esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Short Term Clock Prediction for BDS-2 Real-Time Precise Point Positioning
## 4 Validation of the Predicted Clocks
### Clock Comparison  
First of all, GFZ precise clock products are selected as the reference benchmark to analyze the accuracy of clock prediction. To demonstrate the effectiveness of the improved model (combing SA and GRNN), other three predicted clocks are employed for comparison, i.e., GBU-P (it is a GFZ clock prediction product that uses QP model), spectrum analysis model, and GRNN(ONLY) model. Meanwhile, similar to the previous section, two latest launched satellites of each type are selected as examples to compare their prediction errors, as shown in Figure 8.  
As shown in Figure 8, GBU-P and spectrum analysis prediction have very similar trends, and the spectrum analysis model performs better. Overall, their prediction errors are all within \(\pm 3\) ns, when the prediction time is less than 3 h. The prediction errors of the GRNN (ONLY) model are smaller and more stable than the predictions utilizing the spectrum analysis, and the prediction errors of all satellites are keeping within \(\pm 1.5\) ns. Comparatively speaking, the performance enhancement of the improved model is obvious and prediction accuracy has been increased. Whether the prediction time is 1 h or 3 h, the prediction errors of all satellites are within \(\pm 1\) ns. Particularly, when the prediction time is less than 1.5 h, most of the prediction errors are within \(\pm 0.5\) ns.  
In order to further verify the prediction accuracy and stability of the improved model, 300-day clock predictions are evaluated statistically. The average clock differences between the predictions and GFZ precise clock products at different prediction time are calculated, and they are listed in Table 3, which also demonstrates that the improved model is evidently superior to the others. Compared with GBU-P, with the increase of prediction time from 0.5 h to 3.0 h, prediction errors of spectrum  
Figure 8: Clock differences between prediction and GFZ precise clock products, using (**a**) GBU-P, (**b**) spectrum analysis model, (**c**) GRNN (ONLY) model and (**d**) improved model.  
analysis are decreased by 3.6%, 6.6%, 12.6%, and 17.7% respectively. This shows that the short-term forecasting accuracy of GBU-P is acceptable. When the GRNN (ONLY) is used, maximum 72.2% improvement at 0.5 h and minimum 48.3% improvement at 3.0 h are obtained. When the improved model is employed, prediction errors are generally reduced by 70%, whether the prediction time is 0.5 h or 3.0 h. It demonstrates that the performance of the improved model has good stability.
## 3 Results and Discussion
### Influence of Recipe Composition on Selected Responses
#### 3.2.4 Texture Parameters  
In the RSM analysis, hardness was identified as the key texture-determining factor, with a target value of 2.89 N, consistent with the control sample. All factors, including SFBJ, BG, and PM (\(p\leq 0.001\), \(p\leq 0.001\), and \(p\leq 0.01\)), affected hardness. For most factors, an increase in hardness was observed with increasing content. Only in the case of SFBJ \(\times\) MP interaction was there a decrease in hardness (\(p\leq 0.01\)). Similarly, in the [PERSON] et al. (2024) study [13], it was found that an increase in the proportion of microcapsules resulted in an increase in the hardness of the bread. It was noted that this may be due to the rivalry of microcapsules, starches, and proteins in the dough for water. This results in a reduction in the starch gelation process, which inhibits the formation of a softer dough. In contrast, in [PERSON] et al.'s (2022) study [47], it was found that the use of microcapsules was associated with a reduction in the hardness of the bread. The analysis was repeated on three consecutive days, and on each of these days, the bread withmicrocaps had a lower hardness. However, this investigation used a different core material and a different encapsulation method, which may have affected this result. When using \(\beta\)-glucan, there is also an increase in the hardness of the bread as the concentration of the ingredient increases. This could be due, as in the case of microcapsules, to the high absorption of water by the fiber molecules [22; 49]. However, this correlation does not occur in the case of gluten-free bread, where the use of \(\beta\)-glucan preparation results in a reduction in the hardness of the bread [50]. Thus, it can be noted that the impact of ingredients such as \(\beta\)-glucan or microencapsulated bioactive compounds depends not only on the level of their participation but on their properties (core and matrix material of microcapsules, particle size), as well as the properties of the whole product, such as the presence or absence of gluten.
## 5 Experiments
### Discussion and Comparative Analysis  
The results of our experiments demonstrate the effectiveness of our proposed multimodal emotion recognition system, particularly when applied to human-robot interaction (HRI). The integration of visual, auditory, and textual modalities has led to improved accuracy and robustness in recognizing user emotions, as shown in Figures 4 and 5. For instance, our visual emotion detection model achieved a fine-tuned accuracy of approximately 95%, while the auditory model reached 100% accuracy under controlled conditions. However, these results must be interpreted in the context of their respective limitations.  
