{2^{1-1/p}}{1+p}+\frac{q_{3}}{q_{2}}+\frac{n\gamma 2^{1-1/p}}{q _{2}(1+p)})\sum\limits_{i=1}^{n}|\sigma_{i}|^{1+1/p}+\] \[\frac{1}{(2-1/p)2^{1-1/p}q_{2}^{1+p}}\sum\limits_{i=1}^{n}\rho_{i }^{2-1/p}(\bar{k}_{i}\omega_{i}+\bar{b}_{i}u_{i}+d_{i}).\]  
According to Theorem 2, substituting Formula (16) into (28) yields:  
\[\dot{V}\leq-q_{5}\sum\limits_{i=1}^{n}|\sigma_{i}|^{1+1/p}+q_{6} \sum\limits_{i=1}^{n}|\rho_{i}|^{1+1/p}- \tag{29}\] \[\frac{q_{1}}{(2-1/p)2^{1-1/p}q_{2}^{1+p}}\sum\limits_{i=1}^{n} \rho_{i}^{2-1/p}(\omega_{i}^{p}+q_{2}^{p}\sigma_{i})^{2/p-1}+\] \[\frac{\sum\limits_{i=1}^{n}\rho_{i}^{2-1/p}(\bar{k}_{i}e_{2}+ \delta_{i})}{(2-1/p)2^{1-1/p}q_{2}^{1+p}}.\]  
Because the virtual control law \(\omega_{i}^{*}=-q_{2}\sigma_{i}^{1/p}\), \((\omega_{i}^{p}+q_{2}^{p}\sigma_{i})^{2/p-1}={\rho_{i}}^{2/p-1}\) can be calculated. Substituting Formula (17) into (29) yields:  
\[\dot{V}\leq-q_{5}\sum\limits_{i=1}^{n}|\sigma_{i}|^{1+1/p}-q_{5} \sum\limits_{i=1}^{n}|\rho_{i}|^{1+1/p}+ \tag{30}\] \[\frac{\sum\limits_{i=1}^{n}\rho_{i}^{2-1/p}(\bar{k}_{i}e_{2}+ \delta_{i})}{(2-1/p)2^{1-1/p}q_{2}^{1+p}}.\]  
Because \(\sum\limits_{i=1}^{n}\sigma_{i}^{2}=(L^{1/2}\bar{\theta})^{T}(L^{1/2}\bar{ \theta})\geq\lambda_{2}(L)(\bar{\theta})^{T}(\bar{\theta})\), where \(\bar{\theta}=\theta_{i}-\theta_{j}\) and \(V_{1i}\leq\frac{|\omega_{i}-\omega_{i}^{*}|\left|\omega_{i}^{p}-\omega_{i}^{*} \right|^{2-\frac{1}{p}}}{(2-\frac{1}{p})2^{1-1/p}q_{2}^{1+p}}\leq\frac{|\rho _{i}|^{2}}{(2-1/p)q_{2}^{1+p}}\), one can obtain \(V=V_{0}+\sum\limits_{i=1}^{n}V_{1i}\leq c_{1}(\sum\limits_{i=1}^{n}\sigma_{i} ^{2}+\sum\limits_{i=1}^{n}\rho_{i}^{2})\) and \(c_{1}=\max(\lambda_{2}(L),\frac{1}{(2-1/p)q_{2}^{1+p}})\). The following can be obtained using Lemma 4:  
\[V^{\frac{1}{2}+\frac{1}{2p}}\leq c_{1}^{\frac{1}{2}+\frac{1}{2p}}\left(\sum \limits_{i=1}^{n}\sigma_{i}^{1+1/p}+\sum\limits_{i=1}^{n}\rho_{i}^{1+1/p} \right). \tag{31}\]  
The following formula is obtained by combining Formulas (30) and (31):  
\[\dot{V}\leq-\frac{q_{5}}{c_{1}^{\frac{1}{2}+\frac{1}{2p}}}V^{\frac{1}{2}+\frac {1}{2p}}+\zeta, \tag{32}\]  
where \(\zeta=\frac{\sum\limits_{i=1}^{n}\rho_{i}^{2-1/p}(\bar{k}_{i}\sigma_{2}+\delta _{i})}{(2-1/p)2^{1-1/p}q_{2}^{1+p}}\), denote \(c_{2}=\frac{q_{5}}{c_{1}^{\frac{1}{2
#### 2.2.2 Difference of Characteristic Aroma Compounds in Two Varieties  
Based on the OAVs of the aroma compounds listed, the difference between characteristic aroma compounds was illustrated for the two varieties by means of OAVs. The main aroma family of Tainong No.6 was the same as the main aroma family of Tainong No. 4, namely, esters and ketones. Although 3-(methylthio)propanoic acid ethyl ester was present in the two varieties, the other characteristic aroma compounds of the two varieties were different.  
