esa-sceva/satcom-chunk-collection huggingface dataset:
# Beyond Presence: Exploring Empathy within the Metaverse
## 5 Discussion
### Future Directions and Technological Advancements  
Research has explored the integration of touch and smell into VR and AR systems. This technological advancement is necessary to bridge the limitations of situated empathy and broaden emotional engagement, as explored in this review. For example, [PERSON] et al. suggest that incorporating scent into VR environments could enhance brand experiences, potentially impacting consumer behavior and marketing strategies within virtual reality [49]. Although this study did not specifically investigate empathy, it underscores the significance of incorporating additional human senses into these technologies. In particular, touch has a strong connection to empathy. A study by [PERSON] et al. cites several studies demonstrating how touch can convey emotions and influence social behaviors, such as compliance and relationship dynamics [50]. Additionally, [PERSON] et al. found that social touch cues can bridge the gap in perceived agency between agents and avatars, making agents appear more \"human\" [48]. Expanding upon these findings to explore the elicitation of empathy through touch or smell could yield valuable insights into fostering empathy even in the absence of contextual cues.  
Analysis of EEG and ECG signals has also been conducted during empathy elicitation. Integrating EEG and ECG into VR holds significant potential for revolutionizing empathy in virtual interactions. [PERSON] et al. found that EEG frontal asymmetry synchronization correlates with heightened perceived empathy, suggesting a link between EEG activity and empathetic responses [30]. Extending this study may offer real-time insights into emotional states, which could create more immersive, personalized, and emotionally resonant VR experiences that foster deeper connections among users.
## 4 Experiments and Analysis
### Experimental Results and Analysis
#### 4.4.1 Forecasting Accuracy and Performance Gap
The number of concurrent models at this level represents a critical threshold where additional CPU availability has a particularly significant impact, as it alleviates resource contention and allows the system to approach optimal model selection accuracy.
# A Benchmark for Morphological Segmentation in Uyghur and Kazakh
## 5 Conclusions  
This paper redefines the model evaluation metrics to effectively evaluate the performance of the Uyghur and Kazakh morphological segmentation and stemming models proposed in recent years. Morphological segmentation metrics shift the evaluation focus from the character to the morpheme. We propose two benchmark models for morphological segmentation and stemming for Uyghur and Kazakh: MMSeg and FEMSeg (FEMSeg-CRF). The stability of the models was demonstrated through 10-fold cross-validation. In the unsupervised learning morphological segmentation, MMSeg achieves F1-scores of 47.55% and 35.22% on the Uyghur and Kazakh, respectively. In the supervised learning morphological segmentation, FEMSeg achieves F1-scores of 92.69% and 92.84% on the Uyghur and Kazakh, respectively. In stemming, FEMSeg achieves accuracy rates of 91.51% and 90.70% on the Uyghur and Kazakh, respectively. The above experiments' F1-scores and accuracy rates are higher than the previously proposed models. Finally, we analyzed the advantagesand disadvantages of the different models in the two tasks from various perspectives. Although current research has improved character-level evaluation metrics, there is still room for improvement in morpheme-level or word-level metrics. In future research, we will continue to explore high-performance low-resource language morphological segmentation models by combining large language models and prompt learning methods.  
Conceptualization, G.A.; methodology, G.A.; software, G.A. and S.R.; validation, G.A. and S.R.; formal analysis, G.A.; investigation, G.A. and B.W.; writing--original draft preparation, G.A.; writing--review and editing, K.A.; visualization, G.A.; supervision, A.W.; project administration, A.W.; funding acquisition, A.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported by National Natural Science Foundation of China [No. 62166044] and Natural Science Foundation of Xinjiang Province [No. 2021D01C079].  
Not applicable.  
