esa-sceva/satcom-chunk-collection huggingface dataset:
# Kiwifruit Allergy--Molecular Basis, Diagnostics and Treatment
## 3 Protein Families
### Thuamatin-like Protein  
Thuamatin-like protein (TLP) is a major kiwifruit allergen, which has been registered as Act d 2 [40; 41; 42]. It is a 21-24 kDa single-chain protein. Two isoforms of Act d 2, which differ in their isoelectric point, have been reported. Both isoforms exhibit similar IgE binding ability in skin pick test responses, but their content varies depending on kiwifruit varieties. Belonging to the pathogenesis-related group 5 (PR-5) family, these proteins engage in plant defense mechanisms by inhibiting fungal growth [43]. The antifungal activity of Act d 2 has been demonstrated for _Saccharomyces Carlsbergenis_ and _Candida albicans_[40].  
Act d 2 exhibits high thermal stability and resistance to digestive degradation [44; 45]. Studies have shown that at least 25% of Act d 2 remained intact after in vitro gastric digestion, and it retained comparable IgE reactivity to untreated Act d 2 fractions [44]. However, the application of high-intensity ultrasound disrupted its secondary structure, decreased its solubility, and increased digestive degradation [46].  
A study reported above 60% specific IgE levels against Act d 2 in kiwifruit-sensitized subjects' sera and 50% positive skin pick test responses in tested patients [47]. Unlike Act d 1, Act d 2 is frequently a cause of cross-reactions due to the structural similarity of this protein with the homologous molecules contained in other fruits and vegetables [48]. The A-RISC index (Figure 2B) suggests likely cross-reactivity between Act d 2 and the TLPs from banana, mango, pepper, and golden kiwifruit. One study shows that the TLP from apple (Mal d 2) shares allergenic epitopes with peach (Pru p 2) and Act d 2 [49]. The cross-reactivity between Act d 2 and other fruit TLPs is also associated with PFAS [50]. Furthermore, association between Act d 2 and Alt a 1 (allergen from mold _Alternaria_) reactivity has also been frequently reported, such that 89% of Act d 2-sensitized subjects were also sensitized to Alt a 1 [1]. In fact, it is suggested that sensitization is an outcome of an electrostatic interaction between Alt a 1 and Act d 2, due to the mold penetration inside the fruit pulp [1].
# Dynamic Compensation of a Fluxgate Magnetometer Based on a Hybrid Optimizing Algorithm
## 4 Experiment and Discussion
### Experiment Process  
4.2.1. Calibration of the Geomagnetic Background Field  
As shown in Figure 18, the geomagnetic total field of the experiment area is calibrated by a proton magnetometer first, which is 48,845.53 nT. The single point stability of the geomagnetic field in this area is better than 1 nT, and the gradient is less than 2 nT, which ensures that this area is suitable for carrying out geomagnetic vector measurement experiments.  
It is measured that the magnetic field stability of this area is reliable. Therefore, in order to obtain the calibration value of the geomagnetic vector in this area, static vector sampling is carried out in the front, middle, and back sections of the truss, respectively, to obtain three groups of geomagnetic vector measurement values, and the mean value is taken as the calibration value of the geomagnetic vector. The calibration vectors' values are shown in Table 5. Bx is the north component of the geomagnetic field; By is the east component of the geomagnetic field; Bz is the vertical component of the geomagnetic field.  
Figure 17: The designed experiment system and truss.  
Figure 18: The calibration of the geomagnetic field.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Bx** & **By** & **Bz** \\ \hline Calibration value/nT & 35,034.99 & \(-\)2819.58 & 34,061.87 \\ \hline \end{tabular}
\end{table}
Table 5: Geomagnetic vectors’ calibration values.
## 4 Response Generation with Response Weight  
The proposed RGRW has a sequence-to-sequence architecture composed of a BERT encoder and a GPT-2 decoder as shown in Figure 2. The key feature of the generator is that the decoder is given a response weight \(\mathbf{a}\) as well as the vector representation \(\hat{\mathbf{q}}\) of a query \(\hat{q}\). As a result, the generator can make a response that follows both query and response contexts.  
