esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 History and Rationale of Self-Gated Cardiac MRI
The well-defined and almost constant image acquisition duration of 9 min permitted an easy integration of the sequence into a clinical protocol, just during the wait time between perfusion imaging and late enhancement imaging [8]. Despite this progress, data were still acquired ECG-triggered and thus only during a short interval of quiescence typically in mid-diastole, which is not only highly inefficient but also involves time-consuming ECG lead placement and occasional repositioning of the electrodes in case any artefactual signal overlaid to the ECG is too strong. The first problem can be solved through a continuous, uninterrupted 3D radial golden-angle data collection where the ECG is not used for triggering--but still recorded for gating during reconstruction--as demonstrated by [PERSON] et al. [9]. When applying the above-mentioned respiratory motion correction algorithm that was developed for respiratory SG [5], 3D cine frames can be reconstructed, and the duty cycle improves 15-fold. Subsequently, and to further increase image quality, [PERSON] et al. proposed the reconstruction of [PERSON]'s free-running data using their extra-dimensional golden-angle radial sparse parallel (XD-GRASP) algorithm, with which respiratory and cardiac resolved 3D data with excellent image quality could be reconstructed [10; 11]. Considering that three spatial and two temporal dimensions are involved, the technique is often referred to as 5D. Nevertheless, this prompted interest in applying compressed sensing (CS) reconstruction algorithms to free-running 3D data. Coming back to the second shortcoming mentioned above and remembering the already cited work on cardiac SG by [PERSON] et al. [4], it appears obvious that the SG signal extracted from the repetitively acquired SI profile should not only be exploited for respiratory but also cardiac motion. Considering that it is acquired approximately every 70 ms, mechanical contraction and relaxation of the heart can be resolved using this temporal resolution as no exact ECG pattern needs to be reconstructed from this signal. Spectral analysis and filtering of that signal permit the extraction of both respiratory and cardiac signature curves that, in turn, can be used to bin the acquired data according to their respiratory and cardiac position prior to a CS reconstruction. This 'total self-gating' concept was first reported by [PERSON] et al. and formed the basis for complete free-running cardiac and respiratory motion-resolved 5D whole-heart MRI [12]. Please note, however, that free-running cardiac MRI can be performed using a variety of methods, including both non-Cartesian (spiral phyllotaxis [6; 12], radial stack-of-stars [13; 14; 15], and 3D radial kooshball [16; 17; 18]) and Cartesian (MUSIC [19; 20], centric reordering [21], and CASPR [22; 23]) trajectories. In this review, we will focus on work that either originated from or expanded upon the original description of the FRF employing a 3D radial trajectory and total SG, as first reported by [PERSON] and colleagues.
# A Novel Deep Learning Method for Predicting RNA-Protein Binding Sites
## 3 Results  
### Baseline  
Numerous deep-learning methods have been applied to predict RNA-binding sites. HPNet was evaluated in this experiment and compared to the following methods:  
* GraphProt: This method uses a hypergraph to describe the RNA secondary structure fully and uses a graph kernel approach to extract features from the hypergraph. The model is then trained through machine learning based on the features to predict RNA-binding sites;* Deepnet-rbp [41]: This method added the RNA tertiary structure to the prediction model for the first time. It involves constructing a framework containing the RNA sequence, secondary structure, and tertiary structure, which represent the specificities of the RBP-binding sites in three dimensions, respectively;
* iDeppE: This method uses two CNNs to learn global and local RNA sequence features. For the global CNN, an entire sequence is padded to the same length and used directly as the input. For the local CNN, an RNA sequence is split into multiple overlapping fixed-length subsequences, and each sub-sequence is used as a separate signal channel of the full-length sequence. The two CNNs are trained independently and then combined for prediction;
* RPI-Net: This method introduces GCN and LSTM deep-learning models. The graphical representation of RNA secondary structure can be effectively learned using the GCN and message passing takes place through the LSTM. The learned graphical features are then inputted into the Bi-LSTM to learn the global embedding. An end-to-end learning method is implemented;
* DeepPN: This method consists of a CNN and a GCN, a parallel learning network. The CNN and GCN are used to learn sequence features from different perspectives to compensate for the lack of features.
