esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a Fixed IF Down-Conversion Double-Balanced Mixer for UHF Band Applications
## 4 Simulation and Experimental Results
### Experimental Validation of Down-Conversion Double-Balanced Mixer Prototype
#### 4.5.2 Noise Figure Measurement Using a Y-Factor Measurement Technique  
The 145 MHz IF filter selectivity of the IF port ensures that the IF spectrum will not contain any image frequencies. The SSB noise figure was measured using the Rohde & Schwarz FSWP with an EID (Electronic Instrumentation Division P/N 7626-1 frequency 10 MHz-26 GHz) noise source connected to the RF port. The measurement employs fixed LO down conversion since the LO frequency cannot be swept through the internal generator of FSWP. Details of the noise figure measurement for frequency converting DUT are given in the application note of Rohde & Schwarz [40].  
Figure 26 shows the noise figure measured at 145 MHz. Additionally, the graph also indicates that the bandwidth of the tuned filter is roughly 10 MHz, and the noise figure is 7 dB \(\pm\) 0.1 dB. The gain will reach its peak at the center frequency and then progressively decrease toward the higher and lower frequencies, depending on the Q of the circuit.  
As can be seen in Figures 15 and 26, the noise figure that was measured for the mixer is lower than the result that was obtained from the simulation. According to the Friss Formula, the input stage has the most significant impact on the total noise figure, and stages further down in the chain will contribute less to the overall noise figure. The total mixer noise contribution derived in Equation (31) mainly depends on the internal base resistance and transconductance of each transistor. At the RF input stage, the internal base resistance of Q1 and Q4 generates a thermal noise and directly adds to the input-referred noise of the mixer and ultimately reduces the SNR (signal-to-noise ratio) at the input, which leads to having a higher noise figure. Thus, lower internal base resistance will reduce the thermal noise at the input, improving the SNR at the input and resulting in a lower noise figure. However, due to complex modeling and curve-fitting processes, there will be some inadequacies in the extraction of the transistor's spice parameter. This can be attributed to the fact that the ADS simulator uses the spice parameter library data that Infineon supplied, which may be pessimistic. Inevitably, we rely on the simul  
Figure 26: The noise was measured at the IF port using the Y-factor method. Test conditions: RF frequency: 994 MHz to 1010 MHz. Fixed LO frequency: 855 MHz at 0 dBm.
## 2 Materials and Methods
### Identification of Selected Strain  
The selected isolate was identified using 16S rRNA analysis. Genomic DNA of the 2 De strain was extracted from pure cultures grown from a single colony using a DNA extraction kit (Bacterial and Yeast Genomic DNA Purification Kit, EURx, Gdarask, Poland) according to the manufacturer's instructions. The DNA was used as the template with the universal bacterial primers 8F (5' AGTTTGATCACTGCCTCAG 3') and 1492R (5' GGTTACCTTGTTACGACTT 3') targeting a fragment size of 1484 bp [27]. The identity of the obtained sequence was performed using the ClustalW alignment and compared with published sequences using the NCBI BLAST program in the GenBank database. The Average Nucleotide Identity (ANI) calculator [28] was used to determine the similarity of 16S rRNA sequences between tested and typed strains [29]. In order to elucidate the phylogenetic position of the strain, a phylogenetic tree was constructed based on the longest common fragment of the 16S rRNA gene sequence (1372 bp) selected from ClustalW alignment of the 2 De strain and the 10 closest type strains of other _Pseudomonas_ species obtained from GenBank. The analysis was performed using Mega X software with the maximum likelihood method and Tamura-3-parameter model, assuming that a certain fraction of sites are evolutionary invariable (pl) and 1000 bootstrap replicates [30; 31].
# Testing the Galileo High Accuracy Service User Terminal (HAUT) in Static Scenarios +
## 4 The Galileo HAS User Algorithm for HAS Initial Service
### User Algorithm Description  
The selected position estimation technique for the User Algorithm was an extended Kalman filter (EKF) with state predictions and state updates to improve convergence and accuracy, as it achieves, in the opinion of the authors, an optimum trade-off between the measurement model and the state space model by minimizing the variance of the updated states. The User Algorithm processing follows an uncombined, satellite-to-satellite single-differenced (SD) processing.  
