esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background and Hypotheses
### Diffusion of Innovation Theory (DOI)  
Due to the rapid changes in technology, several studies have recommended integrating TAM with DIT to better understand the users' adoption patterns [23; 24]. The diffusion of innovation theory (DOI) could be considered as one of the most popular theories that have attempted to predict factors that affect an individual to adopt an innovation or a new technology [25; 26]. DIT is an extensive social and psychological theory that seeks to explain the stages that show how new ideas and technology spread among people in different societies, climates, or culture environments. [PERSON] identified five attributes of innovation that are considered to determine the rate of innovation adoption behavior, which are relative advantage, compatibility, complexity, trialability, and observability [25]. According to [PERSON], relative advantage is \"the degree to which an innovation is perceived as being better than the idea it supersedes\" [25]. Compatibility refers to \"the degree to which a service is perceived as consistent with users' existing values, beliefs, habits, and present and previous experiences\" [25]. Trialability is \"the degree to which an innovation may be experimented with on a limited basis\" [25]. Observability is \"the visibility of the innovation\" [25]. Compatibility is a vital feature of innovation as conformance to the user's lifestyle can advance a rapid rate of adoption [27]. An idea that is incompatible with the users' values or needs will not be rapidly adopted compared to an innovation that is compatible. Researchers indicated that compatibility is a significant antecedent in determining consumers' attitude towards internet banking adoption in Malaysia [28]. Compatibility had a significant correlation with computer adoption and use of mobile banking in Saudi Arabia [29]. Based on the literature discussed above, we added compatibility into the expanded TAM model and hypothesize the following:  
**H5.**_Switch user's perceived compatibility will have a positive effect on the perceived ease of use. [COM\(\rightarrow\)PEOU]_
# The Influence of Ionizing Radiation on Paclitaxel-Loaded Nanoparticles Based on PLGA
## 2 Materials and Methods
### In Vitro Drug Release Study  
In vitro drug release tests were performed using the dialysis method on free PTX as a control and PTX-loaded nanospheres that were (a) non-irradiated, (b) irradiated with \(\gamma\)-rays, and (c) irradiated with EB. PBS solution (pH = 7.4), containing Cremophor(r) EL (0.5% (\(w/v\)) as a solubilizer, was used as a release medium. The suspensions were maintained at 37 \({}^{\circ}\)C while stirring at 100 rpm (ES-20, Biosan, Riga, Latvia). The amounts of PTX released from the samples were chromatographically determined at \(\lambda\) = 229 nm, according to Section 2.11.  
The results of the PTX release were analyzed according to zero-order, first-order, Higuchi and Korsmeyer-Peppas mathematical models, as shown in Equations (4)-(7) [24; 25].  
Zero order:  
\[F=kt \tag{4}\]  
First order:  
\[logF=logF_{0}-\frac{kt}{2.303} \tag{5}\]  
Higuchi model:  
\[F=k\sqrt{t} \tag{6}\]  
Korsmeyer-Peppas model:  
\[F=kt^{n},\ for\ F<0.6 \tag{7}\]  
where \(F\) is the fraction of drug released in time (\(t\)), \(F_{0}\) is the initial concentration of the drug, \(k\) is the model constant, and \(n\) is the release exponent.
## 6 Conclusions and Ongoing Work  
A digital beamforming radiometer has been completely developed. This achievement includes the hardware, calibration algorithms, and control software. A comprehensive set of tests have been carried out to understand the capabilities of the instrument: Simultaneous multi-angular measurements can be obtained, and the hardware calibration is satisfactory provided. Receivers' amplitude and phase can be equalized by using a NCO, which is also used to steer the DBF. System stability allows inter-calibration periods to be longer than 30 minutes. First results of the DBF have been presented for the vertical, horizontal and the correlation polarization, fulfilling the design requirements and satisfactorily showing the capabilities of this new instrument and its synthesized beams.  
Next steps will be the preparation of the instrument for ocean and land measurements.
