esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### Correlation Between HMGR Expression Levels and Strawberry Fruit Quality  
Research on HMGR and its influence on fruit quality related to terpenoids remains limited, particularly concerning its effects on fruit color and aroma. The two terpenoid biosynthesis pathways MVA and MEP are not completely isolated, as transporters in the plastid membrane allow metabolite exchange, facilitating a'metabolic cross-talk' [1]. HMGR, which typically catalyzes the conversion of 3-hydroxy-3-methyl-glutaryl-CoA to mevalonic acid in the MVA pathway, has been shown to influence color formation by affecting the MEP pathway and anthocyanin synthesis [10]; it also encodes functional proteins that accelerate carotenoid biosynthesis [23; 25]. Consistently, [PERSON] et al. [10] found that the expression of _VvHMGRs_ in yellow grape varieties was generally higher than in red varieties. HMGR positively regulates terpenoid metabolism, thereby affecting hormone synthesis and indirectly regulating anthocyanin accumulation [24]. Moreover, a truncated form of HMGR (HMG) was found to increase endogenous sesquiterpenes level by 37-fold and phytoene by 100-fold [33]. Negative regulation of HMGR by phyB and HY5 via interaction with PIF3 may further affect anthocyanin synthesis [19]. In this study, strawberries overexpressing _FaHMGR_ exhibited reduced coloration, along with downregulated expression of most anthocyanin pathway genes. Conversely, _HMGR_ silencing by expressing _FaHMGRi_ promoted strawberry coloration, with anthocyanin content increasing by 3.33 mg\(\cdot\)g\({}^{-1}\) compared to the control, aligning with findings by [PERSON] et al. [26] in grape studies.  
Strawberry aroma contributes significantly to fruit quality, enhancing consumer appeal and potential health benefits. Strawberry aroma results from a mixture of more than 350 volatile compounds, making it one of the most complex fruit aromas [34]. Volatiles in strawberry aroma include esters, terpenoids, alkanes, alcohols, acids, aldehydes, and ketones, with terpenes providing the main floral notes [35]. The terpenoids responsible for strawberry aroma primarily consist of monoterpenes (C10), sesquiterpenes (C15), and triterpenes (C30). Monoterpenes were the only type detected in this study. The highest diversity of terpenes, especially monoterpenes, was observed in _FaHMGRi_-expressing strawberries. This finding suggests that inhibiting _HMGR_ expression promotes monoterpene synthesis via the MEP pathway. However, this contrasts with studies in grapes, which indicated a positive correlation between _HMGR_ expression and monoterpene accumulation. This inconsistency suggests species-specific differences in how _HMGR_ influences fruit aroma composition [36].  
Beyond color and aroma, HMGR expression also affects fruit size. HMGR impacts early fruit development by regulating MVA synthesis, a role confirmed in studies of melons and tomatoes. Moreover, reduced HMGR activity has been shown to disrupt normal plant development [37; 38].
# Textile-Based Flexible Coils for Wireless Inductive Power Transmission
## 7 System Performance  
### Output Power of WPT  
The plots of output power versus output current obtained by employing paired coils of the different types are shown in Figure 10. The given standard deviation of maximum output power is calculated based on 10 measurements with each pair of coils.  
The maximum output DC power of the WPT system using the printed coils can be observed to be 1.51 W at 0.37 A output current. For all the coils tested, the errors of maximum output power are 1%, 1.5%, 1.7%, and 1.8% at 5, 10, 15, and 20 mm separation, respectively.
# Path Tracking of Agricultural Vehicles Based on 4 WIS-4 WID Structure and Fuzzy Control
## 2 Materials and Methods
### Path-Tracking Model Based on 4 WIS-4 WID Structure and Fuzzy Control
#### 2.2.1 Relationship between Traditional Pure Tracking Model and Vehicle Structure
From the comparison (Figure 3), we can see that to obtain an equal response of steering (i.e., the same steering radius), the steering angle of each wheel of the 4 WIS-4 WID vehicle structures is more insignificant.
