esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Short-Term Clock Prediction Method for Real-Time Positioning
## 3 Experiment Results
### Accuracy Verification of Short-Term Clock Prediction
#### 3.1.2 Comparison with IGU Product  
In practical PPP technology, two approaches can be implemented to acquire real-time clock products. In addition to the real-time clock estimates and forecasts based on the real-time GPS observations, IGU-P products and other similar products can also be used. Therefore, IGU ultra-fast product clocks from day 1 to day 296 in 2016 are sampled to obtain statistics on the 1-h, 2-h, and 3-h clock prediction accuracy of IGU-P products. Forecast precision is evaluated with a method described in literature [23] with reference to IGS post products at a sampling interval of 30 s.  
Table 4 shows the improvement of the new model with 3-h observation corresponding to the IGU-P product. The same epoch difference between IGU-P and IGS, identified as outliers, is not analyzed. Table 4 indicates that a decrease in prediction time leads to a significant improvement of the new model. For 3-h prediction, nearly one-third of the satellite forecast results are better than the new model, whereas for 1-h prediction only two satellite results are better than the new model. It is proven that this improved strategy is more suitable for short-term prediction than the IGU-P model. This new strategy can not only reflect the advantages of the linear model in short-term prediction, but also reflect the advantages of the periodic model in long-term prediction. Besides, the type of Block IIF obviously gains more from the improved model, and there is no significant difference between Block IIR and Block IIR-M. In summary, the improved model proposed for real-time clock application can be used to replace IGU-P products.
# MSA K-BERT: A Method for Medical Text Intent Classification
## 4 Methods
### Multi-Scale Attention Layer  
The multi-scale attention layer (MSA) is a method that applies weights to the input at different scales [60]. It can extract multi-level information and is suitable for processing text or images of different lengths or resolutions. One implementation method of the MSA is to use multiple convolution kernels of different sizes to convolve the input, then concatenate the convolution feature maps, calculate the attention weights through a fully connected layer or self-attention mechanism, and finally apply the attention weights to the input to obtain the multi-scale attention output.  
For example, in the case of medical text intent classification, we assume the input is a matrix \(X\in R^{n\times d}\), where \(n\) represents the text length. and \(d\) is the dimensionality of the word embeddings. We apply three convolutional kernels of different sizes, \(K_{1},K_{2},K_{3}\) to convolve \(X\), resulting in three feature maps of different scales: \(F_{1},F_{2},F_{3}\), where each \(F_{i}\in R^{n\times c}\) and \(c\) represents the number of output channels. Subsequently, we concatenate these three feature maps to obtain a concatenated feature map \(F\in R^{n\times 3c}\). Next, a self-attention mechanism is employed to compute the attention weights \(A\in R^{n\times n}\) for \(F\), where \(A_{ij}\) represents the attention weight of the \(i\)-th position with respect to the \(j\)-th position. Finally, the attention weights are applied to the input \(X\) to obtain the multi-scale attention output \(O\in R^{n\times d}\), where \(O_{i}=\sum_{j=1}^{n}A_{ij}X_{j}\). In this way, we obtain a text representation that can capture information at multiple scales, which can be used for subsequent intent classification tasks.
# Analytic Continual Learning-Based Non-Intrusive Load Monitoring Adaptive to Diverse New Appliances
## 2 Problem Statement
### The Continual Learning Setting of NILM  
In real-world applications, NILM models need to not only perform well on test data from the collected dataset \(D_{0}\) but also continually learn from streaming data to adapt to new appliances. Streaming data are represented as a sequence of appliance datasets, \(\{D_{1}\sim\{I_{1}^{\mathrm{e}},\,U_{1}^{\mathrm{e}},\,V_{1}\},\,\ldots,\,D_{k} \sim\{I_{k}^{\mathrm{e}},\,U_{k}^{\mathrm{e}},\,Y_{k}\}\}\), arriving in temporal order, with \(k\) denoting the number of data increment rounds [10]. In particular, the diversity of appliances causes variations in waveform distributions across datasets and differences in the appliance types of \(Y_{k}\). Therefore, when new data arrive, a novelty detection model \(f^{\mathrm{n}}(I^{\mathrm{e}},\,U^{\mathrm{e}};\,W^{\mathrm{n}})\), parameterized by \(W^{\mathrm{n}}\), is required to determine whether the appliances are already learned. If unknown appliances are detected, their types also need to be labeled. In the subsequent continual learning process, the NILM model should try to retain its original ability after updating its parameters on new data. For the load identification model, updating \(W_{k-1}^{\mathrm{i}}\) to \(W_{k}^{\mathrm{i}}\) extends its scope from identifying appliances in \(D_{0}\sim D_{k-1}\) to those in \(D_{k}\). For the novelty detection model, updating \(W_{k-2}^{\mathrm{n}}\) to \(W_{k-1}^{\mathrm{n}}\) incorporates appliances in \(D_{k-1}\) into the learned set, alongside those in \(D_{0}\sim D_{k-2}\), while continuing to treat appliances in \(D_{k}\) as novel.
