esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Sources  
We applied the VPM GPP dataset in this study, which is from simulations of the satellite-based Vegetation Photosynthesis Model (VPM) [27]. This dataset has been updated to December 2019, with the spatial resolution of 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\). It can be acquired from [[http://data.tpdc.ac.cn/en/data/582663f5-3](http://data.tpdc.ac.cn/en/data/582663f5-3) be7-4f26-bc45-b56a3c4 fc3b7/]([http://data.tpdc.ac.cn/en/data/582663f5-3](http://data.tpdc.ac.cn/en/data/582663f5-3) be7-4f26-bc45-b56a3c4 fc3b7/) (accessed on 11 August 2023). The VPM GPP dataset was generated using an enhanced light use efficiency theory, forcing by the moderate resolution imaging spectroradiometer (MODIS) data, and reanalysis climate dataset. Furthermore, it utilizes an advanced vegetation index (VI) gap-filling and smoothing algorithm, along with a partition approach for C3/C4 photosynthesis pathways [27; 45].  
Figure 1: _Cont._  
In this study, we selected climate factors that may have a significant impact on vegetation productivity, such as temperature, precipitation, and shortwave radiation. Those data were derived from the China Meteorological Forcing Dataset (CMFD). CMFD offers a spatial resolution of 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) and a temporal resolution of 1 day (Figure 1b-d). These data were downloaded from the \"Cold and Arid Regions Science Data Center at Lanzhou\" ([[http://westdc.westgis.ac.cn/data/7a35329c-c53f-4267-aa07-e0037d913a21](http://westdc.westgis.ac.cn/data/7a35329c-c53f-4267-aa07-e0037d913a21)]([http://westdc.westgis.ac.cn/data/7a35329c-c53f-4267-aa07-e0037d913a21](http://westdc.westgis.ac.cn/data/7a35329c-c53f-4267-aa07-e0037d913a21)) (accessed on 11 August 2023)) [46]. This dataset was produced by integrating data from various sources, encompassing instantaneous near surface air temperature, 3-h mean (ranging from \(-\)1.5 to +1.5 h), surface downward shortwave radiation, etc. The vegetation type data were derived from MODIS product (MCD12Q1) in 2010 (Figure 1e). The vegetation type categories in MCD12Q1 were based on the International Geosphere-Biosphere Programme (IGBP) classifications. As we mainly focused on the forest, savanna, grassland, and cropland types, we combined deciduous evergreen needleleaf forests, evergreen broadleaf forests, deciduous needleleaf forests, and mixed forests into the forest category. We combined woody savannas and savannas into the savannas category.  
Figure 1: The location and 12 water resource regions of the YRB (**a**). The spatial distributions of long-term averages of precipitation (**b**), temperature (**c**), shortwave radiation (**d**), and vegetation cover (**e**) of the YRB.
## 2 Materials and Methods
### Case Study and Data  
In this study, daily meteorological data from 1966 to 2015 from 689 meteorological stations located in the seven climatic zones of China were collected. Meteorological data consisted of mean air temperature (T\({}_{\text{mean}}\)), maximum air temperature (T\({}_{\text{max}}\)), minimum air temperature (T\({}_{\text{min}}\)), relative humidity (RH), global solar radiation (R\({}_{\text{s}}\)), and wind speed (U\({}_{2}\)). The selected data were quality controlled. All weather-related variables were measured at a 2 m height. However, it should be noted that R\({}_{\text{s}}\) was not recorded in all the 689 stations. To fix this problem, the extraterrestrial solar radiation (R\({}_{\text{a}}\)) was used for interpolation through modelling, if the R\({}_{\text{s}}\) data were absent. T\({}_{\text{s}}\) at a depth of 0 cm were measured through probes on the soil surface. Six input combinations were utilized for training the M5P, RF, and XGBoost models. Table 1 showed the types of input combinations. Raw meteorological data used in the study were provided and quality inspected by the National Meteorological Information Center (NMIC) of the China Meteorological Administration (CMA). A further examination was applied to datasets before computing. A number of data were deleted from the original data because they were missing or the ratio between measured T\({}_{\text{s}}\) and theoretical T\({}_{\text{s}}\) was greater than one. T\({}_{\text{mean}}\) was converted from T\({}_{\text{max}}\) and T\({}_{\text{min}}\). The M5P, RF, and XGBoost programs were written in R software (version 3.2.3; The R Project for Statistical Computing). All the simulations were performed in a computer with a single Intel Core i7-6700 at 3.4-4.0 GHz and 16 GB of random-access memory (RAM).
