esa-sceva/satcom-chunk-collection huggingface dataset:
# Initial Student Attention-Allocation and Flight-Performance Improvements Based on Eye-Movement Data
## 3 Results
### Entropy of Fixation  
The entropy of fixation [38] is further calculated as an indicator to measure the difference in eye movement strategies of different pilots. Entropy provides a measure of the statistical randomness or aggregation of participants' eye movements, so that the spatially diffused fixation will lead to higher entropy, while the spatially tightly focused fixation will lead to lower entropy. The entropy of fixation represents the randomness of visual scanning of subjects. The higher the entropy, the more random the scanning strategy. Therefore, if each subject uses a more similar instrument scanning strategy throughout the experiment, their fixation entropy will be lower, as shown in Equation (1).  
\[H_{t}=-\sum_{i=1}^{n}p_{i}\sum_{j=1}^{n}p_{ij}\log_{2}p_{ij} \tag{1}\]  
where \(p_{i}\) represents the probability of fixating on the \(i\)-th AOI, while \(p_{ij}\) denotes the conditional probability of fixating on the \(j\)-th AOI given that the \(i\)-th AOI was previously fixated upon. \(n\) refers to the total number of AOIs, and \(H_{t}\) stands for fixation entropy. The results indicate significant differences in fixation entropy between experimental and control groups (\(p\) = 0.001, cohen'd = 1.41). As shown in Figure 8, the entropy of fixation of the control group was significantly higher than that of the experimental group, indicating that the members of the experimental group were more strategic in instrument scanning after the attention allocation course training.
# Phonocardiography Signals Compression with Deep Convolutional Autoencoder for Telecare Applications
## 4 Experiments
### Results
#### 4.2.1 Segment Length Evaluation  
During the training phase, the batch size is chosen as 60, and the number of the epoch is set to 200. Meanwhile, we use conventional adaptive moments (Adam) optimizer [43] in order to obtain better results. The main parameters associated with the Adam optimizer are learning rate = 0.01, hyper-parameters \(\beta_{1}=0.9\) and \(\beta_{2}=0.999\), weight decay factor \(\lambda=10^{-5}\). Because each audio file's length in the data set is not the same, we first divided them into segments and then randomly split into approximately 80% for training, 10% for validation, and 10% for testing, respectively.  
We fix the CR as 27 and evaluate the resulting PRD under various segment lengths (see Figure 3). Note that the segment length of 2000 or 3000 samples contains roughly two or three cardiac cycles; the segment length of 6000 samples contains roughly four or five cardiac cycles, and the segment length of 8000 samples contains roughly six or seven cardiac cycles. The evaluation results are listed in Table 3, in which we can observe that the longer segment length leads to higher computational cost, and the segment length of 6000 achieves the lowest PRD. Thus, we choose the samples in a segment as 6000.  
As shown in Figure 4, we observe that for the time duration of 3 s (segment length of 6000), the details contained in the compressed signals are less than that for the time duration of 4 s
## 2 Materials and Methods
### The Processing Strategies
#### 2.2.1 Coordinate and Time Systems of the New Prototype Software Platform  
A unified coordinate system and time system are needed before five-system ultra-rapid POD. For the coordinate system, GPS, GLONASS, BDS, Galileo, and QZSS use the WGS84, PZ-90, CGCS2000, GTRF, and GRS80 ellipsoid systems [34; 35; 36; 37; 38], respectively. These coordinate systems are defined to approach the International Terrestrial Reference System (ITRS). According to Section 2.1, the double-difference observation model was used in our software platform, and so the differences between WGS84 and other coordinates can be ignored, except PZ-90 [39]. The transformation between WGS84 and PZ-90 can be realized by the [PERSON] seven-parameter model, and the transformation model is shown below:\[\left(\begin{array}{c}X_{GPS}\\ Y_{GPS}\\ Z_{GPS}\end{array}\right)=\left(\begin{array}{c}\Delta X\\ \Delta Y\\ \Delta Z\end{array}\right)+\left(1+\alpha\right)\left(\begin{array}{ccc}1& \Delta\omega&-\Delta\varphi\\ -\Delta\omega&1&\Delta\epsilon\\ \Delta\varphi&-\Delta\epsilon&1\end{array}\right)\left(\begin{array}{c}X_{ GLO}\\ Y_{GLO}\\ Z_{GLO}\end{array}\right) \tag{12}\]  
where \(\left(\begin{array}{cc}X_{GPS}&Y_{GPS}&Z_{GPS}\end{array}\right)^{T}\) and \(\left(\begin{array}{cc}X_{GLO}&Y_{GLO}&Z_{GLO}\end{array}\right)^{T}\) are the coordinates in the WGS84 and PZ-90 systems, respectively. Furthermore, \(\left(\begin{array}{cc}\Delta X&\Delta Y&\Delta Z&\alpha&\Delta\varphi&\Delta \varphi&\Delta\epsilon\end{array}\right)\) are the seven transformation parameters ([[ftp://ftp.aiub.unibe.ch/BSWUSER52/GEN/DATUM](ftp://ftp.aiub.unibe.ch/BSWUSER52/GEN/DATUM)]([ftp://ftp.aiub.unibe.ch/BSWUSER52/GEN/DATUM](ftp://ftp.aiub.unibe.ch/BSWUSER52/GEN/DATUM))).  