## 3 The Proposed Model
### Image Pre-Processing: GF Technique  
GF, a bandpass filter, is effectively employed for different machine vision and image processing applications [18]. A 2D Gabor function is an oriented complex sinusoidal grating, modulated by a 2D Gaussian envelope. In a 2D coordinate \((a,b)\) system, GF includes an imaginary and a real component, as given herewith:  
\[G_{\delta,\theta,\varphi,\sigma,\gamma}(a,b)=\ \exp\left(-\frac{{a^{\prime}}^{2}+ \gamma^{2}{b^{\prime}}^{2}}{2\sigma^{2}}\right)\times\ \exp\left(j\left(2\pi\frac{{a^{\prime}}}{\delta}+\psi\right)\right) \tag{1}\]  
in which:  
\[{a^{\prime}}=a\ \text{cos}\ \theta+b\ \text{sin}\ \theta \tag{2}\]  
\[{b^{\prime}}=-a\ \text{sin}\ \theta+b\ \text{cos}\ \theta \tag{3}\]  
Now, \(\delta\) characterizes the wavelength of sinusoidal factors, and \(\theta\) signifies the orientation separation angle of the Gabor kernel. Consider \(\theta\) in the interval \([0^{\circ},\ 180^{\circ}]\), since the symmetry makes other directions redundant. \(\psi\) denotes the phase offset, \(\sigma\) indicates the standard derivation of the Gaussian envelope, and \(\psi=0\) and \(\psi=\pi/2\) return the real and imaginary parts of Gabor filter, correspondingly. Variable \(0\) is defined by \(6\), and the spatial frequency bandwidth \({}^{b}\!w^{\prime}\) is determined as follows.  
\[\sigma=\frac{\delta}{pi}\sqrt{\frac{ln2}{2}}\ \frac{2^{bw}+1}{2^{bw}-1} \tag{4}\]
# A Geographical Heuristic Routing Protocol for VANETs
## 3 Background
### Heuristics Adaptations for VANET Routing
#### 3.2.2 Simulated Annealing
Recall that a large value in the denominator of Equation (1) increases the probability \(p\) of random forwarding. Figure 2c includes two possible paths originated by random forwarding operations at the first hop. These paths are labeled as \"1\" with a continuous line and \"2\" with dotted lines. Simulated annealing can also generate long paths, when in fact there is a shorter path in the topology. In Figure 2c, Node O1 in path \"1\" could forward  
Figure 2: An example that shows how Simulated Annealing (SA) could help routing protocols avoid local minimum scenarios. (**a**) Node G1 (or P5 if Tabu is used) is the last hop in the path between S and AP. The path S to G1 (or P5) was built using greedy forwarding. Greedy forwarding can lead to “local minimums”. (**b**) There is a high probability \(p\) to forward the packet randomly, according to the SA principle, when it arrives at P1 because the improvement in the distance (numerator in Equation (1)) by selecting G1 is little. (**c**) There is a high probability \(p\) to forward the packet randomly at the beginning of the path from S to AP because the distance to the destination is long (denominator in Equation (1)). randomly to O2 (red arrow labeled as \"3\") and follow the second path, which is longer than path \"1\". To conclude our example, we point out that the controlled random forwarding of simulated annealing can fail in our example. For instance, simulated annealing could generate the bad paths S\(\rightarrow\)B1\(\rightarrow\)P5 or S\(\rightarrow\)B1\(\rightarrow\)P5\(\rightarrow\)P1. Nevertheless, simulated annealing provides the chance of avoiding the local minimum at G1 that would be unavoidable if the routing protocol made just greedy decisions. Summarizing, with simulated annealing, if the best candidate changes the distance to the destination little with respect to the distance from the current node or/and the current node is far away from the destination node, then the probability \(p\) (of choosing a node farther than the current node) will be high. This probability \(p\) depends on a tuning factor \(\gamma\).
