esa-sceva/satcom-chunk-collection huggingface dataset:
# A Calibration Method for Time Dimension and Space Dimension of Streak Tube Imaging [PERSON]
## 3 Calibration Method
### Time Calibration
#### 3.1.2 Data Processing for Time Calibration  
In Experiment I, the data were only used for time calibration, and angle calibration was not considered. For each streak image, the centroid of the streak signal in every row could be obtained by Equation (1). The data processing results are shown in Figure 5, where the red line is composed of the centroids of each row.  
By taking the average of all centroids (2000 in this work) in the same row under the same delay, we could obtain a more accurate delay centroid relationship of lidar, as shown in Figure 6.  
\[x_{j}^{\prime}=\frac{1}{S}\sum\
olimits_{s=1}^{S}x_{js} \tag{3}\]  
In Equation (3), \(x_{j}^{\prime}\) is the average of the centroids in the \(j\)th row; \(S\) is the number of streak images with the same delay time of sweeping voltage; \(S=2000\) here; and \(x_{j,s}\) is the centroid in the \(j\)th row of the \(s\)th image.  
Figure 4: Time calibration experiment.  
Figure 5: The centroids of streak image.  
As shown in Figure 6, we can find that the accidental error of the centroid of the measurement results was still relatively large, with a standard deviation of 0.84 ns, so it was necessary to reduce the error by calculating the average value.  
After taking all of the average values under every delay, we could obtain a series of curves under different delays, as shown in Figure 7.  
In Figure 7, one curve represents the position of the streak signal on the image under one delay value. These curves were used as a time calibration array for lidar data calibration. However, the points on adjacent curves were separated by 10 ns, and the resolution was not high enough. Therefore, we had to interpolate these curves so that the points on adjacent curves could be separated by 0.1 ns in this work.  
From Figure 7, it can be found that the curvature of the rightmost curve differs greatly from other curves because the highest sweeping voltage was not high enough and because the distortion of the electronic focusing system was more serious at the edges than at other positions. Fortunately, the streak signal hardly appeared at the edge of the image, so the distorted curvature had little effect on the experimental results.
# Application of Machine Learning Techniques to
## 4 Results and Discussion
### Climate Zones
#### 4.1.3 Climate Zones and Reference site  
This study reveals useful details on the efficiency of machine learning algorithms when formulating large, homogeneous regions of precipitation for different seasons. Homogeneous precipitation sites were identified based on the validated number of cluster-values. These station clusters were then plotted on a study area map, as shown in Figure 6. All homogeneous regions are differentiated by different colors to show the general spatial patterns associated with precipitation, the approximate boundaries are drawn on the maps to allow for a clearer representation. The clusters of stations transformation into different climate zones for all seasons have been shown in Figure 8.  
The Jhelum and Chenab river basins are partitioned into clusters 13, 13, 14 and 13 for the warm-wet, cold-dry, cold-wet and warm-dry seasons, respectively, based on the analysis. There were three outliers in the clusters of the warm-wet season, one outlier in the clusters of the cold-dry season, one outlier in the cold-wet and one outlier in the warm-dry season. After merging the outliers with the neighboring clusters, the final demarcations of the basins were mapped for every season. The basins are finally classified into 10, 12, 13 and 13 climate zones for warm-wet, cold-dry, cold-wet and warm-dry seasons. The reference station was identified in each climate zone for the selection of the best-correlated GCM, using the past performance analysis of the GCM at the reference station in each climate zone.  
Figure 7: Dendrograms with cut-off bars to group the stations in clusters as a function of the Euclidean distance, the numbers in bracket indicate the number of branches (group of station) in a truncated branch of Dendogram: (**a**) warm-wet, (**b**) cold-dry, (**c**) cold-wet and (**d**) arm-dry seasons. The truncation is done through black cut-off Bar corresponding to the Euclidian distance for the required optimum number of clusters.