The encoder is a pre-trained BERT, and it takes a query \(\hat{q}\) as an input and then outputs its vector representation \(\hat{\mathbf{q}}\). The decoder has a transformer structure, but the main difference between the decoder and the standard transformer is that it takes two kinds of inputs: \(\hat{\mathbf{q}}\) and \(\mathbf{a}\). When a decoder has multiple encoding inputs, their concatenation is often used as a single input [27, 28]. However, the concatenation of \(\hat{\mathbf{q}}\) and \(\mathbf{a}\) makes it difficult to grasp the key context of \(\hat{q}\) since the concatenation becomes just a lengthened representation of two similar encodings. Therefore, the proposed decoder has two individual attention layers that process \(\hat{\mathbf{q}}\) and \(\mathbf{a}\) sequentially.  
The first encoder-decoder attention layer uses \(\hat{\mathbf{q}}\) for both the key and value and the output of the self-attention layer for query. On the other hand, the second encoder-decoder attention layer uses a scalar multiple of \(\hat{\mathbf{q}}\) by an element of \(\mathbf{a}\) for both the key and value, that is, when the length of a query \(\hat{q}\) is \(m\), the key and value of the second encoder-decoder attention layer is the following:  
\[\hat{\mathbf{q}}^{\mathbf{a}}=\langle\hat{\mathbf{q}}_{1}^{a},\hat{\mathbf{q }}_{2}^{a},\dots,\hat{\mathbf{q}}_{m}^{a}\rangle, \tag{6}\]  
where \(\hat{\mathbf{q}}_{i}^{a}=a_{i}\cdot\hat{\mathbf{q}}_{i}\). The query of this layer is the output of the first encoder-decoder attention layer. Therefore, the decoder grasps the overall context of \(\hat{q}\) in the first encoder-decoder attention layer, and catches the response-related tokens of the query in the second encoder-decoder attention layer.  
RGRW has twelve decoder blocks as shown in Figure 2. Because the proposed structure is partially the same as that of GPT-2 [10], the parameters of the pre-trained GPT-2 are  
Figure 2: The structure of the proposed response generator, RGRW.  
borrowed to avoid excessive resource consumption. That is, only the two encoder-decoder attention layers boxed with red dotted lines in the figure are optimized during the training time, while other layers are all fixed. The AdamW optimizer [29] is used for optimizing the response generator with the cross entropy loss.
## 2 Related Works
### Cluster-Based Energy Consumption Optimization Approaches
In the phase of cluster head selection, we consider the total residual energy and energy balance degree of the cluster head candidate nodes, and the two parameters can effectively guarantee the energy consumption balance of network whereas the existing approaches only consider the distance parameters or residual energy [33; 34; 36; 37; 38]. We also propose a cluster formation algorithm in which the member nodes join in a cluster head by a weight function. However, in the existing approaches [33; 36; 37; 38; 23], the member nodes join the cluster head by only considering distance, and it may cause imbalance load of cluster head energy consumption. energy consumption optimization approaches cannot effectively guarantee energy consumption balance for network with different size because they usually only consider distance parameters and residual energy in the cluster head selection phase. In this paper, we take both area coverage quality and energy consumption into account and propose an approach based on IAPSO to achieve high coverage ratio and low redundancy ratio and energy consumption balance for DSN. The contributions of this paper are as follows:  
1. We propose a multi-objective area coverage optimization model which considers coverage ratio and redundancy ratio in order to reduce coverage blind areas and coverage redundant areas. This model is suitable for the scenario where the target area is multiply covered. 2. We propose a cluster head selection optimization model which considers the total residual energy ratio and energy balance degree of the cluster head candidate nodes to guarantee energy efficiency. We also propose an energy efficiency algorithm in the cluster formation phase. 3. We utilize an improved adaptive particle swarm optimization (IAPSO) to solve multi-objective area coverage optimization model and cluster head selection optimization model to achieve high coverage ratio, low redundancy ratio and energy consumption balance. Compared to traditional PSO, IAPSO has higher convergence ratio and operator precision.
## 3 Results and Discussion  
### Testing Datasets  
Three datasets were selected for testing the methodology, which are characterized by different statistical and probabilistic characteristics:  
* A load profile extracted from a house located in the region of Ruse, Bulgaria, in the period 1-29 February 2024 (Figure 6a). It is characterized by power consumption varying in a wide range and significant peaks during the weekends. Residential load profiles are generally influenced by many factors, such as the type of day (weekday or weekend), hour of the day, meteorological conditions, people's lifestyle, holidays, etc.
