esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results & Discussion
### Parameter Setting  
In parameter settings, we used the one factor at a time (OFAT) procedure to set one parameter sequentially at a time. The OFAT procedure proved to be effective for selecting parameters for the SA algorithm [32]. In this experiment, each parameter has several options to choose from as follows.  
* Initial Temp. (\(T0\)): 30, 60, 90
* Final Temp. (\(Tf\)): 5, 0.5, 0.05
* _Alpha_: 0.25, 0.5, 0.9, 0.99
* _liter_: 25\(N\), 50N, 100N, 250N, 300N (\(N\) = number of customers)  
Figure 3(a) shows the experimental determination of the \(T0\) parameter, while the other parameters are fixed. The \(T0\) determination experiment shows the number 90 in the search for the minimum objective value in an acceptable time. Then the search for the \(Tf\) parameter was carried out with the value \(T0\) = 90, and the other parameters were fixed. Figure 3(b) shows that the obtained \(Tf\) is equal to 0.5, in order to obtain a minimum objective value,  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline  
also in an acceptable time. For the _Alpha_ and _liter_ parameters the same procedure is used as _T0_ and _Tf_. After setting the parameters, from the experimental results, the values of _T0_ = 90, _Tf_ = 0.5, _Alpha_ = 0.99, and _liter_ = 100\(N\). After all these processes, the final parameters are as follows: T0 = 90, _Tf_ = 0.5, Alpha = 0.99, and _liter_ = 100\(N\). Figure 4 shows the analysis of the effect of each SA parameter on the objective value and computation time.
###### Abstract  
Around the world, the increasing drought, which is exacerbated by climate change, has significant impacts on vegetation carbon assimilation. Identifying how short-term climate anomalies influence vegetation productivity in a timely and accurate manner at the satellite scale is crucial to monitoring drought. Satellite solar-induced chlorophyll fluorescence (SIF) has recently been reported as a direct proxy of actual vegetation photosynthesis and has more advantages than traditional vegetation indices (e.g., the Normalized Difference Vegetation Index, NDVI and the Enhanced Vegetation Index, EVI) in monitoring vegetation vitality. This study aims to evaluate the feasibility of SIF in interpreting drought effects on vegetation productivity in Victoria, Australia, where heat stress and drought are often reported. Drought-induced variations in SIF and absorbed photosynthetically active radiation (APAR) estimations based on NDVI and EVI were investigated and validated against results indicated by gross primary production (GPP). We first compared drought responses of GPP and vegetation proxies (SIF and APAR) during the 2009 drought event, considering potential biome-dependency. Results showed that SIF exhibited more consistent declines with GPP losses induced by drought than did APAR estimations during the 2009 drought period in space and time, where APAR had obvious lagged responses compared with SIF, especially in evergreen broadleaf forest land. We then estimated the sensitivities of the aforementioned variables to meteorology anomalies using the ARx model, where memory effects were considered, and compared the correlations of GPP anomaly with the anomalies of vegetation proxies during a relatively long period (2007-2013). Compared with APAR, GPP and SIF are more sensitive to temperature anomalies for the general Victoria region. For crop land, GPP and vegetation proxies showed similar sensitivities to temperature and water availability. For evergreen broadleaf forest land, SIF anomaly was explained better by meteorology anomalies than APAR anomalies. GPP anomaly showed a stronger linear relationship with SIF anomaly than with APAR anomalies, especially for evergreen broadleaf forest land. We showed that SIF might be a promising tool for effectively evaluating short-term drought impacts on vegetation productivity, especially in drought-vulnerable areas, such as Victoria.  
d  
drought; satellite solar-induced chlorophyll fluorescence (SIF); gross primary production (GPP); climate change 2019
# Output Feedback Tracking Control for Vessel with Collision-Avoidance and Performance Constraints
## 4 Collision-Avoidance Switch Control Strategy
### Switching Control Strategy  
Based on the analysis of the DCCR, the CASC can choose between a collision-avoidance controller (CAC) and a prescribed performance controller (PPC). A switching control strategy is proposed as follows:  
\[\tau_{c}=\left\{\begin{array}{ll}\tau_{ppc},&C_{ri}=0,\\ \tau_{cac},&C_{ri}>0,\end{array}\right. \tag{33}\]  
where \(\tau_{ppc}\) is the control input of the PPC and \(\tau_{cac}\) is the control input of the CAC.  
