esa-sceva/satcom-chunk-collection huggingface dataset:
# A Predictive Model to Correlate Amino Acids and Aromatic Compounds in Calabrian Honeys
## 2 Materials and Methods
### Hilic-UPLC-ESI-MS/MS Analysis  
The separation of free amino acids was carried out using a Nexera X2 chromatograph (Shimadzu Corporation, Milan, Italy) with an LCMS-8050 Triple Quad detector (Shimadzu Corporation, Milan, Italy). The UPLC system consisted of a binary pump, an automatic degasser, a column heater, and an autosampler. To mitigate errors stemming from poor repeatability, a HILIC (hydrophilic interaction liquid chromatography) separation method was employed [33; 34]. The chromatographic separation using the hydrophilic interaction was conducted utilizing an Acuity BEH Amide column (100 \(\times\) 2.1 mm \(\times\) 1.7 um) at a temperature of 30 \({}^{\circ}\)C with a flow rate of 0.3 mL/min. The mobile phase composition consisted of a binary gradient of (A) H\({}_{2}\)O:MeOH buffer (45:45:10) and (B) ACN:Buffer (90:10). The buffer solution comprised 20 mmol/L formic acid, 3 mmol/L ammonium formate, and 3 mmol/L ammonium acetate. The gradient elution program involved the following steps: 0-5 min at 100% B, 5-7 min at 90% B, 7-10 min at 70% B, and 10-18 min at 40% B. The composition was then reverted to the initial mobile phase and equilibrated for 8 min prior to the next injection. The MS/MS system incorporated an ESI (electrospray ionization) source operating in the positive ion mode. Optimization of ionization source and MS parameters was conducted individually for each analyte by directly infusing a standard solution (with a concentration of 1 mg/L). Detection was performed in MultipleReaction Monitoring (MRM) mode to ensure high sensitivity and selectivity for each analyte. This was achieved through the in-source generation of protonated molecular ions of amino acids and the generation of specific fragment or through collision-induced processes. For the LC-MS/MS analysis, the following instrumental parameters were used: nitrogen as the drying gas at a flow rate of 10 L/min, nebulizing gas at 3 L/min, and heating gas flow at 10 L/min. The interface voltage and temperature were set at 4 kV and 300 \({}^{\circ}\)C, respectively. The DL (drying line) and heater block temperatures were set to 250 \({}^{\circ}\)C and 400 \({}^{\circ}\)C, respectively. Data acquisition and analysis were carried out using Labsolution software (Shimadzu Corporation). Detailed information about the specific MRM transitions for amino acids, along with corresponding fragmentor voltages, collision energies, and dwell times, is provided in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
###### Abstract  
The sharp change of stiffness in the soil-rock combination stratum is the weak point in the seismic design of the pipe jacking structure. To study the seismic response characteristics of vertical double-layer overlapping pipe jacking, based on the typical soil-rock combination strata in Jinan, two electric power pipe jacking tunnels of 3.6 m diameter were studied as the research objects, where the upper pipe jacking is located in the soil and the lower pipe jacking is located in the composite stratum of half soil and half rock. The soil-rock-overlapping tunnel system was deemed as a plane strain problem. By using the dynamic time history method and considering the non-linearity of material, the seismic response characteristics of a vertical overlapping pipe jacking tunnel under seismic wave were discussed from five aspects, acceleration response, displacement response, stress response, soil interlayer response and the influence of soil-rock combination stratum. The results indicate that under the action of ground motion, the peak acceleration and relative horizontal displacement of the upper pipe jacking are greater than those of the lower pipe jacking; small pipe jacking spacing will lead to the aggravating earthquake failure effect; due to the stiffness difference, the relative horizontal displacement and stress of pipe jacking structure at the soil-rock interface change abruptly. The vertical double-layer arrangement of the pipe jacking increases the buried depth, and the stratum is prone to be uneven hardness. Therefore, the seismic design and relevant structural measures of large diameter vertical overlapping pipe jacking structure should be strengthened.  
