esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Analysis  
### Analysis of the Strain-Stress Relationship of Confined Compaction  
#### 3.1.1 Variation Law of Strain-Stress Curve  
The experimental condition for this test is confined compaction, and the compaction device is made of rigid material. The lateral strain is assumed to be zero, and only axial strain occurs during the compaction process. Therefore, the axial strain can be represented as  
\[\varepsilon=\frac{\Delta h}{h} \tag{1}\]  
where \(\varepsilon\) is the axial strain, \(h\) is the filling height of the sample after vibration, and \(\Delta h\) is the axial displacement during the entire compaction process.  
Figure 5 shows the strain-stress curves of the crushed gangue samples compacted from 0 to 24 MPa under 5 different water content conditions. It can be seen from Figure 5 that with the increasing of the axial stress, the axial strain of the samples under different water content conditions generally shows a continuous increasing trend, and the curve increase rate gradually decreases. According to the characteristics of the axial strain-stress curve, the entire confined compaction deformation process of crushed gangue can be divided into three stages: the rapid compaction stage, crushing compaction stage, and stable compaction stage [36; 37].  
Figure 4: Workflow for confined compaction test of crushed gangue.  
Figure 5: Strain–stress curves of crushed gangue under different water content conditions.  
1. Rapid compaction stage (0-2.8 MPa): At the beginning of loading, the crushed gangue is in a relatively loose state of vibration. As the stress increases, the gangue particles adjust their contact state and continuously fill the pores between the particles. The particles gradually interlock with each other to form a relatively compact skeleton structure, and the crushed gangue samples undergo a relatively large strain under a smaller stress.
2. Crushing compaction stage (2.8-12 MPa): After the rapid compaction stage, the strain-stress curve has a serrated shape, with a continuous increase in the strain and a small fluctuation in the stress. This is the obvious boundary between the crushing compaction stage and the rapid compaction stage. After the gangue particles are gradually compacted, the stress acting on the skeleton particles that play a supporting role exceeds their strength value, causing the skeleton particles to be concentratedly destroyed. The crushed particles fill the remaining pores, and the stress quickly readjusts, resulting in fluctuations. As the stress continues to increase, the gangue particles continue to slide and roll under the mutual squeezing action, and the gangue particles are crushed again, thus continuously filling the remaining pores.
3. Stable compaction stage (12-24 MPa): When the compacted state of the crushed gangue reaches a certain degree, the structure of the gangue particles becomes stable, and the residual pores are very small. Under the larger axial stress, the increase in the axial strain is very small. The relationship between the axial strain and axial stress is basically linear, and the axial strain gradually tends to a constant value.
# Frequency Selective Surfaces: Design, Analysis, and Applications
###### Abstract
Figure 1: Wave interaction with FSS characteristics: (**a**) bandpass, (**b**) bandstop, (**c**) absorber, (**d**) polarization converter [14]. Additionally, optically transparent dual-band FSSs have been proposed for smart surfaces, which are particularly relevant for Internet of Things (IoT) devices that necessitate specific design considerations related to transparency and dual-band functionality [23]. The integration of materials such as indium tin oxide and polyethylene terephthalate in the design of these transparent FSSs enhances their applicability in modern communication systems [24]. The latest research on frequency selective surfaces demonstrates various advancements aimed at enhancing selectivity, miniaturization, tunability, and functionality. These developments underscore the continuous innovation in FSS technology to address evolving requirements in wireless communication, sensing, electromagnetic compatibility, and structural engineering applications. The exploration of reconfigurable FSSs, which can adapt their characteristics through the use of active semiconductor devices, further exemplifies the trend towards dynamic and versatile applications in this field [25]. Moreover, the miniaturization of FSS designs, such as those employing convoluted structures, has proven essential for meeting the demands of modern communication systems, particularly in the millimeter-wave and terahertz frequency ranges [26]. These innovations collectively highlight the significant role of FSS technology in shaping the future of electromagnetic applications [27].