# Radar Emitter Structure Inversion Method Based on Metric and Deep Learning
## 3 RES Modeling  
The importance of RES modeling lies in the construction of a dataset that corresponds to the actual situation and the generation of the required RF signal using the structure feature level modeling method. This is a forward modeling process that provides a basis for subsequent inversion methods. Having a high-quality dataset is the premise and basis for training neural networks. However, due to the confidentiality of military systems, it is very difficult to obtain RF signals under such hetero-mode operating parameters. Therefore, simulation data [46; 47] are mostly used to conduct experiments on electronic reconnaissance systems. Hence, RES modeling is the premise of obtaining a simulation dataset, which is of great importance. Large amounts of data can be obtained via the modeled RES.  
The current methods for constructing simulation data are mainly classified into two categories: single-device modeling and mathematical modeling. In single-device modeling, power amplifiers are mostly studied [48; 49; 50; 51], and behavioral modeling, like Saleh [49], polynomial [50], and Volterra series [51] models, is usually used to simulate nonlinear features and memory effect. In mathematical modeling, there are mainly two types of models: a time-domain envelope model that can reflect the transient and steady-state features of the radar emitter [52], and the phase noise model [53] with the frequency shift mode [54] relative to frequency stability that can reflect the spectrum purity of the radar-emitted signal. Their main shortcomings are that they can only reflect the features of a certain module in the RES model but cannot fully reflect its overall features.  
Therefore, to provide basic conditions for theoretical analyses and the practical verification of this study, this paper builds three classes of RES models by selecting different structure design parameters and using a simulation platform with measured parameters according to the working principle of radars. The S2P file is used for simulation, and the S2P file can be approximately considered as the reflection of a field measurement component in software. Therefore, the simulation data are closer to real data. In addition, under the same RES model, four REI models are built by slightly adjusting the parameters of each module. As a result, there are structural differences among the RES models of different classes and individual differences among different REI models of the same class when building RES in complex situations.
## 1 Introduction
Thus, the master-slave parallel method could be adopted herein to find the global sensitivity numbers. In summary, there is still a lack of an efficient modal modification method in the gradient-based topology optimization of a large-scale, fully coupled thermoelastic system, although there are some works for standard eigenproblems and their sensitivities. This work, therefore, proposes a technological framework to efficiently update the eigenvalues and their sensitivities in gradient-based topology optimization for thermoelastic systems, which is essential when dealing with their complex eigenproblem and global sensitivity analysis for a huge number of design parameters. The proposed framework can be easily implemented in the optimization framework of thermodynamics to improve their computational efficiency [14; 32]. Furthermore, though the proposed method focuses on fully coupled thermoelastic systems, it can also be implemented in dense optimization frameworks for purely static or dynamic systems with simple reductions of the heat conduction equation [16; 33; 34]. A Krylov subspace preconditioner will be constructed based on the Neumann series expansion so that the thermoelastic eigenproblem can be solved in an efficient, low-dimension solver, rather than its original high-fidelity solver. In constructing Krylov reduced-basis vectors, the computational cost of the inverse of the system matrix can be solved efficiently by constructing a diagonal system matrix with the lumped mass and heat generation submatrices. Then, the reduced-basis preconditioner can provide an efficient optimal solver for both the thermoelastic eigenproblem and its eigensensitivity. Furthermore, a master-slave pattern parallel method will be developed to reduce the computational time of computing the global sensitivity numbers, and therefore, the global sensitivity problem can be discretized into elemental-scale problems in a parallel way. Finally, two case studies of topology optimization of two types of micro-beams will be used to illustrate the effectiveness of the proposed method. It is shown that the proposed method can reduce the computational cost remarkably with little loss of accuracy in the iterative optimization process.