* A load profile of a pig farm located in the region of Silistra, Bulgaria, in the period 1-31 August 2023 (Figure 6b). It is characterized by relatively similar daily variations, which can be explained by the agrotechnological requirements. Load profiles in livestock farming are influenced mostly by the schedule of the technological processes (lighting, feeding, ventilation, etc.) and the meteorological conditions.
* A load profile of a printing house, located in the region of Varna, Bulgaria, for the period 1-29 February 2024 (Figure 6c). It is characterized by significant electrical consumption during the working hours of the weekdays and almost zero consumption during the rest of the time. Industrial load profiles are greatly influenced by factors such as the type of day (weekday or weekend), hour of the day, daily schedule, holidays, and the meteorological conditions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Month of the Year** & **Hour of the Day** & **Power, kW** & **Comment (Not Part of the File)** \\ \hline
11 & 17 & 2.4 & Power consumption for 17:15 \\
11 & 17 & 2.48 & Power consumption for 17:30 \\
11 & 17 & 2.28 & Power consumption for 17:45 \\
11 & 18 & 1.6 & Power consumption for 18:00 \\ \hline \hline \end{tabular}  
* The \"Analyze Data\" button estimates 24 hourly and 24 h-change transition matrices, based on the provided dataset and the defined number of states (in the \"Number of states\" field).
* The \"Generate Data\" button generates synthetic data for the defined number of days (in the \"No days\" field) and month (in the \"Month\" field) with a 15 min time step. The synthetic data are automatically exported in a tab-delimited file.
* The buttons “Export matrices of training data” and “Export matrices of generated data” export the hourly and hour-change transition matrices of the training and synthetic datasets, respectively.
* The button “Compare using Frobenius distance” estimates the Frobenius distances between each two corresponding matrices of the original and generated datasets and exports them in a tab-delimited file.
* The button “Compare using R2” estimates the \(R^{2}\) values between each two corresponding matrices of the original and generated datasets and exports them in a tab-delimited file.  
The statistical analysis and the graphical representations of the data metrics and data fragments were implemented in Microsoft Excel 2021, v. 2108.
## 3 The Biosynthesis of Artemisinin (Qinghaosu)  
Terpene biosynthesis [271-276] and its regulation [277,278] in _A. annua_ have been well reviewed, including the central role of amorpha-4,11-diene (**451**) in the biosynthesis of artemisinin (**495**) [279]and the likelihood that glandular trichomes are the location wherein artemisinin biosynthesis actually occurs [244,280] (indeed, it has recently been concluded that artemisinin biosynthesis occurs in the two outer apical cells of the glandular secretory trichomes [104] - the glandular trichomes of _A. annua_ are comprised of 10 cells in total). The biosynthesis of artemisinin will be considered in three phases, as depicted in Scheme 9.  
It should be emphasised that this view of the biosynthetic route to artemisinin is still not universally accepted. Although there is increasing experimental evidence in support of its general correctness, there are a significant number of experimental results (particularly in the \"older\" literature) which might appear to contradict Scheme 9. Thus, for many years, it was assumed that artemisinin acid (R=CH\({}_{2}\) for **473** in place of R=CH\({}_{3}\) for **480** in Scheme 9) [148,215,251,281-284], rather than dihydroartemisinin acid (**480**; R=CH\({}_{3}\)), was the late-stage precursor to artemisinin at the juncture between phases 2 and 3.  
