esa-sceva/satcom-chunk-collection huggingface dataset:
# Solanesol Biosynthesis in Plants
## 4 Key Enzymes in Solanesol Biosynthesis
### Geranyl Geranyl Diphosphate Synthase (GGPPS)  
_GGPPS_ catalyzes the condensation of three IPP molecules with one DMAPP molecule to form one GGPP molecule (Figure 2) [19]. GGPP is a common precursor in the synthesis of diterpenes, tetraterpenes, and polyterpenes, and it participates in the synthesis of chlorophyll, carotenoids, cytokins, gibberellins, abscisic acid, plastoquinone, ubiquitinone, and solanesol [45,46]. Plant GGPPSs may be divided into large or small subunits; nine members of the GGPPS family have been identified in _N. tabacum_, seven of which are large subunits and two of which are small subunits; five members of the GGPPS family have been identified in _N. tometosiformis_ and _N. sylvestris_, among which four are large subunits and one is a small subunit [47]. NtGGPPS3 is a large subunit located in chloroplasts and the plasma membrane, and the _NtGGPPS3_ gene is expressed in all tobacco tissues during all major stages of growth, with especially high levels in leaves and stems [48,49]. NtGGPPS5 is a small subunit, and _NtGGPPS5_ is expressed in the roots, stem, leaves, and buds of tobacco, with expression decreasing in the order: bud > leaf > stem > root [50]. After treatment with methyl jasmonate, the expression of _NtGGPPS1_ increased significantly, whereas its expression decreased following auxin treatment, and silencing of _NtGGPPS1_ significantly lowered the levels of chlorophyll and carotenoid [51]. In the report by [PERSON] et al. [1], transient expression of the potato _GGPPS3_ gene in _N. benthamiana_ significantly increased solanesol content. Hence, _GGPPS_ is a key enzymatic gene in terpenoid biosynthesis, and its overexpression promotes the accumulation of downstream metabolites such as solanesol.
## 4 Results
### SHAP-Driven Feature Significance Analysis  
Figure 8a illustrates a detailed SHAP summary visualization, mapping individual sinkhole instances and their SHAP values relative to input predictors. After training the RF classifier, factors were prioritized based on their influence, with the \(x\)-axis denoting SHAP values (magnitude and direction of impact) and the \(y\)-axis listing the predictors. Each dot corresponds to a sinkhole sample, colored by predictor magnitude--lighter hues (low values) to darker hues (high values). The RF model identified PTF, MAP, and DBC as the three most critical predictors.  
Figure 8b further quantifies predictor importance using absolute SHAP values, emphasizing their relative contributions to sinkhole prediction. Higher mean absolute SHAP values signify stronger influence, with PTF, MAP, and DBC again dominating. Notably, DBC exhibited a paradoxical relationship: elevated DBC values correlated with negative SHAP values (third row of Figure 8a), implying that higher bicarbonate differences amplify sinkhole susceptibility. Conversely, lower PTF and MAP values (lighter dots) aligned with positive SHAP values, indicating reduced PTF/MAP levels heighen  
Figure 8: (**a**) The RF classifier’s SHAP summary plot, (**b**) RF classifier’s global feature importance by absolute SHAP values.  
The analysis also revealed ASP, DTI, and SDP as the least impactful factors. While SHAP summary plots clarify directional influence, they lack granularity on how predictor magnitudes affect outcomes. To address this, SHAP dependence plots are recommended to dissect nonlinear relationships between feature values and model predictions.
## 2 Materials and Methods
### Sample  
The sample used in this study is an unpackaged p-GaN/AlGaN/GaN on Si-based HEMT bare chip. The simplified structure of the sample is shown in Figure 5. The GaN buffer that has a thickness of 4 \(\upmu\)m and p-GaN/AlGaN/GaN heterostructures are grown on a Si substrate. A 2 DEG is formed at the AlGaN and GaN interface due to the difference in band gap between the two materials [56; 57; 58; 59; 60]. However, since the 2 DEG has high electron mobility even when no gate voltage is applied, the HEMT operates normally on. For normally off operation, heavily p-doped GaN is grown on top of AlGaN/GaN layers, which is etched away except for the gate region. As a result, the area under the gate electrode is depleted by the p-GaN, preventing the formation of the 2 DEG unless a voltage is applied. When a forward voltage is applied to the gate, the 2 DEG is formed, and the channel is connected. This structure and operation method are called e-MODE and are generally used in normally off GaN HEMT devices [61; 62; 63].  
Figure 4: (**a**) Overlap image of microscope image and Raman thermography measurement results in p-doped SOI pattern, (**b**) microscope image of p-doped Si residue.  
