esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Hydrogeological Setting in Mountain Regions
### Hydrogeological Properties  
In terms of experimental hydraulic parameters (Figure 2), the storage, permeability, and porosity of regolith are generally higher than those of bedrock [27; 40; 41; 42]. However, groundwater in bedrock is distributed in fracture networks and tends to exist and flow at secondary weak planes or weathered zones with higher permeability [33; 43; 44; 45; 34].  
The effects of surface topography on the groundwater flow system are described and simulated by theoretical and numerical methods [46; 47]. There are three types of groundwater flow systems (i.e., local, intermediate, and regional flows) affected by topographic relief in a basin. Surface topography also generally reflects the spatial distribution of soil moisture and groundwater levels in the process of water infiltration, recharge, and discharge. The properties of SD, river gradient, flow accumulation (FA), drainage density (DD), and TWI can be analyzed from a DEM. The regional geomorphology, land cover or land use, groundwater recharge, and water bodies can be determined by ground investigation or derived from RS [48; 49; 50; 51; 52]. Furthermore, geological lineament structures provide information on regional connectivity of a fracture network, where the groundwater potential could be higher [53; 54; 55]; in addition, spatiotemporal land surface temperature (LST), normalized difference vegetation index (NDVI), and soil moisture index (SMI) provide dynamic evidence on groundwater recharge and discharge [56; 57; 58].  
Therefore, hydrogeological characteristics change with the evolution of regional geology and topography. The target of groundwater exploration in this study was discharge areas that are composed of regolith and fractured bedrock aquifers. It becomes feasible to determine remotely sensed surface characteristics linked to groundwater by using the proposed RS and GIS model.
## 2 Control Strategies Description
### Three-Phase Damping Control Strategy
\[g_{1}=\frac{3\;p_{\text{dc}}^{\bullet}}{\sum|\underline{v}_{x} |^{2}+2\;\sum_{x\
eq y}|\underline{v}_{x}||\underline{v}_{y}|\cos(\theta_{x }-\theta_{y}-\frac{2\pi}{3})}\\ -2g_{d}\frac{\sum|\underline{v}_{x}|^{2}-\sum_{x\
eq y}| \underline{v}_{x}||\underline{v}_{y}|\cos(\theta_{x}-\theta_{y}-\frac{2\pi}{3} )}{\sum|\underline{v}_{x}|^{2}+2\;\sum_{x\
eq y}|\underline{v}_{x}|| \underline{v}_{y}|\cos(\theta_{x}-\theta_{y}-\frac{2\pi}{3})} \tag{12}\]The first fraction of (12) calculates the positive-sequence power, which determines the amount of the injected active power with the grid, while the second fraction is responsible for compensating the power of the zero- and negative-sequence components that are flowing through the inverter. The three-phase damping control strategy is able to mitigate the voltage unbalance at the point of common connection by exchanging asymmetrical currents with the grid. From the presented analytical expressions, it can be seen that these asymmetrical currents are based on the symmetrical component theory and ensures a natural mitigation of the voltage unbalance, which affects not only the rms voltage values but also the phase angles. The asymmetrical current injection is caused by the resistive behavior of the control strategy, which results in larger current injection in the phase with lower voltage and smaller currents in the phases with higher voltage. Thus the voltage unbalance is mitigated. This resistive behavior is determined by \(g_{d}\), which in practice is calculated by using the nominal ratings of the DER:  
\[G_{d}=\frac{P_{\text{dc,nom}}}{V_{\text{nom}}^{2}} \tag{13}\]  
where \(P_{\text{dc,nom}}\) is the nominal power of the inverter and \(V_{\text{nom}}\) is the nominal grid voltage. In a p.u. system the damping conductance is expressed as  
\[g_{d}=\frac{P_{\text{dc,nom}}/P_{\text{dc,base}}}{V_{\text{nom}}^{2}/V_{\text {base}}^{2}} \tag{14}\]  
where \(P_{\text{dc,base}}\) is the base power of the power electronic inverter and \(V_{\text{base}}\) is the base value of the grid voltage.