## 4 Discussion  
### Computational Efficiency  
The highest computational efficiency was achieved by the 2D short implicit transient model with an analytic rigid roller, thanks to the dramatic computational saving by using fewer 2D elements (2516 elements) with reduced degrees of freedom. The 2D model with an elastic roller led to twice the number of elements (6284) and demonstrated 48.6% lower efficiency compared to the 2D model with an analytic rigid roller. Besides the number of elements, the solution method also affects the computational efficiency. The short explicit and implicit transient models comprised an identical number of elements, but the explicit analysis model demonstrated slightly lower efficiency than the implicit analysis model. This was mainly because the explicit solver was more computationally expensive than the implicit solver for problems involving contact with rigid bodies [54]. The uncertainty in efficacy of mass scaling techniques for explicit analysis also affected the computational efficiency. It was interesting to see that although the Eulerian steady-state model used a large number of elements (27,600), it still demonstrated high computational efficiency, similar to the short explicit transient model, with three times fewer elements used (9036). Such a computational saving can be attributed to the steady-state detection option in the Eulerian model, which halted the analysis after rolling along a distance equivalent to 11.6% the modelled wall length. However, the steady-state detection option in Abaqus requires consistent mesh topology in the rolling direction [48], and it does not support the use of multiple CPUs. For all the developed efficient models, the benefits brought by the attempt to further reduce the elements through progressive meshing techniques were deemed to be limited.
## 3 Results and Discussion
### Soft Grippers
#### 3.5.2 Test Results  
In order to examine the deformation behavior according to the pneumatic pressure of the fabricated gripper, a 3 mm pneumatic fitting was inserted and then connected to an air compressor. The input pneumatic pressure was changed to 75, 100, and 150 kPa using a regulator. At this time, a test was performed on a 5 mm grid scale plate to measure the radius of curvature of the deformed gripper. As shown in Figure 18, when the specimen was printed with only soft material B, the properties of the gripper were uniform over the entire area so it was deformed evenly. Additionally, as the pneumatic pressure increased, the amount of deformation increased and the radius of the curvature decreased. On the other hand, when the gripper was printed in a half-and-half structure, the mechanical properties of the gripper were different depending on the region. The hard part printed with material A showed little deformation and only the soft part printed with material B was deformed. At this time, the radius of the curvature according to the pneumatic pressure of the region containing material B where the deformation occurred was measured as being a similar value to the case where only a single material was printed in the previous test. As such, the gripper with the half-and-half structure maintained the properties of each material, enabling selective stiffness variation.  
Figure 18: Deformation of soft grippers with different 3D-printed material structures in the pneumatic test.  
Regarding the gripper printed with materials A and B as a laminar composite structure, the properties were uniform throughout the entire region, similarly to a new mixed material, due to the plate-reinforced effect. As such, it was deformed evenly, as in the case of printing with a single material; however, since hard material A occupied 50% of the printed layer, the mechanical strength was greatly increased and the measured radius of the curvature was relatively large compared to the other specimens. In addition, the gripper was broken at a pressure of 150 kPa due to a decrease in flexibility.
# Integrity Monitoring of PPP-RTK Positioning: Part II: LEO Augmentation
## 4 LEO Augmentation  
The first part of this series of papers [14] introduced the IM procedure of multi-GNSS PPP-RTK positioning. In this section, the differences in the positioning precision and the HPLs after the LEO augmentation will be discussed in detail. The GPS/Galileo/BDS-3 satellites transmitting signals on 1575.42 and 1176.45 MHz, i.e., L1/L5 for GPS, E1/E5a for Galileo and BIC/B2a for BDS-3 on 26 June 2021 are used for test simulations together with the considered LEO constellation. The elevation mask angle is set to 10 degrees for GNSSsatellites and a lower value of 5 degrees for LEO satellites, assuming the user is not located in a high-density urban area. As shown in Figure 7, the same three networks with different sizes are used for simulations as in the first part of this series of papers (see Figure 2 in [14]), and the user at one of the worst locations for ionospheric interpolation is utilized for user processing in each network (see the stars in Figure 7, also see Figure 5 in [14]). Recall that the inter-station distance varies from 42 to 140 km in Network 1, from 101 to 337 km in Network 2 and from 168 to 563 km in Network 3.  