## 3 Results and Discussion  
Both simulations and experiments were applied here with the fluidic systems presented above to assay the OFI signal performance. In the following subsections, the results are demonstrated and discussed in detail to verify our model reliability.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameters** & **Value** \\ \hline Wavelength & \(\lambda=670\) nm \\ Half divergence of the laser & \(B=5^{\circ}\) \\ Lens focal length & \(f_{1}=f_{2}=30\) mm \\ Refractive index of water & \(n_{0}=1.33\) \\ Refractive index of PDMS & \(n_{p}=1.4\) \\ Refractive index of DF & \(n_{s}=1.59\) \\ Refractive index of PS particles & \(N=1.58\) \\ Photon lifetime in laser & \(\tau_{p}=10^{-12}\) s \\ External cavity length & \(L=76\) mm \\ VCSEL aperture diameter & \(DA=6\) μm \\ Laser output cavity mirror & \(r_{2}=0.9995\) \\ reflectivity & \\ \hline \hline \end{tabular}
\end{table}
Table 1: ZEMAX modeling parameters settings.  
Figure 2: (**a**) Schematic of the microfluidic measurement system; (**b**) a polydimethylsiloxane (PDMS) channel and a dry film (DF) channel.
## 3 Results and Discussion  
The first characterizations were performed to investigate the samples' morphological features. Agarose-based structures obtained by freeze-drying and supercritical drying preserved the same shape, but not the same size as the starting hydrogels. Shrinkage was 36% and 20% for cryogels and aerogels, respectively. These values were calculated using sample dimensions (Table 3) and the formula reported in Section 2.5.  
FE-SEM analysis was performed to observe the section and surface of each sample produced; the results are reported in Figure 2.  
FE-SEM images in Figure 2a,b show that the aerogels were characterized by a regular micro- and nano-porous morphology, with open and interconnected pores in the cross-section and on the surface of the sample; whereas cryogels showed a less regular macroporous structure (Figure 2c,d). Supercritical drying is able to preserve the nanofibrous  
Figure 2: FE-SEM images of 3% _w/w_ agarose aerogel: (**a**) section and (**b**) surface. Images of 3% _w/w_ cryogel: (**c**) section and (**d**) surface.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Before** & **Diameter [cm]** & **Height [cm]** & **Volume [cm\({}^{3}\)]** \\ \hline Aerogel & 1.7 & 1.0 & 2.268 \\ \hline Cryogel & 2.0 & 0.8 & 2.512 \\ \hline
**After** & **Diameter [cm]** & **Height [cm]** & **Volume [cm\({}^{3}\)]** \\ \hline Aerogel & 1.7 & 0.8 & 1.815 \\ \hline Cryogel & 1.6 & 0.8 & 1.607 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Cryo/Aerogel dimensions before and after the drying process.  
structure of the starting hydrogel thanks to the zero-surface-tension of the supercritical mixture CO\({}_{2}\) plus organic solvent, formed during the drying process [22; 37]. In the case of freeze-drying, the nucleation of ice crystals and their subsequent removal by sublimation tend to destroy the gel nanostructure. In particular, freezing and sublimation steps led to the development of significant stresses within the polymeric matrix since, during freezing, water expands, and mechanical stresses are exerted on the surrounding polymeric chains; moreover, capillary stresses destroy the gel nanostructure during ice crystals sublimation [22; 33; 34; 35; 36].
# Green Hydrogen Production--Fidelity in Simulation Models for Technical-Economic Analysis
## 4 Simulation Results and Discussion
### Cost Analysis and Economic Feasibility  
The price of producing green hydrogen is a key aspect for its large-scale adoption. Based on the simulations conducted according to Table 4, the results indicate that the energy consumption to produce one kilogram of hydrogen is 64 kWh. Additionally, the hydrogen production per day and year is 0.287 kg/day and 103.32 kg/year, respectively. On the other hand, a cost of 0.06 USD/day and 36 USD/year was considered as a reference for the price of water in the city of Cuenca, Ecuador. In this case, the required water consumption is 0.4 L per hour.  
As shown in Figure 15a, the electrolyzer produces 0.287 kg of hydrogen per day while operating. It is important to note that the electrolyzer runs for 16 h per day and remains off for the remaining 8 h. Therefore, in this case study, the electrolyzer will produce a total of 103.32 kg of hydrogen annually.  
Figure 14: Validation of simulation models through experimental data.  
In addition, based on Figure 15b, the energy consumption to produce 1 kg of hydrogen is expected to stabilize at 64 kWh after the first 9 h. In this case study, conducted in the city of Cuenca, Ecuador, where the cost of electricity is USD 0.09 per kWh, the production cost for one kilogram of hydrogen would be approximately USD 5.76.  
