esa-sceva/satcom-chunk-collection huggingface dataset:
LC in reversed phase (RP) or hydrophilic interaction (HILIC) enables the separation of a wide polarity range of contaminants commonly found in food at low concentration levels [11; 12]. LC coupled to UV has been adopted as the official AOAC method for quantifying patulin in apple juice [13]. The coupling with mass spectrometry (MS) detectors enables high multiclass pesticide detection [10]. The use of high-resolution mass spectrometry (HRMS) enables the effective screening and tentative identification of a large number of species even without the need for standards [14]. Prior to the instrumental determination, sample preparation is the most important step. Several sample preparation protocols have been proposed to isolate pesticide residues and mycotoxins from foods, employing liquid-liquid extraction (LLE); solid-phase extraction (SPE); supercritical fluid extraction (SFE); accelerated solvent extraction (ASE); quick, easy, cheap, effective, rugged, and safe (QuEChERS); and microwave-assisted extraction (MAE), as has already been reviewed [6; 15; 16]. Several microextraction and miniaturized extraction techniques, such as solid-phase microextraction (SPME), liquid-phase microextraction (LPME), gas purge microsyringe extraction (GP-MSE) [17], fabric phase sorptive extraction (FPSE), stir bar sorptive extraction (SBSE), dispersive solid-phase extraction (d-SPE), and magnetic solid-phase extraction (MSPE), have been proposed for the extraction of contaminants [18; 19]. The use of MAE presents several advantages, such as high extraction rate, automation, and the possibility of the simultaneous extractions of samples without interferences [20]. In recent years, the use of microwaves as a method of extracting ingredients, mainly from plant tissues, has been of great research interest. MAE is a process of using microwave energy to heat solvents in contact with a sample in order to transfer the substances to be analyzed from the substrate to the solvent, offering high sample throughput with low solvent consumption [21]. Extraction tests with the MAE method have also been reported for the mycotoxins ochratoxin A and zearalenone as well as for the group of cereal aflat. However, there are no reports of the application of this method to patulin, citrinin, and mycotoxins of the genus _Alternaria_ spp. [22; 23; 24]. The objective of this work was to develop and validate an MAE-LC-MS/MS analytical methodology for the simultaneous determination of two mycotoxins of the _Alternaria_ genus (AOH, AME), two mycotoxins of the _Penicillium_ genus (citrinin, patulin), and pesticide residues in apples. The use of MAE provided automation and rapidity during the extraction.
## 2 Materials and Methods
### Data Processing
#### 2.2.4 Positional Algorithms  
In this step we apply the positional algorithms requested. Three algorithms have been used during this experimentation.  
**(1) Triangulation:**  
The triangulation algorithm such as explain in [19] have been used as it was suitable to our problem. The only minor adjustment we made is that we take into account the zone between all intersection points as a potential position of the visitor. As we are working with data coming from a window of 5 s, a zone is more representative in this context.  
**(2) Trilateration:**  
The trilateration algorithm explained in [20] and often used in BLE based indoor positioning works such as [21; 22; 23] returned poor results in our case. It may come from the non-optimal placement of our sensors within the room (Such as the \"grid\" placement), the exclusive use of BLE signal and the 5 s window of data collection which is not suitable for accurate and precise localization in a single point \((x,y)\) of the visitor. In the majority of cases, the trilateration did not return any position.  
**(3) Minimal zone searching algorithm (MZS):**  
We also used our proposed algorithm, the MZS algorithm, which will be described in the next section. Considering all sensors within the room, it iterates until getting the minimal zone, representing the movement of a visitor.  
Figure 3: Impact of moving average method on raw data collected by one sensor.  
**(4) Recalibration:**  
We used a common method for all positional algorithms. When a calculated point such that circle intersection with the triangulation or a \(\Gamma_{s}\) point (The key part of the MZS that we will explain in the next section) is located outside the studied room, we put it down at the limit of this one as described in Figure 4.  
The angle line in Figure 4 connects a reference point with the point outside the room in order to determine the angle of the intersection with the limit of the room. This reference point is the second intersection point in the triangulation algorithm, and the corresponding \(\Gamma_{s}\) point for sensor s in the proposed algorithm. This simple method has offered significant results in our case.