Compared to an ionosphere-free approach, the proposed uncombined processing avoids the amplification of residual satellite orbit, clock, and phase bias errors, avoids noise amplification and brings no loss of information, which occurs when using measurement combinations. Conversely, ionosphere-free combinations do not require the integration of ionospheric corrections or models. Moreover, the significant bias amplification achieved by the wide-lane combination is considered more detrimental than the benefit of a larger wavelength.  
When comparing the SD approach of the User Algorithm to an undifferenced setup, the elimination of receiver-specific errors and noise amplification by single differences are on a comparable performance level but only if the process noise of the clock and other receiver-specific unknowns can be modeled sufficiently well. Thus, the elimination of these unknowns by SD is the preferred option.  
The algorithm has preprocessing steps, including outlier detection and elimination of single-satellite failure for each GNSS constellation with RAIM/DIA and detection and elimination of complete GNSS constellation outages. Preprocessing also addresses cycle slip detection and correction by using a time-differenced Melbourne-Wubbena combination and ionosphere-free phase combination. With this set of techniques, the proposed UA provides robustness to different fault behaviors on the site of satellites, the level of the correction service and the receiver and its environment.
# Wireless Channel Models for Over-the-Sea Communication: A Comparative Study
## 6 Possible Applications and Improvements  
In this section, we first address the applications of the discussed channel models. We then discuss the possibility of unique and novel applications of the channel models in our point of view. After that, some possible future improvements of the channel models are presented. Finally, we discuss some critical issues, prospects, and phenomena in order to give our insights to readers.
###### Abstract  
This study provides a comprehensive review of molten salt technology, as well as electrochemical and thermochemical processes aimed at hydrogen and syngas production. First, this research illustrates the current types of molten salt mixtures, detailing their main applications and thermophysical properties. Then, the analysis delves into existing thermochemical cycles and their specific operating conditions for producing hydrogen and syngas. Moreover, this study assesses the compatibility of these processes with molten salt integration. This investigation involved a comprehensive review of the existing technical and scientific literature, blending insights and practical experiences to offer detailed data on the topics explored. The findings suggest that molten salts, with their medium-high operating temperatures, can markedly improve the efficiency and sustainability of hydrogen and syngas production. Furthermore, this study outlines the pivotal role these technologies can play in achieving the European Union's ambitious goals by enhancing the use of renewable energy sources and advancing the shift to carbon-free solutions.  
Keywords:thermo-electrochemical processes; molten salt mixture; renewable syngas production; hydrogen production _Review_
## 2 Materials and Methods
### Experimental Details
#### 2.1.1 Meteorological Conditions at the Study Site  
Meteorological conditions for the two paddocks were acquired from a weather station coupled to a data logger (CR-10X, Campbell Scientific Inc., Logan, UT) placed 1.9 meters aboveground on the mown paddock [17; 26; 61]. Briefly, the weather station provided 30 min averaged values of precipitation (SBS500, Campbell Scientific Inc., Logan, UT), air temperature and relative humidity (HMP 45 AC, Vaisala), radiation components (CNR1, Kipp & Zonen), and wind speed (A100L2, Vector Instruments) and direction (W200P, Vector Instruments). Volumetric soil water content data were collected by time domain reflectometry (TDR) probes at 10, 20, 30, 60, 80, and 100 cm depths (CS616, Campbell Scientific Inc., Logan, UT), and soil temperatures were measured at 5, 10, 20, 30, 60, 80, and 100 cm down the soil profile (PT100, Mesurex), but only for the mowed paddock. Soil heat flux was measured at 5-cm-depth (HFP01, Hukseflux), and data were corrected for changes in heat storage in the soil layer above the flux plate [62]. Over the study period (2006-2010), average air temperature and average annual precipitation were 11.2 \({}^{\circ}\)C and 774 mm yr\({}^{-1}\), respectively. Half-hourly meteorological data, i.e., air temperature, global radiation, humidity, and precipitation, were summed/averaged to daily values to be used as driving variables for the CenW model runs of both paddocks.  