Tables 5 and 6 present the electricity prices per hour in Ecuador for residential, commercial, and industrial consumers [40]. Consequently, the EL-500 electrolyzer consumes 2.382 kWh when operated for 16 h per day, resulting in a total daily demand of 38.08 kWh. Considering the manufacturer's reported efficiency range of 60% to 80%, the net energy consumption is between 22.08 kWh and 30.46 kWh. Additionally, during the experimental phase, it was observed that hydrogen storage at 200 bar in a 50-liter tank provided 8.919 kWh over 3 h, 27 min, and 59 s, When the fuel cell is operating at nominal power with the characteristics of Table 7 of the Ecobox-MR30S1 model (Singapore). This corresponds to a round-trip efficiency of 40.45%, which aligns with values reported in the literature [41].  
\begin{table}
\begin{tabular}{l c c c} \hline
**Description** & **Parameter Value** & **Unit** \\ \hline Consumption of energy to produce hydrogen & 64 & kWh/kg \\ Production of hydrogen per day & 0.287 & kg/day \\ Production of hydrogen per year & 103.32 & kg/year \\ Cost of using water & 0.06 & USD/day \\ Cost of using water & 36 & USD/year \\ Water consumption & 0.4 & L/h \\ Water specification & 10 & uS/cm \\ \hline \end{tabular}
\end{table}
Table 4: Costs and results from production.  
Figure 15: (**a**) Hydrogen production over time: daily to yearly. (**b**) Energy consumption per kilogram of hydrogen (kWh/kg).
## 6 Numerical Results  
We evaluate the performance of the proposed and existing schemes through both simulations and practical experiments. We separate our experiments into two main parts: (1) the performance simulation of the cost of MLMS measurements formation and (2) the reconstruction of sensed data in the network by using our proposed termination rule. We employ OMNeT++ to establish our sensor network with different randomly distributed wireless nodes. Due to the data reconstruction performance of the sink node involved in the sparsity of data itself and the chosen of reconstruction algorithm, it belongs to the category of the application layer; as a matter of convenience, we adopt MATLAB to reconstruct the data received by the sink node.
## 3 Methodology
### Stereo Reconstruction
#### 3.3.1 Feature Detection and Matching  
A feature point in an image is a small patch with enough information to be found in another image. Feature points can be corners, which are image patches with significant changes in both directions and are rotation-invariant [46, 47]. They can be keypoints that encode texture information in vectors called descriptors, which besides being rotation-invariant, are also scale-invariant [48, 49, 50, 51, 52]. Additionally, they can be Deep Learning-based features that can also be invariant to repetitive textures and non-rigid deformations [39, 40].  
Feature matching entails finding the same feature point in both images, for this, we compared corner detectors such as Harris [46] and Shi-Tomasi [47] along with feature point detectors such SIFT [48], SURF [49], BRISK [50], FAST [51], KAZE [52], a state-of-the-art Deep Learning method called SuperGlue [39] and DeepMatching [40]. In the case of a corner detector, we use epipolar geometry described in Section 3.2 to reduce the search along the epipolar curve and match the points using image-patch correlation.  
For the case of a feature point detector, we compute the pairwise distance between the descriptor vectors. Two descriptor vectors match when the distance between them is less than a defined threshold \(t\); \(t=10.0\) for BRISK and \(t=1.0\) for SIFT, SURF, KAZE, and FAST. For the case of SuperGlue, we used the outdoor trained model provided by the authors for the outdoor scene and the indoor trained model for the indoor scene. For DeepMatching, after finding the matches using the original implementation, we filter out the wrong matches using epipolar constraints. For this, we measure the Euclidean distance to the epipolar curve and keep the corresponding point in the other image if it lies \(d\) pixels from the epipolar. Figure 5c shows a selected feature as a red mark in the upper panoramic image, and Figure 5d shows the matched point in the lower panoramic image as a red mark along with the epipolar line through which the point should lie.  
Figure 5: Feature point and epipolar curve (best seen in color). (**a**) Feature point (red mark) in the upper catadioptric image, (**b**) Feature point (red mark) and epipolar curve in the lower catadioptric image, (**c**) Feature point (red mark) in the upper panoramic image, (**d**) Feature point (red mark) and epipolar curve in the lower panoramic image.
### System Overview  
The CHP2 system simultaneously enables network communications, distributed coherence, and relative positioning using a single, narrowband waveform. This waveform contains a communications payload and several reference sequences. The communications payload carries network communications packets between users and also contains shared timing information. The reference sequences estimate the ToA at each receive antenna in each frame. The ToA estimates and shared timing information drive a distributed synchronization algorithm, which estimates the ToF between each transmit-receive antenna pair, digitally synchronize the users, and estimates the relative position and orientation of the users. This system architecture is described in greater detail later in the manuscript.  
