esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data Preparation and Methodology
### Score Map  
The aforementioned microseismic waveform has 30,000 sampling points, which means that each waveform has 1 positive point but 29,999 negative points for onset time picking. The optimization function during the model training process will lead to a low error rate of 0.0033% (1/30,000). In this case, the class imbalance problem is generated, affecting the model performance and making the training difficult. To solve this problem, a Gaussian-like distribution is introduced to incorporate the onset time picking of micro-fracture waveforms and is considered on a score map in Equation (1). Here, a commonly used function is adopted, and other functions with a similar meaning can also be used to generate score maps.  
\[\text{Score(x)}=\exp\left(-\frac{\left(\text{x}-\mu\right)^{2}}{2\sigma^{2}}\right) \tag{1}\]  
where Score(x) represents the score or probability of onset time picking and is used to evaluate the error between the predicted and accurate onset time picking. An accurate onset time picking of micro-fracture waveforms is defined as the highest score (i.e., set to 1), and the score of the time window area before and after the accurate onset time picking gradually decreases to 0 (see Figure 3). For the non-micro-fracture waveforms, the score of the onset time picking is set to 0. The variance is the average value of the Gaussian-like distribution, which is set to 0, and the standard deviation \(\sigma\) represents the opening range of the function map (score map), which increases as the value of \(\sigma\) increases. The setting of the value is discussed in detail in Section 3.2. In addition, the input of the waveform is normalized to a value between 0 and 1, which can improve the training performance of the model when the model output from the first section is a score map.  
Figure 2: Semisynthetic noisy waveforms with accurate onset time picking: (**a**) a micro-fracture waveform with a high SNR; (**b**) a noise waveform (**c-e**); a semisynthetic noisy waveform with SNRs of 35.21, 11.68, and 5.66, respectively.
## 1 Introduction
### Cybersickness  
Cybersickness, which manifests as nausea, dizziness, and disorientation, poses a significant challenge in the deployment of Virtual Reality (VR) technologies. This condition is akin to motion sickness and is often triggered by sensory conflicts inherent in immersive VR environments. Factors such as the ergonomic design of VR headsets--particularly their weight and fit--and software-related issues like improper calibration can exacerbate these symptoms [32]. The presence of cybersickness not only impacts the user's comfort but can also compromise the reliability of cognitive assessments conducted in VR, making it a critical issue to address [1,2,32,33,34].  
Recent advancements in VR technology have made significant strides in reducing the incidence and severity of cybersickness. Innovations in hardware, including better weight distribution and enhanced visual fidelity, along with software improvements like teleportation-based movement systems and optimized user interfaces, have contributed to a more comfortable and immersive experience [11,35,36]. These developments are essential for ensuring that VR tools are not only effective but also accessible to a broader population. By minimizing cybersickness, these improvements enhance the reliability and applicability of VR-based cognitive assessments, making them more enjoyable and feasible for users across different demographics [2,35,36,37].
# Application of Single-Electron Transistor to Biomolecule and Ion Sensors
###### Abstract  
The detection and quantification of chemical and biological species are the key technology in many areas of healthcare and life sciences. Field-effect transistors (FETs) are sophisticated devices used for the label-free and real-time detection of charged species. Nanowire channels were used for highly sensitive detections of target ion or biomolecule in FET sensors, however, even significantly higher detection sensitivity is required in FET sensors, especially when the target species are dilute in concentration. Since the high detection sensitivity of nanowire FET sensors is due to the suppression of the carrier percolation effect through the channel, the channel width has to be decreased, leading to the decrease in the transconductance (\(g_{m}\)). Therefore, \(g_{m}\) should be increased while keeping channel width narrow to obtain higher sensitivity. Single-electron transistors (SETs) are a promising candidate for achieving higher detection sensitivity due to the Coulomb oscillations. However, no reports of an SET-based ion sensor or biosensor existed, probably because of the difficulty of the room-temperature operation of SETs. Recently, room-temperature SET operations were carried out using a Si multiple-island channel structure. This review introduces the mechanism of ultra-sensitive detection of ions and biomolecules based on an SET sensor and the experimental results.  