[PERSON] _et al._ (1989; 1991) [3, 4] had determined the odor threshold of 3-(methylthio)propanoic acid methyl ester and 3-(methylthio)propanoic acid ethyl ester to be 180 \(\upmu\)g\(\cdot\)kg\({}^{-1}\) and 7 \(\upmu\)g\(\cdot\)kg\({}^{-1}\). Their respective concentrations indicated their great odor contribution to Tainong No. 4 and No. 6 pineapples according to their respective OAVs. Two thioesters, including 3-(methylthio)propanoic acid methyl ester and 3-(methylthio)propanoic acid ethyl ester, were the characteristic aroma compounds in Tainong No. 4, whereas only one thioester was founded in the Tainong No.6 pineapple, namely, 3-(methylthio)propanoic acid ethyl ester.  
Methyl-2-methylbutyrate and ethyl-2-methylbutyrate were detected in the Tainong No. 6 pineapple. Their odor thresholds were 0.25 \(\upmu\)g\(\cdot\)kg\({}^{-1}\) and 0.006 \(\upmu\)g\(\cdot\)kg\({}^{-1}\), respectively [3, 4]. Their OAVs were calculated and allowed us to consider those two compounds as the major characteristic aromas of the Tainong No. 6 pineapple. Ethyl-2-methylbutyrate is the main aroma in apples [26]. The smell of a typical apple-like aroma [27, 28] could be why Tainong No. 6 is called 'apple' pineapple.  
Decanal and ethyl butanoate are often the most important contributors to the flavor of orange juice [18]. Decanal has a OAV of 16.1, and is considered to be one of the key aroma volatile compounds contributing to the characteristic aroma of the Tainong No. 6 pineapple, as it is one of its aldehyde compounds.  
Characteristic aroma compounds of the two varieties were different from those of the cyenne pineapple. In the cyenne pineapple, the characteristic aroma compounds were ethyl 2-methylbutanoate, ethyl hexanoate, 2,5-dimethyl-4-hydroxy-3(2\(H\))-furanone (DMHF), decanal, ethyl 3-(methylthio)propionate, ethyl butanoate, and ethyl (E)-3-hexenoate in the pulp. In the pineapple core, the main compounds were found to be ethyl 2-methylbutanoate, ethyl hexanoate and DMHF [15].
###### Abstract  
Polarized 3D imaging technology reconstructs the three-dimensional (3D) surface shape of an object by analyzing the polarization characteristics of light reflected from its surface. A key challenge in polarized 3D imaging is accurately estimating the zenith angle. Specular light poses a notable challenge in estimating the zenith angle because it conveys limited information regarding the target. To enhance the accuracy and robustness of zenith angle estimation for specular light, this study proposes a novel zenith angle estimation method that utilizes both specular and diffuse reflections. Based on the estimated zenith angle, the target surface shape was reconstructed. The feasibility of the proposed method was validated using polarimetric images of marine targets, offering a new solution for the accurate identification and 3D imaging of distant maritime targets.  
