esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Heart Rate Variability Indices  
For the selection of HRV indices aimed at analyzing the dynamic behavior of the ANS, the indices that allowed the evaluation of both global activity and the interactions between the sympathetic and parasympathetic branches were chosen. Indices were calculated in the 5 min segments of the 14 blocks previously segmented, following the recommendations established by the Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology [11]. This procedure generated a total of 672 analysis blocks for each patient in a 48 h recording period. The dynamic vector of the HRV is a proposed methodology that allows the temporal evolution of indices to be analyzed by dividing a continuous record into segments or blocks of time. It aims to capture the variability and adaptability of the ANS at different times, providing a more detailed view of its regulation.  
To perform the analysis of the ANS, HRV indices from three domains were selected: temporal, frequency, and non-linear. In the time domain, the SDNN and the RMSSD were included. In the frequency domain, the Welch periodogram method was used to determine the LF (low frequency, 0.04-0.15 Hz) and HF (high frequency, 0.15-0.4 Hz) frequency bands, and the LF/HF ratio was used as an indicator of the autonomic balance between both branches of the ANS. Finally, in the nonlinear domain, the indices derived from the Poincare analysis were also included, SD1 and SD2. The corresponding Equations (1)-(5) and clinical implications of each index are described in Table 1.  
The data obtained from the HRV indices were organized in a 7 \(\times\) 48 matrix, where each row corresponded to one of the 7 HRV indices calculated per patient, and each column represents one hour of the 48 h record. Each value in the matrix represented the hourly value of a specific index, derived from the arithmetic mean of the 14 blocks of 5 min corresponding to that hour.  
\begin{table}
\begin{tabular}{c c c} \hline
**Index (Unit)** & **Equation** & **Clinical Significance** \\ \hline
1. SDNN (ms) & \(SDNN=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(RR_{i}-RR\right)^{2}}\) & (1) & Reflects the total variability over the recording period, capturing both sympathetic and parasympathetic modulations. \\  & & & Measures short-term variability associated primarily with parasympathetic activity. \\
2. RMSSD (ms) & \(RMSSD=\sqrt{\frac{1}{N-1}\sum_{i=1}^{N}\left(RR_{i+1}-RR_{i}\right)^{2}}\) & (2) & Indicates vagal tone in the autonomic nervous system. \\  & & & \\ \hline
3. LF (ms\({}^{2}\)) & Power in the [0.04, 0.15] Hz band calculated by spectral analysis (Welch) &
\begin{tabular}{c} Represents sympathetic and parasympathetic modulations, with sympathetic predominance. \\ \end{tabular} \\  & & \\ \end{tabular}
\end{table}
Table 1: HRV indices. Equations, description, and clinical significance.
# Iterative Named Entity Recognition with Conditional Random Fields
## 3 Results  
### Reference Experiment  
After the first iteration, the model reached an F1-score of 0.786, with precision (0.878) higher than recall (0.711, Figure 4a). During the iterative training procedure, the recall increased strongly by 0.165 to a final score of 0.876, while the precision increased less dramatically to a final score of 0.929, resulting in a final F1-score of 0.902 after the 29 th iteration of data annotation. After this iteration, the training was halted as the increase in model performance had plateaued. In total, 30 models (0 th-29 th iteration) were fitted during the procedure. The classwise F1-score per entity reveals high initial F1-scores of nearly 0.9 or above for the entities belonging to the NUMEX classes 'percent', 'date', and'money' (Figure 4b), with minor improvements obtained after a relatively small number of annotated data points added to the model training dataset. The recognition of these entities was much better than that of entities belonging to the ENAMEX group (organisation, person, location), the initial F1-scores of which were all below 0.7, and whose improvement during iterative training was stronger than that obtained for the abovementioned entities. An exception were tokens classified as the entity 'time', which had an F1-score of <0.5 even after the annotation of 80,000 tokens, and the prediction of which only improved after the annotation of 100,000 tokens, yielding a final score of 0.8.  
Figure 4: (**a**) Overall performance (F1-score) during the first 30 iterations during model training according to the settings of the reference experiment, i.e., with manual annotation of 20 sentences from the lowest quantile and 20 from the quantile 0.2 upwards (‘Ann. 20-0Q, 20-0.2Q; ST’ in Table 3). (**b**) Model performance for each separate entity (F1-score).  
