esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Regeneration of the WPET-SiAMPy Nanofibres  
The regeneration of the WPET-SiAMPy nanofibres was carried out by treating the previously used WPET-SiAMPy nanofibres with 5 M H\({}_{2}\)SO\({}_{4}\) for 60 min, respectively. This procedure was carried out to determine the reusability and lifecycle of the adsorbents. The regeneration experiments were carried out to regenerate the sample used as the 0.01 g dose of WPET-SiAMPy nanofibres. After the application of 0.01 g WPET-SiAMPy to adsorb 1.24 mmol/g of Cu\({}^{2+}\) under constant conditions of pH 5, with an initial Cu\({}^{2+}\) concentration of 100 mgL\({}^{-1}\), with a contact time varying from 1 to 120 min, the acid regenerated WPET-SiAMPy nanofibres were reused for 100 mgL\({}^{-1}\) Cu\({}^{2+}\) adsorption at pH 5 with the contact time held constant at 1 min due to the fast kinetics. This regeneration and reuse cycle was repeated five times to determine the regenerability of the nanofibres. After each of the adsorption-desorption regeneration cycles, the solution was analysed and the recovery efficiency for Cu\({}^{2+}\) was calculated according to Equation (1).  
Figure 15 shows five adsorption-desorption regeneration cycles of previously used WPET-SiAMPy nanofibres. After acid regeneration, the desorbed WPET-SiAMPy nanofibres were reused for the first cycle adsorption and achieved 1.04 mmol/g adsorption capacity of Cu\({}^{2+}\) ions, with a 15.98% decrease in adsorption capacity. Thereafter, the Cu\({}^{2+}\) adsorption of the desorbed WPET-SiAMPy nanofibres gradually decreased to 1.01 mmol/g binding capacity at the fifth cycle, with a 18.48% decrease in adsorption capacity. The decrease in Cu\({}^{2+}\) adsorption on WPET-SiAMPy nanofibres could be due to the gradual accumulation of Cu\({}^{2+}\) traces that were not desorbed from the adsorbent, or to the limited contact time used in the readsorption cycles, or to loss of fibre [61]. Nevertheless, the modified WPET-SiAMPy nanofibres could be regenerated using 5 M H\({}_{2}\)SO\({}_{4}\) after previous use for Cu\({}^{2+}\) adsorption and could be successively reused for at least five adsorption-desorption cycles. This adsorption-desorption regeneration method was successful because after five cycles, the adsorption capacity only decreased by 18.48% compared to the initial adsorption seen in Figure 4. Therefore, the highly replicable Cu\({}^{2+}\) reabsorptions upon regenerated WPET-SiAMPy blended nanofibres proved the reusability of the WPET-SiAMPy blended nanofibres compared to the parent silica supported 2-aminomethylpyridine resin (AMPy) that had slow desorption and kinetics [30], which limited the operational recycling of the resin [14]. The desorption mechanism occurred with the consideration that the Cu\({}^{2+}\) ions fully desorbed from the Cu(WPET-SiAMPy)\({}_{2}\) complex. This reaction was assumed to occur according to the following Equation:  
\[\text{Cu(WPET-SiAMPy)}_{2}+\text{H}_{2}\text{SO}_{4}\rightarrow\text{WPET-SiAMPy }+\text{CuSO}_{4}+\text{SO}_{2}+\text{H}_{2}\text{O}\]  
Figure 15: Five adsorption–desorption/regeneration cycles of Cu\({}^{2+}\) using WPET-SiAMPy nanofibres.
## 3. PCFMIS Algorithm
### Prepared Knowledge  
#### 3.1.1 Definitions for Concepts  
The CFMIS algorithm deals with the tree in which sibling nodes are unordered with labels and the sibling nodes of the same parent node have no repeats. The 'tree' mentioned below is the same tree as the CFMIS processes, assuming that there is no repeat label in a same tree. CFMIS focuses on frequent maximal induced subtree mining.  