single-electron transistor; biosensor; ion sensor; sensitivity; percolation effect
## 3 Results and Discussion
### Determination of the Immersion Enthalpy of Activated Carbons in Phenol Aqueous Solutions  
Figure 7 presents some potentiometric curves obtained during the immersion of activated carbon modified with nitric acid in phenol solutions and subsequently subjected to reflux in ammonium hydroxide solution GNA. The curves presented in this figure correspond to those obtained in the process of immersion of the activated carbon in the phenol solutions. The total calorimetric curves include the calibration process that is carried out for all solids; the contact between activated carbon and the phenol solution is maintained for two hours, so the adsorption of phenol is carried out at the same time.  
In Figure 7, the area under the curve is proportional to the energy transferred as heat during the immersion calorimetry processes; the concentration of phenol of each peak is equal to those used in the generation of the adsorption isotherm. It is well known that the immersion energy is an integral quantity that corresponds to the average interactions with the solid and all constituents of the liquid  
Figure 7: Potentiometric curves obtained during the immersion calorimetry for the activated carbon sample GNA in solutions of phenol; the numbers over peaks correspond to the concentration of phenol at the beginning of the calorimetry immersion in mmol\(\cdot\)L\({}^{-1}\).  
phase [4]. The first peak corresponds to energy produced by the wetting of the carbon surface, which is the largest; the peaks from two to six correspond to the fast adsorption in the isotherm, and it can be see that the area of these peaks decreases as the phenol concentration increases. This suggests that the energy produced by the wetting of activated carbon is used by phenol for adsorption; the last three peaks are the plateau in the adsorption isotherm and show that the immersion energy is similar and therefore there is no increase in adsorption.  
An interesting result of this study can be seen when comparing the maximum capacities of the adsorption Q\({}_{0}\) in the Langmuir model during the calorimetric and the equilibrium process (Figure 8). As can be observed, for the sample GNA, the adsorption on the calorimeter was carried out at 97%. This suggests the energetic interactions produced by the phenol adsorption and determines the heat transferred using the calorimetric immersion for this sample; this corresponds to almost all of the energy produced during the entire adsorption process. This indicates that the adsorption of phenol on activated carbon GNA involves specific interactions that are given by enthalpy-driven processes.
# Developments, Applications, and Innovations in Agricultural Sciences and Biotechnologies
## 6 Phosphorus Fertilization and Crop Quality
### Balancing Nutrient Management and Crop Quality  
Phosphorus is a critical nutrient for plant growth, and its management has significant implications for crop quality and environmental sustainability [21]. The study on lentil seed development explores how phosphorus fertilization influences gene expression and seed quality. The authors' key findings are as follows:  
Gene expression modulation: phosphorus levels affected the expression of genes involved in tannin biosynthesis, impacting seed coat hardness and nutritional value.  
Differential effects across stages: the study revealed that phosphorus availability had varying effects at different stages, highlighting the need for stage-specific nutrient management.
# Efficient Dynamic Deployment of Simulation Tasks in Collaborative Cloud and Edge Environments
## 3 Problem Statements
### Application Background  
With the development of computer and network technology, simulation has been recognized as a powerful tool in the area of training. Compared to the traditional field training, simulation can significantly reduce the operation cost and enhance the convenience of training. If the involved personnel or equipment is relatively few, simulation applications can be executed efficiently in local clusters. However, for a large-scale geo-distributed simulation scenario, cloud computing is necessary benefiting from its unlimited computation and storage resources. To alleviate the long data transmission delay between users and the remote cloud data centers, edge computing is also introduced, which is called collaborative cloud and edge simulation.  
