esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Differential GNSS
### Double Differencing  
Figure 12 illustrates the geometry of double differencing. Here, both receivers \(A\) and \(B\) track simultaneously two satellites \(j\) and \(k\). The single differences \(\Delta L_{AB}^{j}\) and \(\Delta L_{AB}^{k}\) can be calculated as shown in Section 3.1 by using Equation (3). The double difference \(\
abla\Delta L_{AB}^{jk}\) is the difference between the single differences \(\Delta L_{AB}^{j}\) and \(\Delta L_{AB}^{k}\). The upside-down triangular symbol \(\
abla\) is a mnemonic device to emphasizes that the calculated differences are between two points in the sky. Finally, the double difference is [43]:  
\[\begin{split}\
abla\Delta L_{AB}^{jk}&=\Delta L_{AB }^{j}-\Delta L_{AB}^{k}\\ &=\
abla\Delta\rho_{AB}^{jk}+\
abla\Delta B_{AB}^{jk}+\
abla \Delta\epsilon_{AB}\end{split} \tag{4}\]  
Equation (4) includes the phase-range ambiguity \(\
abla\Delta B_{AB}^{jk}\). \(\Delta B_{AB}^{j}\) is reduced in single differencing to \(B_{AB}^{j}=\lambda_{0}(\varphi_{0A}^{j}-\varphi_{0B}^{j})-\lambda_{0}(N_{B}^{ j}-N_{A}^{j})\). Now, in double differencing, the ambiguity reduces to \(B_{AB}^{jk}=\lambda_{0}(N_{B}^{k}-N_{A}^{k})-\lambda_{0}(N_{B}^{j}-N_{A}^{j})\) as it is assumed that each receiver logs all incoming satellite signals at exactly the same time. Doing so, it becomes obvious that the range-phase ambiguity becomes an integer, which is another advantage of the double differencing technique. Finally, estimating and validating the integer ambiguity can be achieved by using the pseudorange solution and then constraining the estimations to an integer solution [45]. The detailed steps of resolving the integer ambiguity can be found for example in [44; 45]. When the integer ambiguity is fixed, the carrier phase range measurements result in precise positioning results in the mm-range.  
Figure 11: Geometry of single differencing with the known position of receiver \(A\) and the unknown position of receiver \(B\). Both receiver measure their distance \(p\) to the same satellite \(j\).  
Figure 12: Geometry of double differencing with the known position of receiver \(A\) and the unknown position of receiver \(B\). Both receiver measure their distance \(p\) to satellites \(j\) and \(k\).
## 4 Applications of CDs in Cancer Theranosis
### Cancer Diagnosis via Fluorescence Imaging  
#### 4.1.1 In Vitro Diagnosis  
Due to their unique optical properties covering UV to NIR, CDs are highly suitable for various imaging applications for cancer diagnosis [134; 135; 136; 137; 138]. This coverage results from several origins. One is the particle size of CDs; by increasing the particle size, the emission wavelength increases [139]. In addition, the graphitic-N, -C-N, -C-O, and -COOH groups [140] and the heteroatom doping, such as N, S, P, or B [141], can change the emission wavelength. Therefore, the wavelength tuning is related to both core synthesis and surface modification and depends on carbon precursors, synthesis conditions, and synthesis methods. Their NIR fluorescence (wavelength = 700-900 nm) with tissue penetration depth of 5-7 mm, depending wavelength and tissue type, is particularly useful as it mitigates interference from background fluorescence produced by tissues within the body, thereby enhancing the signal-to-noise ratio and also allowing a deep tissue imaging; for example, [PERSON] et al. reported the penetration depth of 8.5 mm in the NIR imaging [142]. [PERSON] et al. demonstrated a remarkable tissue penetration depth of 20 mm in chicken breast tissue [143]. Unlike traditional dyes and fluorophores, CDs can offer tunable wavelength properties, making them more attractive for cancer diagnosis [144]. The discrimination of cancer cells from normal cells is quite challenging in cancer diagnosis [145; 146; 147]. CDs can be designed to selectively bind to specific cancer cells by conjugating them with cancer-specific ligands, such as folate receptors overexpressed in cancer cells, and used to distinguish cancer cells from normal cells through fluorescent imaging [148; 149; 150]. [PERSON] et al. synthesized folic acid (FA)-conjugated fluorescent CDs (FA-CDs) for targeted imaging of the Michigan Cancer Foundation (MCF)-7 human breast cancer cells, human liver cancer (HepG2) cells, and pheochromocytoma (PC)-12 rat cancer cells using confocal laser scanning microscopy [151]. They demonstrated that the expression levels of folate receptors in cancer cells depended on cancer cell types; the expression levels of folate receptors were MCF-7 > HepG-2 > PC-12 cells (Figure 8a(i-viiiii)). [PERSON] et al. developed
