esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Positioning Algorithm of Smartphone
### The Point Positioning Model of Smartphone  
GNSS point positioning does not require reference station data and is easy to implement. It is the main application method of smartphone GNSS positioning. This article uses carrier-smoothing pseudorange point positioning and precise point positioning to verify the model effect. The carrier-smoothing formula can be expressed as:  
\[\begin{array}{l}\overline{P}(t_{1})=P(t_{1})\\ \overline{P}(t_{i})=\frac{1}{i}.P(t_{i})+\frac{i-1}{i}.\left[\overline{P}(t_{ i-1})+\varphi(t_{i})-\varphi(t_{i-1})\right]\end{array} \tag{7}\]  
where \(P\) is the pseudorange, \(\overline{P}\) is the pseudorange after smoothing, \(t_{i}\) is the epoch, \(\varphi\) is the carrier. However, for Android smartphones, the difference between pseudorange and the carrier is not fixed. The increments of the pseudorange between adjacent epochs and the carrier are not equal:  
\[\varphi(t_{i})-\varphi(t_{i-1})=P(t_{i})-P(t_{i-1})+d_{i} \tag{8}\]  
where \(d\) is the difference between carrier increment and pseudorange increment. When Formula (7) is used for calculation, the pseudorange obtained will contain the cumulative incremental error. To solve the impact of it on the single point positioning of the carrier-smoothing pseudorange, this article uses a fixed smoothing window (the size of window is 30) to ensure that all pseudorange in the same epoch are smoothed the same number of times, so that all pseudorange have the same cumulative incremental error. This error can be absorbed by the device clock error during the Kalman filtering process and does not affect the positioning result.  
According to the uncertainty of the difference between the GNSS pseudorange and the carrier in the smartphone, it is considered that they have different device clock parameters.  
Currently, the availability of dual-frequency GNSS raw observations of smartphones is poor. In this article, only single-frequency GNSS data is used for PPP positioning, and the Kalman filter is used for parameter estimation. The observation equations of the pseudorange and the carrier are:  
\[\begin{array}{l}P=\rho+c\cdot d\widetilde{t}_{P}-c\cdot dT+d_{trop}+d_{orb }+d_{ion}+\varepsilon\\ \varphi=\rho+c\cdot d\widetilde{t}_{\varphi}-c\cdot dT+d_{trop}+d_{orb}+d_{ion }+\widetilde{N}+\varepsilon\end{array} \tag{9}\]  
where \(\rho\) is the geometric distance between satellite and receiver, \(d\widetilde{t}_{P},d\widetilde{t}_{\varphi}\) are the clock error of pseudorange and the carrier, \(dT\) is the clock error of the satellite, \(\widetilde{N}\) is the ambiguity of whole cycles, \(d_{trop}\) is the tropospheric delay, \(d_{orb}\) is the error of satellite ephemeris, \(d_{ion}\) is the ionospheric delay, \(\varepsilon\) is the residual. Since cycle slips data accounted for more than 20% of the smartphone phase observations and cycle slips occur frequently [20; 21], this article does not fix the carrier phase.
## 4 Discussion
### PPO and POD Activity Levels  
Our results showed that both pasteurization methods led to the deactivation of PPO and a significant reduction in POD activity below the limit of quantification. Similar results were obtained by [PERSON] et al., 2021 [61]. Observations showed that POD activity reached over 90% deactivation within 5 min at set temperatures of 90 \({}^{\circ}\)C and 100 \({}^{\circ}\)C [61]. These studies were conducted on yellow peas using microwave heating at a frequency of 2450 MHz.  
Research by [PERSON] et al., 2024, showed that at 90 \({}^{\circ}\)C, the continuous microwave flow method achieved more than 90% inactivation of PPO, while the conventional thermal method required a temperature of 95 \({}^{\circ}\)C for 5 min [62]. In addition, the continuous microwave flow method showed faster enzyme inactivation and better retention of TPC and AOC.  
