esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Eligibility According to PRISMA Flow Diagram  
The PRISMA diagram shown in Figure 1 reflects the methodology that was carried out together with the results obtained in each of the steps described below. Initially, the search process in the databases provided us with 230 results (49 from Web of Science, 29 from PUBMED, 84 from Scopus, 25 from MEDLINE, 3 from CINAHL and 40 from Science Direct), 65 of which were duplicates, and thus, were eliminated as a first step within the screening process, getting 165 results. The studies not cataloged as research articles were rejected (that is, 36 proceedings and conference articles, 17 book chapters, and 4 posters and editorials) as not being peer reviewed, as described in the methods section. As a consequence, 57 studies were removed in this step, leaving a total of 108 articles, which were submitted to the eligibility process. The inclusion and exclusion criteria described were applied. As a result of this phase, 9 articles were excluded for not using ML techniques, 24 articles did not focus their study on PD, 27 articles did not use EEG recordings, 3 articles considered animal studies, 1 article had pharmacological interventions, 24 articles were reviews with a different purpose, 3 articles performed studies on sleep EEG recordings, 6 articles were based on EEG changes evoked by exogenous stimuli and 2 articles had incomplete descriptions of the methodology used. The sum of all these types of articles resulted in a total of 99 exclusions, leaving us with 9 research articles that were included in this review.
## 2 Problem Description  
A typical centralized wireless real-time mesh network is illustrated in Figure 1. The network typically consists of a gateway node (GW), access points (APs) and field devices (FDs) performing sensor and actuator functionality. The GW provides bridging functionality to host applications and may also host the centralized network manager entity. The field devices connect to one or more access points, and applications (such as control algorithms) run on the gateway. A well-known example for such a centralized system is WirelessHART. In a typical, networked control application, the host application would process sensor measurements and provide control updates to the actuators.
## 2 Materials and Methods
### Analysis and Homology Modeling of the BLUF Domain  
The annotated sequences were further analyzed for the putative secondary structures and function using Predict Protein ([[https://www.predictprotein.org/](https://www.predictprotein.org/)]([https://www.predictprotein.org/](https://www.predictprotein.org/))). Based on the secondary structure analysis, a two-dimensional topology was generated using POTTER ([[http://wlab.ethz.ch/protter/#](http://wlab.ethz.ch/protter/#)]([http://wlab.ethz.ch/protter/#](http://wlab.ethz.ch/protter/#))) [52]. Three-dimensional models of the predicted BLUF sequences and associated effector domains were created using the Phyre2 modeling tool [53], employing an integrated combinatorial approach comprising comparative modeling, threading, and _ab initio_ modeling [54]. All the energy-minimized models of the annotated BLUF domains and BLUF, in combination with the effector domains, were further evaluated for structural errors and the stereochemistry quality, as well as for manual curation. The variety of the models were performed, in terms of bond angles, distances, stereochemical analysis, and vocabulary, by the UCLA Structure Analysis and Verification Server (SAVES) with the PROCHECK and ERRAT programs [55; 56]. Finally, the most acceptable models were finalized based on Ramachandran plot analysis and structural fit for each annotated sequence. All of the predicted BLUF sequences were aligned for an analysis of conservation and variation of residues using the Clustal Omega program.
# Crop Land Change Detection with MC&N-PSPNet
## 2 Materials and Methods
### Attention Mechanism  
To improve the training accuracy and speed of the model, this study introduces attention mechanisms into the PSPNet model. The essence of attention mechanisms lies in applying human perception and attention to machines, enabling them to discern important from unimportant parts of the data. In this paper, attention modules, namely CBAM and NAM, are respectively incorporated into the backbone feature extraction network and after feature fusion.
# A New Multivariate Approach for Real Time Detection of Routing Security Attacks in VANETs
## 6 Results and Discussion  
This section starts with an analysis of the influence of black hole attack on the considered network metrics mainly: throughput (TH), dropped packets ratio (DPR), and overhead traffic ratio (OTR). Further, an evaluation of the proposed scheme is performed by simulating two scenarios of AODV routing protocol: one normal AODV without attacks and a second with AODV black hole attack. Analysis and discussion of the obtained results are also presented.