# Improving a Street-Based Geocoding Algorithm Using Machine Learning Techniques
## 4 Experimental Evaluation
### Experimental Case 2: Address Matching with a Similarity Metric  
In the address matching with a similarity metric, we tested Jaro-Winkler Sorted and Mlipns in edit-based similarity metrics, and Tversky and Monge-Elkan in token-based similarity metrics. Among the four metrics, Jaro-Winkler Sorted and Tversky, which have the highest accuracy when training data are used (Table 5), also achieve higher accuracy over 89% using Input data 3 than the other two metrics. Mlipns and Monge-Elkan have approximately 14% lower accuracy and 4-5 times more mismatches than Jaro-Winkler Sorted and Tversky, as shown in Table 3. The accuracy of Mlipns and Monge-Elkan becomes much lower than Jaro-Winkler Sorted and Tversky in Input data 1 and 2 than Input data 3. Regarding the quality of matches, Monge-Elkan has higher mismatched addresses than Mlipns, which means that it tries to match similar incorrect names more times. In total, 38.71% of all the input addresses are present on the corresponding parcels with the side offset in Jaro-Winkler Sorted with 70% of correct matches in input addresses, and the rest of addresses (61.29%) are outside corresponding parcels.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Matched Input Address** & **Mismatched Input Address** & **Unmatched Input Address** \\ \hline Input data 1 * & 34.12\% & & 65.88\% \\ Input data 2 * & 52.82\% & 0\% & 47.18\% \\ Input data 3 * & 71.78\% & & 28.22\% \\ \hline  & **Geocoded on** & **Geocoded Outside** & **Mean Distance \(\pm\) Standard** \\  & **Corresponding Parcels** & **Corresponding Parcels** & **Deviation** \\ \hline Input data 1 * & 15.09\% & 84.91\% & 4.53 \(\pm\) 7.12 m \\ Input data 2 * & 25.20\% & 74.80\% & 4.45 \(\pm\) 7.73 m \\ Input data 3 * & 32.74\% & 67.26\% & 4.43 \(\pm\) 7.43 m \\ \hline \hline \end{tabular}  
* Input data 1: 30% of correct matches; Input data 2: 50% of correct matches; Input data 3: 70% of correct matches.  
\end{table}
Table 2: Performance of address matching process without any similarity metrics, percentage of geocoded input addresses on corresponding parcels, and distances between geocoded addresses and the corresponding parcels.  
Figure 9: Geocoded 1524 input addresses in Gyeonggi Province, Suwon City, Paldal District, Korea.  
The mean distances of Jaro-Winkler Sorted and Tversky are around 4.40 m, which is lower than Mlipns and Monge-Elkan when using Input data 1. Tversky has the lowest mean distance, whereas Monge-Elkan shows the highest mean distance in all the three kinds of input data.
## 5 Conclusions  
The variation in soil aggregate size distribution along the precipitation gradient reflected the impact of environmental factors on soil structure. A more humid climate is conducive to the formation of larger soil aggregates. With increasing soil depth, the SOC content and cumulative mineralization decrease and the impact of precipitation on SOC mineralization gradually diminishes. Precipitation primarily controlled the SOC mineralization characteristics in the 0-50 cm soil layer, while in the 50-100 cm layer, SOC mineralization was more closely related to soil-associated carbon. Additionally, the distribution and cumulative mineralization of soil organic carbon in different grassland types of the Loess Plateau also decreased significantly with the reduction in precipitation gradient.  