It is worth noting the study by [PERSON] et al. in 2015 conducted studies on strawberry puree and showed that regardless of the temperature (90 \({}^{\circ}\)C and 120 \({}^{\circ}\)C), PPO activity was reduced by about 80% in just 10 s [28]. They also observed that shortening the microwave processing time to 7 s at a temperature of 90 \({}^{\circ}\)C reduced PPO activity to about 62%, highlighting the significant impact of processing time on enzyme activity. Moreover, the study emphasized that PPO shows better temperature resistance than peroxidase (POD) [63].  
During the studies of [PERSON] et al., 2017, it was shown that higher power and shorter working time in a continuous, semi-industrial microwave oven allowed for obtaining cocktails of higher quality with more significant enzyme reduction than conventional thermal processing [25]. Our studies observed higher quality with more significant enzyme reduction until the sixth month of storage. Additionally, studies by [PERSON] et al. in 2017 showed that microwave heating, especially at higher power and shorter times, such as 1600 W/206 s and 3600 W/93 s, reduces the residual enzymatic activity of peroxidase, pectin methyl esterase, and polygalacturonase [25].  
The research results indicate that both pasteurization methods (MFP and TP) effectively deactivated polyphenol oxidase (PPO) and significantly reduced peroxidase (POD) activity below the detectable limit in both juices. This means that both MFP and TP methods can effectively control enzymatic activity in fruit juice, which is essential for its quality and shelf life.
## 4 IRS-Aided Wireless Network Performance and Deployment
### IRS as a Wireless Network Signal Reflector
#### 4.1.2 Ergodic Metrics  
The ergodic metric determines the average network performance [24]. Ref. [93] showed that the data rate between the BS and a mobile user can be improved even if the design of the IRS's optimal phase shifts is dependent on limited feedback from the mobile user. An IRS with perfect channel state information (CSI) reduces positioning error bound and orientation error bound [94]. Moreover, when the direct path channel between the transceivers is characterized by a low rank matrix, through optimization an IRS can improve the rank of channel matrix and lead to significant capacity gains [95].
# A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt
## 2 Materials and Methods
### Datasets  
The datasets used in this study are listed in Table 1. The details of each dataset are presented in subsequent sections.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data** & **Variables** & **Unit** & **Source** & **Spatial Resolution** \\ \hline Satellite data [54] & Normalized difference vegetation index (NDVI) & - & MODIS & 250 m \\ Enhanced vegetation index (EVI) & - & MODIS & 250 m \\ Primary production [55] & Gross primary production (GPP) & kg C/m\({}^{2}\) & MODIS & 500 m \\ Soil [56] & Available water content (AWC) & cm & gSSURGO & 10 m \\ Available water storage (AWS) & mm & gSSURGO & 10 m \\ Cation exchange capacity (CEC) & meq/100 g & gSSURGO & 10 m \\ Bulk density & g/cm\({}^{3}\) & gSSURGO & 10 m \\ Percent clay & Percent & gSSURGO & 10 m \\ Percent sand & Percent & gSSURGO & 10 m \\ Field capacity & cm/cm & gSSURGO & 10 m \\ Organic carbon & g C/m\({}^{2}\) & gSSURGO & 10 m \\ pH & - & gSSURGO & 10 m \\ Saturated hydraulic conductivity & \(\mu\)m/sec & gSSURGO & 10 m \\ Wilting point & cm/cm & gSSURGO & 10 m \\ Precipitation & mm & PRISM & 4 km \\ Minimum temperature & \({}^{\circ}\)C & PRISM & 4 km \\ Maximum temperature & \({}^{\circ}\)C & PRISM & 4 km \\ Mean temperature & \({}^{\circ}\)C & PRISM & 4 km \\ Minimum vapor pressure deficit & hPa & PRISM & 4 km \\ Maximum vapor pressure deficit & hPa & PRISM & 4 km \\ Mean dew point temperature & \({}^{\circ}\)C & PRISM & 4 km \\ \hline \hline \end{tabular}
\end{table}
Table 1: The datasets used in the study.  
Figure 1: The map of the study area (Corn acreage by county in 2020).