maritime targets; polarized 3D reconstruction; mixed reflection model; zenith angle; Fresnel's law +
Footnote †: journal: applied sciences
# Numerical Study of Cone Penetration Tests in Lunar Regolith for Strength Index
## 2 Numerical Model
### Contact Model Parameter Calibration
After the calibration according to the above ideas, the discrete element simulation parameters are listed in Table 2. The selected porosity (\(e\)) is 0.58, and the relative density of the sample is 0.83, which corresponds to the average relative density of lunar soil at a depth of 0\(\sim\)60 cm on the lunar surface. Researchers [17; 22; 23] have established a lunar regolith particle size range of 0.5 to 9 mm in simulation studies, yielding results that align well with experimental findings. Considering the size effect and simulation efficiency, the particle size for the contacting portion is between 2 and 4 mm, with the particle size distribution illustrated in Figure 3. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Particle Density (kg/m\({}^{3}\))**} & \multirow{2}{*}{**Porosity \(e\)**} & **Contact Effective Modulus \(E_{\text{eff}}\)** & **Contact Stiffness Ratio k** & **Contact Friction Coefficient \(\text{m}\)** & **Particle Shape Factor \(\times\)** \\ \hline
3100 & 0.58 & 50 & 1.0 & 1.5 & 0.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The three-axis simulation parameters. Figure 2: Contact model mesoscopic parameter analysis results. The stress-strain curves calibrated using discrete element simulation software are compared with the triaxial tests results in Figure 4, revealing a substantial softening phenomenon. The peak deviator stress of the calibrated curve generally aligns with that of the test curve, whereas the residual deviator stress in the later stage is slightly higher, though not significantly different. This discrepancy is attributed to the numerical simulation not accounting for the strength reduction caused by particle crushing. Overall, the selected parameters were considered reasonable and suitable for subsequent simulation studies.
## 3 Results
### Interaction Mechanisms of Collector and Depressants on Fluorite Flotation
These authors found that the results of the adsorption and zeta potential tests indicate that tannic acid is an effective depressant for separating fluorite from calcite, demonstrating that the interaction of tannic acid on the fluorite surface has little effect on the chemisorption of the collector, while it strongly weakens the calcite surface which favors its separation. However, dextrin or similar compounds have been poorly invested in fluorite flotation. [PERSON] et al. [42] studied the flotation of schelite, calcite, and fluorite using dextran sulfate sodium as a depressant and sodium oleate as a collector. The authors observed significant drops in the zeta potential for all minerals in the absence of a depressant, indicating that the collector can interact with them and change their surface charge. In the presence of the depressant, the drop in zeta potential is different for each mineral and depends on the collector concentration, indicating that the depressant interferes with the interaction between the collector and minerals. Finally, [PERSON] et al. [21] proposed, for the first time, the enrichment of fluorite in laboratory and industrial flotation tests using oleic acid as a collector and tannin and dextrin as depressants. The authors conclude that two-stage flotation using dextrin to remove SiO\({}_{2}\) and tannin to remove CaCO\({}_{3}\) improves the fluorite flotation process. The results obtained in this work are consistent with those found by other researchers and are a good starting point for scaling the process.
## 4 Simulation and Result Analysis
### System Performance Validation  
#### 4.3.1 System Peak-to-Average Power Ratio  
The peak-to-average power ratio is defined as:  
\[PAPR=10\log\left(10\cdot E\left(\frac{\max(s)^{2}}{E(s)^{2}}\right)\right) \tag{18}\]  
Figure 8 shows the complementary cumulative distribution function (CCDF) curves of the PAPR of the UWAOSDM/PN system with different vector length M. As shown in the figure, the peak-to-average power ratio (PAPR) decreases with the increase of system vector length.  
In addition, the PAPR reflects the advantage of the OSDM from another perspective. High PAPR has low signal energy utilization and short propagation range, while low PAPR has high signal energy utilization and long propagation range. Therefore, the relationship between the vector length and propagation range can be shown in Figure 9. The appropriate vector length can be selected according to propagation range, that is to say, OSDM is a PAPR tunable modulation method.  