Compared to prior studies, such as [10], which utilize fixed datasets for emotion recognition, our approach demonstrates the adaptability of a multimodal system through the application of fine-tuning. This fine-tuning is key to tailoring the system to specific users, addressing the inherent variability in emotional expression between individuals. Unlike previous work, where general models are applied across datasets without user-specific adjustments, our methodology enables explicit personalization, making it particularly suitable for domains like healthcare and education. However, this advantage comes with challenges, including the need for user-specific data collection and a dependency on stable environmental conditions during the fine-tuning process.  
Moreover, while our system achieves high accuracy in emotion recognition, it currently lacks a fully automated mechanism for balancing the weight of individual modalities (e.g., visual versus auditory emotions) in ambiguous cases. This limitation is an area of active research and presents opportunities for future advancements in multimodal systems.  
Another notable strength of our approach is its ability to integrate seamlessly with Pepper's existing framework, demonstrating real-world applicability. Unlike prior works that focus primarily on simulation or controlled settings, our results indicate the feasibility of deploying such systems in practical scenarios. Nevertheless, the reliance on Pepper's outdated hardware and limited computational power poses challenges for real-time operation and scalability, which we aim to address in future iterations of the system.  
In conclusion, the findings of this study not only validate the proposed framework but also highlight areas for further development, including hardware improvements, the automation of fine-tuning processes, and broader generalization to diverse user groups. This discussion underscores the potential impact of multimodal emotion recognition in advancing the field of HRI while acknowledging the scientific and practical challenges that remain.
## 7 Superframe Duration Allocation Schemes
### Illustrative Example
\begin{table}
\begin{tabular}{c c c} \hline  & \(SO_{i}\) & \(SD_{i}\) \\ \hline \(CH_{1}\) & 3 & 8 \(\times SD_{min}\) \\ \(CH_{2}\) & 2 & 4 \(\times SD_{min}\) \\ \(CH_{3}\) & 1 & 2 \(\times SD_{min}\) \\ \(CH_{4}\) & 0 & \(SD_{min}\) \\ \(CH_{5}\) & 0 & \(SD_{min}\) \\ \(CH_{6}\) & 0 & \(SD_{min}\) \\ \hline \end{tabular}
\end{table} 
Table 1: Superframe Durations for all Clusters. For depth 2 (\(CH_{2}\)), the set of higher priority message streams is composed of message streams \(S_{3}\), \(S_{4}\), \(S_{7}\), \(S_{8}\) and \(S_{9}\). Thus, we have:  
\[\Theta^{1}_{10}(CH_{2}) =\tfrac{1}{2}+\left\lfloor\tfrac{\tfrac{1}{2}\times 5}{4} \right\rfloor\times 28+\tfrac{1}{2}\times 5=3 \tag{45}\] \[\Theta^{2}_{10}(CH_{2}) =\tfrac{1}{2}+\left\lfloor\tfrac{\left\lceil\tfrac{3}{60}\right \rceil\times\tfrac{1}{2}\times 3+\left\lceil\tfrac{2}{70}\right\rceil \times\tfrac{1}{2}\times 2}{4}\right\rfloor\times 28+\left\lceil\tfrac{3}{60} \right\rceil\times\tfrac{1}{2}\times 3+\left\lceil\tfrac{3}{70}\right\rceil \times\tfrac{1}{2}\times 2=3 \tag{46}\]  
In turn, for depth 1 (\(CH_{1}\)), the set of higher priority message streams is composed of message streams \(S_{1}\), \(S_{2}\), \(S_{3}\), \(S_{4}\), \(S_{5}\), \(S_{6}\), \(S_{7}\), \(S_{8}\), \(S_{9}\), \(S_{11}\) and \(S_{12}\). Thus, we have:  
\[\Theta^{1}_{10}(CH_{1}) =\tfrac{1}{2}+\left\lfloor\tfrac{\tfrac{1}{2}\times 11}{8} \right\rfloor\times 24+\tfrac{1}{2}\times 11=6 \tag{47}\] \[\Theta^{2}_{10}(CH_{1}) =\tfrac{1}{2}+\left\lfloor\tfrac{\left\lceil\tfrac{6}{60} \right\rceil\times\tfrac{1}{2}\times 6+\left\lceil\tfrac{6}{70}\right\rceil \times\tfrac{1}{2}\times 5}{8}\right\rfloor\times 24+\left\lceil\tfrac{6}{60} \right\rceil\times\tfrac{1}{2}\times 6+\left\lceil\tfrac{6}{70}\right\rceil \times\tfrac{1}{2}\times 5=6 \tag{48}\]  
Finally, applying Equation (25) for the case of bottom-up scheduling, we have:  
\[R_{i}=\sum_{j=1}^{N_{CH}}SD_{j}+\gamma_{i}+\sum_{d=1}^{depth_{i} }\Theta^{\text{pr}}_{i}(CH_{j}(d)) \tag{49}\] \[R_{10}=\sum_{j=1}^{N_{CH}}SD_{j}+\sigma_{10}+(BI-SD_{5})+\Theta^ {\text{pr}}_{10}(CH_{5})+\Theta^{\text{pr}}_{10}(CH_{2})+\Theta^{\text{pr}}_{10 }(CH_{1})\] (50) \[R_{10}=17+\tfrac{1}{2}+(32-1)+1+3+6=58,5 \tag{51}\]  
