esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Seismic Waves and Seismic Signal Processing Techniques
The Fourier transform is also used to filter out noise and unwanted signals from seismic data, improving the accuracy of earthquake detection and analysis [112]. Another advanced signal processing technique used in seismic wave analysis is wavelet analysis, which is used to analyze signals that are both time-varying and non-stationary [113; 114; 115]. In earthquake early warning systems, wavelet analysis is often used to detect and analyze seismic waves that have complex frequency components, such as those generated by slow earthquakes or volcanic activity [116; 117]. By decomposing a seismic waveform into its constituent frequency components, wavelet analysis can provide more detailed information on the characteristics of seismic waves, such as their frequency content, duration, and amplitude [118; 119]. In addition to these advanced signal processing techniques, earthquake early warning systems also rely on a variety of specific parameters to optimize their performance [120; 121]. These parameters include sampling rates, window sizes, and filter cutoff frequencies, among others. Sampling rates determine how often seismic data is collected and stored, while window sizes determine the length of time over which seismic data is analyzed. Filter cutoff frequencies determine which frequency components of a seismic waveform are analyzed and are often used to remove noise and unwanted signals from seismic data. In conclusion, earthquake early warning systems rely on a variety of advanced signal processing techniques and specific parameters to detect and analyze seismic waves in real time. By providing more detailed information on these techniques and parameters, we aim to enhance the technical rigor of our paper and improve the understanding of the underlying technology. By optimizing the performance of earthquake early warning  
Figure 3: Earthquake measurement evolution. systems through advanced signal processing techniques and specific parameters, we can improve the accuracy and effectiveness of these systems, ultimately helping to save lives and reduce the impact of earthquakes on communities.
# The Effects of Compression on the Detection of Atrial Fibrillation in ECG Signals
###### Abstract  
Although Atrial Fibrillation (AF) is the most frequent cause of cardioembolic stroke, the arrhythmia remains underdiagnosed, as it is often asymptomatic or intermittent. Automated detection of AF in ECG signals is important for patients with implantable cardiac devices, pacemakers or Holter systems. Such resource-constrained systems often operate by transmitting signals to a central server where diagnostic decisions are made. In this context, ECG signal compression is being increasingly investigated and employed to increase battery life, and hence the storage and transmission efficiency of these devices. At the same time, the diagnostic accuracy of AF detection must be preserved. This paper investigates the effects of ECG signal compression on an entropy-based AF detection algorithm that monitors R-R interval regularity. The compression and AF detection algorithms were applied to signals from the MIT-BIH AF database. The accuracy of AF detection on reconstructed signals is evaluated under varying degrees of compression using the state-of-the-art Set Partitioning In Hierarchical Trees (SPIHT) compression algorithm. Results demonstrate that compression ratios (CR) of up to 90 can be obtained while maintaining a detection accuracy, expressed in terms of the area under the receiver operating characteristic curve, or at least 0.9. This highlights the potential for significant energy savings on devices that transmit/store ECG signals for AF detection applications, while preserving the diagnostic integrity of the signals, and hence the detection performance.  