To achieve the prescribed error constraints and collision avoidance, a novel control strategy, which consists of a prescribed performance controller and a collision-avoidance controller with an anti-windup compensator algorithm based on switching logic, is proposed. The proposed controller is presented step by step.
# Enhancing Propaganda Detection in Arabic News Context Through Multi-Task Learning
## 5 Results and Discussion
### Discussion
In contrast, integrating emotion, which is considered a task characterized by greater subjectivity and noise, likely introduces optimization difficulties, resulting in task interference and weaker shared representations [59, 60]. Among the proposed loss weighting schemes that were evaluated, UW emerged as the most reliable, narrowly outranking HW and dynamic-difficulty weighting. This superiority can be attributed to two factors. First, the homoscedastic uncertainty parameters adaptively down-weight noisy gradients from the auxiliary heads, thereby stabilizing the joint optimization. Second, unlike PGRW variants, UW maintains continuous, differentiable control over the weight simplex, allowing for finer adjustments as training progresses, instead of sensitivity to sampling distributions. This underscores the need for stable weighting mechanisms in MTL. Nevertheless, the marginal gap between UW and HW, 0.13% based on Macro-F1, indicates that hierarchical loss ordering, where the main task is always given precedence, remains a competitive, computationally cheaper alternative for scenarios in which uncertainty estimation is infeasible. Even feature-enhanced STL models such as AraBERT, with features outperformed by 1.04% in Macro-F1, highlighting MTL's capacity to implicitly capture feature interactions without manual engineering. Furthermore, our proposed model surpassed prior MTL approaches such as MARBERT by 4.58%, based on Macro-F1, likely owing to optimized task-weighting and the integration of sentiment/emotion. This is a novel contribution to Arabic propaganda detection.
## Abstract  
This paper presents an all-digital background calibration method for gain, time skew, and bandwidth mismatch in \(M\)-channel under-sampling time-interleaved analog-to-digital converters (TI-ADCs) systems. Firstly, the characteristics of offset, gain, time skew, and bandwidth mismatch on the TI-ADCs system are analyzed. Secondly, a parameter vector is constructed to correct gain, time skew, and bandwidth mismatch. Then, the constructed parameter vector is calculated with the bandpass fractional delay filter and least squares (LS) algorithm. Based on the bandpass fractional delay filter, the proposed technique can work for ultra-high frequency signals. Additionally, the constructed parameter vector has a smaller number of filter taps than the derivative filter or Hilbert filter. Therefore, fewer computing resources are used to correct the input signal after obtaining the proposed parameter vector. Finally, there are matrix inversions in the LS algorithm. Additionally, implementing matrix inversion within FPGA is complex. For this reason, solving a system of linear equations is used to replace matrix inversions. The LS algorithm is affected by quantization error and white Gaussian noise. The simulation results verify the effectiveness of the proposed algorithm when the SNR of sub-ADC is from 30 dB to 100 dB or the ENOB of sub-ADC is from 5-bit to 16-bit. They show that the proposed algorithm is not limited by the first sub-ADC Nyquist. Additionally, the measurement results show that the proposed method is effective in the actual time-Interleaved system.  