## 1 Introduction
Therefore, in this paper, a concentrated solar ORC system, as designed and built under the EU-funded project Innova MicroSolar [19] by several universities and companies, is considered as a case study to investigate the potential of fuzzy logic control in optimizing the operation of small-scale solar ORC plants. More precisely, advanced models of the different subsystems are developed by the authors in MATLAB/Simulink in order to take into account the dynamics of the system under varying ambient and operating conditions. In addition, a fuzzy logic control strategy is included in the simulator, aimed at performing load following by dynamically adapting the plant operation to the user load profile. Hence, the main contributions of the work rely on (i) the design of a fuzzy controller of a small-scale solar ORC plant for thermal load following and (ii) the use of the proposed control logic approach on complex solar energy systems by means of an advanced simulation tool developed by the authors. Therefore, the paper is structured as follows: After this introduction, the methods used in the analysis and the developed models are presented in Section 2. Section 3 reports the impact of the proposed control logic on the operation and performance of the plant under investigation, while in Section 4, the main conclusions are discussed.
## 4 Methods
### Geometric Consistency Discrimination Module (GCDM)  
Initially, a coarse registration process is performed in this study. Given the point correspondence matrix\(H_{x}^{\prime}\) output by the feature matching module, the preliminary rotation matrix \(\mathbf{R}\) and translation vector \(\mathbf{T}\) are solved using Singular Value Decomposition (SVD).  
Subsequently, the Geometric Consistency Discrimination Module (GCDM) is deployed to validate the coarse registration results. Based on the premise that the translation distance of each point should theoretically remain consistent during the registration process, the coarse registration results are analyzed. Specifically, the frequency distribution of translation errors for the registered keypoints in the \(x\), \(y\), and \(z\) directions is statistically analyzed, and points that fall within the concentrated error distribution intervals in all three directions are identified. These point pairs are considered correctly registered pairs. Subsequently, another round of SVD is performed on these correctly registered pairs to obtain the final rotation matrix \(\mathbf{R}\) and translation vector \(\mathbf{T}\). Finally, the transformation and restoration of Gy are achieved through this process, as shown in Figure 4. The colored arrows in the figure indicate the schematic diagram of the Cartesian coordinate system of the two subfigures.  
Through the above steps, the Geometric Consistency Discrimination Module effectively enhances the accuracy of registration, ensuring that the final registration results maintain geometric consistency. This process strengthens the robustness and reliability of the model.
# Predicting Wheat Potential Yield in China Based on Eco-Evolutionary Optimality Principles
## 3 Results  
### Model Evaluation  
The PY predictions from this study align with the global patterns reported by EARTHSTAT (Figure 3). Wheat in the Northeast Plain and North China Plain shows higher PY due to abundant energy and water resources, while the arid climate and limited irrigation infrastructure in the Mongolian Plateau result in lower PY. In contrast, typical crop models fail to capture these regional variations in PY. The statistical metrics, MAE (mean absolute difference) and RMSE (root mean squared error), highlight the superior performance of our model: MAE = 1.58 ton/ha and RMSE = 1.44 ton/ha, both of which are notably better than those of other models. Furthermore, our model predicts a PY frequency distribution that is closer to the EARTHSTAT benchmark, with more regions exhibiting PY levels between 4 and 6 tons per hectare, compared to the less than 4 tons per hectare predicted by other crop models (Figure 4). We attribute this superior performance to the accurate prediction of wheat traits based on EEO theory, rather than relying on static parameters. The subsequent analysis is conducted based on our model's predictions.  
Figure 3: Comparison of wheat PY prediction maps from various sources against EARTHSTAT (**a**). In addition to the typical crop models shown in panel (**b**), other models include (**c**) CLMcrop, (**d**) EpicBoku, (**e**) EpicIIASA, (**f**) EpicTAMU, (**g**) Gepic, and (**h**) ORCHIDEE-crop. MAE and RMSE are calculated after masking nonwheat grids. The units for both MAE and RMSE are tons per hectare.
