esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Virtual Reality in Learning Quantum Mechanics
## 3 Teaching Experiment
### System Satisfaction  
In order to understand students' opinions about using virtual teaching modules to learn quantum mechanics, this study conducted a questionnaire survey on system satisfaction using a five-point Likert scale for scoring (strongly disagree: 1 point, disagree: 2 points, general: 3 points, agree: 4 points, strongly agree: 5 points). The researchers discussed with experts and scholars to ensure the correctness of the questionnaire. The evaluation items of this questionnaire are divided into three parts: learning content, interface design, and system operation, containing a total of 14 questions (Table 4). The average score of all questions is 4.39, and the standard deviation is 0.43, indicating students were mostly satisfied with the virtual teaching modules.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **Learning Motivation Questionnaire** & **Min** & **Max** & **Mean** & **S.D.** \\ \hline
1. & I think learning in virtual reality is fun. & 3.00 & 5.00 & 4.17 & 0.75 \\
2. & I want to use virtual reality more for learning. & 3.00 & 5.00 & 4.23 & 0.77 \\
3. & After studying with virtual reality, I think natural science is interesting. & 1.00 & 5.00 & 3.67 & 1.15 \\
4. & I would like to actively use virtual reality to study sciences. & 1.00 & 5.00 & 3.57 & 1.10 \\
5. & Compared with traditional teaching, I think using virtual reality is helpful to my study. & 3.00 & 5.00 & 4.23 & 0.68 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical results of learning motivation scale.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & **Cognitive Load Questionnaire** & **Min** & **Max** & **Mean** & **S.D.** \\ \hline
1. & It is difficult for me to learn knowledge using virtual reality. & 1.00 & 3.00 & 1.93 & 0.52 \\
2. & In this learning activity, I had to spend a lot of effort to answer the questions. & 1.00 & 4.00 & 2.17 & 0.91 \\
3. & In this learning activity, answering questions made me feel very troublesome. & 1.00 & 3.00 & 1.80 & 0.61 \\
4. & In this learning activity, answering questions frustrated me. & 1.00 & 3.00 & 1.50 & 0.57 \\
5. & I didn’t have enough time to answer questions. & 1.00 & 2.00 & 1.60 & 0.50 \\
6. & In this learning activity, both the teaching method and the presentation of the learning & 1.00 & 5.00 & 2.03 & 0.96 \\
7. & In this learning activity, I had to put in a lot of effort to complete the & 1.00 & 5.00 & 2.17 & 0.95 \\
8. & In this learning activity, the teaching style was difficult to follow and understand. & 1.00 & 4.00 & 1.57 & 0.73 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical results of cognitive load scale.
## 3 Signal Demodulation  
We consider the demodulation problem as a symbol recognition one. This section describes the proposed demodulation scheme at the receiver.
## 2 Materials and Methods  
### Flux Tower Data  
Dynamics of carbon dioxide (CO\({}_{2}\)) exchange and other ecosystem resource characteristics are being measured by flux towers at observation sites throughout the world. For short stature vegetation (crops, shrubs, and grasses), the flux tower fetch area is about 250 m. NEP flux data sets were at the 30 min time scale and subsequently aggregated to daily and weekly time steps. A flux tower utilizes systems, such as eddy covariance and Bowen Ratio methods, to continuously measure the exchanges of CO\({}_{2}\), water vapor, and energy between terrestrial ecosystems and the atmosphere [23]. Globally, there are over 500 active flux towers currently operating on a long-term and continual basis. These flux towers are located in a diverse range of land cover types, such as, forests, croplands, grasslands, shrublands, wetlands, and tundra. For this study, flux tower data sets from sites located in grassland and cropland within or near the Great Plains and during the 2000-2008 time frame were identified, acquired, and processed (Table S1). We used NEP at the 30 min time step from the flux towers to quality control for possible instrument related outliers. The quality controlled carbon flux data sets were partitioned into gross primary production (GPP) and ecosystem respiration (Re) components utilizing light response and vapor pressure-based partitioning of carbon flux into GPP and Re components [2; 3; 4]. Partitioning the fluxes into GPP and Re facilitated a more functional filling of temporary flux tower data gaps [24]. Partitioned and gap-filled GPP and Re and NEP used in this mapping effort were largely from focused regional flux tower synthesis for grain crops [4], leguminous crops [3], and grasslands [2]. We selected the carbon flux light response and vapor pressure, or \"non-rectangular hyperbolic method\" flux partitioning models [2; 3; 4] over \(Q_{10}\) and short-term exponential fit models (which model Re as a function of temperature based on night-time data) and rectangular hyperbolic fit models (which use the relationship between photosynthetic active radiation (PAR) and daytime NEP to model Re) because it produces the most reasonable C flux estimates and data gap filling [25], particularly in non-forest, low canopy height systems. NEP data from the flux towers were summarized into weekly periods aligning with 7-day expedited Moderate Resolution Imaging Spectroradiometer (eMODIS) Normalized Difference Degetation Index (NDVI) composites [26]. The training of the grassland and cropland NEP mapping models for weekly NEP was focused on records from 30 flux towers--15 measuring CO\({}_{2}\) exchange (CFlux) in cropland sites and 15 measuring CFlux in grassland sites (Figure 1, Table S1). The 30 flux towers represent a total of 76 site years of data (33 site years for grassland and 43 site years for cropland).