## 2 Establishment of the Seasonally Frozen Ground-Pipeline System Model
### Basic Assumptions and Fundamental Governing Equations of the Physical Fields
#### 2.2.3 Moisture Field Governing Equations  
The distribution of the moisture field in frozen ground is closely related to moisture migration, water content, porosity, concentration, soil-water potential, and temperature distribution. Moisture migration is the controlling process of the moisture field distribution and is the fundamental cause of frost heave and thaw settlement. Assuming that the moisture migration law in frozen ground is similar to that in thawed soil, this paper adopts the Richards equation, which considers the water-ice phase change as the differential equation governing moisture migration:  
\[\frac{\partial\theta_{u}}{\partial t}+\frac{\rho_{I}}{\rho_{w}}\frac{\partial \theta_{I}}{\partial t}=\
abla[D(\theta_{u})\
abla\theta_{u}+k(\theta_{u})] \tag{5}\]  
In Equation (5), \(D(\theta_{u})\) is the diffusion rate of water (m\({}^{2}\)/s), calculated using the formula \(D(\theta_{u})=k(\theta_{u})/c(\theta_{u})\cdot I\); \(I\) represents the impedance factor \(I=10^{-100t}{}_{i}\); \(k(\theta_{u})\) is the permeability coefficient of the soil (m/s), indicating the ease with which fluid migrates through the pore framework.  
During the freezing process of water, moisture migration is hindered by ice, and the permeability coefficient changes over time, as well as with the temperature of the frozen soil, ice content, and liquid water content. The expression is  
\[k(\theta_{u})=k_{s}\cdot S^{l}\Big{(}1-\Big{(}1-S^{1/m}\Big{)}^{m}\Big{)}^{2} \tag{6}\]  
\(k_{s}\) is the permeability coefficient of saturated soil; \(c(\theta_{u})\) is the specific water capacity (\(1/m\)), and the calculation method is as follows:  
\[c(\theta_{u})=a_{0}m/(1-m)\cdot(\theta_{s}-\theta_{r})\cdot S^{1/m}{(1-S^{1/m })}^{m} \tag{7}\]where \(a_{0}\), \(m\), and \(l\) is a constitutive parameter that varies with soil type; \(S\) is the relative saturation of the frozen soil:  
\[S=(\theta_{u}-\theta_{r})/(\theta_{s}-\theta_{r}) \tag{8}\]  
in which \(\theta_{r}\) is the residual moisture content, \(\theta_{s}\) is the saturated moisture content. In the calculations, \(S\) is chosen as the independent variable for hydrothermal coupling solutions.  
In the heat conduction equation and the moisture migration equation, it is necessary to introduce the solid-liquid ratio \(B(T)\) as a coupling term to link the hydrothermal equations. This ratio represents the volume ratio of ice to unfrozen water in the soil, and the calculation formula is:  
\[B(T)=\frac{\theta_{I}}{\theta_{u}}=\begin{cases}1.1\Big{(}T/T_{f}\Big{)}^{B}- 1.1&T<T_{f}\\ 0&T\geq T_{f}\end{cases} \tag{9}\]  
where \(T_{f}\) represents the freezing temperature of the soil (\({}^{\circ}\)C); \(B\) is a constant that varies with soil type and salinity.
## 1 Introduction
introduced DeepmRNALoc [17], a model that applies deep learning technologies to precisely predict mRNA localization in various subcellular compartments. By leveraging neural networks, this model demonstrated significant improvements in predictive performance and offered new opportunities for RNA localization research. [PERSON] et al. proposed the DM3 Loc model [18], focusing on multi-label mRNA subcellular localization prediction. This model addressed the challenge of predicting mRNA that localizes to multiple subcellular compartments simultaneously, introducing innovative strategies for multi-label learning in biological applications. [PERSON] et al. introduced the Clarion model [19], another notable tool for multi-label mRNA localization prediction. The Weighted Series (WS) method was introduced as the ensemble framework for Clarion. This approach incorporates prior information about the labels during the model training process and improvesprediction performance by optimizing the weight parameter, balancing the contributions of the non-label module and the fusion-label module. Currently, only two prediction models, DM3 Loc by [PERSON] et al. and Clarion by [PERSON] et al., consider the possibility that mRNA may simultaneously localize to multiple subcellular locations. Although some prediction models and computational methods have emerged in the related field, there are still many shortcomings in understanding the precise location and function of mRNA, especially in multi-site prediction. Additionally, traditional feature extraction methods are not effective in capturing and representing sequence information. To address these issues, this paper proposes a novel multi-site prediction model called CSpredR. This model converts sequences into graphs and uses a combination of two encoding methods to extract features from each graph node. It then employs a three-layer neural network model to extract high-level features from the graph. Finally, it uses an MLP to process the feature data and complete the prediction task. We conducted extensive experiments to evaluate the performance of CSpredR. By using different encoding methods and network structures, we demonstrated the effectiveness of CSpredR in predicting mRNA subcellular localization. CSpredR has two major advantages: (1) Using two encoding methods to extract features from graph nodes enriches the semantic information of the graph. (2) CNN effectively extracts local features, while Bi-LSTM excels at capturing long-term dependencies and global context information. Combining these two allows the model to focus on local details while considering the global context, leading to a more accurate understanding of the sequence data. By using the outputs of CNN and Bi-LSTM as inputs to the multi-head attention mechanism, the model can further integrate these features and generate a more robust and rich representation.
