esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Instrumental Texture Analysis  
#### 3.6.1 Shear Force  
Low shear force values of cooked meat are desirable as they reflect greater tenderness of ready to eat product. Tendermess is related to myofibrils and connective tissue proteins and their changes during heat treatment, mainly denaturation of myofibrillar proteins and solubilization of connective tissue [33].  
The maximum force noted during a shear test was selected to characterize the texture of the samples (Table 3). The highest shear force was noted for meat cooked at 60 \({}^{\circ}\)C/4 h (25.61 N), and the lowest for the sample cooked at 65 \({}^{\circ}\)C/3 h (13.29 N). At two lower temperatures, i.e., 60 and 65 \({}^{\circ}\)C, the most tender meats were obtained when heating was conducted for 3 h (17.63 and 13.29 N, respectively), while at two higher temperatures, i.e., 70 and 75 \({}^{\circ}\)C, the lowest shear force values were observed for meats cooked for 2 h (14.66 and 14.61 N, resp.). It can be pointed out that when the same cooking time at all temperatures was taken into consideration (2 h), the shear force values decreased with increased temperature of the heat treatment. Both temperature and time of cooking had a significant effect (\(p<0.001\)) on meat shear force, but their interaction effect was not significant.  
Our results are partially in agreement with the experiment of [PERSON] et al. [20], where the shear force values of pork _Longissimus dorsi_ were in the range 12.6-41.1 N and decreased with cooking temperature increases, while the effect of cooking time was not uniform and depended on the cooking temperature. In the study by [PERSON] et al. [34], the shear force of pork loin was affected by process temperature, but process time had significant effect on meat tenderness only at 50 \({}^{\circ}\)C. It was suggested that the tenderization of longer cooked (24 vs. 12 h) meat was supported by activity of intrinsic proteases which remained active at this low temperature. Thermal inactivation of proteases at temperatures higher than 55 \({}^{\circ}\)C was the probable reason that heat induced structural changes of meat proteins had a decisive role in meat tenderness formation. Correlation between cathepsins activity and shear force of pork has been recently reported by [PERSON] et al. [19]. Likewise, [PERSON] et al. [21] observed a significant effect of cooking temperature on the shear force values of pork ham sous vide cooked at 61 \({}^{\circ}\)C and 71 \({}^{\circ}\)C, while cooking time effect was noted for meat cooked at 71 \({}^{\circ}\)C. Increased meat tenderness upon cooking is associated with the collagen conversion to gelatin [38,45]. [PERSON] et al. [52] reported increasing collagen solubility in beef cooked at 45, 65 and 75 \({}^{\circ}\)C, while [PERSON] et al. [45] observed an increase in collagen solubility with temperature (80-100 \({}^{\circ}\)C) and time of cooking (30-60 min). In our study temperatures higher than 55 \({}^{\circ}\)C were applied. Therefore, changes in meat texture apparently have been a result of heating and not of proteases activity.
# CPM-OFDM Performance over Underwater Acoustic Channels
## 3 System Architecture
### Continuous Phase Modulation (CPM)  
The complex data symbols \(s_{k}\) for the \(k\)th subcarrier at the output of the CPM mapper follow the rules of continuous phase modulation. These complex symbols can be represented as  
\[s_{k}=\cos\theta_{k}+j\sin\theta_{k} \tag{2}\]  
where  
\[\theta_{k}=\pi\sum_{i=-\infty}^{n-1}h_{i}l_{i}+\pi h_{n}l_{n}q(t-nT_{s}) \tag{3}\]  
\(\{l_{i}\}\) represents the sequence of \(M\)-ary symbols of information that are typically selected from the set \(\pm 1,\pm 3,\ldots,\pm(M-1),\;\{h_{i}\}\) are called modulation indices, and \(q(t)\) represents the pulse shape. If \(h_{i}=h\) for all \(i\), the modulation index is fixed for all symbols and such a scheme is called \"single \(h\)\" CPM. A scheme in which \(\{h_{i}\}\) is different from symbol to symbol is called \"multi-\(h\)\" CPM. In multi-\(h\) CPM, the \(\{h_{i}\}\) varies in a cyclic manner through all the \(h\) values. The most useful feature of a CPM scheme is that it contains memory--represented by the first term in Equation (3). It can be seen that this term is the accumulation of all symbols up to time \((n-1)T_{s}\) where \(n\) represents the present symbol index [13,17]. Figure 2 shows the values of \(\theta_{k}\) as a function of symbol times when \(h=1/2\) for a binary CPM. Solid lines indicate transition due to \(I=+1\) (when the information bit is a 1), whereas broken lines indicate transition when \(I=-1\) (when the information bit is a 0). The latest value of \(\theta_{k}\) is computed by adding \(\pi h\) (if \(I=+1\)) or subtracting \(\pi h\) (if \(I=-1\)) to the previous value of \(\theta_{k}\). It is noted that the transition of \(\theta_{k}\) from previous to the current value is discrete in nature, although transitions in Figure 2 are shown using  
Figure 1: Proposed CPM-OFDM transceiver for underwater acoustic channel.  
continuous lines. Regardless of the value of \(\theta_{k}\), the complex numbers shall always lie on a circle.  