## 4 Power Consumption Models  
### LTE Base Station Model  
The majority of the energy in wireless networks is consumed in the base stations, also in the defined cooperative scenarios. From the base station point of view, it is crucial to study the supply power consumption rather than radio frequency (RF) transmission power to see the total effect. Supply power consumption \(P_{\text{sup}}\) for a single RF chain showing the relation between supply power and RF transmission power \(P_{\text{tx}}\) is [21]:  
\[P_{\text{sup}}=\begin{cases}P_{0}+\Delta_{p}P_{\text{tx}},&0<P_{\text{tx}}<P_{ \text{max}}\\ P_{\text{sleep}},&P_{\text{tx}}=0\end{cases} \tag{1}\]  
where \(P_{0}\) is the minimum active power consumption, \(\Delta_{p}\) is a linear transmission dependence factor, and \(P_{\text{sleep}}\) is the power consumption in the sleep mode. When there are \(N_{\text{trx}}\) RF chains included, the total supply power consumption \(P_{\text{tot}}\) is  
\[P_{\text{tot}}=N_{\text{trx}}\times P_{\text{sup}} \tag{2}\]  
Measured parameter values of LTE base stations (macro, remote radio head, micro, pico, femto) can be found from [22]. The values are summarized in Table 1. The model and the values are based on commercially-available base stations, providing sufficient foundation for our energy estimations. We adopt this model since it is simple, based on vigorous measurements, and easy to use in the analysis. We note that there are also other models recently published, such as in [47], where a general conclusion is drawn as: \"Modeling a linear dependence between the emitted power and the energy consumption, as well as between the traffic volume and the energy consumption, is a very good approximation, and it is strongly confirmed by real data\".
## 3 Results and Discussion
### Comparison of SPME Fibers Using TpGAS -Germacrene A Synthase  
Germacrene A was included as an example of a germacrene with a different positioning of double bonds compared to germacrene D (Figure 1). _Tp_GAS has previously been described as a germacrene A synthase [29]. Expression of _Tp_GAS in _N. benthamiana_ followed by extraction using HS-SPMS and analysis on GC-MS with the different fiber materials PDMS, PDMS/DVB, PDMS/CAR, and PDMS/CAR/DVB at an injection port temperature of 250 \({}^{\circ}\)C only resulted in rearrangement products and did not yield any germacrene A (**2**) (Figure 5, Supplementary Figures 5 and 6).  
With PDMS/DVB, PDMS/CAR and PDMS/CAR/DVB at 250 \({}^{\circ}\)C the identified major products were (-)-\(\alpha\)-selinene (**20**) and (+)-\(\beta\)-selinene (**21**), with **6** as a minor product and some minor unknown sesquiterpenoids (Figure 5b, Supplementary Figures 5 and 6). **20** and **21** are the eudesmane type of sesquiterpens and are acid-induced rearrangement products of **2**, as has also been detected previously [13]. **6** is the result of thermal Cope rearrangement of **2**, as was described above [38]. At 160 \({}^{\circ}\)C **2** was not detected using PDMS/DVB and PDMS/CAR (Supplementary Figures 5 and 6). For PDMS/DVB/CAR **2** was detected along with **20**, **21**, and one unknown sesquiterpenoid (Figure 5b).  
For PDMS, **6** was detected as the major product at 250 \({}^{\circ}\)C. **2** was the major product at 160 \({}^{\circ}\)C followed by a few minor unknown products (Figure 5a). **6** was not identified at 160 \({}^{\circ}\)C for any fibers (Figure 5, Supplementary Figures 5 and 6), which correlates with germacrenes in general being stable at lower temperatures. At 110 \({}^{\circ}\)C there were no products detected, most likely owing to the lower quantity of product produced by _Tp_GAS compared to _T_ITPS7414. The product profile of _Tp_GAS was less complex than _T_ITPS7414 for all four types of fibers, mainly due to the composition of the double bonds in **2**.  
\begin{table}
\begin{tabular}{l|l|l|l|l|l|l} \hline \hline
**Temperature** & **110 \({}^{\circ}\)C** & **SD** & **160 \({}^{\circ}\)C** & **SD** & **250 \({}^{\circ}\)C** & **SD** \\ \hline Fiber & & & & & & \\ \hline PDMS/DVB/CAR & 55.6 & 4.7 & 30.3 & 3.5 & 8.1 & 0.6 \\ PDMS/DVB & 29.5 & 6.4 & 13.1 & 3.2 & 6.9 & 0.7 \\ PDMS/CAR & 46.1 & 9.8 & 18.7 & 5.3 & 6.3 & 0.5 \\ PDMS & 88.4 & 2.0 & 90.5 & 2.7 & 66.9 & 4.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of germacrene D (**1**) levels (% of total sesquiterpenoids) using different fiber materials. SD is the standard deviation of at least three biological replicates.