###### Abstract  
This paper investigates the possibility of the application of the HArdening State Parameter (HASP) constitutive model for numerical modelling of overconsolidated soft clay under embankment loading. The HASP constitutive model is a critical state soil model with a combined hardening rule that uses a state parameter to determine the dilatancy of overconsolidated clay. The model overcomes some shortcomings of the Modified Cam Clay (MCC) model in the prediction of overconsolidated clay's behavior, while preserving the simplicity and the same set of five parameters. The HASP model was implemented in finite element software. In order to verify the applicability of the model in predicting the behavior of soft overconsolidated clay due to embankment loading, two examples reported in the literature are analyzed. The numerical predictions of the HASP model are compared with the field measurements of ground settlements and pore water pressures, and with the MCC model's predictions. The results indicate that the HASP model predicts the development of the settlements of the overconsolidated soft clay deposits with a high accuracy from an engineering point of view. There are certain deviations from the field measurements in predicting the pore pressure development, which is often observed for other models as well. For the embankment settlement assessment, as important serviceability issue, the HASP model has an advantage over more complex models that require a large number of parameters. Since the parameters of the HASP model are obtained from standard laboratory tests, it can be easily applied for routine geotechnical analyses.  
n  
numerical modelling constitutive modelling; soft clay; overconsolidated clay; HASP; PLAXIS; embankment
## 2 Background  
### Condition Indicators  
The analysis of signals can be done with several techniques and the use of signals on different domains can be useful to enrich the information obtained from a signal, leading us to a better understanding of its nature. If we are interested in quantifying some signal properties, we can use mathematics, statistics-based values or condition indicators to measure the different signal characteristics; this can help revealing the hidden information inside the signal. These condition indicators (CIs) are often called _features_. In the present work, the approach used in this feature extraction phase is a combination of different features computed from the signal in both time and frequency domains.  
In case of time domain, we used 30 statistical indicators resulting from a mixture of common features mainly used in fault diagnosis and features coming from the Electromyography (EMG) field, such as described in [9]. Additionally, we used frequency-based CIs due to the fact that the frequency domain can reveal valuable information about the change in the monitored system condition in a different shape.  
The frequency spectrum \(X(k)\) of a discrete-time signal \(x(i)\) can be computed by using the Fast Fourier Transform (FFT). The spectral analysis shows all the harmonic components of a signal, leading to a better understanding of the underlying phenomenon behavior. Another common approach to search signal characteristics in the frequency domain is the Power Spectral Density (PSD). The power spectrum of a signal \(P(k)\) is given by  
\[P(k)=\Big{(}\frac{1}{K}\Big{)}|X(k)|^{2} \tag{1}\]  
where \(X(k)\) is a previously obtained frequency spectrum and \(K\) denotes the number of points in the power spectrum. The PSD measures the average power of a signal in terms of the frequency, and it also shows periodicities [22; 23]. The knowledge about the power distribution among the frequency components contained in a signal is also useful to understand the signal nature.  
In frequency domain, we have used 24 condition indicators: 15 of them computed over the frequency spectrum and nine over the power spectrum. These 24 condition indicators are presented in Table 1; here, \(f_{k}\) is the frequency value of the spectrum in the corresponding frequency bin \(k\), whereas \(K\) denotes the total number of samples in the frequency and power spectrum.
## 4 Results
### Comparisons between GA and Proposed CBGA
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Product** & **GA (No Cluster)** & **CBGA** & **Improvement** \\ \hline L07955 & 0.618 & 0.604 & + \\ \hline L26969 & 90.532 & 72.927 & + \\ \hline L28560 & 1.890 & 1.782 & + \\ \hline L29573 & 1.010 & 0.97 & + \\ \hline S06845 & 34.77 & 40.164 & \(-\) \\ \hline S09818 & 0.529 & 0.371 & + \\ \hline S32615 & 44.613 & 40.236 & + \\ \hline S35483 & 9.436 & 9.256 & + \\ \hline S52509 & 0.771 & 0.968 & \(-\) \\ \hline S57551 & 13.313 & 11.48 & + \\ \hline S69806 & 24.180 & 24.608 & \(-\) \\ \hline S71465 & 0.236 & 0.268 & \(-\) \\ \hline S96291 & 2.7586 & 2.184 & + \\ \hline S99262 & 1881.622 & 1843.244 & + \\ \hline S09278 & No Sales Data & No Sales Data & \# \\ \hline S69806 & No Sales Data & No Sales Data & \# \\ \hline \hline \end{tabular} \(+\): There is improvement, and \(-\): There is worsening.  