digital calibration; gain; time skew; bandwidth mismatch; under-sampling; least squares +
Footnote †: journal: Applied  
[PERSON] and [PERSON]
###### Abstract  
The Temperature-Vegetation-Precipitation-Drought Index (TVPDI) has a good performance in drought monitoring in China. However, different regions have different responses to troughs due to terrain differences. In southeastern Tibet, the drought monitoring capacity of some drought indices without soil information has to be assessed on account of the poor sensitivity between temperature and soil humidity. Therefore, soil moisture was added to calculate a new drought index based on TVPDI in southeastern Tibet, named the Temperature-Vegetation-Soil-Moisture-Precipitation-Drought Index (TVMPDI). Then, the TVMPDI was validated by using the Standardized Precipitation Evapotranspiration Index (SPEI) and other remote sensing drought indices, including the Vegetation Health Index (VHI) and Scale Drought Conditions Index (SDCI), during the growing seasons of 2003-2018. The Standardized Precipitation Index (SPI) and SPEI were used to represent meteorological drought and Gross Primary Productivity (GPP) was used to represent agricultural drought. The relation between TVMPDI and these drought indices was compared. Finally, the time trends of TVMPDI were also analyzed. The relation coefficients of TVMPDI and SPEI were above 0.5. The correlations between TVMPDI and drought indices, including the Vegetation Health Index (VHI) and Scale Drought Conditions Index (SDCI), also had a good performance. The correlation between the meteorological drought indices (SPI and SPEI) and TVMPDI were not as good as for the TVPDI, but the temporal correlation between the TVMPDI and GPP was greater than that between the TVPDI and GPP. This indicates that the TVMPDI is more suitable for monitoring agricultural drought than the TVPDI. In addition, historical drought monitoring had values that were consistent with those of the actual situation. The trend of the TVMPDI showed that drought in the study area was alleviated from 2003 to 2018. Furthermore, GPP was negatively correlated with SPEI (r = -0.4) and positively correlated with Soil Moisture (SM) drought index (TVMPDI, SMCI) (r = 0.4) in the eastern part of the study area, which suggests that SM, rather than precipitation, could promote the growth of vegetation in the region. A correct understanding of the role of soil information in drought comprehensive indices may monitor meteorological drought and agricultural drought more accurately.  
drought monitoring; TVMPDI; soil moisture; meteorological drought; agricultural drought; Southeastern Tibet 2022146125. [[https://doi.org/10.3390/rs14236125](https://doi.org/10.3390/rs14236125)]([https://doi.org/10.3390/rs14236125](https://doi.org/10.3390/rs14236125))
## 5 Results
### Combined All Features  
The results of combining all the previous feature sets (statistical, wavelet analysis, higher-order spectra, Hjorth, and fractal dimensions) indicate 11,748 total features. Subject No. 1 performed the best, with LinearSVM reaching 82.05%, yet GSVM performed poorly compared to the latter, reaching 53.47%. Subject No. 2 achieved moderate results. LinearSVM reached 64.81%, yet the lowest accuracy was reached by GSVM (27.40%). Subject  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **LinearSVM** & **CART** & **GSVM** & **SVM (Kernel = “Poly”)** \\ \hline Subject No. 1 & 84.96 & 64.90 & 84.24 & 83.50 \\ Subject No. 2 & 69.62 & 45.55 & 69.62 & 62.59 \\ Subject No. 3 & 90.37 & 74.44 & 90.37 & 85.18 \\ Subject No. 4 & 65.26 & 38.52 & 63.32 & 56.04 \\ Subject No. 5 & 82.07 & 53.05 & 79.41 & 79.38 \\ Subject No. 6 & 47.88 & 31.03 & 47.89 & 50.62 \\ Subject No. 7 & 92.96 & 84.16 & 91.48 & 86.70 \\ Subject No. 8 & 90.88 & 62.52 & 89.75 & 88.99 \\ Subject No. 9 & 87.31 & 77.20 & 86.87 & 84.76 \\ \hline Avg. Success Rate & 79.04 & 59.04 & 78.11 & 75.31 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The results of fractal dimension features.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **LinearSVM** & **CART** & **GSVM** & **SVM (Kernel = “Poly”)** \\ \hline Subject No. 1 & 83.11 & 64.90 & 71.40 & 66.63 \\ Subject No. 2 & 68.88 & 45.55 & 50.37 & 41.48 \\ Subject No. 3 & 88.14 & 74.44 & 52.96 & 57.03 \\ Subject No. 4 & 64.86 & 38.14 & 53.70 & 48.07 \\ Subject No. 5 & 78.66 & 53.05 & 65.68 & 48.12 \\ Subject No. 6 & 48.81 & 31.03 & 31.51 & 34.24 \\ Subject No. 7 & 90.02 & 84.16 & 69.75 & 60.88 \\ Subject No. 8 & 87.44 & 62.52 & 54.54 & 59.49 \\ Subject No. 9 & 84.76 & 77.20 & 53.18 & 54.41 \\ \hline Avg. Success Rate & 77.19 & 59.00 & 55.90 & 52.26 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The results of Hjorth features.  
