esa-sceva/satcom-chunk-collection huggingface dataset:
# Feasibility Study on Earthquake Prediction Based on Imending Geomagnetic Anomalies  
[PERSON]  
1 Institute of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China, [EMAIL_ADDRESS] (Y.H.)  
[PERSON]  
2 School of Geophysics and Geomatics, China  
the smallest geomagnetism showed a significant enhancement change from 20 April to 10 June 2013, exceeding the threshold, and the anomaly continued for about 50 days [6] A typical example is the VAN method (VAN is the abbreviation of three Greek scientists, [PERSON], [PERSON] and [PERSON]) [7] This technology was been developed in Greece in the early 1980s and has also been applied in Japan since the 1990s. The seismic electrical signal in the VAN method is the transient DC ground potential change observed before earthquakes by burying the dipole of the electrode. From both the experimental and theoretical perspectives, the VAN method is by far the best-equipped method in this category. However, in modern society, artificial magnetic interference from electrical equipment seriously affects the accuracy of the VAN method. In addition, its prediction time exceeds one week, reaching several weeks, which is still too long to be practical [8].  
The mechanism of geomagnetic anomalies before and after an earthquake is still in being researched. However, some articles have verified using experimental observation that squeezing, friction, impact and the accompanying rock fracturing process will produce electromagnetic anomalies, with frequencies ranging from 0 Hz to 10 kHz [5]. Before the earthquake, compression, friction, impact and rock fracturing processes will be formed, which can explain the rationality of geomagnetic anomalies before and after the earthquake.  
From the cases observed in the above literature, it can be seen that they are all cases of geomagnetic anomalies observed before large earthquakes, and there are no cases of small earthquakes. Most of the articles describe only one observation of geomagnetic anomalies before an earthquake, rather than consecutive occurrences, and most of the literature is post-earthquake data analysis. These articles can only explain that the geomagnetic anomaly caused by the earthquake is occasionally observed, or the data are found inversely after the earthquake. But can it be observed before every earthquake? This paper, through a large number of observations of anomalies using the intensive observation method, proposes an independent and innovative algorithm to capture the data for geomagnetic anomalies before earthquakes. It shows that the geomagnetic anomalies caused by earthquakes can be observed before earthquakes with high probability, and can be used for earthquake prediction.
## 3 Wireless Communication Networks
### Synchronization  
It is the process where the WDs define the alignment of transmission parameters, which allows the establishment of communication links, i.e., to define the information necessary to ensure the hookup and linkup among WDs. This process plays an important role given that it has impact in both physical and higher levels.
# FL-SDUAN: A Fuzzy Logic-Based Routing Scheme for Software-Defined Underwater Acoustic Networks
## 2 Related Work  
### Software-Defined Routing Technologies in UANs  
Many recent studies have focused on SDN-based routing techniques for underwater acoustic networks. [PERSON] et al. [6] introduced Software, a software-defined underwater communication system that supports various applications and enables the interoperability of devices from different manufacturers. [PERSON] et al. [7] developed a software-defined platform in hydroacoustic networks with runtime configuration. The platform worked well while maintaining flexibility. [PERSON] et al. [8] proposed a routing system that relies on software-defined underwater nodes. Each DN obtained a prediction of the communication quality of a potential link with a one-hop neighbor by receiving beacon signals from the aggregation node. [PERSON] et al. [9] proposed an SDN-based routing technique to improve the quality of service (QoS) in the underwater Internet of Things (IoT). The experimental results showed that this method significantly outperformed other non-SDN methods concerning the packet loss rate, end-to-end latency, and energy consumption. [PERSON] et al. [10] presented an SDN-based routing testbed for hydroacoustic networks with a one-hop RF channel in the control plane and a hydroacoustic channel as the data plane.  