**Definition 1**.: _Induced subtree. A tree \(T^{\prime}=(V^{\prime},E^{\prime},r^{\prime})\) is an induced tree of \(T=(V,E,r)\), denoted as \(T^{\prime}\subset T\) if \(V^{\prime}\subset V\), \(E^{\prime}\subset E\), where \(V\) is the set of nodes; \(E\) is the set of edges in which \((x,y)\in E\) represents that \(x\) is the parent of \(y\); \(r\) is the root node._  
**Definition 2**.: _Frequent subtree. \(D=\{T_{1},T_{2},\ldots,T_{n}\}\) is a tree set, \(\varepsilon\) is the frequency threshold, \(Occ(T_{i},T^{\prime})\) represents whether \(T^{\prime}\) occurs in \(T_{i}\), if \(T^{\prime}\subset T_{i}\), then \(Occ(T_{i},T^{\prime})=1\), else \(Occ(T_{i},T^{\prime})=0\). \(Frq(T^{\prime})=\sum_{i=1}^{n}Occ(T_{i},T^{\prime})\). \(T^{\prime}\) is frequent subtree if \(Frq(T^{\prime})\geqslant\varepsilon\)._  
**Definition 3**.: _Maximal subtree. \(F\) is the frequent subtree set of \(D\). \(T^{\prime}\) and \(T^{\prime\prime}\) are two frequent subtrees in \(F\), \(T^{\prime}\) maximize \(T^{\prime\prime}\) (denoted as \(T^{\prime\prime}\stackrel{{ m}}{{\rightarrow}}T^{\prime}\)) if and only if \(T^{\prime\prime}\subset T^{\prime}\), there is no \(T^{\prime\prime\prime}\) in \(F\) to make \(T^{\prime\prime}\stackrel{{ m}}{{\rightarrow}}T^{\prime\prime\prime}\) happen. \(T^{\prime}\) is called maximal subtree._  
**Definition 4**.: _Compression tree sequence(CTS). \(CTS\) is an ordered sequence composed by node\({}^{pd}\), \(CTS=(node_{0}^{pd_{0}},node_{1}^{pd_{1}},\ldots,node_{n}^{pd_{n}})\), \(pd\) is the index pointing to the parent node for each node except the root node. Let the parent of node\({}_{i}^{pd_{i}}\) be node\({}_{j}^{pd_{j}}\), and then \(pd_{i}=|i-j|\), where node\({}_{0}^{pd_{0}}\)is the root node._  
For example, the CTS of the example tree in Figure 1 can be \(a^{0}b^{1}c^{2}d^{1}e^{2}f^{3}\). \(a^{0}\) is the root element. \(a\) is the parent element of \(b\), due to the difference between the positions of \(a\) and \(b\) in the CTS is 1 while the index superscript of \(b\) is 1. \(c\) is the parent element of \(f\), due to the difference between the positions of \(c\) and \(f\) in the CTS being 3 while the index superscript of \(f\) is 3. The same rule applies to other nodes, so the information about tree structure and all the edges in the tree can be obtained from CTS.  
**Definition 5**.: _Tree list length. The number of node\({}^{pd}\) in CTS is the length of CTS._
## 4 Results and Discussion
### Indices Selection and Seasonal Variation of All Indices
#### 4.3.2 Model Fitting and Optimal Model Selection  
Based on the comprehensive comparison of large to small VIP values, six bio-indicators were selected to build and calibrate the empirical models, _i.e._, MCARI/OSAVI[705,750], Vogelmann Index, Chlorophyll Index RedEdge 710, EVI, SPRI*EVI, and SPRI*NDVI (Table 3).  
In order to improve the coefficients of determination between the six bio-indicators and GPP and Re, linear models, polynomial quadratic, exponential growth, and logarithmic model fittings were employed to conduct empirical calibration and to develop inversion models (Figure 8).  
SPRI*NDVI had the highest coefficients of determination (R\({}^{2}\)) to GPP and Re (R\({}^{2}\) = 0.86 and 0.89, \(p<0.0001\), respectively).  
The two calibration functions listed below were used to inverse the regional forest ecosystem GPP and Re.  
