esa-sceva/satcom-chunk-collection huggingface dataset:
# GRU Neural Network Based on CEEMDAN-Wavelet for Stock Price Prediction
## 3 GRU Neural Network Based on CEEMDAN-Wavelet
### The Framework of GRU Neural Network Based on CEEMDAN-Wavelet  
This section will discuss the steps of the model we proposed, which consists of two main stages. Firstly, we denoise the original input data using CEEMDAN to form various IMF components. In normal situations, experiments will give up some high-frequency components, because high-frequency IMF components tend to contain more noise and lower signal-to-noise ratio (SNR). However, containing more noise does not mean that high-frequency components do not have effective information. Thus, discarding high-frequency components simply will inevitably destroy the integrity of the information [45], which will always have an impact on the second part of the prediction process. Therefore, we use the wavelet threshold denoising method for the separation of effective information from noise for high-frequency IMF components. The high-frequency signal is passed through a wavelet transform, and then the wavelet coefficients generated by the signal often contain important information about the signal. The wavelet coefficients of a valid signal are always larger than the wavelet coefficients of the noise. Consequently, we just need to choose an appropriate threshold to filter the useful signal and discard useless noise; finally, the denoised signal is reconstructed to ensure that the useful information of the signal is retained to the maximum extent. The second part is to train denoised signals using GRU neural network and forecast close prices in the future. The main steps can be seen in Figure 5.  
Figure 4: GRU structure model.  
Figure 5: Architecture of the denoise system.
## 1 Introduction
### Motivation  
We target our research for space flight instrumentation. For gravitational microlensing measurements, obtaining microlensing parallax is of critical importance in order to acquire properties of the lensing system. Our research enables development of a space flight instrument telescope constellation to acquire microlensing parallax measurements. These measurements would not be feasible on any SmallSat type instruments in a telescope constellation due to limited on-board resources available on these instruments--unless an intelligent and efficient way of sampling and compressing data is available, such as the one we demonstrate here using compressive sensing techniques.
## 4 Numerical Results
### Numerical Test 2 (Parameter Variation): Homogeneous Boundary Conditions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**DEIM** & **Average Error** & **Avg Runtime** & **GPOD** & **Average Error** & **Avg Runtime** \\ \(m\) & & **(Scaled \%)** & \(m\) & \(q\) & **(Scaled \%)** \\ \hline
10 & \(2.4654\times 10^{-3}\) & 0.19\% & 10 & 40 & \(7.6060\times 10^{-4}\) & 0.39\% \\
20 & \(2.8762\times 10^{-4}\) & 0.20\% & 20 & 50 & \(1.6507\times 10^{-4}\) & 0.40\% \\
30 & \(2.3596\times 10^{-4}\) & 0.22\% & 30 & 60 & \(4.7335\times 10^{-5}\) & 0.40\% \\
40 & \(2.3799\times 10^{-5}\) & 0.25\% & 40 & 70 & \(3.9397\times 10^{-6}\) & 0.40\% \\
50 & \(5.6333\times 10^{-6}\) & 0.28\% & 50 & 80 & \(2.7384\times 10^{-7}\) & 0.41\% \\
60 & \(1.8494\times 10^{-7}\) & 0.29\% & 60 & 90 & \(1.3214\times 10^{-7}\) & 0.42\% \\
70 & \(4.0323\times 10^{-8}\) & 0.32\% & 70 & 100 & \(1.2307\times 10^{-8}\) & 0.42\% \\ \hline Full & - & 100\% & Full & - & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: [Test 2] Average runtime and average error of the POD-DEIM and POD-GPOD reduced systems with various parameter values and POD basis with dimension \(k=50\). The average runtime in Table 4 shows that POD approach can speedup the simulation of the full-order model to roughly 2.1-2.5 times faster (using 39.37% to 46.91% of the average runtime for the full-order system). In Table 5, the POD-DEIM approach and POD-GPOD approach are shown to give around 300-500 times and 240-255 times, respectively, for the speedups. Although GPOD seems to use more simulation time than DEIM, it has already reduced the runtime of the full-order system (more than 200 time speedup) and, more importantly, it can provide approximation with less error than DEIM around one order of magnitude when the dimension \(m\leq 50\). Table 6 summarizes the accuracy and the scaled average computational time of the reduced systems when compared with the full-order system (the runtime of full-order system is normalized to be one), for a special case fixed dimensions \(k=50\), \(m=40\), and \(q=70\).