# A Truly Second-Order and Unconditionally Stable Thermal Lattice Boltzmann Method
## 3 Unconditionally Stable Thermal Lattice Boltzmann Method (USTLBM)
### Boundary Conditions  
In its computational process, USTLBM calls for boundary values of both macroscopic variables and non-equilibrium distribution functions. The physical boundary condition, which is related to macroscopic variables, can be directly implemented. Specifically, Dirichlet boundary condition can be imposed by artificially assigning boundary values of macroscopic variables on boundary meshes; and Neumann boundary condition can be fulfilled by adopting information on several inner layers of meshes to achieve desired order of accuracy.  
Boundary values of non-equilibrium distribution functions are required in the corrector step of USTLBM, as shown in Equations (43) and (44). It has been proven in previous studies that the non-equilibrium extrapolation scheme can ensure the second order of accuracy in space [17]. In this treatment, the boundary values of non-equilibrium distribution functions are set equal to those on the first inner layer of mesh points. Such boundary treatment can be flexibly applied in uniform and non-uniform meshes, and will be used in the present method in determining boundary values of non-equilibrium terms.
## 3 Results and Discussion  
### Results of Steady-State DC Static Voltage Responses on Battery Terminals  
All measurement points are shown in Table 1. Some points data, before closed AC injection, are off records because they are almost the same as the last check point. The time domain outputs of the detection circuit are shown in Figure 10. Procedures of pulse and step were used to reduce sampling points and to act as low-pass filtering. In Figure 10, outputs are mainly in the transient process at the beginning, and gradually approaching to 660 mV after one hour. Then, the outputs are nearly stable around 660-670 mV, and the maximum voltage fluctuation, 10 mV in the secondary side, will not exceed 0.25 mV in the primary side, which is significantly less than the magnitude (3.2 mV in the primary side) obtained by \(FPLC_{DCqu}\) simulation in Figure 4. Therefore, the experiment can prove that steady-state DC static superpotential on electrode-electrolyte interfaces is indeed zero under fixed frequency AC current inputs.  
\begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Pulse Procedures** & **Timing (s)** & **Output (mV)** \\ \hline
1 & Before 0.5 Hz/5 A injection & 740 & 451 \\  
efore 0.5 Hz/5 A injection & 4280 & 648 \\ \hline \end{tabular}
\end{table}
Table 1: Measurement timings and results.  
Figure 9: Capacities and EIS plots of batteries in accelerated aging tests: (**a**) batteries’ capacities of tests rounds; (**b**) EIS of E-52#; (**c**) EIS of E-11#; (**d**) EIS of E-02#; (**e**) EIS of E-20#.  
Under sinusoidal AC current injections, steady-state DC static voltage responses on electrode-electrolyte interfaces are nearly zero in this detection experiment. Results of the detection circuit are almost ideal; some small errors can be tolerated and may be caused by ambient temperature fluctuations and battery self-heating by injection currents. Maybe a little steady-state DC static voltage response does appear on battery terminals, although it does not affect \(FPLC_{ACqu}\) in engineering applications.
## 2 Materials and Methods  
### Sample Collection and Growth Environment  
The 'Red Globe' (_Vitis vinifera_ L.) grapes selected for investigation were separately cultivated at the experiment bases of Xinjiang Institute of Agricultural Sciences in Turpan and Urumqi, China. The cultivated environment was reported in a recent study [2]. In brief, during fruit development in Turpan, the average temperature was about 40 \({}^{\circ}\)C, average humidity was about 40% and light intensity was around 1500-1800 \(\upmu\)mol\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\). By contrast, growing conditions in Urumqi included an average temperature of about 30 \({}^{\circ}\)C, average humidity of about 50% and light intensity of around 900-1200 \(\upmu\)mol\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\). In contrast to the temperate continental climate of Urumqi, Turpan possesses an arid, warm continental climate owing to its basin topography with longer sun exposure time. Fifteen grapevines with the same ages and growth trends that had been cultivated at the experimental bases of each district were selected for experimental use. At each stage, a bunch of grapes with similar growth trends from each grapevine was selected for sample collection. Five to ten well-developed berries without worms were collected from each of the bunches. Collected berries were divided into three parts for three replicates. Then the berries were immediately frozen by liquid nitrogen and stored at \(-80\)\({}^{\circ}\)C until use. Berries collected at 45 days after flowering were labelled in accordance with their collection time as Stage 1 (S1). Then, berries were collected at intervals of fourteen days and labelled as Stages 2 to 5 (S2 to S5). External profiles of the selected grape berries are shown in Figure S1.  