Similar to the simulated GNSS observations in [14], the LEO observations are simulated taking into consideration the observation noise, the random-walk ZTDs, hardware biases, and spatially correlated ionospheric delays. The sampling interval of the data is set to 1 s. The standard deviations of different parameters for accuracy and continuity purposes are given in Table 4 of [14], and those for integrity purposes are given in Table 6 of [14].
# Blood Pressure Monitoring Based on Flexible Encapsulated Sensors
## 4 Blood Pressure Measurement
### Data Preprocessing
#### 4.3.1 Feature Extraction  
To reduce computational cost, this study extracted feature parameters (shown in Table 2) for blood pressure prediction.
# XI2S-IDS: An Explainable Intelligent 2-Stage Intrusion Detection System
## 2 Materials and Methods
### Evaluation Metrics  
Evaluation of machine learning models often considers various metrics, with accuracy being one of the most commonly used. While accuracy provides a general measure of model performance, it can be misleading in the context of intrusion detection, especially when low-frequency attacks are present. For IDS evaluation, accuracy alone is insufficient, as it does not account for the critical balance between false positives (incorrectly flagged benign traffic) and false negatives (missed attacks). A model with high accuracy may still yield an unacceptable number of false negatives, which pose severe security risks. Conversely, a model with lower accuracy but fewer false negatives could be more desirable if it reliably detects critical threats. Moreover, in imbalanced datasets typical of IDS applications, accuracy can obscure true performance, as it often overlooks minority classes crucial for threat detection [41].  
Therefore, in this study, we prioritize false negative rate (FNR) as it captures the percentage of missed attacks, along with precision, recall, F1-score, and accuracy, to offer a comprehensive performance assessment. These metrics are essential for understanding both detection capability and error balance in IDS, particularly under imbalanced data conditions. The formulas for these metrics are provided in Equations (1)-(5).  
\[FNR=\frac{FN}{FN+TP} \tag{1}\]  
\[Precision=\frac{TP}{TP+FP} \tag{2}\]  
\[Recall=\frac{TP}{TP+FN} \tag{3}\]  
\[F1\text{-}score=2\frac{Precision*Recall}{Precision+Recall}=\frac{2 TP}{2 TP+FP+FN} \tag{4}\]  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{5}\]
# RMS Modeling and Control of a Grid-Forming E-STATCOM for Power System Stability in Isolated Grids
## 3 GFM Converter Modeling and Control
### Exciter Model
This model aims to determine the internal voltage magnitude \(E\) of the GFM, with its reference \(E^{*}\) calculated according to Equation (17) as the product of \(V_{dc}\) and the modulation function \(f(m_{a})\). To account for converter dynamics, a first-order function relating \(E\) and \(E^{*}\) has been employed. The modulation index, \(m_{a}\), is derived from a voltage droop regulator, as is shown in Equation (18). \[m_{a}=m_{a0}-n_{q}(V_{G}-V_{G}^{*})+\Delta m_{a} \tag{18}\]  
The first term, \(m_{a0}\), represents a constant value. The second is the droop term, \(-n_{q}\Delta V_{G}\), which has a negative sign and is proportional to the voltage increment \(\Delta V_{G}=V_{G}-V_{G}^{*}\). The control signal \(\Delta m_{a}\) modifies the modulation index if the reactive current \(I_{rG}\) exceeds any of its limits; otherwise, its value remains zero. The formulation of Equation (18) is based on previously established methodologies for voltage control in grid-forming (GFM) converters. Specifically, similar expressions have been proposed in the literature to regulate the internal voltage magnitude based on system conditions and modulation strategies, as discussed in [15]. This approach is fundamental in ensuring stable voltage reference generation in weak and isolated grids. Equation (18) can be interpreted as follows: when the voltage \(V_{G}\) exceeds its reference value \(V_{G}^{*}\) (i.e., \(V_{G}>V_{G}^{*}\)), \(E\) should be decreased to reduce the generated reactive power and consequently lower the voltage \(V_{G}\). Conversely, when \(V_{G}\) is lower than its reference (\(V_{G}<V_{G}^{*}\)), \(E\) should be increased. The reactive current limiter (RCL) block, illustrated in Figure 5, computes \(\Delta m_{a}\) based on \(I_{rG}\) and \(I_{rG}^{max}\). Figure 6 shows the RCL logic: when  
Figure 5: GFM exciter model. \(I_{rG}\) exceeds \(I_{rG}^{max}\), a proportional regulator, \(K_{q}\), generates a negative \(\Delta m_{a}\) to reduce the internal voltage magnitude \(E\), thereby limiting \(I_{rG}\). If the reactive current remains within limits, such as when \(I_{rG}<I_{rG}^{max}\), the regulator output saturates at \(\Delta m_{a}=0\). A symmetrical regulator is employed for negative values of \(I_{rG}\).
