esa-sceva/satcom-chunk-collection huggingface dataset:
# Extraction of Joint Entity and Relationships with Soft Pruning and GlobalPointer
## 4 Experiment
### Result and Analysis
#### 4.4.2 Result Analysis on Different Sentence Types
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{\(\mathbf{N=1}\)} & \multicolumn{3}{c}{**NYT**} & \multicolumn{3}{c}{**WebNLG**} \\  & & \(\mathbf{N=2}\) & \(\mathbf{N=3}\) & \(\mathbf{N=4}\) & \(\mathbf{N\geq 5}\) & \(\mathbf{N=1}\) & \(\mathbf{N=2}\) & \(\mathbf{N=3}\) & \(\mathbf{N=4}\) & \(\mathbf{N\geq 5}\) \\ \hline CopyRE\({}_{\textit{OneDexder}}\) & 66.6 & 52.6 & 49.7 & 48.7 & 20.3 & 65.2 & 33.0 & 22.2 & 14.2 & 13.2 \\ CopyRE\({}_{\textit{MultiDexder}}\) & 67.1 & 58.6 & 52.0 & 53.6 & 30.0 & 59.2 & 42.5 & 31.7 & 24.2 & 30.0 \\ GraphRel\({}_{1p}\) & 69.1 & 59.5 & 54.4 & 53.9 & 37.5 & 63.8 & 46.3 & 34.7 & 30.8 & 29.4 \\ GraphRel\({}_{2p}\) & 71.0 & 61.5 & 57.4 & 55.1 & 41.1 & 66.0 & 48.3 & 37.0 & 32.1 & 32.1 \\ OrderCopyRE & 71.7 & 72.6 & 72.5 & 77.9 & 45.9 & 63.4 & 62.2 & 64.4 & 57.2 & 55.7 \\ ETL-Span & 85.5 & 82.1 & 74.7 & 75.6 & 76.9 & 82.1 & 86.5 & 91.4 & 89.5 & 91.1 \\ CasRel & 88.2 & 90.3 & 91.9 & 94.2 & 83.7 & 89.3 & 90.8 & **94.2** & 92.4 & 90.9 \\ DualDec & 88.5 & 90.8 & 92.4 & 95.5 & 90.1 & 85.8 & 90.5 & 88.9 & 89.9 & 85.4 \\ RMAN & 84.3 & 86.0 & 86.6 & 92.5 & 76.1 & - & - & - & - & - \\ \hline SGNet & \(\mathbf{89.2}\) & \(\mathbf{91.6}\) & \(\mathbf{92.7}\) & \(\mathbf{95.9}\) & \(\mathbf{90.6}\) & \(\mathbf{89.4}\) & \(\mathbf{91.1}\) & 93.7 & \(\mathbf{93.4}\) & \(\mathbf{91.7}\) \\ \hline \hline \end{tabular} Bold data in the table indicates the best results.  
\end{table} 
Table 3: F1 score of extracting relational triples from sentences with a different number (denoted as \(N\)) of triples. Figure 5: F1 score of extracting relational triples from sentences with the different overlapping patterns. (**a**) F1 of Normal Class, (**b**) F1 of SEO Class, (**c**) F1 of EPO Class.