\end{table} 
Table 6: Change in MSE value comparisons before clustering and after at Cluster 1 products. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Product** & **GA (No Cluster)** & **GA (Cluster Exists)** & **Improvement** \\ \hline L04259 & 0.471 & 0.349 & + \\ \hline M27306 & 0.032 & 0.025 & + \\ \hline S00363 & 0.775 & 0.675 & + \\ \hline S08812 & 0.171 & 0.112 & + \\ \hline S24285 & 103.940 & 91.473 & + \\ \hline S75341 & 0.068 & 0.059 & + \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Change of solution before and after the cluster 5 analysis. see a summary the GA and CBGA comparison graph showing the general situation. The vertical axis shows the product numbers for the relevant cluster. When Figure 8 is examined, the cluster-based genetic algorithm model, CBGA, is a method that increases success according to the MSE performance criteria. The forecast was even better in 40 out of 47 products for the 50 selected products. As there were no sales data for the other three products, a comparison could not be made.
## 3 Predictive Torque Control with Switchable Delta-Star Winding Configuration
### Considerations about Flux, Torque, and Speed Control
#### 3.1.3 Utilizable Speed Range  
The base speed, i.e., the maximum achievable rotor speed under a nominal excitation and nominal stator current, can be expressed as [27]  
\[\omega_{\rm r(base)}=\frac{U_{\rm max}}{L_{1}\sqrt{\sigma^{2}I_{\rm max}^{2}+ (1-\sigma^{2})I_{1d(\rm n)}^{2}}}. \tag{30}\]When the winding connection is switched from delta to star, the maximum achievable voltage is reduced by the factor \(\sqrt{3}\). If the flux is kept at the same level, it follows that  
\[\omega^{\prime}_{\rm r(base)}=\frac{1}{\sqrt{3}}\omega_{\rm r(base)}. \tag{31}\]  
Using the relationship between the stator flux linkage vector magnitude and the \(d\)-axis flux-producing current component (see Appendix A for derivation)  
\[i_{1d}=\frac{L_{2}}{L_{\rm m}}\sqrt{\frac{\psi_{1}^{2}-L_{1}^{2}\sigma^{2}l_{ \rm Imax}^{2}}{L_{\rm m}^{2}+2L_{1}L_{2}\sigma}}, \tag{32}\]  
it can be derived that for the preservation of the same base-speed region, the nominal \(d\)-axis flux-producing current component must be scaled by the factor  
\[k_{\rm scale}=\frac{1}{\sqrt{3}}\sqrt{1+\frac{2l_{\rm Imax}^{2}\sigma^{2}}{( \sigma^{2}-1)l_{1d(n)}^{2}}}\, \tag{33}\]  
and, consequently, the stator flux reference adjusted as  
\[\psi_{1(Y)}=\sqrt{L_{1}^{2}\sigma^{2}l_{\rm Imax}^{2}+\frac{k_{\rm scale}^{2} l_{1d(n)}^{2}(L_{\rm m}^{4}+2L_{\rm m}^{2}L_{1}L_{2}\sigma)}{L_{2}^{2}}}. \tag{34}\]  
After the enumeration of (33) and (34) using the machine parameters from the experimental setup (Appendix B), it follows that (34) can be approximated as  
\[\psi_{1(Y)}\approx\frac{1}{\sqrt{3}}\psi_{1\rm{In}(\Delta)}. \tag{35}\]  
If the machine operates in a delta connection with a nominal stator flux, then to preserve approximately the same base-speed region after a winding changeover from delta to star, the reference flux should be scaled as in (35).