## 4 Methodology
### System Architecture  
The overall system architecture is meticulously designed to enable seamless interaction between the user and [PERSON]. The interaction process is initiated when the user records a voice message through a mobile application. When the user presses the recording button on the mobile interface, a signal is sent to activate [PERSON]'s camera. The camera begins capturing frames of the user's facial expressions with a slight temporal delay, which are then passed through a CNN to analyze the predominant facial emotion over the recording duration. Simultaneously, the system records the audio message. Once the recording is complete, the audio is processed by a separate CNN to extract auditory emotions, while the text transcription of the audio is analyzed for textual emotion detection. These modalities are integrated to determine the overall emotional state of the user during the interaction. Once the recording is complete, the system processes the audio data, transcribed text, and visual inputs to ascertain the predominant emotion expressed by the user. The transcribed text undergoes intent analysis to determine the user's request, which then directs [PERSON] to perform corresponding actions such as setting reminders, controlling movement, initiating chatbot conversations, or activating object recognition functionalities. Figure 1 provides a visual representation of the system's architecture.  
Figure 1: Interaction flow diagram.
## 3 ATON Framework
### Access, Manage and Publish Content  
The framework provides a built-in authentication system that allows content creators and publishers to access, manage and modify their own collections and scenes on the deployed instance. The most basic setup allows to place and organize such content directly in the main collection folder of the DN (e.g., 3D models, audio, panoramas, etc.). The framework offers a built-in lightweight, responsive back-end (\"_Shu_\") where authors, editors or content creators can authenticate to publish and manage 3D scenes with ease. The local authentication middleware is based on passport.js, allowing a fine-grained control on requests involving content access, modification or other tasks. Furthermore, passport.js middleware offers a wide range of integrations with different authentication strategies (Facebook, Google OAuth, and many more) thus providing a flexible and extensible system for more advanced back-ends.  
Shu allows authenticated users (Figure 5A) to create online scenes with ease starting from their collections (or remote content) in private galleries (Figure 5B). Authenticated users can then publish their scenes on the main landing page (Figure 5D,E) with public access, thus allowing remote users to consume the 3D scene on every device. The landing page also provides a search box to filter public scenes by author, term or keyword, very useful to create custom galleries (e.g., collection of museum objects). Administrators have additional control on the instance and web-apps currently hosted by the framework (Figure 5C). The standard workflow thus involves authenticated users to upload, manage or modify their content into collections, then arrange and publish a 3D scene. Several options enabling remote access to local collections and scenes are available, although a very flexible and comfortable solution for content creators, editors and publishers is the cloud integration (see assessment section). Indeed, the REST API and the modular structure of the framework allow the development of custom or more advanced back-end solutions to access and manipulate content.
# Reducing the Uncertainty of Lidar Measurements in Complex Terrain Using a Linear Model Approach
## 2 Materials and Methods
### Test Site and Measurement Campaign
#### 2.1.1 Local Conditions  
This site has been the subject of many coordinated studies [16; 18; 19; 20]. A small remotely-piloted aircraft was used to measure the flow conditions [18]. Detailed Detached-Eddy Simulations (DES) of the flow have been validated using different measurement methods [15; 16]. Wind tunnel tests and CFD simulations have also been to model the flow over the site [20].  
The wind at this site is mostly from the west to north-west (Figure 2). Around 38% of winds are perpendicular to the _Albrauf_, and so changes in wind direction of 10\({}^{\circ}\) can result in significantly different inflow profiles downwind of the _Albrauf_[21].  