The dataset used in this paper can be accessed by [[http://thuuymorph.thunlp.org/](http://thuuymorph.thunlp.org/)]([http://thuuymorph.thunlp.org/](http://thuuymorph.thunlp.org/)) (accessed on 20 May 2024).  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### Data Acquisition and Pre-Processing  
This study used satellite data (Table 1) to monitor the mangrove changes between 1973 and 2020. Landsat images acquired in 1973, 1990, 1995, 2000, 2009, and 2020 were downloaded from the US Geological Survey Global Visualization Viewer (USGS Glovis) site. Persistent clouds cover the study areas all year round. The Landsat MSS (Multispectral Scanner) collected data from Landsat 1-5. Landsat TM (Thematic Mapper) and ETM+ (Enhanced Thematic Mapper Plus) have 7 spectral bands with a spatial resolution of 30 m for bands 1-5 and 7, while band 6, a thermal infrared, is acquired at a 120 m and 60 m resolution, respectively. The Landsat 8 data have 9 spectral bands with a spatial resolution of 30 m for bands 1-7 and 9. Both Landsat 7 and 8 acquire the panchromatic band, i.e., band 8 at a pixel size of 15 m. Google Earth images and topographical maps and aerial data obtained from the Department of Forestry and the Department of Land Surveying Zanzibar were used as reference datasets to determine the accuracy of the classified results.  
Figure 1: Location of Chwaka Bay and Menai Bay in Unguja, Zanzibar.  
The Landsat module tool in the TerrSet IDRISI software version 18.1 was used to radiometrically correct the Landsat images. The raw DN (digital number) values were converted to surface reflectance. Using the dark-object subtraction algorithm, atmospheric correction was carried out to remove atmospheric haze. All images were projected onto the UTM zone 30 North projection system. The Landsat images were resampled to a 15 m spatial resolution using the nearest neighborhood resampling technique [28,44].
## 2 State-of-the-Art
### Text Mining
#### 2.3.2 Pre-Processing  
The objective is to obtain clean and actionable data from the data collected through the application of different debugging and cleaning techniques. There is no specific order to perform the pre-processing task, so this must be determined empirically [23], experimenting with each technique individually, comparing the results, and combining those that perform best. The pre-processing of social media data presents certain challenges due to the use of informal language and the length of the messages (for instance, tweets are very short in length) [24].  
The techniques commonly used in text pre-processing are the following [10,11,25,26,27,28]:  
1. Normalization and noise reduction: this process unifies the text and removes irrelevant and meaningless elements. The literature recommends the deletion of URLs, HTML tags, special characters, emojis, or emoticons.
2. Remove punctuation marks: as punctuation marks do not add additional information, the elimination of them helps reduce the size of the data and increase the model efficiency.
3. Remove stopwords: these are terms that help build sentences but do not commonly provide meaning, like prepositions or articles.
4. Processing of abbreviations, acronyms, and entities.
5. Spell correction.
6. Tokenization: it splits text into significant words delimited by blank spaces, commas, periods, or any other special character.
7. Lemmatization: it is the search for the words' lemma to unify the terms that give the same information (for example, derivational forms of verbs).
8. Part of Speech Tagging (PoS): grammatical tagging of each word.
## 2 Materials and Methods
### Implementation of SMOTE
#### 2.3.3 Synthetic Sample Generation  
The core of the SMOTE algorithm lies in its synthetic sample generation process. This step is where the algorithm demonstrates its power in addressing class imbalance by creating new, artificial samples of the minority class. For each minority sample \(x_{i}\), SMOTE generates \(N\) synthetic samples, where \(N\) is determined by the desired oversampling rate. The process for generating each synthetic sample \(x_{\text{new}}\) is as follows:  
1. Randomly select \(x_{j}\) from \(\text{KNN}(x_{i})\).
2. Generate a synthetic sample as follows: \[x_{\text{new}}=x_{i}+\lambda(x_{j}-x_{i})\] (4) where \(\lambda\) is a random number in \([0,1]\).  
This approach ensures that the synthetic samples are created along the line segment between the minority sample and its neighbor, effectively populating the feature space in the vicinity of the existing minority samples. By using a random \(\lambda\), SMOTE introducesvariability in the synthetic samples, preventing the creation of exact duplicates and reducing the risk of overfitting [18].  
If \(\lambda\) were fixed, all synthetic samples derived from the same pair of minority points would end up in the same location, risking overfitting or poor generalization. By contrast, a random \(\lambda\) distributes the generated points more evenly, approximating the underlying minority data distribution in a superior manner. As a result, this approach can help machine learning models form more balanced decision boundaries, ultimately improving predictive performance on imbalanced datasets, as seen in previous studies [19; 20].
