esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Metaverse Classroom: A Layered Architectural View
### Personalization and Accessibility Layer
#### 3.3.3 Multilingual Support  
Multilingual support in a Metaverse classroom ensures an inclusive learning environment for a diverse, global student body [7]. Real-time translation allows students to participate in discussions, engage with content, and collaborate on projects without language barriers [151]. For example, students can receive live translations during virtual lectures, enabling them to understand complex topics in their preferred language [44]. This feature promotes equitable access to education and encourages international participation. Real-time translation is especially valuable during group discussions and collaborative activities, where language differences could otherwise hinder participation [144]. Students can share ideas and work together regardless of their native language, fostering intercultural exchanges [27]. For instance, in a science project, students from different countries can express ideas in their native languages while the system provides real-time translations for all participants [92]. This feature enhances global connectivity and cultural understanding, enriching the learning experience.  
Beyond translation, localized content adaptations further support students by tailoring educational materials to their cultural contexts [152]. These adaptations include culturally relevant examples and illustrations that make lessons more relatable [153]. For example, a history lesson might feature regional events or figures that resonate with specific student groups [104]. This customization bridges understanding gaps and makes learning more meaningful for students worldwide. Voice and text translation tools enhance accessibility for students who prefer alternative communication methods [145]. Voice-to-text translation allows students to speak in their native language and receive translations in text format, or vice versa, depending on their needs [154]. This flexibility is helpful for students with limited literacy in the language of instruction or those with disabilities who find text-based communication challenging [155]. For example, a visually impaired student might use voice-to-text in their preferred language, while others can type responses and have them translated in real time [142]. This adaptability ensures that all students can participate in a way that best suits them. Cultural inclusion within multilingual support fosters a deeper sense of belonging and respect [137]. Recognizing multiple languages in the classroom validates students' diverse identities and encourages them to see their cultural backgrounds as assets in the learning process [156]. For instance, students might share phrases or traditions from their cultures during language or social studies lessons, contributing to a more globally-minded classroom. Multilingual support transforms the Metaverse into a multicultural space that celebrates diversity and prepares students for communication in an interconnected world.
## 4 Experimental Evaluation
### Scenario 2: Distance between STA and AP
#### 4.3.1 Experimental Environment of Scenario 2  
Figure 7 illustrates the measurement environment for evaluating the communicable distance. In the measurement, the distance between STA and AP is taken every 10 m up to 60 m and every 5 m after 60 m. The operation PC connects to the AP via a wired line and to the STA via an ad hoc connection.  
Figure 5: RTTs for TCP and UDP communications on both sides (Scenario 1).  
Figure 6: RSSI results for TCP and UDP communications (Scenario 1).  
Figure 7: Measurement environment for evaluating communication distance.
## 3 Proposed Approach
### Selection of Object Detection Methods and Instance Segmentation Methods
#### 3.8.2 Instance Segmentation: YOLOv10  
YOLOv10, illustrated in Figure 10, comprises three main components: a backbone for feature extraction, a neck for multi-scale feature enhancement, and a head for object detection and segmentation. While YOLOv10 normally provides bounding-box predictions, it can be adapted for instance segmentation by adding a dedicated branch in the head to perform pixel-wise classification. As described in [26], \"dual label assignments\" enable one-to-many and one-to-one matching, supporting instance-level predictions when required. In this setup, the bounding-box head handles standard object detection (regression and classification), while an auxiliary segmentation branch converts feature maps into per-pixel logits.  
This study used Vision Transformer (ViT), Shifted Window Transformer (Swin Transformer), and Pyramid Vision Transformer (PVT) as backbones, chosen for their trade-offs between accuracy and computational efficiency [27; 28]. For the segmentation task, two classes (\"building\" and \"background\") are defined, and the final layer employs a single output channel with a sigmoid activation for binary classification.
# Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-Prediction
## 4 Proposed Fast CU Size Selection Algorithm
### Level 2 SVMs (L-2): Features and Optimal Weight Calculation
#### 4.2.1 Data Collection  
The number of training samples collected for the L-2 is maintained at \(N=1000\) for each CU depth level. In this case, the number of training samples to be used are determined empirically, which achieves the best trade-off between the encoding time reduction and model accuracy. Similar to L-1 models, L-2 models at each CU depth level are discarded once a certain number of predictions are made. In this case, existing L-2 models are discarded and recreated with a new data set keeping the ratio between the training and predicted samples at 1:200. This ensures that the models generated are content-adaptive and CU size predictions are content relevant. Similar to the L-1, the ratio is selected empirically, following the same process described in Section 4.1.1.