No. 3 achieved strong results, with LinearSVM reaching 84.07%; however, other classifiers, including GSVM, struggled, with a score of 29.25%. Subject No. 7 achieved the highest overall accuracy with LinearSVM (85.23%), and solid performance with CART (84.54%), while GSVM performed poorly, reaching 49.46%. Subject No. 6 had the lowest success rate overall, its best being LinearSVM (42.86%), and the rest of the classifiers performed poorly. Subject No. 9 achieved relatively good results, with LinearSVM reaching 77.57% accuracy.  
In terms of classifiers, the lowest overall average accuracy was reached by GSVM, at 39.44%. SVM with a polynomial kernel had better accuracy (46.41%), followed by the CART, reaching 59.13%. However, LinearSVM still shines and has the best score, with a 72.94% average success rate. The results of applying all combined feature methods in feature extraction are presented in Table 7.
## Materials and Methods
### Isolation and Cultivation of Human ASCs  
The isolation of adipose precursor cells was performed as follows [21; 27]. Adipose tissue biopsies obtained after surgical procedures were aseptically transferred to the laboratory in a sterile sealed container. The tissue was rinsed with Dulbecco's phosphate-buffered saline (PBS; Sigma-Aldrich, St. Louis, MO, USA), and any fibrous material and blood vessels were carefully removed through dissection. Subsequently, the tissue was cut into small pieces (approximately 1-2 mg) and was subjected to digestion in a buffer solution (Hank's Balanced Salt Solution (HBSS) containing 200 U/mL collagenase (CLS Type II, Worthington Biochemical Corp., Lakewood, NJ, USA) and 2% \(w/v\) BSA (Sigma-Aldrich, St. Louis, MO, USA)) under continuous stirring for 60 min at 37 \({}^{\circ}\)C, using a ratio of 1 mg adipose tissue to 3 mL digestion buffer. Following digestion, the dispersed tissue was centrifuged at room temperature for 10 min at 200\(\times\)\(g\). The floating adipocytes were aspirated, and the remaining pelleted cells representing the stromal vascular fraction (SVF) were suspended in an erythrocyte lysis buffer (0.155 M NH\({}_{4}\)Cl, 5.7 mM K\({}_{2}\)HPO\({}_{4}\), 0.1 mM EDTA, pH 7.3) and incubated for 10 min at room temperature. To eliminate tissue debris, the cell suspension was passed through a nylon mesh filter (100 uM pore size; Thermo Fisher Scientific, Waltham, MA, USA). The pelleted SVF obtained after another centrifugation step (10 min at 200\(\times\)\(g\)) was resuspended in ASC medium (a mixture of Dulbecco's Modified Eagle's Medium (DMEM) and F-12 medium (1:1) with HEPES and L-glutamine; Thermo Fisher Scientific, Waltham, MA, USA), supplemented with 33 uM biotin, 17 uM pantotheanate (both from Sigma-Aldrich, St. Louis, MO, USA), 10 ng/mL epidermal growth factor (EGF), 1 ng/mL basic fibroblast growth factor (bFGF), 500 ng/mL insulin, 2.5% fetal bovine serum, and 12.5 uM/mL gentamicin (all from Sigma-Aldrich, St. Louis, MO, USA). The SVF cells were seeded in T175 flasks at a density of 50,000 cells/cm\({}^{2}\).  
Once the cells reached 70% confluence, they were washed with PBS and detached using 0.05% trypsin-EDTA 1x solution (Sigma-Aldrich, St. Louis, MO, USA). The trypsin was inactivated by adding ASC medium supplemented with 10% FBS, and the cells were pelleted by centrifugation at 300\(\times\)\(g\) for 5 min. Subsequently, the cells were seeded at a density of 5000 cells/cm\({}^{2}\) and allowed to reach 70% confluence before further passaging. For this study, adipose-derived stem cells (ASCs) at passages 3-4 were used. In the experimental design, the cells were treated with various concentrations of nicotinamide riboside (Medko Biosciences, Morrisville, NC, USA. cat#329479).