# A High-Resolution Magnetic Field Imaging System Based on the Unpackaged Hall Element Array
## 3 Experiments and Analysis
### Imaging Samples of Magnetic Materials  
#### 3.1.1 Imaging Ring-Shaped Magnets  
In the first experiment, we imaged a pattern of four ring-shaped magnets. The material of the ring-shaped magnet is NdFeB. As shown in Figure 9a, each ring-shaped magnet has an outer diameter of 10 mm, an inner diameter of 5 mm, and a height of 10 mm. Four ring-shaped magnets are placed on the right side of the unpackaged Hall linear array, which is detected by the array scanning. The magnetic field distribution of the four ring-shaped magnets is shown in Figure 9b. The colors in the figure indicate the magnetic flux density in Tesla (T). The image shows well the magnetic field polarity and shape of the four ring-shaped magnets. In order to compare with the experimental results measured by this magnetic field imaging system, the magnetic field of four ring-shaped magnets was simulated with Maxwell software. The simulation result is shown in Figure 9c. According to the image comparison, the results of the measured and simulated plots are very similar in terms of magnet shape, magnetic field polarity, and magnetic field values. This indicates that the magnetic field imaging system is capable of detecting and visualizing magnetic fields in two dimensions.
# InoT Sensor Network for Wild-Animal Detection near Roads
## 1 Introduction
As for battery life, NB-IoT [36] here loses to LoRa [36; 37] and Sigfox [37; 38] because it consumes additional energy needed for synchronous communications and QoS handling. In addition, OFDM/FDMA consumes more peak power than DBPSK (Sigfox) or LoRa [25; 26]. Sigfox has the smallest data payload, only 12 bytes, and a limited message count per day. The LoRa payload is bigger, but it has restrictions for packet time on air. NB-IoT has the biggest payload and data rate [36]. In rural areas, theoretically, Sigfox is the choice because one base station can cover the biggest area, but Sigfox works only in some parts of the world [39]. In the city environment, Sigfox can cover up to 10 km, and LoRa can cover up to 5 km. The NB-IoT radius depends on the mobile network of a certain area, but it uses a well-developed mobile operators' network and infrastructure. The main advantage of the usage of LoRa for the wild-animal detection system is the possibility to use its own network at no cost per one device or one message sent. So, in summary, each technology has its own advantages, but for the implementation of wild-animal tracking near the road system, LoRa is considered by the authors as a better choice. Various wireless technologies can be used at LPWAN [27; 28; 29; 30]. Table 1 shows a summary of the most used nowadays. IoT technology, to the authors' best knowledge, has not been widely reported to be applied to wild-animal detection systems, and the research aimed at this topic is considered to be relevant. The emerging long-range data-transmission energy-efficient technology (LoRa) can significantly extend the possibilities and flexibility of the system and increase the physical length of monitored roadway segments using a flexible-solutions network. The solutions may benefit from the decreased energy consumption using the low-power solutions and autonomic functioning of the nodes powered by renewable energy sources. In this article, the IoT sensor-network structure and implementation that are presented can detect animals near roads in various weather conditions and seasons. The proposed animal detection system uses an IoT nodes network for the detection of wildlife near main roads. Nodes were mounted on roadside-marker posts and can detect wild-animal presence near the road. The nodes are designed to detect animal presence near the road and inform drivers using a flashing LED light. Each IoT node has a solar panel and runs on harvested solar energy. The testing system has a gateway with a thermal vision camera placed near the IoT node network. The thermal camera takes thermal photos of the road periodically. For research purposes, the gateway gathers raw node-activation data to be compared with the thermal photo footage.
# Experimental Approaches in Delineating mTOR Signaling
## 4 Identification and Characterization of mTOR Signaling Pathway Components
### Biochemical Approaches to Identify mTOR Signaling Pathway Components
#### 4.1.5 DEPTOR Was Identified in Low Salt mTOR-IPs  
Using the same low salt buffer condition (150 mM NaCl, 1% Triton) for PRAS40 characterization [34], mTOR-IPs from HEK293E cells also led to identification of a 48 KD DEPTOR protein with an N-terminal DEP domain and a C-terminal PDZ domain [35]. Further DEPTOR-IPs confirmed that DEPTOR was associated with both mTOR1 and mTOR2 complexes, and depletion of DEPTOR in cells led to increased activities of both mTOR complexes [35]. These data support DEPTOR as an endogenous mTOR inhibitor.
