esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Word Representation
#### 3.1.1 Pre-Trained Model  
Transformer attracts a lot of attention at present. The Transformer-based model is pre-trained on large-scale text data, which has a strong ability to capture language features, and  
Figure 1: The overall architecture of our model EINET contains three components, Word Representation, Named Entity Recognition, and Relation Extraction. Blue vectors and red vectors are entities representations, and local context is represented by the orange vector.  
it can provide relatively high-quality word vector representation for joint entity and relation extraction. The multi-head attention mechanism, the core component of Transformer, is used to capture multi-angle features of language. BERT is a typical Transformer-based pre-trained model. In order to obtain high-quality word vectors, we choose the BERT model to provide the initial word vector representation for EINET.  
The initialized sentence representation is passed into the BERT pre-trained model. BERT uses Byte-Pair Encoding. For example, the words \"loved\", \"loving\", and \"loves\" are disassembled into \"lov\", \"ed\", \"ing\" and \"es\". This method can better reduce the number of vocabulary, improve the training speed, and also link the phenomenon of OOV (Out of Vocabulary).  
However, the labels of SRL are given to complete words, so in order to fuse the word embedding obtained by BERT with label representations, the subword representations encoded by BERT need to be aggregated into complete word representations. Inspired by [PERSON] et al. [6], we adopt convolution and maxpooling to aggregate subwords. \((s_{1},s_{2},\ldots,s_{l})\) is the subword sequence of \(x_{i}^{b}\), where \(l\) is the subword sequence length.  
First, the subword sequence is passed to a one-dimensional convolution layer:  
\[\mathbf{e}_{i}=\mathbf{W}_{1}[\text{BERT}(s_{i}),\text{BERT}(s_{i+1}),\ldots,\text{ BERT}(s_{i+k-1})]+\mathbf{b}_{1} \tag{1}\]  
where \(k\) is the convolution kernel size, \(\mathbf{W}_{1}\) and \(\mathbf{b}_{1}\) are trainable vectors. \(\text{BERT}(.)\) is vector representation from BERT. Then, subword embeddings are aggregated into a word-level representation vector after Maxpooling:  
\[\mathbf{x}_{i}^{b}=\text{Maxpooling}[\text{ReLU}(\mathbf{e}_{i}),\ldots,\text{ReLU}( \mathbf{e}_{l-k+1})] \tag{2}\]  
\[\mathbf{X}^{b}=\left(\mathbf{x}_{1}^{b},\mathbf{x}_{2}^{b},\ldots,\mathbf{x}_{n}^{b}\right) \tag{3}\]  
where \(n\) is the length of the input text sequence, and ReLU is a common activation function.
## 4 Conclusions  
In this paper, extended version of the multi-carrier OGFDM system with 32 frequency subcarriers and sampling frequency equals to 6 GHz is proposed, examined, and evaluated. The developed scheme can further support a wider number of mobile network users and a higher overall channel capacity than the initial multi-carrier OGFDM. Experimentally speaking, the upgraded OGFDM has five levels of manipulation (acceleration, filtration, oversampling, allocation, and modulation) that directly impacts the performance of the transmission system. Because of the close bonding between the acceleration and filtration levels, the experiment shows 1.5 dB and 3 dB improvements via moving from the preliminary OGFDM and GFDM to the developed OGFDM. In the oversampling level, mainly at the bad transmission conditions, the channel capacity of the OGFDM with the proposed dual sampling (DOV) far outweigh the transmission capacity of the OGFDM and GFDM at normal sampling (NOV) by around 6 dB and 12 dB respectively. However, at good transmission cases, the findings indicate that by using the NOV with the promoted OGFDM, the recorded bit-rate can be better than the transmission rate of the OGFDM and GFDM with the DOV by about 2.6 dB and 5.25 dB correspondingly. As a  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline No. of frequency centers & 16 \\ F\({}_{\text{DAC/ADC}}\) & 6 GHz \\ SNR & 23–26 dB \\ Roll-off & 0–1 \\ System mode & Multi-carrier \\ Number of subcarriers & 32 \\ OGFDM symbols & 2000 \\ Filter length & 32 \\ Modulation format & Fixed \& Adaptive \\ Oversampling & Normal (32) \& Dual (64) \\ Filter type & RRC \& Hilbert filters \\ \hline \hline \end{tabular}
\end{table}
Table 2: System parameters of multi-carrier OGFDM.  