## 3 Numerical Results and Discussion
### Wave Energy Converter Integrated with Breakwater
Additionally, it can be seen that the heave RAO converged to 2.0 in the low-frequency region. The power generation of the WEC was calculated using the PTO force to determine the effect of motion amplification on power generation. In this study, the mean generated power was calculated using Equation (23) in [34], assuming that the PTO force is linearly proportional to the motion of the WEC. \[P_{abs}=\frac{1}{2}\ b_{pto}w^{2}/z_{3}j^{2} \tag{23}\]  
\[CW=\frac{P_{abs}}{J} \tag{24}\] \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Case Number**} & \multicolumn{2}{c}{**Motion Increment of WEC**} \\ \cline{2-3}  & In Low-Frequency Range & In Resonance Range \\ \hline Case 1 & 1.87 & 2.24 \\ Case 2 & 1.70 & 2.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Maximum motion increment of the WEC compared to the case of no breakwater. Figure 17: Motion RAOs of the WEC under different conditions. where \(b_{pta}\) is the PTO damping coefficient. The capture width (_CW_) is defined as the ratio of the extracted wave power, \(P_{abs}\) (in _W_), to the input wave energy, \(J\) (in _W_/_m_) [35]. Thus, CW is interpreted as the wave-front width completely absorbed by the WEC from the incoming wave. The capture width ratio (CWR) is the ratio of CW to the characteristic length of the WEC. Therefore, the CWR reflects the efficiency of the hydrodynamic absorption of a WEC. The characteristic length (_B_), which is generally used to calculate the CWR, is the width of the WEC [36]. In this study, \(B\) is the same as the diameter of the WEC (_D_). A case study was performed to determine the optimal PTO damping coefficient for each incident wave frequency. Figure 18 shows the contour of the heave RAO of the WEC according to \(b_{pta}\) and \(w\). As the PTO damping coefficient increases, the heave RAO of the WEC decreased significantly near the resonant frequency (_w_ = 3.2 rad/s). This is where the kinetic energy of the floating body is replaced with PTO damping (energy extraction). In the low-frequency region, the CWR increases as the PTO damping (extraction) coefficient increases (Figure 19). More incident wave energy is extracted at the lower-frequency wave region. With the breakwater, the CWR increases by approximately 3.67 times compared to without the breakwater. This is a significant improvement in the hydrodynamic efficiency of the WEC because of the breakwater. However, the CWR is very low in some frequency ranges (_w_ = 2.0 - 2.8 rad/s) compared to the WEC without the breakwater. This is because the wave reflected by the breakwater at \(w=2.0\) - 2.8 rad/s (when the WEC is located at the node of the standing wave) interferes with the movement of the WEC. This phenomenon has been reported in some studies [37; 38]. Therefore, to increase the energy extraction efficiency of the WEC, it is necessary to design the WEC system to be integrated with the breakwater, considering the sea conditions of the installation area. Figure 18: Heave RAO of the WEC with various PTO damping coefficients in Case 1.
## 3 Results and Discussion
### Method Validation
#### 3.2.5 Matrix Effect and Extraction Efficiency  
Biological samples have a complex matrix, and usually when analyzed by LC/MS co-eluting endogenous substances could interfere with the signal of the target analyte. Matrix effect was evaluated by performing a series of dilutions of the serum sample prior to analysis and plotting the ratio of AA peak area to IS peak area against the calculated concentrations to check for a linear relationship. It was found that the relationship was linear (R\({}^{2}\) = 0.9924) in the tested range and exhibited a regression equation y = 1.3279x \(-\) 0.3141, slope 1.327, y intercept 0.3141.  
Another approach to evaluate a potential matrix effect is by comparing the slopes of solvent-based and authentic matrix-matched calibration curves. We found slope variability of 2.5%, which is below the target value of 5%, and therefore the slopes are considered to be equivalent [25]. When the matrix effect is calculated against a surrogate matrix, the result does not differ significantly from that obtained with a solvent-based matrix, 7.6% vs. 6.4%. We consider that the present method was not interfered with by endogenous substances in the matrix.  
The absolute extraction efficiency was 102.7% for d-8 AA (IS) at a concentration of 50 \(\upmu\)g/mL; 87.2% for AA at a concentration of 0.67 \(\upmu\)g/mL; and 89.64% for AA at a concentration of 2.67 \(\upmu\)g/mL.