# Study on the Impact of the Coastline Changes on Hydrodynamics in Xiangshan Bay
## 2 Materials and Methods
### Harmonic Analysis Results
#### 3.2.2 Comparative Experiments Harmonic Analysis Results  
The M\({}_{2}\) tidal amplitudes and phases from experiments A-E are shown in Figure 6A-E, respectively.  
Figure 5: M\({}_{2}\) tidal amplitudes and phases from the control experiment.  
Figure 6: _Cont._In Experiment A, the main changing area was near Meishan Island, where the phase angle had some variation. Compared to the control experiment, the 36\({}^{\circ}\) phase contour line on the west side of Meishan Island, which originally had a slightly northwest-southeast orientation, changed to a mainly north-south orientation. Meanwhile, the amplitude distribution showed a similar trend, with a slight increase on the west side of Meishan Island, approximately 0.1 m.  
In Experiment B, the main changing area was near Dasong. Due to coastline changes, there was a significant variation in phases in this area, especially for the 36\({}^{\circ}\) phase contour line. This contour line, which was originally roughly parallel to the latitude lines, has now become curved and open towards the mouth of the bay. There was also some variation in the amplitude, with the 1.5 m amplitude contour line approximately coinciding with the 36\({}^{\circ}\) phase contour line. Overall, the phases near Dasong showed a slightly decreasing trend, unlike the amplitude, which showed a slightly increasing trend. The above results indicated that the arrival time of the tidal wave at this location advanced, whereas the tidal amplitude at this location decreased.  
In Experiment C, the main changing area was near Tongzhao, where the amplitude in the Tie inlet increased. In Experiment D, the main changing area was north of the Tie inlet. After the area was reclaimed, the amplitude in the remaining areas of the Tie inlet decreased slightly, by approximately 0.05 m. In Experiment E, the main changing area was near Qiangjiao. Compared to the control experiment, the overall amplitudes and phase distribution did not change significantly, with only a slight decrease in amplitude in the surrounding sea areas.  
In summary, experiments A and B had a greater impact on the amplitudes and phases due to the changing coastline areas located on the outside of Xiangshan Bay, near the mouth of the bay. Experiments C, D, and E showed relatively minor changes, as the changing  
Figure 6: M\({}_{2}\) tidal amplitudes and phases from comparative experiments (**A**–**E**).  
coastline areas were located closer to the head of the bay, with only a slight decrease in amplitude in the surrounding changed areas.
###### Abstract  
The maximum light use efficiency (LUE) (\(\epsilon_{0}\)) is a key essential parameter of the LUE model, and its accurate estimation is crucial for quantifying gross primary production (GPP) and better understanding the global carbon budget. Currently, a comprehensive understanding of the potential of seasonal variations of \(\epsilon_{0}\) in GPP estimation across different plant functional types (PFTs) is still lacking. In this study, we used a phenology-based strategy for the estimation of \(\epsilon_{0}\) to find the optimal photosynthetic responses of the parameter in different phenological stages. The start and end of growing season (SOS and EOS) from time series vegetation indices and the camera-derived greenness index were extracted across seven PFT flux sites using the methods of the hybrid generalized additive model (HGAM) and double logistic function (DLF). Optimal extractions of SOS and EOS were evaluated, and the \(\epsilon_{0}\) was estimated from flux site observations during the optimal phenological stages with the light response equation. Coupled with other obligatory parameters of the LUE model, phenology-based GPP (GPP\({}_{\text{phe-based}}\)) was estimated over 21 site-years and compared with vegetation photosynthesis model (VPM)-based GPP (GPP\({}_{\text{VPM}}\)) and eddy covariance-measured GPP (GPP\({}_{\text{EC}}\)). Generally, GPP\({}_{\text{phe-based}}\) basically tracked both the seasonal dynamics and inter-annual variation of GPP\({}_{\text{EC}}\) well, especially at forest, cropland, and wetland flux sites. The R\({}^{2}\) between GPP\({}_{\text{phe-based}}\) and GPP\({}_{\text{EC}}\) was stable between 0.85 and 0.95 in forest ecosystems, between 0.75 and 0.85 in cropland ecosystems, and around 0.9 in wetland ecosystems. Furthermore, we found that GPP\({}_{\text{phe-based}}\) was significantly improved compared to GPP\({}_{\text{VPM}}\) in cropland, grassland, and wetland ecosystems, implying that phenology-based \(\epsilon_{0}\) is more appropriate in the GPP estimation of herbaceous plants. In addition, we found that GPP\({}_{\text{phe-based}}\) was significantly improved over GPP\({}_{\text{VPM}}\) in cropland, grassland, and wetland ecosystems, and the R\({}^{2}\) between GPP\({}_{\text{phe-based}}\) and GPP\({}_{\text{EC}}\) was improved by up to 0.11 in cropland ecosystems and 0.05 in wetland ecosystems compared to GPP\({}_{\text{VPM}}\), and RMSE was reduced by up to 5.90 and 2.11 g C m\({}^{-2}\) 8 day\({}^{-1}\), respectively, implying that phenology-based \(\epsilon_{0}\) in herbaceous plants is more appropriate for GPP estimation. This work highlights the potential of phenology-based \(\epsilon_{0}\) in understanding the seasonal variation of vegetation photosynthesis and production.  