considering \(\sigma_{10}\) as approximately \(\tfrac{1}{2}\times SD_{min}\). Thus, the timing constraint for message stream \(S_{10}\) is satisfied, as follows:  
\[R_{10} \leq P_{10} \tag{52}\] \[58,5 \leq 70 \tag{53}\]  
Table 2 shows the timing constraints for all message streams, where it is clear that all deadlines can be met (a probabilistic guarantee, as the underlying message duration is probabilistic). The purpose of this example is just to illustrate the use of allocation scheme Load-SDA. Noticeably, for a real cluster-tree network, this analysis must be extended to the overall set of cluster-heads.
## 3 Experiment and Results
### Evaluation Results of SBAS Orbit and Clock Corrections
In general, the accuracies of satellite orbits and clocks calculated with current GPS broadcast navigation data are comparable or even better than that of SBAS-derived orbits and clocks, especially for the MSAS- and GAGAN-derived orbits and clocks. The benefits of applying SBAS corrections to GPS broadcast orbits and clocks are marginalized. The results are consistent with the previous literature [14]. Table 2 presents the SISRE values of SBAS-derived orbits and clocks for each SBAS satellite. SISRE is calculated according to Equation (13) by using 181-day orbit and clock error time series of all available GPS satellites with SBAS corrections. It is observed that the SISRE value is smaller than the addition of the RMS values of radial orbit errors and clock offset errors for each SBAS satellite. This situation should be attributed to the compensation effect of the radial orbit errors and the clock errors when computing the combined orbit and clock SISRE as mentioned above. In addition, orbit and clock corrections provided by the same SBAS system show slight differences in term of SISRE. The SISREs of EGNOS and SDCM systems are significantly smaller than the SISREs of the remaining three SBAS systems, followed by WAAS with the value less than 1 meter. The average SISRE values of MSAS- and GAGAN-derived orbits and clocks are 1.931 and 1.325 meters, respectively. The worse accuracy of MSAS and GAGAN orbit and clock corrections may be attributed to the limited geographic distribution of reference stations [43]. The SISRE of GPS broadcast orbits and clocks is also calculated and given in Table 2. Its value approximates to that of EGNOS- and SDCM-derived orbits and clocks, but significantly smaller than the SISRE values of WAAS-, MSAS- and GAGAN-derived orbits and clocks. The comparison results confirm that the benefits of applying SBAS corrections to GPS broadcast orbits and clocks are marginalized at present in terms of SISRE.
## 3 Computation Model
### Many-Core Structure and Communication of the Sunway TaihuLight
In order to further reduce the communication time, we divide the data in each MPE into the form shown in Figure 6, which are internal data and boundary data, respectively, where boundary data is the part that needs to communicate with other processes. When CPEs conduct internal data computation, the MPE is responsible for communicating the boundary data with related processes. In addition to the double-buffering mode, we can further reduce the communication time by using register communication between CPEs, as shown in Figure 7 (only the first line CPE is displayed, lines 2 to 8 are similar). Step 1: The data of CPEs 1, 3, 5, and 7 are transmitted to the CPEs 0, 2, 4, and 6 through register communication. Step 2: The data of CPEs 2 and 6 are transmitted to the CPEs 0 and 4 through register communication. Step 3: The data of CPE 4 are transmitted to the CPE 0 through register communication. Step 4-Step 6: Similar to Step 1 to Step 3, Step 4 to Step 6 operate on the column, and finally all the data that needs to pass will be transmitted from 64 CPEs to CPE 0. Figure 5: Data storage order adjustment. Figure 6: Boundary communication hiding. Step 7: CPE 0 communicates with the main memory by DMA. With this approach, CPE 0 is mainly responsible for the communication between the CPEs and MPE, so CPE 0 will be responsible for fewer computation tasks than other CPEs to achieve the purpose of reducing the communication time. Since register communication is much faster than access to the main memory, this method can greatly reduce the communication time.