[PERSON] et al. [11] proposed a software-defined routing protocol that supports deep learning in hydroacoustic networks. Simulation experiments showed that the survival time of the protocol improved by 15% compared to conventional techniques. [PERSON] et al. [12] modeled the underwater multimodal routing problem as an optimization problem and solved it using a convex relaxation method in the full duplex case. [PERSON] et al. [13] employed the paradigm of SDN technology and proposed an SDN-based underwater cooperative searching framework for AUV-based UWNs. [PERSON] et al. [14] suggested a software-defined clustering mechanism that achieved a trade-off between multiple constraints and improved the performance of hydroacoustic networks. [PERSON] et al. [15] proposed an SDN-based hydroacoustic network paradigm and introduced a clustering method through an improved Birch algorithm. [PERSON] et al. [16] proposed an SDN-based delay-sensitive routing algorithm in hydroacoustic networks. The simulation results showed that the method could accurately reflect the spatio-temporal characteristics of the network state. [PERSON] et al. [17] recommended an SDN-based routing protocol for underwater acoustic networks. A central controller on the surface buoy provided a global view, while a local controller on the autonomous underwater vehicle (AUV) offered a local view.
# Antimicrobial Peptides--Mechanisms of Action, Antimicrobial Effects and Clinical Applications
## 3 Antimicrobial Effects of AMPs
### Antiviral Activity of AMPs  
Due to the increasing resistance of viruses and the limited effect of common drugs, antiviral peptides play an essential role as therapeutic agents [43]. AMPs with antiviral activity are referred to as antiviral peptides (AVPs) [53; 54]. There are several different mechanisms by which antiviral peptides block viruses at various stages of their cycle [34]. Some AVPs can destabilize the viral membrane or prevent infection by neutralizing the virus by integrating it into the viral envelope and cell membrane [2; 55]. Similarly, they can bind to viral glycoproteins, after which the viruses can no longer bind to the host cell surface [2; 56], while some bind to host cell receptors, preventing the virus from binding to its target receptor and thus inclavitating it (e.g., heparan for herpes simplex virus). Some block viral attachment and prevent membrane fusion in the virus cell (e.g., influenza virus) [55; 57]. An essential mechanism of action of AVP on the influenza virus is the regulation of the human immune system with the increased expression of cytokines and chemokines on the antigen complex, activation of cells of the immune system, and inactivation of viral pathogens (e.g., H1N1 virus) [58; 59; 60]. In addition, AVP blocks its activity by acting on the replication cycle [61]. AVP shows similar effects to those described for influenza virus in inhibiting human immunodeficiency virus 1 (HIV-1), herpes simplex virus 1 (HSV-1), herpes simplex virus 2 (HSV-2), hepatitis B virus (HBV), and hepatitis C virus (HCV) infections [61]. The target site of AVPs can be DNA and RNA [62; 63], and the goal of their action is to destroy the viral envelope, leading to its instability, as in the Junin virus (JV), HIV-1, and HSV-2 [64]. Another way to inhibit viruses is to modify or interfere with cell signaling pathways [65], such as GF-17 (17-mer-derived peptide from human cathelicidin LL-37) and BMAP-18 (bovine myeloid antimicrobial peptide-18), which can inactivate the Zika virus (ZIKV) by interfering with interferon type 1 signaling [54]. Recently, there has been an increasing number of studies on the antiviral effect of AVPs on coronaviruses (such as SARS-CoV). In short, the current dogma of this type of research endeavors is based on AVP's inhibitory effect on the viral cell membrane and its inability to enter the host cell [66; 67; 68; 69].
# Robot Operating System 2 (ROS2)-Based Frameworks for Increasing Robot Autonomy: A Survey
## 3 General Architecture for Enhancing Cobot Autonomy
### Motivation  
The reason behind the need for such an architecture is that the inherent characteristics of cobots alone are not yet sufficient to perform complex tasks in dynamic industrial scenarios that require a higher degree of autonomy. This is because industrial collaborative robots are currently on the market equipped with few exteroceptive sensors, and as a result, they cannot fully perceive the external environment and thus cannot be used at their full potential. These limitations are also due to the closed interfaces with limited functionality that most cobot manufacturers provide for users to control and program their products. They typically make available both a GUI (graphical user interface), very intuitive and user friendly but with standard predefined and non-customizable functionalities, and APIs (application program interfaces), specifically provided for further user-customized functionalities but to be properly integrated into the user application.  