# A Review of Advanced CMOS RF Power Amplifier Architecture Trends for Low Power 5G Wireless Networks
## 1 Introduction  
The first most primitive radio transmitter that was used for telegraphy was developed in the early 1890s by [PERSON]. This invention spawned the wireless telegraphy or \"spark\" era, named due to the transmitter having spark gaps, and lasted for several decades. As a result, this became the starting point for the search for more efficient and rapid ways to exchange wireless information [1]. The largest leap in the domain of wireless information transfer started with the invention of the transistor, as this allowed research and development of portable devices and led to the launch of the first commercially automated cellular network (1G generation), which later evolved into the currently widespread 3G and the maturing 4G technology and is paving the way to the 5G realm. This is possible due to the massive growth in the global mobile communication sector revenue, which increased from \(\copyright\)174 billion in 2010 [2] to \(\copyright\)2.7 trillion in 2017 and is expected to reach over \(\copyright\)4 trillion by 2020 [3].
## 4 A Framework for Improving Accessibility of Serious Games in Handheld AR
### Implementation of Technology Platform
#### 4.2.5 Serious Game SG2  
SG2 is a prototype game with an implemented mechanism for adapting selected settings (UI parameters and interaction mechanisms) for an individual user based on retrieved settings data from the database. It is developed in Unity and its functionalities are similar to SG1. It comprises six tasks in which the user is given the interaction mechanism according to the calculated settings. The settings for an individual user are accessed by sending a request to a defined access point on the server part of the system. The response to the request contains the settings values in JSON format. The main character of SG2 is a car with which the user needs to perform different actions depending on the task. As in SG1, skipping the task and changing the UI settings is possible. Before entering the game, the user logs in with a username, after which the settings for that user are retrieved. If a user did not previously play a full session of SG1, they will play SG2 with the default settings. The user first starts a game by scanning the surface on which the virtual objects are then created. Afterwards, the user performs the tasks with assigned interaction mechanisms (touch gesture or indirect control). The six tasks of SG2 are related to changing the car's colour, translating, scaling and rotating the car, and entering the number from the car registration in the input field. Figure 7 shows screenshots from SG2 where tasks with interaction mechanisms assigned for a test user are shown.  
Figure 6: UML activity diagram illustrating a method for determining the interaction mechanism in related tasks.  
By having implementation of the next three modules: module to support automated tracking of user interaction data, module to analyse user interaction data and calculate adaptation settings for an individual user, and module to support adaptation of interaction mechanisms in selected tasks and UI parameters in serious games in AR, key parts of the framework that enable the goal of improving accessibility of AR-based serious games are developed. The framework in its entirety is shown in Figure 8. To determine the theoretical and practical validity of the framework proposed in this paper, an experiment with a developed technology platform was conducted. The experiment included user testing with people with disabilities and people of different ages.
# Reconfigurable Antennas: Switching Techniques--A Survey
## 5 Reconfigurable Antennas with Smart Materials  
Reconfigurable antennas with smart materials are a very new subject of research, and many challenges such as reliability and efficiency still need to be further investigated. However, some designs with notable achievements have been reported recently [66; 67; 68]. In this type of reconfiguration technique, the characteristics of the antennas can be reconfigured by pumping fluid into a hollow placed behind the antenna to change the characteristics of the substrate in terms of relative electric permittivity or magnetic permeability [69; 70; 71].  
In [72], a broadband polarization reconfigurable antenna is proposed. Its structure, shown in Figure 9a, is a spiral antenna radiator fed by a parallel stripline. The antenna utilizes two water arms and is mounted above a large ground plane for unidirectional radiation. Two water channels are mounted above the ground plane to generate different polarizations. By controlling the water flow along the water channels, the polarization of the antenna can be switched between right-hand and left-hand circular polarizations. The antenna operation band covers a frequency range of 1.2-1.84 GHz.  
Another design of the reconfigurable antenna with smart materials is introduced in [73]. It uses a low-loss transformer oil at high frequency to tune the operation frequency of a coaxial-fed patch antenna. As shown in Figure 10a, a two-layer substrate is employed between the radiation patch and ground plane of the proposed antenna. By changing the height of the oil layer, the volume ratio of air to liquid is varied, which leads to tuning the effective permittivity of the entire substrate of the patch radiators to generate the frequency reconfiguration. Figure 10b shows the fabricated prototype of the design. An aluminum plate is used as the ground plane. As can be observed from Figure 10c, for different heights of the transformer oil (0-9 mm), the operation frequency of the patch antenna can be tuned in a wide range from 1.42 to 1.96 GHz.  