Keywords: atrial fibrillation; heart rate variability; shannon entropy; compression +
Footnote †: journal: Applied Sciences
## 1 Introduction
Lastly, the assumption of a specific constant traffic generation state at HAP drones in existing research does not align with the stochastic and deterministic nature of traffic generation in practice, rendering conventional static matching algorithms inapplicable [13]. Therefore, in order to address the issues mentioned above, a tripartite matching problem among LEO satellites, HAP drones, and traffic types is investigated for the space-air-ground integrated network in this paper. Specifically, the main contributions of this paper are as follows:  
* First, the network architecture and working mechanism of the space-air-ground integrated network is introduced, which aims at achieving user access and traffic backhaul in remote areas. Different from the conventional static traffic generation state with deterministic variables, the traffic generation state at HAP drones is modeled as a mixture of stochasticity and determinism, which aligns with real-world scenarios. * Then, different from the conventional schemes that treat all traffic types as equally important, we develop a tripartite matching problem among satellites, HAP drones, and traffic types based on the different values of different traffic types. The problem can be decoupled into two sub-problems: traffic-drone matching and LEO-drone matching. Traffic-drone matching is simplified into multiple separate sub-subproblems through mathematical analysis, which can be addressed independently. LEO-drone matching cannot be solved by conventional optimization solvers since the traffic generation state at drones is a mixture of stochasticity and determinism. Thus, reinforcement learning is adopted. Moreover, due to the significant propagation latency between terrestrial traffic center and LEO satellites, a conventional centralized scheme cannot obtain the latest status of the network. Therefore, it cannot devise LEO-drone matching strategies in a timely manner. In addition, the state space of the LEO-drone matching sub-problem is continuous. Therefore, a multi-agent deep reinforcement learning approach with centralized training and decentralized execution is proposed, in which the value network is centrally trained at the terrestrial traffic center and the LEO-drone matching strategy is timely devised at LEO satellites in a decentralized manner. * Finally, the convergence performance of the proposed matching approach is discussed and analyzed through simulations. In addition, the proposed algorithm is compared with state-of-the-art algorithms under different network parameters to validate its effectiveness. The rest of the paper is organized as follows. The related works are discussed in Section 2. The system model and working mechanism are illustrated in Section 3. Section 4 formulates and simplifies the tripartite matching problem. In Section 5, the formulated problem is solved by the multi-agent deep reinforcement learning algorithm. Simulation results are presented and discussed in Section 6. Future work is summarized in Section 7. Finally, conclusions are drawn in Section 8.
# A Privacy-Preserving Protocol for Utility-Based Routing in DTNs
## 5 Performance Evaluation
### Performance Comparison  
PPUR is compared with the routing protocols of PROPHET [2] and FaceChange [10] to evaluate its performance. The PPUR has 1-4 APs denoted as PPUR1, PPUR2, PPUR3, PPUR4, respectively, to test the influence of the number of APs. We are interested in the following metrics for performance evaluation: message delivery ratio, average delivery delay, and overhead ratio. The delivery ratio is defined as the ratio of successfully delivered messages to total messages generated in the network. Delay is measured by the delay sum of all delivered messages divided by the number of delivered messages. Overhead ratio is defined as the ratio of the total number of forwarding to the number of delivered messages, which denotes how many times forwarding is needed to successfully deliver a message. We evaluate the influence of buffer size and TTL on performance.
## 2 Materials and Methods
### Methodology
In Tables 2 and 3, the results were obtained using the proposed methodology and the literature approaches using greedy sampling (GSx) [56] and random sampling are reported. Acquisition dates are referred with the symbol \(\mathrm{D_{i}}\). The first five columns report, for each date, the number of samples exploited for PLSR model calibration. The red-framed cell indicates the prediction date. The sixth column expresses the total number of samples used for model calibration. Quantitative data are provided in terms of the root mean squared error (RMSE). The values reported in the last column of the tables (RMSE\({}_{\mathrm{p}}\)) refer to predictions for acquisition \(\mathrm{D_{i}}\) made using a model calibrated using data collected up to the date \(\mathrm{D_{i-1}}\). Data concerning the random sampling approach refer to the maximum and average RMSEs obtained following 1000 experiments. As for the dry mass experiment (see Table 2), the RMSE with respect to ground truth ranged between 0.14 t/ha obtained on 15 May 2014 and 4.97 t/ha registered on 14 October 2014. The average RMSE was of 2.05 t/ha. The number of samples selected for PLSR ranged from 48 for the acquisition made on 15 May 2014 to 21 referred to the flight made on 26 October 2017 with an average of about 30 samples per date. The values of RMSE\({}_{\mathrm{p}}\) were useful to assess the forecasting capability of the fitted model. They have been calculated by using the model fitted at time \(\mathrm{D_{i-1}}\) with the data acquired at time \(\mathrm{D_{i}}\). From the Table, it arises that the model failed the prediction at time \(\mathrm{D_{2}}\) and \(\mathrm{D_{3}}\). Starting from time \(\mathrm{D_{4}}\) predictions show reasonable RMSE, although its value was much higher than that the one obtained using active learning. Random sampling approach has been evaluated by computing the maximum RMSE and the average one. In the first case the range was between 2.83 t/ha and 9.93 t/ha. In the second, between 2.26 t/ha and 3.77 t/ha. The GSx approach returned an average RMSE of 1.96 t/ha. As for the nitrogen content experiment, (see Table 3), the obtained RMSE ranged between 1.10 kg/ha, obtained on 15 May 2014, and 8.84 kg/ha, registered on 14 October 2014. The average value was 5.20 kg/ha. The number of samples selected for PLSR ranged from 47 for the acquisition made on 15 May 2014 to 20 referred to the flight made on 26 October 2017. For this experiment, further data about the RMSE calculated by averaging data based on the fertilization treatment (see Figure 1) have been reported in analogy with the results discussed in [9]. In this case, the obtained RMSE\({}^{*}\) ranged between 2.96 kg/ha, obtained on 9 May 2017, and 7.22 kg/ha, registered on 14 October 2014, with an average of 4.68 kg/ha. As for forecasting, the model calibrated with data collected up to time \(\mathrm{D_{i-1}}\) fails
## 1 Introduction
### Related Work
In [50], the authors developed a mathematical propulsion energy model for rotary-wing UAVs with the goal of minimizing the total energy consumption of the UAV while keeping all ground node data rates in consideration. The authors in [51] take the problem of UAV-based communicating systems in a more complete manner, where they optimize both access and backhaul links simultaneously. In particular, they consider low earth orbit (LEO) satellites as a backhauling option and try to obtain an optimal backhaullink through BS-satellite association. Besides, the transmit power, channel to be used, and the trajectory of UAV BSs are also optimized with the help of RL.