To better understand the limitations of the cobots currently on the market, the following is a brief overview of the main sensors they are equipped with and the typically available functionality enabled by them:  
* Torque and force sensors: These are the sensors that allow collision detection and subsequent emergency stop of the robot. Typically, the GUI provided by commercial cobot manufacturers allows users to set safety stop criteria in terms of maximum values of parameters such as TCP (tool center point) force and joint torques. In this way, along with speed limitations, collisions are not anticipated and avoided, but they are made not dangerous. To handle them differently (e.g., with trajectory re-planning), different sensor types such as vision systems or distance sensors are needed.
* RGB camera: Most collaborative robots are also on the market with models equipped with an RGB camera mounted on the end of the manipulator (i.e., eye-in-hand arrangement). The integration of this vision system makes it possible to teach the robotic arm to recognize and grasp specific, identical objects from the same plane. Since depth information is not available, it is necessary to manually reprogram the robot if the objects to be grasped or the plane on which they are located changes. Again, the use of different types of sensors (e.g., depth cameras) could overcome these limitations.  
As a concrete example of the aforementioned, Figure 1 shows sensors integrated into some commercially available collaborative robots, from which it emerges that, while sensor types remain consistent, manufacturers can make different choices regarding which and how many sensors to integrate into their individual products.
# Engineering of Ti:Sapphire Lasers for Dermatology and Aesthetic Medicine
## 2 Experimental Results and Discussion
### The Characteristics of Short Cavity Ti:Sapphire Gain-Switched Laser under Nanosecond Pumping
DD has diffraction efficiency \(\sim\)75%, which led to a reduction in pump efficiency in comparison to MLA. Due to this, we selected MLA as a main component for homogenization in our laser devices. We can conclude that subnanosecond pulses with durations of less than 500 ps and energy \(\geq\)300 mJ can be generated by the simple gain-switched Ti:Sapphire laser with a short (several millimeters) laser cavity when pumping is produced by a Q-switched laser with a pulse duration \(\sim\)4 ns or less, with the appropriate homogenization of pumping spot. Based on these results, we developed a TR device, HELIOS-4, which combines the modified HELIOS-3 and the Ti:Sapphire laser module. A part of the HELIOS-4 laser head, with the Ti:Sapphire module, is shown in Figure 8. Pump radiation enters the module from the right side. \begin{table}
\begin{tabular}{c c c} \hline
1 & Maximum pulse energy & mJ & 300 \\ \hline
2 & Pulse energy stability & \% & 2 \\ \hline
3 & Minimum pulse duration & ps & 460 \\ \hline
4 & Pulse duration stability & \% & 1 \\ \hline
5 & Maximum pulse repetition rate & Hz & 10 \\ \hline
6 & Maximum energy efficiency & \% & 43 \\ \hline
7 & Central wavelength & nm & 785 \\ \hline
8 & Spectral width & nm & \textless{}20 \\ \hline
9 & Laser beam profile & & Flat-top \\ \hline \end{tabular}
\end{table} 
Table 2: Characteristics of Ti:Sapphire laser generation. Reprinted with permission from ref. [17]. Copyright 2019 © The Optical Society. Figure 7: Dependence of E on EPUMP: (**a**)—pump spot 3.4 \(\times\) 3.4 mm, L = 7.5 mm; (**b**)—pump spot 5.6 \(\times\) 5.6 mm, L = 7.2 mm. Reprinted with permission from ref. [17]. Copyright 2019 © The Optical Society.