Figure 8: (**a**) Different configurations and (**b**) measured/simulated return losses of the antenna (redrawn from [60]).  
Figure 9: (**a**) Fabricated prototype, (**b**) different configurations and (**c**) measured/simulated results of the antenna (redrawn from [72]).
## 4 Thick Ellipsoid State Estimation Algorithm
### Thick Ellipsoid Correction Step  
The correction step of the thick ellipsoid state estimator at the point \(k+1\) is given by the application of Theorem 5 with the following step-by-step procedure (in this description, we prefer the version of maximized inner bounds to reduce the likelihood of empty inner enclosures; however, the task of minimizing the outer volume can be stated in full analogy):  
1. Determine the inner shape matrix on the basis of Equation (47) according to \[\mathbf{\mathrm{Q}}_{k+1}^{\prime}=2\cdot\left(2\cdot\left(\left(\xi_{1,k+1}^{2} \cdot\mathbf{\mathrm{Q}}_{k+1}^{1}\right)^{-1}+\left(\xi_{2,k+1}^{2}\cdot\mathbf{ \mathrm{Q}}_{\mathrm{m},k+1}\right)^{-1}\right)\right)^{-1}\enspace,\] (80) where (based on the change of the ellipsoids' midpoint positions according to (43)) \(\mathbf{\mathrm{Q}}_{k+1}^{1}\) from (75) is the inner bound of the previous prediction; \(\mathbf{\mathrm{Q}}_{\mathrm{m},k+1}\) characterizes the measurement uncertainty (possibly given in terms of a degenerate ellipsoid).
2. Use Equation (48) to determine the intermediate outer bound \[\mathbf{\mathrm{Q}}_{k+1}^{\prime}=4\cdot\left(2\cdot\left(\left(\xi_{1,k+1}^{2} \cdot\mathbf{\mathrm{Q}}_{k+1}^{\mathrm{O}}\right)^{-1}+\left(\xi_{2,k+1}^{2} \cdot\mathbf{\mathrm{Q}}_{\mathrm{m},k+1}\right)^{-1}\right)\right)^{-1}\] (81) with \(\mathbf{\mathrm{Q}}_{k+1}^{\mathrm{O}}\) from (76).
3. Finally, Equation (49) yields the thick Dikin ellipsoid as the result of the measurement-based correction step.
# Model-Free-Based Single-Dimension Fuzzy SMC Design for Underactuated Quadrotor UAV
## 7 Conclusions  
The proposed system in this manuscript is a quadrotor unmanned aerial vehicle. This is known as one of the most underactuated systems because it has a smaller number of control inputs than degrees of freedom (DOF). This is a major issue that is very hard to control and stabilize. This manuscript shows a comparative performance analysis between sliding mode control (SMC), conventional fuzzy-based sliding mode control (FSMC) and single-dimension-based fuzzy sliding mode control without a model-free approach (SDF-SMC) and with a model-free approach (MFSDF-SMC).  
First, the paper addresses that the sliding mode control (SMC) algorithm is highly sensitive to external disturbances and unmodelled dynamic factors. Thus, by implementing this SMC design one may see that the four brushless DC (BLDC) motors of the quadrotor craft are experiencing a high number of oscillations known as Zeno phenomenon or chattering noise. The main reason for this noise is the design of manifold or sliding surface design for SMC algorithm.  
Secondly, to have a smooth sliding surface, the paper discusses the amalgamation of fuzzy logic control with SMC design, but this increases the execution time as well as the control input energy (CIE). This results in slow convergence of all error rates as well as sluggish maneuvers of quadrotor because of further sub-steps involved in FSMC algorithm such as fuzzification, inference of rules and defuzzification. In addition to this, both SMC and fuzzy-based SMC algorithms cannot enable the drone to follow the helical trajectory properly and generate some transient as well as steady-state issues. One may see the Zeno effect in both algorithms.  
Thirdly, the paper suggests the single-dimension fuzzy sliding mode control (SDF-SMC) design to tackle the unmodelled dynamic factors and deal with the high number of oscillations known as the Zeno phenomenon or the chattering effect. One can see the employment of a mathematical approach named as single-dimension method to turn the two-dimensional rules into a single dimension. In this technique, there is still noticeable diversion while tracking the helical trajectory in the presence of unmodelled dynamic factors but comparatively better transient and steady-state performance than SMC and conventional FSMC algorithms.  
