esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The Crustal Movement Observation Network of China (CMONOC) has begun receiving BeiDou Navigation Satellite System (BDS) observations since 2015, and accumulated more than 2.5 years of data. BDS observations has been widely applied in many fields, and long-term continuous data provide a new strategy for the study of crustal deformation in China. This paper focuses on the evaluation of BDS positioning performance and its potential application on crustal deformation in CMONOC. According to the comparative analysis on multipath delay (MPD) and signal to noise ratio (SNR) between BDS and GPS data, the data quality of BDS is at the same level with GPS measurements in COMONC. The spatial distribution of BDS positioning accuracy evaluated as the root mean square (RMS) of daily residual position time series on horizontal component is latitude-dependent, declining with the increasing of station latitude, while the vertical one is randomly distributed in China. The mean RMS of BDS position residual time series is 7 mm and 22 mm on horizontal and vertical components, respectively, and annual periodicity in position time series can be identified by BDS data. In view of the accuracy of BDS positioning, there are no systematic differences between GPS and BDS results. Based on time series analysis with data volume being 2.5 years, the noise characteristics of BDS daily position time series is time-correlated and corresponding noise is white plus flicker noise model, and the derived mean RMS of the BDS velocities is 1.2, 1.5, and 4.1 mm/year on north, east, and up components, respectively. The imperfect performance of BDS positioning relative to GPS is likely attributed to the relatively low accuracy of BDS ephemeris, and the sparse amount of MEO satellites distribution in the BDS constellation. It is expectable to study crustal deformation in CMONOC by BDS with the gradual maturity of its constellation and the accumulation of observations.  
BDS; CMONOC; data quality; positioning accuracy; BDS derived velocity field 10 September 2018; 3353; doi:10.3390/s18103353
## 2 Materials and Methods
### Material Preparation and Testing
#### 2.4.2 Consolidated (Isotropic) Undrained Triaxial Test (CIU)  
Using a computer-controlled stress-path cell, consolidated (isotropic) undrained triaxial test (CIU) was performed in accordance with BS EN ISO 17892-9-2018 on the compacted and cured samples (natural and stabilised).  
* Sample saturation  
It should be noted that the compacted and cured samples were in a state of partial saturation. Hence, it is generally practical to use naturally saturated or soils which have been brought to a state of saturation before the commencement of testing. This is to prevent the effect of compressible pore fluid, which may distort the results of effective stresses [28]. The samples were subjected to values of effective pressures (i.e., cell pressure and back pressure) in multiple steps to ensure saturation in excess of 95%. A specimen can be considered as being saturated if the value of pore pressure coefficient B (\(\Delta\text{u}_{3}/\Delta\sigma_{3}\)) is at least 0.95 [29]. Figure 2a,b indicate the detailed plot of each \"stepped\" pressure inputs to achieve the desired saturation of the samples. The specimens were subjected to saturation with the value of B ranging between 0.95 and 0.98 (Figure 2c).  
Figure 2: Stepped input pressure: (**a**) unstabilised soil; (**b**) stabilised soil; (**c**) B-value range of unstabilised and stabilised soils.  
Sample consolidation and shearing  
Following the saturation stage, two each of the triaxial specimens were subjected to constant effective consolidation pressures (i.e., cell pressure--back pressure) of 100 kPa and 200 kPa, representing values high enough to exceed any pre-existing preconsidation pressures and the range commonly encountered in most earthwork applications [29; 30]. Thereafter, shearing was conducted on the same constant effective consolidation pressures as in the consolidation stage.