###### Abstract  
Background: Adenosine plays the role of regulating cell differentiation, proliferation, and apoptosis in various kinds of cells through the B-cell lymphoma 2 (BCL2) pathway. Objectives: Since anti-apoptotic (BCL2) expression plays a role in controlling apoptosis in some cell lines, this study was designed to investigate whether adenosine analogue, NECA (non-selective adenosine receptors agonist), selective adenosine A2B receptor antagonist, PSB 603, and a selective adenosine A2A receptor agonist, CG21680, affect BCL2-gene expression in the skeletal muscle cells of rats. The purpose of this investigation was to test the hypothesis that CG21680 treatment would significantly intensify BCL2 gene expression in rat skeletal muscle. Methods: Flasks measuring 25 cm\({}^{2}\) were employed in culturing the rat L6 skeletal muscle cells. After treating these differential cells, the relative mRNA expression level for the BCL2 gene, at varying conditions of treatment, was measured using quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Results: From the qRT-PCR analysis results, it was concluded that BCL2 expression was markedly amplified after selective adenosine A2A receptor agonist, CGS21680 (\(p<0.01\)) treatment. More prospective validation for the adenosine receptors' contribution in modulating apoptosis by NECA was delivered by the outcomes from the combined pre-treatment of the cells with NECA and PSB 603. These outcomes show that when starved skeletal muscle cells are treated with a combination of NECA and 100 nM PSB 603, there was a substantial decrease in comparison to either treatment used on its own. Conclusions: This study's results showed, for the first time, an increase in BCL2 gene expression within skeletal muscle after CGS21680 treatment. Hence, the prospective escalation in BCL2 protein expression might have a protective role to play against apoptosis and avert damage to the skeletal muscle.  
NECA; CGS21680; PSB 603; insulin; skeletal muscle cells; mRNA gene expression; BCL2 and apoptosis
# Single-Mode Polymer Ridge Waveguide Integration of Organic Thin-Film Laser
## 5 Results and Discussion
Laser thresholds could be found to be in the range from approximately 25 J/cm\({}^{2}\) to approximately 170 J/cm\({}^{2}\). Moreover, with increasing the Bragg grating period, and therefore increasing the laser wavelength, the laser threshold decreases and the slope of the lasers increases. At a certain turning point, the laser threshold increases and the laser slope decreases. This can be attributed to the spectral response of the optical modal gain of this material (cf. Figure 8a). The turning point corresponds with the modal gain maximum where the stimulated cross section is the highest [12,13]. The spectral width of the modal gain increases with increasing excitation energy density. Thus, for shorter laser wavelengths, higher excitation energy is needed until the optical modal gain overcomes absorption and optical losses. For higher wavelengths, the optical modal gain decreases, which can be seen in the laser threshold of the laser with 666.82 nm laser wavelength. The lowest lasing threshold is one magnitude higher than classic OLAS without waveguides (cf. [2]) but still up to 210-times lower than the thresholds reported in [3,4]. By inserting a polarization filter in front of the detection fiber into the optical set-up (cf. Figure 7), a polarization measurement could be done to get the TE- and TM-proportions. The radiated emission was clearly linear polarized with a polarization extinction ratio (PER) of 15.5 : 1. The intensity difference between the values at 0\({}^{\circ}\) and 360\({}^{\circ}\) can be addressed to a slight degradation of the sample during this measurement. Figure 8: (**a**) Lasing lines out of the integrated OLAS with different DFB grating periods with the optical modal gain in the background; and (**b**) the spectral response of the integrated OLAS with (**c**) increasing excitation energy density.
# The Modified Void Nucleation and Growth Model (MNAG) for Damage Evolution in BCC [PERSON]
## 3 Results and Discussion
### The MNAG Model: Size Effects  
Additional simulations were performed to evaluate the sensitivity of the current results of the system size. These simulations involved the use of larger cell dimensions for the (001) single-crystal Ta and the re-fitting of the MNAG model parameters to the new MD results. The evolution of the number density and damage as a function of the system size is shown in Figure 7. This figure showed that the contribution from void nucleation, growth, and coalescence to total void volume decreased with increasing cell dimensions. This suggested that greater cell dimensions reduced the rate of all three stages of damage evolution due to the lower strain rate involved (the strain rate was a function of cell dimensions). The fitted void volume fraction was close to that of MD, although the fitted void number density showed minor deviations from MD at larger cell dimensions. However, the overall trends in void number density and damage did not change with the system size, even though there were changes in the quantitative values. Therefore, the analytical form of the MNAG model was general and could qualitatively represent damage evolution as a function of time.  