###### Abstract
The lateral recirculation zone (dead zone) acts as a surface storage zone, which induces anomalously long residence times of solutes via trapping effects, then drive the non-Fickian tailing behaviors [34; 35]. Thus, it is critical to accurately predict the onset and size of the flow separation in meander bends for successfully capturing the non-Fickian transport signatures in riverine environments. As aforementioned, the prediction of the flow separation is highly sensitive to turbulence models. The \(k-\varepsilon\) model has popularly been adopted for simulations of solute mixing in curved channels [16; 36; 37], whereas the impact of turbulence models on solute transport is rarely assessed. Furthermore, the non-Fickian mixing properties associated with the horizontal flow recirculation within the meander bend have not been deeply discussed with resepct to turbulence models and channel sinuosity. The present work sheds light on the impact of turbulence closure models on solute transport and the non-Fickian mixing behaviors in meandering channels having different sinuosity levels. First, 3D flow characteristics in meandering open channels are reproduced using the 3D RANS model incorporated with a variety of turbulence closure schemes including the \(k-\varepsilon\), \(k-\omega\) and SST \(k-\omega\) models. The flow simulation results are compared by changing meander geometry to scrutinize the influence of the turbulence models on the secondary flow and flow separation structures in channels across a diverse range of sinuosity. With the 3D flow fields obtained from the three different turbulence models, the solute transport is then simulated adopting the 3D Lagrangian particle-tracking (LPT) algorithm. This study eventually aims to unravel how the turbulence models influence the solute transport simulations in meandering open-channel flows taking into account their performance in reproducing the meander-driven flow features, especially the separated flow recirculation.
# Direct Multistep Wind Speed Forecasting Using LSTM Neural Network Combining EEMD and Fuzzy Entropy
## 5 Case Study
### Error Evaluation Index  
The selection of evaluation indexes has an important impact on the assessment of wind speed forecasting methods. The evaluation indexes selected in this paper are _MAE_, _RMSE_ and Mean Absolute Percentage Error (_MAPE_). The calculation formulas of _MAE_ and _RMSE_ are given by Formulas (9) and (10), and the formula for _MAPE_ is as follows:  
\[MAPE=\frac{1}{N}\sum_{i=1}^{N}\frac{\left|v_{i}-v_{pi}\right|}{v_{i}} \tag{11}\]  
Figure 5: The variety law of _MAE_ and _RMSE_ with correlation length: (**a**) _MAE_; (**b**) _RMSE_.
# Self-Gated Respiratory Motion Rejection for Optoacoustic Tomography
## 2 Materials and Methods
### Spiral Volumetric Optoacoustic Tomography  
The spiral volumetric optoacoustic tomography (SVOT) scanner is schematically depicted in Figure 1a (top). A detailed description of the system is available elsewhere [32]. Briefly, a spherical ultrasound array of piezocomposite elements (Imasonics SaS, Voray, France) is mounted on motorized rotating and translating stages and scanned around the animal following a spiral trajectory. The array consists of 256 elements with a central frequency of 4 MHz and \(-6\) dB bandwidth of \(\sim\)100%, arranged in a hemispherical surface with angular coverage of 90\({}^{\circ}\). The excitation light beam is guided via a fiber bundle (CeramOptec GmbH, Bonn, Germany) through a cylindrical aperture in the center of the sphere. SVOT enables imaging of the entire mouse with a nearly isotropic 3D spatial resolution in the 200 \(\upmu\)m range [31]. In the experiments, light excitation was provided with a short-pulsed laser (\(<\)10 ns duration pulses with 25 mJ per-pulse energy and up to 100 Hz pulse repetition frequency) based on an optical parametric oscillator (OPO) crystal (Innolas GmbH, Krailling, Germany). The pulse repetition frequency of the laser was set to 25 Hz and the wavelength was maintained at 800 nm, corresponding to the isosbestic point of hemoglobin. The array was scanned for 17 angular positions separated by 15\({}^{\circ}\) (total angular coverage in the azimuthal direction of 240\({}^{\circ}\)) and for 30 vertical positions separated by 2 mm (total scanning length of 58 mm, a full-body scan requires approximately 10 min). 50 frames were captured for each position of the array, for which all signals were simultaneously digitized at 40 megasamples per second with a custom-made data acquisition system (DAQ, Falkenstein  
Figure 1: A schematic diagram of the steps involved in the motion rejection algorithm. (**A**) Two- and three-dimensional scanning systems, (top) spiral volumetric optoacoustic tomography (SVOT) based on a spherical array of transducers and (bottom) cross-sectional optoacoustic tomography based on a full-ring array of cylindrically focused transducers. (**B**) Sequence of frames (sinograms) acquired at a single position of the scanner. (**C**) Rearrangement of the data corresponding to the entire sequence into a single matrix. (**D**) Correlation coefficients of the autocorrelation matrix of the columns in (**C**). (**E**) K-means clustering of the correlation coefficients matrix into two groups, namely, selected (static) frames and rejected (motion) frames. (**F**) Volumetric image of a blood vessel reconstructed with data from the selected versus the rejected-frames.  