# Software Engineering Techniques for Building Sustainable Cities with Electric Vehicles
## 2 Literature Review
### Potential of Intelligent Transportation Systems in Smart Cities  
Intelligent transportation systems (ITS) have been widely researched, primarily due to their potential to improve transportation systems' efficiency, safety, and sustainability [33; 34].  
The literature suggests that ITS can significantly improve traffic flow in smart cities by providing real-time information on traffic conditions, thus reducing congestion, lowering travel times, and improving the overall quality of life for city residents [35; 36]. Research has also demonstrated ITS's potential in enhancing safety, as these systems can provide real-time information on traffic conditions, weather, and other hazards, helping drivers make more informed decisions and avoid accidents [37; 38].  
In terms of economic implications, studies suggest that ITS can promote economic growth and job creation by fostering new business opportunities and stimulating innovation [39]. The literature, therefore, indicates that the potential impact of ITS in smart cities is significant. However, the deployment of these technologies requires a collaborative effort between different stakeholders and the development of innovative solutions to address the unique challenges of the smart city infrastructure.  
In summary, the literature underscores the instrumental role of software engineering in the development of EV-integrated smart cities, the benefits and challenges of EVs, and the potential of ITS in smart cities. The integration of these themes forms a comprehensive understanding of the complex interplay between these areas, highlighting the need for further research and development.  
The existing body of knowledge also suggests a need for more in-depth studies on software solutions that can address the unique challenges of EV-integrated smart cities. For instance, the development of robust software systems for managing charging infrastructure, optimizing energy consumption, and enhancing traffic flow remains a critical area of research.  
The literature also points to the need for ongoing research to address the environmental impact of EVs, particularly concerning battery production and disposal. Further investigation into the use of renewable energy sources for charging EV batteries can provide insights into how to mitigate these environmental impacts.  
Finally, while the potential of ITS in smart cities is widely acknowledged in the literature, many studies call for further exploration into this area. This includes research on how ITS can be effectively deployed in smart cities, the ways these systems can improve traffic flow and safety, and how they can stimulate economic growth.
# Lichens as a Natural Source of Compounds Active on Microorganisms of Human Health Interest
## 3 Results
### Screening of the Antimicrobial Activity of Lichen Extracts  
The antimicrobial activity of LC1 and LC2 lichen extracts, tested at the concentration of 1000 \(\upmu\)g/mL by the agar well diffusion assay against relevant human pathogens, is shown in Table 1. Both extracts showed the same antimicrobial activity 1 day after the extraction procedure, albeit with some differences among the indicator strains according to the diameter of the inhibition zone. No activity was detected for the DMSO solution used as a control.  
According to the results, 24 h after the extraction procedure, _B. cepacia_ MM 08 was the least sensitive microorganism to lichen extracts, as shown by the small diameter of the inhibition zone (6 mm), followed by _S. aureus_ ATCC 6538 (8 mm), _K. pneumoniae_ MM 01 (12 mm), and _B. subtilis_ ATCC 6633 (16 mm). More relevant inhibition halos were observed for the other indicator strains (_E. faecalis_ ATCC, _E. coli_ ATCC 8739, _P. aeruginosa_ ATCC 902, and _C. albicans_ ATCC 10231) compared to the previous ones. The highest sensitivity was detected against _Candida albicans_ ATCC 10231, recording a diameter of 40 mm. Analyzing the long-term antimicrobial effects (7, 15, and 30 days after the extraction procedure), both lichen extracts were active for no longer than 1 day against the majority of indicator strains. The two extracts maintained their activity up to 7 days (Figure 1) against the three Gram-positive pathogens _E. faecalis_ ATCC 29212, _S. aureus_ ATCC 6538, and _B. subtilis_ ATCC 6633, whereas the activity was retained up to 30 days toward _C. albicans_ ATCC 10231.