**Scheme 9.** Three phases in the biosynthesis of artemisinin (**495**).  
A large number of other cadinane and amorphane sesquiterpenes, including: artemisinin B (**462**) [248,285]; _epi_-deoxyarteannuin B (**478**) [286]; dihydroarteannuin B (**479**) [249]; dihydro-_epi_-deoxyarteannuin B (**485**) [286]; the _seco_-cadinane (**494**) [217]; and artemisinin (**497**) [211,248,284] have also been suggested as late-stage intermediates at or around this point in the biosynthesis (in addition, \(\alpha\)-epoxyartemisinic acid (**487**) has been stated not to be a biosynthetic intermediate to artemisinin [194]). The experimental evidence for each of these precursors is discussed individually in some detail in Section 3.3 (phase 3 of the biosynthesis of artemisinin). However, as the length of the foregoing list shows, there have been so many different proposals for phase 3 of the biosynthesis, that not all of them can be correct. In fact, none of **462**, **478**, **479**, **485**, **494** or **497** feature in the most likely biogenetic route to artemisinin (**495**) which is discussed first in Section 3.3, although many of these compounds have been implicated as side-products, in reactions which diverge away from this main biosynthetic route to (**495**) (see Schemes 5, 6 and 7 in Section 2.6.3 for example).
## 4 Control Algorithm  
### Leader-Follower Swarm-Control Algorithm  
Determination of goal formation between USVs is crucial for leader-follower swarm control. The primary motivation for researching swarm control of USVs in this study was that it enabled the use of multiple entities in missions, such as underwater surface exploration in the target area using an imaging sonar [15; 16]. The utilization of multiple entities for exploration is advantageous in terms of time, energy, and efficiency. However, if the swarm formation is nonlinear, i.e., it morphs into different shapes, post-processing steps may be required after the mission. Therefore, a linear formation is preferred for exploratory missions as it helps streamline subsequent data processing and other procedures after mission completion.  
Therefore, in this study, a (\(d\), \(\psi\)) formation control that can theoretically generate a linear swarm formation was employed as the swarm-formation controller. However, platforms operating in the marine environment, regardless of the sensitivity of the designed controller, may experience slower thruster responses due to various coefficients between water and air, such as fluid viscosity and friction coefficients. Consequently, if the swarm controller is used directly, maintaining a linear relationship between the leader and the followers becomes impractical.  
To address this issue, in this study, a virtual point based on the leader USV and another virtual point based on the target point (waypoint) were trialed to generate the mission trajectory for the follower USVs. The virtual point based on the leader USV was employed to maintain a constant distance between the leader and its followers, while the one based on a waypoint was used to create the mission trajectory for the follower USVs. Figure 3 illustrates a schematic depicting the relationship among the leader, follower, and generated virtual points.  
Figure 3: Relationship diagram of leader–follower USVs and virtual points.
# NaYF4 Microstructure, beyond Their Well-Shaped Morphology
## 3 Results and Discussion
### Microstructural Analysis
\begin{table}
\begin{tabular}{l l l l l l l} \hline  & \multicolumn{2}{c}{**TEM Size (nm)**} & \multicolumn{1}{c}{**AR**} & \multicolumn{1}{c}{**Gd**} & \multicolumn{1}{c}{**Crystallite Size (nm)**} \\  & \multicolumn{1}{c}{**L**} & \multicolumn{1}{c}{**D**} & \multicolumn{1}{c}{**(mol\%)**} & \multicolumn{1}{c}{**min**} & \multicolumn{1}{c}{**max**} \\ \hline NR01 & 835 & 113 & 7.4 & 0 & 77 & 100 \\ NR02 & 1390 & 234 & 5.9 & 0 & 143 & 153 \\ NR03 & 718 & 108 & 6.6 & 5 & 79 & 180 \\ NR04 & 935 & 160 & 5.8 & 5 & 132 & 142 \\ NR05 & 375 & 70 & 5.4 & 15 & 65 & 77 \\ NR0 As previously said, the smallest length of the crystallites is associated to the directions orthogonal to c-axis. Moreover, for NPs with a diameter below 100 nm, this length is almost always slightly smaller of few nanometers than the diameter of the NPs. This fact was explained with the HRTEM observation. In Figure 7a,b, we observed an amorphous phase or a less crystallized shell around the NPs. Another observation, more surprising from our point of view, is the possibility to have a well crystallized phase in an orthogonal direction surrounding the core of the NPs. The inset of Figure 7a shows the FFT of the area delimited by the dotted green line. This FFT presents a rectangular signal characteristic from an hexagonal structure seen orthogonally of the c-axis. Even though the NPs in Figure 7c is a rod well defined, the FFT of the area delimited by the continuous red line is characteristic from an hexagonal phase through the c-axis. Note that the crystallinity of the NPs presented is not altered by the electron beam of the TEM. This observation is important if we want to analyze the luminescence of the NPs. It is well known that lanthanide ions have a specific luminescence correlated with their local environment. For a structure like P6\({}_{3}\)/m, the doping ions have access to only one type of site as mentioned in Table 2. The luminescence and in particular the polarized properties will be depending of this site (here \(C_{3h}^{2}\)) [28]. But if the crystallinity of the NPs is not good meaning polycrystalline nanorods instead of monocrystalline and the lanthanide-site are not well aligned with a well-defined c-axis along the whole rod, the optical properties will be affected starting with a reduction of the polarization degree. Moreover, for singles NPs studies, due to statistical variation from one NP to another, it will be difficult to analyze the optical properties.