The predominant wind direction was from the southwest and a secondary peak from the northeast (Figure 1b).  
Throughout the five years of the study, daily maximum air temperature exceeded 25 \({}^{\circ}\)C for 10.5% of the time, with a maximum of 35.5 \({}^{\circ}\)C. Daily minimum air temperature was negative for 13.3% of the time, with a lowest value of \(-\)11.0 \({}^{\circ}\)C (data not shown). Summer months (June-September) were hot and dry with average monthly air temperature ranging between 15.6 and 19.4 \({}^{\circ}\)C and precipitations between 48 and 71 mm mth\({}^{-1}\) (Figure 2a). The wettest and coldest month are November (98 mm mth\({}^{-1}\)) and December (3.6 \({}^{\circ}\)C), respectively (Figure 2a). Among the five years of the study, 2006 was the warmest (11.9 \({}^{\circ}\)C) and wettest (888 mm yr\({}^{-1}\)), while 2010 was the coldest (10.5 \({}^{\circ}\)C) and driest (697 mm yr\({}^{-1}\)) year.
### Motivation  
Modern monitoring technology has evolved significantly, as optimised systems allow data to be collected at multiple measurement points, obtaining a large quantity of data that, when processed with artificial intelligence, have obtained deep and meaningful research results [7]. The lack of dedicated IoT datasets with specific protocols and conditions limits the emergence of novel mechanisms to recognise abnormal network behaviour. The detection of traffic anomalies can be improved by the proposal of new datasets representing real systems with wireless sensor networks and light communication, where IoT protocols are modified.  
The implementation of models that recognise abnormal circumstances is possible due to the representative knowledge of the environment. These facts are motivated the presentation of CIDAD (available at [[https://github.com/dad-repository/cidad.git](https://github.com/dad-repository/cidad.git)]([https://github.com/dad-repository/cidad.git](https://github.com/dad-repository/cidad.git))), URL (accessed on 30 October 2022)) a complete and labelled CoAP-IoT dataset based on the reproduction of certain traffic with a sufficient number of samples, specific feature extraction, and mixed anomalous scenarios.  
An incident that happened at the Centre for Information and Communications Technology Research (CITIC) [8] served as the inspiration for the scenario design in this article. The temperature sensors initially failed to deliver accurate data to the cooling system due to a software issue that resulted from an erroneous sensor configuration in the data centre. The system failed due to a hardware breakdown brought on by changes in the device temperatures.  
In addition, to validate CIDAD for traffic anomaly detection on CoAP-IoT networks, five shallow machine learning models were applied. Indeed, ML cannot work with raw data in suitable conditions; thus, as a preliminary stage, it was necessary to extract and analyse the representative features and remove redundant information. Consequently, the data had to be pre-processed to be perfectly interpreted by the ML algorithms.
# Trajectory Control Approach for Single-Stage Soft-Switching Grid-Tied Inverters
## 6 Conclusions and Future Work  
This paper presented a trajectory control model using finite state machines (FSMs) for a single-stage soft-switching grid-tied inverter. By using a series resonance filter network and high frequency (HF) transformer in the grid-tied inverter, the introduced control scheme provided zero-voltage switching (ZVS) on the DC side and zero-current switching (ZCS) on the AC side. By employing the FSM model, the control of complex switching operations was efficiently managed on a digital signal processing platform. By directly controlling the energy within the series resonant circuit, the model delivers a fast transient response while minimizing switching actions across all quadrants of operation. The control scheme was implemented on a TI 28379D digital signal processor and validated through PLEXIM RT-BOX Hardware-In-the-Loop testing. This is expected to lead to higher efficiency and improved grid synchronization for module-integrated photovoltaic inverters. Future work will focus on implementing the control scheme in a physical prototype, leveraging FPGAs which supports simultaneous parallel processing of signals to enhance response times and high-speed control. This development aims to address practical challenges and hardware limitations in single-stage soft-switching grid-tied inverters.  