## 2 Materials and Methods
### Scene Data Generation Quality Assessment  
After obtaining the generated scene images, we evaluated the classification accuracy of the generated images. Firstly, a visual discrimination method was used to judge the generated images with the naked eye, and they were compared with the images directly generated by other generated adversarial networks. Secondly, the three conventional convolutional neural networks (CNN)-ALEX-NET [15], VGG-NET [16], and RES-NET [17] were used to carry out classification experiments on the data sets of NWPU-RESISC45 [18]. Then, the generated scene images were classified. In total, 60 images from each of the 5 categories were added to the original data set to form an enhanced data set, and classification experiments were carried out on the enhanced data set. In addition, we also conducted a separate classification experiment on the scene images generated by the proposed approach.  
Figure 2: The texture samples from five classes in the data set.
## 2 Preliminaries
### Qc-LDPC Codes  
QC-LDPC codes belong to a class of structured LDPC codes which can be represented by an \(M_{b}\times N_{b}\) base matrix \(\mathbf{H}_{b}\). Each element in \(\mathbf{H}_{b}\) corresponds to a circulant permutation matrix [28] of size \(Z\times Z\) such that we use integers between \(0\) and \(Z-1\) to specify the position of the one-entry in the first row of the circulant and use \(\infty\) to represent an all-zero matrix. Let \(\mathbf{H}\) be the parity-check matrix (PCM) of a QC-LDPC code. Accordingly, there are \(M=M_{b}\cdot Z\) rows and \(N=N_{b}\cdot Z\) columns in \(\mathbf{H}\).
## 3 Proposed CS-DDSG Scheme
### Measurement Matrix Formulation  
In this subsection, we present the formulation procedure for the measurement matrix. As we introduced above, in Stage 4, after the mobile sink queries the \(M\) nodes, which are denoted by \((n_{i_{1}},n_{i_{2}}\cdots,n_{i_{k}},\cdots n_{i_{M}}),i_{1}<i_{2}<\cdots<i_{M}, i_{k}\in[1,N]\), the measurement matrix \(\Phi\) is constructed based on the \(M\) packets. Suppose \(\Omega_{k}\) is the index of node ID and its definition is expressed as follows:  
\[\Omega_{k}=S(n_{i_{k}}).\text{id.} \tag{11}\]  
Initially, \(\Phi\) is an all-zero \(M\times N\) matrix, then \(\Phi\) is formulated at this step which is given by Equation (12):  
\[\blacksquare(k,j)=\left\{\begin{array}{l}\mathbf{1},\;j=\Omega_{k}\\ \mathbf{0},\;\text{otherwise}\end{array}\right.. \tag{12}\]  
For example, assume there are five nodes in the network (i.e., \(N=5\)). If the mobile sink queries two nodes (i.e., \(M=2\)), then \(\Phi\) can initially be expressed as follows:  
\[\mathbf{\Phi_{2\times 5}}=\left(\begin{array}{cccc}\mathbf{0}&\mathbf{0}& \mathbf{0}&\mathbf{0}\\ \mathbf{0}&\mathbf{0}&\mathbf{0}&\mathbf{0}\end{array}\right). \tag{13}\]  
Suppose that nodes 2 and 4 are selected by the sink, and their packets components are as follows:  
\[\begin{array}{l}S(2).\text{id}=[2,5]\\ S(4).\text{id}=[1,4],\end{array} \tag{14}\]  
then \(\varphi_{1,2}=\varphi_{1,5}=1\) and \(\varphi_{2,1}=\varphi_{2,4}=1\). Finally, the matrix \(\Phi\) becomes:  
\[\mathbf{\Phi_{2\times 5}}=\left(\begin{array}{cccc}\mathbf{0}&\mathbf{1}& \mathbf{0}&\mathbf{0}&\mathbf{1}\\ \mathbf{1}&\mathbf{0}&\mathbf{0}&\mathbf{1}&\mathbf{0}\end{array}\right). \tag{15}\]  
Moreover, the measurement vector \(Y\) is expressed as follows:  
\[Y=(S(2).\text{data},S(4).\text{data})^{\text{T}}. \tag{16}\]  
Obviously, \(\Phi\) is a sparse matrix, whose sparsity degree is influenced by \(p\), \(p_{1}\) and \(p_{2}\). Furthermore, Equation (12) indicates that \(\Phi\) is constructed by relying on the gathered data, which precludes the need to measure lost data. Thus, \(Y\) is not influenced by lost packets at all. Therefore, CS-DDSG is resistant to the packet loss rate.