For the time system, GPS, GLONASS, BDS, Galileo, and QZSS use the GPST, GLNT, BDT, GST, and QZSST systems, respectively. These time systems conform to UTC (USNO), UTC (SU), UTC (BSNC), UTC (PTB), and UTC (NICT), respectively. Their transformation model is shown below [34; 35; 36; 37; 38; 39]:  
\[\left\{\begin{array}{l}GPST=GLNT-3h+\Delta t_{LS}+\delta_{GPST-GLNT}\\ GPST=BDT+1356 weeks+14s+\delta_{GPST-BDT}\\ GPST=GST+\delta_{GPST-GST}\\ GPST=QZSST+\delta_{GPST-QZSST}\end{array}\right., \tag{13}\]  
where \(\Delta t_{LS}\) is the leap second of \(GLNT\). \(\delta_{GPST-GLNT}\), \(\delta_{GPST-BDT}\), \(\delta_{GPST-GST}\), and \(\delta_{GPST-QZSST}\) are the micro-deviations between GPST and the GLNT, BDT, GST, and QZSST systems. In our software platform, these micro-deviations are ignored.
# Speech Inpainting Based on Multi-Layer Long Short-Term Memory Networks
## 3 Proposed Speech Inpainting Methods and Model Architecture
### Hyperparameter Optimisation
#### 3.5.1 Batch Size  
As recommended in [42], four batch sizes were considered based on our proposed models: 256, 512, 640, and 1024. The average MOSs of the inpainting performance under different batch sizes and gap lengths are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Batch Size**} & \multirow{2}{*}{**Bandwidth**} & \multicolumn{8}{c}{**Gap Length (ms)**} \\ \cline{3-10}  & & **20** & **40** & **50** & **100** & **200** & **500** & **1000** \\ \hline \multirow{2}{*}{256} & NB & 3.95 & 3.80 & 3.78 & 3.67 & 3.37 & 2.93 & 2.34 \\  & WB & 3.84 & 3.70 & 3.62 & 3.51 & 3.06 & 2.22 & 1.69 \\ \hline \multirow{2}{*}{512} & NB & 4.03 & 3.92 & 3.91 & 3.79 & 3.59 & 3.25 & 2.74 \\  & WB & 4.03 & 3.93 & 3.87 & 3.72 & 3.44 & 2.88 & 2.27 \\ \hline \multirow{2}{*}{640} & NB & 4.08 & 3.95 & 3.92 & 3.82 & 3.62 & 3.23 & 2.72 \\  & WB & 4.01 & 3.87 & 3.80 & 3.68 & 3.37 & 2.72 & 2.06 \\ \hline \multirow{2}{*}{1024} & NB & 4.08 & 3.90 & 3.89 & 3.77 & 3.52 & 3.15 & 2.62 \\  & WB & 4.07 & 3.93 & 3.90 & 3.80 & 3.45 & 2.83 & 2.19 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average MOSs of the inpainted speech with different gap lengths under different batch sizes.  
