esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
For example, high VAF by a limited number of synergies represents decreased complexity of motor control, which is often associated with neuromuscular pathologies such as cerebral palsy and stroke [6; 27; 29] and characterized by increased levels of co-activation between individual muscles [30]. However, healthy aging is associated with a more gradual process of physical decline and thus changes in complexity of motor control may not manifest in a decreased number of synergies [8]. Consequently, age-related changes in motor control may be better reflected by comparisons of VAF for a fixed number of synergies [29; 31; 32] or by assessing changes in spatio-temporal organization of muscle synergies, such as altered module composition and shifts in activation patterns [2; 6]. Differences in spatio-temporal organization within a stable number of synergies can arguably be considered more subtle than differences in the total number of synergies as they tend to reflect compensatory or alternative motor strategies in order to overcome increased or altered task challenges, whereas a decreased number of synergies is usually used as an indication of neural impairments. Although it is currently unknown how aging and task intensity affect muscle synergy organization during stair ascent, analyses of underlying differences could provide a basis to improve detection of pre-clinical age-related deterioration in neuromuscular control and more effectively target fall prevention programs at individuals most at risk. Therefore, the purpose of this study was to explore muscle synergy recruitment during step ascent in forward and lateral directions and with incremental step heights in young and older adults. Specifically, we aimed to assess the effects of age-related changes, task intensity, and their interaction on complexity and organization of motor control by comparing the number of extracted synergies, variance accounted for (VAF) by a fixed number of synergies, and spatio-temporal characteristics of the extracted synergies across conditions.
# Time-Domain Sound Field Reproduction with Pressure and Particle Velocity Jointly Controlled
## 2 Review: Frequency-Domain Velocity-Assisted Sound Field Reproduction
### EVD-Based Approach with Conjugate Gradient Algorithm  
While the joint control of sound pressure and particle velocity for sound field reproduction is especially suited to non-uniform loudspeaker array setup with reduce number of loudspeakers and control points required, the proposed time-domain reproduction method has the potential to be used in real-time applications. However, for long-length RIRs and control filters, the inverse solution in (16) requires very high computational complexity. To solve this problem, the eigenvalue decomposition (EVD) based approach is adopted with the conjugate gradient (CG) method to search for the optimal solution in an iterative manner.  
In (19), the matrix \(\mathbf{R}\) is a symmetric positive definite matrix of size \(LJ\times LJ\), where \(L\) is the number of loudspeaker used for reproduction and \(J\) is the length of the control filter for each loudspeaker. Solving the problem with the direct inverse operation requires \(\mathcal{O}((LJ)^{3})\) operations. Instead, we assume that the space spanned by the spatial autocorrelation matrix \(\mathbf{R}\) can be approximated by its \(I\) dominant eigen vectors, where \(I\leq LJ\). Then, the CG method, which searches the solution in a set of orthogonal directions, can be used to find the solution iterative. The CG method adopted in this work has the advantage of reducing the computational complexity to \(\mathcal{O}(I(LJ)^{2})\) by setting the dimension of search direction as \(I\). The flow of the algorithm is summarized in Table 1.
# Enhancing Data Integrity in Blockchain Oracles Through Multi-Label Analysis
## 3 Methods
### Characteristics and Variables Data of Coffee Futures Contracts
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Variable** & **Type of Variable** & **Values** & **Reference Phase** \\ \hline Altitude site & Numerical variable & 1200, 1800 (meters above sea level) & Production phase \\ Temperature site & Numerical variable & 20, 25, 30 (degrees Celsius) & Production phase \\ Humidity site & Numerical variable & 60, 65, 70, 75 (\% relative humidity) & Production phase \\ Precipitation site & Numerical variable & 80, 100, 120, 150 (mm/month) & Production phase \\ Variety & Categorical variable & Arabica, Robusta & Production phase \\ Phosphorus (P) & Numerical variable & 20, 30, 40, 50 (mg/kg soil) & Production phase \\ Potassium (K) & Numerical variable & 150, 200, 250 (mg/kg soil) & Production phase \\ Calcium (Ca) & Numerical variable & 2, 3, 4 (mg/kg soil) & Production phase \\ Magnesium (Mg) & Numerical variable & 1, 1.5, 2 (mg/kg soil) & Production phase \\ Drip irrigation & Categorical variable & Yes, No & Production phase \\ Pest control & Categorical variable & Yes, No & Production phase \\ Nitrogen (N) & Numerical variable & 40, 50, 60 (mg/kg soil) & Production phase \\ Phosphorus (P) (fertil usage) & Numerical variable & 30, 40, 50 (kg/ha) & Production phase \\ Potassium (K) (fertil usage) & Numerical variable & 20, 200, 220 (kg/ha) & Production phase \\ Calcium (Ge) (fertil usage) & Numerical variable & 2, 3, 3.5 (kg/ha) & Production phase \\ Magnesium (Mg) (fertil usage) & Numerical variable & 1, 1.5, 2 (kg/ha) & Production phase \\ Pulping & Categorical variable & Mechanical, Manual & Post-harvest phase \\ Fermentation & Numerical variable & 24, 36, 48 (hours) & Post-harvest phase \\ Washing and drying & Categorical variable & Washed, Natural & Post-harvest phase \\ Roasting & Categorical variable & Light, Medium, Dark & Processing phase \\ Cup Score & Numerical variable & 80, 85, 90, 95 (points) & Marketing phase \\ Coffee Load Price & Numerical variable & 3000, 3500, 4000 (COP per load) & Marketing phase \\ Price USD to COP & Numerical variable & 3500, 3550, 3600 (COP per USD) & Marketing phase \\ Contract type & Categorical variable & Direct sale, Export, Cooperative & Marketing phase \\ Packaging & Categorical variable & Vacuum, Paper bags, Glass & Marketing phase \\ Storage & Numerical variable & 15, 20, 25 (degrees Celsius) & Transformation phase \\ Distribution & Categorical variable & Direct sale, Export, Cooperative & Marketing phase \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Variables for understanding coffee futures contracts. Table 2 details these aspects for the key variables, providing a structured approach to addressing the challenges related to data integrity.
## 4 Queuing Model  
Figure 2 presents a theory-based queuing model for the specified architecture. The term \"theory-based\" refers to Queue Theory, which was used as the basis for the model development. As aforementioned, the JMT tool was used, which allows the development of models based on queueing theory. In the model, there are two entry points and two  
Figure 1: Scenario illustration of a health monitoring system for elderly people.  
departure points. There are also five lineups with related services and four transfer times displayed. Table 1 lists all the model's components.  
The recommended model shows the service time for each component. The data flows from left to right. Within a preset period, sensors generate requests based on a probabilistic distribution. These requests need a service time based on the time it takes to collect and transport data from the sensors when they arrive at the gateway. Transmission 1 (T01) is a component that simulates a network delay by delaying the transmission of a request. It lacks a clearly defined service. The data is sent from the gateway to the router, determining whether it should be sent to the fog or the cloud. Cloud and fog service time refer to the time it takes to save data and communicate them to other analytic services so that warning signals may be sent out. While the fog is closer to the sensors, the cloud is often stronger. After processing in the cloud/fog, the model contains two more connections, transmissions 03 and 04, used in the model. Both the cloud and the fog can send information or alerts to a family member or in the event of an emergency. Following the end of the data processing loop, there are two options for exit (Exit 1 and Exit 2). Exit 1 indicates that data processing is complete before reaching the Family Member. When an emergency occurs, Exit 2 signals the end of data processing.  
The arrival rate \(\lambda_{i}\) assumes that the times between the arrivals of each service in the system are independent and exponentially scattered. A FIFO (First-In, First-Out) queueing discipline is expected to be used to manage health data received in each component of the overall system. To replicate the Gateway, Cloud, Family Member, and Emergency, the M/M/c/K queue model is employed. The M/M/c/K model refers to the c service stations (servers). Each service station has a maximum capacity of (K) nodes. Finally, the concept may be used by anybody who requires realtime monitoring, not only the elderly.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
\begin{tabular}{c} Represents the end of data processing in the \\ Represents the end of data processing in Emergency. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the model components.  
Figure 2: Analytical model to assess a health monitoring scenario.