## 4 Energy Consumption Model
### Battery  
The battery of a behavior monitoring device was modeled as a container with a minimum and maximum level of store-able energy (\(mA-hr\)) and efficiency of charge. Battery level could not exceed the maximum value, and was considered completely discharged at the minimum value to avoid damage to the battery. The maximum capacity was selected as 800 \(mA-hr\) (fully charged) and the minimum as 10 \(mA-hr\) (fully drained). Net current draw from modeled components was converted to \(mA-hr\), for each time step, then added or subtracted from the battery. Battery state of charge (\(SOC\)), defined as the ratio between present battery level \(Q(k)\) (\(mA-hr\)) and maximum battery capacity \(Q_{Max}\) (\(mA-hr\)), was used to represent available battery energy. SOC was updated at each time step \(SOC(k)\) by dividing the net current entering or leaving the system \(Q(k)\), then adding it to the previous value \(SOC(k-1)\) (See Equation (3)). For the occurrence of the first calculation of SOC, the battery was assumed to be fully charged, therefore having an initial SOC of 100%.  
\[SOC(k)=SOC(k-1)+\frac{Q(k)}{Q_{Max}}\times 100 \tag{3}\]
## 1 Introduction
However, so far it has not been verified by laboratory investigations focusing on the selectivity of the membrane process and determination of physicochemical properties of low-sugar product obtained by such membrane treatment. The application of membrane technologies for the production of low-sugar juices fits well with sustainable food processing (SFP), an integral part of the sustainable food supply chain and sustainable development based on the use of low energy, low-impact processing schemes to produce food with excellent quality and nutritional value close to (or better than) that of a fresh product [13]. Membrane technology can be classified as a sustainable food production technique, as it is environmentally friendly due to low energy consumption [11; 14]. Moreover, thanks to mild conditions, the membrane treatment does not deteriorate a product's quality as much as traditional thermal processing. Apart from the improvement of healthy food quality and nutritional value, SFP additionally focuses on the simultaneous exploitation of food processing wastes, which is also connected with the proposed technology. The proposed juice membrane treatment, when implemented into industry, would give the benefit of reduced waste during juice processing. According to [PERSON] et al. [15], in the case of fruit processing, the production of apple juice generates a huge volume of waste considering the annual processed tonnage of up to 12 million tons. At present, membrane processing of juices on an industrial scale is carried out in order to replace the stages of filtration, decanting and partially evaporation during the production of concentrated and clear juices, and the subject of producer's interest is the ultrafiltration permeate (clear juice) [16; 17]. During such processing at the UF stage, a significant amount of by-product is generated (UF retenstate), although it contains the compounds of high nutritive potential: dietary fiber, pectins, polyphenols, and oligosaccharides [11; 15; 18]. [PERSON] et al. [19] presented that the levels of flavonoids and chlorogenic acid in apple juice were reduced to between 50% (chlorogenic acid) and 3% (catechins) compared to fruit. In the proposed technology, the approach is opposite--UF/DF retenstate (optionally subjected to further treatment), containing potentially valuable components (e.g., pectins) and lower sugar content than the feed juice, is the subject of interest. At the same time, the stream of generated permeate can be also treated as a clear juice, or can be utilized for the production of traditional fruit beverages. Apple juice is one of most popular fruit juices, while cranberries are increasingly popular, mainly due to high phytochemicals content and positive implications for health [20; 21]. Thus, the aim of work was to investigate (1) the performance of ultrafiltration and diafiltration of apple-cranberry cloudy juice; (2) the selectivity of both membrane techniques; (3) the physicochemical properties of obtained products (retantates); (4) the values of sugars reference intake connected with the consumption of obtained products (retantates).
## 3 Results and Discussion
### Volatile Acidity  
Volatile acidity, mainly comprising acetic acid, is regulated by current legislation, with the maximum value for this parameter, for commercialized wines, being 1.2 g/L. The concentration of finished wines varies, being approximately 0.5 g/L. Acetic acid is mainly produced by the _Acetobacter_ species, and, to a lesser extent, by yeasts during alcoholic fermentation, being considered a spoilage characteristic in excessive quantities [62]. Its synthesis depends on several factors, such as grape sanity, techniques and processes, stage of vinicification, species and strains of microorganisms present in the medium, exposure to oxygen and nutrient imbalance, which lead to competition between existent yeast and bacterial populations [63; 64].  