# Fine-Grained Boundary Conditions in Field-Based Routing
## 3 Revisit of Field-Based Routing Protocol
### Protocol Properties  
The potential value of a mesh node is determined primarily by proximity to gateways and its traffic load level. The key properties of the protocol are from its simultaneous consideration of these two factors. These properties can be categorized into two main aspects:  
* Proximity-aware Autonomous Load-Balancing: Field-based routing autonomously achieves load balancing because of its congestion-aware nature while considering the proximity to gateways. This facilitates traffic distribution and enables packets to effectively avoid congestion under the given proximity to gateways.
* Simplicity: The protocol achieves the aforementioned proximity-aware autonomous load-balancing by maintaining a routing field through the simple exchange of local information among only one-hop neighbors.  
In the context of this paper, it is important to note that boundary conditions can significantly enhance the aforementioned advantageous characteristics, ultimately leading to improved routing performance.
# Hybrid Impedance-Admittance Control for Upper Limb Exoskeleton using Electromyography
## 6 Conclusions  
This paper presents a hybrid control system for the real-time operation of a robotic arm. The proposed system was validated and evaluated using a proof of concept with two degrees of freedom to mimic the movements of a human arm.  
The experiments performed evaluated the system's response to elbow and shoulder flexion and extension movements, with and without load. The average error of the elbow movement experiment, without load, was 2.452%. This average error increased in the experiment involving compound movements of the elbow and shoulder; however, it was less than 4.5%, even when a load was added. These results show that the proposed hybrid control model is valid, as it manages to mimic the movements of the upper limbs of a human in real-time. In addition, the evolution of methods such as this, of relative simplicity, aim to popularize the method and make its implementation cheaper, so that it can be used more widely.  
As future work, we intend to investigate how to increase the system's robustness by adding control variables for the user's posture variations. We also intend to develop a more accurate model and investigate methods to reduce the computational cost and speed up the calibration of the coefficients.  
Conceptualization, L.D.L.d.S.; methodology, L.D.L.d.S. and T.F.P.; software, L.D.L.d.S.; validation, L.D.L.d.S.; writing--original draft preparation, L.D.L.d.S. and T.F.P.; and writing--review and editing, C.A.Z., L.O.S., and V.R.Q.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior, Brasil (CAPES) (Finance Code 001), Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq) (Processes 315287/2018-7 and 436982/2018-8), Fundanacao de Amparo a Pesquisa do Estado de Santa Catarina (FAPESC) (Grant 2019 TR169), and Fundacao para a Ciencia e a Tecnologia under Project UIDB/04111/2020.  
The authors also like to acknowledge the collaboration of Computer Laboratory 7 of Instituto de Telecomunicacoes--IT Branch Coviha--Portugal.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ADC & Analog-to-Digital \\ AI & Artificial Intelligence \\ BPNN & Back Propagation Neural Network \\ CMA & Contraction for Maximum Angle \\ CAD & Computer-aided Design \\ CVA & Cerebrovascular Accident \\ CMRR & Common-mode Rejection Ratio \\ DAC & Digital-to-Analog Converter \\ EMG & Electromyography \\ ESP & Espressif \\ FSR & Force Sensitive Resistor \\ LAN & Local Area Network \\ MQTT & Message Queuing Telemetry Transport \\ MVC & Maximum Voluntary Contraction \\ PC & Personal Computer \\ PLA & Polylactic Acid \\ POC & Proof of Concept \\ RAM & Random Access Memory \\ RMSE & Root Mean Square Error \\ sEMG & Surface Electromyography \\ \end{tabular}
# Optimization Environment Definition for Beam Steering Reflectarray Antenna Design
## 3 Evolutionary Algorithms
### Biogeography Based Optimization  
Biogeography Based Optimization (BBO) is a biologically inspired algorithm developed in [31]. The main idea of this algorithm is to mimic the migrations of species in an archipelago. BBO has been used in antenna optimization, but it often leads to an early convergence: with the aim of reducing this behavior, two different modifications have been implemented and here adopted.  
The first one, called mBBO, has been presented in [32] and introduces a new operator in the algorithm, the cataclysm. It aims to renovate the entire population when the algorithm stagnates for more than 10 iterations. The second modification used in mBBO is the use of a cosine immigration and emigration probability functions.  