In this kind of simulation, three kinds of simulation federates are involved, namely live (real person and equipment), virtual (various simulators), and constructive (computer generated forces) federates, which is also called LVC simulations. For example, a pilot in a plane simulator can fire a simulated missile at a real tank with many sensors and receivers. These federates may be distributed geographically and they also show different characteristics. Live federates usually refer to the trainees or some real equipment. For these members, they need to upload a great amount of data to update their corresponding digital avatars maintained in the unified simulation environment. At the same time, they have certain maneuverability and can move between adjacent areas. For virtual federates, namely simulators, they are usually located in some fixed locations. Since simulators themselves can process data locally, a much lower volume of data are needed to update their avatars. As for constructive federates, they are essentially computer programs and can be deployed anywhere throughout the whole network.
## 3 Results and Discussion
### Aromatization in Medical Facilities  
Aromatization of space is also widely used in medical facilities of various types, from hospitals to dispensaries and different places providing healthcare. The sterile scent typical of these facilities can be replaced by something more pleasant to distract patients from their health problems, make it more bearable for them to wait before an examination, or calm them down before the procedure itself. Proof was shown, for example, in the study by [PERSON] et al. [88], conducted in a dentist's waiting room, which confirmed that patients exposed to the scent (orange or lavender) had lower levels of anxiety, were in a better mood, and were generally calmer. From the methodological point of view, only classical feedback in the form of a self-assessment questionnaire with the Likert scale was used to assess the emotional states researched. Further evidence of the use and impact of aromatization in medical settings was provided in the study by [PERSON], [PERSON], and [PERSON] [89], which focused on whether scents and aroma can influence evaluations of a service experience and perceptions of personal wellness. The testing took place in the hospital's pediatric ward, examining three situations: odorless, with a relaxing scent (exotic fruit), and with a stimulating scent (citrus fruit). From the research methods, traditional methods were used, such as in-depth interviews conducted directly with hospitalized children, with their parents, or with medical staff, along with added insight from observing children in contact with nurses or other patients. Although the study confirmed the importance of aromatizing such spaces, the authors indicated the need for further research in this area due to the current study's limitations, either due to sample size or the possible influence of other factors on the variables studied. Another study was performed in the waiting room environment of a plastic surgeon [90]. The subject of their research was the influence of ambient scent or aroma (lavender) and music (instrumental music) on patient anxiety, evaluation of the waiting environment, and perceived waiting time. From a methodological point of view, patients evaluated the monitored indicators only through a questionnaire. This study concluded that both scent or aroma and music effectively reduce patient anxiety, but only if used separately. When both are active, it is necessary to carefully consider the possible interactions.
# Robust DOA Estimation Using Multi-Scale Fusion Network with Attention Mask
## 5 Experiments and Discussion
### Baselines  
MUSIC carries out DOA estimation by searching for spectral peaks of noise subspace \(V_{n}\) obtained from decomposition of eigenvalues of the signal covariance matrix. The DPD test is widely used to combat reverberation by selecting the TF-bins dominated by the source from all TF-bins. Handling the noise subspace by DPD testing can greatly improve the accuracy of the MUSIC algorithm in reverberation environments, referred to as DPD-MUSIC.  
Based on the spherical harmonic domain decomposition, the magnitude and phase of SHC are taken as SHPM features. A CNN network consists of three convolutional layers, and two fully connected layers (FC) are used to output the estimation. In order to maintain as much comparability as possible with MF-AMnet, we use the output strategy of regression. This approach is called SHPM-R. The complex version of SHPM-R is denoted SHPM-CR.  
For acoustic models, a multi-stream network based on Fourier transform uses two independent branches to process the real and imaginary parts and then fuse them [54]. SHC features are used as inputs to this framework, called MS.  
CV-CNN is a CVNN that uses multiple complex-valued layers, including C-Conv, C-FC, complex-valued batch normalization, and complex-valued dropout, to estimate DOA [55]. It has been shown to perform well under low SNR and with fewer snapshots.  