# A Novel Multi-Task Performance Prediction Model for Spark
###### Abstract  
Performance prediction of Spark plays a vital role in cluster resource management and system efficiency improvement. The performance of Spark is affected by several variables, such as the size of the input data, the computational power of the system, and the complexity of the algorithm. At the same time, less research has focused on multi-task performance prediction models for Spark. To address these challenges, we propose a multi-task Spark performance prediction model. The model integrates a multi-head attention mechanism and a convolutional neural network. It implements the prediction of execution times for single or multiple Spark applications. Firstly, the data are dimensionally reduced by a dimensionality reduction algorithm and fed into the model. Secondly, the model integrates a multi-head attention mechanism and a convolutional neural network. It captures complex relationships between data features and uses these features for Spark performance prediction. Finally, we use residual connections to prevent overfitting. To validate the performance of the model, we conducted experiments on four Spark benchmark applications. Compared to the benchmark prediction model, our model obtains better performance metrics. In addition, our model predicts multiple Spark benchmark applications simultaneously and maintains deviations within permissible limits. It provides a novel way for the assessment and optimization of Spark.  
spark performance prediction; multi-head attention mechanism; convolutional neural network; prediction model +
Footnote †: journal: Applied sciences
## 3 Data and Data Processing
### Groundwater-Level Data  
Groundwater data were obtained from a single well named 52258, which is located in the center of the study area (Figure 1). The well measurements were provided from the General Directorate of State Hydraulic Works and provide daily water level values for the time period of 2014-2018.
# Data-Decoupled Scattering Imaging Method Based on Autocorrelation Enhancement
## 3 Results and Analysis
### Model Training  
The experiment uses 6000 datasets of the basic elements collected using the above optical path design, which is pre-processed by autocorrelation to obtain the training set of the model. The simultaneously collected Fashion-MNIST was used as the test set, which totalled 2000 sets. The speckle recovery algorithms designed in this paper were all run on a computing platform based on the Pytorch deep-learning framework with CPU i7-8700 and GPU RTX 2080 Ti, accelerated by Pytorch 1.9 and CUDA10.1. The Epoch was set to 100, and the loss curves during model training are shown in Figure 8.  
Figure 8: Loss–function curve.  
Figure 7: Experimental data representation and display of autocorrelation reprocessing results. GT is the truth value of the target, Speckle refers to the target speckle images, GT_corr represents the truth value autocorrelation, 220 Spec_corr is the speckle autocorrelation under 220-mesh frosted glass, and 600 Spec_corr is the speckle autocorrelation under 600-mesh frosted glass.  
From Figure 8, it can be seen that after 40 iterations, the loss curves of the training and validation sets tend to converge.
# A Novel Spectrum Scheduling Scheme with Ant Colony Optimization Algorithm  
[PERSON]  
1 School of Software, Central South University, Changsha 410075, China; [EMAIL_ADDRESS] (L.L.); [EMAIL_ADDRESS] (Z.C.); [EMAIL_ADDRESS] (L.G.)  
[PERSON]  
Correspondence: [EMAIL_ADDRESS]; Tel.: +86-152-7310-835411 These authors contributed equally to this work.1 School of Software, Central South University, Changsha 410075, China; [EMAIL_ADDRESS] (L.L.); [EMAIL_ADDRESS] (Z.C.); [EMAIL_ADDRESS] (L.G.)  
[PERSON]  
1 School of Software, Central South University, Changsha 410075, China; [EMAIL_ADDRESS] (L.L.); [EMAIL_ADDRESS] (Z.C.); [EMAIL_ADDRESS] (L.G.)  
[PERSON]  
1 School of Software, Central South University, Changsha 410075, China; [EMAIL_ADDRESS] (L.L.); [EMAIL_ADDRESS] (Z.C.); [EMAIL_ADDRESS] (L.G.)  
are treated as two partite sets and an allocation scheme can be seen as a matching of the corresponding bipartite graph. A mathematical cross-layered model for the cognitive radio network link scheduling problem under the interference temperature model is presented in [7], which maximizes the number of scheduled links within a time frame while satisfying interference temperature constraints. In [8], [PERSON] integrates an auction model and Stackelberg game theory to deal with the different stage issues for spectrum sharing in CR networks. Game theory is adopted in [9] to reduce power waste caused by some cognitive users' SINR over the target value. To maximize system utility in this paper, we perform spectrum allocation based on graph theory. However, the graph-theory-based allocation issue is a nondeterministic polynomial (NP) problem whose solution can be found in polynomial time on a non-deterministic machine. Intelligent optimization algorithms are effective methods to find a close-to-optimal solution for NP-hard issues.  