soil-rock combination stratum; double-layer vertical pipe jacking; seismic response; numerical calculation
# A High Gain Modified Quadratic Boost DC-DC Converter with Voltage Stress Half of Output Voltage
## 2 Evolution of the Proposed Modified Quadratic Boost Converter
### Quadratic Boost Converter with Dual Voltage Lift Cell  
Further, the output voltage can be boosted by implementing the same technique with the first inductor, L\({}_{1}\). The proposed circuit topology by [PERSON] et al. [37] is shown in Figure 4 where two capacitors, C\({}_{1}\) and C\({}_{2}\), are connected in series with the inductors L\({}_{1}\) and L\({}_{2}\) respectively.  
During the first mode of operation, the first voltage lift cell (i.e., L\({}_{1}\) and C\({}_{1}\)) is charged by the source voltage through diode D\({}_{2}\). In this mode, the necessary charging current for the capacitors C\({}_{1}\) and C\({}_{2}\) is provided by the source through the diode D\({}_{1}\) and D\({}_{3}\), respectively. Whereas, the second voltage lift cell (i.e., L\({}_{2}\) and C\({}_{2}\)) is charged by the capacitor C\({}_{3}\). During mode 2, the four passive components (i.e., L\({}_{1}\), L\({}_{2}\), C\({}_{1}\), C\({}_{2}\)) transfer the energy to the load with a voltage gain following \(\frac{(2-d)^{2}}{(1-d)^{2}}\). In [37], the characteristics of the converter were proposed and studied.  
Figure 3: Circuit configuration of modified QBC with voltage lift capacitor integrated with series with Inductor L\({}_{2}\).  
Figure 2: Circuit configuration of Conventional QBC.
## 3 Demonstration of Tunable Shielding Effectiveness in GHz Bands  
### Design and Experiments of Different Shielding Film Structures  
#### 3.1.1 Definition of Shielding Effectiveness  
The shielding effectiveness of material is defined as the ratio of electric field strength, magnetic field strength or power in the free space before and after the shielding body is placed, which can be expressed as [40; 41]:  
\[\text{SE}=20\text{lg}(E_{i}/E_{t})=20\text{lg}(H_{i}/H_{t})=10\text{lg}(P_{i}/P_ {t}) \tag{2}\]  
According to the definition of the S parameter in a dual-port network, \(S_{21}=\left(b_{2}/a_{1}\right)_{a_{2}=0}\), \(S_{12}=\left(b_{1}/a_{2}\right)_{a_{1}=0}\), where \(a_{1},b_{1},a_{2},b_{2}\) represent the field strength of incident and reflected waves of port 1 and port 2, respectively. Thus, the SE of a material can be described by the transmission coefficient (T) as [41]:  
\[\text{SE}=-20\text{lg}|S_{21}|=-20\text{lg}|S_{12}|=-10\text{lg}(\text{T}) \tag{3}\]  
Figure 3: (**a**) The adopted CVD graphene grown on PET film; (**b**) the fabricated GIG sample (8 \(\times\) 8 cm); (**c**) GIG sample bent at a certain angle.  
The SE of graphene greatly depends on the position of its Fermi level. With the increase of bias voltage, doping between graphene levels leads to the increase of conductivity and the shielding effectiveness towards the electromagnetic waves.
# Navigation Scenario Permutation Model for Training of
## 2 Materials and Methods
### Navigation Element Selection  
Navigation elements are the factors that comprise any given navigation situation. In this study, we collected navigation elements that have been used previously in other simulated navigation-related studies. The collected navigation elements were such as \"distance between waypoints,\" \"course altering at waypoints\" [14], \"speed acceleration\" [15], \"heading,\" \"safe passing distance\" [16], and others. Among the various navigation elements, we selected the \"course altering angle at the waypoint\" and \"distance between waypoints.\" The reason for selecting these particular navigation elements was that they are both components of a navigation route, able to compose a basic form of navigation scenario, and considered to be sufficiently independent of each other.