## 3 Results and Discussion
### Effect of Reaction Time on Photocatalysis Treatment  
For this experiment, the effect of reaction time on the output parameters was studied. The reaction time was varied from 20 min to 100 min, while the mixing speed and catalyst load were kept constant at 60 rpm and 0.9 g/L, respectively. The reaction time had minimal effect on the pH (Figure 2a), as the pH values remained almost constant, ranging from 6.79 to 7.11, further validating the minimal effect reaction time had on pH, as the pH also remained close to the characterized wastewater pH of 6.85. Constant pH suggests that the products of photocatalysis had no significant influence on pH.  
The removal efficiencies of the parameters colour (Figure 2b), turbidity (Figure 2c), and COD (Figure 2d) were highest in the first 20 min and then decreased thereafter. The initial increase is because, with an increase in contact time, the availability of hydroxyl radicals for the oxidation of pollutants present in wastewater increases [35]. [PERSON] and [PERSON] [29] studied the effect of reaction time on the photodegradation of methyl green and also found that an increase in reaction time increased the degradation efficiency, which they attributed to increased interaction time between the pollutant and the surface of the photocatalyst. The decrease thereafter might be because the TiO\({}_{2}\) photocatalyst aggregated with the pollutants, and with time, the interactive surface of the photocatalyst became saturated and dissociated, contributing to secondary complex pollutants [15; 16].
## 1 Introduction
Thus, OFDM is widely applied in broadband communication systems, including three types of fixed-line networks, namely, high-speed digital subscriber line (HDSL), very high-speed digital subscriber line (VDSL), and asymmetric digital subscriber line (ADSL) [16]. Other common applications include digital audio broadcasting (DAB) and broadband wireless access (BWA) systems, as well as the IEEE 802.11 specifications of wireless local area network (WLAN) [17; 18; 19; 20]. In this study, we apply several dispersion equalizer algorithms to construct an electronic dispersion compensation (EDC) equalizer of electrical domain plus with DCF of optical domain which suits for the OFDM-OCDMA with Walsh-Hadamard codes along 10 km-long SMF and 4 km-long DCF. The remainder of this paper is organized as follows. In Section 2, the configuration of the proposed OFDM-OCDMA RoF is described. In Section 3, the effect that chromatic dispersion makes to the OFDM-OCDMA RoF system is introduced. In Section 4, simulation result analysis and compensation mechanism of dispersion compensation fiber DCF and electronic dispersion compensation equalizer in the proposed system is interpreted. Brief conclusions are presented in Section 5.
## 4 Designing an Ontology Framework: A Case Study on Asthma
### Validation for the Extended Ontology of Asthma Based on Specific Metrics
#### 4.9.2 Reusability  
Reusability assesses whether the ontology can be used in other contexts or integrated with standards such as SNOMED CT [75]. The asthma ontology is modular and aligns with medical standards, enabling its components to be reused in other healthcare ontologies. For example, its structure fits well within SNOMED CT's hierarchy for respiratory conditions. Reusability ensures that the ontology can adapt to different medical datasets, increasing its practical value.