The modulation index \(h\) is selected to be \(0<h<1\) and rational, i.e., ratio of two relatively prime integers \(p\) and \(q\), the total number of points in the CPM constellation are kept to a reasonable number and catastrophic phase states are avoided [34]. When \(p\) is even, the phase states \(\theta_{k}\) are given as,  
\[\theta=\left\{0,\pi\frac{p}{q},2\pi\frac{p}{q},\ldots,(q-1)\pi\frac{p}{q}\right\} \tag{4}\]  
In contrast, if \(p\) is odd, the phase states \(\theta_{k}\) are given as,  
\[\theta=\left\{0,\pi\frac{p}{q},2\pi\frac{p}{q},\ldots,(2q-1)\pi\frac{p}{q}\right\} \tag{5}\]  
It follows from Equations (4) and (5) that there are \(q\) points in the signal constellation when \(p\) is even and \(2q\) when \(p\) is odd. For instance, there are five points in the signal constellation if \(h=2/5\), and \(8\) points if \(h=1/4\). Figure 3 shows the two scenarios.
# Optimal Fleet Transition Modeling for Sustainable Inland Waterways Transport
## 1 Introduction
Given the nascency of research on optimizing electric passenger vessel fleet transitions, this work presents a novel methodology. The simplicity and yet high practical utility of the greedy heuristic algorithm are tailored to this problem. It balances solution quality and speed to provide essential insights into community-level electric fleet transition feasibility and implementation requirements. The Materials and Methods section will detail the optimization model developed for this study. This model, designed to determine the minimum number of electric vessels required to replace the existing diesel-based fleet, takes into account various parameters such as capacity, range, charging time, and costs of the available electric vessel models. Following this, the Case Study section will introduce Orta lake, the local lake in Italy that serves as the focus of this study. This section will provide an overview of the lake, its current diesel-based fleet, and the specific challenges and considerations related to its transition to sustainable inland waterways transport. The Results section will present the findings obtained from applying the optimization model to the case study of Orta lake. This will include the optimal fleet configuration, the feasibility of the transition, and the implications of the transition. Finally, the Conclusions section will provide conclusive remarks on the study. It will summarize the key findings, discuss their implications for sustainable inland waterways transport, and suggest potential directions for future research in this area.
## 5 Numerical Comparison of the Existing Mass-Rig Systems
### Comparison and Discussion  
Comparisons between the proposed IMS and the other two inertial configurations using the distributed plasticity technique are presented in this section. Figures 16 and 17 illustrate the calculated seismic performance in terms of displacement histories and force-displacement hysteresis for each numerical model. Additionally, the experimental hysteresis curve used for calibration is presented. In general, the results show that Model I replicated the initial stiffness, strength and displacement capacities, pinching effect, and stiffness and strength deterioration of the tested specimen reasonably well. Then, using the same calibrated parameters in Model II, it can be seen that the proposed IMS closely matched the calculated performance of Model II, which represents the ideal scenario for testing cantilever bridge columns in a shake table, i.e., placing the mass on the specimen's top. On the other hand, by applying the conditions imposed by the mass-rig setup [15] to the proposed IMS, Model III had deficiencies capturing the strength capacity of the tested specimen. Additionally, the displacement capacity and pinching effect were not captured well.  
Figure 15: Model III.  
Figure 16: _Cont._  
Figure 16: Column displacement histories comparison: (**a**) Curico X record; (**b**) Capitola X record.  
Figure 17: _Cont._  
Figure 17: Force-displacement hysteresis comparison: (**a**) Curico X record; (**b**) Capitola X record.  
Figure 18 presents the total experimental dissipated energy contrasted to those obtained from the numerical models. The total energy dissipated was computed by summing the enclosed area from each cycle of the force-displacement hysteresis curve. Model III tends to overestimate the cumulative energy dissipated by the specimen by approximately 16% and 12% for the Curico X and the Capitola X records, respectively. This overestimation is primarily caused by the secondary moments (P-A effects) that the mass-rig transferred to the column. In contrast, Models I and II present a negligible overestimation in the total dissipated energy, with errors of 0.87% and 0.31% for the Curico X record, and 3% and 2% for the Capitola X record, respectively.  