## 2 Materials and Methods
### In Situ Meteorological Observations and Carbon Flux Data
The meteorological variables were measured simultaneously at each site including air temperature, rainfall, solar radiation, photosynthetically active radiation (PAR), relative humidity and soil moisture. VPD was calculated using measured relative humidity and actual vapor pressure. The meteorological data were measured at automatic weather stations at every 10 min interval, which were carefully checked for quality and summed into 30 mins and daily timescales. The raw EC measurements of 10 Hz data were processed into half-hourly flux data using the flux processing software Eddypro ([[http://www.licor.com/env/products/eddy_covariance/software.html](http://www.licor.com/env/products/eddy_covariance/software.html)]([http://www.licor.com/env/products/eddy_covariance/software.html](http://www.licor.com/env/products/eddy_covariance/software.html))) developed by LI-COR Biosciences (Lincoln, NE, USA). The flux data processing steps included spike detection, coordinate rotation, time-lag correction, coordinate rotation, sonic virtual temperature correction, frequency-response correction, and density correction [44; 45]. Then, the flux data were gap-filled using the marginal distribution sampling (MDS) method and partitioned into GPP and ecosystem respiration (Reco) following the flux partitioning algorithms from the REddyPro package [46]. Figure 2: Plots of monthly accumulated precipitation, monthly averaged air temperature (T) and monthly averaged vapor pressure deficit (VPD) over HRB.
# NFT-Based Framework for Digital Twin Management in Aviation Component Lifecycle Tracking
## 4 Discussion
### Challenges of NFT-Based Approach for Aviation Component Lifecycle Management  
While the NFT-based approach for aviation-component lifecycle management offers significant benefits, it also faces several key challenges:  
* Integrating blockchain, NFTs, digital twins, and decentralized storage creates a complex ecosystem that requires significant expertise to develop, maintain, and upgrade.
* Efficiently storing, managing, and ensuring the quality of vast amounts of data generated throughout component lifecycles poses a significant challenge.
* While blockchain enhances security, protecting the system from cyber attacks and balancing transparency with privacy protection remain crucial concerns.
* Obtaining approval from aviation authorities for blockchain and NFT technologies in critical systems and ensuring compatibility with international regulatory frameworks present significant hurdles.
* Overcoming resistance to change, integrating with legacy systems, and providing comprehensive training across the industry are key challenges for widespread adoption.
* Maintaining fast transaction speeds and network capacity as the system grows to encompass more components and stakeholders is a critical technical challenge.
* Significant investment is required for the initial setup, ongoing operations, and transitioning from existing systems to the new NFT-based approach.
* Seamless operation across different blockchain platforms and integration with various external systems require careful standardization efforts.
* Clarifying the legal status of NFTs as representations of physical components and determining liability in cases of system failures need to be addressed.  
Addressing the potential high energy consumption of blockchain networks, especially in the context of the aviation industry's sustainability goals, is an important consideration.  
Despite these challenges, the potential benefits of this innovative system make it a promising avenue for improving safety, efficiency, and transparency in the aviation industry.
## 3 Proposed Dynamic Fuzzy Logic for Broadcasting
### Reinforcement Learning Integration (Same as Previous Work)
#### 3.5.4 Maintaining the BC/DNB Ratio  
Once converged, the Q-table effectively encodes which fraction of nodes should broadcast versus those that should not broadcast under various network conditions (e.g., low or high density, strong or weak signal, etc.). If the network coverage remains high despite fewer nodes participating in packet transmissions, the RL agent gradually steers the system toward more DNB decisions. By contrast, if Coverage degrades significantly, the agent shifts toward more BC decisions. Thus, the primary role of RL in our framework is to continuously fine-tune this BC/DNB ratio based on real-time performance, thereby reducing Redundancy and Overhead while maintaining sufficient Coverage.
# Comprehensive Annual Ice Sheet Velocity Mapping Using Landsat-8, Sentinel-1, and RADARSAT-2 Data
## 4 Discussion
### Implications for (Future) SAR Missions for Ice Velocity Measurements
The ice speed is coded on a logarithmic scale overlaid on MOA background mosaic [32].
# Review of Vibration-Based Structural Health Monitoring Using Deep Learning
## 1 Introduction
Failure data in the field are difficult to obtain owing to the limited availability. Required data for training are generated from laboratory testbeds or from a smaller system to acquire labelled data for training. Testbed data are not guaranteed to those expected in the actual system data. To fill this gap, many deep-learning-based approaches have been proposed. Recent progress in machine learning technology has rapidly expanded its application to vibration-based structural health monitoring. This review summarizes recent attempts at actual applications. This information will be useful for proposals of relevant methods for specific systems requiring inspection and maintenance. Studies using deep learning for health monitoring with respect to vibration characteristics are introduced.