# Affection Enhanced Relational Graph Attention Network for Sarcasm Detection
## 4 Experiments
### Results  
Tables 2-4 show the experimental results on six benchmark datasets. We observe that our model achieves state-of-the-art performance in all six datasets; the best-improved results of the four metrics are more than 4%, compared with the previous state-of-the-art version. It is worth noting that, since there are no unified datasets among existing studies, we conduct comparison experiments of baselines on our datasets with open source code or reproduced code (the results of basic models are from [PERSON] et al. [6]). Experiments show that our proposed model, which simultaneously considers the affection information and relational dependencies, contributes to the performance of sarcasm detection. We observe that basic models are much worse than attention-based and graph-based models. Those models only capture local semantic information, which is insufficient to recognize complex irony expressions or long-term incongruity between words. The attention-based models achieve a slight improvement compared with the basic models, demonstrating the effectivity of the attention mechanism. The SAWS shows that sentence snippets capture more useful sarcastic information than words among the attention-based methods. The graph-based methods (i.e., ADGCN and ARGAT), which simultaneously utilize the affection and semantic information, show the strong power to capture incongruity among word pairs. The performances of graph-based models suggest that affective information plays an essential role in sarcasm detection. Noting that all models get the best results in Tweets sets, and the performances decrease when the length of sentences is \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**IAC-V1**} & \multicolumn{4}{c}{**IAC-V2**} \\ \cline{2-10}  & **Precision (\%)** & **Recall (\%)** & **F1 (\%)** & **Acc. (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 (\%)** & **Acc. (\%)** \\ \hline NBOW & 57.17 & 57.03 & 57.00 & 57.51 & 66.01 & 66.03 & 66.02 & 66.09 \\ CNN & 58.21 & 58.00 & 57.95 & 58.55 & 68.45 & 68.18 & 68.21 & 68.56 \\ GRNN & 56.21 & 56.21 & 55.96 & 55.96 & 62.26 & 61.87 & 61.21 & 61.37 \\ CNN-LSTM-DNN & 55.50 & 54.60 & 53.31 & 55.96 & 64.31 & 64.33 & 64.31 & 64.38 \\ \hline ATT-LSTM & 58.98 & 57.93 & 57.23 & 59.07 & 70.04 & 69.62 & 69.63 & 69.96 \\ SIARN & 63.94 & 63.45 & 60.52 & 62.69 & 72.17 & 71.81 & 71.85 & 72.10 \\ MARN & 63.88 & 63.71 & 63.18 & 63.21 & 72.92 & 72.93 & 72.75 & 72.75 \\ SAWS & 66.22 & 65.65 & 65.60 & 66.13 & 73.25 & 73.40 & 73.43 & 73.55 \\ \hline ADGCN & 68.08 & 68.08 & 68.06 & 68.06 & 76.96 & 76.98 & 76.97 & 76.99 \\ \hline ARGAT (**proposal**) & **72.26** & **72.26** & **72.25** & **72.25** & **78.41** & **78.19** & **78.21** & **78.22** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results on IAC datasets. The best results are in bold and the results of four basic models are from [6].
## 6 Discussion
Based on the obtained findings from both static and PWV validations, the following points can be concluded:  
* Decimeter- to meter-level 3D positioning accuracy and few-millimeter PWV precision can be achieved utilizing the ZED-F9P receiver in the real-time domain using the BKG, CNE, GFZ, IGS, and WHU products;
* The real-time GPS PPP processing model outperforms the Galileo, GLONASS, and BeiDou real-time PPP processing models for both 3D positioning and PWV results;
* The Galileo solution outperforms the GLONASS and BeiDou PPP solutions in the 3D position and PWV domains;
* The products' accuracy has a significant impact on both GLONASS and BeiDou PPP accuracies, as well as the satellite geometry;
* The GPS PPP solution using WHU products outperforms the other products, with 0.164 m 3D position accuracy and 2.73 mm PWV precision;
* BKG GLONASS products provide better GLONASS PPP solution than other products, with 1.123 m 3D position accuracy and 8.72 mm PWV precision;
* For the Galileo PPP solution, BKG provides 3D positioning accuracy superiority compared to the others, with a 0.419 m RMSE value; however, both GFZ and WHU have superior PWV precision compared to other products, with 3.98 mm and 5.37 mm STD values;
* Both GFZ and WHU products offer better BeiDou PPP solutions in comparison with IGS products, with 2.151 m and 1.402 m 3D position accuracy, respectively, and 9.45 and 10.90 mm PWV precision, respectively. * For future work, using different types of low-cost antennas will be investigated.