Complex Residual Angle Estimation Network (CVRAEN) is also proposed for DOA [56]. CVRAEN processes the input signal in two stages using the initial feature extraction module and the deep feature extraction module, which includes complex-valued linear layers and C-Conv.  
SADOAnet is a deep learning framework with a sparse signal enhancement layer [57]. A sparse representation of the original signal is formed by using a binary mask. Then, the signal is enhanced by signal embedding and position coding. Finally, the DOA estimation output is obtained by a CNN.  
According to the characteristics of circular harmonic domain signal, the TF-bins are filtered by the power spectrum of 0-th degree 0-th order components of the signal. Then, DOA is estimated by the neural network. We denote it as circular domain localization (SDL) [58].
# Assessment of Dental Arch Reproduction Quality by Using Traditional and Digital Methods
## 4 Discussion
There is little research on the accuracy of dental models created by 3D printing. Most of them show sufficient accuracy and may be suitable for clinical use [25; 26; 41]. However, the accuracy of the arch dimensions reproduction was not measured in these studies. Several elements may affect the final accuracy of the model obtained in the 3D printing process. When preparing the data describing the virtual model to generate a physical model, it is necessary to divide it into layers. During this process, errors related to the triangulation may occur. Additionally important are the thickness of the layer of the polymerized material, the amount of polymerization shrinkage, the amount of overcuring or the properties of the light beam and the rate of exposure. The layer thickness is the fundamental factor determining the surface structure of the generated objects. The surface of the object depending on it may be more or less detailed and smooth. Final curing by irradiation with UV light and heating of the generated objects is necessary to solidify unreacted or partially reacted monomers in the 3D printing process, thus improving the mechanical properties of the object. This process can also lead to extra contraction [25]. In summary, we can say that, traditional methods proved to reproduce the dental arch and all three linear dimensions more accurately to a statistically significant degree than digital methods. The linear dimension between the molars was reproduced with the highest degree of accuracy and the dimension between the amines was reproduced with the lowest accuracy, regardless of the method used. A limitation of the present study was its in vitro design. Another limitation was that only one IOS device was tested. A significance of this study is that the amount of publications with similar methodology, concentrating on this topic are scarce. Another important aspect of this research is the modern equipment. We were probably the first in the world to use the E3 scanner for this type of research. The development of new technologies is much faster than the pace of research. The results of this research are in line with previous studies [17; 23; 31; 41; 42] in the statement that, in the case of more extensive reconstructions or the need to obtain maximum accuracy across the arch, traditional methods are still the gold standard. However, conventional impressions with model casting and optional digitization using extraoral scanners remain the recommended procedure [17].
## 8 Conclusions  
In this study, we successfully used UAV remote sensing for the dynamic monitoring of construction and the collection of monitoring information. This involved three main modules: processing data and identifying illegal buildings; handling the data based on the WebGIS network information management system; and using a mobile grid management terminal system for data communication. The main method for managing illegal buildings was to use a regulatory platform to supervise a series of functions: target image recognition, field verification, investigation and process monitoring, and historical tracking of illegal buildings. When aerial imagery postprocessing software was used, we were able to generate a digital orthophoto map, a digital surface model (DSM), 3D images, and other data. On the basis of preliminary computer selection, supplemented by manual visual inspection, a 3D image and a 720\({}^{\circ}\) panoramic view provided artificial verification, then the unexpected stronghold was obtained and the corresponding thematic maps were produced. After computer-automated processing, new DSM data were obtained from elevation differences in two-stage images, and illegal buildings could be identified. The investigated algorithm achieved the goals of \"seeing from the sky\", \"managing on the ground\", and \"searching on the Internet\", essential tools for stopping and preventing illegal construction.  
Investigation, Y.H., W.M., Z.M., and C.C.; Methodology, Y.H., Z.L., C.-F.Y., and W.F.; Formal analysis, Y.H., Z.L., W.F., and C.C.; Writing--original draft preparation, Y.H., W.M., C.-F.Y., and Z.M.; Writing--review and editing, C.-F.Y., Y.H., W.M., and C.C.  