Since this study focused on implementing the proposed IMS to assess the seismic performance of large-scale bridge column models experimentally, it can be deduced from the results that test setups considering reduced-scale models could reproduce non-linear  
Figure 18: Energy dissipation comparison: (**a**) Curico X record; (**b**) Capitola X record.  
earthquake effects of real structural models. While extrapolating the behavior of the prototype model could be challenging when large-scale specimens are planned, the use of an external device on the specimen, such as the one proposed here, capable of accommodating large amounts of additional mass, can simulate the effects caused by earthquake inputs in real components, which is supported by the results.
## 4 Conclusions  
The method developed for controlling the temperature of a nozzle heated by HF currents, using the proposed phase-amplitude resonant measurement method, made it possible to ensure a constant quality of regulation in the accessible range of operating temperature of the nozzle, to reduce the nozzle temperature control error from 20 to 0.2 \({}^{\circ}\)C, as well as to decrease the control delay by more than six times. Thus, high accuracy and speed of nozzle temperature control were provided to achieve the low-delay response  
Figure 14: Tensile stress-strain curves: (a) PA 6; (b) PC; (c) PEEK.  
needed to precisely control the extruded material temperature invariant to toolhead speed. This allows to successfully print objects with the complex geometries (with the need to manipulate extrusion speed while maintaining the constant extrusion temperature when printing smaller elements, overhangs, outer shell, and bridges) and/or getting successful prints when using high-grade temperature-dependent materials such as PEEK.  
Due to the application of the developed method for controlling the temperature of the nozzle and the results of numerical studies of the temperature fields of the deposited layers of the product, for the first time, it will be possible for FDM technology to control the thermal cycle of the FDM process, which will improve the quality of interlayer fusion. Moreover, it will be possible to achieve a more equal distribution of retained stress and reduce mechanical deformations introduced by non-uniform heat shrinkage.  
Conceptualization, D.N.T., I.I.B. and A.A.O.; methodology, A.A.O., I.I.B. and D.N.T.; software, A.A.O. and I.I.B.; validation, A.A.O., D.N.T. and I.I.B.; formal analysis, A.A.O. and D.N.T.; investigation, A.A.O. and I.I.B.; resources, A.A.O., I.I.B. and D.N.T.; data curation, A.A.O., D.N.T. and I.I.B.; writing--original draft preparation, A.A.O.; writing--review and editing, A.A.O. and I.I.B.; visualization, A.A.O.; supervision, D.N.T. and I.I.B.; project administration, D.N.T.; funding acquisition, D.N.T. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge financial support under the Mega-grants program, contract no. 075-15-2021-578 of 31 May 2021, hosted by Perm National Research Polytechnic University.  
Not applicable.  
The data presented in this study are available on request from corresponding author. The data are not publicly available due to further study will be carried out using the same data.  
The authors declare no conflict of interest.