# Enhanced Path Planning by Repositioning the Starting Point +
## 4 Experimental Evaluation
### Evaluation Results
#### 4.2.3 Finding the Best Centering Approach
The statistical reliability of these findings becomes evident when examining the minimal variations between datasets, which typically range from \(\pm\)1-2%. While this suggests that the original 650-scenario dataset was sufficient to capture the essential patterns, the expanded dataset offers researchers additional confidence in the statistical significance of the results. This enhanced confidence is reflected in the superior performance of the geometric median approach across both datasets. When compared to other methods for centering the starting point in the mTSP, the geometric median consistently achieves optimized total distances. The brute-force approach produces identical results, further affirming the geometric median's effectiveness as an optimal solution. The practical implications of these findings become clear when considering algorithm efficiency. The geometric median approach maintains its position as the optimal centering method across both datasets, with results consistently matching the computationally expensive brute-force approach. This consistency across different sample sizes reinforces the practical value of the geometric median method as an efficient alternative to exhaustive search approaches. To provide additional statistical validation, we applied the Wilcoxon Signed-Rank Test [50] to compare the total distance produced by the standard mTSP against the total distance after repositioning the starting point using the geometric median approach. The test yielded a statistic value of 2145.0 and a \(p\)-value of \(1.203\times 10^{-12}\), indicating very strong evidence against the null hypothesis. This suggests a significant difference between the paired samples, confirming that the observed improvements are not due to random chance but represent a meaningful enhancement in performance. To further validate these findings and ensure the reproducibility of our results, we conducted an additional round of testing using a completely new set of 65,000 randomly generated scenarios (another 520,000). This independent validation dataset, generated with different random station configurations, produced virtually identical results to our original expanded dataset (Figures 13 and 14). The consistency between these two independent large-scale datasets reinforces the validity and reliability of our findings, demonstrating that the observed performance patterns are robust and not dependent on specific random configurations. This additional validation strengthens the statistical evidence supporting the superiority of the geometric median approach.
## 3 Background
### The Transformer Encoder Architecture  
The Transformer encoder architecture (illustrated in Figure 1), introduced in Ref. [47], is widely used in natural language processing (NLP) for sequence-to-sequence tasks such as machine translation and language modeling. It is known for its ability to effectively capture long-range dependencies and its parallelizability.  
The key components of the Transformer encoder architecture include input embeddings, positional encoding, multi-head self-attention, layer normalization, position-wise feed-forward networks, residual connections, and layer stacking.  
Input embeddings represent the meaning of input tokens and are learned during training. Positional encoding is added to address the lack of inherent positional information in the Transformer architecture, allowing the model to capture the sequential order of tokens in the input sequence.  
The multi-head self-attention mechanism is at the heart of the Transformer encoder, enabling the model to weigh the importance of different tokens and capture long-range dependencies efficiently. Layer normalization is applied after self-attention to normalize the outputs, aiding in training stability. Position-wise feed-forward networks capture complex interactions between tokens, and residual connections help with gradient flow and alleviate the vanishing gradient problem.  
The Transformer encoder architecture typically consists of multiple identical layers stacked on top of each other, with the outputs of each layer fed as inputs to the subsequent layer, allowing the model to capture hierarchical representations of the input sequence.  
In summary, the Transformer encoder architecture is characterized by its self-attention mechanism, layer normalization, and residual connections, which work together to make it a powerful architecture for sequence-to-sequence tasks in NLP.
## 5 Proposed Model
### An Enhanced Distributed and Adaptable PDP Model Based on ML Technology for Access Control
#### 5.2.1 Balancing Access Control Policy Datasets  
The actual access policy set is an imbalanced data set. In a dataset of access policies, there can be substantial variation in the number of policies permitted and denied. For example, the number of approved policies in Kaggal Amazon's access control dataset is more significant than that of refused policies. The model's performance will suffer if these unbalanced datasets are used. Samples representative of the entire population can be generated using adaptive synthetic sampling (ADASYN). The computation procedure is detailed below.  