In this paper, we adopt the ant colony optimization algorithm (ACO) for available licensed spectrum allocation. ACO [10] was first introduced by Dorigo in 1992, which is a popular means of dealing with assignment issues. The basic idea of ACO comes from the natural phenomena that ants can find the shortest path between nest and food efficiently due to their positive feedback attribute [11]. However, ACO is prone to being premature and stagnate when the problem scale is too big. These flaws would certainly drag the effectiveness of the algorithm down [12]. Based on the above, we design a novel spectrum allocation algorithm (IACO) based on ACO, which introduces the differential evolution (DE) [13] process to accelerates convergence speed by the monitoring mechanism, in addition, we employ a variable neighborhood search (VNS) process [14] to avoid falling into the local optimum. We can see from the simulation results that either the system utility or the other performance of IACO are greatly promoted.
# Domain Adaptation Speech-to-Text for Low-Resource European Portuguese Using Deep Learning
## 4 Proposed System
### Architecture  
A pipeline was assembled to create models for transcribing Portuguese speech into the respective transcription using deep learning to facilitate connections between each necessary stage of this process. Figure 1 illustrates the pipeline built, whose stages are described below:  
* Dataset Acquisition--explore and acquire new speech datasets in Portuguese (audio recording files and respective transcriptions);
* Content Analysis--dataset analysis, assessment of the dataset's initial structure, documentation, content quantity and quality, and audio file encodings;
* Pre-Processing--dataset restructuring, data pre-processing, and manifest creation;
* Model Creation--model creation with the pre-processed data;
* Results Evaluation--testing and evaluation of the created model.  
Figure 1: System pipeline and toolkit.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Quality Label** & **Hours** \\ \hline OK & 152.99 \\ NOISE & 30.82 \\ GARBAGE & 1.04 \\ OTHER & 0.34 \\ NO\_PTO & 0.90 \\ TOTAL & 186.09 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Hours of audio recordings for each audio quality label.
## 5 Simulation Setting and Results
### RODP or Packet Delivery Ratio (PDR)  
Figure 11 indicates that the RODP or PDR of the CoEERD scheme is higher than the CoDBR, EERD, and ODBR schemes. The reason of achieving high RODP of the CoEERD scheme is the avoidance of the adverse channel effects by looking at reduced BER. In addition, it uses the function parameters of highest residual energy and lowest distance for the selection of RLN and DSN. The cooperation of the single RLN node with the DSN in the CoEERD scheme reduces the number of contributing nodes, which consumes minimum energy and they remain alive for a long time to forward the packets. The CoDBR scheme uses two RLNs with the DSN, which consumes high energy and drains its battery power at a quicker rate, so the results show decreased RODP. The EERD is non-cooperative scheme, which achieves low RODP value due to the single best forwarder node as compared to the cooperative schemes. The ODBR scheme is also a non-cooperative scheme which decreases the RODP by forwarding the information over single path. Initially, the CoEERD, and CoDBR schemes with the same value of RODP that starts from 1, while the EERD and ODBR schemes start from 0.5. From start to end, the CoEERD scheme has the maximum RODP as compared to all of the schemes, while the CoDBR scheme has the higher RODP value than the EERD and ODBR schemes from start to round 100, and for some instant it decreases due to long transmission path. Moreover, at round 400, the RODP values of the CoDBR, EERD, and ODBR schemes are 0.38, 0.35 and 0.25, respectively, while at the same round, the CoEERD has high RODP value nearer to 0.5, which ensures a reliable network.  
Figure 10: Initial energy available for 1000 rounds.
# Experimental Investigation on Self-Excited Thermoacoustic Instability in a Rijke Tube
## 2 Theorical Background  
### Thermoacoustic Instability  
The basic principle of thermoacoustic instability was first described qualitatively by [PERSON] (1954). The attenuation or amplification of pressure fluctuations due to heat release is dependent on whether their phases are in or out. The [PERSON] criterion can be formulated mathematically as [PERSON] (1954).  
\[\int\limits_{V}\int\limits_{\tau}q^{\prime}(\overrightarrow{x},t)p^{\prime}( \overrightarrow{x},t)dtd\overrightarrow{x}>\int\limits_{V}\int\limits_{\tau} \sum\limits_{i}L_{i}(\overrightarrow{x},t)dtd\overrightarrow{x} \tag{1}\]  
where \(q^{\prime}\) represents heat variation, \(p^{\prime}\) pressure oscillation, \(L_{i}\)\(i\)-th wave energy dissipation process, \(V\) volume of interest, \(\tau\) period of the oscillation. This inequality shows that during every cycle, only when the energy addition by heat release to acoustic field surpasses the total energy dissipation resulting from oscillation, will the thermoacoustic instability be excited and strengthened. It should be noted that the [PERSON] criterion provides the basic understanding of thermoacoustic instability, but it is difficult to obtain more valuable information only relying on the criterion.  