# Analyzing the Characteristics of Soil Moisture Using GLDAS Data: A Case Study in Eastern China
###### Abstract  
In this paper, we use GLDAS (Global Land Data Assimilation System) to analyze the effects of air temperature and precipitation on the characteristics of soil moisture in the eastern region of China from 1961 to 2011. We find that the temperature and precipitation in different seasons have different degrees of influence on the characteristics of soil moisture in each layer. The results show that over the last 50 years, the soil moisture in eastern China has a tendency to dry out, especially between the late 1970s to the early 1980s. The change of soil moisture with the depth of soil layer has similar inter-annual and seasonal patterns. Soil moisture in different depths (0-200 cm) positively correlates with the air temperatures of spring, summer and autumn, but negatively correlates with the air temperature in winter at 0-10 cm, 40-100 cm and 100-200 cm. Similarly, soil moisture is positively related to the precipitation in spring, summer and autumn, and the opposite is true in winter. The results also show that precipitation has a significant effect on the shallow soil moisture (0-10 cm), while air temperature most affects the deep soil moisture (100-200 cm).  
Keywords:soil moisture; air temperature; precipitation; GLDAS data; eastern China _Applied_
# Effects of Phase Shift Errors in Recurrence Plot for Rotating Machinery Fault Diagnosis
## 1 Introduction
In all cases, vibration spectra must be conditioned, and this process produces several phase shifts errors that affect the accuracy of fault diagnosis in rotating machinery.
###### Abstract  
In western Jilin Province, China, the presence of soda saline-alkali soil poses a significant threat to the raising of rice seedlings due to its harsh soil properties. The scarcity of suitable seedling-raising soil resources has become increasingly pronounced. A short-term soil-improvement experiment was conducted using the original saline-alkali soil sourced from the rice-growing region of Jilin Province, followed by the rice-seeding-raising test in the improved soil to identify an effective soil-improvement strategy. Four distinct treatments were established: no amendment (JCK); gypsum and straw (JCW); gypsum, straw, and sulfuric acid (JCWH); and gypsum, straw, and chemical fertilizer (JCWF). The effects of these amendment treatments on the soil physicochemical properties (pH, electrical conductivity, exchangeable sodium, total alkalinity) were evaluated, as well as the effects on soil organic carbon (SOC) and its components including humic acid carbon (HAC), and fulvic acid carbon (FAC). The results indicated that, compared to the control group, all amendment treatments effectively reduced the average soil pH by 0.53 to 0.79 units and decreased exchangeable sodium by 56.7% to 74.8%. Furthermore, the average SOC, HAC, and FAC increased by 48.3%, 89.4%, and 56.0%, respectively. Among the treatments, JCWH proved to be the most effective. After two years of improvement, the rice seedlings in the JCWH-treated soil exhibited the highest dry weight and plant height, surpassing those grown in the farmer's seedling-raising soil. The scheme of utilizing soda saline-alkali soil for rice-seeedling raising, following a short-term improvement treatment with corn straw, gypsum, and sulfuric acid (JCWH), provides technical support and an effective solution to the soil scarcity issue faced by seedling farmers in saline-alkali regions.  
s +
Footnote †: journal: Applied sciences  
Article
## 3 Results
### Evaluation of the Assimilation System
#### 3.1.2 Quantitative Evaluation of Rainfall Forecasts  
To quantitatively assess the impact of POD-4 DEnVar assimilation on precipitation forecast, the results of 24-h Equitable Threat Score (ETS) from 2016 and 2017 are shown in Figure 7, for five thresholds 0.1 mm, 10 mm, 25 mm, 50 mm and 100 mm per 24 h. The ETS results are aggregated by 11 forecast results per year, compared with the 0.1\({}^{\circ}\times 0.1^{\circ}\) precipitation data from the China meteorological data sharing service network ([[http://data.cma.cn/data/](http://data.cma.cn/data/)]([http://data.cma.cn/data/](http://data.cma.cn/data/))).  