Figure 8: Complementary cumulative distribution function (CCDF) curve of peak-to-average power ratio (PAPR) of the UWAOSDM/PN system.  
Figure 7: Performance comparison of UWAOSDM/PN system with different vector lengths: (**a**) system performance under channel model 1 (**b**) system performance under channel model 2.
## 4 Materials and Methods
### Microscale Thermophoresis (MST)  
Binding constants for protein-ligand pairs were established using the MST method. The hNudt16 E76Q was labeled with RED-tris-NTA 2 nd Generation dye (Monolith HIS-tag labeling Kit, NanoTemper Technologies, Munchen, Germany) according to the manufacturer's instructions. Measurements were carried out in a wide range of ligand concentrations, selected after preliminary tests for each of the ligands. The results obtained were used to determine of binding parameters for a given ligand. Each MST measurement consisted of a series of 16 serial 1:2 dilutions of the ligand, and was performed at a constant dye-labeled protein concentration of 50 nM. After 10 min of pre-incubation, dye-labeled protein-ligand samples were manually loaded into Monolith(tm) NT.115 Premium capillaries (NanoTemper Technologies, Munchen, Germany) and analyzed using the Monolith NT.115 (NanoTemper Technologies). As we observed two separated inflection points indicating two non-equivalent binding sites, analysis with standard NanoTemper software that enables using only two models, namely (1) assuming one binding site (or equivalent binding sites) and (2) cooperative binding sites (Hill Equation), was insufficient. Therefore, the results were analyzed using the model of two independent binding sites, as described previously by [PERSON] et al. [35] Analysis was performed using Origin package (OriginLab, Northampton, MA, USA) and dissociation constants were fitted globally for a series of at least six MST pseudo-titration experiments for each ligand. Up to 10 replicates were performed for some ligands.  
For the dissociation constants obtained, we also calculated the free energy associated with the dissociation of the complex \(\Delta G_{\text{diss}}\) according to the equation: \(\Delta G_{\text{diss}}=-\text{RTln}\text{K}_{\text{d}}\)[52].
###### Abstract  
Carbon nanotube (CNT) film can be used as thin film electrodes and wearable electronic devices due to their excellent mechanical and electrical properties. The femtosecond laser has the characteristics of an ultra-short pulse duration and an ultra-high peak power, and it is one of the most suitable methods for film material processing. The ablation and patterning of CNT film are performed by a femtosecond laser with different parameters. An ablation threshold of 25 mJ/cm\({}^{2}\) was obtained by investigating the effects of laser pulse energy and pulse number on ablation holes. Raman spectroscopy and scanning electron microscope (SEM) were used to characterize the performance of the pattern groove. The results show that the oligomer in the CNT film was removed by the laser ablation, resulting in an increase in Raman G band intensity. As the laser increased, the ablation of the CNTs was caused by the energy of photons interacting with laser-induced thermal elasticity when the pulse energy was increased enough to destroy the carbon-carbon bonds between different carbon atoms. Impurities and amorphous carbon were found at and near the cut edge while laser cutting at high energy, and considerable distortion and tensile was produced on the edge of the CNTs' groove. Furthermore, appropriate cutting parameters were obtained without introducing defects and damage to the substrate, which provides a practical method applied to large-area patterning machining of CNT film.  
