esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling Semantic-Aware Prompt-Based Argument Extractor in Documents
## 2 Related Work
### Joint Event Extraction  
Joint event extraction, a pivotal focus area within natural language processing, is dedicated to pinpointing events and their associated arguments within text. Initial research endeavors were chiefly concentrated on sentence-level extraction, with the primary aim of identifying event triggers and corresponding arguments confined to individual sentences. However, contemporary research has underscored the significance of document-level event extraction, which entails extracting events and their arguments from entire documents while accounting for the intricate relationships and dependencies between events. For instance, [PERSON] et al. [42] proposed a Neural Gibbs Sampling (NGS) method that combines neural networks with Gibbs sampling to model the joint distribution of event arguments, thereby improving the correlation modeling among event arguments. [PERSON] et al. [43] introduced CasEE, a joint learning framework with cascade decoding, which sequentially performs type detection, trigger extraction, and argument extraction to handle overlapping events. [PERSON] et al. [44] developed a Heterogeneous Graph-based Interaction Model with a Tracker (GIT), which constructs a heterogeneous graph to capture global interactions among sentences and entity mentions, and incorporates a tracker to model the interdependencies among events. [PERSON] et al. [16] proposed a Token-Token Bidirectional Event Completed Graph (TT-BECG) method, which designs a novel graph structure to accurately decode event arguments and event types, transforming the document-level event extraction into a prediction and decoding task of the token-token adjacency matrix. Another approach, also by [PERSON] et al. [45], proposed a novel method for document-level event extraction that introduces a token-event-role data structure and a multi-channel argument role prediction module, effectively merging entity extraction and multi-event extraction into a unified task.  
These studies collectively advance the field of joint event extraction by addressing the challenges of argument scattering, event overlapping, and interdependencies among events. However, challenges such as long-distance dependencies and event co-occurrence remain. Existing methods often struggle to capture the complex interactions between events and their arguments, particularly when these elements are dispersed across multiple sentences or paragraphs. Additionally, some approaches face limitations in computational efficiency and the ability to handle overlapping events accurately. To address these challenges, our work introduces a novel approach, the Semantic-aware Prompt-based Argument Extractor (SPARE) model, which enhances both the efficiency and accuracy of joint event extraction.
## 5 Experiments
### Experimental Results
The benefits are not confined to accuracy alone; we also observed substantial reductions in the elapsed times required for embedding detected objects and finding the k-nearest neighbors. In the nine-sprite dataset, the times recorded for \"E. Backdrops\" and \"Finding kNN\" were 0.27198 and 0.32854 with YOLOS-Ti, and mere values of 0.03610 and 0.04508 with CutLER, respectively. This indicates that the use of the unsupervised model significantly reduces the false positives of the detected objects when employing CutLER while simultaneously increasing the true positives. In summary, our experimental results underscore that the combination of ResNet-18 and CutLER yields the best performance on our dataset, excelling in both speed and accuracy. For scenarios where a small model size is prioritized, pairing ResNet-18 with YOLOS-Ti could prove beneficial, especially when further optimized with techniques such as Faiss or HNSW. Unfortunately, existing Vision Transformer-based embedding models fell short of expectations on our dataset. This might be attributed to the Transformer models' need for substantial data volumes to attain high accuracy, suggesting that further training on more extensive datasets could enhance their performance.