Figure 9: Channel capacity of the multi-carrier OGFDM vs. GFDM with adaptive modulations and F\({}_{\text{DAC}}\) = 4 and 6 GHz.  
result, the hybrid solution (normal and dual) is recommended with the advanced OGFDM to extremely support the high channel capacity at good and bad transmission conditions. In the allocation and the modulation levels, the results declare that the improved OGFDM can achieve around 1.66 dB and 3.32 dB gains in comparison with the initial OGFDM and GFDM. By improving the overall channel capacity of the upgraded OGFDM, a good transmission bit rate can be supplied for each subscriber in the mobile network.  
This research was funded by the Ministry of Higher Education and Scientific Research, Republic of Iraq, grant number 2633 and the APC was funded by the University of Northampton, UK.  
The author declares no conflict of interest.
# Misalignment-Resilient Propagation Model for Underwater Optical Wireless Links +
## 3 Simulation Results
### Propagation Distance  
The MALO calculation was performed while sweeping the divergence using the parameters from Table 1 and a seven-lens scheme for different lengths between the emitter and receiver and for the two extremes of water turbidity: clear and harbor II waters.  
The obtained results are plotted in Figure 12, showing similar curves that exhibit an optimum divergence that depends on the distance. For a distance of 1 m, there was a larger tolerance to misalignment (higher MALO) than for 0.5 m. This can be attributed to the higher number of scattering events and the opening of the beam due to propagation. The consequence was that the maximum was obtained at a lower value of divergence and required a more accurate configuration (lower range of divergence angles) due to the rapid decay of the MALO.  
On the other hand, above the ideal divergence, the MALO degraded rapidly at 1 m. Here, the increased scattering and propagation distance made the beam spread so much that the minimum amount of power did not reach the sensors. The same behavior was observed for both the clear and harbor II waters, from which we concluded that the geometry of the link and the distance, in particular, was the limiting performance factor, which could be somewhat overcome by adjusting the divergence angle. For instance, in clear waters and at a distance of 0.5 m, the MALO was 0.37 m, whereas when the distance was increased to 1 m, the MALO was actually increased to 0.45 m by adjusting the divergence angle from 22\({}^{\circ}\) to 17\({}^{\circ}\). If the divergence was maintained at 22\({}^{\circ}\), then the MALO actually decreased to 0.4 m.  
Figure 11: MALO calculation as a function of the divergence of the emitter for different water types, considering a seven-lens scheme and a Tx–Rx distance of 1 m.
## 3 Problem Definition and Formulation
### Constraints  
In the optimization of resource allocation and task scheduling in satellite networks, several constraints must be considered to ensure the feasibility and practicality of the optimization solutions. These constraints are classified into resource constraints, task constraints, latency constraints, computational complexity constraints, path selection constraints, and connectivity constraints, to account for the practical needs and limitations in the optimization process.
# GPS-Based Indoor/Outdoor Detection Scheme Using Machine Learning Techniques
## 4 I/O Detection Scheme
### Data Collection
#### 4.1.2 Data Description  
The collected data comprise GPS signals measured by the mobile phone application (GPS signal magnitude mode) and recorded. The dataset described in this subsection was collected using this mode.  
Figure 9 also shows the GPS signal magnitude obtained with the dataset. The significant offset between indoor and outdoor values is the result of the substantial difference and attenuation variation in radio signal propagation due to wall or roof obstacles in the indoor environment. However, in some cases, this difference overlaps due to variation in the environment. Each data is denoted as vector X, with components \(x_{1}\), \(x_{2}\)... \(x_{i}\), where \(x_{i}\) is a set with nine integer numbers. The dataset includes 1020 vectors X and 1020 corresponding classes Y. Training and test sets were collected without simulation or  
Figure 8: Outdoor environment for data collection: (**a**) under a highway, (**b**) walking road, (**c**) high obstacles, (**d**) thin forest, (**e**) beside a mountain, (**f**) University campus, (**g**) street, (**h**) playground.  
adding noise. Note that some data were collected from a semi-indoor environment (this problem will be discussed later).