## 5 Measurement Scenario  
In this section, the measurement scenario to the study of MIMO A&F and D&F relay Node through the implemented platform is presented. A series of measurements in an indoor scenario--in particular, on the fourth floor of building C of the School of Telecommunications at the Polytechnic University of Madrid--were carried out, in order to verify the operations of the relay nodes with the developed testbed.  
The LTE network real-scenario in Figure 11 is composed of one eNodeB, which will serve the one UE through the RN (A&F, A&F-ZF, A&F-MMSE or D&F). Besides, we considered the scenario shown in Figure 11, where the eNB and RN have Line-of-Sight (LOS) with distance between them of 16 meters. On the other hand, the UE was placed inside the room C-404, positioned about 22 and 9 meters, respectively, from the eNB and the RN. Furthermore, inside the office, tables, chairs, and informatics equipment were present. It should be noticed that the UE node has Non-Line-of-Sight (NLOS) with respect to the eNodeB and the RN, and due to that an experimental measurement scenario is taken into account, the path-loss, and fading effects are considered.  
Our results characterize the performance of the downlink, obtained by means of an eNB which was emulated using an LTE signal generated using the Matlab LTE Toolbox. Furthermore, the simulation parameters that were used during the realization of the system were as given in Table 1. For each transmission (20), 50 Frames were sent, where the  
Figure 10: Demodulation-&-Modulation Algorithm Processing Blocks.  
relay processed the signal and forward it, and the UE carried out two kinds of decoding: Decoding from the eNB (direct link) and from the RN (access link). The UE calculated the considered metrics for the two kinds of link-level individually.  
On the other hand, the authors have performed several measurements to experimentally estimate the SNR using the MS2090A handheld Spectrum Analyzer. In the NI-USRP 2944R used to emulate the eNB, its internal gain has been varied and the SNR on the receiver side has been estimated with the MS2090A, for each transmitted power level. This  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Signal bandwidth, \(F_{s}\) & 5 MHz \\ Carrier Frequency, \(f_{s}\) & 2.105 GHz \\ Tx/Rx schemes & SISO and 2 \(\times\) 2 MIMO \\ LTE Duplex Scheme & LTE-FDD \\ RMC number & R.6 and R.11 \\ Modulation & 64-QAM \\ Target Code Rate & 3/4 \\ Samples Rate & 7.68 MHz \\ Cyclic Prefix & Normal \\ Number of FFT & 512 \\ Number of Subcarriers \(\times\) Symbols in a Frame & 300 \(\times\) 140 \\ Number of pilot symbols per Frame & 8000 \\ Number of data symbols per Frame & 34,000 \\ Subcarrier spacing & 15 kHz \\ Number of Cell ID & 0 \\ Number of transmission & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main parameters to emulate the eNB.  