The goal of this study is to compare the data from the met mast and the lidar to assess different measurement and data processing approaches. It is therefore essential that there is minimal external influence on the results, for example as a result of differences in flow between the lidar and tower that might be introduced by the ground cover or the terrain. The impact of these differences can be judged using the International Electrotechnical Committee (IEC) 61400-12-1 Standard (2005, [22]) for power performance testing of wind turbines, which quantifies the potential impact of obstacles as a function of size and distance. At 1400 m south-west of the mast, there is a small forest (not visible in Figure 1), which, according to the Standard, is far away enough that it should not have an influence. East of the met mast, the terrain rises again up to a height of 720 m above sea level. There are also several commercial wind turbines near to the mast which could influence the flow. These obstructions would therefore reduce the free stream sector (according to [22]) from 171\({}^{\circ}\) to 341\({}^{\circ}\). However, this large a sector still introduces the chance for flow differences between the devices.  
Other studies of this site can be used to understand the polar location and the met mast. The vertical wind profile at the points M1-M4 in Figure 1a was simulated for wind from 295\({}^{\circ}\) using CFD that was validated against measurements from an unmanned aerial vehicle [15; 16]. Results from the CFD (Figure 3) showed a speed-up of \(\approx\)20% at 100 m above ground at M4, closest to the escarpment. At M3 the speed-up factor at 100 m above ground is \(\approx\)1.15, while at M1 the speed up effect has disappeared and the vertical profile is fully recovered. From these profiles it can also be seen that the 100-m wind speed at M2 is within 2% of M1, i.e., the mean wind speed difference between M2 (near the lidar) and M1 (near the met mast) is minimal. Based on the results from the CFD study, it was decided to limit the data used to a 70\({}^{\circ}\) inflow wind direction sector from 245\({}^{\circ}\) to 315\({}^{\circ}\).  
Figure 2: Windrose at the experiment site.
# Distinct Signatures of Host Defense Suppression by Plant-Feeding Mites
## 2 Results
### Steroidal (glyco)alkaloids  
Solanaceous plants typically produce steroidal alkaloids. These nitrogen-containing secondary metabolites are commonly glycosylated, presumably to reduce their autotoxicity, forming steroidal glycoalkaloids (SGAs) [107; 108]. Steroidal (glyco)alkaloids are generated from cholesterol [108] (which in turn is produced from the triterpene (C\({}_{30}\)) squalene [109]) via a series of hydroxylation, oxidation, transamination, reduction and/or glycosylation reactions that are catalyzed by various GLYCOALKALOID METABOLISM (GAME) enzymes, of which ten have been characterized in tomato thus far [107; 108; 110; 111]. In addition, the JA-responsive transcription factor GAME9/JRE4 functions as a master regulator of steroidal (glyco)alkaloid metabolism by controlling, either alone or in association with MYC2, the expression of genes coding for key enzymes in the MVA, cholesterol and SGA biosynthesis pathways [112; 113; 114].  
We observed that expression of eight out of nine _GAME_ genes present on our microarray was down-regulated in tomato leaves dual infested with _T. urticae_ and _A. Iycopersici_ (Figure 7). Although the FCs were smaller, the same genes were down-regulated upon single infestation with _A. Iycopersici_, for six of them this down-regulation was statistically significant. _GAME9/JRE4_ was the most strongly down-regulated _GAME_ gene in both Tu+Al and Al samples, followed by _GAME1_ and _GAME18_. The latter two code for glycosyltransferases that are responsible for two out of four sugar-attachment steps required for the conversion of timatidine into \(\alpha\)-tomatine, which is the major SGA present in green tomato tissues [107; 108]. The genes encoding the other two glycosyltransferases, _GAME2_ and _GAME17_[108], are not present on our microarray. Expression of some _GAME_ genes appeared to be down-regulated in the Tu samples, but this was only statistically significant for _GAME9/JRE4_. No changes were found in the Te samples. We verified the expression patterns of _GAME1_ and _GAME9/IRE4_ with qPCRs (Figure S2C,D). Very similar expression patterns were found for the putative cholesterol biosynthesis genes [109] (Figure 7) as well as for genes encoding enzymes of the upstream MVA pathway, as discussed earlier (Figure 6). Our results suggest that the steroidal (glyco)alkaloid contents of tomato leaves may be reduced and the sterol composition altered upon infestation with _A. Iycopersici_, possibly with _T. urticae_ as well, and that those changes are most severe when mites from both species simultaneously attack the plant.