The thermoacoustic modeling includes acoustic models, flame models and entropy wave models ([PERSON], 1954). In the following, only the models concerning entropy waves are revised as they are significantly related to temperature.  
Figure 2 presents the interaction between an unsteady heat source and acoustic waves in a Rijke tube. The incident flow with mean velocity, \(\overline{u}_{1}\), mean temperature, \(\overline{T}_{1}\), and acoustic wave \(p^{\prime}{}_{1}\) travels through region 1. When the flow encounters the flame, i.e., heat source, \(q^{\prime}\), the partial acoustic wave will be reflected back to region 1 and the rest transmits through the flame and propagates downstream in region 2. Meanwhile, the entropy wave, \(s^{\prime}\), emerges in region 2. The mean velocity and temperature in region 2 are represented as \(\overline{u}_{2}\), \(\overline{T}_{2}\), respectively. Fluid parameters can be divided into the summation of an average term \(\overline{(}\overline{)}\) and fluctuating term (\(0^{\prime}\), e.g., \(\overline{T}=\overline{T}+T^{\prime}\), for small perturbations. It has been verified that entropy fluctuations result from density and acoustic oscillations ([PERSON], 1954):  
\[s^{\prime}=c_{p}\frac{p^{\prime}}{\overline{p}}-c_{p}\frac{\rho^{\prime}}{ \overline{p}} \tag{2}\]  
where \(s\) is entropy, \(p\) pressure, \(\rho\) density, \(c_{p}\) specific heat at constant volume and \(c_{p}\) specific heat at constant pressure. Combining Equation (2) with ideal gas relation, we can obtain  
\[\frac{s^{\prime}}{c_{p}}=\frac{T^{\
## 2 Equations Derivation of the Direct Calculation Method (DCM)
### Procedure of Numerical Analysis for the FEM  
To examine the results obtained by DCM, a numerical analysis that provides rigorous results for comparison, such as the finite element method (FEM), is investigated in this study. The finite element computing processor developed in the laboratory includes (1) the pre-processor (matrix optimization, boundary condition needed, mesh modeling, applied force, input data of material used, and geometry), (2) the main program (mechanic models, tolerance used, and the core of calculation including the increment steps), and (3) the post-processor (iso-value illustrations, drawing the contour line of displacement/stress, and output data).  
The mesh made by finite element analysis includes 1971 total nodes and 658 elements (118 T6 elements and 540 Q8 elements), using three components of calculation (ground, excavation, and lining), and the analysis boundary of the roller support is 20 times the tunnel excavation radius. The simulation results obtained by the FEM comprise the displacements/stresses of the ground around the periphery of the tunnel. In addition, Figure 3 shows a plot of simulation results for tunnel excavation under initial isotropic stress condition and includes the plastic zone, the stress field, the total displacement, the minor principal stress, the major principal stress, and the iso-value illustrations of the initial stress. The input data used for the calculation are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ground** & \multicolumn{2}{c}{**Support (Shotcrete-Lining)**} \\ \hline Parameter & Value & Parameter & Value \\ Vertical stress, \(\sigma_{v}\) (MPa) & 1.0 & Poisson’s ratio, \(v_{\text{side}}\) & 0.2 \\ In-situ stress ratio, \(K_{v}\) & 1.0 & Elastic modulus, \(E_{\text{side}}\)/GPa & 25.0 \\ Cohesion, \(c\) (MPa) & 0.1 & Unit weight, \(v_{\text{side}}\) (MPa/m) & 0.025 \\ Friction angle, \(\varphi\) (\({}^{\circ}\)) & 30.0 & Uniaxial compression & 20.0 \\ Dilation angle, \(\varphi\) (\({}^{\circ}\)) & 30.0 & Thickness, \(E_{\text{side}}\) (MPa) & 0.2 \\ Poisson’s ratio & 0.25 & Unsupported spend, \(d\) (m) & 0.53,137 \\ Elastic modulus, \(E\) (MPa) & 300.0 & Coefficients, \(m\) & 0.75 \\ Confinement loss, \(\lambda\) & 0.0-1.0 & Tunnel radius, \(R\) (m) & 5.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Input data of the numerical computation of DCM and finite element method (FEM).  
Figure 2: Computation flowchart of the algorithmic process of direct calculation method (DCM).