# Matching Consignees/Shippers Recommendation System in Courier Service Using Data Analytics
## 3 Methodology
### Step 1: Product Association  
To identify product associations, rules are extracted from the product category of consignees' transactions using the FP-Growth algorithm [51]. This involved the following:  
1. FP-Growth was used to determine items in the set that have been frequently delivered together in a certain fraction of transactions.
2. Evaluation of the association rule used Min sup and Min conf thresholds [47] measured by Equations (1) and (2). Thresholds are defined for expected values of minimum revenue. \[\text{\it Support}\ (X==>Y)=\frac{\text{\it number}\ \text{\it of}\ \text{\it transactions}\ \text{\it with}\ X\ \text{\it and}\ Y}{\text{\it total}\ \text{\it number}\ \text{\it of}\ \text{\it transactions}}\ \leq\text{\it Min}\ \text{\it sup}.\] (1) \[\text{\it Confidence}\ \ (X==>Y)=\ \frac{\text{\it support}\ (X,Y)}{\text{\it support}\ (X)}\ \leq\text{\it Min}\ \text{\it conf}.\] (2)
# Embedded Based Real-Time Monitoring in the High-Pressure Resin Transfer Molding Process for CFRP
## 4 Results and Discussion
### Detection of Abnormal States
#### 4.2.1 Abnormal Curing Condition  
Figure 13 shows the detected sensor signals when the initial temperature of the mold was low. The carbon fiber layups used only eight sheets in identical experimental conditions. The cavity pressure CP2 in Figure 13a was maintained at a constant in the curing step (\(\lx@sectionsign\)3) but the cavity pressure CP3-4 was reduced by about 20 bar, as compared to CP2 unlike the normal states in Figure 13a. The cavity pressures CP3 and CP4 also varied at about 480 s and 180 s in the curing step. In Figure 13b, the temperature T1-2 increased to 4 \({}^{\circ}\)C and T3-4 to 2 \({}^{\circ}\)C compared to the initial temperature after the curing step. The deviation in the end-gap between LVDT1-2 and LVDT3-4 was 0.2-0.3 mm in Figure 13c and the thickness of the CFRP specimen was as shown in Figure 13d. The deviation of the end-gap between LVDT1-2 and LVDT3-4 was 0.2-0.3 mm as shown in Figure 13c. The thickness of the CFRP specimen in Figure 14d was 2.55 mm on average and deviated by 0.2 mm. In the curing step, the temperature T1-2 started to cure at about 105 \({}^{\circ}\)C but the maximum reaction temperature was 4 \({}^{\circ}\)C despite the slow reaction rate compared to the normal state. The cavity pressure CP2 was maintained at a constant at this time. The temperature T3-4 started at 100 \({}^{\circ}\)C due to the initial temperature of the mold, which was lower than the set value of 110 \({}^{\circ}\)C. The reaction rate of the resin had slowed, the maximum reaction temperature was not 4 \({}^{\circ}\)C, and the pressure had also become unstable. Therefore, curing occurred up to the central portions (CP2 and T2) of the mold but only occurred partially at the end portions (CP3-4 and T3-4). The CFRP specimen was not uniformly compressed because it had some uncured layers and the thickness variation increased. Due to the presence of some uncured layers and the thickness variation increasing, the CFRP specimen was not uniformly compressed.  
Figure 13: Results of the HP-RTM process: (**a**) Cavity pressure; (**b**) Temperature; (**c**) End-gap; (**d**) The CFRP thickness.
# A Novel Hybrid Whale-Chimp Optimization Algorithm for Structural Damage Detection
## 1 Introduction
For instance, [PERSON] et al. [52] improved the optimization ability of the chimp algorithm by using three strategies of highly damaged polynomial edge, Spearman's rank correlation coefficient, and beetle antennae operator, respectively. [PERSON] et al. [53] proposed a boosted ChOA by studying different variants of the ChOA. [PERSON] [54] proposed a hybrid ChOA based on the Sine-cosine function and the spotted hyena optimizer attack strategy. The paper proposes an improved hybrid whale-chimp optimization algorithm (W-ChOA). Firstly, the Sobol sequence [55] is introduced to initialize the population to enhance the initial convergence speed of the algorithm. Secondly, the multi-strategy hunting mechanism of the whale optimization algorithm [56] is integrated with the hierarchical hunting mechanism of the ChOA to avoid falling into the local optimum. Simulations show that the W-ChOA has good performance in structural damage detection. The article is divided into five sections. The presented W-ChOA is deduced in Section 2. Then, Section 3 details the mathematical model of structural damage detection by the W-ChOA. Numerical simulations of the W-ChOA for structural damage detection and the comparison results with the ChOA are demonstrated in Section 4. Finally, some conclusions are discussed in Section 5.