## 4 Discussion
### The Ability of SIF to Monitor Drought  
Our research has demonstrated that SIF can capture the spatiotemporal variations of drought (Figure 5). It was discovered that SIF is susceptible to variations in water availability and strongly correlated with precipitation (Table 3). SIF can track the physiological and structural changes in vegetation caused by drought [53; 54; 55]. During mild or short-term drought events, the chlorophyll content of vegetation does not significantly decrease, and the vegetation canopy does not exhibit noticeable changes. VIs based on greenness remain relatively stable, but fluorescence emissions may decrease immediately. This is because changes in VIs based on greenness lag behind and cannot promptly detect changes in vegetation physiological status. They can only capture long-term droughs, which lead to wilting of plant leaves. [PERSON] et al. (2019) found that VIs lag behind changes in moisture conditions, while the relationship of precipitation with VIs is more significant [31]. [PERSON] et al. (2018) found that the seasonal mean of SIF significantly decreases under severe drought and extreme drought conditions, while NDVI only significantly decreases under extreme drought conditions [56]. Moreover, SIF is more sensitive to the occurrence of drought, while NDVI is suitable for monitoring drought on longer time scales. Therefore, NDVI cannot timely monitor early-stage drought, while SIF possesses the ability to monitor drought in real time, making it an effective indicator for agricultural disaster monitoring.
# Radiometric Method for Determining Canopy Stomatal Conductance in Controlled Environments
## 2 Materials and Methods
### Chamber Wind Speed  
Wind speed above and within the canopies was measured with heat transfer needle anemometers (Model AN-27, Soiltronics, Burlington, WA, USA). These anemometers were well-suited for making wind measurements within canopies because they are small, have fast response times (half-life t\({}_{\mathrm{1/2}}\) = 1 s), and are omnidirectional. The anemometers were calibrated in a wind tunnel for windspeeds between 0.05 and 5 m s\({}^{-1}\)[33]. Vertical gradients in mean wind speed above and within the canopies were measured with anemometers spaced between 4 and 6 cm apart. Each chamber was modified to include variable speed centrifugal blowers so that wind speed above the vegetation could be controlled over a wide range. Three wind speed settings (high: 2.3 m s\({}^{-1}\); medium: 1.7 m s\({}^{-1}\), and low: 0.8 m s\({}^{-1}\)) were used in the chambers, but the majority of the measurements were made at the medium setting.
## 4 Results
### Evaluation of Calibration Bias
The number of radar pairs with a mean bias greater than 1.0 dB decreased. As we mentioned, the GNG radar was a good reference because the \(\epsilon_{\text{SC}}\) was close to 0.0 dB. The mean biases of MYN-GNG pair were \(-2.85\) dB for CASE I and \(-0.03\) dB for CASE II. The results indicate that the MYN radar was recalibrated between CASE I and CASE II. We determined the change in the calibration bias using the RCA in the previous section. We focused on the evaluation of \(\epsilon_{\text{SC}}\) based on inter-comparison. However, the inter-comparison not only evaluated the consistency of data over the radar network, but also monitored the calibration bias during the rain period.
# The Biosynthesis and Medicinal Properties of Taraxerol
###### Abstract  
Taraxerol is a pentacyclic triterpenoid that is actively produced by some higher plants as part of a defense mechanism. The biosynthesis of taraxerol in plants occurs through the mexulonate pathway in the cytosol, in which dimethylallyl diphosphate (DMAPP) and isopentyl pyrophosphate (IPP) are first produced, followed by squalene. Squalene is the primary precursor for the synthesis of triterpenoids, including taraxerol, \(\beta\)-amyrin, and lupeol, which are catalyzed by taraxerol synthase. Taraxerol has been extensively investigated for its medicinal and pharmacological properties, and various biotechnological approaches have been established to produce this compound using in vitro techniques. This review provides an in-depth summary of the hypothesized taraxerol biosynthetic pathway, the medicinal properties of taraxerol, and recent developments on tissue culture for the in vitro production of taraxerol.  
taraxerol; in vitro; medicinal properties; biosynthesis; triterpenoids 20220201080  
double bond between positions 14 and 15 [17] (Figure 1). This compound is also known by a few other synonymous names, which are isoolean-14-en-3b-ol, skimmiol, alnulin, and tiliadin. Taraxerol can be extracted from various plant families and species found in nature. However, the synthesis of taraxerol is challenging and depends on natural resources that have a negative impact on biological conservation. Hence, the ongoing research on taraxerol production and its distribution provides vital information for future investigations.