Samples grown in Turpan are labelled as TS, and samples growing in Urumqi are labelled as US.
## 3 Results
### Enzymatic Activity  
The Brown-Forsythe test (equality of variance) showed that there was a significant difference between the soils with _p_-value = 0.005 (Table 2--Enzymatic activity). Further pairwise comparison established that fluvisol was significantly different from both cambisol and chernozem, with the highest difference being between fluvisol and chernozem (_p_ = 0.023), while cambisol and chernozem exhibited very similar behaviours (_p_ = 1.000).  
The graph (Figure 2) is based on all the individual enzymatic replicates for each soil replicate (i.e., one soil replicate has four enzymatic replicates for each enzyme measured (lactase and Mn-enzymes) separately), hence the presence of a higher number of outliers. The measurement unit is U (umol.min\({}^{-1}\)), which represents the enzymatic unit from the SI that measures the amount of enzyme that catalyzes the conversion of one micro mole of substrate per minute under the specified conditions of the assay method.  
Overall, fluvisol had the highest median activity (371 U), followed by chernozem (308 U) and cambisol (296 U). On the other hand, cambisol presented the biggest variation in its values, registering the highest individual enzymatic measurement throughout the experiment (1652 U) (Table 2--Enzymatic activity values at each sampling time show differences in and between soils).  
The highest median in the experiment was associated with fluvisol at day 3 (527 U), and it also had the highest values in four out of six sampling points. The boxes and whiskers show that the distribution of the values was more spread out in this soil. Furthermore, the graph shows that it presented much clearer differences between the sampling times, with the most important one being between 24 h and 3 days. For this soil, the least intense activity was after one week into the experiment.  
Cambisol and chernozem showed less variation between sampling times, and the values were more concentrated (smaller boxes). For cambisol, Table 2 shows that higher enzymatic activity was registered in the first 24 h and afterwards, alternated between sampling times. Chernozem, on the other hand, started with a slow and constant decrease in activity between 4 h and 24 h, and exhibited its highest values at 28 days.  
Irrespective of these differences, all three soils had the most important change in enzymatic activity at 24 h-3 days, where all three saw an important increase in activity.
# FhMYB108 Regulates the Expression of Linalool Synthase Gene in _Freesia hybrida_ and _Arabidopsis_
## 3 Results
### FhMYB108 Is a Transcription Factor with Strong Activation Activity  
The transient expression vector harboring _FhMYB108_ fused with a GFP tag was constructed and subsequently transfected into protoplas. Intense green fluorescence emanated from the nucleus upon fluorescence, indicating the nuclear localization of FhMYB108 similar to that of the red NLS nuclear localization signal marker. These findings suggest that FhMYB108 functions as a nuclear-localized transcription factor (Figure 3A).  
GD can interact with Gal4 and subsequently modulate _GUS_ gene expression through GD-associated proteins. In this study, the plasmids pUC19-GD (negative control), pUC19-GD-FhMYB108, and PUC19-GD-VP16 (positive control) harboring the GD label were co-transfected with Gal4: GUS, respectively, followed by the subsequent evaluation of GUS activity. The robust transcriptional activation potential of FhMYB108 is evident, highlighting its role as a proficient transcriptional activator (Figure 3B).  
Figure 2: Analysis of the differential expression patterns of _FhTPS1_ and _FhMYB108_ in floral tissues. (**A**) The expression level of _FhTPS1_ in different tissues. (**B**) The expression level of _FhTPS1_ in different blooming stages of _Red River®_. (**C**) Amino acid sequence alignment diagram between FhMYB108 and its homologous proteins. (**D**) The phylogenetic analysis of FhMYB108. (**E**,**F**) The spatial and temporal expression pattern of _FhMYB108_ in _Red River®_ flowers. In (**A**,**B**,**E**,**F**), data are shown as the mean \(\pm\) SD of three biological replicates.  