## 2 Beam Dynamics Design
### Choice of Beam Dynamics Parameters  
#### 2.2.1 Input Beam Emittance  
At first, we should give the input beam emittance. Beam emittance characterizes the distribution of particles in a phase space, serving as a measure of beam quality. Emittance in a specific plane is linked to the area \(A\) occupied by all particles within that phase space, typically measured in mm\(\cdot\)mrad or cm\(\cdot\)mrad. In our design, the input emittances of the proton beam in the \(x\), \(y\), and \(z\) planes are determined by the output emittances of the ion source, which are \(\varepsilon_{x}=\varepsilon_{y}=0.020\) cm\(\cdot\)mrad, \(\varepsilon_{z}=0\).
# On the Study of Influences of Different Factors on the Rapid Topospheric Tomography
## 5 Tomography Experiments and Results  
SWD at 93 GNSS stations in Guangdong Province on the day 119 in 2017 and the meteorological observations (air temperature, pressure, and relative humidity) at GNSS station were simulated on the basis of ERA5. The tomographic initial values are from GPS forecast data. In this section, we will conduct tomographic experiments based on the simulated observations to study the influences of meteorological observation data, multi-GNSS observations, ground-based station distribution, and tomographic horizontal resolution on the tomographic results.  
The tomography region covers the whole Guangdong Province and surrounding area in south China with latitude from 20\({}^{\circ}\) N to 25.5\({}^{\circ}\) N and longitude from 109\({}^{\circ}\) E to 118.5\({}^{\circ}\) E. In the control case (CTRL), the horizontal resolution is set to be 0.5 degrees and the vertical space is divided into layers with resolution of 300, 500, and 1000 m for height below 3 km, between 3 and 5 km, and between 5 and 12 km, respectively, resulting in \(12\times 20\times 24=5760\) voxels in total. The tomographic interval is set to be 30 min.  
Besides the control case, seven experiments (CS01-CS07) as described in Table 1 are setup to study the influences of different factors on tomographic results. In Table 1, \"MET Obs\" means meteorological observations, \"Computational Time\" means the time required to calculate half an hour data (The tomographic results are output every half an hour) (PC configuration: CPU, Intel Core i7 - 7700 @ 3.60 GHz, 3.60 GHz; RAM, 8G), \"No\" means the specific observations are not included in the tomography, \"N/A\" stands for not applicable, and \"Measured\" and \"Simulated\" mean that the GNSS station coordinates are from actual station coordinates or from simulation, respectively.  
Figure 5: The root mean square (RMS) of the STD differences between simulated and Global Navigation Satellite System (GNSS) STD with satellite and satellite elevation angle.
## 3 Simulation Results
### Sea Surface Reflection and Transmission of Lidar  
Then, we applied our model for polarized lidar (i.e., \(S_{0}=[1,1,0,0]^{\text{T}}\)). Figure 6a-c present the Stokes parameters I, Q, and U of reflected light for the air-incident case, respectively. Figure 6e-g present the Stokes parameters of transmission light accordingly. Figure 6d,h present the DoP of reflected and transmitted light, respectively. Figure 7 shows the results of reflection and transmission of the ocean surface for the water-incident polarized lidar (\(S_{0}=[1,1,0,0]^{\text{T}}\)). Specifically, Figure 7a-c present the Stokes parameters I, Q, and U of reflected light for the water-incident case, respectively. Figure 7e-g present the Stokes parameters of transmission accordingly. Similarly, Figure 7d,h present the DoP of reflected light and transmission light of water-incident lidar, respectively. Compared with Figure 5a, Figure 6a shows that the reflectivity of polarized lidar was much lower than that of unpolarized light. In reality, the reflectivity varies with the polarization status of incident light. show the apparent depolarization of reflection. For the reflection of the air-incident case, the DoP reached  
Figure 5: Comparison of the reflectance of our model with [PERSON]’s. (**a**) Unidirectional reflectivity of the ocean surfaces vs. the zenith or nadir angle of the light source \(\theta_{0}\). Our calculations are shown as solid lines; [PERSON]’s outputs are highlighted with asterisks on dashed lines. (**b**) Absolute percentage deviation, with solid lines and dashed lines denoting the air-incident case and water-incident case, respectively. (**c**) Regression plots of outputs of air-incident case. (**d**) Regression plots of outputs of water-incident case.  
the minimum value near \(\theta=53^{\circ}\), which is the Brewster angle. As the wind speed increased, the reflectivity decreased, while depolarization increased. With a proper incident angle near \(53^{\circ}\) shown in Figure 6a, more energy would be transmitted into the water, which could expand the detective depth for oceanographic lidar.