The intrinsic resolution of this TWR system is proportional to the inverse of the signal bandwidth, which is approximately 30 m for a 10 MHz waveform. We leverage integration gain from the long navigation sequences to maintain high ISNR (\(\sim\)45 dB) to support oversampling techniques that improve this resolution by a factor of 200 (\(<\)2 cm). This precision may be further improved by Kalman filter tracking solutions and more efficient firmware implementations. For multi-antenna platforms, these ToF estimates are extended to estimate relative position and orientation, the precision of which depends on the specific network configuration. This technology may be installed on numerous types of vehicles or stationary platforms, enabling a highly-reconfigurable network that can quickly adapt to different use cases. An example network topology is depicted in Figure 1.  
Figure 1: CHP2 is flexible and reconfigurable, which enables diverse network configurations on a variety of platforms. CHP2 supports both ground and aerial users, and can be tuned for both close-range and long-range applications. G2G links enable applications such as traffic management, knowledge distribution, and automatic navigation. A2G links enable applications such as ATM; flexible traffic surveillance; PNT; automated landing; and CNS. A2A links enable airborne applications such as collision avoidance, formation control, PNT, and CNS without relying on satellite links (which are susceptible to spoofing cyberattacks) or ground links—which are not always available.
# Exploring Data Model Relations in OpenStreetMap
## 4 Experimental Analysis
### Relation Analysis
#### 4.2.6 Contributors to Relations  
Our analysis presented in this paper is limited to the current version of OSM data for our four case-study cities. Subsequently, it is not possible to provide an in-depth analysis of the contribution patterns to relations in these cities without analysis of the edit history. Each relation contains the username and user-id of the OSM contributor who edited the current version of the corresponding relation. To align this work with more formal investigations of contribution patterns in OSM [41; 42; 43], we observed the edits performed by the ten most frequent contributors to relations in each city. There is no immediately obvious patterns in the absence of a more longitudinal analysis of the entire history of edits. However, it is interesting to see that the top ten contributors certainly provide a high percentage of editing effort. The total percentage of the current version of all relations edited in each city by the top ten most frequent contributors to relations are as follows: Vienna 53.44%, Zurich 57.08%, Madrid 61.13% and Rome 85.23%. Through further investigation in the Rome dataset, we found that one particular user (username = [PERSON]) was the most frequent contributor, and responsible for 32% of the current versions of relations in Rome. It appears that these relations are mostly part of a bulk import or strategic editing of public transportation related relations in Rome.
# Ribosomal Hiberation Factor Links Quorum-Sensing to Acid Resistance in EHEC
## 2 Materials and Methods
### Acid Stress Resistance Profiling  
Stationary-phase cultures were normalized to OD\({}_{600}\) = 1.0 in pre-warmed LB (pH 7.4). For acid challenge, 1 mL aliquots were transferred to LB acidified to pH 2.5 or pH 5.5 using sterile HCl (1 N), followed by static incubation at 37 \({}^{\circ}\)C. At specified intervals (0, 1, 2, and 3 h), samples were immediately diluted 10-fold in ice-cold phosphate-buffered saline (PBS; 137 mM NaCl, 2.7 mM KCl, 10 mM Na\({}_{2}\)HPO\({}_{4}\), 1.8 mM KH\({}_{2}\)PO\({}_{4}\), pH 7.4), subjected to serial dilutions, and plated on LB agar. Viable colonies were enumerated after 12 h of incubation at 37 \({}^{\circ}\)C. Survival rates were calculated as CFU at time h (CFU\({}_{h}\)) normalized to t = 0 values (CFU\({}_{0}\)). For quorum sensing (QS) activation, filter-sterilized 3-oxo-C6-acyl-homoserine lactone (AHL; Sigma-Aldrich [#K3007], Merck KGaA, Darmstadt, Germany) was added to final concentrations of 10 \(\upmu\)M or 100 \(\upmu\)M immediately before acid exposure.