The results obtained for this parameter are discriminated in Figure 7, and statistical analysis using the Kruskal-Wallis test showed no significative differences between control, enzymatic treatment and PEF (H= 0.226, \(p=0.893\)).  
Given the lack of published information on PEF application in white grape must, we were forced to compare our results with the existing data for both red and white musts. Recalling that the PEF protocol used for this assay was 1 kV/cm and 2 kJ/kg, the information is in favor of what was concluded by [PERSON] et al., 2018, when applying a PEF protocol of 1.5 kV/cm and 22 kJ/kg to white variety Garganega grapes. However, when [PERSON] applied the same electrical field strength and lowered the specific energy to 11 kJ/kg, the difference was considered significantly higher (Tukey HSD test, \(p<0.05\)) [33]. [PERSON] et al. performed a comparison between enzyme and PEF (5 kV/cm; 3.67 kJ/kg) application to study the effects on the maceration of Cabernet Sauvignon must, concluding that neither PEF nor enzymes usage interfered with this parameter [7].
# A Deep Learning Model for Detecting Diabetic Retinopathy Stages with Discrete Wavelet Transform
## 1 Introduction
The demand for efficient and cost-effective techniques for early DR identification is of utmost importance [18]. As each dataset varies in demographics and clinical situations, this research is essential to the study of DR because it employs a unique strategy, combining two different datasets to improve the model's capacity to generalize the unseen DR images. Our work aimed to develop a deep learning model that successfully predicts DR images using a CNN model. The model was trained, validated, and tested on APTOS-DDR fundus datasets. Images were preprocessed using contrast-limited adaptive histogram equalization (CLAHE) to perform histogram equalization and then discrete wavelet transform (DWT) to extract the coefficients. The CNN model can extract the relevant information or features from the preprocessed images. To train the model, a multi-class classification  
Figure 1: The stages of DR from the APTOS dataset. (**a**) No DR or normal retina, (**b**) mild DR, (**c**) moderate DR, (**d**) severe DR, and (**e**) proliferative DR.  
technique categorizes DR into five stages: normal or no DR, mild, moderate, severe, and PrDR. Limited academic research evaluates CLAHE and DWT with combined datasets, focusing on their performance in categorizing the DR stages. Significant contributions to this study include the following:  
* The study utilized two public datasets, including APTOS and the Dataset for Diabetic Retinopathy (DDR), to categorize each stage of DR. The APTOS and DDR datasets were merged to train, validate, and test the model. Generalizability can be mitigated by blending diverse datasets and evaluating the model's performance on an unseen test set. * A CNN model has been constructed to predict the DR stages. The learning rate is tuned during the model training, and optimizers such as adaptive moment estimation (Adam), root mean square propagation (RMSProp), Adamax, adaptive gradient algorithm (Adagrad), and stochastic gradient descent (SGD) are evaluated on the model. Data augmentation is used in the training set to mitigate the issue of overfitting. * We provide a novel image classification framework that carefully blends many effective processing algorithms to improve classification performance. Each image is preprocessed using CLAHE. Altering the intensity distribution in specific regions of the picture, this approach enhances the image contrast. We then employ DWT to divide the images into frequency components to recover spatially covered features. * Our methodology also offers new research paths, notably in integrating DWT with other deep learning architectures and applying it to complicated image processing problems. The consequent portions of the article are categorized as follows: The background research in Section 2 shows the literature review of the DR, followed by an overview of the investigation methodology in Section 3. The results are presented and examined in Section 4, and Section 5 discusses them. Finally, Section 6 provides a conclusion to the study.
## 3 Results and Analysis
### GPS/BDS Single-Frequency KOD Analysis of Luojia-1A Satellite
As shown in Table 2, when using observations from satellites between \(-10^{\circ}\) and \(5^{\circ}\) elevations, the orbital RMS precision values reach 1.640, 1.729, and 0.988 m, which are improved by 22.1%, 16.4%, and 6.5% in the radial, along-track, and cross-track directions, respectively. As expected, GPS/BDS-combined KOD results are better than that of the GPS-only case with the black line since BDS makes a contribution to both the number of available satellites and the PDOP in the Asia-Pacific region. Some low-quality measurements can be also eliminated easily and do not remarkably decrease the number of redundant observations. For the GPS-only solution, the RMS values of the orbital error are 2.302, 1.965, and 0.993 m in the radial, along-track, and cross-track directions, respectively. The GPS/BDS combination improves the estimation precision of the ambiguity parameter, and the residuals are reliable since outliers are removed correctly. As a result, the orbital RMS precision values of the GPS/BDS-combined solution are improved by 28.8%, 12.0%, and 0.5% in the radial, along-track, and cross-track directions, respectively.