This research was supported by the Bisa Research Grant of Keimpung University in 2021 (Project No: 20210721).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The author declares no conflict of interest.
## 3 Discussion
Regarding conventional BIA, besides the inherent limitations of the two-compartment model and the lack of athlete-specific equations for different disciplines and levels of sports practice, there is often a lack of knowledge about the predictive equations used by BIA manufacturers to estimate FFM and/or FM. For all these reasons, the error of estimation may constitute a real handicap to the use of BIA, because, as expected, a smaller difference is observed between FM/FFM estimated by BIA and by reference methods (DXA) when BIA data are used in athlete-specific equations. Inconsistent results have been found in studies that compared anthropometry and conventional BIA to the gold standard techniques (i.e., DXA) for BC assessment in sports. [PERSON] et al. demonstrated that in a group of professional male soccer players, only 12 of 31 anthropometric equations for body FM% estimation presented nonsignificant differences with DXA. Only 4 of them showed narrower limits of agreement [39]. [PERSON] et al. also observed significant differences in the estimation of FM% by using DXA, anthropometry (Slaughter equation), conventional BIA, and air displacement plethysmography in young soccer players, concluding that such methods do not present good agreement in this population [40]. According to all the authors, the results of body composition analysis are extremely dependent on the methods used. Although the most common use of conventional BIA is to estimate body compartments (i.e., FM, FFM, and/or TBW), BIA analysis offers more possibilities, such as other types of quantitative BC assessments and the qualitative analysis of BIVA. BIVA has the significant advantage of not making biological assumptions that rarely hold true in sports [32]. It is equally possible to obtain a great deal of information about BC using raw data, for example, the phase angle, the parameter that best represents the ICW/ECW ratio and reflects the integrity of the cell membranes [30]. BIA analysis is a simple, inexpensive, and extremely fast technique that can be used for both large-scale evaluations and individualized monitoring of athletes at \"on-site work\". As a result, despite its limitations, conventional BIA, especially BIVA, is often employed to evaluate BC in sports [13; 41; 42]. [PERSON] et al. have focused their research on the application of BIVA in sports. They showed that it was possible to discriminate between different categories of somatotypes by the bioelectrical properties of athletes. This was due to the predictive role of their raw measurements on muscle mass and body fluid distribution, which change according to FM and FFM [13]. They concluded that BIA and especially BIVA could be utilized to monitor the nutritional status and seasonal changes in body composition of athletes as well as to provide accurate intra- and interathlete comparisons [43].
## Materials and Methods
### Radiative Transfer and Gross Primary Production (GPP) Models  
A simple canopy photosynthesis model [50] ([PERSON]'s model) was used to explore the links between plant traits and GPP. This model integrates a non-rectangular hyperbola leaf photosynthetic light response curve through the canopy. Its main inputs are incident photosynthetically active radiation (PAR), leaf light response parameters, leaf transmittance, light saturated leaf photosynthetic rate (A\({}_{\text{leaf}}\)), leaf area index (LAI), and canopy light extinction coefficient.  
The PROSAIL radiative transfer model was used to explore the links between plant traits and spectral reflectance. PROSAIL couples leaf (PROSPECT 5) and canopy (4 SAIL) radiative transfer models to simulate the effects of plant traits, soil reflectance, and view and sun geometry on land surface reflectance [51]. PROSAIL inputs include leaf chlorophyll per area (C\({}_{\text{ab}}\)), Leaf Mass per Area (LMA), leaf structure (number of air-cell wall interfaces), carotenoid and brown pigment content, water content per leaf area (W\({}_{\text{c}}\)), and Leaf Area Index (LAI).  