# Phosphogypsum Recycling: A Review of Environmental Issues, Current Trends, and Prospects
Wet, freshly formed phosphogypsum has a low pH (because of the presence of water-soluble fluorine compounds: H\({}_{2}\)SiF\({}_{6}\), Na\({}_{2}\)SiF\({}_{6}\), K\({}_{2}\)SiF\({}_{6}\), and HF), traces of unwashed phosphoric and sulfuric acid, and phosphoric salts, and shows high corrosive activity. The issue of phosphogypsum disposal is still very relevant, for the following reasons:* The storage of phosphogypsum on the territory of the enterprise deteriorates the sanitary condition of the site and the adjacent territory;
* The transportation and storage of phosphogypsum in dumps are connected with rather high costs--about 18% of the cost of construction of phosphoric acid production itself--and they significantly increase during the transition to more reliable hydrotransport for phosphogypsum. Operating costs are approximately 12% of the cost of raw material processing [21];
* The need to alienate large areas to create dumps. These areas may exceed the size of industrial sites of enterprises;
* The exploitation of the dumps poses a potential threat to the environment and residential landscapes adjacent to the dump. It is noteworthy to mention that phosphogypsum valorization has still its limitations; particularly, because of the high cost of its removal, new alternatives must be found to reduce the use of land for phosphogypsum storage [22]. This paper aims to analyze the directions of phosphogypsum recycling (a by-product of phosphate fertilizer production) and possible alternatives to its use. To achieve this aim, the following tasks were set:  
* The analysis of phosphogypsum processing methods and highlighting key trends;
* The visualization of the network system by phosphogypsum research areas and consideration of alternative solutions for the disposal of phosphogypsum.
## 4 Discussion  
Risk analysis using the SORA method is an effective method of determining the risk of an operation and its mitigation. However, it is intended for civilian applications and it is necessary to adapt this method to the needs of rescuers using unmanned aerial vehicles. One of the biggest problems with this method is its time-consuming nature, as it takes many steps to complete it correctly. Another aspect that is very important for flight safety, which is not addressed by the SORA methodology, is the hazards characteristic of rescue operations, not occurring in civil applications. Examples of such threats include, among others:  
* Manned aviation--air medical assistance, water discharge planes, etc.;Environmental conditions during the action--dynamic physicochemical phenomena occurring during a fire, e.g., convective movements of hot air, fire gases and smoke, high temperature, humidity, etc.;
* Flight in difficult conditions (operator's stress) and changing weather conditions;
* Civil and commercial flights are planned and usually take place in good weather, and FR flights are not planned and take place when necessary;
* The methodology does not take into account safety-related aspects, e.g., intrusion of another unidentified UAV into the area of operation;
* Additionally, the SORA methodology does not take into account the use of a swarm of drones--a relatively new possibility of the simultaneous use of several UAVs at the same time, which cooperate. Such an application is also envisaged in the ASSISTANCE project, and the authors' team believes that such solutions will appear more and more often and more widely among various FRs.  
The above additional threats, occurring during the mitigation of catastrophes with the use of UAVs, indicate how important it is to use reliable UAV equipment by a qualified and experienced pilot.
## 2 Materials and Methods  
### Sample Preparation  
Undersized kiwifruits Actinidia chinensis cv \"Jintao\" (average weight of \(60.7\pm 1.6\) g, \(13\pm 1\)\({}^{\circ}\)Brix) were obtained from the Jingold Consortium (Cesena, Italy). Fruits were stored at \(4\pm 1\)\({}^{\circ}\)C until they were used, for a period not longer than one week. Fruits were washed, peeled and sliced in order to obtain discs of 3 mm thick.  
Kiwifruit discs were subjected to the OD process and/or PEF treatment with different combinations, selected on the basis of preliminary experiments, as reported in Table 1. Different sequences of the combined treatment were applied such as OD followed by PEF, PEF followed by OD or PEF followed by OD and again PEF treatment, in order to clearly understand the differences in terms of treatment effect through the different work sequences applied on the food product. OD was carried out in 40% (_w/w_) trehalose solution at 35 \({}^{\circ}\)C for 2.5 h, showing a good efficiency of the process, reflected in ahigh water loss/solid gain ratio in the range of 3.5-3.6.Trehalose was chosen because of its potential positive effect in the preservation of biomolecules and low variogenic potential when compared to sucrose [18; 19]. The treatment was performed in continuous stirring using a fruit/OD solution ratio of 1:4, in order to avoid concentration changes of the solution during the treatment [11].  