# Classification and Mapping of Paddy Rice by Combining Landsat and SAR Time Series Data
## 2 Study Area and Data
### Reference Data  
Ground reference data were collected from the 2004 Sutter County land use survey data and United States Department of Agriculture (USDA) 2009 Cropland Data Layer (CDL) with 1:100,000 scale (site A) and 2002 Korea land cover data with 1:25,000 scale (site B), which were provided by California Department of Water Resource, USDA, and Korea Ministry of Environment, respectively. USDA CDL was produced using Landsat data and Resourcesat-1 based on a decision tree approach [41], and Korea land cover was produced using Landsat, Indian Remote Sensing satellite-1C (IRS-1C), and Satellite Pour l'Observation de la 5 (SPOT 5) Terre data. Since there was a 2-3 years gap between ground reference data and satellite data, we refined ground reference data using high resolution Google Earth images that were collected in the corresponding years through visual inspection. There are nineteen (site A) and twenty-two (site B) land cover classes in the ground reference data and they were aggregated to six (site A) and eight (site B) land cover classes by grouping similar classes (Table 3). A total of 1100 sample points were randomly extracted from input variables at each site. These samples were divided into training data (800 samples) and test data (300 samples), and the number of samples for each class was determined considering the area percentage for each class throughout the study area. The original land cover, aggregated land cover, the number of samples, and the area percentage of each class are summarized in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline
**RADARSAT 1** & **Landsat 5** \\ \hline
18 February 2004 & 12 February 2004 \\  & 13 March 2003 \\
25 March 2005 & 18 March 2005 \\  & 29 March 2003 \\  & 31 March 2004 \\
6 May 2003 & 30 April 2003 \\
30 May 2003 & 1 June 2003 \\  & 3 June 2004 \\
27 September 2003 & 21 September 2003 \\
15 October 2004 & 9 October 2004 \\  & 12 December 2004 \\ \hline \end{tabular}
\end{table}
Table 2: Acquisition dates of satellite data at site B.
# Compression Behavior of Rubber-Bentonite Mixture under Different Salinities
###### Abstract  
This study investigated the engineering properties of a rubber-bentonite mixture under different salt solution concentrations, mass ratios, and consolidation pressures. In addition, the effects of different solute ion concentrations on the compression index of the samples were compared. The results showed that the compression coefficient could be reduced effectively by increasing the weight percentage of rubber without being affected by the salt solution. However, with the increase in the salt solution concentration, the compression coefficient of mixed materials with different mass ratios increased, and when the salt solution concentration exceeded 0.5 mol/L, the compression coefficient increased more obviously. In a 0.1 mol/L NaCl solution, the addition of different levels of rubber could increase the compression modulus of the mixed material and reduce the compression ratio of the mixed material. This showed that in an environment with a low salt solution concentration, adding rubber into the mixed material could enhance its compressive deformation resistance. However, when the rubber content exceeded 50%, significant pores appeared in the sample, and the effect of high salt solution concentration intensified. The rubber content also had an effect on the swelling properties of soil, and the degradation of rubber in the salt solution showed reduced mechanical properties. This emphasizes the need to consider the stability and resistance of saline-alkali areas to salt erosion.  