## 2 Materials and Methods  
### LMO Monitoring for LM Canola in Natural Environments  
In 2008, the LMO Act came into effect in South Korea, leading to the initiation of a nationwide survey in 2009 to monitor the unintentional release of LM canola. The monitoring sites were strategically selected based on the proximity to food and feed factory locations, and the transportation routes from import harbors. The monitoring was conducted during the growing season of canola, and suspicious Brassica samples were collected within a 100-m radius of the monitoring site. Field photos, environmental data, and GPS co-ordinates (using a Garmin Montana 680, Garmin Inc., Olathe, KS, USA) were recorded for each area where suspicious samples were found. At survey sites, each plant sample was collected individually in separate paper bags to prevent contamination. The leaves were then divided for immunochemical testing, drying, and PCR analysis. If there were LMO suspicious plants, we sampled 10 individual plants from each quadrat within a 100-m radius, using a randomized selection method to ensure the sample accurately represented the plant population at each site. A 100-m radius was estimated from a central point at each site using Google Maps and GPS co-ordinates. An immunochemical test (Envirologix Inc., Portland, ME, USA) was performed on-site at the survey locations. The leaf samples were then dried with silica gel and stored at 4 \({}^{\circ}\)C. Each collected sample underwent LMO identification through PCR analysis and sequencing [43; 44].
## 5 Conclusions and Future Work  
For the application of visible-light wearable gaze trackers, YOLOv3-tiny-based deep-learning pupil tracking methodology is developed in this paper. By applying YOLO-based object detection technology, the proposed pupil tracking method effectively estimates and predicts the center of the pupil in a visible-light mode. By using the developed YOLO-based model to test the pupil tracking performance, the accuracy is up to 80%, and the recall rate is close to 83%. In addition, the average visible-light pupil tracking errors of the proposed YOLO based deep-learning design are smaller than 2 pixels for the training mode and 5 pixels for the cross-person testing mode, which are much smaller than those of the previous designs in [15; 21], without using deep-learning technology under the same visible-light conditions. After the calibration process, the average gaze tracking errors using the proposed YOLOv3-tiny-based pupil tracking models are smaller than 2.9 and 3.5 degrees for the training and testing modes, respectively. The proposed visible-light wearable gaze tracking design performs up to 20 frames per second on the Nvidia Xavier embedded platform.  
In this design, the application distance between the user's head and the screen is fixed during the operation, and the direction of the head pose tries to keep fixed. In future works, a head movement compensation function will be added, and the proposed wearable gaze tracker will be more convenient and friendly for practical uses. To raise the high-precision recognition ability of the pupil location and tracking, the deep-learning model will be updated with online training using the big image databases in the cloud to fit the pupil position for different eye colors and eye textures.  