# Monocular Depth Estimation Based on Dilated Convolutions and Feature Fusion
## 3 Method Design and Implementation  
The network architecture proposed in this study, utilizing dilated convolutions and feature fusion, is illustrated in Figure 4. The system primarily comprises three key components: the feature extraction module, the multi-receptive field feature fusion module, and the depth perception module. The feature extraction module is tasked with the initial extraction of features from the input image data to acquire preliminary feature information. The information is subsequently processed by the multi-receptive field feature fusion module to produce enhanced feature representations. Finally, the feature information is transmitted to the depth perception module, where a correlation is established between the feature maps and depth categories, culminating in the generation of the image's depth estimation result.
## 2 Background  
### Eeg  
Electroencephalograms (EEGs) are widely used in various research fields, including neural engineering and neuroscience [14]. An EEG measures the brain's electrical activity [15]. This technique involves placing electrodes on the scalp to detect and measure voltage fluctuations resulting from ionic flow within brain neurons. EEG signals are categorized into different frequency bands: delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), and gamma (30-100 Hz), each associated with different brain states and functions. Compared to other tests like fMRI, EGG offers advantages such as low cost,portability, and noninvasive measurement, reducing the burden on subjects and minimizing side effects.  
EEG-based emotion recognition analyzes human emotions using EEG signals detected noninvasively by measuring electrical brain activity with electrodes placed on the scalp. By capturing the brain's electrical activity, we aimed to classify emotional states on EEG signal patterns and characteristics.
# Removal of Chromium(VI) by Chitosan Beads Modified with Sodium Dodecyl Sulfate (SDS)
## 2 Materials and Methods
### Adsorption Isotherm  
Adsorption isotherms are commonly used to reflect the performance of adsorbents in adsorption processes. To examine the relationship between the metal uptake (\(q_{e}\)) and the concentration of metal ions (\(C_{e}\)) at equilibrium, adsorption isotherm models are widely employed for fitting the data. To investigate the equilibrium data, initial concentrations of metals were varied while the adsorbent weight of each sample was kept constant. Langmuir and Freundlich isotherms models were applied to evaluate the adsorption data obtained in this study.  
The Langmuir model assumes monolayer adsorption on a surface and is given by  
\[\frac{C_{e}}{q_{e}}=\frac{C_{e}}{q_{max}}+\frac{1}{K_{L}q_{max}} \tag{2}\]  
where \(C_{e}\) and \(q_{e}\) are the concentration of Cr at equilibrium (mg:L\({}^{-1}\)) and the amount of adsorption of Cr(VI) at equilibrium (mg:g\({}^{-1}\)), respectively; \(q_{\max}\) is the maximum adsorption capacity on the surface of the chitosan bead (mg:g\({}^{-1}\)); \(K_{L}\) is the equilibrium adsorption constant (L:mg\({}^{-1}\)). A plot of \(C_{e}\)/\(q_{e}\) versus \(C_{e}\) gives a straight line with a slope of 1/\(q_{\max}\) and intercept of 1/(\(K_{L}q_{\max}\)).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Condition** \\ \hline
**Plasma Conditions** & \\ RF frequency & 27.1 MHz \\ RF power & 1400 W \\
**Gas conditions** & \\ Plasma gas flow & 15 L \(\cdot\) min\({}^{-1}\) \\ Carrier gas flow & 1.2 L \(\cdot\) min\({}^{-1}\) \\
**Sample conditions** & \\ Sampling depth & 6.5 mm \\ Sample uptake rate & 0.5 mL \(\cdot\) min\({}^{-1}\) \\ Measurement point & 3 points/peak \\ Integration time & 1.0 sec/point \\ Measured isotope & \({}^{52}\)Cr \\ \hline \hline \end{tabular}
\end{table}
Table 1: Operating conditions of inductively coupled plasma mass spectrometry (ICP-MS).  
On the other hand, the linearized Freundlich model isotherm is represented by the following equation:  
\[\lg q_{e}=\lg K_{F}+(1/n)\lg C_{e} \tag{3}\]  
where \(K_{F}\) is the adsorption capacity and \(1/n\) indicates the adsorption intensity of the system. The plots of \(q_{e}\) versus \(C_{e}\) on a log scale can be plotted to determine the values of \(1/n\) and \(K_{F}\) depicting the constants of the Freundlich model.