Figure 5: Schematic diagram of epi structure of p-GaN/AlGaN/GaN HEMT on Si substrate.  
The field plate is connected to the source electrode. It provides a stable electric field to the channel, increases electrical stability, and has a heat dissipation effect [64; 65; 66]. The overall structure of the sample features a comb-shaped electrode design enabling high-current operation and power efficiency [67; 68].  
The sample is bonded with Al wire on the PCB substrate. The source and drain electrodes are connected to the PCB with 10 wire bonds each for high-voltage applications, while the gate electrode is connected with a single wire since it does not require a high voltage.
# True Random Number Generator Based on Fibonacci-Galois Ring Oscillators for FPGA
## 3 True Random Number Generators Design and Evaluation on FPGA Technology
### Candidate Solutions
#### 3.2.2 Meta-Ring Oscillator (Meta-RO)  
Meta-RO necessitates of a reliable delay line between the activation signal and the sampling clock. This delay can be obtained employing a PLL, which has been configured to produce the enable signal at the same frequency of the clock signal (100 MHz) with a phase shift of \(-1\) ns. In our case, the PLL used is hardware primitive of the FPGA. Unlike TERO, in Meta-RO elements, the number of oscillations is not significant for the output statistic. Because of this, the testing proceeded in a slightly different way: eight instances of the same locked Meta-RO element have been positioned in different LABs and the parallel output value, interpreted as an 8-bit unsigned number (_Sample Value_, 0-255), has been evaluated, as showed in Figure 9. This test has been repeated multiples times, generating new bitstreams to understand dependencies from placing and routing. Every run has acquired 131,072 samples from eight parallel Meta-RO elements. Figure 10 shows histograms of the output values: even if the figures of merit of the oscillator itself can be considered satisfying (as shown in [28]), data are far from being uniformly distributed and the sawtooth shape highlights a predominance of zeros over ones, which reflects in low-quality output.  
Figure 8: Number of oscillations histogram for \(10^{5}\) trigger events for Transition Effect Ring Oscillator (TERO) True-Random Number Generator (TRNG).
## 6 Conclusions  
From the positive correlations and \(R^{2}\) results between RE parameters (curve slope and reflectance) and the NDVI of vegetation canopy in different illumination/shadow proportions, we derived two fundamental conclusions: **(1)** the RE parameters of a vegetation canopy are able to distinguish the shadow proportion of the canopy quantitatively without being influenced by tree species, and **(2)** the NDVI is rarely influenced by canopy shadow proportion and guarantees a stable value for vegetation morphological characteristics. A normalized difference canopy shadow index (NDCSI) was proposed that incorporated the above analysis, and was verified to be accurate enough for a quantitative differentiation of canopy sunlight conditions.  
Based on a Sentinel-2 multispectral image, NDCSI was applied for linear spectral mixture analysis (LSMA) combined with bare soil index (BSI). Positive correlations between measured brightness values and fractional illuminated vegetation cover (FIVC) proves NDCSI's capacity of utilization for satellite images to calculate and extract the illuminated enough vegetation canopy.  
Conceptualization and Methodology, Q.T.; Investigation and Data Acquisition, K.X. and S.T.; Data Analysis and Writing--Original Draft Preparation, N.X.; Validation and Writing--Review & Editing, J.T.; All authors read the manuscript, contributed to the discussion, and gave valuable suggestions to improve the manuscript.  
This work was supported by; National Key R&D Program of China [2017 YFD0600903]; the National Natural Science Foundation of China [41771370]; High-resolution Earth Observation Project of China [03-Y20A04-9001-17/18, 30-Y20A07-9003-17/18]; the Fundamental Research Funds for the Central Universities [090414380022].  
We sincerely thank [PERSON], [PERSON] and [PERSON] for work of ground data collection; and other members of the Hyperspectral program of International Institute for Earth System Science, Nanjing University.  
The authors declare no conflict of interest.
# JFET Integration Using a Foundry SOI Photonics Platform
## 2 JFET Design and Characteristics
A differential JFET amplifier works as the second stage, and provides a regulated output by adjusting the reference voltage V\({}_{\mathrm{REF}}\). All the resistors were realized by an \(n\)-type or \(p\)-type implant on the monolithic silicon chip. Figure 8 shows the optical testing setup, where a tunable laser, Luna Phoniex 1400, and an HP 4145 semiconductor analyzer were used. Successful rectifying characteristics were demonstrated in Figure 9: with an incident light power of 100 \(\upmu\)W, the photocurrent of the integrated Ge waveguide photodiode was measured as 10 times higher than the dark current at the 1550-nm wavelength. The photocurrent obtained was intended to be further amplified through the LBJTs and JFETs amplifiers demonstrated in Figure 7.  