Finally, the model-free approach is merged with single-dimension fuzzy-based sliding mode control (MFSDF-SMC). The simulation results in comparison with SMC, FSMC and SDF-SMC without model-free approach have been demonstrated. These results prove the robustness of the proposed algorithm for the stability and control of an underactuated quadrotor UAV under aggressive maneuvers and for tracking the helical trajectory in the presence of unmodelled dynamic factors and external disturbances.  
Figure 25: Flight mode of quadrotor within a closed environment.
## 5 Data Processing and Experimental Results: SAR Mode
### Data Processing
In particular, these approximations become unsuitable in the presence of large trajectory deviations or steep topographic variations [73; 75] thus impairing the accuracy of the overall focusing procedure [76]. To circumvent these kinds of problems, Back Projection (BP) focusing algorithms [77], which operate in the time domain, are more appropriate, since they do not carry out any approximation, at expenses of a computational efficiency lower than that ensured by the spectral domain focusing algorithms. In the case at hand, considering the strong track deviations affecting the SAR acquisitions, we applied the BP focusing strategy (see again Figure 9) by exploiting the information provided by the INS-GPS data and the SRTM DEM of the observed area.
# Metabolic Profiling of _Conyza sumatrensis_ ([PERSON].) [PERSON] from Lugazi, Uganda
## 3 Results and Discussion
### Compound Prediction with In Silico Techniques and Database Matches  
In addition to SIRIUS compound class prediction, we performed an automated search against the spectral libraries from GNPS and MassBank of North America (MoNA) (as described in Section 2.8) and mapped the hits in the network (see Table 3 for the database hits). SMILES codes of the library hit structures were provided by GNPS databases, and based on these SMILES, the structures were generated with ChemDraw (version 22.0.0) (Figure 3C). The structure predictions were validated by MS2 LDA analysis (see supplementary file Table S3). Therefore, an unsupervised substructure annotation of the fragmentation spectra was performed with the MS2 LDA Web app (22). This approach uses the Latent Dirichlet Allocation (LDA) algorithm to discover co-occurring fragments and neutral losses in MS/MS spectra (Mass2 Motifs) that are indicative of specific structural features. We found 15 clusters with at least three connected nodes, also referred to as molecular families, as well as 17 clusters with two nodes and 607 singletons. In the dominating Cluster 1 (Figure 3C), the prediction showed lipids and lipid-like molecules, including terpenoids and fatty acids. Three spectral matches showed acylated phytosterols in the ethanolic extracts. This correlates with the in silico prediction discussed above (Figure 4), suggesting the presence of lipids and lipid-like molecules in _C. sumutrensis_. Phytosterols are cholesterol-like compounds and include sterols and stanols, which differ in the structure of the alkyl side chain and the degree of saturation. It has been reported that a diet rich in phytosterols may reduce the risk of developing certain cancers in humans and animals (40); the ability of phytosterols to lower cholesterol levels in humans was already demonstrated in the 1950s (41). Cluster 2 was predicted to contain chlorins, mainly porphyrin metabolites, and two spectral library hits (pheophorbide A and 10 S-hydroxy pheophorbide) support these findings. Cluster 3 was assigned to quinic acid derivatives with three spectral matches, including cynarin. MS2 LDA analysis showed a cumaric acid-related pattern (rhamn_motif 28) for four nodes, which supports the findings. Most of the nodes in this cluster are present in both aqueous and ethanolic extracts. However, the node predicted as cynarin and another node with \(m/z\) 585.1238 are present only in aqueous extracts.
# Breath Analysis: A Promising Tool for Disease Diagnosis--The Role of Sensors
## 3 Types of Nanomaterial-Based Sensors in Breath Analysis
### Optical Sensors  
Optical gas sensors detect analytes by measurable changes of absorption, luminescence, scattering, reflectivity, refractive index or optical path length [40], due to the interaction of the radiation with the desired gas or a selective layer [143]. In the first two cases, light intensity or wavelength are measured [40].