1. Determine the degree of imbalance between the minority group (policies denied as \(M_{\text{d}}\)) and the majority group (policies allowed as \(M_{\text{a}}\)) by using this equation, where \(B\) indicates the degree of imbalance. \[B=\frac{M_{\text{d}}}{M_{\text{a}}},\quad B\in[0,1].\] (1)
2. Compute quantity of dataset that should be synthesised. When \(\alpha=1,GN\) equals the ratio between the denied policies dataset (minority group) and the allowed policies  
Figure 3: Distributed and adaptable PDP model based on ML technology for access control.  
dataset (majority group). In this step, the numbers from the minority and majority groups are accurately balanced within the synthesised dataset. \[GN=\alpha(M_{\text{a}}-M_{\text{d}}),\quad\alpha\in[0,1].\] (2)
3. Each sample from the minority group has \(F\) neighbours, where \(F\) is determined by Euclidean distance and \(N_{\text{a}}\) is the quantity of samples from the majority group within \(F\) neighbours. \[e_{\text{i}}=\frac{N_{\text{a}}}{F},\quad e_{\text{i}}\in[0,1].\] (3)
4. Compute the number for the majority group within the minority group \[\widehat{e_{\text{i}}}=\frac{e_{\text{i}}}{\sum_{i=1}^{m_{\text{a}}}e_{\text{ i}}},\quad\sum_{i=1}^{m_{\text{a}}}e_{\text{i}}=1.\] (4)
5. Determine the number of samples that should be synthesised for each minority group. \[gn_{\text{i}}=\widehat{e_{\text{i}}}\cdot GN.\] (5)
6. When performing data synthesis (\(SY\)), choose one minority group sample (\(v_{zi}\)) from the \(K\) samples that are closest to each minority group sample (\(v_{\text{i}}\)). (\(K\) = number of nearest neighbours). \[SY_{\text{i}}=v_{\text{i}}+\beta\cdot(v_{zi}-v_{\text{i}}),\quad\beta\in[0,1].\] (6)
## 4 Performance Evaluation  
To evaluate the performance of the proposed routing protocol, we utilized LEACH, a representative routing protocol, and did performance analysis. The performance evaluation criteria considered the number of surviving nodes and the amount of energy consumed by the surviving nodes after each round of transmission.
# Emotion Analysis AI Model for Sensing Architecture Using EEG
## 4 Application and Usage of Brainwave-Based Fine-Tuned Model in Architectural Spaces
### Fine-Tuned Model Validation and Performance Evaluation
#### 4.1.4 Performance Metrics Computation and Results  
As the OpenAI API does not compute performance metrics automatically, scikit-learn methods were employed to assess classification performance. The evaluation included accuracy, precision, recall, F1 score, and Cohen's Kappa, ensuring a thorough assessment of the model's capability in EEG-based emotion classification.  
The fine-tuned model underwent eight iterations of preprocessing and training, culminating in the development of eight fine-tuned versions. Each version was evaluated under identical test conditions, allowing for direct performance comparisons across iterations.  
The findings demonstrated notable improvements in classification accuracy, with Version 8 achieving the highest accuracy of 60.3%, compared to 34% in earlier iterations. A key distinction between Versions 1-7 and Version 8 was in the dataset structure. Earlier versions explicitly included EEG channel names in the JSONL training data, whereas Version 8 removed channel names and relied solely on normalised EEG data. This structural modification likely contributed to the observed performance enhancement (Table 4).
# An Enhanced Energy Balanced Data Transmission Protocol for Underwater Acoustic Sensor Networks
Therefore, we focus on the efficient utilization of energy resources and propose two new routing protocols for UASNs; Efficient and Balanced Energy consumption Technique (EBET) and Enhanced EBET (EEBET). Both the techniques avoid long distance direct transmissions for balancing energy consumption. These protocols perform well under both sparse and dense network conditions. Both of the protocols initially establish communication links between sensor nodes on the basis of optimal distance threshold. For balanced energy consumption, initial energy of sensor nodes is divided into energy chunks termed as Energy Levels (\(RLs\)). After link establishment, nodes communicate with each other based on their energy levels. As the energy levels of communicating nodes differ, the predecessor node selects high residual energy level node within its vicinity as a next  
Figure 1: Underwater Acoustic Sensor Network (UASN) architecture. potential relay node. Consequently, the energy consumption of sensor nodes become balanced which is proved using Lemma 1. Furthermore, our techniques save the excessive transmit energy needed for direct data transmission over long distance. So, the technique improves network lifetime and is best suited for large scale UASNs. It is worth mentioning that this research work is an extended version of our previous work in [14]. The rest of the paper is organized as follows. Section 2 provides an overview of literature and the motivation behind this work. Section 3 illustrates the underwater energy consumption model. Section 4 explains detailed description of proposed schemes along with the mathematical model. In Section 5, performance evaluation is done through simulations and discussion of results is provided. Finally, overall work is concluded in Section 6.