## 1 Introduction  
Atomic high-order harmonic generation (HHG) was discovered in 1980s [1; 2]. It is a nonlinear nonperturbative process, which converts low-frequency fundamental femtosecond (1 fs = \(10^{-15}\) s) laser fields to coherent high-energy attosecond (1 as = \(10^{-18}\) s) pulse trains or an isolated attosecond laser pulse [3]. The typical laser intensity \(I\) used for HHG is around \(10^{14}\) W/cm\({}^{2}\). The general feature of atomic HHG which also appears in molecular high-order harmonic generation (MHOHG) is a fast decay of the lower order harmonics, then a long plateau, and a short cutoff [4]. The mechanism of HHG is interpreted by a three-step model [5; 6; 7] neglecting the role of excited states due to their short lifetime in strong laser field. First, the bound state electron is freed by tunnel ionization through the static barrier created by the instantaneous laser field and Coulomb potential. Then, the electron oscillates in the laser field and acquires kinetic energy. It may finally return to the parent ion and recombine to the ground state to emit HHG photons. The maximal kinetic energy \(E_{{}_{k}}\) of electrons returning to the origin is \(3.17\,U_{{}_{p}}\), where \(U_{{}_{p}}=E^{2}\big{/}4\omega^{2}\) is the ponderomotive energy of the electrons oscillating in the laser field, \(E\) and \(\omega\) are the amplitude and frequency of the laser field, respectively (atomic units \(e=m_{{}_{e}}=h=1\) are used throughout the paper unless otherwise specified). As a consequence, the cutoff energy of HHG, _i.e._, maximal kinetic energy \(E_{{}_{k}}\) plus ionization potential \(I_{{}_{p}}\), is:  
\[\Omega_{{}_{cutoff}}=3.17U_{{}_{p}}+I_{{}_{p}} \tag{1}\]  
In MHOHG, the ionized electron can recombine with neighboring ions in the same molecule leading to a much larger cutoff energy depending on the internuclear distance \(R\)[8; 9; 10]. This larger cutoff energy comes from longer acceleration distance in the above process.  
From Equation (1), one has to increase the ionization potential \(I_{{}_{p}}\), laser intensity \(I\) and wavelength \(\lambda\) to extend the cutoff energy \(\Omega_{{}_{cutoff}}\) of the HHG spectrum. In short laser fields with very high intensity, the depletion of the ground state and rapid change of intensity \(I(t)\) will lead to nonadiabatic effects in HHG, such as red [11] and blue [12] shifts of HHG. We review the recent progress in these nonadiabatic spectral shifts of HHG and in particular MHOHG in this article.
# Wi-GC: A Deep Spatiotemporal Gesture Recognition Method Based on Wi-Fi Signal
## 5 Experimental Design and Analysis
### Experimental Evaluation
#### 5.2.5 Method Performance Evaluation  
To evaluate the performance of the Wi-GC method, we conducted experiments on the Widar3.0 dataset [47], the CSIDA dataset [48] and our data set. Widar3.0 is composed of two sub-datasets, contributed by researchers at Tsinghua University, and has six gestures: push-pull, sweep, clap, slide, circle, and zigzag. CSIDA is a CSI-based gesture data set with six gestures: up, down, left, right, circle, and zigzag, proposed by [PERSON] et al. Experimental data are from the office environment; the results are shown in Figure 20.  
As can be seen from Figure 20, the Widar 3.0 dataset has the highest accuracy rate, with an average accuracy of 93.7%. This is because it is a relatively simple office environment, which produces fewer multi-path effects. The difference in accuracy between the CSIDA dataset and our dataset is small, due to their similar office layouts, with the perception area in the middle of two rows of desks. The environment is more complex than the Widar 3.0 dataset and, thus, slightly less accurate. Overall, the average accuracy of all three datasets was above 91%, indicating the excellent performance of our method.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Environment** & **Method** & **OK** & **ASL** & **ASR** & **Push** & **RO** & **RD** \\ \hline \multirow{3}{*}{Conference Room} & Ref. [28] & 89.7\% & 88.5\% & 88.2\% & 90.5\% & 87.4\% & 89.9\% \\  & CrossGR & 89.5\% & 89.1\% & 88.6\% & 90.2\% & 86.9\% & 90.2\% \\ \cline{1-1}  & WiNum & 87.5\% & 87.1\% & 86.7\% & 88.3\% & 85.4\% & 88.1\% \\ \cline{1-1}  & Wi-GC & 96.8\% & 95.4\% & 95.7\% & 96.5\% & 95.1\% & 96.3\% \\ \hline \end{tabular}
\end{table}
Table 1: Results of Wi-GC versus other methods.