Figure 5: Block diagram of a complementary metal oxide semiconductor (CMOS) photonic circuit. Figure 1:0 Microscopic view of the integrated optoelectronic circuit, showing the grating couplers, Ge photodiodes, and transimpedance amplifier fabricated by the IME process. Figure 1:1 A setup for Ge photodiode measurement. Figure 1:2 Transimpedance amplifier constructed by lateral bipolar junction transistors (LBJTs) and JFETs. Figure 1:3 Measured current–voltage characteristics of the Ge photodiode under 1550-nm optical power (100 \(\mu\)W) and dark condition. The fabricated JFET behaves well, including the expected drain characteristics (see Figure 3) and transfer characteristics (see Figure 4), with an estimated 1 GHz cut-off frequency. With the revised SPICE model parameters in Table 1, electronic circuitry could be designed and monolithically integrated with the photonic components provided by the IME technology [10; 13].
## 6 Conclusions  
In this study, we investigated the effects of background porosity on seismic anisotropy in fractured rocks by conducting laboratory experiments. P- and S-wave velocities were measured on a set of synthetic samples with the same fracture density but different porosities (ranging from 15.1% to 30.8%) under water-saturated conditions. The results exhibited a significant effect of background porosity on P-wave anisotropy for the measured porosity range. The P-wave anisotropy fluctuated slightly when porosity increased from 15.1% to 22.1%, and then declined sharply when porosity continued to increase to 30.8%. However, SWS stayed fairly constant for the entire porosity range. The results were compared with predictions from equivalent medium theories. In both the Eshelby-Cheng model and the Gurevich model, increasing porosity reduces P-wave anisotropy, but has little influence on SWS. The experimental results show good qualitative agreement with the theoretical predictions.  
Understanding the influence of background porosity on seismic anisotropy could improve the characterization of different fractured reservoirs. The findings of this study have potential applications for fracture detection in different areas with different reservoir physical properties. However, considering the inevitable differences between synthetic samples and natural rocks, further studies concerning this topic using field observationsare required, such as post-survey and post-drilling calibrations of the seismic anisotropy after VSP.  
Sample manufacturing and ultrasonic measurement, Y.Z.; data analysis, Y.Z., F.G. and L.L.; supervision, B.D.; writing, Y.Z. and L.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that supports the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
## 2 System Description  
Unlike the WIOBSS-AA, we developed another system branch based on WIOBSS in 2018. The specific architecture is shown in Figure 1. The transmitter is a single channel subsystem. To obtain a large pulse compression gain, a sequence of pseudo-random code is employed as the modulating signal [18]. The multi-channel receiver has six independent channels. Correspondingly, six antennasform a miniaturized antenna array. In addition, a calibration reference source is included to ensure the amplitude and phase consistency of the channels. This system operates in the HF band (3-30 MHz), generally works in the frequency step scan mode for the oblique backscatter sounding. In order to make full use of the transmitting power, the system adopts the modulated continuous wave for transmitting. This will cause serious interference in the near field of the transmitting end. Thus, the receiving and transmitting subsystems are set up in different places. To ensure the time-frequency synchronization of them, synchronization modules are included by both.
# Examining the Link between Isokinetic Strength Metrics and Ball Speed in Women's Soccer
## 2 Materials and Methods
### Design and Procedures  
The study was completed in four consecutive phases, as depicted in Figure 1. In the first phase, procedures such as recording players' demographic information, informing them about the study, and verifying their eligibility were carried out. Additionally, during this phase, the players' dominant foot was determined. To identify their dominant foot, the players were assigned tasks such as kicking a ball, picking up a marble, tracing shapes, and stamping out a simulated fire with their feet. The foot that the players preferred or were more competent at using during these tasks was considered their dominant foot [28].  
On the second day of the data collection phase, players' body composition, body weight, height, and body mass index (BMI) values were determined. Additionally, players were provided with information and instructions about the isokinetic force measurement device, the tests, and measurement protocols by an assistant. This sharing of information  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & \(\mathbf{X}\pm\mathbf{SD}\) & **Range** \\ \hline Age (yrs.) & 17.77 \(\pm\) 1.74 & 16.00–23.00 \\ TSE (yrs.) & 4.50 \(\pm\) 2.00 & 2.00–8.00 \\ TPSE (yrs.) & 2.46 \(\pm\) 1.25 & 0.33–5.00 \\ BW (kg) & 51.72 \(\pm\) 4.33 & 45.30–58.20 \\ Ht (cm) & 160.92 \(\pm\) 4.8 & 152.00–174.00 \\ BMI (kg/m\({}^{2}\)) & 19.96 \(\pm\) 1.33 & 17.70–21.99 \\ LBM (\%) & 40.62 \(\pm\) 2.9 & 36.8–45.8 \\ SLM (\%) & 37.65 \(\pm\) 2.68 & 34.00–42.50 \\ WHR (\%) & 0.71 \(\pm\) 0.01 & 0.68–0.72 \\ \hline \hline \end{tabular}  
* TSE: total soccer experience; TPSE: total professional soccer experience; BW: body weight; Ht: height; BMI: body mass index; LBM: lean body mass; SLM: soft lean mass; WHR: waist-to-hip ratio.  