## 3 Results and Discussion
The influence of the TEC controlling was also measured during the experiments. First, a TEC controlling source with 0.1 degree precision was adopted to control the temperature of the LD module, and then the output energy was varied (in a large amplitude, from 300 to 350 mJ,) with a pumping current of 80 A. A new controlling source with 0.01 degree precision was then adopted to increase the relative stability of the output energy. Both the output energy and center wavelength change as temperature changes; therefore, appropriate temperature control is a critical component of the system; we were able to maintain the LD modular temperature more steadily using the controlling source with a 0.01 degree precision. Finally, at a pumping current of 80 A, a pump pulse duration of 200 us, and a pump repetition rate of 10 Hz, we obtained a laser pulse energy of 335-350 mJ at a wavelength of 1064 nm, a pulse duration of 10 ns, and an output energy relative stability of 5%. The beam diameter was measured as 6.73 mm, with a divergence angle of less than 0.8 mrad, and the beam quality was \(M^{2}<4\).
# New Insights into the Mechanism of Spatiotemporal Scent Accumulation in Orchid Flowers
## 3 Discussion
Thus, this temporal specificity of the _RCDXS_s and _RCHDS_s expression profiles may be the main reason for KOVA flowers producing fragrance at D8. On the other hand, MTPSs, as the enzymes involved in the final step of the MEP pathway, determine the production and types of monoterpens in flowers. ama0c15, a myrcene synthase, has been found to be expressed at a high level in flowers and contributes to myrcene emissions from _Antirinhunm majus_ flowers [22]. AmNES/LIS-2 (_A. majus_ meridololol/linalool synthases-2) is located in plastids and is responsible for linalool formation in _A. majus_ flowers [40]. Transiently overexpressing _PptPS5_ or _PptPS10_ from _P. bellina_ in _Phalanetopsis_ cultivar _P._ I-Isin Venus flowers increased linalool production by 7.7- or 29.0-fold compared with the control, respectively [24]. However, there is limited understanding of the spatiotemporal expression pattern of _MTPS_ in different perianth parts. In KOVA flowers, the total monoterpene content and the expression levels of _RcMTPS_s were the highest in the hypochile (Figures 3-5). These results indicate that the expression of _RcMTPS_s might be the main reason that the monoterpene emissions from the hypochile were higher than those from the other parts. In summary, the high expression of _RcDX_s and _RCHDS_s in KOVA flowers at D8 may be responsible for the high amounts of monoterpens biosynthesized during the full flowering at stage D8. Additionally, the higher emissions of monoterpens from the hypochile compared with the other flower parts may result in the hypochile possibly being more fragrant to pollinators. These findings provide new insights into the biochemical and molecular mechanisms of the spatial differences in floral scents between different flower parts, which offers some clues for breeding novel orchid cultivars with special floral scents. Finally, the specific functions of _RcDX_s, _RCHDS_s, and _RcMTPS_s in monoterpens biosynthesis in orchid flowers need to be further investigated in the future.
# An Overview of FIR Filter Design in Future Multicarrier Communication Systems
## 3 FIR Prototype Filter Design  
As discussed in Section 2, there are some design criteria to customize the performance of FIR. Thus, there must be some specific methods of FIR design corresponding to these criteria. In this work, the filter design methods are divided into three major categories: the frequency sampling methods, the windowing based methods, and the optimization based methods. For each type of method, we firstly introduce their basic concepts and subsequently some typical design examples are given. At last the advantages and disadvantages of the summarized methods are discussed.
## 7 Towards a Simplified Optical Transceiver
### Full Coherent Optical System
#### 7.1.1 Optical Coherent Receiver Configurations  
The COH system can effectively detect the full optical field [2; 18; 19]. Furthermore, Co-Rxs can be broadly based on two configurations, which are intradyne/homodyne and heterodyne systems. As illustrated in Figure 14, in the former (i.e., Figure 14a), a pair of balanced detectors is required per polarization, while for the latter (i.e., Figure 14b,c), a detector whose bandwidth is twice that of the former, per polarization, is usually em  
ployed [19; 21]. It is noteworthy that the polarization-diverse intradyne scheme is mainly adopted in the existing Co-Rxs [18; 21]. In general, a full Co-Rx implementation (i.e., Figure 14a) is usually based on components such as an LO laser, optical hybrids, either a polarization diversity system or an optical polarization tracking setup, and balanced PDs (BPDs) (i.e., four pairs) in order to suppress the SSBN at relatively lower LO powers. In Section 7.1.2, we expatiate on the fundamentals of optical coherent detection for different configurations. Furthermore, detailed information on the DSP for long-haul COH systems is available in [10; 133; 132]. In spite of the fact that a full Co-Rx might be an effective long-term scheme, currently, it is not a cost-efficient solution for cost-sensitive and short-reach link applications such as DCNs [18; 19]. Simplified COH optical schemes have been presented to address the associated challenges of the full COH system implementation.