## 3 Results
### Rayleigh Wave Detection and Monitoring  
Rayleigh waves were detected using the setup described in Section 2.2.4. An example of spatio-temporal wave evolution obtained for the orthophthalic polyester composite sample OP2 is shown on Figure 2. This representation allows us to visualize the displacement of the wave envelope (light blue) in space and time (Figure 2). Thus, this spatio-temporal figure summarizes all the time signals along the 400 measurement points along the sample.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Pure**} & \multicolumn{2}{c}{**Composite**} \\ \hline
**Sample** & \(\rho\) **(kg\(\cdot\)m\({}^{-3}\))** & \(E\) **(GPa)** & \(\rho\) **(kg\(\cdot\)m\({}^{-3}\))** & \(E\) **(GPa)** \\ \hline OP1 & 1021 \(\pm\) 50 & 3.63 \(\pm\) 0.16 & 1251 \(\pm\) 63 & 5.83 \(\pm\) 0.89 \\ OP2 & 1190 \(\pm\) 68 & 4.55 \(\pm\) 0.54 & 1702 \(\pm\) 50 & 8.66 \(\pm\) 1.04 \\ IP & 1097 \(\pm\) 60 & 4.17 \(\pm\) 0.26 & 1620 \(\pm\) 99 & 7.43 \(\pm\) 0.82 \\ EP & 1170 \(\pm\) 63 & 3.88 \(\pm\) 0.67 & 1198 \(\pm\) 54 & 4.31 \(\pm\) 0.46 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of density (\(\rho\)) and Young’s modulus (\(E\)) of the different samples.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & \(\mathbf{vp_{1}}\) **(m\(\cdot\)s\({}^{-1}\))** & \(\mathbf{vp_{2}}\) **(m\(\cdot\)s\({}^{-1}\))** & \(\mathbf{vp_{3}}\) **(m\(\cdot\)s\({}^{-1}\))** & **Average \(\mathbf{vp_{2}}\) **(m\(\cdot\)s\({}^{-1}\))** \\ \hline OP1 & 2255 & 2243 & 2198 & 2231 \(\pm\) 30 \\ OP2 & 2459 & 2458 & 2313 & 2411 \(\pm\) 84 \\ IP & 2442 & 2396 & 2401 & 2413 \(\pm\) 25 \\ EP & 2427 & 2342 & 2521 & 2430 \(\pm\) 90 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of longitudinal wave velocities of the different pure samples.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & \(\mathbf{vp_{1}}\) **(m\(\cdot\)s\({}^{-1}\))** & \(\mathbf{vp_{2}}\) **(m\(\cdot\)s\({}^{-1}\))** & \(\mathbf{vp_{3}}\) **(m\(\cdot\)s\({}^{-1}\))** & **Average \(\mathbf{vp_{2}}\) **(m\(\cdot\)s\({}^{-1}\))** \\ \hline OP1 & 2577 & 2566 & 2405 & 25 For this sample, the Rayleigh wave velocity was estimated to be about 1383 m\(\cdot\)s\({}^{-1}\). Table 5 shows the Rayleigh wave velocities for all the pure and composite resins. The values of this property ranged from 1075 to 1140 m\(\cdot\)s\({}^{-1}\) and from 1145 to 1383 m\(\cdot\)s\({}^{-1}\) for pure and composite samples, respectively. The maximum discrepancy in velocity was estimated to be \(\pm\)80 m\(\cdot\)s\({}^{-1}\) and was used for measurement presentation purposes.
## 4 Results
### Comparison on an Annual Scale  
For annual scale comparison, regional scatter plots and statistics are shown in Figure 7a-d and Table 3, respectively. At region 1 (Figure 7a), GPPs had shown overall positive linear relationship with regression coefficient >0.65; deviations are obvious and mostly the in-situ precipitation is overestimated. Figure 7b shows that at region 2, TMPA outperformed the other GPPs in terms of linear relationship with in-situ, having a regression coefficient of 1.01. PERSIANN-CDR overestimated observed precipitation, while CHIPRS and MSWEP underestimated in-situ precipitation. In region 3, GPPs consistently overestimated in-situ precipitation products, as can be seen from Figure 7c, where TMPA outperformed rest of the GPPs. Figure 7d shows that in region 4, MSWEP and TMPA performed relatively well, while CHIRPS underestimated and PERSIANN-CDR overestimated in-situ precipitation. Table 3 suggest that at annual scale the performance of all GPPs is relatively weaker in region 1, with lower regression values except PERSIANN-CDR, differences and biases in mean precipitation are also obvious for PERSIANN-CDR (RMSD = 9.41 mm, Bias = -32.5 mm) and MSWEP (RMSD = 17.38 mm, Bias = -36.48) respectively. Overall, TMPA relatively outperformed all the other GPPs, followed by CHIRPS and MSWEP. The performance of GPPs on an annual scale suggests an improvement towards semi-arid regions with decrease in mean differences, biases and improved correlation for all GPPS; but a larger number of regions require study before such a trend can be accepted as definitive. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Region** & **Name** & **Regression Coefficient** & **RMSD (mm)** & **R** & **STD** & **Bias (mm)** \\ \hline \multirow{4}{*}{\(1\)} & MSWEP & 1.81 & 34.56 & **0.72** & 0.40 & –36.0 \\  & CHIRPS & 0.73 & 21.12 & 0.56 & **0.78** & –23.0 \\  & PERSIANN-CDR & 0.83 & 24.82 & 0.55 & 0.67 & –32.0 \\  & TMPA & **0.86** & **20.43** & 0.65 & 0.75 & \(\mathbf{-9.0}\) \\ \hline \multirow{4}{*}{\(2\)} & MSWEP & 0.73 & 19.63 & 0.92 & 1.27 & 12.0 \\  & CHIRPS & 0.74 & 15.11 & 0.95 & 1.28 & 9.2 \\  & PERSIANN-CDR & 1.18 & 28.57 & 0.93 & 0.79 & –31.0 \\  & TMPA & **0.96** & **12.43** & **0.98** & **1.01** & \(\mathbf{-3.0}\) \\ \hline \multirow{4}{*}{\(3\)} & MSWEP & 0.75 & 13.01 & 0.80 & 1.05 & 1.8 \\  & CHIRPS & 0.58 & **12.03** & 0.75 & 1.28 & **1.0** \\  & PERSIANN-CDR & 0.65 & 17.63 & 0.65 & **0.99** & –10.0 \\  & TMPA & **0.93** & 13.91 & **0.84** & 0.89 & \(\mathbf{-2.4}\) \\ \hline \multirow{4}{*}{\(4\)} & MSWEP & **0.98** & **11.18** & 0.96 & **0.92** & \(\mathbf{-1.0}\) \\  & CHIRPS & 0.76 & 15.37 & 0.90 & 1.17 & 3.0 \\ \cline{1-1}  & PERSIANN-CDR & 1.29 & 21.66 & 0.93 & 0.71 & –20.0 \\ \cline{1-1}  & TMPA & 1.14 & 12 & **0.97** & 0.85 & –3.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Descriptive statistics of GPPs versus in-situ precipitation at monthly time scale. Figure 7: Scatterplots of annual mean precipitation for in-situ and GPPs at selected locations. (**a–d**) are equivalent to regions 1, 2, 3 and 4 in Table 1.
## 3 Signal Processing
A trigonometric interpolation [28] provides an appropriate method as reported in [14]. The correction described above does not change the initial signal envelope. Consequently, the 3D topography can be obtained from the corrected correlograms by using widespread CSI signal processing algorithms which use the interference contrast and phase of the signal [29, 30, 31, 32]. In this work, the CSI signals are evaluated by a phase evaluation algorithm using the lock-in technique similar to Equation (4) and additionally, an envelope detection using the Hilbert transform in order to increase the unambiguous range of two adjacent height values. In this way, an axial repeatability in the subnanometer range is achieved. These algorithms and their combination is described in more detail in [24]. Figure 3: Measured IDS signal (blue) sampled during a CSI depth scan and the corresponding actuator excitation (brown). The excitation is caused by the voltage curve (green). The evaluation windows are marked by the colored areas where the nearly linear mirror deflection leads to a constant frequency \(f_{\rm e}\) of the interference signal. \(u_{\rm cam}\) represents the camera trigger signal at the turning point of the mirror deflection.
## 2 Materials and Methods
### Brain Data Acquisition  
The EEG data were acquired from an EEG Emotiv EPOC+ system. This consists of 16 saline-based electrodes and a wireless amplifier. The electrodes were located at positions AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, AF4, according to the international 10-20 system. The 10-20 system is an internationally recognized method that allows EEG electrode placement to be standardized. It ensures that inter-electrode spacing is equal and that electrode placements are proportional to skull size and shape. Two electrodes located just above the ears (P3, P4) were used as a reference. The data were collected at a sampling rate of 128 Hz and transferred to the computer via Bluetooth. The choice of this device was based on several advantages compared to more conventional EEG systems. The main advantages of such technologies include quick and easy positioning due to saline electrodes and wireless Bluetooth data transmission, opening new perspectives for the study of cerebral activity in natural or ecological settings.  
The installation time of each Emotiv Epoc+ system is considerably shorter, about 5 min less than gel-based systems, where the gel application for each electrode can take up to one hour, which extends considerably the duration of experiments.  
Figure 1: Course of the experiment.