\end{table}
Table 1: Physical and demographic characteristics of the players.  
Figure 1: Flow chart of the research.  
aimed to ensure players' understanding of the isokinetic dynamometer and the measurements, as well as their mental preparedness. On the second day of the study, players were reminded not to engage in strenuous activities and to arrive for the isokinetic measurements in a well-rested state.  
The third phase involved the isokinetic measurements, which consisted of consecutive phases of concentric (Con), eccentric (Ecc), and isometric (Iso) contractions. Players were divided into three different groups (5, 4, and 4 players, respectively) for each isokinetic phase to mitigate fatigue-related performance losses and enable testing at similar time intervals. The participants visited the laboratory on consecutive days, and the isokinetic tests began as previously recommended (around 16:00 and approximately 3 h after a standard meal) [29].  
In the fourth phase, all players were asked to visit the research facility on the same day, and ball speeds during penalty kicks were examined. Penalty kicks were initiated at the same time that previous testing occurred.
# Mining Deformation Life Cycle in the Light of InSAR and Deformation Models
## Materials and Methods
### Sentinel-1 Data and DInSAR Methodology
#### 2.1.5 Geospatial and Statistical Analyses
The correlations between the two sets of data--(\(D\)) and (\(M\))--and the levelling (\(L\)) are presented by [PERSON]'s coefficient:  
\[R_{L,D}=\frac{\sum(w_{L}-\overline{w}_{L})(w_{D}-\overline{w}_{D})}{\sqrt{ \sum(w_{L}-\overline{w}_{L})^{2}\sum(w_{D}-\overline{w}_{D})^{2}}}\text{ and }R_{L,M}=\frac{\sum(w_{L}-\overline{w}_{L})(w_{M}-\overline{w}_{M})}{\sqrt{ \sum(w_{L}-\overline{w}_{L})^{2}\sum(w_{M}-\overline{w}_{M})^{2}}}. \tag{7}\]  
The set of benchmarks (\(n_{bm}\)) was used to first calculate the subsidence differences between levelling and DInSAR (\(dw_{L-D}\)) and a second to calculate time the differences between levelling and the modelled (\(dw_{L-M}\)) values in the same locations. The averaged differences were calculated as follows:  
\[\overline{dw}_{L-D}=\frac{\sum_{i=1}^{n_{bm}}dw_{L-D_{i}}}{n_{bm}}\text{ and }\overline{dw}_{L-M}=\frac{\sum_{i=1}^{n_{bm}}dw_{L-M_{i}}}{n_{bm}}. \tag{8}\]  
The standard deviations for the two series were calculated in a common manner:  
\[\sigma_{\overline{dw}_{L-D}}=\sqrt{\frac{\sum\Bigl{(}dw_{L-D}-\overline{dw} _{L-D}\Bigr{)}^{2}}{n_{bm}}}\text{ and }\sigma_{\overline{dw}_{L-D}}=\sqrt{\frac{\sum \Bigl{(}dw_{L-D}-\overline{dw}_{L-D}\Bigr{)}^{2}}{n_{bm}}}. \tag{9}\]
## 3 Simulation Results
The results for \(r_{s}=36.0\) are shown in Figure 5. In the case of \(\theta=0.5\), the coupling strength is very strong (\(\Gamma\approx 20\)). The difference between the MDF and the Fermi distribution becomes more significant. The first maxima and minima of the PCFs become more pronounced, also, the second maxima starts to appear at \(r\approx 120a_{0}\); however, the number of electrons should be increased for better resolution. In the case of \(\theta\approx 2\) (\(\Gamma\approx 9.8\)), the MDF has quite a distinct \"tail\". The PCFs \(g_{uu}\), \(g_{dd}\), and \(g_{ud}\) are completely the same and have maxima and hardly distinguishable minima. In the case of \(\theta\approx 4.0\), the coupling strength \(\Gamma\approx 4.9\), the MDF slightly differs from the Fermi distribution and there are maxima on the PCFs. Figure 4: Left plots: single-particle MDFs of the unpolarized UEG at \(r_{s}=28\) and \(\theta=0.5,1,4\) calculated with the SMPIMC method. The results are compared with the SMPIMC for the IFG at the same conditions and with the analytical Fermi distributions. Right plots: PCFs \(g_{uu}\), \(g_{dd}\), and \(g_{ud}\) of the UEG. In addition, we calculated the average kinetic energy \(E_{kin}\) and potential energy \(E_{pot}\) of the UEG, supplementing our results from [34] with the strongly coupled states of UEG. These results are presented in Table 2 and Figure 6 and compared with the internal (kinetic) energy of the IFG, calculated with the SMPIMC method (\(E_{IFG}\)) and obtained from the analytical Fermi distribution directly (\(E_{IFG0}\)). The