Conceptualization, W.-L.O. and T.-L.K.; methodology, T.-L.-K. and C.-C.C.; software and validation, W.-L.O. and T.-L.K.; writing--original draft preparation, T.-L.K. and C.-P.F.; writing--review and editing, C.-P.F.; supervision, C.-P.F. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Ministry of Science and Technology, Taiwan, R.O.C. under Grant MOST 109-2218-E-005-008.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Towards a Near-Real-Time Protocol Tunneling Detector Based on Machine Learning Techniques +
## 3 Background
### Artificial Neural Networks  
Artificial neural networks (ANNs) are deep learning models that have been successfully applied to a vast number of knowledge fields ranging from computing science to arts [33]. They are internally constituted by groups of multiple neurons, which can be thought of as mathematical functions that take one or more inputs. In ANNs, inputs are only processed forward and are multiplied by weights within each neuron and summed up to be then passed to an activation function, which becomes the neuron's output. In general, artificial neural networks consist of three different layers: input, hidden, and output; the first layer accepts inputs, while the hidden layers process them to learn the optimum weights. Finally, the output layer produces the result.
# An Acoustic Feature-Based Deep Learning Model for Automatic Thai Vowel Pronunciation Recognition
## 4 Materials and Methods
### The Classification by Convolutional Neural Networks Model
#### 4.3.1 Baseline Structure  
In the baseline CNN model, the researcher started with shallow convolutional neural networks which consisted of two convolutional layers and one pooling layer. ReLU activation function [58], Adam optimizer, and a batch size of 32 were used in this model. The first convolutional layer consisted of 32 filters (2 \(\times\) 2), the ReLU activation function, and max pooling (2 \(\times\) 2). The second convolutional layer consisted of 64 filters (2 \(\times\) 2) and ReLU activation function but without the pooling layer. The fully connected layer consisted of 64 hidden units. In the final layer, the Softmax activation function was used for classification.  
The LSTM layer is designed for modeling the time signal and learning the long-term contextual dependencies from the sequences [50] including the baseline LSTM model and the reshaped dimension of the input layer. A dropout of 0.35 is used after the input layer. Then, the output passes through the LSTM layers. The first and second LSTM layers consist of 512 units, hyperbolic tangent activation function (tanh), and dropout (0.35). Finally, the Adam optimizer and Softmax activation function are applied to the classification.  
The LSTM has a special mechanism to control the flow of information using four components. Cells with a self-recurrent connection, an input gate, a forget gate, and an output gate are used as components in the LSTM layer. The LSTM unit is updated at every timestep \(t\) as explained by Equations (3)-(7) [50]:  
\[i_{t}=\alpha(W_{t}y_{t}^{l-1}+U_{t}y_{t-1}^{l}+b_{t}) \tag{3}\]  
\[f_{t}=\alpha(W_{t}y_{t}^{l-1}+b_{t}y_{t-1}^{l}+b_{t}) \tag{4}\]  
\[c_{t}=f_{t}c_{t-1}+i_{t}\tanh(W_{t}y_{t}^{l-1}+U_{t}y_{t-1}^{l}+b_{c}) \tag{5}\]  
\[o_{t}=\alpha(W_{o}y_{t}^{l-1}+U_{o}y_{t-1}^{l}+b_{o}) \tag{6}\]  
\[y_{t}^{l}=o_{t}\tanh c_{t} \tag{7}\]  
where \(y_{t}^{l-1}\) denotes the input of the LSTM unit, \(y_{t}^{l}\) denotes the output of the LSTM unit; \(i_{t},f_{t}\), and \(o_{t}\) denote gate vectors; \(c_{t}\) denotes the LSTM unit state; \(W\), \(U\), and \(b\) denote the parameter matrices and vectors; \(\alpha\) denotes the sigmoid function; and \(\tanh\) denotes hyperbolic tangents. In Equations (3)-(7), the superscript \(l-1\) and \(l\) denote the indices of the input and output features. The subscript \(i,f,o\), and \(c\) in Equations (3)-(6) denote the input gate, forget gate, output gate, and cell, respectively.
## 2 Preliminaries and Problem Formulation
### Model of Underactuated Marine Surface Vessel  
The dynamics and kinematics equations for the underactuated MSV are defined by [9]. The kinematics model of the underactuated MSV can be written as follows:  
\[\left\{\begin{array}{l}\dot{x}=u\cos(\psi)-v\sin(\psi)\\ \dot{y}=u\sin(\psi)+v\cos(\psi)\\ \dot{\psi}=r\end{array}\right. \tag{4}\]  
where \([x,y,\psi]^{T}\) represents the surge position, the sway position and the heading angle of the MSV in the earth-fixed inertial frame; \([u,v,r]^{T}\) represents the surge velocity, the sway velocity, and the yaw rate of the MSV in the body-fixed frame.  