\[GPP = 2.170+0.034\times e^{12.951\times SPRI*NDVI} \tag{5}\] \[Re = 1.357+0.261\times e^{7.762\times SPRI*NDVI} \tag{6}\]
## 1 Introduction
Our work is inspired by the characteristic of attention mechanisms. With the help of an attention mechanism as a bridge, sentiment linguistic knowledge and deep learning methods can be perfectly integrated to enhance the sentiment feature of text. To alleviate the aforementioned limitations, in this study, we propose a sentiment-feature-enhanced deep neural network (SDNN) for text sentiment classification. First, we propose a novel sentiment attention mechanism that uses a traditional sentiment lexicon as an attention source attending to context words via the attention mechanism. Its goal is to learn more comprehensive and meaningful sentiment-aware sentence representations as input to the deep neural network, establishing an effective relationship between sentiment linguistic knowledge and deep learning methods. Second, we designed a new model based on deep learning, combining GRU and CNN, to further enhance the representation quality of textual structure information. Above all, CNN performs poorly in learning sequential correlation information, while GRU lacks the ability to extract context local features. By using our design, this model can effectively compensate for their own defects and maximize the potential of their respective strengths. The main contributions of our work are summarized as follows:  
1. We leverage traditional sentiment lexicon and a current popular attention mechanism to design a novel sentiment attention mechanism. The sentiment attention mechanism shows strong capability of interactively learning sentiment and context words to highlight the important sentiment features for text sentiment analysis. 2. Both text sequential correlation information generated by recurrent neural network and context local features captured by the convolutional neural network are essential in the text sentiment classification task, so we designed a deep neural network to effectively combine a GRU-based recurrent neural network and a convolutional neural network to enrich textual structure information. 3. Extensive experiments have been conducted on two real-world datasets with a binary-sentiment-label and a multi-sentiment-label to evaluate the effectiveness of the SDNN model for text sentiment classification. The experimental results demonstrate that the proposed SDNN model achieves substantial improvements over the compared methods. The remainder of this paper is organized as follows. Section 2 presents the SDNN architectures for text sentiment classification in detail, including the feature-enhanced word-embedding module, bidirectional GRU network module, convolutional neural network module, and sentence classifier module. The experiment is introduced in Section 3. Section 4 presents the conclusions and discusses future work.
## 3 Results
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline  & \multicolumn{10}{c}{**Competition Category**} \\ \cline{2-10}  & \multicolumn{4}{c}{**Junior N = 38 (51.4\%)**} & \multicolumn{4}{c}{**Absolute N = 36 (48.6\%)**} \\ \cline{2-10}  & **Mean** & **SD** & **Median** & **IQR** & **Mean** & **SD** & **Median** & **IQR** & **_p_** \\ \hline Right forefoot & 17.6 & 7.4 & 16.1 & 12.8–20.3 & 19.3 & 6.2 & 18.7 & 15.4–23.1 & 0.063  \\ \hline Left forefoot & 22.1 & 6.3 & 21.1 & 17.7–26.7 & 23.3 & 8.0 & 22.8 & 17.0–29.5 & 0.479  \\ \hline Right rearfoot & 30.0 & 7.5 & 31.5 & 26.9–34.2 & 27.5 & 8.1 & 27.1 & 25.4–32.9 & 0.086  \\ \hline Left rearfoot & 30.3 & 7.0 & 31.9 & 25.2–35.1 & 29.9 & 11.5 & 29.1 & 24.7–33.6 & 0.187  \\ \hline Whole right foot & 47.6 & 4.3 & 47.1 & 45.6–50.4 & 26.8 & 10.1 & 48.3 & 46.9–50.4 & 0.312  \\ \hline Whole left foot & 52.4 & 4.3 & 52.9 & 49.6–54.4 & 53.2 & 10.1 & 51.7 & 49.6–53.1 & 0.312  \\ \hline Both forefeet & 39.6 & 12.5 & 37.4 & 31.5–48.2 & 42.6 & 11.9 & 42.8 & 34.3–51.2 & 0.144  \\ \hline Both rearfeet & 60.4 & 12.5 & 62.7 & 51.8–68.5 & 57.4 & 11.9 & 57.3 & 48.8–65.7 & 0.144  \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Plantar pressure distribution (%) under the forefoot, rearfoot, and whole foot.
# Directed Network Disassembly Method Based on Non-Backtracking Matrix
## 2 Related Works  
As described in the previous section, many network dismantling methods have been proposed in recent years. Next, I will introduce two methods that are compared with this article, namely the GND algorithm and Min-Sum algorithm.  
GND method. This method considers the case where the node removal cost is equal to the node weight and is not a unit cost. First, perform spectral division of the Laplacian matrix for which the operators are node weights of the network. After the division is successful, the node weight coverage algorithm is applied to the edges connecting different divisions to find the minimum weight point set that can divide the network, so as to find the minimum cost disassembly set. Compared with the previous algorithm, GND is more general and applicable. It considers the influence of node weights in undirected networks on the network disassembly problem. However, the operator in the GND method is a node adjacency matrix.  
Min-Sum method. [PERSON] et al. proposed a three-stage Min-Sum algorithm to dismantling networks. They first decycle a network with a variant of the Min-Sum message passing algorithm. After all cycles are broken, they break the remaining trees into small components until the largest component is smaller than the desired threshold. Finally, they refine the node set of network dismantling by moving some of them back to the original network. However, this kind of method tends to delete irrelevant nodes during the loop removal step and then moves them back to the original network in the following node re-inserting step, which reduces the disassembly efficiency.  