# Variations in the Equatorial Ionospheric F Region Current during the 2022 Tonga Volcanic Eruption
###### Abstract  
In this work, the temporal variations in the ionospheric F region current (FRC) during the 2022 volcanic eruption in Tonga are reported for the first time. The FRC increased and underwent several polarity reversals following the eruption, as observed by the dual Swarm satellites. By combining neutral wind observations derived from the Ionospheric Connection Explorer (ICON) satellite, we attempt to reveal the potential physical mechanisms responsible for these variations. The ICON and Swarm satellites were located at almost the same distance from the epicenter of the volcanic wave, thus providing a good opportunity for analyzing coordinated observations. The F-layer neutral wind data reflect eastward and westward fluctuations that are basically consistent with the polarity reversals identified in the FRC. The E-layer neutral wind also exhibits polarity reversals that affect the equatorial ionosphere electron density anomaly. These results show that the atmospheric fluctuations caused by the analyzed volcanic eruption affected the wind pattern distribution in the E-F region, thus further affecting the spatial distribution of the FRC and electron density in the F layer.  
ionospheric radial current; neutral wind; equatorial ionosphere electron density anomaly; volcanic eruption wave 2022146241 [[https://doi.org/10.3390/rs14246241](https://doi.org/10.3390/rs14246241)]([https://doi.org/10.3390/rs14246241](https://doi.org/10.3390/rs14246241))
# Detection of Precipitation and Fog Using Machine Learning on Backscatter Data from Lidar Ceilometer
## 4 Weather Detection
### Data Analysis  
The observational data of AWS range from 1 January 2015 to 31 December 2016. The performance of learning algorithms may decrease if they are not provided with enough training data. For precipitation, the presence or absence of precipitation in the AWS hourly data was used. In general, the lower the visibility sensor value was, the higher the probability of fog was. Hence, the visibility sensor data were categorized into 1000 m or below, between 1000 m and 20,000 m, and 20,000 m or higher. Table 7 shows that precipitation data account for 6.38% of all data (1120 cases), and Table 8 shows that visibility sensor data that fall into 1000 m or below account for 1.16% of all data (11,082 cases).  
The precipitation data has a value of 0 or 1, indicating only presence or absence, thus forming a binomial distribution. However, the visibility sensor values range from 0 m to 20,000 m and the data distribution can be represented as a histogram in Figure 1. The MOR in the figure indicates the visibility sensor value, and the values at the bottom of the figure are the mean (\(\mu\)) and standard distribution (\(\sigma\)) of all visibility sensor values. The line indicates a normal distribution, and the values on the \(X\) axis are the values obtained by adding or subtracting the standard deviation to or from the mean.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Field** & **Missing** & **Precipitation** & **Non-Precipitation** & **Total** \\ \hline \#data & 558 & 1120 & 15,866 & 17,544 \\ Percentage & 3.18\% & 6.38\% & 90.44\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Statistics of hourly AWS data related to precipitation.  
Figure 1: Histogram on visibility data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Field** & **20,000 m** & **[0 m, 1000 m]** & **(1000 m, 20,000 m)** & **Total** \\ \hline \#data & 84,767 & 11,802 & 921,553 & 1,018,122 \\ Percentage & 8.33\% & 1.16\% & 90.51\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Statistics of visibility data.  
The observation interval of ceilometer backscatter data is irregular at approximately 30 s. The observation interval of AWS data is irregular at 1 h, and the observation interval of visibility sensor data is irregular at 1-3 mins. The intervals of observational data were adjusted to those of the backscatter data.