Figure 3: Estimation of the annotation time as a function of the number of tokens annotated.
## 2 Methodology  
We start by reviewing various distance measuring sensors: laser, LiDAR, and ultrasound. These were assessed for accuracy, range, and reliability through tests against a stationary wall at varying distances. From this evaluation, we selected the most suitable candidates for outdoor experiments. In our outdoor test, the sensors were attached to a stationary bike on the roadside to gauge their proficiency at detecting passing vehicles. Our preferred choice was then integrated with the OAK-1 AI camera and mounted on a bicycle. Real-world data collection was simulated as the bike navigated actual roads with traffic. The system was programmed with Python using  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Setup** & **Data Collection Technique** \\ \hline [PERSON] and [PERSON] [20] & Video footage with “screen ruler”. & Manual \\ \hline [PERSON] et al. [21] & Ultrasonic sensors, GPS receiver, and video camera. & Manual \\ \hline [PERSON] et al. [12] & LiDAR sensors, video camera, GPS, and event button. & Manual \\ \hline [PERSON] et al. [22] & Video footage. & Manual \\ \hline [PERSON] et al. [7] & Ultrasonic sensor (10 Hz), Arduino, event button. & Manual \\ \hline [PERSON] et al. [15] & Global positioning system, ultrasonic sensors, 5 car video camera DVR black boxes, 8 proximity switches, variable resistor, multi-function logger. & Semi-automatic \\ \hline [PERSON] and [PERSON] [14] & Driving simulator. & Automatic \\ \hline [PERSON] et al. [23] & 2 video cameras, GPS, LiDAR sensor (20 Hz). & Manual \\ \hline [PERSON] et al. [24] & 3 Video cameras, GPS tracker, laser sensor, laptop, laser pointer. & Manual \\ \hline [PERSON] et al. [25] & Video camera, ultrasonic sensor (10 Hz). & Manual \\ \hline [PERSON] et al. [26] & Microcontroller, GPS, 2 ultrasonic sensors (20 Hz), motion sensor. & Automatic \\ \hline [PERSON] et al. [13] & Driving simulator. & Automatic \\ \hline [PERSON] et al. [11] & LiDAR sensor, push button. & Manual \\ \hline [PERSON], et al. [27] & Ultrasonic sensors (25 Hz), Arduino, and two Garmin video cameras. & Manual \\ \hline [PERSON], et al. [17] & GoPro, C3 FT. & Semi-automatic \\ \hline [PERSON] et al. [19] & 2 ultrasonic sensors (20 Hz), microcontroller, GPS. & Automatic \\ \hline [PERSON] et al. [28] & Ultrasonic sensor, video camera. & Manual \\ \hline [PERSON] et al. [18] & LiDAR sensor, GPS, Arduino microcontroller. & Automatic \\ \hline \hline \end{tabular}
\end{table}
Table 2: A comparison of setups used by different research papers.  
the Raspberry Pi, employing modules like Pyserial and Smbus to facilitate communication between the sensors and the Pi. The AI camera was programmed through the DepthAI API by Luxonis. Post-data collection, noise was filtered out to emphasize clusters indicative of vehicle passes. Finally, the machine learning clustering method DBSCAN was employed to automatically identify vehicle passes in the data collected, the accuracy of which was validated using GoPro footage.
# Influence of Baffle Location on the Diodicity of Tesla Valves
## 4 Conclusions  
This research provides an in-depth analysis of the impact of baffles within the flow field. By examining the positional effects of baffles, the following conclusions have been derived:  
1. This research has established that modifications to the flow field substantially affect the diodicity of the TV. The position of baffles at the outlet is instrumental in enhancing optimization, with the most significant improvements being observed when baffles are positioned below the outlet.
2. When augmenting the number of baffles, it is imperative to meticulously consider their layout and interactions to minimize flow losses and mitigate adverse effects on flow field structures. A setup with two baffles that generates a non-uniform flow field exerts both beneficial and deleterious effects on the optimization of the diodicity of Tesla Valves (TVs). Positioning baffles below the inlet can enhance diodicity to a certain degree, whereas positioning them above the inlet is counterproductive to the optimization process.