However, when analyzing directed networks, most spectral methods using node adjacency matrices will use symmetric adjacency matrices to make the network undirected [20]. This processing method will inevitably lose some information in the network [21], resulting in the search process for the minimum set of nodes to be disassembled in the directed network which will add some unnecessary nodes and cause unnecessary disassembly.
## 3 Vehicle-Embedded Speech Recognition Front-End for Multi-UAV Control
### Voice Trigger for Multiple UAVs Control
#### 3.3.1 Conventional Voice Trigger  
The voice trigger is similar to keyword spotting that detects one or more predefined keywords from a sequence of speech signals [31; 32]. It is widely used to wake up personal assistant devices. Thus, this task is also known as wake-up-word detection or hotword detection [33; 34]. Most conventional voice trigger approaches attempt to detect a single trigger word that is pertinent to the target device [31; 33; 35]. Some systems can consider multiple trigger words, thus selecting a trigger word and switching to one of other words. However, the device allows a single selected word among multiple trigger words as a wake-up-word because handling a single word is efficient relative to enhancing the detection correctness. Thus, a single acoustic model is sufficient for the operation. In recent years, several studies have attempted to operate multiple trigger words by allowing any predefined multiple words. Here, it is not important to classify which word is detected. Thus, it is sufficient to employ a single model.  
Figure 6 illustrates the standard voice trigger procedure when handling a single acoustic model [32]. As can be seen, a single trigger model for a trigger word is used to determine the trigger. For each input voice entered into the device, the trigger module produces a recognition result that represents the similarity between the input voice and the trigger model. The result is compared to a predetermined threshold to determine if the input voice corresponds to the trigger word. When it is determined to be a trigger word, the following voice signals are considered a voice command to recognize. In this study, we refer to this technique as a single-channel voice trigger in terms of the use of a single trigger model.
# Anti-Multipath Performance Improvement of an M-ary Position Phase Shift Keying Modulation System
## 3 The Scheme of Multipath Delay Estimation and Normalized Symbol Joint Decision
### Modulation Parameter Setting  
Assume that the delay of the sub-path relative to the main path is \(\Delta\tau\). First, the integer part value \(m\) of \(\frac{\Delta\tau}{T}\) is obtained, and \(m\) represents the number of symbol periods in which the sub-path is delayed from the main path. Then the parameter \(\Delta\tau\)-\(mT\) is calculated, which is a specific position of sub-path in the \((M+1)\)-th symbol period of main path. In order to separate the main path and the sub path signal, the condition is that the phase jump of the sub path after the delay cannot overlap with the phase jump of the main path. In this paper, taking only two paths of multipath as an example, according to the waveform characteristics of the MPPSK signal, the separation conditions of the main path and the sub path can be obtained.  
1. When \(0<\Delta\tau-mT<(M-1-r_{g})KT\), the main path delays m symbol periods to form a sub-path waveform whose waveform will fall within the modulation segment \((0-(M-1-r_{g})KT)\) in the symbol period, so the sub-path will be aliased with the main path waveform.
2. When \((M-1-r_{g})KT\leq\Delta\tau-mT<NT-(M-1-r_{g})KT\), the main path delays m symbol periods to form a sub-path waveform, and the waveform will fall outside the symbol modulation period \((0-(M-1-r_{g})KT)\) in the symbol period, so multipath does not affect the main path waveform. In order for this condition to be true, it is also necessary to satisfy \(2(M-1-r_{g})K<N\)  
Figure 6: Block diagram of LMSTDE.  
Therefore, in order to satisfy this condition, after the carrier frequency of the transmitted signal is fixed, it is necessary to select the parameters \(K\) and \(N\) of the MPPSK pulse modulation signal according to the delay to satisfy the separation of the main paths and sub-paths.
# WON-OCDMA System Based on MW-ZCC Codes for Applications in Optical Wireless Sensor Networks
## 3 System Description  
Figure 1 illustrates the layout of the combined OCDMA and WON system. The OCDMA utilises a multiwavelength laser source based on [31]. This multiwavelength laser generates 90 wavelengths \(\lambda_{1}\) to \(\lambda_{M}\) spectrally separated by 20 GHz into so-called wavelength chips. Set of \(k\) wavelength is then distributed towards \(V\) video surveillance transmitters while a set of \(p\) wavelengths is distributed towards \(B\) transmitters used for structural vibration sensing. Note \(V\times k+B\times p=M\) were the number of generated wavelength by the laser.  