gros primary production; light use efficiency model; maximum LUE; phenology-based; hybrid generalized additive model +
Footnote †: journal: remote sensing
# Urban PM2.5 Concentration Prediction via Attention-Based CNN-LSTM
## 3 Data and Method
### Method
#### 3.5.2 LSTM Network  
As a special kind of RNN, the LSTM network [10] is capable of learning long-term dependencies. It has the advantage of connecting previous information to the present task [23; 24]. Because of its special memory cell architecture, the LSTM network overcomes the defects of the traditional RNN, especially the problems of gradient disappearance and gradient explosion. The architecture of an LSTM memory cell is shown in Figure 7, where each cell has three \"gate\" structures, namely, the input gate, the forget gate, and the output gate. A chain of repeating cells forms the LSTM layer. The calculation process of the spatiotemporal feature matrix \(L=[l_{1},l_{2},\cdots,l_{t}]\) in the LSTM layer is given in Equations (4)-(9).  
\[f_{t}=\sigma\Big{(}W_{f}\cdot[h_{t-1},l_{t}]+b_{f}\Big{)}, \tag{4}\]  
\[i_{t}=\sigma(W_{i}\cdot[h_{t-1},l_{t}]+b_{i}), \tag{5}\]  
\[\overline{c}_{t}=\tanh(W_{c}\cdot[h_{t-1},l_{t}]+b_{c}), \tag{6}\]  
\[c_{t}=f_{t}\circ c_{t-1}+i_{t}\circ\overline{c}_{t}, \tag{7}\]  
\[o_{t}=\sigma(W_{o}\cdot[h_{t-1},l_{t}]+b_{o}), \tag{8}\]  
\[h_{t}=o_{t}\circ\tanh(c_{t}), \tag{9}\]  
where \(W_{f}\), \(W_{i}\), and \(W_{c}\) denote the weight vector of the input gate, output gate, and forget gate, respectively, whereas \(b_{f}\), \(b_{i}\), \(b_{c}\), and \(b_{o}\) are the bias vectors for the three gates, and \(\sigma\) denotes the sigmoid activation function.  
Actually, Equation (4) represents the forget gate and it decides what information should be thrown away from the cell state, where \(f_{t}\) denotes the output of the forget gate. Equations (5) and (6) represent the input gate, which decides what new information should be stored in the cell state, where \(i_{t}\) and \(\overline{c}_{t}\) denote the output of the input gate, and \(c_{t}\) denotes the activation vector of the current cell. Equations (8) and (9) represent the output gate, where \(o_{t}\) denotes the output of the output gate. \(h_{t-1}\) is the hidden state of the last cell, and \(h_{t}\) is the state of the current cell. The feature state matrix \(H=[h_{1},h_{2},\cdots,h_{t}]\) is the output of the LSTM layer.
# Experimental Damage Identification of a Model Reticulated Shell
###### Abstract  
The damage identification of a reticulated shell is a challenging task, facing various difficulties, such as the large number of degrees of freedom (DOFs), the phenomenon of modal localization and transition, and low modeling accuracy. Based on structural vibration responses, the damage identification of a reticulated shell was studied. At first, the auto-regressive (AR) time series model was established based on the acceleration responses of the reticulated shell. According to the changes in the coefficients of the AR model between the damaged conditions and the undamaged condition, the damage of the reticulated shell can be detected. In addition, the damage sensitive factors were determined based on the coefficients of the AR model. With the damage sensitive factors as the inputs and the damage positions as the outputs, back-propagation neural networks (BPNNs) were then established and were trained using the Levenberg-Marquardt algorithm (L-M algorithm). The locations of the damages can be predicted by the back-propagation neural networks. At last, according to the experimental scheme of single-point excitation and multi-point responses, the impact experiments on a K6 shell model with a scale of 1/10 were conducted. The experimental results verified the efficiency of the proposed damage identification method based on the AR time series model and back-propagation neural networks. The proposed damage identification method can ensure the safety of the practical engineering to some extent.  