PEF treatment was applied by using a pulse generator, S-P7500 60A 8 kV (Alintel srl., Bologna, Italy). The following process parameters were used: electric field strength of 200 V/cm, and 1000 near-rectangular-shaped pulses, with a fixed pulse width of 10 \(\upmu\)s and a frequency of 100 Hz. The total treatment time was set to 10 s and total energy input was of 1.92 kJ/kg. Table 1 summarizes the pre-treatment applied and the samples obtained at each drying temperature.  
The differently pre-treated samples were subjected to air drying at 50, 60 and 70 \({}^{\circ}\)C, until a target water activity of 0.2 was reached. The drying was performed in a tray drier, CLW 750 TOP+ (Pol-Eko-Aparatura SP.J., Wodzislaw Slaski, Poland), with transverse air flow, air velocity of 2 m/s and an air renewal fee of 50%.  
The total time of the drying processes varied as a function of the applied pre-treatment and temperature, as reported in Table 2.
# Optimizing Mobile Vision Transformers for Land Cover Classification
## Appendix A Training and Testing Accuracy Values  
\begin{tabular}{l l l l l l l l l} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Split Category**} & \multicolumn{3}{c}{**Dataset**} & \multicolumn{3}{c}{**Split Category**} \\ \cline{3-10}  & & **20-80** & **40-60** & **50-50** & **60-40** & **20-80** & **40-60** & **50-50** & **60-40** \\ \hline \multirow{9}{*}{AID} & ViT & 99.25 & 99.1 & 99.14 & 98.82 & & 99.52 & 99.76 & 98.86 & 99.76 \\ \cline{2-10}  & MViT & 99.05 & 99.4 & 99.46 & 99.28 & & 98.57 & 98.69 & 99.76 & 98.95 \\ \cline{2-10}  & MViT-Depth & 99.45 & 99.62 & 99.46 & 99.9 & & 100 & 99.64 & 100 & 99.84 \\ \cline{2-10}  & MViT-Combined & 100 & 99.5 & 99.96 & 99.93 & & 100 & 100 & 100 & 100 \\ \cline{2-10}  & SWIN-Reg & 97.1 & 97.12 & 97.34 & 97.18 & & 75.71 & 90.48 & 92.38 & 98.25 \\ \cline{2-10}  & SWIN-Conv Block & 99.95 & 99.73 & 99.62 & 99.45 & & 100 & 99.88 & 100 & 99.92 \\ \cline{2-10}  & SWIN-ShuffleNet & 99.95 & 99.67 & 99.36 & 99.47 & & 100 & 100 & 99.9 & 99.84 \\ \cline{2-10}  & ViT & 99.8 & 99.5 & 99.6 & 99.44 & & 100 & 99.25 & 99.8 & 99 \\ \cline{2-10}  & MViT & 99.8 & 99.8 & 99.84 & 99.85 & & 98.51 & 99.25 & 99.8 & 96.35 \\ \cline{2-10}  & MViT-Depth & 99.78 & 99.87 & 99.75 & 99.63 & & 99 & 98.51 & 99.6 & 99.67 \\ \cline{2-10}  & MViT-Combined & 99.96 & 99.67 & 99.88 & 99.83 & & 99 & 100 & 100 & 100 \\ \cline{2-10}  & SWIN-Reg & 98.41 & 98.5 & 98.56 & 97.89 & & 88.06 & 95.27 & 94.82 & 96.02 \\ \cline{2-10}  & SWIN-Conv Block & 99.8 & 99.65 & 99.62 & 99.77 & & 100 & 100 & 100 & 100 \\ \cline{2-10}  & SWIN-ShuffleNet & 99.55 & 99.68 & 99.52 & 99.61 & & 100 & 100 & 99.8 & 100 \\ \hline \multirow{9}{*}{AID} & ViT & 56.71 & 66.22 & 68.7 & 71.5 & & 50.77 & 62.22 & 67.05 & 75.71 \\ \cline{2-10}  & MViT & 73.96 & 82.9 & 85.82 & 86.58 & & 63.39 & 81.35 & 87.14 & 87.62 \\ \cline{2-10}  & MViT-Depth & 75.29 & 84.28 & 87.44 & 88.25 & & 76.9 & 83.25 & 91.71 & 86.55 \\ \cline{2-10}  & MViT-Combined & 76.89 & 85.67 & 86.52 & 89.42 & & 80.48 & 88.02 & 92.1 & 90 \\ \cline{2-10}  & SWIN-Reg & 39.47 & 49.47 & 45.08 & 53.65 & & 35.06 & 42.7 & 50.57 & 55.12 \\ \cline{2-10}  & SWIN-Conv Block & 41.96 & 45.7 & 53.74 & 56.77 & & 41.37 & 48.41 & 60.1 & 58.1 \\ \cline{2-10}  & SWIN-ShuffleNet & 40.89 & 50.87 & 58.12 & 52.75 & & 44.4 & 54.76 & 57.43 & 56.43 \\ \cline{2-10}  & ViT & 91.78 & 92.65 & 92.97 & 94.43 & & 46.77 & 57.38 & 60.83 & 60.45 \\ \cline{2-10}  & MViT & 95.71 & 96.36 & 