# Simultaneous Determination of 12 Marker Components in Yeonkyopaedok-san Using HPLC-PDA and LC-MS/MS
## 3 Results and Discussion
### Simultaneous Determination of the 12 Marker Components in YPS Sample Using UPLC-MS/MS MRM Mode  
For a quality evaluation of YPS, quantification of the 12 marker components was carried out using the optimized UPLC-MS/MS MRM assay. The retention times (min) of the 12 marker components were as follows: 3-CQA, 1.98; CFG, 4.30; FA, 4.65; LlQA, 4.66; NAR, 5.12; 5-MVS, 5.30; NAG, 5.30; NHES, 5.61; OPDH, 6.58; GA, 8.10; ARC, 8.20; and 6-GIN, 8.94 min (Figure 2). The amounts of these analytes ranged from 2.35 to 853.11 \(\upmu\)g/g (Table 5).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Batch No.**} \\ \cline{2-7}
**Compound** & \multicolumn{2}{c}{**1**} & \multicolumn{2}{c}{**2**} & \multicolumn{2}{c}{**3**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Mean (mg/g) \(\pm\)**} & \multicolumn{2}{c}{**RSD (\%)**} & \multicolumn{2}{c}{**Mean (mg/g) \(\pm\)**} & \multicolumn{2}{c}{**RSD (\%)**} & \multicolumn{2}{c}{**Mean (mg/g) \(\pm\)**} \\  & \multicolumn{2}{c}{**SD (\(\times\) 10\({}^{-1}\))**} & \multicolumn{2}{c}{**SD (\(\times\) 10\({}^{-1}\))**} & \multicolumn{2}{c}{**SD (\(\times\) 10\({}^{-1}\))**} & \multicolumn{2}{c}{**SD (\(\times\) 10\({}^{-1}\))**} & \multicolumn{2}{c}{**RSD (\%)**} \\ \hline
3-CQA & 4.28 \(\pm\) 0.66 & 1.53 & 4.22 \(\pm\) 0.56 & 1.32 & 4.15 \(\pm\) 0.83 & 1.99 \\ CFG & 0.62 \(\pm\) 0.05 & 0.80 & 0.61 \(\pm\) 0.07 & 1.19 & 0.60 \(\pm\) 0.12 & 2.03 \\ LlQA & 3.05 \(\pm\) 0.26 & 0.84 & 2.90 \(\pm\) 0.65 & 2.24 & 2.96 \(\pm\) 0.39 & 1.31 \\ FA & 0.32 \(\pm\) 0.04 & 1.23 & 0.30 \(\pm\) 0.07 & 2.27 & 0.30 \(\pm\) 0.08 & 2.74 \\ NAR & 1.30 \(\pm\) 0.15 & 1.19 & 1.27 \(\pm\) 0.25 & 1.93 & 1.24 \(\pm\) 0.24 & 1.95 \\
5-MVS & 0.59 \(\pm\) 0.03 & 0.49 & 0.58 \(\pm\) 0.10 & 1.71 & 0.50 \(\pm\) 0.15 & 2.97 \\ NAG & 9.00 \(\pm\) 0.74 & 0.82 & 8.93 \(\pm\) 0.07 & 0.08 & 8.84 \(\pm\) 0.65 & 0.74 \\ NHES & 6.48 \(\pm\) 0.42 & 0.65 & 6.39 \(\pm\) 0.59 & 0.92 & 6.28 \(\pm\) 0.94 & 1.50 \\ OPDH & 0.69 \(\pm\) 0.05 & 0.78 & 0.67 \(\pm\) 0.10 & 1.53 & 0.64 \(\pm\) 0.17 & 2.64 \\ ARC & 0.94 \(\pm\) 0.09 & 0.96 & 0.92 \(\pm\) 0.03 & 0.31 & 0.92 \(\pm\) 0.12 & 1.28 \\ GA & 6.02 \(\pm\) 0.51 & 0.85 & 5.95 \(\pm\) 0.37 & 0.61 & 5.85 \(\pm\) 0.95 & 1.62 \\
6-GIN & 0.15 \(\pm\) 0.02 & 1.19 & 0.15 \(\pm\) 0.04 & 2.65 & 0.14 \(\pm\) 0.02 & 1.16 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Amount of the 12 marker components in YPS using HPLC–PDA (n = 3).  
Figure 2: _Cont._
###### Abstract  
This paper presents the concept and design of a system that embeds piezoelectric sensors to measure the voltage of a mechanical load applied to it. COMSOL Multiphysics, a finite element simulation tool, was used to design the system and analyze the data to find a possible fingerprint of voltage changes. The sensors' voltage readings were affected by the load applied to the surface of the structure with different magnitudes and speeds. The analyzed data show the effect of position and mass on the voltage readings and indicates the possibility of speed prediction. The obtained dataset results validated the concept of the proposed system, where the collected data can serve as a digital data pipeline model for future research on different artificial intelligence (AI) or machine learning (ML) modeling applications. From the obtained data, a reasonable view shows that voltage reading matrices can be utilized for the detection of vehicle speed, location, and mass if used as training data for machine learning modeling, which can benefit the Internet of Things (IoT) technology.  