We applied variance-based global sensitivity analyses [52; 53] to the Thornley and PROSAIL models to identify the plant traits and other inputs that drive GPP and the VIs. The analysis of the Thornley model was run using the R sensitivity package v1.18.0. The analysis on PROSAIL was run using the Global Sensitivity Analysis module v.1.08 of the ARTMO toolbox v.3.25 [54]. Previous sensitivity analysis on VIs have emphasized crop species, and parameter ranges has been either tailored for such ecosystems or arbitrarily selected [20; 24]. The results of global sensitivity analyses depend heavily on the range of inputs specified, and we selected ranges that represent the variation across our study sites or from literature datasets that covered a diverse array of species and biomes (see Appendix A for details). The global sensitivity analyses of both models used 2000 samples per input parameter following a Sobol sampling scheme.  
Once the drivers and factors driving GPP and VIs were identified, we compared the results of Thornley and PROSAIL models forced with in-situ information from these main drivers to further explore how plant traits link VIs to GPP (see Appendix B for details).  
All analyses used either MATLAB, including the ARTMO toolbox v.3.25 [54], or R v3.5.1. (the R Foundation for Statistical Computing), including the hsdar v1.0.0 [55] and sensitivity v1.18.0 packages.
# A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges
## 4 Vehicular Ad-Hoc Networks (VANETs)
### Vehicle-To-Vehicle (V2V) Communication  
It is also called inter-vehicle communication (IVC) that allows the vehicles to communicate with each other and share the necessary information about traffic congestion, accidents and speed limits [4]. V2V communication can generate the network by connecting different nodes (Vehicles) using a mesh (partial or full) topology [5]. Depending upon the number of hops used for inter-vehicle communication, they are classified as single-hop (SIVC) or Multi-hop (MIVC) systems [9]. The SIVC can be used for short-range applications such as lane merging, ACC, etc., whereas MIVC can be used for long-range communication such as traffic monitoring. The V2V communication provides several advantages such as BSD, FCWS, automatic emergency braking (AEB) and LDWS [4].
## 3 Results
### Dosimetry for Therapeutic X-ray Beams  
#### 3.2.1 Percentage Depth Dose (PDD)  
The measurements of the central pixel of the edgeless array are depicted in Figure 5. The obtained results are benchmarked with the measurement of the PTW 30013 Farmer IC in a solid water phantom for depths exceeding d\({}_{\text{max}}\) = 1.5 cm and to GEANT4 simulations for the build-up region. Measurements were normalized based on measurement at d\({}_{\text{max}}\).  
Figure 4: Dose linearity assessment for the central diode as measured on the Clinac 21 iX at SCCC. Error bars are contained within the symbols. The first subplot shows the relative deviation of each measurement from the linear fit as a function of the dose. The second subplot is the error bars which are based on the standard deviation of three measurements with the edgeless array at each dose.  
Figure 5: The PDD measurement for the central pixel of the edgeless array, considering variations in thickness without accounting for the EPOM. The dependencies of PDD are systematically compared with the PTW 30013 Farmer IC measurement downstream of the d\({}_{\text{max}}\) and the GEANT4 simulation [34] upstream of d\({}_{\text{max}}\).  
The edgeless output exhibits consistency with the IC within 1.4% for depths larger than d\({}_{\text{max}}\). Notably, a marginal systematic under-response is discerned at greater depths, with the most pronounced discrepancy observed at 25 cm in solid water, amounting to \(-\)1.38%. In order for the detector to accurately estimate the PDD in the build-up region, an estimation of the EPOM is required.  
Effective Point of Measurement  
The examination of the build-up region of the PDD is shown in Figure 6 compared to the GEANT4 prediction. The EPOM that minimizes the \(\chi^{2}\) variable in Equation (1) and calculated for the central pixel was determined to be \(\Delta z\) = 789 \(\pm\) 46 \(\upmu\)m. The measurement at d\({}_{\text{max}}\) is excluded from this analysis, presuming the PDD to be 100% at this depth.