In Table 2, it can be seen that when the gap length was less than or equal to 200 ms, the batch sizes that achieved the highest average MOSs were 640 (NB) and 1024 (WB), respectively. For gap lengths in the range of 500 ms\(\sim\)1000 ms, the highest average MOS was obtained when the batch size = 512.  
As specified in the 3 GPP standards for voice encoder/decoder (CODEC) for 4G/5G mobile communication systems [43; 44; 45], the maximum sampling rate of input voice signals is 48 kHz. This corresponds to 960 sample points for a frame of 20 ms. To ensure the model's capability in learning the features and correction among speech frames during training, optimising speech communication quality across different bit rates, and ensuring the model's adaptability to a variety of speech CODECs, the batch size should be close to the number of samples in one frame. Consequently, a batch size of 1024 was selected for final-tuning the model.
## 3 Results and Discussion
### Phase Transformation of NiTiNb Prepared by LSF
[61] found that the martensite transformation temperature increases with the increase of laser power, indicating that Ni loss increases with the increase of input energy. Therefore, the L7 sample with high laser power without heat treatment has a higher phase transformation temperature than the L8 sample. As the laser energy increases from the heat-affected zone to the center of the molten pool, the loss of nickel increases, and the martensite transformation temperature gradually increases [62]. This will result in different phase transformation temperatures at different locations of the same sample. The R phase transformation can be induced by various thermomechanical treatments, such as annealing after deformation, thermal cycling, or aging of nickel-rich nickel-titanium alloys [52]. The thermal cycle of remelting solidification is continuously carried out in the LSF process, and the accumulated stress may also be one of the reasons for the R phase transformation. The XRD curve of this study shows the presence of Ni\({}_{4}\)Ti\({}_{3}\), and Ni\({}_{4}\)Ti\({}_{3}\) particles have a strong resistance to the formation of B19\({}^{\prime}\), but their resistance to the formation of the R phase is much smaller [63]. Therefore, in a nickel-rich NiTi alloy that produces Ni\({}_{4}\)Ti\({}_{3}\) precipitation, a one-step martensitic transformation B2-B19\({}^{\prime}\) changes to a two-step transformation B2-R-B19\({}^{\prime}\). Therefore, the main reason for the change in the phase transformation behavior of the sample may be the precipitation of Ni\({}_{4}\)Ti\({}_{3}\) particles caused by the heat treatment. The existence of grain boundaries or similar defects in nickel-rich NiTi alloys and the content of nickel control whether the precipitation phases are distributed non-uniformly or uniformly [63]. Due to the complexity of the LSF processing history, the microstructure and Ni content will change, resulting in the uneven distribution of Ni\({}_{4}\)Ti\({}_{3}\) particles. This is also one of the reasons for the different phase transformation temperature and different phase transformation behavior in the same sample. In the future, we can consider adjusting the LSF parameter combination to further optimize the phase transformation temperature characteristics of LSF-NiTiNb. [PERSON] and [PERSON] et al. simulated the twinning and microstructures in martensitic transformation [64; 65] and temperature- and stress-induced phase transformations [66; 67] by the phase-field method. The above work laid a foundation for our in-depth study of LSF-NiTiNb transformation by the phase-field method in the later stage.
# Analysis of the Water Circulation Structure in the Paldang Reservoir, South Korea
## 3 Results
### Water Circulation of Paldang Reservoir
#### 3.2.2 Relationship between the Flow Velocity and Discharge  
In this study, a discharge analysis was performed to determine the relationship between site-specific flow velocity and discharge from the PDD, the CPD, and the IPW from 12 May to 17 June 2016. The results showed that the average discharges from the PDD and the CPD were 292.0 and 163.5 m\({}^{3}\)/s, respectively, which were values slightly lower than the values of 353.9 and 175.6 m\({}^{3}\)/s for the average discharges from these dams, respectively, during the same period for the previous decade. The average discharges obtained in the first survey period accounted for 80% and 93% of the previous 10-year average discharge for the PDD and the CPD, respectively. Furthermore, the average discharge of the IPW was 108.4 m\({}^{3}\)/s during the first survey period, which accounted for 77% of the previous four-year average discharge of 139.3 m\({}^{3}\)/s.  