Figure 11: Indoor-to-Indoor experimental scenario with MIMO Relay Node.  
measurement has been carried out for different levels of gain transmitted by the NI-USRP 2944R, using the average of each point as the SNR value.
## 4 Landslide Susceptibility Mapping and Validation  
The probability for landslide susceptibility was predicted by reflecting the relative influence indexes of predictor variables calculated in the ANN and BT models. The predicted landslide  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Normalized Weights Based on ANN**} & \multicolumn{2}{c}{**Normalized Weights Based on BT**} \\ \hline Soil thickness & 0.00 & Soil material & 0.00 \\ Plan curvature & 0.05 & Soil thickness & 0.11 \\ Aspect & 0.14 & Plan curvature & 0.13 \\ Slope length (SL) & 0.19 & Soil topography & 0.18 \\ Mid-slope position (MSP) & 0.22 & Landforms & 0.21 \\ Soil topography & 0.24 & Mid-slope position (MSP) & 0.27 \\ Topographic position index (TPI) & 0.25 & Stream power index (SPI) & 0.33 \\ Soil land-use & 0.30 & Soil land-use & 0.34 \\ Timber diameter & 0.31 & Convexity & 0.36 \\ Terrain ruggedness index (TRI) & 0.35 & Topographic position index (TPI) & 0.42 \\ Soil material & 0.37 & Timber density & 0.43 \\ Stream power index (SPI) & 0.39 & Aspect & 0.45 \\ Timber age & 0.43 & Slope length (SL) & 0.65 \\ Convexity & 0.45 & Slope gradient & 0.66 \\ Landforms & 0.54 & Topographic wetness index (TWI) & 0.67 \\ Timber density & 0.58 & Terrain ruggedness index (TRI) & 0.71 \\ Slope gradient & 0.60 & Timber diameter & 0.73 \\ Topographic wetness index (TWI) & 1.00 & Timber age & 1.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the influence weights of predictor variables for Artificial Neural Network (ANN) and Boosted Tree (BT).  
susceptibility index was classified into four classes based on area for simple and visual interpretation: very high, high, medium, and low index ranges in 5%, 10%, 15%, and 70% of the study area, respectively (Figure 5a,b).  
Susceptibility maps were verified and compared by using known 41 actual landslide events as a validation set that were not used in the ANN and BT training to evaluate whether they could effectively reflect future landslide hazard areas. The landslide susceptibility indexes were sorted in descending order, and divided by 100 classes with cumulative 1% intervals. The cumulative distributions of landslide occurrence were compared with receiver operating characteristics (ROC) curves in 100 classes [52]. The ANN and BT models had a reasonable performance of 82.25% and 90.79% as percentage of area under ROC curves, respectively (Figure 6).  
Figure 5: Landslide susceptibility maps based on (**a**) ANN; and (**b**) BT approaches. The rank was divided into four classes based on area: very high, high, medium, and low index ranges in 5%, 10%, 15%, and 70% of the study area, respectively.