# Closing Connectivity Gap: An Overview of Mobile Coverage Solutions for Not-Spots in Rural Zones
## 3 Potential Solutions
### Other Engaging Solutions
#### 3.2.1 Altitude Platforms  
The altitude platforms are grouped into LAPs (Low-altitude platforms) and HAPs (High-altitude platforms). [PERSON] et al. in [104] granted the main difference about the prior  
Figure 6: UAS addressing in 3 GPP standards.  
categories. LAPs gather the aerial platforms at an altitude of 20 km. UAVs, drones, and blimps fall into this group since they cannot support higher payload capacities, and their autonomy relies on SWAP constraints [105]. As Section 3.1 discussed, UAVs can perform far-flung coverage, increase redundancy, and increase survivability, leveraging the swarm FANET architecture [104].  
LAPs have lent dynamic and scalable networks that can quickly cover broader regions, although there are by-payloads stuck. In this case, there are two method to limit this situation: First, developing a suitable propagation model that includes the elevation angle--deployed at several altitudes--along with the MIMO output antenna diversity gain, especially for the last mile connectivity [106]. In the UAV case, the strategy may contain a formulation of statistical assessment of A2G propagation by either using Ray Launching or Ray Tracing geometrical optics models [107]. Second, Drone-to-Drone communication arises as a reliable collision avoidance system [105].  
On the other hand, HAPs operate in a quasi-stationary position at an altitude of 20 to 50 km, becoming a viable option to furnish capacity and coverage enhancement [104]. The authors in [108] have envisioned these platforms as a super macro BS (HAPs-SMBS) to unfold high-traffic-volume networks in a metropolitan area in bargaining with smart city paradigms. Facing the LEO constellation shortcomings, HAPs-SMBS can mask the high path loss and high mobility effects.  
The potential uses of HAPs--to tackle rural not-spots--sheld light on dynamically managed radio resources and mitigate the crossed interference [109]. The rural environment has admitted more prevalence to network coverage instead of higher capacity density. The reason for this is that HAPs requires lower investment and provides high quality--even providing higher terrestrial QoS--and this alternative has been carried out to cover rural and remote areas [110]. At this point, the likely exploitation of radio environment maps and artificial intelligence in the ongoing infrastructure may allow a radius coverage area of more than 30 km, as [PERSON] highlighted in [110].
###### Abstract  
Prolonged inundations are altering coastal forest ecosystems of the southeastern US, causing extensive tree die-offs and the development of ghost forests. This hydrological stressor also alters carbon fluxes, threatening the stability of coastal carbon sinks. This study was conducted to investigate the interactions between hydrological drivers and ecosystem responses by analyzing daily eddy covariance flux data from a wetland forest in North Carolina, USA, spanning 2009-2019. We analyzed temporal patterns of net ecosystem exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (RE) under both flooded and non-flooded conditions and evaluated their relationships with observed tree mortality. Generalized Additive Modeling (GAM) revealed that groundwater table depth (GWT), leaf area index (LAI), NEE, and net radiation (Rn) were key predictors of mortality transitions (R\({}^{2}\) = 0.98). Elevated GWT induces root anoxia; declining LAI reduces productivity; elevated NEE signals physiological breakdown; and higher Rn may amplify evapotranspiration stress. Receiver Operating Characteristic (ROC) analysis revealed critical early warning thresholds for tree mortality: GWT = 2.23 cm, LAI = 2.99, NEE = 1.27 g C m\({}^{-2}\) d\({}^{-1}\), and Rn = 167.54 W m\({}^{-2}\). These values offer a basis for forecasting forest mortality risk and guiding early warning systems. Our findings highlight the dominant role of hydrological variability in ecosystem degradation and offer a threshold-based framework for early detection of mortality risks. This approach provides insights into managing coastal forest resilience amid accelerating sea level rise.  
coastal forest decline; carbon fluxes; groundwater table depth; ghost forests; early warning thresholds; Generalized Additive Models (GAM)
# Characterization of a Modified Clinical Linear Accelerator for Ultra-High Dose Rate Beam Delivery
## 5 Conclusions  
The LINAC Elekta SL 18 MV has been successfully upgraded to deliver 10 MeV UHDR electron beams. This modification is completely reversible, and switching between the conventional modality and the UHDR modality takes only a few minutes. The LINAC is now capable of reaching a maximum of 2.2 Gy/ pulse and an instantaneous dose rate of over 10\({}^{5}\) Gy/s at the crosshair foil position in a pulse lasting 2 us. A silicon sensor device assesses the stability and repeatability of the pulses across different beam deliveries and days.  