femtosecond laser machining; carbon nanotube film; material threshold; Raman spectroscopy; laser-induced processing Article
## 3 Hierarchical State-of-the-Art Control
### Voltage and Frequency Restoration for Secondary Control  
As well-known, the deviation of voltage and frequency in the primary droop-based controller in a microgrid is permanent. The secondary control is proposed in the hierarchy level to compensate these deviations. Therefore, numerous approaches have been proposed to enhance the research efforts to tackle this issue by considering whether or not to require a central controller. 1. _Centralized controller_: The principle of the centralized control approach is identical with the inner loop control, as described in Section 3. As previously mentioned, MGCC is responsible for power management, voltage and frequency regulations, and the interconnection with the utility grid. The tprincipal operations are via a communication link that receives the information from the primary network when operating in grid-connected mode and passing the references value to the primary control level. The studies in [20; 27; 66] show examples of this approach. They mainly concern the coordination of the control strategy that depends on the communication network to provide a satisfactory operation and power sharing of generation and storage devices. Worth mentioning, all the decision making, collaboration, and prioritization are achieved more efficiently when information is collected at a single point through the central controller. However, the significant weakness in the MGCC approach is the dependency of the regulations on the central controller, which means that the system performance degrades during the presence of a malfunction at a single point of communications in the central controller. Besides, the communication link among devices offers unreliable operation in terms of maintenance and cost considerations. 2. _Decentralized controller_: A distributed secondary control or MAS network is reportedly a promising method for improving microgrid stability and performance while enhancing the reliability and scalability of the microgrid. The permanent inherent limitations of the primary control, especially the deviations of voltage and frequency, could be counteracted through finite-time and event-triggered control methods, among others. * _Consensus algorithm (CA)_: CA is widely employed in voltage and frequency regulation schemes, wherein the reference values are distributed among all the primary controllers. The most popular methods are through a distributed cooperative finite-time secondary control [22; 116] by employing a neighbor-based linear consensus algorithm that allows each controller to communicate with each other and also by employing the so-called sparse
# Ellipsoidal K-Means: An Automatic Clustering Approach for Non-Uniform Data Distributions
## 4 Experimental Study
### Results and Technical Discussion
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Datasets** & **MOCK** & **VGAPS** & **GCUK** & **SSym\({}_{\text{PESA}\text{II}}\)** & **SSym\({}_{\text{AMOS4}}\)** & **SAELLC** \\ \hline Data\_5\_2 & 0.55 & 0.25 & 0.18 & 0.35 & 0.25 & **0.00** \\ Data\_10\_2 & 1.01 & 0.84 & 0.15 & 0.57 & 0.13 & **0.11** \\ Mixed\_3\_2 & 0.82 & 0.22 & 0.80 & 0.72 & 0.22 & **0.15** \\ Sym\_3\_2 & 0.69 & **0.12** & 0.81 & 0.51 & **0.12** & 0.15 \\ Ellip\_2\_2 & 0.74 & **0.00** & 0.86 & 0.72 & **0.00** & **0.00** \\ Square1 & 0.19 & 0.19 & 0.31 & 0.81 & 0.19 & **0.14** \\ Square4 & 0.60 & 0.52 & 0.51 & 0.73 & 0.49 & **0.35** \\ Sizes5 & 0.64 & 0.22 & 0.48 & 0.66 & 0.25 & **0.12** \\ Iris & 0.82 & 0.62 & 0.58 & 0.55 & 0.54 & **0.40** \\ Cancer & 0.40 & 0.37 & 0.38 & 0.39 & 0.37 & **0.32** \\ Newthyroid & 0.82 & 0.58 & 0.81 & 0.74 & 0.75 & **0.52** \\ Lungcancer & 0.96 & 0.97 & 0.84 & 0.60 & 0.69 & **0.59** \\ Wine & 0.90 & 0.97 & 0.96 & 0.89 & 0.92 & **0.83** \\ LiverDisorder & 0.98 & 0.98 & 0.99 & 0.96 & 0.96 & **0.95** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of SAELLC with other clustering techniques using Minkowski score for different artificial and real-world datasets. We present a comparison with a stochastic-based approach, expectation maximization (EM), which assumes that the input data are drawn from a normal distribution. In contrast, the proposed SAELLC, as a \(K\)-means-based clustering method, does not impose any prior assumptions or statistical restrictions on the nature of the distribution from which the input data are derived [48]. Table 4 compares the performance of SAELLC and EM in terms of the Minkowski score for several of the datasets used [49]. The results demonstrate that the proposed approach outperformed EM, even without relying on its underlying assumption of normality.