A second modification of the original algorithm has been tested: it is called nBBO and it introduces seasonality, i.e., an important modification of the island environment. This operator has been implemented with a Gaussian mutation with 0.01 as standard deviation.
## 3 Settlement Calculation with Error Corrections and Resampling
### MDD/GNSS Data Error Correction  
#### 3.4.1 MDD/GNSS Data Analysis by the LOESS Trend Model  
It should be emphasized that the presence of random errors and bias errors in the measurement results is noticeable, particularly in GNSS monitoring. Random errors are caused by fluctuations in measurements, while bias errors include factors like tropospheric delays and multipath effects. To ensure accurate monitoring, we adapted an error-correction method that effectively reduces both random and bias errors.  
The utilization of a trend model has shown promising results in estimating measurements and reducing random errors. The trend model, specifically the LOESS (Locally Weighted Scatterplot Smoothing) trend model, is a statistical tool capable of identifying patterns and trends in data over time [23]. We used it in this study in analyzing Multi-Depth Deflectometer (MDD) and GNSS measurement data for slab tracks to aid in the detection of deformation changes. The LOESS trend model utilizes a locally weighted average to fit a smooth curve to the data, making it advantageous for handling non-linear relationships and outliers. It is a flexible approach for capturing underlying patterns in the data and exploring variable relationships [24]. The LOESS trend model was employed to smooth time series data from GNSS and MDD vertical displacement measurements in railway tracks, utilizing similar calculation equations as the general LOESS trend model for time series data smoothing. The LOESS regression model is defined as follows [23]:  
\[\dot{\hat{y}}=f(x,t)=\sum w(i,t)y_{i}, \tag{7}\]  
where \(\hat{y}\) is the predicted value of the response variable (vertical displacement), \(x\) is the predictor variable, t is the time index, \(y_{i}\) is the observed value of the response variable, and \(w(i,t)\) is the weight assigned to the \(i\)th data point at time \(t\).
# Deep Reinforcement Learning-Based Deployment Method for Emergency Communication Network
## 4 Proposed DRL-Based Method
### Design of State, Action and Reward
#### 4.3.2 Action  
In this problem, each selected deployment location for the current element is defined as an action \(a_{t}\). Specifically, the deployable area is divided into an \(n\times n\) grid, where each cell corresponds to a distinct action. Consequently, the action space \(A\) contains \(n\times n\) discrete actions \(a_{t}\). The agent processes the current state \(S_{t}\) through a value network and outputs a Q value matrix \(Q_{w}(S_{t},a)\), which quantifies the expected cumulative reward for selecting action \(a_{t}\) under state \(S_{t}\). During decision-making, a greedy strategy is employed to select the optimal action corresponding to the maximum Q value in the matrix:  
\[a_{t}=\underset{A}{argmax}Q_{w}(S_{t},a) \tag{6}\]
# Education Mobility as a Service: A Study of the Features of a Novel Mobility Platform
## 2 Review of New Technologies in Education and Lifelong Learning
### Videoconferencing Systems  
These tools can be a great help in creating visual instructions, which are extremely important when designing the didactic process. These so-called LCSs (learning communication systems) are most often used for real-time communication, i.e., with live classes (text and voice contact with the possibility of video transmission, sharing documents, virtual board/desktop, saving recordings from sessions/videoconferences). It is worth noting that an alternative name for these systems is the so-called virtual classroom system (VCS). A LCS is often a component module of LMS and LCMS platforms. Tools that allow a course participant to communicate with lecturers or other course participants or edit graphic or text files (in most popular formats) on a shared virtual board are helpful when conducting webinars or employing group work methods in the form of discussions (brainstorming). An example of an LCS is Adobe Connect or Microsoft Teams. Both of these tools allow educators to:  
* conduct classes for any number of recipients,
* participate in courses without traveling,
* teach classes without traveling,
* participate in courses without leaving home (important for sick and disabled people),
* run facilitation classes thanks to a keyboard server with additional software.  
A joint virtual meeting of many participants connecting using various devices and videoconferencing systems is also possible using other more or less recognized systems such as Skype or TrueConf. Tools for remote meetings using videoconferencing terminals can also take the form of a \"cloud\" service.