For precipitation in 2016 (Figure 7a), the improvements are mainly found in threshold smaller than 50 mm. Compared to CON, POD produces a 2.21% ETS improvement rate for the 0.1 mm precipitation, and 3.25% improvement for 10 mm. Scores of 25 mm in POD is close to CON, with only 0.78% improvement to CON. It shows that weaker precipitation is better represented in 2016 when data assimilation is applied. For heavy rainfall (threshold larger than 50 mm), POD performs worse than CON. For 2017 precipitation (Figure 7b), POD shows comparable or improved ETS for all thresholds. Especially for heavy rainfall in 2017, ETS from POD is improved for 50 mm and 100 mm relative to CON. According to observed precipitation, there has been a heavy rainfall event in Hunan province, China from 29 June 2017 to 1 July 2017 (Figure 8). Results indicate the ability to make a better forecast on heavy rainfall in 2017 when carrying out assimilation. Moreover, relative 0.78% improvement in  
Figure 6: The average RMSE evolution as forecast time increase for (**a**) U at 10 m, (**b**) V at 10 m, (**c**) T at 2 m, and (**d**) Qv at 2 m. The blue line represents results from the baseline experiment (CON) and red represents results from the second experiment (POD).  
0.1 mm, 5.89% in 10 mm and 3.89% in 25 mm are also found in POD relative to CON experiment. Overall, the assimilation of satellite and radar data with POD-4 DenVar method show benefits on the rainfall forecast.
# Experimental Analysis of Shale Cuttings Migration in Horizontal Wells
## 3 Experimental Results
### Experimental Phenomenon
#### 3.1.2 Destruction Stage of Cuttings Bed  
The destruction of the cuttings bed occurs as a result of the continuous movement of small particles, driven by both fluid rate and the rotational speed of the drilling tool. The fluid rate generates horizontal forces that push the leading edge of the cuttings upward, gradually moving the entire cuttings bed forward. At the same time, the rotational speed of the drill creates lateral forces on the cuttings, causing them to jump and migrate across the bed's surface. Together, these forces promote the suspension of cuttings within the annulus, enhancing their overall transport.  
From observations of cuttings migration in horizontal sections, three primary modes of movement have been identified: sliding, rolling, and suspension. Based on experimental data, the degradation of the cuttings bed is shown to occur in three distinct stages, each defined by different patterns of particle movement.  
Early stage.  
In the early stage of bed destruction, the cuttings bed begins to decrease in length, while its height gradually increases. The drilling fluid exerts a horizontal drag force on the windward slope of the cuttings bed, causing the foremost particles to move upward. This movement gradually accumulates the cuttings into a \"hillside\" formation, primarily characterized by rolling. At the rear end of the bed, a significant height difference leads to accelerated movement of the cuttings, where jumping and suspension behaviors become more prevalent (Figure 8a).  
Suspension stage.  
As the fluid rate increases, rotational speed imparts both lateral and horizontal forces on the cuttings. This causes some particles within the cuttings bed to intermittently jump and become suspended in the drilling fluid. The suspension of cuttings is most pronounced at the rear end of the cuttings bed, particularly under conditions of high fluid rate. In some cases, portions of the cuttings are completely dislodged from the bed, advancing forward and forming smaller, secondary cuttings beds (Figure 8b).  
Rolling stage  
In this stage, the cuttings primarily undergo rolling movement, especially on the elevated slope of the cuttings bed and in areas with variable height at the rear. A limited fluid rate results in a continuous accumulation of cuttings at the rear, and the rolling action becomes more dominant. This rolling mechanism drives the steady degradation of the cuttings bed as particles move forward (Figure 8c).
## 4 Results
### Qualitative Analysis of Model Interpretability
#### 4.2.2 Interpretability of Models on the Constellation Phase Histogram Dataset
Due to the complex modulation scheme, the phase clustering points are noticeably denser compared with the other two modulation formats. Therefore, Grad-CAM, which tends to annotate in large blocks, obviously struggles to clearly mark the phases. In comparison, the performance of LIME and SHAP is satisfactory, with some overlap between the annotated phase features and the phase clustering points. However, LIME still provides some annotations that do not overlap, while SHAP, conversely, fails to capture certain phase clustering points. In AMI-CNN, the model's annotated weights almost perfectly align with the phases of each clustering point. Even at positions with varying numbers of clustering points, there are differences in weights. For instance, at positions like 45\({}^{\circ}\) with two clustering points, the color is closer to white, while at slightly sparser phases like 22.5\({}^{\circ}\), the color is closer to black, indicating lower weights in that region. In conclusion, compared with the other three interpretable methods, AMI-CNN demonstrates the best interpretability across the three modulation formats.