## 1 Introduction
In summary, the review of studies dedicated to reactive power compensation methods based on time-domain filtering reveals two key limitations. First, the influence of filter type on the performance of reactive power compensator control systems has not been thoroughly investigated. Second, the filters in these studies are typically designed with fixed cut-off frequencies, which hinders the compensator's ability to operate correctly under varying frequencies of the monitored signals. The objective of this study is to investigate the influence of filter type on the performance of reactive power compensator control systems in traction drives of railway rolling stock equipped with asynchronous motors. The research contributions of the article are as follows:  
* A novel approach is proposed in which, instead of compensating higher-order voltage harmonics within the line voltage system, compensation is performed within the stator phase voltage system. * For the examined filter types, a structural scheme is developed that enables dynamic adaptation of the filter cut-off frequencies to variations in the supply frequency of the asynchronous motor. * An algorithm is proposed for evaluating convergence, defined as the time required for the modulus of the spatial vectors of the stator currents in the studied configuration to match those in a reference system powered by pure sinusoidal voltages. * The effect of the filter approximation method on the reactive power compensator's effectiveness is thoroughly investigated. * Within an FOC framework, the impact of adapting the filter cut-off frequency to the supply frequency is assessed with respect to efficiency, torque ripple coefficient, convergence behavior, and total harmonic distortion (THD). The structure of the paper is as follows: Section 2 provides the rationale for selecting the power circuit configuration and the control strategy of the reactive power compensator. It also presents the derivation of transfer functions for high-frequency filters and the development of an integrated model of the output stage of the traction drive system incorporating the compensator. Section 3 presents the simulation results and their subsequent analysis. Section 4 is devoted to the discussion of the obtained findings and their comparison with the results reported in related studies. Finally, the paper concludes with Section 5, which summarizes the main conclusions.
# Cooperative Transmission Mechanism Based on Revenue Learning for Vehicular Networks
## 1 Introduction
Nowadays, with the rapid development of science and technology, various kinds of algorithms emerge in an endless stream. Machine learning is widely used in the field of artificial intelligence due to its characteristics of constantly learning to acquire new knowledge or ability and reconstructing the existing knowledge structure to continuously improve its performance. Reinforcement learning, as one of the techniques of machine learning, was first proposed by [PERSON] in 1954. It is a kind of continuous trial training between agents and the outside world to learn the optimal strategy that can maximize the reward. \"Trial and error\" is the core of reinforcement learning, and the purpose of reinforcement learning is to constantly obtain benefits from \"trial and error\". The reinforcement learning approach is applied to vehicular network users, so that vehicular network users can learn to obtain communication benefits in addition to communication. The users near the vehicular network collaborate in communication, reduce the waste of system resources, and at the same time enhance the communication rate of vehicles and the base station. In the multi-point cooperative transmission of vehicular networks, if the method of reinforcement learning can be used to make idle vehicles constantly learn to obtain benefits and improve the efficiency of information transmission, it is bound to promote the improvement of the communication ability of unmanned driving. As pointed out above, vehicular networks can adopt multiple base stations for a user with services, which can easily interfere with other cells, occupy large channel resources,and require high delay. So, the terminal vehicles begin to use continuous reinforcement learning to obtain their own benefits. In this paper, our innovation is to utilize the idle users of the surrounding vehicular networks to carry out self-learning through the Q-learning algorithm with the help of reinforcement learning and assist the vehicle users of the vehicular networks to carry out collaborative communication. This method not only can reduce the interference with other surrounding areas and occupy channel resources, but also can make full use of system resources and improve the overall performance of the vehicular networks. To boost the development of self-driving technology, the first thing to do is to improve the communication ability of the vehicular network coordination. Therefore, this topic adopts a benefit learning method to discuss how to effectively choose relay nodes, improve the performance of the coordination transmission system, and finally realize the vehicular network traffic capacity ascension.
## 4 Experiments
### Analysis of Experimental Results  
* Unknown environment Experiment 1 is a path planning experiment conducted in an unknown environment with obstacles. Two sets of unknown obstacles are added to the known global map to form an unknown environment map, which is used to test the effectiveness of local path planning. The results are shown in Figure 13, A is defined as the start point and B is defined as the target point.  
It can be seen from Figure 13 that the three algorithms can avoid obstacles and successfully reach the target point. However, the deviation between the path planned by the other two algorithms and the central axis of the tunnel excavation area is relatively large, which does not meet the requirements of underground mine vehicles. The deviation between the path planned by the fusion algorithm and the central axis of the simulated tunnel excavation area is generally small, with an average deviation controlled at \(\pm\)0.109 m, and the maximum deviation is \(\pm\)0.214 m at the corner. The path planned by the hybrid algorithm is smoother and more reasonable when the vehicle avoids obstacles.  
* Dynamic environment This is a dynamic obstacle path planning experiment for the three algorithms mentioned above. The yellow dot represents the obstacle moving at a constant speed. A is defined as the start point. B is defined as the target point. The result is shown in Figure 14.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Equipment** & **Model** \\ \hline \hline  & CPU i7-9700 \\ Computer & Graphics card \\  & RTX3060 \\ LIDAR & Velodyne VLP-16 \\ IMU & LPMS-IG1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Information on experimental devices.  