geotechnical engineering; bentonite; rubber; consolidation; deformation +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Energy Balance Evaluation  
The energy gains obtained by using this technology were calculated for the existing digester operated under mesophilic conditions, with a capacity of 2500 m\({}^{3}\), an average feedstock flow rate of 125 m\({}^{3}\)/d, and an HRT of 20 d. To perform this evaluation, the following equations were used:  
\[Q_{m}=Y_{m}\cdot L_{VS}, \tag{3}\]  
\[Q_{t}=Q_{m}\cdot Q_{i}, \tag{4}\]  
\[Q_{1}=V\cdot\left(T-T_{f}\right)\cdot CSS, \tag{5}\]  
\[Q_{2}=24\cdot\left(T-T_{a}\right)\cdot U\cdot F, \tag{6}\]  
\[F=3.14\cdot D^{2}, \tag{7}\]  
\[Q_{c}=\beta\cdot\left(Q_{1}+Q_{1}\right), \tag{8}\]  
\[Z=\left(\frac{Q_{t}-Q_{c}}{Q_{c}}\right)100\%, \tag{9}\]  
\[E_{d}=Q_{m}\cdot Q_{ie}, \tag{10}\]  
\[N_{t/e}=Q_{m}\cdot Q_{ie}\cdot\eta_{t/e} \tag{11}\]  
where \(\rm Q_{m}\) is the daily methane production (m\({}^{3}\) CH\({}_{4}\)/d), \(\rm L_{VS}\) is the volatile solid (VS) load in the feedstock (kg VS/d), \(\rm Q_{i}\) is the theoretical amount of thermal energy obtained from the combustion of methane (MJ/d), \(\rm Q_{1}\) is the thermal energy required for heating the feedstock(kJ/d), \(\rm Q_{2}\) is the thermal energy required to cover the heat loss through the walls of the digester (kJ/d), CSS is the specific heat of the sewage sludge (4200 kJ/m\({}^{3}\) K), D is the diameter of the cylindrical part of the digester (m), \(\rm E_{d}\) is the daily energy production (kWh/d), F is the surface of the digester walls (m\({}^{2}\)), \(\rm N_{t/e}\) is the theoretical electric/thermal power production (kW), \(\rm Q_{c}\) is the thermal energy demand (kJ/d) \(\rm Q_{i}\) is the heating value of methane (35.8 MJ/m\({}^{3}\)), \(\rm Q_{ie}\) is the calorific value of methane (10 kWh/m\({}^{3}\)), T is the anaerobic digestion temperature (K), Ta is the air temperature (K), T\({}_{t}\) is the temperature of the feedstock in two- and three-component digestion (K), U is the heat loss coefficient by permeation through the walls of the digester (4.0 kJ m\({}^{2}\)/h K), V is the feedstock flow rate (m\({}^{3}\)/d), Z is the profit of thermal energy (%), \(\beta\) is the margin factor (1.1), and \(\eta\)t/e is the thermal/electric efficiency of the CHP engine (\(\rm\eta_{t}=0.43\), \(\rm\eta_{e}=0.38\))
## 2 Design and Implementation
### SoC Design
An additional advantage of the digitally-assisted self-quenching architecture is the reduced quench period, which can increase the maximum data rate. Figure 4b shows how the SR-Rx works, with and without the 27 MHz RF carrier being present. Counter1 in the FPGA generates a 250 kHz real-time clock from the 48 MHz crystal output. The quench stays low for 20 \(\upmu\)s to suppress the previous oscillation sufficiently, and stays high for 40 \(\upmu\)s to detect the 27 MHz RF carrier. If an oscillation is detected within the 40 \(\upmu\)s oscillation window, counter2 in the FPGA counts the number of oscillations and resets the quench after detecting 16 oscillations. The 40 \(\upmu\)s oscillation window adjusts the sensitivity of the SR-Rx to \(-\)73 dBm to have 5 dB safe margin, considering the 10 dBm output power from the 27 MHz Tx in the TDS-UI and the 78 dB worst-case attenuation at the maximum operating distance of 22 cm [39]. Data is demodulated as '1' or '0' according to the counter1 value at the falling edge of the quench signal. Both counters are initialized at the falling edge of the quench signal to be ready to detect the presence of the 27 MHz RF carrier for the next bit. The resulting signal period is equal to or shorter than 60 \(\upmu\)s, depending on the strength of the 27 MHz RF carrier. Considering that we adopted a 5 kbps data rate (200 \(\upmu\)s period), the SR-Rx has an oversampling ratio of more than 3.3, which is sufficient to recover the OOK-modulated data [40].