# Asynchronous Chirp Slope Keying for Underwater Acoustic Communication
### Research Problem  
We investigate a modulation scheme that is intended for short messages, which in our application of diver status and localization packages are intended to carry an unique ID and a short amount of status information, as well as limited telemetric data, similar to the protocol of the AHOI modem [18]. We found in our previous investigations [36] that conventional chirp slope keying demodulation approaches, e.g., as proposed in the works of [8; 23; 37; 38], show a critical flaw for the application in the transmission of very short multi-band packets, where the probability that a single channel is comprised entirely of one symbol state is high. This leads to one of the matched filter outputs having only the low-valued crosscorrelation of up vs. down chirp to estimate the symbol timing. One common solution is in the use of well known preambles, that feature an adequate amount of all states in all bands, to ensure symbol synchronization, with the draw-back that precious time slots in the channel are occupied by the overheads. Another solution is to implement a coding scheme, which avoids patterns that lead to single channel featuring only a single symbol state. Furthermore, the target application of our communication scheme in diver or UAV communication implicates an underwater environment, where reverberating surfaces are close-by, e.g., the water surface as well as a ship's hull in case of maintenance drivers or the ground for scientific UAVs. Therefore, the assumption of semi-infinite bodies of water does not hold. We emulate this environment by using a small pool for our measurements, as shown in Section 2.5, which introduces strong reverberations from multi-path propagation. Finally, we simulate the overall BER for an idealized channel in Section 4 to provide performance estimators, which can be compared to other systems, along with the PER that is often neglected in other works.
## 3 System Model Description  
**Notation:** In the following, boldface lowercase letters are used to denote vectors whereas boldface uppercase lettersso denote matrices. \((\cdot)^{T}\) and \((\cdot)^{\dagger}\)denote the transpose and conjugate transpose of their matrix arguments, respectively. \(I(x;y)\) denotes the mutual information function between \(x\) and \(y\). \(\mathbf{R}_{+}\) denotes the nonnegative real domain. \(\mathbf{I}_{n}\) denotes the \(n~{}\times~{}\)unitary matrix.
# Enhancing Communication Security in Drones Using QRNG in Frequency Hopping Spread Spectrum
## 4 Methodology and System Architecture
### True Random Frequency Hopping  
With an RPU, the sequence \(\{f_{o}\}\) is generated by a truly random process. If \(f_{o}\) is chosen uniformly from a set of available frequencies \(F\), the probability distribution of selecting any particular frequency is:  
\[P(f_{o}=f_{z})=\frac{1}{|F|} \tag{8}\]  
where \(|F|\) is the cardinality of the set \(F\). The unpredictability of the frequency hops, characterized by entropy \(H\), is maximized:  
\[H=-\sum_{f\in F}P(f)\log P(f)=\log|F| \tag{9}\]  
This high entropy ensures that even with complete knowledge of the hopping algorithm, an adversary cannot predict the sequence, as it is truly random.  
The primary security advantage of true random frequency hopping lies in its anti-jamming capabilities. Given that the sequence is entirely unpredictable, a jammer must either cover the entire bandwidth (which requires considerable power) or fail to disrupt the communication. The jamming margin, defined as the difference between the bandwidth of the spread spectrum and the jamming bandwidth, is maximized.  
The security of the system is further enhanced because eavesdroppers cannot anticipate the next frequency hop. The information-theoretic security provided by the truly random sequence means that even with infinite computational power, an adversary cannot predict future hops without the actual random sequence.  
The primary challenge with true random FHSS is synchronization between the transmitter and receiver. Both ends must be perfectly synchronized in terms of the hopping sequence. This can be achieved through secure key distribution mechanisms or synchronization protocols.  
The true random sequence or the seed value for the RPU must be securely shared between the communicating parties. This can be done using public-key cryptography or other secure channels.  
The implementation of our RPU-based FHSS system presents both challenges and opportunities for enhancing communication security. One key consideration is the rate at which the RPU can generate random numbers, as this directly impacts the frequency hopping rate and, consequently, the system's resilience to jamming attempts. To address potential latency issues, we employ a buffer system that pre-generates a pool of random frequencies, ensuring a continuous supply for rapid hopping. Additionally, we implement a dynamic hopping rate that adapts to the perceived threat level; under normal conditions, the system may use a moderate hopping rate to conserve power, but it can switch to a more aggressive rate when jamming or interception is suspected. The RPU's true randomness also allows for the implementation of a frequency exclusion list, dynamically avoiding frequencies that are detected to be under interference, without compromising the unpredictability of the overall sequence. This adaptive approach, coupled with the inherent unpredictability of the RPU, provides a significant advantage over traditional PRNG-based systems in terms of security and flexibility.