## 2 Quality Analysis of BeiDou Measurements
### Satellite Visibility  
Figure 2 shows the visibility of BeiDou satellites at CHDU on September 24, 2012. It can be seen from the figure that four GEO satellites C01, C03, C04 and C05 are tracked all day long, while five IGSO satellites C06-C10 are not tracked continually without breaks for 24 h. The lengths of the data gaps for all the IGSO satellites are about 4.5 h. Two MEO satellites (C11 and C12) are tracked in a shorter time than that of GEO and IGSO satellites. It is also seen that at least three IGSO satellites are tracked at every moment so at least seven BeiDou satellites are tracked for every epoch, which guarantees the realization of BeiDou PPP.  
Figure 1: The distribution of “BETS” and the tracks of the sub-satellite points of four GEO, five IGSO and two MEO BeiDou Satellites.  
Figure 2: Visibility of BeiDou satellites at CHDU on September 24, 2012 (the mask elevation is 7\({}^{\circ}\)).  
Figure 3 shows the sky plots of five IGSO and four GEO satellites against the azimuths and elevations. It can be seen that the elevations and azimuths of C01 are within 30-45\({}^{\circ}\) and within 120\({}^{\circ}\)-135\({}^{\circ}\) and that of C03 are within 45\({}^{\circ}\)-60\({}^{\circ}\) and within 210\({}^{\circ}\)-225\({}^{\circ}\), respectively. The elevations of C04 and C05 are lower than that of C01 and C03, as they are further away from the station. Meanwhile, the five IGSO satellites have a ground track of an incomplete \"8\" shape as their elevations are below the mask angle and the satellites cannot be observed. Furthermore, we can find that for mainland China, the BeiDou GEO and IGSO satellites cover the southern hemisphere most of the time.
## 2 A Brief Comment on Geometrophysical Modeling
### Application to Highway Regulations  
Regarding highway regulations, geometrophysical models can be used to optimize road design, improve safety standards, and enhance traffic flow efficiency. By applying these models, engineers can determine the optimal curvature of roads that balances safety and speed or establish speed limits that are based on the physical capabilities of the road and typical vehicle performances. One application example related to modeling accidents and highway geometric design relationships is found in [16]. In that study, the capabilities and limitations of regression models are explored to help understand the relationships between vehicle accidents and highway geometric design, emphasizing the need to develop proper statistical models to handle the variability and distribution of accident data. In [17], there is another application example. This study discusses the impact that geometric design characteristics have on traffic safety. Statistical negative binomial regression modeling is used to better understand factors related to highway geometric influencing accident frequency, identifying operating speeds (the central parameter in this present study) as one of the main factors contributing to the total number of accidents. These examples, among others, illustrate how geometrophysical considerations can contribute to a better understanding of how modeling and design factors are linked to regulatory outcomes and safety on highways.
## 4 Discussion
### Comparison of Different Prediction Methods  
To validate the effectiveness of the PDI forecasting model based on the TPA-LSTM, various prediction methods were compared:  
Figure 19: RMSEs of different data imputation models.  
Figure 20: RMSEs of different input lengths.  
RNN:** The classic RNN is the basic framework of other recurrent models. For each timestamp, the structure and the parameters of the RNN cell are shared [42].  
* **LSTM:** This model is an improved version based on the RNN. The LSTM calculates the hidden state for the long-term memory and the cell state for the short-term memory. The input gate, the forget gate and the output gate are introduced to control the transmission of information [43].
* **GRU:** This model is a variant of LSTM and has a simpler structure. The forget gate and the input gate are merged to form the update gate. Hence, the calculation efficiency of the GRU is relatively higher [44].
* **Support vector regression (SVR):** This model reflects the low-dimensional time series data to the high-dimensional feature space through the kernel function. Then, the regression result with the minimum error is searched through iterations [45].  