## 3 From Understanding GNSS Data to Problem Definition
### Problem Definition  
By now, we can clearly define the problem we aim to tackle in this paper. The major task is to profile location context based on raw GNSS measurements. In particular, we consider a large volume of GNSS data records collected by various GPS receivers (of capable smartphones), each _record_ containing _samples_ obtained within a time segment \(\mathcal{T}\) but from one of \(\mathcal{N}\) visible satellites and each sample includes \(\mathcal{M}\) fields. Our goal is to first process and pack all the records of dimension \(\mathcal{T}\times\mathcal{M}\times\mathcal{N}\) into a valid feature map; then, we identify a _semantic representation_ through unsupervised learning, aiming at compactly characterizing these seemingly sophisticated data structures.  
Figure 1: Preliminary studies on GNSS data. (a) Outdoor, long, wide street, (b) inside a glass building and an a void deck, (c) sheltered street, (d) enclosed, high-rise area, (e) building surrounded by street, and (f) the same as (e) but at a different time. We put ground-truth and GPS traces on Google Maps snapshots in the first row, the corresponding photographic views in the second, then satellite distribution patterns (with black dot indicating the position of receiver) in the third, and finally GPS error distributions (in meters) in the last.  
The outcome of this profiling process should yield a representation that satisfies the following criteria to ensure its effectiveness:  
1. It can be further derived and used to estimate the accuracy level of GPS localization performance.
2. It has specific semantic meaning that can be further clustered or classified to represent various urban location contexts.
3. Its dimension should be sufficiently reduced, so as to be computationally efficient for other applications.  
Although the ultimate purpose of this profiling is to assist an integrated localization service, we would refrain from being over-ambitious in this preliminary study. Instead, we use the above requirements (1) and (2) to act as two immediate applications, in order to demonstrate the usefulness of our proposed GNSS profiling.
# Low-Latency, Three-Phase PMU Algorithms: Review and Performance Comparison
## 2 Measurement Algorithms  
In this paper, we present a comparison among three algorithms for estimating positive sequence synchrophasor, frequency and ROCOF starting from a three-phase signal. The considered techniques are the SV filtering algorithm (SV-F) [24], the SV Taylor-Fourier algorithm (SV-TF) [26] and the SV lpDFT algorithm (SV-lpDFT) [25]. All of them feature high flexibility: their parameters can be tuned in order to meet specific requirements in terms of accuracy and latency according to the application. In this respect, we focus on fast response, fixing the algorithm latency at about one and half nominal cycles. The P-class algorithm suggested by the IEC Std, which is used as reference to compare the performance obtained with the three aforementioned algorithms, has a different latency, but always below two cycles. In the following, the common ground and useful definitions are firstly introduced, then all the considered algorithms are explained in detail.
## 3 Results
### Gravimetry  
Gravimetry was focused on measurements regarding (a) weight loss (WL) and (b) sorbed moisture (SM). WL was calculated for reconditioned samples, namely at dried state (DS), according to:  
\[\text{WL}_{\text{INTx}}\left(\%\right)=(\text{SW}_{\text{INTx}-1}-\text{SW}_{ \text{INTx}})/\text{SW}_{\text{INTx}-1}\times 100 \tag{1}\]  
where \(\text{SW}_{\text{INTx}-1}\) is the sample weight measured in the previous AWE treatment interval (\(1\leq x\leq 5\)) and \(\text{SW}_{\text{INTx}}\) is the sample weight measured in the current AWE treatment interval. In the same manner, SM was calculated according to:  
\[\text{SM}_{\text{INTx}}\left(\%\right)=(\text{SW}_{\text{HS/INTx}}-\text{SW}_ {\text{DS/INTx}})/\text{SW}_{\text{DS/INTx}}\times 100 \tag{2}\]  
where \(\text{SW}_{\text{HS/INTx}}\) is the sample weight in humid state (HS) for AWE treatment interval INTx (\(1\leq x\leq 5\)) and \(\text{SW}_{\text{DS/INTx}}\) is the sample weight in DS for the same treatment interval. Due to small variations of sample weights, \(\text{SM}_{\text{INTx}}\) (%) was recalculated in regard to the specific sample weight. A complementary 1000 h exposure of samples in a humidity chamber (100% RH/40 \({}^{\circ}\)C) revealed that all samples, regardless of filler content, were capable of withholding additional moisture content, leading to the conclusion that saturation level was not achieved during the treatment process. Moreover, the 7 day medium vacuum exposure at 0.1 mbar was not sufficient to fully remove saturation-level SM, which is estimated to have deeply penetrated the epoxy matrix.  