kinetic energy rapidly decreases by many orders of magnitude as the parameter \(r_{s}\) increases, similar to the ideal case (for the Boltzmann gas \(E_{kin}\propto r_{s}^{-2}\)). \(E_{kin}\) starts to exceed \(E_{IFG}\) at high values of \(r_{s}\) due to the effect of \"quantum tails\"; for example, \(E_{kin}\) becomes almost 30% higher than \(E_{IFG}\) at \(r_{s}=36\), \(\theta=0.5\). The potential energy is negative and rapidly falls as \(r_{s}\) grows, while the dependence of \(E_{pot}\) on \(\theta\) at fixed \(r_{s}\) is more sharp at low values of \(r_{s}\) and becomes weaker at high values (for the Boltzmann gas \(E_{pot}\propto-r_{s}^{-1}\)). Figure 1.0 Left plots: single-particle MDFs of the unpolarized UEG at \(r_{s}=36\) and \(\theta=0.5,1,4\) calculated with the SMPIMC method. The results are compared with the SMPIMC for the IFG at the same conditions and with the analytical Fermi distributions. Right plots: PCFs \(g_{uu}\), \(g_{dd}\), and \(g_{ud}\) of the UEG.
# A Review of Transformer-Based Approaches for Image Captioning
## 4 Transformers in Captioning
### Entangled Transformer  
[PERSON] et al. [85] introduce the Entangled Attention (ETA) transformer that tries to exploit the semantic and visual information simultaneously and thus bridge the semantic gap. The semantic gap arises due to difficulties in attention mechanisms identifying accurately the equivalent visual signals, especially when predicting highly abstract words. With their entangled attention, semantic information is injected into the visual attention process; similarly, visual information is injected into the semantic attention process, hence the name \"entangled\".  
They also presented the Gated Bilateral Controller (GBC) which is a gating mechanism that controls the path through which information flows. The GBC controls the interactions between the visual and semantic information. The overall attention model includes a visual sub-encoder and a semantic sub-encoder and a multimodal decoder, as shown in Figure 5. Each sub-encoder consists of N identical blocks, each with a multi-head self-attention and feed-forward layer.
# Handling Skewed Data: A Comparison of Two Popular Methods
## 3 Results
### Application to Real-Life Data
#### 3.2.2 The Catheters Data  
We have real data from a sample of 592 children who received multiple attempts to start peripheral IV catheters in the inpatient setting. The data were obtained from a study conducted at two southeastern US hospitals from October 2007 through October 2008 [21]. We are interested in checking the difference in children's weight between children who lost IV vs. those who did not. The weight variable was skewed and fitted to a gamma distribution according to AICc values. The Q-Q plot and the histogram of the untransformed and log-transformed weight variable are given in Figure 6.  
Then, the \(t\)-test after log transformation was used; we got \(t_{394,29}=\ -3.39\) with a \(p\)-value \(=\ 0.0004\), indicating a significant difference between the two groups; the average for the children who lost the IV weight was lower than that of the other group, who did not lose the IV. An application of the GLM to the original data with gamma distribution and log link function returned a \(p\)-value \(=\ 0.0042\). For this data, the results from the \(t\)-test with log-transformed data and the GLM indicated evidence of a difference in mean for the weight variable.
## 4 Case Study
### Control Group Design and Results
#### 4.2.2 Illumination Condition  
Two types of illumination conditions were simulated: with background light and without background light (see Figure 5). The biggest influence of lighting conditions on the projected image  
Figure 4: Comparison of projection results in two model sizes, (**a,b**) images shot from different angles.  
was color. The virtual image appeared to be closest to the original color scheme when the projection did not have background lighting. There was a higher degree of color contrast in the darker environment (no background lighting), especially on the lines and ridges of the model, such as the brick seams and window frame edges, which gave the projected model more depth and a stereoscopic effect.