# Optimization Algorithms in SDN: Routing, Load Balancing, and Delay Optimization
## 5 Comparative Overview  
Heuristic algorithms offer a practical approach to solving complex optimization problems that are otherwise computationally infeasible. By employing strategies such as swarm intelligence, simulated annealing, and tabu search, these algorithms can provide good-enough solutions within a reasonable timeframe.  
Advantages: Flexibility in addressing a wide range of problems, capability to find satisfactory solutions where exact algorithms would be impractical.  
Limitations: Solutions are not guaranteed to be optimal; performance heavily depends on the specific heuristic and problem instance.  
Application in SDN: Particularly useful for dynamic and complex network configurations, such as controller placement, where exact solutions are not computationally viable.  
Latency optimization algorithms focus specifically on minimizing delays within the network. These algorithms are critical in environments where real-time data transmission is essential, and any delay can lead to significant performance degradation.  
Advantages: Directly address network performance by minimizing delays, enhancing user experience, and optimizing real-time data flow.  
Limitations: May require frequent recalculations in dynamic network environments, potentially leading to overhead.  
Application in SDN: Essential for applications requiring real-time interaction, such as VoIP, online gaming, and financial transactions, where delays must be minimized.  
Load-balancing algorithms aim to distribute network traffic or computational load evenly across resources, preventing any single node from becoming a bottleneck. These algorithms ensure efficient resource utilization and maintain high levels of service availability.  
Advantages: Improve network resource utilization, prevent the overloading of individual network components, and enhance overall network performance and reliability.  
Limitations: Complexity in implementation increases with the scale and variability of network traffic; may require continuous adjustment to maintain balance.  
Application in SDN: Vital for managing data center networks, multi-server architectures, and cloud services, where traffic volume can be highly variable and unpredictable.  
Table 6 represents a summary of the Algorithms discussed in the articles above with their advantages and limitations while Table 7 summarizes the targeted networks.
## 1 Introduction  
Automatic modulation recognition (AMR) has grown in significance in wireless communication [1]. AMR methods mainly included likelihood-based (LB) and feature-based (FB) [2]. The LB method requires complex computation to calculate the comparison for recognition [3]. The FB method needs to design classifiers such as Support Vector Machines (SVM) [4], Decision Trees (DT) [5], artificial neural networks [6], etc. In recent years, with the development of Deep Learning (DL), it has a good ability of classification, induction and migration, and has made a major breakthrough in the field of signal modulation recognition [7]. In 2016, [PERSON] et al. used a 2 \(\times\) 128 matrix as input to a convolutional neural network for the first time to recognize 11 modulated signals. In the following year, the recognition of 24 modulated signals was completed by using residual neural network and other methods [8; 9; 10]. CNN algorithms solely took into account the signal's spatial characteristics, ignoring its temporal properties [11]. [PERSON] used the temporal characteristics of the signal that were taken out of the unidirectional Recurrent Neural Network (RNN) [12], but this technique ignores the problem of the signal being distorted by offset interference. In order to reduce the influence of carrier frequency and phase offset on modulation recognition, a learnable offset correction model can improve classification accuracy and correct channel errors [13]. However, this scheme only considers the combination with CNN, resulting in too large parameters. [PERSON] et al. combined learnable correction modules with CNN and Gated Recursive Units (GRUs) to make lightweight improvements [14]. To obtain a reasonable tradeoff between classification accuracy and computational complexity, we propose an efficient DL-AMR scheme using predictive correction combined with double GRUs via a lightweight framework. After error learning and estimation, the signal is restored by inverse transformation to realize signal correction, and after that, CNN and two GRU layers are used to extract the spatial and temporal characteristics for classification. This method not only improves the recognition accuracy, but also achieves low computational complexity.
## 5 Energy-Harvesting Applications
### Cognitive Radio for Wireless Sensor Networks
#### 5.1.3 Energy-Efficient Spectrum Usage  
In WSNs, energy efficiency is a critical concern [94; 95]. The CR technique can result in energy savings by optimizing spectrum consumption and decreasing unwanted transmissions.  
* Spectrum Optimization: By keeping away congested or under-utilized bands, it uses spectrum bands more intelligently.
* Adaptive Power Control: Modifies the transmission power based upon the observed spectrum environment in order to reserve the energy.