Additionally, the _TPS1_ gene promoter was cloned to generate a HA-pUC19-FhProTPS1: GUS plasmid, which was co-transfected with the transient expression vector HA-pUC19-_FhMYB108_ into _A. thaliana_ protoplasts (HA-pUC19-CAT serving as a negative control). The result suggests that FhMYB108 has the ability to significantly enhance GUS activity, indicating its autonomous binding capability to the promoter region of _FhTPS1_ and potential for transcriptional activation (Figure 3C). With truncation analysis, it has been determined that FhMYB108 is likely to exert regulatory control over the expression of _FhTPS1_ by exhibiting a strong binding affinity towards the \(-1016\)\(\sim\)\(-916\) bp region of the _FhTPS1_ promoter (Figure 3D).
# Phase Balancing and Reactive Power Support Services for Microgrids
## 4 Microgrid Central Controller
### Microgrid Central Controller (MGCC)
Furthermore, the sharing constant was limited in the region [0.01 100] to ensure stable operation of the MGCC. The proposed central controller allows coordination of the PV and ESS inverters to provide Q-PB service, which can improve the power factor and asymmetries at the MG's PCC. Further, the MGCC estimates the priority that should be given to each ancillary service defined according to the sharing constant (\(k\)).
# Tightly-Coupled GNSS/Vision Using a Sky-Pointing Camera for Vehicle Navigation in Urban Areas
## 5 Algorithm Development: GNSS/Vision Integration
### Visual Odometry  
The visual odometry estimate vector \(\mathbf{x}_{c}\) consists of rotations \(\mathbf{r}_{c}\) and translations \(\mathbf{t}_{c}\) (where the subscript \(c\) stands for the camera) that can be represented as:  
\[\mathbf{x}_{c}=\begin{bmatrix}\mathbf{r}_{c}&\mathbf{t}_{c}\end{bmatrix}^{ \mathsf{T}} \tag{19}\]In fact, the camera and the land vehicle coordinate frames are not the same. Thus, the rotations and the translations of the camera should be transformed into the vehicle coordinate frame following Equations (20) and (21).  
\[\mathbf{r}_{c_{b}}=\mathbf{R}_{c}^{b}\mathbf{r}_{c}\mathbf{R}_{b}^{c} \tag{20}\]  
where \(\mathbf{R}_{c}^{b}\) is the matrix used for the rotation from the camera to the body frame, \(\mathbf{R}_{b}^{c}\) the rotation matrix from the body frame to the camera frame and \(\mathbf{r}_{c_{b}}\) represents the rotated \(\mathbf{r}_{c}\) into the body frame. Likewise, the translation should also be rotated into the body frame as follows:  
\[\mathbf{t}_{c_{b}}=\mathbf{R}_{c}^{b}(\mathbf{t}_{c}+\mathbf{r}_{c}l_{b}^{c}-l _{b}^{c}) \tag{21}\]  
where \(l_{b}^{c}\) denotes the lever arm from the camera to the body frame.
# Evaluation of Ultrasonic Bonding Strength with Optoacoustic Methods
## 3 Experimental Procedure
### Electronic Speckle Pattern Interferometry (ESPI)  
Electronic speckle pattern interferometry (ESPI) was used to visualize bending motion in the above vibration test. In addition, this technique can measure the resonant frequency and vibration mode simultaneously [29; 30; 31; 32; 33; 34]. Figure 6 illustrates the schematic of ESPI to measure out-of-plane displacement. A semiconductor laser with wavelength of 660 nm was used for the light source. The light was expanded by a lens and split into two paths (arm 1 and arm 2) with a beam splitter, and used to irradiate the specimen and a reference plane. An A6061-T6 sheet, which is the same material as the bonded specimen, was utilized for the reference plane. The roll direction of the reference plane was aligned with the specimen. When the laser is irradiated on a rough surface, the lights of the random phase overlap and interfere with each other due to the coherence of the laser, thereby a speckle pattern in formed. The superposed speckle pattern of the reference plane and the specimen were photographed by a charge coupled devise (CCD) camera. By subtracting a photographed image before vibration from another image after deformation, and then calculating the absolute value of them, a subtract image was created to recognize the deformation of the bonded specimen. The subtracted speckle intensity changes with the phase difference of the object beam (arm 2) relative to the reference beam (arm 1). The phase difference is caused by the displacement of the specimen.  