data pipeline; piezoelectric sensor; voltage reading; stress/strain; COMSOL Multiphysics
## 4 Conclusions  
In this work, we investigated the remediation effect on diesel-contaminated farm-land soil using different bioremediation methods, including natural volatilization (S), natural attenuation (C), biostimulation (N), bioaugmentation (B), and the biostimulation-bioaugmentation joint (N-B) remediation method. Quantitative analysis of bacterial genes and functional genes was carried out by real-time PCR. The functional enzyme activity, functional gene content, and soil total DNA metagenomic information were determined to evaluate the impact of diesel pollution on soil microbial ecology and biological functions by monitoring the soil's physical and chemical properties to evaluate the feasibility of the experimental program. The results show that the presence of diesel severely damages the soil's nitrogen cycle system and reduces soil cultivation capacity. The combined remediation of the biostimulation-bioaugmentation can reduce the diesel content in the soil and restore the micro-ecological structure of the soil. This joint remediation process can improve the functional enzyme activity of the soil, strengthen the coding expression of the soil nitrogen cycle functional gene, and stimulate the increase of degradation genes _alkB_ and the expression of the gene _nah_. The in situ field experiment shows that the diesel content decreases from 2.7% \(\pm\) 0.3 to 1.12% \(\pm\) 0.1, and the soil electrical conductivity, available ammonium nitrogen, and available potassium, restore to normal levels. The Miseq sequencing results showed that the microecological diversity returned to normal levels. Among them, the _Actinomycetes_, _Acidobacteria_, _Bacteroidetes_ all reached or exceeded normal soil. This work will help us to better understand how to regulate the micro-ecological structure of the biosimulation-bioaugmentation joint remediation process and to further improve the degradation efficiency.  
Conceptualization, X.L.; methodology, X.L. and Y.C.; software, X.L. and Y.C.; validation, Y.C., X.D.; formal analysis, J.Z.; investigation, X.L.; resources, D.L. and Z.L.; data curation, X.L.; writing--original draft preparation, X.L., Y.C.; writing--review and editing, X.L., Y.C.; visualization, Y.C.; supervision, X.D. and J.Z.; project administration, D.L.; funding acquisition, D.L. All authors have read and agreed to the published version of the manuscript.  
This study is supported by the Independent Project from the State Key Laboratory of Petroleum Pollution Control (No. RISEAW2019002) and \"The National Key Research and Development Program of China under Grant No. 2018 YFA0902200\" and the Chinese National Natural Science Foundation under Grant No. 21878175 and State Key Laboratory of NBC Protection for Civilian (No. SKLNBC2020-16).  
Not applicable.  
The authors would like to thank the CNPC Research Institute of Safety and Environmental Technology for its strong assistance and support in this work.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Research on Misalignment Fault Isolation of Wind Turbines Based on the Mixed-Domain Features
## 2 Establishment of Mixed-Domain Feature Library
### Feature Extraction of Vibration Signal in the Time-Frequency Domain  
In this paper, EMD is improved by the method of mirror extension, and the energy entropy of the vibration signal is extracted by the improved EMD (IEMD). Assuming [21]:  
\[E_{i}=\int|c_{i}(t)|^{2}dt=\sum\limits_{k=1}^{n}\Big{|}\chi_{ik}^{2}\Big{|}, \tag{14}\]  
\[E=\sum\limits_{i=1}^{n}E_{i}, \tag{15}\]  
\[p_{i}=\frac{E_{i}}{E}, \tag{16}\]  
where \(i=1,2,3,...,n\); \(x_{ik}\) is the amplitude of each discrete point. The expression of IEMD energy entropy is  
\[P_{i}=-\sum\limits_{i=1}^{n}p_{i}|\mathrm{g}p_{i}. \tag{17}\]  
According to Ref. [7], the energy entropy of the first eight IMF components of IEMD decomposition is the time-frequency domain indexes. Collecting the vibration signal of the high-speed shaft at normal and three misalignment conditions with the speeds of 81.3\({}^{\circ}\)/s, 104.4\({}^{\circ}\)/s, 110.7\({}^{\circ}\)/s and 117.0\({}^{\circ}\)/s, respectively, one gets 114 samples at each speed with each case, so the total number of samples is \(4\times 4\times 114\). The features oftime-domain, frequency-domain and time-frequency domain of the samples are extracted to construct the mixed-domain fault feature library, which are shown in Table 1.  
Tables 2-4 are partial data of the extracted time-domain, frequency-domain and time-frequency domain indexes. Type 0, 1, 2, 3 represent normal state, parallel misalignment, angle misalignment and comprehensive misalignment, respectively. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature Library** & **Feature** & **Index** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Mixed-domain \\ feature library \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} Time-domain \\ feature library \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} root mean square, square root amplitude, variance, \\ standard deviation, kurtosis, waveform index, peak \\ index, pulse index, margin index, kurtosis index \\ \end{tabular} } \\ \cline{2-3}  & & & \\ \cline{2-3}  & Frequency-domain & \
\begin{tabular}{c} centroid frequency, mean square frequency, \\ variance frequency \\ \end{tabular} \\ \cline{2-3}  & & Time-frequency domain &
\begin{tabular}{c} energy entropy of the first eight IMF components of \\ IEMD decomposition \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mixed-domain feature library.