Keywords:reticulated shell; damage detection; time series modeling; Levenberg-Marquardt algorithm; impact experiment; neural networks +
Footnote †: journal: Applied Sciences
# Signal Separation Based on Knowledge Representation
## 2 Theory  
### Problem Setup  
The signal separation problem can be described as a mixed signal matrix \(\mathbf{X}\) consisting of \(N\) target source signals \(\mathbf{S}_{n}\) and noise \(\mathbf{I}\).  
\[\mathbf{X}=\sum_{n=1}^{N}\mathbf{S}_{n}+\mathbf{I} \tag{1}\]  
When provided with the observed mixed signal matrix \(\mathbf{X}\), the objective of signal separation is to estimate its source signals \(\mathbf{\hat{S}_{n}}\), aiming to closely resemble the true underlying signals. In blind source separation (BSS) problems [33; 34], the number of target source signals is usually unknown. Therefore, the key to solving the signal separation problem lies in accurately extracting prior knowledge from the mixed signal \(\mathbf{X}\) and utilizing these knowledge representations to effectively recover the target source signals, as well as reconstructing the mixed signal \(\mathbf{X}\). Assuming the knowledge of the number of source signals \(N\), this problem can be formulated as a mathematical optimization problem:  
\[\begin{split}&\underset{\theta_{1},\cdots,\theta_{N}}{\arg\min} \left\|\mathbf{X}-\mathbf{\tilde{X}}\right\|+\\ & M(\mathbf{\hat{S}}_{1},\mathbf{K}_{1},\mathbf{\hat{S}}_{2}, \mathbf{K}_{2},\cdots,\mathbf{\hat{S}}_{N},\mathbf{K}_{N})\\ &\text{s.t.}\quad\mathbf{\hat{S}}_{i}=\Phi_{\theta_{i}}( \mathbf{X})\\ &\mathbf{\hat{X}}=\sum_{i=1}^{N}\mathbf{\hat{S}}_{i}\\ &\left[\mathbf{K}_{1},\mathbf{K}_{2},...,\mathbf{K}_{N}\right]^ {\mathsf{T}}=R(\mathbf{X})\end{split} \tag{2}\]  
This optimization problem seeks to minimize the discrepancy between the observed mixed signal matrix \(\mathbf{X}\) and its reconstructed signal \(\mathbf{\hat{X}}\), where \(\|\cdot\|\) represents the function operation used for the reconstruction error of the observed mixed signal, and in the context of signal separation, the most critical requirement is that the separated signals must accurately reconstruct the observed signal, ensuring that the reconstructed signal is exactly equal to the observed signal. The \(L_{1}\) norm is adopted in our formulation because it inherently enforces equality constraints, making it well suited for this purpose. By minimizing the \(L_{1}\) norm, the formulation guarantees that the sum of the separated signals strictly equals the observed signal, thereby preserving its fidelity and preventing information loss. \(R(\cdot)\) represents a nonlinear function for knowledge extraction and representation of the observed mixed signal; \(\mathbf{K}_{i}\) represents the knowledge corresponding to the \(i\)-th separated target; \(\Phi_{\theta_{i}}\) represents a neural network function capable of predicting the separated \(i\)-th source signal; \(\mathbf{\hat{S}_{i}}\) denotes the \(i\)-th separated source signal predicted by the neural network function; and \(\theta_{i}\) represents the trainable parameters of the \(i\)-th branch of the neural network. Finally, \(M(\cdot)\) represents the function that combines the results of applying knowledge from different target signals. It can be either a linear or a nonlinear function.