In the future, the LINAC could be exploited as a facility for testing beam monitoring and dosimetry devices, as well as for radiobiological experiments to further investigate the FLASH effect. The use of applicators with different geometries and various perforated templates could enable the study of the effects of SFRT and its combination with UHDR irradiation.  
Conceptualization, U.D., A.C., C., E.A.D., A.F., S.G., E.M., D.M.M.O., V.M., R.S., E.S. and A.V.; methodology, U.D., A.C., C.E.A.D., A.F., S.G., E.M., D.M.M.O., V.M., R.S., E.S. and A.V.; software, U.D., A.C., E.M., D.M.M.O.; validation, U.D., A.C., F.M., D.M.M.O., R.S. and A.V.; formal analysis, U.D., A.C., E.M. and D.M.M.O.; investigation, U.D., A.C., E.M.D., E.A.D., V.F., A.F., S.G., O.M.V., F.M.M., E.M., D.M.M.O., F.M., V.M., R.S. and A.V.; resources, U.D., A.C., C.C., E.M.D., E.A.D., V.F., S.G., F.M.M., E.M., D.M.M.O., F.M., V.M., R.S., E.S. and A.V.; data curation, U.D. and A.C.; writing--original draft preparation, U.D., A.C. and D.M.M.M.O.; writing--review and editing, U.D., A.C., E.M., D.M.M.O., V.M., R.S. and A.V.; visualization, U.D., A.C. and D.M.M.O.; supervision, C.C., R.C., E.A.D., S.G., V.M., R.S., E.S. and A.V.; project and administration, R.C., S.G., R.S. and A.V.; funding acquisition, R.C., S.G., R.S. and A.V. All authors have read and agreed to the published version of the manuscript.  
The research was supported by the National Institute for Nuclear Physics (INFN), CSN5 Call 2021, project \"FRIDA\" and by VIGA S1921 EX-POST 21 01 grant by Compagnia di San Paolo. The installation of the LINAC at the Physics Department was financed by Compagnia di San Paolo grant \"OPEN ACCES LABS\" (2015), CRT Foundationgrant n.2015.AI1430.U1925 and INFN.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.  
We thank the INFN CSN5 funded project \"eXFlu\" for the collaboration and \"Fondazione Pisa\" for funding CPFR with the grant \"prog. n. 134/2021\". The authors are also grateful to Elekta S.p.A. for the technical support in the LINAC upgrade and maintenance.  
[PERSON] and [PERSON] are employees of Elekta S.p.A., Agrate Brianza (MB), Italy. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.
## 1 Introduction
Additionally, there has been little effort to explore whether the pixel-based, PLS-based discriminant algorithm obtained from the PlanetScope dataset can effectively and consistently map the occurrence of different crop diseases. Research progress and practical applications in remote sensing have motivated us to develop a novel approach for mapping damage in rice caused by different diseases. In this paper, the multiple-disease outbreak of rice dwarf, rice blast, and glume blight was selected for case study. The outbreak occurred in the rice planting area of Guanxi Province, China, during the autumn of 2017. The cloud-free, bi-temporal, high spatial resolution PlanetScope (PL) images acquired before and after the outbreak were used as a basis for analysis. The aims of this study were (1) to propose a series of normalized two-stage vegetation indices (VIs) that characterize the host-pathogen interaction of individual diseases; (2) to evaluate partial least squares discriminant analysis (PLS-DA) for mapping the spatial distribution of rice diseases using the proposed normalized two-stage VIs; (3) to produce the damage map of rice diseases at fine spatial scales. To our knowledge, this study is also the first attempt using the commercial PL datasets in precision agricultural management.