97.98 & 97.69 & & 66.04 & 79.77 & 81.31 & 64.18 \\ \cline{2-10}  & MViT-Depth & 92.08 & 97.71 & 97.67 & 96.49 & & 63.81 & 79.77 & 81.11 & 77.36 \\ \cline{2-10}  & MViT-Combined & 97.35 & 97.8 & 97.32 & 97.08 & WHU-RS19 & & 61.32 & 80.43 & 82.11 & 86.32 \\ \cline{2-10}  & SWIN-Reg & 86.48 & 89.6 & 90.8 & 90.77 & & 34.08 & 49.92 & 52.49 & 50.75 \\ \cline{2-10}  & SWIN-Conv Block & 86.03 & 85.61 & 89.65 & 85.04 & & 54.73 & 53.57 & 60.24 & 60.45 \\ \cline{2-10}  & SWIN-ShuffleNet & 87.23 & 92.07 & 91.92 & 91.94 & & 52.61 & 59.54 & 59.64 & 62.19 \\ \hline \hline \end{tabular}
## 2 Related Work
### Semantic Network and Knowledge Fusion  
As a formal knowledge representation, ontology plays an important role in knowledge acquisition, storage, sharing, and reuse. [PERSON] and [PERSON] [13] proposed an ontology of design knowledge for DR capturing. Following this, several studies on DR ontology were carried out [26; 35; 39; 44]. Semantic Web Rule Language (SWRL) is an expressive OWL-based rule language, which provides deductive reasoning capabilities. W3C's Semantic Web technology promotes the application of ontology in knowledge representation and acquisition. The knowledge graph was originally used by Google to improve search engine performance [56]. [PERSON] et al. [57] define the knowledge graph as a graph of data intended to accumulate and convey knowledge of the real world, whose nodes represent entities of interest, and whose edges represent relations between these entities. The semantic network and knowledge graph provide the basic method for ontology turtle modeling, which can be utilized to construct a formalized knowledge network. Knowledge fusion is an effective solution to the problem of acquiring knowledge from different sources [58]. Addressing the multi-dimensional, heterogeneous, and time series issues on the data collection of the manufacturing process, [PERSON] et al. [59] use the knowledge fusion process to disambiguate, integrate, and reason the knowledge, and to obtain high-quality knowledge after eliminating a series of redundant and erroneous information. The knowledge fusion process includes the entity alignment or cognition matching of entities, relations, and attributes, which is one of the fundamental techniques for building knowledge graphs [59; 60; 61]. [PERSON] et al. [62] proposed a BERT-based semantic similarity calculation method to realize the fusion of entities and relations in the knowledge graph.  
These ontology-based semantic webs, knowledge graphs, and knowledge fusion technologies are the basis for the DKN construction, which also provides operable methods for the improvement of knowledge networks.
## 4 Application of Methodology  
The implementation of the presented method was coded using Python language. The proposed methodology was tested on the KDD '99 and UNSW-NB15 datasets. The step-by-step implementation of the proposed methodology is explained in detail for KDD '99, which is the most widely used dataset in the literature. Only the steps of the KDD '99 dataset are given in detail, as the processing was performed using the same methods and its application to the UNSW-NB15 dataset is also briefly explained. The results obtained are presented in tables for both datasets. Test procedures on other datasets are also briefly explained in order to avoid repetition. The details of the applications made on the dataset are given in the following subsections.