3. In TVs, the forward flow predominantly traverses the straight channel branches, leading to minimal variation in the drag experienced by the fluid. Consequently, a structure that directs fluid preferentially into arc channels to enhance reverse flow resistance and prevents obstructions in areas prone to vortex formation, such as above the inlet, achieves superior optimization outcomes.
4. Baffles positioned both below the inlet and the outlet yield the optimal enhancement in diodicity, with an optimization magnitude exceeding 40%. These outcomes are predicated on the specific baffle parameters delineated in the preceding text. When a single baffle is positioned, one situated below the outlet achieves a notable increase in diodicity, surpassing 30%.  
Overall, this study, employing computational fluid dynamics (CFD) simulations coupled with experimental validation, has demonstrated the potential to enhance the diod  
Figure 17: Comparison of experimental and simulation results.  
icity of TVs by modulating flow field uniformity. The findings offer valuable insights for future design optimizations of TVs, potentially augmenting their efficacy in fluid control applications.  
Conceptualization, P.W.; Methodology, L.L.; Writing--original draft, S.Z.; Writing--review & editing, S.Z.; Supervision, C.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, with grant number 52205072.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.
# Graph Convolutional Networks for Privacy Metrics in Online Social Networks
## 4 Framework Design  
### GCNs  
Our framework is based on GCNs, so we first give a brief overview of GCNs. Similar to deep neural networks, such as CNNs (convolutional neural networks) and RNNs (recurrent neural networks), GCNs are also multilayer stack structures. Each hidden layer consists of three steps: feature propagation, linear transformation, and nonlinear transformation. For ease of description, we express the input of the nth layer as \(H^{(n-1)}\). The input of the first layer is the initial feature matrix:  
\[H^{(0)}=V \tag{2}\]  
**Feature Propagation** In brief, the purpose of feature propagation is to aggregate the information of other nodes within n hops of each node into the node itself so that each node can learn the information of the surrounding nodes in the network structure. The specific method is to use the adjacency matrix of nodes to construct a standardised correlation matrix \(P\):  
\[P=D^{-\frac{1}{2}}\overline{A}D^{-\frac{1}{2}} \tag{3}\]  
Feature propagation can be expressed as:  
\[\overline{H}^{(n-1)}\gets PH^{(n-1)} \tag{4}\]  
By stacking an additional layer of feature propagation, a node can aggregate information from nodes that are one more hop away.  
**Linear Transformation** To ensure that the model achieves the purpose of learning, there is a weight matrix in each layer of linear transformation. The weight matrix is accompanied by the learning of samples, and the value is adjusted continuously by reducing the loss function:  
\[\overline{\overline{H}}^{(n-1)}\leftarrow\overline{H}^{(n-1)}\Theta^{(n-1)} \tag{5}\]  
**Nonlinear Transformation** Nonlinear transformation uses nonlinear functions to make the model learn the nonlinear characteristics of samples. The most commonly used function in GCNs is the RELU function. The final output of the hidden layer is:  
\[H^{(n)}\leftarrow\text{ReLU}(\overline{\overline{H}}^{(n-1)}) \tag{6}\]  
Finally, the output layer can choose different functions according to specific application scenarios, such as Sigmoid and SoftMax.
# MAOA: A Swift and Effective Optimization Algorithm for Linear Antenna Array Design
## 4 Results and Discussion
### Antenna Current Amplitude Optimization
#### 4.2.1 Design 6: Peak Side Lobe Level Minimization of a 10-Element LAA  
The fitness function used to minimize the PSLL of an LAA is mentioned in Equation (25). The antenna current amplitude has been optimized for minimizing the PSLL in the region \(\theta=[0^{\circ},74^{\circ}]\) and \(\theta=[106^{\circ},180^{\circ}]\). The MAOA and other metaheuristic algorithms are applied to compute these optimized amplitudes. The simulation results are shown in Table 8, highlighting PSLL and the optimized current amplitude of the right-sided five array elements. In this design, the number of antenna elements is even; hence, for making a symmetrical array, no element is placed at the origin. The simulation outcome indicates that MAOA provides considerable PSLL minimization from \(-12.97\) dB to \(-24.32\) dB, as compared to the conventional ULA, offering a reduction of 11.35 dB. Also, in comparison with other algorithms, MAOA offers a reduction in PSLL of 0.86 dB with DA, 0.03 dB with STO, and 6.57 dB with dung beetle optimization (DBO) optimized antenna array. The array pattern is highlighted in Figure 12. The converging nature of all the algorithms is highlighted in Figure 13, which suggests that MAOA optimizes the current amplitude in less time compared with other optimization algorithms.  