## 1 Introduction
In this paper, a methodology for estimating the global cost and the carbon impact of a PVCS for EVs is detailed, with an improvement strategy in order to reduce the global carbon impact. The PVCS includes PV sources, charging terminals, stationary storage, and connection with the public grid. Therefore, this work brings the following improvements by providing:  
1. A calculation methodology of the global cost of the PVCS, including the costs of investment, maintenance, exploitation, and externalities, to offer to the decision-maker a choice of infrastructure compatible with his spatial and budgetary constraints. 2. Concretely, a definition of a methodology for calculating the carbon impact of a PVCS by defining an equation, making it possible to calculate the carbon impact of each subsystem composing the PVCS, in order to assess their usefulness compared to the grid-powered charging station (PGCS), using the LCA method based on the ISO 14,067 standard. The carbon impact estimation offers to the decision-maker a choice of infrastructure compatible with his ecological constraint. In addition, it is committed to researching the most relevant carbon emission coefficient, making it possible to better assess the carbon impact of the components of each subsystem. 3. An identification of the levers of action, i.e., components that strongly influence the global carbon impact of PVCS, and on which it would be possible to act to reduce the carbon impact of such infrastructures. This reduction solution of the carbon impact is based on new data and recycled materials for the most emitting elements of CO\({}_{2}\). A numerical application of the proposed calculation methodology of the global cost and carbon impact for a case study of an installation of the PV parking shade, located in the Innovation Center of the Universite de Technologie de Compiegne, has been discussed in the end of this paper, to validate the methodology and the proposed carbon impact reduction solutions. A comparison of the result with an EV charging station, similar but powered exclusively by the power grid, is proposed. The rest of the paper is structured as follows: Section 2 describes the methodology for calculating the global cost of the PVCS, Section 3 shows the methodology for calculating the carbon impact of the PVCS, Section 4 depicts a numerical application and assessment of the global cost and the carbon impact of PVCS versus PGCS, and finally, Section 5 concludes this work and opens new perspectives.
# A Global Overview of SVA--Spatial-Visual Ability
## 2 Rationale behind the Study  
Study in the realm of spatial skills has been consistent throughout the last century and has proved to be a determinant of success in science and technology fields [7]. Spatial visualisation has been deemed an integral ability for engineers, scientists, and even artists [8], with architects and interior designers relying heavily on this skill to conceptualize and create innovative built forms. The aforementioned ability goes beyond simply picturing objects in space; it comprises mental rotation, observation of perspective, and the capacity to comprehend and control intricate spatial connections. Strong spatial skills are associated with academic achievement in disciplines such as computation, engineering, and mathematics because they make individuals better able to solve problems and design solutions that require spatial reasoning [9]. Consequently, educational environments have placed a greater emphasis on spatial ability, with initiatives designed to cultivate and improve these capabilities in students in order to equip them for prospective careers in STEM.  
It is imperative to examine the effects of external influences such as family socioeconomic status on cognitive abilities, brain volume, and the features of neural tissue. Psychological characteristics and general well-being are determined by parental education and parental assets [10; 11].  
At present, there is a lack of comprehensive studies that target students of architecture and interior design beyond undergraduate studies [12]. Subject to development and directives, spatial ability is adaptable. This necessitates the development of a domain-specific evaluation of spatial ability and a critical examination of the elements underlying the spatial skills required in interior design. Therefore, design educators should foster students' capacity to extend visualisation from 2D to 3D when they are teaching design communication [13].  
A comprehensive investigation of the process is required to comprehend the category of spatial thinking distinct to architecture and design; this understanding will lead to curriculum changes and benefit novice students of architecture and design [12]. Incorporatingcomputer-aided design (CAD) and virtual-reality (VR) technologies into the curriculum can improve students' spatial abilities [3; 14; 15; 16; 17]. Educational institutions can overcome the divide between academic competence and practical application by ensuring students comprehend and employ theoretical concepts. This integrative approach equips students for the evolving needs of the architectural and interior-design professions.