We concluded that in high precipitation areas, the turnover rate of SOC was faster compared to low precipitation areas, leading to an increase in CO\({}_{2}\) emissions into the atmosphere. In the scenario of global warming, these findings provided new insights into predicting the stability and response of soil organic carbon mineralization in grasslands to climate change and they were significant for maintaining the functionality of grassland soil ecosystems, plant growth, and the global carbon balance. In future research, more attention should be paid to the carbon dynamics of soils in high-precipitation areas and their interactions with climate.  
**Author Contributions:** [PERSON] and [PERSON] jointly designed the research; [PERSON], [PERSON], [PERSON] and [PERSON] conducted the research; [PERSON] and [PERSON] wrote the manuscript; [PERSON] and [PERSON] commented. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Second Tibetan Plateau Scientific Expedition and Research Program (2019 QZKK0606-2) and the International Cooperation Project of Science and Technology Department of Jilin Province, China (2020402061 GH).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to laboratory regulations.
## 3 Data Acquisition and Processing
### Selecting Performance Degradation Index
#### 3.2.3 Features of EEMD Space  
The Ensemble Empirical Mode Decomposition (EEMD) method [38] is used to extract features; vibration signal \(x(t)\) is decomposed into multiple Intrinsic Mode Function (IMF) by the EEMD method. In order to judge which \(IMF\) is the real component of the signal or meaningless components, the correlation coefficient between the IMF and \(x(t)\) is as an index. All \(IMFs\) are normalized, and the normalized correlation coefficient between \(IMFs\) and \(x(t)\) is described as follows,  
\[r_{j}=\frac{\sum_{t=1}^{N}(x(t)-\overline{x})(c_{j}(t)-\overline{c}_{j})}{ \sqrt{\sum_{t=1}^{N}(x(t)-\overline{x})^{2}*\sum_{t=1}^{N}(c_{j}(t)-\overline{ c}_{j})^{2}}} \tag{20}\]  
where, \(x(t)\) is the original signal, \(c_{j}(t)\) is the \(j^{th}\)\(IMF\), \(r_{j}\) is the correlation coefficient of the \(j^{th}\)\(IMF\), and \(t=1,2,\cdots,N\) is the sampling points of the signal.  
The threshold value \(TH\) is set, which is the standard deviation of the correlation coefficient \(TH=std(r_{j})\).  
\[TH=\sqrt{\frac{1}{n-1}\sum_{j=1}^{n}\left(r_{j}-\overline{r}\right)^{2}} \tag{21}\]  
If \(r_{j}>TH\), the \(j^{th}IMF\) is retained; otherwise, it is removed.  
The major performance degradation index of EEMD space is extracted by SVD in different working conditions, which are shown in Figure 6c.
# Flexible Simulation Platform for Generating Realistic Waveforms with Voltage Notches
## 4 Analysis of Key Parameter Dependencies, Limitations, and Impacts
### Impact of Key Parameters on Voltage Notch Waveforms
#### 4.2.1 Scenario 0: \(S_{sc}\), \(S_{r}\), \(X_{com}\), and \(\alpha\)  
This scenario considers only the six-pulse controlled rectifier in Figure 3, neglecting snubber circuits (\(R_{sn}=1\) M\(\Omega\), \(C_{sn}=\infty\)), yielding classical rectangular voltage notches for qualitative sensitivity analysis of the key parameters influencing voltage notch waveforms. The results of the simulations are depicted in Figure 8.  
* Simulations vary \(S_{sc}\) from 760 kVA to 5 MVA.
- Fixed parameters are \(S_{r}=11.4\) kVA, \(\alpha=30^{\circ}\), \(X_{com}=245.3\) m\(\Omega\), and \(I_{D}=20.15\) A.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{Scenario} & \multicolumn{6}{c}{**Component Incorporated in the Simulation**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Six-Pulse Controlled Rectifier**} & \multirow{2}{*}{**Linear Loads**} & \multirow{2}{*}{**Capacitor Bank**} & \multirow{2}{*}{**Background Unbalance**} \\ \cline{2-7}  & **Snubber Disabled** & & & & & **and Distortion** \\ \hline
0 & \(\checkmark\) & & & & & \\
1 & & \(\checkmark\) & & & & \\
2 & & \(\checkmark\) & \(\checkmark\) & & & \\
3 & & \(\checkmark\) & & \(\checkmark\) & & \\
4 & & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & & \\
5 & & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Scenarios for analyzing the impact of key parameters on voltage notch waveforms through simulations.  