Figure 5: Experimental setup for measuring the resonant frequency by a piezoelectric actuator and a capacitance displacement gauge. I/O = input/output; PC = personal computer.  
Figure 6: Experimental setup of electronic speckle pattern interferometry (ESPI).  
The specimen was vibrated in the same manner as the measurement of frequency response. The input voltage to the piezoelectric actuator was 5 mV, which is lower than that used in the measurement of frequency response described in the Section 3.2, because the ESPI cannot detect the large displacement owing to high sensitivity. The frame rate of the CCD camera was 30 frames per second (fps). The brightness of the subtract images was multiplied by 10 to make the images brighter.
# Corner Centrality of Nodes in Multilayer Networks: A Case Study in the Network Analysis of Keywords
## 3 Experimentation
### Experiment 1: Biplex Networks
#### 3.1.2 Florentine Families  
In this experiment, we applied our method to the Florentine Families multiplex network [28]. This multiplex network is composed of two layers. One layer describes the business dealings between sixteen Florentine families in the XV century, and the other layer illustrates their alliances due to marriage. Figure 3 shows the married and business relationships between the Florentine families. In Table 5, the code associated with the name of every family member is presented. To apply the corner centrality measure to this network, we assigned the node's degree value as the node's initial importance value in every layer. The method was then compared to the Pagerank versatility [26].  
In Table 5 and Figure 4, the value and ranking of the corner centrality and Pagerank versatility methods are shown. For both methods, the [PERSON] family (node 9 in Figure 4) was the node with the highest centrality value. Unlike PageRank versatility, the corner centrality method assigns a zero value to a node with an initial value of importance equal to zero in at least one layer. This is so because corner centrality looks for those nodes that maintain high importance throughout all the layers. It is a logical concept, for example, when we need an expert in different fields or a peace mediator in the different countries of a conflict.  
Figure 3: This figure illustrates the Florentine Families multiplex network. For the experiment described in Section 3.1.2, we built a multiplex network using information about business alliances in one layer, and marriage in the other one.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Corner Centrality**} & \multicolumn{2}{c}{**PageRank Versatility**} \\ \hline
**Family Name** & **Node Id** & **Value** & **Rank** & **Value** & **Rank** \\ \hline [PERSON] & 1 & 0 & 15 & 0.8638167 & 6 \\ [PERSON] & 2 & 0 & 12 & 0.8389033 & 13 \\ [PERSON] & 3 & 0.87675385 & 2 & 0.8567245 & 8 \\ [PERSON] & 4 & 0.250263979 & 5 & 0.8732232 & 4 \\ [PERSON] & 5 & 0.268755428 & 4 & 0.8557555 & 7 \\ [PERSON] & 6 & 0.083220632 & 8 & 0.8347692 & 15 \\ [PERSON] & 7 & 0.223183775 & 6 & 0.8823537 & 3 \\ [PERSON] & 8 & 0.109123972 & 7 & 0.8561786 & 9 \\ [PERSON] & 9 & 1.229130987 & 1 & 1 & 1 \\ [PERSON] & 10 & 0.041761825 & 10 & 0.830739 & 16 \\ [PERSON] & 11 & 0.278145671 & 3 & 0.8975903 & 2 \\ [PERSON] & 12 & 0 & 16 & 0.8638167 & 5 \\ [PERSON] & 13 & 0 & 13 & 0.8370955 & 14 \\ [PERSON] & 14 & 0.071072416 & 9 & 0.8374698 & 12 \\ [PERSON] & 15 & 0 & 14 & 0.8496274 & 10 \\ [PERSON] & 16 & 0.025402425 & 11 & 0.8430032 & 11 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Corner centrality values and Pagerank versatility for the Florentine Families multiplex network.