This study was supported by Longyan City Science and Technology Plan Foreign Cooperation Project of China (Grant No.2018 LYF7006) and Open Fund Programs of Key Laboratory of Ecological Environment and Information Atlas (Putian University) Fujian Provincial University of China (Grant No.ST17002). This work was also supported by projects under the Nos. MOST 108-2221-E- 390-005 and MOST 108-2622-E-390-002-CC3.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline
**Project** & **In This Paper** & **Others [22; 23]** \\ \hline Basic data & UAV remote sensing data (0.06 m/pix) & Satellite remote sensing data (2.5 m/pix) \\ Convenience for remote sensing & & Difficult \\ data & & Post comparison of multitemporal \\  & & image classification, change detection \\ Illegal building extraction method & Multitemporal elevation difference & combining strategies at the pixel and the \\  & calculation, man–machine interaction & feature levels, building variation pixels \\  & & were detected by k-means clustering \\ Accuracy of illegal buildings’ & & technique \\ extraction & 78\% & 75\% [23] \\ Dimension of illegal building & & Three-dimensional \\ supervision & & \\ \hline \end{tabular}
\end{table}
Table 1: Comparison table of illegal buildings’ identification and monitoring research. UAV: unmanned aerial vehicle
## 5 Discussion  
FEM software with an open structure like, for example, Abaqus gives the possibility to implement parts of constitutive models (e.g., evolution of the yield surface) by applying user-defined subroutines. In particular, in case of the Modified Drucker-Prager/Cap model, the evolution of the Cap yield surface is defined in terms of the volumetric inelastic strain, that being the sum of plastic volumetric strain and creep volumetric strain (\(\varepsilon^{cr}_{vol}\)). The \(\varepsilon^{cr}_{vol}\) is related to modelling the secondary consolidation phenomenon. The consolidation creep mechanism in the Modified Drucker-Prager/Cap is defined by specifying a constitutive  
Figure 11: Relationship between \(\sigma^{\prime}\) and strain produced by the model for \(\eta=20\) MPa \(\cdot\) min.  
Figure 10: Relationship between \(\log\sigma^{\prime}\) and strain produced by the model for \(\eta=20\) MPa \(\cdot\) min for two different strain rates.  
law for \(\varepsilon^{cr}_{vol}\) in a form of differential equation in terms of an equivalent creep pressure and \(\varepsilon^{cr}_{vol}\). Three laws for creep are implemented in Abaqus, one of them being the Singh-Mitchell law. However, any equation for calculating \(\varepsilon^{cr}_{vol}\) can be given by a user subroutine CREEP. Thus, the equations presented in the manuscript for transient consolidation can be implemented as an extension to the Modified Drucker-Prager/Cap model. Rheological structures serve as a useful tool for building creep laws. In the article, the Burgers structure was analysed in detail; however, even more complicated viscoelastic models can be used in order to relatively easily find equations describing creep.  
The steady-state consolidation model presented in the manuscript, in which the Kepes element was used, can be also utilized in order to build and to implement a complete material model for consolidation analysis. Geotechnical FEM software (i.e., Plaxis) allows user definitions of material models programmed in Fortran. The authors plan to extend the presented approach to a three-dimensional case and implement the constitutive model using the UMAT user subroutine available in Abaqus software.  
The right-hand branch of the steady-state consolidation model can be further modified to obtain a more suitable solution. This will be done in future works. Moreover, both incremental loading (IL) and constant rate of strain (CRS) oedometer tests will be carried out by the authors in order to further validate the proposed model.  
The equation of flow can be modified to simulate the flow of water more realistically [4]. In a transient FEM analysis the equations of flow are solved numerically. Moreover, according to test data, the coefficient of permeability changes in time as the consolidation progresses and the density of the material increases [30]. The authors plan to assume a non-constant function of this parameter in the equation of flow in future works.