## 5 Experiments
### The Defection Detection Results
Figure 9: Segmentation results of scratch defects and dent defects: (**a**,**b**) are the respectively acquired defect images and segmentation results of scratch defects; (**c**,**d**) are the respectively acquired defect images and segmentation results of dent defects. \[\text{R}= \frac{\text{TP}}{\text{TP}+\text{FN}}, \tag{7}\]  
where the false negative (FN) denotes the number of undetected defect regions. Industrial production always wants the qualification rate to be as high as possible after production inspection, which means strict inspection criterion to make the false negative number small. However, strict inspection criterion may cause a high false positive, which results in low productivity and high costs. A good defection detect system would result in high precision and high recall. The deep-learning method proposed in this paper is superior to the traditional method. The traditional segmentation method is mainly composed of background correction, image filtering, and morphological operations [29]. Verified by the test experiment with more than 10,000 image samples, the inspection results of the traditional machine vision method and proposed deep learning method are shown in Table 1. The performance of the proposed method is significantly better with higher precision and recall on the defects of dent, scratch, and discoloration. Meanwhile, the average precision of the traditional method is 85.2%, while the proposed deep learning method achieves a precision of 91.8%. The average recall of the proposed method is 4.6% higher than that of the traditional method. The inspection performance on discoloration is obviously better than other defects because the size of discoloration is relatively large and the variance value is higher. The dent defect has the lowest recall because of the inevitable dust particles. The detection result is also better than the manual inspection result.
## 4 Discussion
### Classification Performance
Previous studies successfully classified a higher number of intertidal sediment classes, but these often involved the use of higher resolution imagery [31; 103; 108], or their workflows were fully optimized to be focused only on sediment mapping, using SAR [32; 109] or DSLR cameras mounted on unmanned aerial vehicles [103]. The use of higher resolution satellite data may help to increase the number of sediment classes, but these products often have much less radiometric resolution or are of limited access or may come at very high monetary and computational costs (see [29]). In addition, the exclusive focus on sediment types will likely disregard other important intertidal habitats. We believe our approach constitutes a good balance between performance and replicability in our study area.
## 3 SnBi-Based Solders  
### Microstructure  
After adding Ni nanoparticles into SnBiAg solder, the Bi-rich phase and Ag\({}_{3}\)Sn IMC was refined because the formation of Ni-Sn IMCs could act as nucleation sites and inhibit the grain growth [55]. With the addition of 0.4 wt.% Ag nanoparticles, a mass of dispersed nano-Ag\({}_{3}\)Sn particles formed, which inhibited the grain growth and provided a refined microstructure [56]. It is worth noting that refinement effect does not always increase with the decrease of nanoparticles dimension. Because of small size and high surface energy, Ag nanoparticles with a size of 31 nm have a stronger tendency to agglomerate to larger particles and cannot disperse in the solder matrix in comparison with those of 76 nm and 133 nm [57]. As a result, SnBi solder-doped 31 nm Ag nanoparticles presents larger grain size and interphase spacing, as shown in Figure 8.  
[PERSON] et al. [58] has reported the influence of Cu nanoparticles on the microstructure of SnBi nano-composite solder. The formation of Cu\({}_{6}\)Sn\({}_{5}\) IMC phase was detected by analyzing XRD patterns of SnBi-3 Cu solder. During solidification, the Cu\({}_{6}\)Sn\({}_{5}\) nanoparticles served as nucleation sites and greatly increased the nucleation rate, which led to the grain refinement of the solder bulk. [PERSON] et al. [59] found that with 0.05 wt.% Cu\({}_{6}\)Sn\({}_{5}\) nanoparticles addition, the grain refinement of SnBi-nano Cu\({}_{6}\)Sn\({}_{5}\) composite solder reached the maximum. Smaller amount of Cu\({}_{6}\)Sn\({}_{5}\) nanoparticles had no obvious effect on the nucleation rate, however when the content was excessive, the agglomeration of Cu\({}_{6}\)Sn\({}_{5}\) nanoparticles would weaken its refinement effect.  
Figure 7: Schematic representation of the inhibition effect of graphene nanosheets (GNSs) on oxygen diffusion [54].  
Ni can react with SnBi solder matrix to form some stable intermetallic compounds such as Ni\({}_{3}\)Sn\({}_{4}\) and Ni\({}_{3}\)Sn, thereby increasing the bonding force between Ni-CNTs and the matrix. As a result, Ni-CNTs distribute more uniformly and become harder to agglomerate in comparison with pure CNTs [60,61]. Therefore, Ni-CNTs can effectively suppress the growth of interfacial IMCs because of their higher stability in the solder joints during soldering.