## 3 Computation of PDs of Surface-Wave Phase Velocities  
In this section, we derive the algorithms, based on matrix multiplication, for computation of PDs of surface-wave phase velocities. In fact, the surface-wave dispersion equation \(F\) also depends on  
Figure 1: A multilayered earth model with each layer being homogeneous, isotropic and elastic. the physical parameters of the subsurface. To facilitate the description, let a vector \(\mathbf{Q}\) indicate these parameters [35]. In other words,  
\[\mathbf{Q}_{\mathrm{R}}=(\alpha_{1},\cdots,\alpha_{n+1},\beta_{1},\cdots,\beta_ {n+1},\rho_{1},\cdots,\rho_{n+1},h_{1},\cdots,h_{n}) \tag{2a}\]  
for the case of Rayleigh waves, or  
\[\mathbf{Q}_{\mathrm{L}}=(\beta_{1},\cdots,\beta_{n+1},\rho_{1},\cdots,\rho_{n +1},h_{1},\cdots,h_{n}) \tag{2b}\]  
for the case of Love waves. Hence, the completed form of \(F\) is  
\[F(\omega,c,\mathbf{Q}_{\mathrm{R}})=0\text{ or }F(\omega,c,\mathbf{Q}_{\mathrm{L}} )=0. \tag{3}\]  
Suppose we want to compute \(\partial c/\partial p_{t}\), where \(p\) represents a generic physical parameter such as \(\alpha\), \(\beta\), \(\rho\), or \(h\) of the objective layer \(t\). Making use of the implicit function theory, \(\partial c/\partial p_{t}\) can be written as:  
\[\partial c/\partial p_{t}=-\frac{\partial F/\partial p_{t}}{\partial F/ \partial c}. \tag{4}\]  
In the following, let us first consider \(\partial F/\partial c\). On the basis of recursion (1) and the derivative rules, the expression for \(\partial F/\partial c\) can be derived as follows:  
\[\partial F/\partial c=\frac{\partial\overline{\mathbf{X}}_{n+1}}{\partial c }\overline{\mathbf{V}}_{n+1}+\overline{\mathbf{X}}_{n+1}\frac{\partial \overline{\mathbf{V}}_{n+1}^{*}}{\partial c}. \tag{5}\]  
Obviously, the calculation of \(\partial\overline{\mathbf{V}}_{n+1}/\partial c\) is straightforward, whereas \(\partial\overline{\mathbf{X}}_{n+1}/\partial c\) has to be calculated recursively starting from the first layer via formula (1):  
\[\partial\overline{\mathbf{X}}_{m+1}^{*}/\partial c=\frac{\partial\overline{ \mathbf{X}}_{m}}{\partial c}\overline{\mathbf{T}}_{m}^{*}+\overline{\mathbf{X }}_{m}^{*}\frac{\partial\overline{\mathbf{T}}_{m}}{\partial c},\ m=1,2,\cdots,n. \tag{6}\]  
For a clearer description of the algorithm (Table 1), let us introduce a new vector, i.e., \(\overline{\mathbf{Y}}_{m}=\partial\overline{\mathbf{X}}_{m}/\partial c\), thus (6) can be reformulated as follows:  
\[\overline{\mathbf{Y}}_{m+1}=\overline{\mathbf{Y}}_{m}\overline{\mathbf{T}}_{ m}^{*}+\overline{\mathbf{X}}_{m}^{*}\frac{\partial\overline{\mathbf{T}}_{m}^{*}}{ \partial c},\ m=1,2,\cdots,n. \tag{7}\]  
This is obviously a zero vector as \(\overline{\mathbf{X}}_{1}\) is a constant vector. A similar algorithm can be derived for \(\partial F/\partial p_{t}\) (Table 2), except that there are two cases that need to be taken into account according to the