# Electron Impact Cross Sections and Transport Studies of C\({}_{3}\)F\({}_{6}\)[PERSON]
###### Abstract  
Electron impact scattering from C\({}_{3}\)F\({}_{6}\)O is studied in this work. The R-matrix method was used for the calculations of elastic, momentum transfer, and excitation cross sections. The attachment cross section was obtained through a parametric estimator based on the R-matrix outputs. The Binary-Encounter-Bethe (BEB) method was used for computing the ionization cross section. The obtained cross section set was used for the transport studies using the BOLSIG+ code, which is a two-term Boltzmann equation solver. The present calculation was performed for steady-state Townsend experimental conditions for E/N, covering a range of 100-1000 Td. The critical dielectric strength of pure C\({}_{3}\)F\({}_{6}\)O was found to be 475 Td, which is much greater than that of SF\({}_{6}\) (355 Td). The effect of the addition of different buffer gases, such as CO\({}_{2}\), N\({}_{2}\), and O\({}_{2}\), was also examined. For the C\({}_{3}\)F\({}_{6}\)O-CO\({}_{2}\), C\({}_{3}\)F\({}_{6}\)O-N\({}_{2}\), and C\({}_{3}\)F\({}_{6}\)O-O\({}_{2}\) mixtures with 65%, 55%, and 60% C\({}_{3}\)F\({}_{6}\)O, respectively, the critical dielectric strength was determined to be essentially the same as that of pure SF\({}_{6}\). The presence of synergism was confirmed for these gas mixtures. We further derived the Paschen curve using a fitting method with the transport parameters as the basic inputs. The minimum breakdown voltage of C\({}_{3}\)F\({}_{6}\)O accounted for only 55% of that of SF\({}_{6}\). The buffer gas mixture improved the condition; however, the performance of CO\({}_{2}\) and O\({}_{2}\) mixtures was not satisfactory. The addition of N\({}_{2}\) as the buffer gas significantly improved the breakdown property of the gas. The mixture of \(\geq\)99% of N\({}_{2}\) or \(\leq\)1% of C\({}_{3}\)F\({}_{6}\)O gave a better breakdown characteristic than SF\({}_{6}\). Any proportion \(\geq\)90% of N\({}_{2}\) or \(\leq\)10% of C\({}_{3}\)F\({}_{6}\)O was suitable in the higher pressure ranges. The present work demonstrates the potential of C\({}_{3}\)F\({}_{6}\)O as a substitute gas for SF\({}_{6}\) with a negligible environmental threat.  
cross section; transport study; rate coefficient; dielectric strength; synergistic effect; breakdown voltage +
Footnote †: journal: Applied Sciences  
Academic Editor: [PERSON]  
Received: 30 October 2023  
Revised: 17 November 2023  
Accepted: 21 November 2023  
Published: 23 November 2023
## 4 The Dynamic Collision Avoidance Algorithm for the USV
### Fitness Function Based on Collision Avoidance Model  
Based on the collision avoidance model, the fitness function of the CS algorithm can be determined. According to (12), the USV can escape from the collision domain by changing course and velocity, so its fitness function can be expressed as  
\[f(\Delta v_{U},\Delta\alpha)=k_{1}|\Delta v_{U}|+k_{2}|\Delta\alpha| \tag{23}\]  
where \(f(\Delta v_{U},\Delta\alpha)\) represents the sum of course variation \(\Delta\alpha\) and velocity variation \(\Delta v_{U}\) in the optimization process, and the variation constraint conditions are shown in  
\[\begin{cases}\frac{\sin\varphi}{v_{UD}}\Delta v_{U}+\frac{v_{U}\cos\varphi}{v _{UD}}\Delta\alpha\geq\mu_{1}-\gamma&\quad\gamma\geq 0\\ \frac{\sin\varphi}{v_{UD}}\Delta v_{U}+\frac{v_{U}\cos\varphi}{v_{UD}}\Delta \alpha\leq-\mu_{2}-\gamma&\quad\gamma<0\end{cases}. \tag{24}\]  
In the process of collision avoidance, under the condition that the USV can escape from the collision domain and meet the constraints of the collision avoidance rules, the USV sails in pursuit of a smaller attitude change and smooth motion, then the change in course and velocity should be as small as possible when seeking a collision avoidance strategy. In other words, the smaller value of fitness in the optimization process indicates the better position of the individual. The weight coefficients \(k_{1}\) and \(k_{2}\) are used to give priority to the adjustment of \(\Delta\alpha\) and \(\Delta v_{U}\). If the value of \(k_{1}\) makes the value of \(k_{1}|\Delta v_{U}|\)larger when adjusting the velocity, the course should be adjusted first. If \(k_{2}\) makes \(k_{2}|\Delta\alpha|\) larger when adjusting the course, the velocity should be adjusted first. Since it is more effective to adjust the course for collision avoidance, the adjustment of the course will be given priority in this paper.