# A Tri-Satellite Interference Source Localization Method for Eliminating Mirrored Location
###### Abstract  
With the increase in satellite communication interference, the tri-satellite time difference of arrival (TDOA) localization technique, which is an effective method to determine the location of the interference using sensors or antennas, has been developed rapidly. The location of the interference source is determined through the intersection of the TDOA lines of position (LOP). However, when the two TDOA LOP have two mirrored intersection points, it is theoretically difficult to determine the real location. Aiming at this problem, a method for eliminating mirrored location based on multiple moment TDOA is proposed in this paper. First, the TDOA results are measured at multiple moments using the cross-ambiguity function (CAF), and the localization equation set is established based on the World Geodetic System (WGS)-84 earth ellipsoid model. Then, the initial location result can be obtained by solving the equation set through the Newton iteration method. Finally, the high-precision location result after eliminating the mirrored location is obtained after the single moment localization based on the initial location. Simulation experiments and real measured data processing results verify the effectiveness of the proposed method. It still has good robustness under the condition of large measurement errors and deviations from the prior initial values.  
tri-satellite localization; interference source localization; cross-ambiguity function 202124483. 24 June 2021244 June 202124483. 24 June 202124483. 24 June 2021
## 4 Conclusions  
The authors of the article are aware of numerous studies by other scientists on the effects on soil caused by the air pressure in tractor tires (\(P_{w}\)) and the vertical load (\(N_{w}\)), as well as of the studies focused on the effect of \(P_{w}\) and \(N_{w}\) on the depth of the furrow after the passage of the tractor wheels. However, the impact of each of these parameters on the soil was considered separately over time.  
The undoubted novelty of this research is that the influence of the parameters \(P_{w}\) and \(N_{w}\) on the depth of the furrow traced by the tractor wheels is considered in their overall  
Figure 8: Degree of influence on the PTL track depth of parameters \(P_{w}\), \(N_{w}\), \(\rho\) and \(H\) when changing their values by 25%.  
Figure 9: Diagram for determining MTU coordinates on a field with PTL.  
effect and in combination with other important parameters of the soil such as its density (\(\rho\)) and cone index (\(H\)). In the experimental study, the authors of the article did not operate separately on each of the parameters \(\rho\), \(H\), \(P_{w}\), and \(N_{w}\) (as all the researchers did), but on the parametric functions \(\pi_{1}\) and \(\pi_{2}\). Under the field conditions, modifying \(\pi_{1}\) and \(\pi_{2}\) with the method of a full factorial \(2^{2}\) experiment is anything but a trivial task. However, its solution allowed us to obtain new and useful results both for the scientific community and for practical users.  
In conclusion, to obtain a greater depth of PTL tracks (\(h\)), the soil must have a low cone index. The soil's low-density \(\rho\) is important, but its influence on the parameter \(h\) is less than the influence of the cone index \(H\).  
The second step in obtaining a greater depth of PTL tracks is to increase tractor front tires inflation pressures \(P_{w}\). Ballasting, (i.e., placing ballast weights on the wheels) of the wheels to solve this problem is the least effective, since increasing the vertical load \(N_{w}\), on them has the least effect on the value of the parameter \(h\).  
The obtained results of the research can be used in practice when choosing tractor parameters (\(P_{w}\), \(N_{w}\)) and taking into account soil parameters (\(\rho\), \(H\)) in order to obtain sufficiently visible (moreover, in a long-term crop rotation of agricultural crops) traces of the PTL without the use of navigation systems.  
Conceptualization and methodology, V.B., S.P. and V.A.; formal analysis, investigation and data curation, S.P., F.S., V.A. and V.K. ([PERSON]); writing--original draft preparation, V.B., S.P., V.N., F.S., V.A. and V.K. ([PERSON]); writing--review and editing, V.K. ([PERSON]), S.P., F.S. and V.N.; Supervision, V.B., S.P., V.N., V.K. ([PERSON]) and V.K. ([PERSON]). All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.  
The authors declare no conflict of interest.