Figure 11: Convergence profile of design 5.  
Figure 12: Array pattern of a 10-element LAA for design 6.  
Figure 13: Convergence profile of design 6.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Normalized Excitation Current Amplitudes**} & \multirow{2}{*}{**PSLL (dB)**} \\ \cline{2-2} \cline{4-6}  & **I** & **II** & **III** & **IV** & **V** & \\ \hline ULA & 1.0000 & 1.0000 & 1.0000 & 1.0000 & 1.0000 & \(-\)12.97 \\ \hline DA & 1.0000 & 0.9030 & 0.7388 & 0.5314 & 0.4530 & \(-\)23.46 \\ \hline STO & 1.0000 & 0.9011 & 0.7251 & 0.5145 & 0.4216 & \(-\)24.29 \\ \hline DBO & 1.0000 & 0.9902 & 0.7384 & 0.7384 & 0.7384 & \(-\)17.75 \\ \hline MAOA & 1.0000 & 0.9024 & 0.7267 & 0.5188 & 0.4210 & \(-\)24.32 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Optimized current amplitude of positive half elements and PSLL for design 6.
# Deadlocks Detection in Multithreaded Applications Based on Source Code Analysis
## 7 Using the Source Code Model of the Multithreaded Application in Practice
### Deadlock Detection Using Petri Nets  
The deadlock phenomenon can be located by PN, and the AP1 application net is shown in Figure 9. This net is very extensive and has many more elements than the operation graph from Figure 8. Also, not all elements included in the operation graph from Figure 8 are in the net shown. Shared resources were omitted. Representation of the AP1 application in the form of PN is also not fully compatible with the representation that would be created if all the principles described in work [23] were applied. However, PN simplification does not adversely affect the process of locating the resource conflict, causing the deadlock. The status of tokens in Figure 9 corresponds to the situation where the thread \(t_{2}\) finished its operation without releasing the lock. The token in place P1,5, which corresponds to operation \(o_{1,5}\) will not move further because the lock was released, which should be manifested by the return of one of the tokens from place P2,15 to place P18.  
The further simulation will move the token from P2,15 to P2,18 and will lock the entire net. This shift may suggest that operation \(o_{2,18}\), which of course is not true. However, after this shift, no further simulation is possible, and the presence of the token in place P1,5 indicates deadlocked thread \(t_{1}\).  
The disadvantage of using PN to locate deadlocks is the need to simulate application work. These simulations can be time-consuming for very large applications. It is also worth noting that the simplified PN for the AP1 application has many more elements than the graphical representation of the application in accordance with the source code model of the multithreaded application. Analysis of PN states also seems to be more complicated than analysis of operation graphs. In the net presenting applications with resource conflict, some states are very difficult to interpret.  
Figure 9: Petri net for the leading example.
###### Abstract  
This study experimentally investigates the effects of gas extraction/addition, via multiple vertical membrane panels, on the hydrodynamics in different regions of a pilot-scale gas fluidized bed membrane reactor (FBMR), based on differential pressure signals measured at different vertical bed sections at high temperature. In a bed section where membrane panels were installed and activated, the extraction of gas caused the average bubble size to increase, but decreased the number of small- and medium-sized bubbles. This effect of gas extraction penetrated into bed sections above the active membrane panel, but attenuated with increasing distance away from the extraction location. The attenuation rate was much faster in FBMR with lower bed voidage, mainly due to the large decrease of the drag force exerted by gas extraction on fluidizing gas in a denser bed. With the same inlet gas velocity, gas addition favored the growth of bubbles, especially in the upper bed sections compared with operation without gas permeation. The increase of the effective fluidizing velocity was the major reason for the increase of the bubble size during gas addition. These findings preliminarily suggest that membrane units should not be installed in or below fast-reacting zones in a scale-up FBMR, and operation with a lower bed voidage is preferable to avoid the formation of large bubbles enhanced by gas extraction.  