Figure 7: Conceptual diagram illustrating the interrelationships among key parameters.  
* Increasing \(S_{sc}\) decreases \(d_{com}\), indicating a weaker PCC leads to more severe voltage notches.
* Simulations vary \(S_{r}\) from 10 kVA to 75 kVA with \(S_{sc}\) = 5 MVA.
- Fixed parameters are \(\alpha\) = 30\({}^{\circ}\) and \(X_{com}\) = 37.3 m\(\Omega\).
- Increasing \(S_{r}\) increases \(\Delta t_{com}\) (and line currents), resulting in more severe notches.
* Simulations vary \(X_{com}\) from 37.3 m\(\Omega\) to 111.8 m\(\Omega\)
- Fixed parameters are \(S_{sc}\) = 5 MVA, \(S_{r}\) = 25 kVA, and \(\alpha\) = 30\({}^{\circ}\).
- Increasing \(X_{com}\) decreases \(d_{com}\) but increases \(\Delta t_{com}\) without affecting the voltage notch area significantly. It is therefore designed to prevent deep voltage notches while maintaining a trade-off with their duration.
* Simulations vary \(\alpha\) from 10\({}^{\circ}\) to 31\({}^{\circ}\)
- Fixed parameters are \(S_{sc}\) = 5 MVA and \(S_{r}\) = 25 kVA.
- Increasing \(\alpha\) increases \(d_{com}\) and decreases \(\Delta t_{com}\). It affects line current commutation time and phase angle of the fundamental component, directly impacting the DPF. These simulations help understand how varying key parameters such as \(S_{sc}\), \(S_{r}\), \(X_{com}\), and \(\alpha\) influence voltage notch waveforms and line currents.
# Dynamic Tuning and Multi-Task Learning-Based Model for Multimodal Sentiment Analysis
## 6 Discussion  
The DMMSA model has achieved remarkable performance across multiple datasets, which can be attributed to several key factors. First, its multi-task learning strategy incorporates single-modality sentiment classification tasks and contrastive learning tasks, enabling the model to gain a more comprehensive understanding and analysis of sentiment signals. This strategy not only enhances the model's comprehension of single-modality  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Acc-3** & **Acc-5** & **MAE** & **Corr** \\ \hline DMMSA & 69.63 & 46.92 & 0.3778 & 0.66 \\ w/o MSC & 68.41 & 44.68 & 0.3807 & 0.656 \\ w/o CL & 69.41 & 46.74 & 0.3828 & 0.651 \\ w/o GDWG & 69.32 & 46.17 & 0.3776 & 0.663 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The ablation experiments on CH-SIMS. “w/o CL” signifies the exclusion of the contrastive learning (CL) task.  
sentiment but also optimizes the feature extraction and decomposition process through the contrastive learning mechanism. Second, the Global Dynamic Weight Generation (GDWG) strategy dynamically adjusts task weights based on the convergence speed of tasks, effectively avoiding the negative transfer issues in multi-task learning. This mechanism ensures that the model balances the learning progress of different tasks during training, thereby improving overall performance. Additionally, by introducing coarse-grained sentiment classification tasks, the DMMSA model more accurately predicts sentiment intensity. This task simplifies the complexity of sentiment analysis by restricting the prediction range, thereby enhancing the model's prediction accuracy. Compared with existing baseline models, the DMMSA model has achieved significant improvements in multiple key metrics. For instance, on the CH-SIMS dataset, the DMMSA model's Acc-3 and Acc-5 values were 1.27% and 3.20% higher than those of the ConFEDE model. On the MOSI and MOSEI datasets, the DMMSA model outperformed all baseline models in metrics such as Acc-2, Acc-7, MAE, and Corr. These results indicate that while optimizing the model structure is important, optimizing the training objectives can also effectively enhance model performance.
# A Robust UWSN Handover Prediction System Using Ensemble Learning
## 2 Background
### Decision Tree  
The DT model is currently one of the most widely used mechanisms in several applications for predictive modeling, including regression and classification. The decision tree algorithm works very well and continuously if the data are discontinuous, even if noise appears. Moreover, it can handle collinearity efficiently and provide excellent prediction explanation. On the other hand, DTs suffer from higher complexity, especially when dealing with complicated datasets, and consequently may lose valuable information (i.e., in the case of continuous variables).  