The main parameters of the above models are listed in Table 7.  
The next sample was predicted with every 12 previous points. The prediction results of the above methods are shown in Figure 21. Additionally, the criteria are listed in Table 8. For this single-step time series prediction task, all the compared models achieved good results on the training set. Most of the methods also performed well in the test set, except for the SVR. Due to the introduction of the temporal pattern attention mechanism, the TPA-LSTM achieved the best results on both the training set and test set, which indicates that the TPA-LSTM has better accuracy, as well as a better generalization ability. However, due to the more complex structure, TPA-LSTM is more time-consuming.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Parameter** & **Value** \\ \hline RNN & Number of hidden units & 32 \\  & Number of hidden layers & 2 \\ \hline LSTM & Number of hidden units & 32 \\  & Number of hidden layers & 2 \\ \hline GRU & Number of hidden units & 32 \\  & Number of hidden layers & 2 \\ \hline SVR & Regularization coefficient & 10 \\  & Epsilon & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Main parameters of the compared prediction methods.  
Figure 21: Prediction results of the compared models.
## 4 Summary  
The COMET Program's online education and training resources include numerous offerings in the area of satellite meteorology. The resources support domestic and international training related to both geostationary and low Earth orbiting satellite systems to enhance the capabilities and performance of environmental forecasters. These same resources also reach a large university audience to provide full-career professional development for meteorologists and other geoscience professionals.  
Instructional designers and staff scientists at COMET make use of well-established instructional principles, maintain an awareness of emerging trends, and clearly delineate forecaster needs to develop training that is operationally relevant and that best suits the intended audience of forecasters and other users of satellite information. The inclusion of interactive exercises as well as the availability of booster questions helps reinforce the content for professionals needing to apply satellite information across a variety of applications and topic areas, including but not limited to, weather analysis and forecasting, hazard monitoring, land and ocean monitoring, climate monitoring, and decision support.  
As we look toward the future, COMET's commitment to sound science and effective and innovative instructional design will continue to guide our offerings. New lessons will emphasize satellite advancements in the context of forecasting applications, IDSS, and overall environmental monitoring. The combination of changing workforce needs and new satellite capabilities coming online, including future Meteosat and EUMETSAT Polar System satellites in Europe, the expanding capabilities of the GOES-R and JPSS satellite series in the Americas, and other new satellite systems from various international satellite operators, will require additional contextual education and training for forecasters and geoscience professionals worldwide.  
Writing--original draft, A.S., P.D. and T.M.; writing--review and editing, Br.Gu., T.A. and E.P.  
**Funding:** COMET is grateful for the support of our primary satellite training sponsors, including NOAA's GOES-R Program, JPSS Program, and National Weather Service; EUMETSAT; and the Meteorological Service of Canada. This paper was funded by UCAR under the cooperative agreement award #NA16 NW54670042 from NOAA, U.S. Department of Commerce. The statements, findings, conclusions, and recommendations expressed herein are those of the authors and do not necessarily reflect the views of NOAA or any of its sub-agencies.  
**Acknowledgments:** We thank COMET graphic designer [PERSON] for assistance in preparing final graphics, and COMET instructional designer [PERSON] for his consistent guidance in instructional design best practices.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Experimental Setup
The alternating currents (ac) from the two detectors were input with a positive/negative power combiner and recorded by a spectrum analyzer (SA). An electronic attenuator (EA) was used to attenuate the current output from PD1 to be equal to that from PD2 before the positive/negative power combiner. The direct currents (dc) from PD1 was used to lock the length of the SRO cavity employing the Pound-Drever-Hall (PDH) technique [37]. The intensity of the signal and idler are defined as \(I_{1}\) and \(I_{2}\), respectively. The noise of the intensity difference \(I_{-}=I_{1}-I_{2}\) between the two down-conversion beams is expected to be below the corresponding SNL. The corresponding SNL is the shot noise level for a beam with an intensity of \(I_{1}+I_{2}\)[8; 9; 10; 11].