## 2 Theoretical Analysis of CBIS
### Basic Concept of the CBIS
#### 2.1.2 The Cable-Bracing System
\begin{array}{ll}\cos(\alpha)&\text{simple cable -- bracing system}\\ \eta\cos(\alpha)&\text{cable -- bracing lever system}\\ \frac{2\cos(\gamma)\cos(\alpha)}{\sin(\alpha+\gamma)}&\text{cable -- bracing seesaw system}\end{array} \right. \tag{6}\]  
Figure 3 presents the variation of the displacement transition ratios concerning the cable installation angle \(\alpha\), in case of the lever arm ratio \(\eta=1,1.5,2\), and the seesaw angle \(\gamma=20^{\circ},30^{\circ},40^{\circ}\). Note that the simple cable-bracing system is a specific form of the cable-bracing lever system, of which the arm ratio equals to one. From Figure 3, we can see that as the cable installation angle increases, the displacement transition ratios of all cable-bracing systems therein decrease. When the cable installation angle is fixed, the displacement transition ratio of the cable-bracing lever system is proportional to the lever arm,  
Figure 3: The displacement transition ratio of the cable-bracing inerter system. while the displacement transition ratio of the cable-bracing seesaw system decreases with an increase in the seesaw angle. When the cable installation angle is small, the cable-bracing seesaw system shows a better displacement transition performance than that of the cable-bracing lever system. However, the seesaw cable system's displacement transition efficiency decreases rapidly with an increase in the cable installation angle. Besides, it is difficult to adjust the displacement transition ratio by changing the seesaw angle, when the cable installation angle is large. In contrast, the cable-bracing lever system's displacement transition ratio declines relatively slowly and still maintains the adjustability under a large cable installation angle. Thus, we conclude that the cable-bracing seesaw system is suitable for the CBIS with a small cable installation angle and a high demand for the displacement transition ratio, and the cable-bracing lever system is the opposite. It is worth noting that although the cable-bracing system can obtain a higher displacement transition ratio by choosing a larger lever arm or a smaller seesaw angle, this is often accompanied by an increase in the complexity of the cable-bracing system. For simplicity, during the design procedure, the displacement transition ratio is selected as an optimization variable instead of considering the specific cable-bracing system's form. The cable installation angle is determined according to specific structure characteristics, such as the story height, column-span, etc. Considering the displacement transition ratio and the cable installation angle, the designers can select the appropriate cable-bracing system.
## 4 Discussions
### About the Spatio-Temporal Correlation  
In the spatio correlation analysis, Granger causality test [30] is often used to analyze the causality between series of different sits. To discuss the effectiveness of the Granger causality test based method in the PM\({}_{2.5}\) concentration prediction, the PM\({}_{2.5}\) concentration of each site is analyzed in this paper. The results show that the pollution trends are highly similar, without time delay. One example of the comparison of PM\({}_{2.5}\) concentration between Aotizhongxin and Dongsi sites is shown in Figure 7. The MAE and RMSE of these two sites PM\({}_{2.5}\) concentration are 14 and 23.2, respectively, so the PM\({}_{2.5}\) concentration of each site reflects the similarity rather than the causality. The result means that the Granger causality test is not effective in the spatio correlation analysis. In this paper, Pearson correlation coefficient is used to measure the spatial correlation of PM\({}_{2.5}\) concentration at 12 sites. The results show that all correlation values are higher than 0.80 (\(p\)-value \(<\) 0.05), indicating that there is a high correlation between PM\({}_{2.5}\) concentrations among sites, which means the PM\({}_{2.5}\) concentration of neighboring sites is helpful to predict the PM\({}_{2.5}\) concentration at the central site.  
Figure 5: The eighteenth hour prediction using different methods for a total of 100 h.  
Figure 6: Effect of the improved attention mechanism.  
In the time correlation analysis, the autocorrelation function is used in this paper to measure the time correlation between PM\({}_{2.5}\) concentration time series at each site. It can be seen from Figure 8 that the autocorrelation coefficient of PM\({}_{2.5}\) concentration after 24 h is above 0.4, and the correlation is moderate or above. Therefore, it is feasible to predict PM\({}_{2.5}\) concentration within 24 h with historical data. In addition, the autocorrelation coefficient of each site in the figure decreases with the increase of time lags. Earlier events have less impact on the current state.