In order to simplify the dynamics model, it is necessary to make reasonable assumption as follows:  
**Assumption 1**.: _[_21_]___  
1. _The heave, pitch and roll motions are neglected in path following on the horizontal plane._
2. _The vehicle is rigid body and its mass distribution is homogeneous, and the vehicle is port/starboard symmetric._
3. _The origin of body-fixed is located at the center of gravity of the vehicle._  
_The dynamics model of the underactuated MSV can be written as follows:_  
\[\left\{\begin{array}{l}m_{11}\dot{u}=\underbrace{m_{22}vr-d_{11}u}_{f_{u}}+ \tau_{wu}+\tau_{u}\\ m_{22}\dot{v}=-\underbrace{m_{11}ur-d_{22}v}_{f_{v}}+\tau_{wv}\\ m_{33}\dot{r}=\underbrace{(m_{11}-m_{22})uv-d_{33}r}_{f_{r}}+\tau_{wv}+\tau_{r} \end{array}\right. \tag{5}\]  
_where the positive constant terms \(m_{11}\), \(m_{22}\), \(m_{33}\) represent the vessel inertia including added mass. The positive constant terms \(d_{11}\), \(d_{22}\), \(d_{33}\) represent the hydrodynamic damping, which can be obtained by using system identification. Therefore, \(f_{j}(j=u,v,r)\) have the model parametric uncertainties. \(\tau_{wj}(j=u,v,r)\) are the time-varying environment disturbances. The surge force \(\tau_{u}\) and the yaw moment \(\tau_{r}\) are the control inputs. In practical engineering, because of the physical constraints of the vessel's actuators, the control force and moment can be expressed as follows:_  
\[\tau_{i}=\begin{cases}\tau_{i\max},\text{if }\tau_{ic}>\tau_{i\max}\\ \tau_{ic},\quad\text{if }\tau_{i\min}\leq\tau_{ic}\leq\tau_{i\max}\text{, }i=u,r\\ \tau_{i\min},\text{if }\tau_{ic}<\tau_{i\min}\end{cases} \tag{6}\]  
**Assumption 2**.: _The compound disturbances \(d_{j}=\tau_{wj}+f_{j}(j=u,v,r)\) and their respective derivatives are bounded, which satisfy the following inequalities: \(\left|d_{j}\right|\leq d_{j}\), \(\left|d_{j}\right|\leq C_{d_{j}}\), where \(d_{j}\) and \(C_{d_{j}}\) are positive constants._
## Materials and Methods
### Determination of Substrate Specificity  
Unless otherwise described, 42 ug\(\cdot\)mL-1 of purified EstDR4 in 50 mM Tris-HCl buffer (pH 8) was used for all testing. Substrate specificity on chain length of EstDR4 was determined at 30 \({}^{\circ}\)C using _p_NP esters with various acyl chain lengths as substrates, including _p_-nitrophenyl acetate (_p_NPC2), _p_-nitrophenyl butyrate (_p_NPC4), _p_-nitrophenyl caprylate (_p_NPC8), _p_-nitrophenyl laurate (_p_NPC12), and _p_-nitrophenyl palmitate (_p_NPC16). Esterase activity was determined by measuring hydrolysis production of _p_-nitrophenyl (_p_NP) based on spectrophotometric analysis at 410 nm. Regioselectectivity determination of EstDR4 was used 1-naphthyl acetate (_\(\alpha\)_-NA) and 2-naphthyl acetate (_\(\beta\)_-NA) as substrates, by measuring hydrolysis production of naphthol based on spectrophotometric analysis at 315 nm. Enantioselectivity determination of EstDR4 was used (R)-methyl 3-hydroxy-2-methylpropanoate ((R)-MHM) and (S)-methyl 3-hydroxy-2-methylpropanoate ((S)-MHM) as substrates with pH shift method. In addition, other substrates such as 2-methylbutyl acetate (2-MA), tert-butyl acetate (TA), linalyl acetate (LA), glyceryl tributyrate (GTB), glyceryl trioctanoate (GTO) and olive oil (OO) were also measured by pH shift method.