Figure 13: Comparison of experiments on three path planning algorithms in an unknown environment. (**a**) The results of the A*+DWA path planning experiment; (**b**) the results of the A*+APF path planning experiment; (**c**) the results of the fusion method path planning experiment.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Parameters** \\ \hline \hline   Shape & 2490 \(\times\) 1550 \(\times\) 616 mm \\   Bearing spacing & 1900 mm \\   Wheel spacing & 1355 mm \\   Maximum speed & 40 km/h \\   Steering type & Four-wheel steering \\   Braking type & Four-wheel disc brake \\ \hline \hline \end{tabular}
\end{table}
Table 5: Structural parameters of the vehicle.  
In Figure 14, it can be seen that the paths planned by the A*+DWA and A*+APF algorithms cannot smoothly bypass dynamic obstacles, and the generated paths are not smooth enough. Before encountering dynamic obstacles, the fusion algorithm can predict the current path and choose a safer path to bypass the obstacles. The path security is better, and the path basically conforms to the global path, with a smoother path, ensuring the global optimum of the path.
# Wintering Conditions and Heat Loss during Hibernation in the Brown Long-Eared Bat
## 3 Results
### Heat Loss Calculation  
(a) Based on fat burned  
Next, the \(ER\) of an average _Plectusauritus_ specimen was calculated following the assumption that the mean loss of mass was directly related to the burning of 2.6 g of fat in order to cover the expenditure during hibernation.  
\[ER=2.6\cdot 39.75=103,350\ J\]  
The heat generated by a specimen during hibernation (Equation (2)) amounted to  
\[Q_{hib}=\frac{103,350\ J}{126\ days\cdot 24h\cdot 3600\ \mathrm{s}}=9.493\ E-3,\ W\]  
The heat loss relative to 1 m\({}^{2}\) of the surface area of a specimen's body (Equation (3)) equalled  
\[q_{hib}=\frac{9.493\ E-3\ W}{3.048\ E-3\ \mathrm{m}^{2}}=3.115\frac{\mathrm{W}}{ \mathrm{m}^{2}}\]  
(b) Based on refugioclimate parameters  
Table 3 presents the heat loss calculated based on the parameters of the refugioclimate (Equations (5)-(10)). The values represent the mean value (\(q_{hib}\)) for each day of measurement.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Date** & **T,** & **sd T,** & **Cv,** & **Rh,** & **sd H\({}_{\mathrm{r}}\),** & **Cv,** & **v,** & **sd v,** & **Cv,** & **P,** & **n,** \\  & \({}^{\circ}\)**C** & \({}^{\circ}\)**C** & **\%** & **\%** & **\%** & **\%** & **m/s** & **m/s** & **\%** & **hPa** & **-** \\ \hline
9 January 2007 & 10.0 & 0.39 & 4 & 91.0 & 0.63 & 1 & 0.50 & 0.29 & 58 & 1000 & 5 \\ \hline
7 February 2007 & 7.0 & 0.77 & 12 & 89.0 & 3.36 & 4 & 0.95 & 0.35 & 36 & 1000 & 25 \\ \hline
8 February 2007 & 8.0 & 0 & 0 & 84.0 & 0 & 0 & 0.61 & 0.04 & 7 & 1000 & 4 \\ \hline
9 February 2007 & 7.0 & 0.45 & 6 & 85.0 & 10.32 & 12 & 0.54 & 0.03 & 6 & 1000 & 8 \\ \hline
11 February 2007 & 10.0 & 1.15 & 11 & - & - & - & 0.53 & 0.53 & 100 & 1000 & 8 \\ \hline
13 February 2007 & 7.0 & 1.35 & 18 & 84.0 & 6.03 & 7 & 0.22 & 0.19 & 83 & 1000 & 74 \\ \hline
14 February 2007 & 9.0 & 0.41 & 4 & 88.0 & 0.37 & 0 & 0.20 & 0.04 & 18 & 1000 & 6 \\ \hline
15 February 2007 & 9.0 & 1.13 & 12 & 78.0 & 5.94 & 8 & 0.08 & 0.05 & 66 & 1000 & 86 \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Measurement Date** & \(q_{hib}\), **W/m\({}^{2}\)** & **sd\(q_{hib}\), W/m\({}^{2}\)** & **Cv, \%** & **n, -** \\ \hline
4 January 2002 & 2 & 0.2 & 7 & 30 \\ \hline
19 January 2002 & 2 & 0.4 & 18 & 30 \\ \hline
1 February 2002 & 2 & 0.3 & 15 & 34 \\ \hline
22 February 2002 & 2 & 0.2 & 11 & 28 \\ \hline
8 December 2002 & 2 & 0.2 & 11 & 28 \\ \hline
21 December 2002 & 2 & 0.2 & 8 & 26 \\ \hline
5 January 2007 & 5 & 1.6 & 28 & 58 \\ \hline
6 January 2007 & 4 & 1.2 & 31 & 55 \\ \hline
7 January 2007 & 2 & 0.7 & 37 & 3 \\ \hline
8 January 2007 & 2 & 0.7 & 36 & 24 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Heat (\(q_{hib}\)) lost from the surface of a specimen’s body into the surroundings in the MRU.  