# Hierarchical Gated Recurrent Unit with Semantic Attention for Event Prediction
## 4 Experiments
### Evaluation
Compared with previous models, the prediction accuracy of HS-GRU has been improved. It indicates that adding prior knowledge can modify the prediction result which used only time-based information. Prior knowledge of current event links plays an important guiding role in event prediction. In datasets, lengths of event descriptions are different. To verify the predictive performance of our model for different length events, we divided the event link into long and short event links to compare the performance of HS-GRU. We took event links with length in the top 30% of datasets as short event links, and the rest as long event links. We compared our model with the basic model HRED. The results are shown in Table 4. Experimental results demonstrate that, for both international news and China news, error rates of short event links are higher than those of long event links. This is because short event links contain incomplete descriptions of events, which can easily cause deviations in event prediction. For the same dataset, compared with the basic model, the error rate optimization percentage (\(\Delta\%\)) of long event links is higher than short event links. This manifests that our model is more capable \begin{table}
\begin{tabular}{l c c} \hline \hline  & \multicolumn{2}{c}{**Error Rate**} \\ \hline  & **International News** & **China News** \\ \hline Backoff N-Gram [[PERSON], 2001] & 96.71\% & 95.94\% \\ Modified Kneser-Ney [[PERSON], 2001] & 93.52\% & 94.75\% \\ \hline GRU [[PERSON] et al. 2014] & 54.44\% & 66.47\% \\ HRED [[PERSON] et al. 2015] & 51.30\% & 64.57\% \\ \hline HS-GRU (ours) & 48.48\% & 62.33\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Error rate on datasets. of extracting long-range information from event links. Based only on time-based information, the model may forget past events. Prior knowledge can provide more guidance information for event prediction. Our model uses prior knowledge provided by historical events to determine the importance of current events to predictions. It improves the accuracy of predictions. We used HS-GRU to predict the possible next event. For current event link \(L=\{E_{1},E_{2},\cdots,E_{m}\}\), the model generates \(E_{m}\) given \(E_{1:m-1}\). Taking the news event links of the Indonesian tsunami as an example, the output of different models is shown in Figure 4. The results manifest that our model is the closest to the real result. Among predictions of each model, although predicted results of GRU and HRED are related to _\"earthquake\"_ and _\"Chinese\"_, they do not predict the information about _\"kill\"_. In the event link on the left of Figure 4, _\"kill\"_ was mentioned in the first event. However, in GRU and HRED, they pay more attention to information such as _\"earthquake\"_ and _\"tsunamis\"_. Our model predicts \"kills\", which further shows that adding prior knowledge can make event prediction more accurate.
# Modifications to ArduSub That Improve BlueROV SITL Accuracy and Design of Hybrid Autopilot
## 2 ArduSub Alterations
### Changes to ArduSub and Motivation
#### 2.2.5 Simulation Control Allocation  
In Section 2.2.1 and Algorithm 2, the process of how desired forces and torques are converted to thruster activations was detailed. In this section, how the updated SITL code calculates state updates given these thruster inputs is explained. The simulation code receives the vector of forces from the eight thrusters and determines the body forces and torques by applying the thruster allocation matrix introduced in Section 2.2.1 and Equation (1). Unlike Equation (1), however, the matrix operation \(\mathbf{T}:\mathbf{m}\mapsto\mathbf{\tau}\) is performed one thruster at a time using software array looping, and the inputs representing the motors are in units of PWMs not force since the simulation is reading the RC_Output channel. There is also a lookup table that complements the one used in Algorithm 2; where the one used in the algorithm used linear interpolation to find PWMs from forces, the simulation code determines forces from PWMs. The 6 DOF force and torque effect of each thruster is determined with column-wise multiplication with the thruster allocation matrix Equation (4). The force and torque values are then divided element-wise with the inertia coefficients \((\mathbf{M}+\mathbf{M_{A}})\) from Equations (A7) and (A8) of the hydrodynamic model described in Appendix B, to solve for intermediate values of linear and angular accelerations that are stored in a buffer. The simulation code then uses the velocity states of the given time step to perform drag force and torque calculations for each degree of freedom using the generalized model of Equation (11), where element-wise multiplication of the corresponding \(\mathbf{B_{L}}\) from Equation (A10) scales first-order terms and \(\mathbf{B_{NL}}\)) from Equation (A11) scales second-order terms, to determine 6 DOF forces and torques resulting from drag. These drag forces and torques are then element-wise divided with \((\mathbf{M}+\mathbf{M_{A}})\) as well to determine linear and angular accelerations which are added to the buffer. Buoyancy forces and torques and resulting accelerations are also calculated based on the virtual vehicle's depth and orientation, which are then element-wise divided with \((\mathbf{M}+\mathbf{M_{A}})\) and added to the buffer.