Figure 10 shows the relationship between the flow velocity observed at different sites and the water discharge from the CPD, the PDD, and the IPW. A comprehensive analysis was conducted for the entire period of data collected, and the results showed that no relationships existed between the flowvelocity and discharge, which can be attributed to the complex three-dimensional water circulation of the Paldang Reservoir. The PC1, PC2, and PC3 monitoring sites were used to examine the relationships with the discharge from the CPD, the IPW, and the PDD, respectively.  
At PC1, a linear relationship between the flow velocity in the downstream direction and the discharge was observed during the vertical mixing period, and a linear relationship between the flow velocity in the downstream direction and the discharge was observed during the stratification period as well. In the surface layer, furthermore, the flow velocity was relatively low compared to that in the middle and lower layers, and flow occurred in the upstream direction during the period of zero discharge from the CPD. At PC2, no significant discharge-velocity relationship was observed, but in the stratification period, flow occurred in the middle and lower layers in the upstream direction during the period of zero discharge from the CPD. At PC3, a linear relationship between the discharge and flow velocity in the downstream direction was observed during the vertical mixing period, and the flow velocity in the lower layer was relatively low compared to that in the surface and middle layers. Moreover, the discharge and flow velocity in the downstream direction had a linear relationship during the stratification period.
## 4 Experiments and Discussion  
The experimental results demonstrate that the DCTransformer technique is notably more effective than other classification techniques. The ViT failed to consider the spatial-spectral features, resulting in poor precision across all indicators. The SpectralFormer has the worst result in OA and AA, meaning the model performs poorly in special categories due to the deficiency of local detail spatial information, but it has higher kappa metrics, which may indicate it performs well in considering consistency and randomness in classification. The SFFT and MorphFormer performed well, in contrast. Furthermore, the classification performance was assessed using various window sizes. The final window size of \(11\times 11\) was chosen by considering the OA, AA, and kappa. Table 9 shows our method's parameters on the Houston datasets and Table 11 shows different window sizes' impacts on the results. Our method demonstrates that highly frequent information is important to the HSI classification and proposes an effective model to capture the spatial-spectral features to achieve an **SOTA** performance.
# A Brief Review on Challenges in Design and Development of Nanorobots for Medical Applications
## 2 Challenges in the Design and Development of Nanorobots
### Powering and Navigation
#### 2.3.4 Bio-Inspired Nanorobots
Another key challenge with robot locomotion engineering methods is to duplicate or replicate the critical functions of their natural counterpart while keeping the complexity minimal in order to construct the device. Creating complex instruments at the micro and nanoscale has become increasingly difficult, as the integration of different parts is more challenging at such scales. Therefore, it is critical to retain the required functionalities of nature and to reduce complexity [24]. Despite the difficulties mentioned, bio-inspired nanorobots developed based on different requirements include helical propellers, flexible filaments, flexible magnetic composites, and catalytic nanorobots.
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 2 Materials and Methods
### Provincial Forecasting Methodologies  
Provincial dispatch centers employ a variety of forecasting methodologies depending on their specific needs, data availability, and technical capabilities. These methods generally fall into three categories:  
Traditional statistical models like ARIMA and Holt-Winters capture temporal patterns through autoregression, differencing, and seasonal components. While effective for stable load patterns, they struggle with complex nonlinear relationships and extreme weather conditions.  
Machine learning approaches, including LSTM and GRU networks, offer improved capabilities for modeling nonlinear relationships and long-term dependencies in load data. These models can better capture complex seasonality and special event effects but require significant computing resources.  
Advanced attention-based architectures like Transformer variants provide enhanced feature extraction capabilities by modeling relationships between multiple variables simultaneously. These models excel in handling complex forecasting scenarios with both long-term trends and short-term fluctuations.  
Rather than focusing on any specific model, our research addresses the critical challenge of integrating these diverse provincial forecasts through a systematic evaluation and weighted fusion methodology.