# Calculation of View Factors for Building Simulations with an Open-Source Raytracing Tool
## 3 Validation of RADIANCE Generated Results against Analytical Solutions
### Differential Element to Finite Parallel Rectangle  
The view factor calculation between a differential element and a finite parallel rectangle is shown in Figure 11. As indicated by the figure, the edge lengths \(a\) and \(b\) of the rectangle are both equal to unity. The distance \(c\) of the differential element from the rectangle is one. Additionally, the normal to the differential element passes through a corner of the rectangle. In Figure 11b, the command line parameters -h and -w are intended to turn off information headers and warnings respectively. The parameter -I sets _reontrib_ in \"Irradiance\" mode, a setting that is a pre-requisite to calculating view factors.  
The analytical solution for this scenario is well-known [33] and can also be accessed through an online interface [34]. The solution can be expressed through Equation (6) as:  
\[F_{dA1-A2}=\frac{1}{2\pi}\left\{\frac{A}{\sqrt{(1+A^{2})}}\tan^{-1}\left[ \frac{B}{\sqrt{(1+A^{2})}}\right]+\frac{B}{\sqrt{(1+B^{2})}}\tan^{-1}\left[ \frac{A}{\sqrt{(1+B^{2})}}\right]\right\} \tag{6}\]  
where \(A=a/c\) and \(B=b/c\). For \(a=1=b=c\), the analytical solution calculated through Equation (6) is 0.1385 when rounded off to four decimal places. The value calculated  
Figure 11: View factor calculation between a differential planar element and finite parallel rectangle. The geometry is shown in (**a**) and a screen-capture of the corresponding RADIANCE calculation is shown in (**b**).  
through RADIANCE, as shown in Figure 11b, is 0.1384. The percentage error calculated as per Equation (7) is 0.072%.  
\[\text{Error}=\frac{\text{Absolute}(\text{Analytical Solution }-\text{ Radiance Result})}{\text{Analytical Solution}}\times 100 \tag{7}\]
# Probabilistic Condition Monitoring of Azimuth Thrusters Based on Acceleration Measurements
## 2 Materials and Methods
### Condition Monitoring Algorithm
#### 2.2.1 Data Quality Control  
In data quality control, the acceleration signals are discarded if their quality is inappropriate for automated condition monitoring. The signals may become adversely affected by the movements of the vessel, by the maneuvering of the thruster or by other disturbances in the measurement system. The disturbed signals are not accepted in condition monitoring and must be identified based on automated procedures. On the other hand, the rejection of useful signals should be avoided.  
Therefore, a sample is rejected due to the following reasons:  
1. The range of moving average of the acceleration signal is greater than a predefined limit.
2. The acceleration values are constant.
3. The absolute mean of the acceleration signal is greater than a predefined limit.  
In this study, the range limit for the moving average was set 0.5 \(\times\) 9.81 m/s\({}^{2}\) (or 0.5 g) in the moving windows of 100 data points. The window size should be relatively large to avoid the rejection of signals with large impulses inflicted by defects. The limit for the  
\begin{table}
\begin{tabular}{c c c} \hline
**Process** & **Parameter(s)** & **Example Value** \\ \hline Quality control & range limit (g), (1 g \(\approx\) 9.81 m/s\({}^{2}\)) & 0.5 \\ (QC parameters) & absolute mean limit (g) & 2 \\ \hline Feature extraction & kinematic frequencies & - \\ (System parameters) & sampling rate & - \\ \hline Inference & \(\alpha\): probabilistic threshold & 0.001 \\ (Model parameters) & \(\pi\): moving window size & 5 \\ \(\beta_{0}\), \(\beta_{1}\), \(\mu\), \(\sigma\), \(\Sigma\): regression models, normal distributions & - \\ \hline Speed range selection & minimum sample size & 60 \\ \hline Filter sample & speed range & - \\ \hline \end{tabular}
\end{table}
Table 2: Parameters used in different processes in the condition monitoring algorithm.  
Figure 2: Flowcharts of proposed algorithm: “Measurement data” arrows describe the flow of acceleration signals, their transformations and shaft speed in the algorithm, whereas the dotted arrows for “parameters” describe the flow of parameters defined by user, monitored system or algorithm.  
absolute mean was set 2 g but a lower limit could be preferable in the practical application with a lot of data. In the signal processing stages that follow quality control, the mean must be subtracted from the accepted signals in order to obtain commensurable values in feature extraction.  
Figure 3 provides a demonstration of the quality control for three signals. The signal shown on top was accepted based on all checks, whereas the signal in the middle was rejected based on the range of moving average. The signal on the bottom was rejected based on the range of moving average and absolute mean checks.