Several techniques have been used to determine the best way to split the input data. One of the main goals of the DT model is to find the most significant splits between tree nodes and further optimize the data into correct classifications. To achieve this goal, it is necessary to apply proper decision rules to the proposed data, which significantly affects the algorithm's performance.  
Information gain (IG) is used with the DT model. The decision tree model aims to find the best split node that guarantees high accuracy. The IG method seeks to find the most suitable nodes that return the highest information gain, which can be measured using an entropy factor. The entropy factor is used to determine the degree of disorganization in the system. The entropy for the output can be calculated using the following formula:  
\[E(s)=\sum_{i=1}^{c}-p_{i}\log_{2}p_{i}. \tag{1}\]
# Trade-Off Analysis for Array Configurations of Chipless RFID Sensor Tag Designs
## 7 Trade-Off Analysis
### Future Optimised Array Design Considerations  
In assessing the trade-offs of various array configurations for chipless RFID tags, it is evident that the planar array structure enhances the RCS but decreases the Q-factor compared to unit cell and linear arrays. This reduction in the Q-factor arises from the simple replication of unit cells without optimising their spatial arrangement and coupling effects. More advanced designs could mitigate these issues while retaining improved detectability.  
Future designs should explore different array configurations to reduce mutual coupling and enhance resonance. Techniques such as staggered placements of resonators, varying element spacing, or utilising phase-tuned structures could improve the Q-factor while maintaining or increasing RCS. Additionally, incorporating metamaterial-inspired designs or defected ground structures (DGSs) may enhance resonance stability and frequency selectivity.  
Another vital consideration is the array compactness for practical applications. The current planar design requires considerable surface area, which may not be feasible for space-constrained scenarios. Future research should focus on creating compact, high-performance arrays that retain detection and scattering efficiency. Techniques like using higher-permittivity substrates or innovative folding methods could provide a more space-efficient design. These enhancements can improve the trade-offs among linear and spherical detectability, RCS, and Q-factor, making chipless RFID systems more suitable for real-world applications.
## 3 Materials and Methods
### Applied Algorithms in the Use Case  
In our four-step use case, we can find two algorithmic steps; the first is an AF detection process, the second is a classification method that predicts based on the output of the first phase. The observed AF detection algorithm [16] was implemented using Residual Networks (ResNet) [17] that is an architecture of Convolutional Neural Networks (CNNs) [18]. This project brings a pre-trained model to classify 30 s long single-lead ECG segments. The neural network performed successfully in Computing in Cardiology (CinC) Challenge 2017 [19; 20] with an F1 score of 79%. The neural network makes an image processing and identifies abnormalities based on learnt ECG signal patterns. Figure 3 illustrates a pattern of AF. The further patterns and the methodology are well-described in [21].  
In this study, we collected the AF classification methods that are often used or have clinical relevance. The first classification method [22] distinguishes four classes, first detected AF, paroxysmal AF, persistent AF and permanent AF. The algorithm makes decisions based on the duration of AF episodes. The second classifier [23] is used during anticoagulation therapy and measures the length of AF episodes. Based on the length of AF episodes, the use of antiarrhythmic drugs may be needed. Here, five classes are defined, patients with AF episodes longer than 24 h, between 6 and 24 h, from 1 to 6 h, between 10 min and 1 h and shorter than 10 min. The third algorithm was also presented in [23] but now the recurrence rate plays an important role. The first class has patients with low recurrence rate (less than five episodes) and the second class contains patients with high recurrence rate (at least five episodes). The result may change the anticoagulation therapy. The fourth classification method [24] is also in connection with anticoagulant treatments. Here, the inspected feature of AF is the length of the episode again, but with different lengths. We have four classes, AF episodes longer than 5.5 h, between 20 s and 5.5 h, between 15 s and 20 s and shorter than 15 s. The output can determine the severity  
Figure 3: A pattern of AF.  
Figure 2: Schematic diagram of the entire AF detection and classification process.  
of thromboembolic risk. The last observed classification algorithm [25] takes into account length of AF episodes, which is proportional to the risk of stroke. Only short periods are examined-- two classes contain patients having AF episodes that last at least 20 s and patients with AF episodes shorter than 20 s.