Figure 6: Damage evolution based on (**a**) the NAG model, (**b**) the Cocks–Ashby model with different choices of the parameter n, (**c**) the Meyer–JMAK treatment with different choices of the parameter \(N_{o}\), (**d**) the Tepla and Tonks models compared to the MD results for (001) single-crystal Ta. The three distinct stages SI, SII, and SIII are marked with dashed lines.
# Improving Singing Voice Separation Using Curriculum Learning on Recurrent Neural Networks
## 4 Evaluation  
We performed singing voice separation experiments and compared the result of the proposed approach with that of baseline approaches. We adopted a stacked RNN [4; 5; 6] and a U-Net [7; 29] as baseline models. Separation experiments were carried out on the simulated vocal-instrument recordings generated by mixing the sound sources from MIR-1K [30], ccMixter [31], and MUSDB18 [32] datasets.
# A Survey of Applications of Deep Learning in Radio Signal Modulation Recognition
## 3 Radio Modulation Recognition Methods Based on Deep Learning
### Rnn
A preprocessed signal representation was proposed, which combined the orthogonal, fourth-order statistics of the modulated signal. The accuracy was improved by 8%. [PERSON] et al. [50] proposed an AMR model based on improved LSTM. The model achieved accuracy of 76% under all SNR. [PERSON] et al. [58] proposed an automatic recognition of digital modulation based on depth LSTM model. This method did not need any feature extraction and directly input the modulated signal into the system. The classification accuracy reached 94.72%. Figure 4: LSTM cell structure.
# Integrated Magnetic Management of Stored Angular Momentum in Autonomous Attitude Control Systems
## 2 Attitude Control System
### Integrated Magnetic Management of Stored Angular Momentum
Note that the low velocity threshold can stop the desaturation command for each individual wheel by zeroing only the single velocity component in the stored angular momentum calculation:  
\[\mathbf{h}_{R}(i)=I_{R}\omega_{R_{i}}=0\quad\text{if}\quad\omega_{R_{i}}< \omega_{R}^{min}. \tag{23}\]  
Analyzing the proposed method at the system level, the continuous integration of magnetic torque during the operative modes can interfere with some magnetic components on-board the spacecraft or with the magnetic cleanliness of the platform. If this is not allowed by system-level constraints, the proposed method should be actuated with different actuators. Specifically, the control torque \(\mathbf{t}_{M}\) should be commanded to nonmagnetic actuators (e.g., thrusters). Alternatively, if magnetic torquers are allowed, particular care should be taken with respect to the magnetometer's measurements. Indeed, magnetic torquers generate a field that strongly disturbs the magnetic measurements. This issue needs to be avoided by properly alternating the usage of magnetometers and magnetic actuators. In such a way, the magnetometer's measurements are not acquired at the same time of the magnetic control commands. Generally, the time gap between magnetic measurements and control should be at least three times the characteristic period of the magnetic actuators to allow the attenuation of the generated magnetic dipole.
# In Silico SwissADME Analysis of Antibacterial NHC-Silver Acetates and Halides Complexes
## 3 Results and Discussion
### Blood-Brain Barrier (BBB) Permeation  
The BOILED-Egg diagrams for complexes **A** and **S** are presented in Figure 14. This type of analysis allows for intuitive evaluation of passive gastrointestinal absorption (HIA) and brain penetration (BBB) of molecules in function of lipophilicity (WLOGP) and polar surface area (TPSA). Additionally, the points colored in blue are predicted to be actively effluxed by P-gp (PGP+) and those in red are predicted as a non-substrate of P-gp (PGP\(-\)). A first glance at both graphs indicates that the more biologically active complexes are characterized by lower lipophilicity and a larger polar surface. To this group belong the 4-nitrobenzyl-substituted NHC-silver acetate complexes **26**, **28**-**31**, the methylated caffeine-based silver acetate complexes **1**, **5**, and **24**, dimethyl-imidazolium-derived complex **2**, as well as the cyanuric chloride-derived NHC-silver chloride complex **123**. The remaining compounds in group **S** do not differ in this model from the less active complexes in group **A**. It should be noted that complexes **32** and **165**-**168** from cohort **A**, as well as compounds **150**, **152**, **169**, **170** and **173** from group **S**, were not included in the charts because their predicted lipophilicity parameters exceeded 8.  
Figure 14: Cont.  
Figure 13: Distribution of the skin permeability coefficient (\(K_{\text{p}}\)) within the groups **A** + **S**, **A**, and **S**.