fluidization; membrane reactor; pressure measurement; gas permeation; hydrodynamics Article
## 4 Microstructural Analysis
### Digital Core Analysis  
Moreover, the representative elementary volume (REV) of arbitrary size (larger than the spatial resolution of CT) can be placed inside the 3D reconstructed model of the whole specimen. The extraction of pore and solid can then be performed for the REV in the same manner as it is done for the whole specimen. In the present work, three REVs, each of which was sized 400 pixels \(\times\) 400 pixels \(\times\) 400 pixels, were placed in three different positions for each sample (see Figure 8). Note that the actual size of the REV depends on the resolution of the CT scan. For the sandstone sample, whose resolution of scan was 30.45 \(\upmu\)m, 400\({}^{3}\) voxels corresponded to an actual volume of 12.18\({}^{3}\) mm\({}^{3}\). The carbonate samples, for which the resolution of scan was 61.75 \(\upmu\)m, 400\({}^{3}\) voxels corresponded to an actual volume of 24.7\({}^{3}\) mm\({}^{3}\). In the reconstructed model, the pore network was represented by a stack of voxels. Higher scan resolution leads to smaller voxels and a more refined representation of the pore network. As is shown in Figure 9, extraction of the pore network and the solid part could be performed for each REV, based on which further geometrical and morphological analysis could be conducted.  
Table 3 lists the volumetric fractions of pores for all the REVs. Due to the complex diagenesis process of carbonate rock, the pores are heterogeneously distributed in the specimen, resulting in quite different porosity readings among the REVs that were sampled from different positions of a specimen. The porosity that was measured from the 3D REVs was more intuitive compared to the cross sectional porosity in Section 3.4, when it came to revealing the volume and distribution of pores. A combination of the cross-sectional porosity and the REV porosity could help locate large pores in the specimen.  
Figure 7: The 3D reconstructed model of sample A. (**a**) Total. (**b**) Pore network. (**c**) Solid.
# Enhanced Upward Translations for Systems with Clusters
## 5 Simplified Enhanced Translations
The question arises, if the maximal errors of the orders disclosed are acceptable for using the simplified translation (12) in practical calculations by KI-FMM? To answer the question, recall that the starting intensities of leaf sources at the beginning of iterations are found quite roughly. The intensities notably change on successive iterations of solving an algebraic system of a BEM. The exact solution of this system already contains errors of discretization the boundaries and approximation of the density. Thus, the final maximal error of tenths of a percent is normally acceptable for practice. When solving the same system by a KI-FMM, it is desirable to retain the overall error below this level. The need for notably less maximal error \(E\) for an _individual_ translation arises merely due to the accumulation of individual errors. The error grows on a _chain_ of translations from a leaf source to a target source. For a chain consisting of \(N_{tr}\) translations, the upper bound of the accumulated overall error increases nearly proportional to \(N_{tr}\) ([18], p. 610). The number \(N_{tr}\) of individual translations in a chain is defined primarily by the number of M2L translations. The latter, as known (e.g., [33]), in 3D problems is at most 189. Thus, the overall error arising after chain translations may be two orders greater than the error \(E\) of an individual translation. When the errors of individual translations are on the needed small level, the error of a selected single translation may be \((N_{tr}-1)\)-fold greater. In particular, this refers to the singled-out starting S2M translation, which is the first in the chain. This implies that the maximal relative error caused by the simplified improved translation (12), is commonly acceptable for practical calculations. In many cases, even the error on the overestimated level of \(E=0.051\) may be acceptable. Furthermore, for the 3D Navier BIE, which is of special significance for fracture mechanics, the accuracy of the simplified approach is on the level of non-simplified modified translations. It is notably better than the accuracy of the conventional translations (cf. Tables 7 and 8). We conclude that for the most practical applications, the simplified modified translations (12) provide acceptable accuracy for far fields translated from the clusters distinguished. This excludes the need to build and inversion the matrices for any of them. When doubting, the applicability of the simplification may be checked in preparatory control calculations performed with solving the system (9).