## 5 Integrating Language Modeling with STEP  
So far, STEP has been implemented and tested in the biomedical domain having the _NCBO Annotator_[8] as an underlying concept extraction technique and the _NCBO Recommender_[19] as helper in the _concept consolidation_ phase (see [7] for details). The annotator's underlying technology is domain-agnostic; first, the biomedical free text is provided as input to the concept recognition tool used by the annotator along with a dictionary. The dictionary (or lexicon) is constructed using ontologies configured for use by the annotator. As most concept recognizers take as input a resource and a dictionary to produce annotations, the only customization to the biomedical domain, would be the biomedical ontologies used by the annotator. However, the NCBO Annotator is predominantly used in the biomedical domain and was the only means of annotating micro-contributions in the initial phase of our research [7]. Consequently, it's had a massive influence on the final results, as effectively, accuracy of the final profiles was heavily dependent on the accuracy of annotations performed by the annotator. Therefore, its versatility has come at the price of extraction efficiency, as an exact match is required between the terms present in text and the labels of ontological concepts, in order for annotations to be detected. For example, a simple usage of the plural of a noun (e.g., Flagella) is enough to miss an ontological concept (such as Flagellum); furthermore, in some cases, only constituents of a phrase are annotated (e.g., \"tibial shaft\"); aggregating partial annotations does not accurately convey the semantics of the whole term (e.g., consolidating concepts representing \"shaft\" and \"tibial\" does not convey the same semantics as concepts representing \"tibial shaft\").  
We have tried to minimize the impact of this problem by consolidating semantically similar concepts. And while the _Concept Consolidation_ resulted in a significant improvement of the results produced by the annotator, we observed that some instances where either not included in the resulting expertise profiles, or concepts included in the profiles were not ranked accurately.  
The exhaustive discovery and resolution of sub-optimal performance by the NCBO Annotator in the context of our use cases, is outside the scope of this research. However, our experiments clearly indicate that the accuracy of resulting profiles is directly influenced by the quality of annotations produced by the annotator. Since the STEP methodology provides a pluggable architecture, in order to reduce the effects of domain-specific annotation tools on the accuracy of the generated profiles, we propose integrating _Language Models_ with the _Concept Extraction_ module of the methodology. The following sections outline our proposed methods, which are _domain-agnostic_ in order to ensure that the overall architecture remains _domain-independent_.
# A Proposal of a Troposphere Model in a GNSS Simulator for VANET Applications
## 3 Introduction to GNSS
### Why Use GNSS Simulator  
The applications utilizing GNSS technology is in constant increase. Many of these applications have strong requirements so it is necessary to integrate GNSS with other technologies to meet these requirements. To ensure that GNSS is reliable and guarantees reliable performance it is important that the development process is based on adequate tests from concept to production. In order to test a GNSS product three main methods are possible:  
* Field tests using real-time signals.
* Registration test.
* Reproduction and laboratory simulation.  
Today, best practices indicate that most tests are performed under controlled and repeatable conditions in a safe laboratory [24]. This allows testing of all conditions, including testing up to the real and theoretical performance limits. This also allows for the development of receivers for GNSS systems that are currently unavailable or lacking a complete constellation. Field testing of the real-world signal has significant disadvantages.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **GPS** & **GLONASS** & **Galileo** & **CNSS** \\ \hline Country & USA & Russia & Europe & China \\ Number of Satellite & 32 & 24 & 30 & 35 \\ Number of orbital planes & 6 & 3 & 3 & \(10^{2}\) \\ Orbital height (km) & 20.200 & 19.140 & 23.222 & 21.150 \\ Orbital period & 11 h 58 m & 11 h 15 m & 14 h 06 m & 12 h 48 m \\ Orbital Inclination & 55\({}^{\circ}\) & 64.8\({}^{\circ}\) & 56\({}^{\circ}\) & 55.5\({}^{\circ}\) \\ Coding & CDMA & FDMA & CDMA & CDMA \\  & 1575 & 1559–1592 & 1579 & 1590 \\  & 1228 & 1243–2063 & 1279 & 1561 \\ Carrier Frequencies (MHz) & 1176 & & 1207 & 1269 \\  & & & 1192 & 1207 \\  & & & 1176 & 1192 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of various GNSS.  