The mean values of \(q_{\text{high}}\) on each day of measurement showed a normal distribution. The mean heat loss during hibernation (\(q_{\text{mean}}\)), calculated as the arithmetic mean of the results provided in Table 3, amounted to 4 W/m\({}^{2}\).
## 5 Results and Discussion
### Parametric Analysis  
Based on the analysis in Sections 3 and 4, the numerical results were validated by the experimental data. To gain an in-depth insight into the effects of the related material properties on the vibration and acoustic properties of the corrugated composite panels, this section presents a parametric study based on the validated FE models. The parametric matrix was designed based on a single variable principle, and the related parameters for discussion are listed in Table 3. When a parameter is discussed, the values of the other parameters are kept as the basis values, as indicated in Table 3. To focus on the effects of the related material properties, the parametric studies were only performed on the CFCPs.
## 2 Analytical Model and Equations
### Mechanism of Tower Deviation Error Formation
In Figure 1, \(h_{1}\) is the height of the tower; \(L_{b}\), \(L_{s}\) are anchor cable and buckle cable lengths before the tower deviates, respectively; \(L_{b}^{\prime}\), \(L_{s}^{\prime}\) are the anchor cable and buckle cable lengths after the tower deviates; \(h_{2}\), \(h_{2}^{\prime}\) are the distance between the top of the tower and the center of the arch foot before and after the deviation; S is the chord length of the arch rib section; is the angle between the chord length of the front arch rib and the horizontal line. In accordance with the geometric relationship, \(\theta\) is the angle between the chord length of the front arch rib and the horizontal line. According to the simplified model, the relationship between tower deflection and arch rib error can be established as follows:  
\[\cos\alpha_{1}=\frac{{{a_{1}}^{2}+{h_{1}}^{2}-{L_{b}}^{2}}}{2{a_{1}}{h_{1}}}, \tag{1}\]  
\[\beta=\arctan(\frac{\delta}{{{h_{1}}}}) \tag{2}\]  
\[L_{b}^{\prime}=\sqrt{{{a_{1}}^{2}+{h_{1}}^{2}-2{a_{1}}{h_{1}}\cos(\alpha_{1}- \beta)}} \tag{3}\]  
\[\cos\alpha_{2}=\frac{{{a_{2}}^{2}+{h_{1}}^{2}-{h_{2}}^{2}}}{2{a_{2}}{h_{1}}} \tag{4}\]  
\[h_{2}^{\prime}=\sqrt{{{a_{2}}^{2}+{h_{1}}^{2}-2{a_{1}}{h_{1}}\cos(\alpha_{2}+ \beta)}} \tag{5}\]  
\[\cos\gamma=\frac{{{h_{2}}^{2}+({h_{2}})^{\prime 2}-\delta}}{2{h_{2}}{h_{1}}} \tag{6}\]  
\[L_{s}^{\prime}=L_{b}+L_{s}-L_{b}^{\prime} \tag{7}\]  
\[\cos\phi=\frac{{{h_{2}}^{2}+{S}^{2}-{L_{b}}^{2}}}{2{h_{2}}{S}} \tag{8}\]  
\[\cos(\gamma+\phi+\Delta\phi)=\frac{{{h_{2}}^{2}+{S}^{2}-{L_{S}}^{2}}}{2{h_{2} }{S}} \tag{9}\]  
Thus, the vertical displacement from \(A\) to \(A^{\prime}\) point is: \(\Delta_{y}=S\sin\theta-S\sin(\theta-\Delta\phi)=S\sin\theta-S\sin\theta\cos \Delta\phi+S\cos\theta\sin\Delta\phi\approx S\sin\theta-S\sin\theta+S\Delta \phi\cos\theta\), where \(\cos\Delta\phi\approx 1\); \(\sin\Delta\phi\approx\Delta\phi\). Equations (1)-(9) are the influence of deviation of buttress tower on the elevation of arch rib section, where the buttress tower has elastic deformation under unbalanced force. The elevation of control points is higher than the predicted linear shape under the impact of tower deviation, which is consistent with the measured data. Therefore, the impact of tower structure should be considered in construction control to guarantee the safety of bridge during construction.