Video surveillance data from each transmitter are carried by a dedicated MW-ZCC code with a code weight of \(k\). Each code uses the assigned subset of wavelengths \(i\) to \((i+k)\) from a dedicated \(k\times 1\) array waveguide grating (AWG). A Mach-Zehnder modulator that follows performs NRZ on-off keying modulation of the Video surveillance signal on the code. Finally, signals from all video transmitters are combined by a \((V+B)\times 1\) power coupler and send for optical amplification before being broadcasted into free space towards the receiver. On a reception side, a \(1\times(V+B)\) power coupler sends a copy of the received spectrum to each decoder. The code detection is handled by fibre Bragg gratings (FBGs). Decoded data are sent to a user photodetector (PIN) for optical to electrical conversion. Finally, data are analysed. In this experiment, a bit error rate (BER) analyser is used to assess the quality of received signals in order to evaluate the system performance.  
For vibration sensing, a set of \(B\)\((p\times 1)\) AWGs is used to form a unique code with the code-weight of \(p\) at each vibration sensing point. (see Figure 1). There, a collimator mounted on the sensing box works as a code modulator to imprint the vibrations. In conducted simulations, a Mach-Zehnder modulator has been utilised to imitate the detrimental behaviour of the collimator used in vibration sensors. Three different frequencies of 70, 140 and 210 MHz were used to represent low, medium and high vibration, which are adopted from previous experimental research [6]. In the real world, the vibration intensity is not the discrete number; its range depends on the physical nature of the monitored structure and variations of the quiver. Before broadcasting, data from all vibration sensors are sent to an \((V+B)\times 1\) power coupler followed by an optical amplifier. On the receiving side, FBGs are used to filter out the individual codes. The decoded codes carrying vibration data are then converted into electrical domain by PIN photodetectors and sent for analyses; this experiment uses an oscilloscope with fast Fourier transform module.
# Variational Quantum Circuits for Machine Learning.
## 2 Quantum Technology Approaches
### Photonic Chips  
A photonic quantum computer uses particles of light (i.e., photons) unlike other technologies, which use electrons. Photons can take advantage of quantum phenomena in a very efficient way because they do not have significant coupling with the outside. Optical quanta have a wide freedom in terms of frequency, linear momentum or spin, on which qubits can be implemented in a relatively simple way. Thus, these systems contain a set of optical devices, such as light sources, splitters, mirrors and detectors, which process photons. Each time a photon interacts with the splitter, the photon takes two paths simultaneously. Consequently, a quantum superposition state is achieved [30]. Among the latest advances, a quantum device that can perform photon entanglement with very high levels of precision and control on a large scale shows the potential of this design [31]. This technology could make mass fabrication of future optical quantum computers possible.  
According to research at Xanadu, since optical quantum chips operate at room temperature, the underlying technology is more scalable than other cooling-based technologies such as superconducting and can be integrated into optical-based telecommunications infrastructures [32].
## 2 Structure and Modeling of the Proposed System
### Structure of the Proposed System
The changing voltage amplitude corresponds to the transmission information, forming a comprehensive power information flow, realizing MASK modulation, and achieving the purpose of simultaneous transmission.
## 5 Results  
### Theoretical Evaluation  
Tables 2 and 3 displays the outcomes delivered by Louvain and I-Louvain and GraphSAGE with k-means. In this experiment, we obtain identical results for both datasets when using ground truth. These results confirm the effectiveness of employing the deep graph learning method: GraphSAGE. For Product Space, the Mutual Info Score of GraphSAGE is equal to 0.498, when it is equal to 0.378 for Louvain. Moreover, with a Mutual Info Score of 0.498, GraphSAGE outperforms I-Louvain which obtains only 0.408. We obtain the same results on the Cora dataset, except for I-Louvain. This variance can be elucidated by the utilization of one-hot encoding for Cora's textual data, so the calculation of inertia is less efficient with binary data. For all the tests we have carried out on Product Space, the order of performance is almost always the same, with I-Louvain slightly outperforming Louvain and GraphSAGE outperforming the other two modularity-based methods by a larger margin. These findings validate the value of deep graph learning in enhancing community detection. Indeed, the Louvain algorithm tends to restrict the number of discovered communities, which can explain the results according to the Rand Index. By contrast, the number of clusters for GraphSAGE tended to be higher and were better matches for the expected number of categories. However, the whole Louvain process is relatively faster than with the computationally intensive GraphSAGE method. This is an advantage when applying these approaches to large networks. There were no significant differences in computation time between the GraphSAGE and I-Louvain methods. However, it is during the pre-processing of textual data into numerical representations using Word2 Vec that the learning time seems to increase in proportion to the volume of textual data to be encoded.