## 2 Characteristics of Surface Potential Distribution for DC Grounding Electrode
### Characteristics of Surface Potential Distribution for DC Grounding Electrodes
#### 2.3.2 Breakage in the Ring Region
(1) Firstly, we arrange one measuring point at each filter well's position; (2) Then, five measurement points are arranged equidistantly on the line AB of the filter wells, as shown in Figure 11a. Similarly, the filter wells unit in other areas is treated in the same way. Therefore, a total of 23 measuring points need to be arranged in the ring area on one side of the DC grounding electrode, and a total of 46 measuring points on both sides can complete the detection of the ring area. The position and number of all measurement points are shown in Figure 12.  
Figure 11: The change in surface potential after breakage between filter wells 6 and 7. (**a**) One feeder rod broken (In the diagram, A and B represent No.7 and No.6 filter-wells respectively, and ��⃝\(\sim\)⃝\(\sim\)⃝\(\sim\)⃝ are the number of measurement points); (**b**) Three feeder rods broken; (**c**) Five feeder rods broken; (**d**) Ten feeder rods broken. Figure 12: Layout of measurement points for surface potential of DC grounding electrode. Operating State Detection Method (OSDM) of DC Grounding Electrode Based on Surface Potential Distribution Characteristics  
In Section 2, it is pointed out that when the local area of the DC grounding electrode is partially broken, the distribution characteristics of the ground surface potential change significantly. Therefore, it is considered to detect the operating state of the DC grounding electrode according to the surface potential in the DC grounding electrode field. The OSDM method proposed in this paper mainly includes three steps:  
(1) According to the arrangement of surface potential measurement points of the DC grounding electrode in Figure 11, the surface potential under various breakage conditions is calculated according to the finite element method, and the data set is established. (2) According to the sample data set, the regression prediction model is constructed to realize the accurate prediction of the surface potential distribution of the DC grounding electrode under different breakage conditions. (3) The objective function is constructed according to the change in surface potential characteristics, and the network prediction model is called by the improved genetic algorithm to complete the iterative optimization of the distribution current of the DC grounding electrode, so as to realize the operation state detection of the DC grounding electrode. The structure of the OSDM model is shown in Figure 13.
## 4 Discussion
### Process-Based vs. Empirical Modeling  
Unsurprisingly, our locally calibrated empirical model performed better for our local site than a continental-scale, process-based model (RAP). We stress that these results are not intended to undermine the value of RAP as a tool. RAP was calibrated to work well when comparing sites across vast spatial scales encompassing highly variable biophysical and climatic conditions. However, good performance at broad scales may tradeoff with good performance at fine scales. In particular, the production assumptions built into RAP may not be detailed enough to capture variation among cosites within the same region. Our study demonstrates that the relationship between APAR and ANHP does vary by ecosite, but RAP currently uses the same parameters to convert APAR to GPP (and eventually to ANHP) for all grasslands, which were calibrated using data from more humid, productive,and taller structured grassland sites in Oklahoma and California [16]. In our study area, RAP tended to predict biomass production in a fairly narrow range and was especially poor at predicting when observed biomass was high (1500-3500 kg ha\({}^{-1}\) for this study). This suggests that RAP's underlying parameters may be (a) overestimating plant maintenance respiration, (b) underestimating APAR, or (c) underestimating light use efficiency in this system. For example, with respect to (c), it is possible that RAP's parameters overestimate the limitations of temperature extremes and high vapor pressure deficit on vegetation in the shortgrass prairie, which has evolved under highly volatile temperature and precipitation regimes. While the underlying reasons for inconsistencies warrant further investigation, in the meantime, managers trying to understand production differences at fine spatial scales may need to recognize that the performance of RAP may vary across different regions and different conditions within a given region.
# Agriculture 5.0 in Colombia: Opportunities Through the Emerging 6G Network
## 4 Results and Discussion
### Current Colombian Agricultural Outlook
#### 4.3.1 Soil Fertility and Nutrient Management  
AGROSAVIA offers a soil analysis service accredited under ISO/IEC 17025 [63], using over 300,000 samples and AI-powered recommendation models for more than 100 crops. These systems are already in use to guide nutrient management practices in regions like Tolima, Sucre, and Meta [64]). With 6G's ultra-low latency and real-time connectivity, soil data could be transmitted instantly to remote farms, enabling dynamic fertilization decisions based on changing environmental conditions.