## 4 Electron Transfer-Based Assays
### Folin-Ciocalteu Method  
#### 4.5.1 Principle of the Method  
The Folin-Ciocalteu method is considered one of the most widely used assays in laboratories worldwide for quantifying the total polyphenol content [105]. It is based on the oxidation of hydroxyl groups in phenolic compounds by the Folin-Ciocalteu reagent (FCR) [106]. The blue color of the test solution corresponds to the reduced form of the FCR. The intensity of this hue is directly proportional to the concentration of phenolic compounds in the analyzed sample. An alkaline environment (pH \(\approx\) 10), usually achieved by using an aqueous solution of sodium carbonate (Na\({}_{2}\)CO\({}_{3}\)) or sodium hydroxide (NaOH), accelerates the reaction. Under these conditions, phenols are more readily deprotonated to phenolics, which have a lower redox potential and thus react more efficiently with FCR. During the redox reaction, molybdenum (Mo) ions in the reagent are reduced from the +6 to the +5 oxidation state, leading to the characteristic blue color of the mixture [107]. Accordingly, the FC assay operates via the SET mechanism and assesses the overall reducing capacity of the sample, using the phenolic content as a primary indicator of antioxidant potential [108]. The general reaction mechanism between phenolic compounds and FCR can be represented as follows:  
\(\text{MoO}_{4}^{2-}\) (in the reagent, yellow color) \(--\)phenolic compounds\(--\)\(>\text{MoO}_{3}^{2-}\) (reduced form, blue color)  
The popularity of the FC assay is attributed to its simplicity, speed, and low cost. However, a major limitation of the FC assay is its lack of specificity. Originally developed for protein quantification [106; 109], the method is not selective for phenolic compounds. The Folin-Ciocalteu reagent (FCR) can react with hydroxyl groups found in various non-phenolic compounds, such as tryptophan and hydroxylamine [28]. Additionally, reducing agents other than phenolic compounds--such as ascorbic acid and sulfites--can also interfere with FCR reactivity [102]. Due to this lack of specificity, the FC assay should not be directly referred to as a method for determining total phenolic content (TPC) [105]. Another important limitation of the FC assay is the absence of a standardized protocol. Significant methodological variations across studies hinder the direct comparison of results obtained from different experiments.  
Due to a lack of specificity and inconsistencies in methodology, it is difficult to properly estimate the TPC in analyzed foods. Consequently, the content of the TPC may be overestimated or underestimated. This may be the reason for the erroneous determination of the antioxidant potential of food products.
## 5 Performance Analysis
### Performance Metrics
\(P_{\mathit{Trans}}\) is the number of packets transmitted by the source nodes. \(n\) is the number of source nodes. \(5\). End-to-end delay (EED): the time taken for a packet to be transmitted across the network from a source node to the destination sink node. To evaluate the EED, Equation (5) was assumed, as adopted from [34; 35]. \[EED=\frac{\sum_{i=1}^{P_{\mathit{Rec}_{i}}}\left(T_{\mathit{Rec}_{i}}-\;T_{ \mathit{Trans}_{i}}\right)}{P_{\mathit{Rec}}}\] (5) where \(T_{\mathit{Rec}}\) is the time when data packet is received by the sink node. \(T_{\mathit{Trans}}\) is the time when each data packet is transmitted by a source node. \(P_{\mathit{Rec}}\) is the total number of data packets received at the destination sink node.