Figure 7a presents WL in regard to previous AWE treatment interval. The bare resin initially (INT0) exhibited weight gain up to 288 h of treatment (Figure S2, Addendum) and, at INT576, WL commenced, reaching a maximum at INT1152. In the following treatment intervals, WL continued up to INT2880, but at a lower rate. According to the literature [43],  
Figure 6: Contact angle change due to AWE treatment.  
two antagonizing phenomena occur during photodegradation, namely (i) photo-oxidation due to chemical interaction of oxygen with the polymeric matrix, which leads to weight gain and (ii) polymeric chain scissions due to decomposition reactions. Additionally, it is important to note that, since weight change measurements were implemented in atmospheric conditions, samples cannot be considered as fully dry and thus, WL cannot be ascribed merely to decomposition reactions. The same stands for ZnO composite samples.  
On the other hand, bare samples exhibited maximum SM content at INT1728, in consequence to maximum WL observed at INT1152 (Figure 7a). The same behaviour was observed for ZnO-loaded samples, leading to the conclusion that moisture retention is fluctuating in response to WL as AWE treatment proceeds (Figure 7b).
## 5 Conclusions  
The MS system installed at tunnels of the Shuangjiangkou hydropower station can capture a lot of signals. Firstly, MS signal waveforms were recognized by using the HHT. Furthermore, introducing HHT instantaneous time-frequency analysis technology conducted MS event dynamic analysis and predicted rockbursts. The main conclusions are as follows:  
1. For rockburst events, there was relatively high energy distribution in the low-frequency band, and the signal characteristics were relatively complex. If the energy released by an MS event was relatively high, there was obvious energy distribution for the event in the low-frequency band.
2. Before the occurrence of a rockburst, there were several indicators that needed to be noted. The event FD range had a significant downward shift phenomenon over time. The frequency points of some events had good continuity. The energy distribution of some events was in the low-frequency range. The FD range of some events gradually narrowed. Before the rockbursts, only some of these indicators may have occurred, or all of these indicators have occurred. For the comparison of multiple events, the energy distribution of some events increased in a relatively low-frequency band over time.  
The HHT spectrum can well reflect the MS activity characteristics and timely feedback the anomalies inside the surrounding rocks on site. In this paper, the HHT instantaneous frequency analysis technology was introduced to reveal the instantaneous frequency change laws of the single-channel signal before rockbursts. A new idea of using a single-channel signal to predict rockbursts was realized.  
**Author Contributions:** Conceptualization, R.X.; methodology, R.X.; software, R.X.; formal analysis, Y.K.; investigation, Y.K.; resources, N.W.; data curation, N.W.; writing--original draft preparation, R.X.; writing--review and editing, N.W.; supervision, N.W.; project administration, R.X.; funding acquisition, R.X. and N.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No. 42167023, 41941018) and Research Program of Science and Technology at Universities of Inner Mongolia Autonomous Region of China (No. NJZY22363) for which the authors are very grateful.  
Not applicable.  
The authors thank the anonymous reviewers, who provided valuable suggestions that improved the manuscript.  
The authors declare no conflict of interest.