Mikrosysteme GmbH, Taufkirchen, Germany) triggered with the Q-switch output of the laser. The acquired data was eventually transmitted to a PC via Ethernet.
# Double-Consensus Based Distributed Optimal Energy Management for Multiple Energy Hubs
###### Abstract  
This paper presents a novel distributed double-consensus algorithm to solve the optimal energy management problem for multiple energy hubs interconnected with each other. The objective is achieved by establishing two interactive and paralleled consensus procedures modified by their corresponding feedback terms. Meanwhile, a novel projection operation method is proposed to map the infeasible values into the feasible operating region. The proposed algorithm can effectively handle the coupled variables problem existing in the objective function and constraint limits. Moreover, the optimality and convergence analysis are performed strictly under strong connectivity conditions only. Simulations performed on standard test cases are provided to illustrate the effectiveness of the proposed distributed algorithm.  
Keywords:consensus; energy management; distributed algorithm; energy hub +
Footnote †: journal: Applied Sciences
## 4 Case Studies of Real Wind Power Data
### Experimental Results  
#### 4.2.1 10 min Ahead Forecast  
In Tables 1 and 2, we compare the 10 min ahead forecast under different models for the whole year data and ramp events in the year 2010, respectively. The forecast results in terms of the MAE and RMSE are normalized using the nominal capacity of 300.5 MW of the wind farm.  
From Tables 1 and 2, we observe that the proposed approach (DSN) outperforms the benchmarks. Compared with the non-NN-based benchmarks (the AR, MC, and SVM-MC), the proposed approach improves the MAE at least 24.9% for the whole year data and at least 13.8% for the ramp events, respectively. Compared with the NN-based benchmarks, the improvement in the proposed approach (DSN) in terms of the MAE is at least 2.5% for the whole year and at least 1.3% for the ramp events. Such improvements are because of the splitting of the non-ramp and ramp events, which enables the DSN to more effectively learn the different dynamics of the GE and Mitsubishi turbines measurements under non-ramp and ramp events.  
Figures 10-12 illustrate the prediction intervals for the three representative ramp events. The first chosen event is 5 January 2010 because there is a wind power ramp-up event from 4 a.m. to 5 a.m. with a ramp-up rate of 85 Megawatts per hour (MW/H). The second chosen event is 19 March 2010 because of the significant wind power fluctuation from 7 p.m. to 9 p.m. with both ramp-up and ramp-down events of an average ramp rate around 100 MW/H. The final chosen event is 9 October 2010 because of a remarkable ramp-down event from 3 a.m. to 5 a.m. with an average ramp rate of 66.5 MW/H. As demonstrated in those pictures, the actual wind farm generation is mostly confined in the prediction interval achieved from (19), regardless of the sharp ramps.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Error** & **AR** & **MC** & **SVM-MC** & **NEAT** & **SNEAT** & **LSTM** & **SSEN** & **ANN** & **DSN** \\ \hline MAE(\%) & 2.441 & 2.413 & 2.214 & 1.734 & 1.778 & 1.799 & 1.704 & 1.826 & **1.661** \\ RMSE(\%) & 3.974 & 3.524 & 3.342 & 3.030 & 3.074 & 3.072 & 3.023 & **2.993** & 2.996 \\ \hline \end{tabular}
\end{table}
Table 1: Forecast under different models over the whole year 2010.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Error** & **AR** & **MC** & **SVM-MC** & **NEAT** & **SNEAT** & **LSTM** & **SSEN** & **ANN** & **DSN** \\ \hline MAE(\%) & 2.945 & 2.856 & 2.657 & 2.363 & 2.416 & 2.469 & 2.320 & 2.426 & **2.288** \\ RMSE(\%) & 4.403 & 3.837 & 3.654 & 3.593 & 3.667 & 3.679 & 3.534 & 3.580 & **3.518** \\ \hline \end{tabular}
\end{table}
Table 2: Forecast under different models over all ramps of the year 2010.