A summary of the benefits of testing with GNSS simulators, compared to live testing with true GNSS constellations, is listed below:  
* Complete control over the signals from the constellations.
* Complete control over environmental conditions.
* Repeatability.
* No unwanted interference.
* No unwanted signal effects.
* Ability to easily test scenarios with GNSS constellation errors.
* Ability to test in the laboratory without involving vehicles.
* Ability to test constellations not yet present.  
A GNSS simulator provides an effective and efficient mean of testing GNSS receivers, which will process simulated signals just like those from real GNSS satellites. It emulates the environment of a GNSS receiver on a dynamic platform by modeling the movement of the vehicle, satellite, signal characteristics, atmospheric effects and others, making the receiver actually navigate according to the parameters of the test scenario. Additionally, a GNSS simulator offers a superior alternative for testing, compared to using actual GNSS signals in a live environment. Unlike road tests, simulator tests provide full control of simulated satellite signals and simulated environmental conditions.
## 3 Progress in Atmospheric Density Inversion for Low Earth Orbit Satellites
### Advances in Atmospheric Density Inversion Using Orbital Data  
[PERSON] et al. used the attenuation occurring in the orbital half-length axis of the onboard high-precision GPS observation data, combined with the mean advection parameter M and the rebound channel coefficient B, to solve the inversion using Tiangong-1 as an example and compared the inversion results with the observation data of Tiangong-1 [17]. [PERSON] et al. used the scientific orbit data (RSO) provided by the CHAMP and GRACE satellites to perform atmospheric density inversion using the semi-long-axis attenuation method and reduced the influence of the fixed-orbit error propagation in the inversion by improving the setting of the orbit integration time interval [36]. [PERSON] used the TLE data provided by the GRACE satellite to invert the atmospheric density along the orbital direction, analyzed the reasons for the errors of inversion, model, and observation values, and improved the accuracy of model inversion by combining the space environment index [37]. [PERSON] utilized the CHAMP satellite precision orbit data to invert the thermospheric atmospheric density using the energy attenuation method; the inversion results were consistent with the semi-long-axis attenuation method, and the accelerometer data can also be calibrated at the same time. The accuracy of the inversion density is related to satellite altitude and local time [38].
## 3 Results and Discussion
### UV-Curing Behaviour  
To study the reactivity of the synthesized PEAs towards UV-curing, the resins were examined by means of Photo-DSC after the addition of 5% of Irgacure 1173 as the photoinitiator. In all cases, the rate of polymerization (ROP) of the PEAs is in the same range as for other UV-curing itaconic acid-based materials [33]. In addition, the ROP of the PEAs is lower than the one obtained from the neat OE. In addition, it decreases with increasing amide content (Figure 5 and Table 3). Both observations can be explained by the fact that the double bond density decreases with an increasing amount of amide. However, there is a major difference between the reactivity of the first and second series. The PEAs obtained by the pseudo-one-pot procedure exhibit reduced reactivity, which can be explained by the lower double bond density, as well as the isomerization to the mesaconate observed by NMR analysis. The internal double bond of the mesaconate is less reactive towards UV-curing compared to the itaconic counterpart. However, despite the lower reactivity, the double bond conversion is, in some cases, higher than for the neat OE. Especially, PEA-003 has a conversion of 71.4% compared to 58% of the neat OE.  
The curing of the samples is also visible in the FT-IR spectra before and after curing. Figure 6 shows the FT-IR spectra of PEA-002. The C=C stretch (1639 cm\({}^{-1}\)) and C=C deformation (816 cm\({}^{-1}\)) vibrations are significantly reduced after curing.  
Figure 5: Photo-curing of OE and PEAs. (a) rate of polymerization of PEA-001-005; (b) rate of polymerization of PEA-101-103; (c) free double-bond conversion PEA-001-005; (d) free double-bond conversion PEA-101-103.  
Figure 6: FT-IR-spectra of PEA-002 before and after UV-curing.  
In addition to the photo-DSC study, the resins with 5% photo initiator were also applied as films and cured with a radiation dose of 500 mJ/cm\({}^{2}\). The number of curing runs needed to obtain tack-free surface was noted. For the first series, all films, but the last were fully cured after five curing runs. However, only PEA001-003 exhibited a non-sticky surface after curing. The films of the second series were all not fully tack-free after curing (Table 4). This could be a result of the lower reactivity towards UV-uring due to the isomerization of the double bond. Compared to polyester from itaconic acid reported earlier, the reactivity towards UV-uring is lower, especially at high amide content [25]. Figure 7 shows the films obtained from PEA-001 and 003.