## 1 Introduction
Due to the unpredictable trajectory of UAVs and the limited battery power, the fairness of vehicle selection in each transmission window needs to be considered to enhance the real-time coverage rate [17]. Owing to the limited battery power of UAVs, the maximum achievable QoS is limited. Therefore, FL-based mechanisms to ensure an optimal UAV trajectory to maximize the battery power utilization of UAVs must be investigated for low-latency communications between vehicles and UAV [18]. Some recent works have proposed FL to design energy-saving algorithms by jointly optimizing the UAV flight distance and flight height [19]. Maximizing the minimum remaining energy of the UAV is also investigated as a viable solution to extend the UAV battery usage. Federated reinforcement learning (FRL) is also shown to provide superior performance in co-operative UAV-vehicle communications [20]. In certain Markovian approaches, the UAV and vehicles observe their individual states and also learn the state information of other agents to improve the level of co-operation among participating agents. However, obtaining the actions of UAV and other vehicles increases the dimension of the state space [21]. Multi-agent co-operation is also shown to have slow convergence in high-dimensional spaces. For high-dimensionalspaces and multi-agent cooperation, the learning efficiency can be improved using FL-based optimization algorithms that assist the participating agents in faster decision-making [22]. A UAV must achieve wider ground coverage due to line-of-sight (LoS) channel links from an optimum UAV altitude [23]. However, in a multi-vehicle network with spatio-temporal correlation in transmitted data, the UAV energy constraints, flight trajectory restrictions, and QoS requirements need to be jointly considered [24]. Because of the multipath propagation effects, vehicles encounter non-line-of-sight (NLoS) links, and the channel quality and transmission rate deteriorate. The signal frequency varies due to the relative motion between the UAV and the vehicles [9]. The UAV trajectory must be optimized considering the constraints of the data rates and bit error rate (BER), taking into account the Doppler spread [10]. Accumulating and processing data from different transmission time intervals (TTIs) leads to delay [25].
## 9 Challenges
**Scalability of Defense Mechanisms:** As the number of participating devices increases, the scalability of the defense mechanisms becomes a critical concern. Techniques that are computationally intensive or require significant communication overhead may not be practical for large-scale FL systems. Research should focus on developing lightweight andscalable defense strategies that can efficiently handle a growing number of clients without degrading performance.
## 2 Materials and Methods
### Dynamic Model of the Lower Limb Prosthesis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Notations** & **Definitions** & **Notations** & **Definitions** & **Notations** & **Definitions** \\ \hline \(M\) & inertia matrix & \(q_{1}\) & angle for ankle joint & \(R_{j}\) & amplitude \\ \hline \(C\) & centripetal–Coriolis matrix & \(q_{2}\) & angle for knee joint & \(g_{0}\) & constant \\ \hline \(G\) & gravitational torque & \(g\) & the gravity & \(R_{0}\) & total offset \\ \hline \(q\) & real angle of joints & \(q_{d}\) & desired trajectory & \(q_{i0}\) & raw trajectory \\ \hline \(\tau\) & control torque & \(q_{di}\) & desired trajectory & \(T\) & gait cycle \\  & & & of i-th joint & & \\ \hline \(\tau_{e}\) & interaction torque & \(r_{0}\) & total offset & \(J\) & cost function \\ \hline \(m_{1}\) & mass of 1-th limb & \(r_{j}\) & amplitude & \(\lambda\) & weight \\ \hline \(m_{2}\) & mass of 2-th limb & \(\omega_{j}\) & frequency & \(\widetilde{\gamma}\) & estimation error \\ \hline \(d_{1}\) & distance from joint & \(t\) & time & \(\Gamma\) & gradient descent \\  & to center of 1-th limb & & & \\ \hline \(d_{2}\) & distance from joint & \(s_{j}\) & phase & \(k_{cl}\) & gain \\  & to center of 2-th limb & & & \\ \hline \(l_{1}\) & distance from ankle & \(g_{j}\) & constant & \(y_{p}\) & auxiliary output \\  & joint to knee joint & & & \\ \hline \(l_{2}\) & distance from knee & \(\Omega_{j}\) & frequency & \(\Phi\) & auxiliary regressor \\  & joint to mass center of & & & \\  & human body & & & \\ \hline \(I_{1}\) & moment of inertia & \(S_{j}\) & phase & \(\Psi\) & auxiliary regressor \\  & of 1-th limb & & & \\ \hline \(I_{2}\) & moment of inertia & \(\psi_{ms,0}\) & initial value & \(L\) & observer gain \\  & of 2-th limb & & & \\ \hline \(W_{past}\) & previous parameter & \(C_{d}\) & damping matric & \(P_{\tau e}\) & observer gain \\ \hline \(J_{past}\) & previous cost function & \(K_{d}\) & stiffness matric & \(A\) & constant matrix \\ \hline \(W^{*}\) & suitable parameter & \(q_{r}\) & reference trajectory & \(\sigma_{\tau e,1}\) & upper bound \\ \hline \(v_{na}\) & neural activation & \(e\) & tracking error & \(\sigma_{\tau e,2}\) & upper bound \\ \hline \(s_{emg}\) & amplitude of sEMG & \(r\) & assistant variable & \(\beta_{\tau e}\) & convergence rate \\ \hline \(\alpha_{na}\) & constant & \(\widetilde{\
u}\) & upper bound & \(\tau_{e}\) & estimation error \\ \hline \(\beta_{na,1}\) & constant & \(\bar{a}\) & upper bound & \(V_{1}\) & Lyapunov function \\ \hline \(\beta_{na,2}\) & constant & \(\alpha\) & constant & \(V\) & Lyapunov function \\ \hline \(C_{na,1}\) & constant & \(Y\) & regressor matrix & \(\Delta V\) & Lyapunov function error \\ \hline \(C_{na,2}\) & constant & \(k_{r}\) & constant gain matrix & \(\psi_{ms}\) & pennation angle \\ \hline \(F_{ms}\) & neuromuscular force & \(\widehat{Y}\) & estimation & \(l_{ms,0}\) & initial value \\ \hline \(l_{ms}\) & length & \(\hat{\tau}_{e}\) & estimation & \(v_{ms}\) & extension speed \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mathematical notations and definitions.
# Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review
## 3 Unmanned Aerial Vehicles (UAVs)
### UAV Characteristics  
#### 3.3.1 Speed and Flight Time  
Smaller UAVs can fly at a speed lower than 15 m/s. In contrast, large UAVs can fly at a speed up to 100 m/s. When any UAV follows any intended trajectory to enhance its spectral or energy efficiency, its speed must be properly checked at different turning points of this trajectory. In ref. [52], the authors focused on trade-off between UAV's turning agility and speed.  
While the flight time of a UAV refers to maximum time, it can fly until its battery drains out. Size, weight, and weather conditions have a strong impact on battery life of UAVs. Large UAVs can travel for hours, while smaller UAVs can fly for a limited time of 20-30 min. Flight time is also affected by the autopilot system and GPS. The flight time of a UAV is of paramount importance along with cost and price. The research fraternity should investigate innovative solutions to overcome the limited endurance of UAVs, which plays a crucial role in their full-scale deployment and successful mission accomplishment.
## 2 Related Works  
### Traditional Label Propagation Algorithm  
The label propagation algorithm (LPA) [19] is a heuristic algorithm that follows the principle that labels propagate among network nodes according to certain rules, and the process is finished when the labels of every node are settled or stop changing; then, nodes with the same label will belong to the same community. The process can be described in discrete stages:  
**Initialization stage**: a unique label is initially allocated to each node in the network.  
**Propagation stage**: the label with the highest frequency among its neighbors is selected to update, executing iteratively for each node until the stop condition is reached.  
**Stopping Criterion**: the algorithm will terminate if the labels of all the nodes in the network no longer change or the maximum number of iterations is reached.  
Characteristics of the LPA include its simplicity, easily implemented fast execution, and suitability for large-scale networks. However, due to its random and local nature, it is probably unlikely to find the globally optimal community structure and may be affected by the initial label allocation and the structure of the network.