## 2 Fundamental Principles of Quasi-Zero Stiffness
### Basic Types of QZS Vibration Isolators
#### 2.2.2 Buckled Beam as Negative Stiffness Mechanism  
Typically, this mechanism utilizes a deformed flexure beam as its negative stiffness mechanism. Its most remarkable characteristic is its compact overall structure. The performance of the mechanism significantly impacts it, leading to unstable nonlinear characteristics. Figure 6a illustrates that [PERSON] [41] connected a pressurized Eulerian flexure beam in parallel with a linear spring. This system exhibits zero dynamic stiffness at the equilibrium position while retaining the load-bearing capacity of the spring, thus presenting a novel approach to isolating low-frequency vibrations. Figure 6b illustrates that [PERSON] [42] designed a QZS unit utilizing cams, pulleys, and Euler beams. The unit adjusts the clamping angle to decrease the initial isolation frequency, thus adapting to the impact of load variation on the performance of the QZS isolator. This addresses the deficiency of unstable system isolation performance caused by load variation. [PERSON] [43] developed a novel QZS vibration isolator employing a slotted conical disc spring as its negative stiffness mechanism. They adjusted the structural parameters to broaden the displacement range of the vibration isolator near the equilibrium position. The Helmholtz-Duffing equations under overload and underload conditions have been derived, and the primary resonance response of the nonlinear system has been investigated by employing the harmonic equilibrium method. However, this system exhibits sensitivity to the external excitation amplitude and frequency, rendering it challenging to adapt to changing working conditions. [PERSON] [44] proposed a QZS metamaterial capable of achieving an extremely low-frequency band gap. The negative stiffness unit of the metamaterial comprises flexural beams. The elliptic integration method was employed to forecast the negative stiffness trend of flexural beams under significant deformation. The minimum band gap frequency can descend to 20 Hz, rendering it a feasible solution for filtering or attenuating extremely low-frequency waves via theoretical and simulation analyses.
## 2 Methodologies  
### Generative Adversarial Networks  
Generative adversarial network (GAN) is a framework that can capture the real data's distribution and then generate fake data very similar to real data. It consists of two modules: generator \(G\) and discriminator \(D\), as shown in Figure 1a.  
Different from the traditional generative models, GAN does not compute the exact probability density function \(p_{X}(x)\) of the data distribution (which is difficult), it models \(p_{X}(x)\) by transforming a latent distribution \(p_{Z}(z)\), such as a standard Normal distribution \(\mathcal{N}(0,I)\). This transformation is called a generator \(G\), which mapping from the latent feature space to data space by a feed-forward network \(\tilde{x}=G(z),G:\Omega_{z}\rightarrow\Omega_{x}\). Another module called discriminator \(D\) aims to distinguish between samples from the data space or generator \(G\). Through adversarial training of \(G\) and \(D\), \(p_{G}(x)\) gradually approaches \(p_{X}(x)\) and finally matches it. Therefore, GAN can learn arbitrarily complex real data distribution and then create real-like data.  
The following minimax objective formulates the training of GAN:  
\[\begin{split}\min_{G}\max_{D}V(D,G)&=\mathbb{E}_{x \sim p_{X}}[\log D(x)]+\\ &\mathbb{E}_{z\sim p_{Z}}[\log(1-D(G(z)))]\end{split} \tag{1}\]  
where \(D(x)\) represents the probability that discriminator will determine the data \(x\) from the true distribution \(p_{X}\) to be true, and \((1-D(G(z)))\) means the probability that \(D\) will judge the sample generated by the generator \(G(z)\) is false. The goal of \(D\) is to maximize the above probabilities, therefore the loss function of the discriminator is:  
\[\begin{split}\mathcal{L}_{D}=&-\mathbb{E}_{x\sim p _{X}}[\log D(x)]\\ &-\mathbb{E}_{z\sim p_{Z}}[\log(1-D(G(z)))]\end{split} \tag{2}\]  
The goal of generator \(G\) is to minimize the probability that \(D\) successfully discriminates generated samples. The first term of Equation (1) makes no reference to \(G\), so it can be ignored. Therefore the loss function of the generator is:  
\[\mathcal{L}_{G}=\mathbb{E}_{z\sim p_{Z}}[\log(1-D(G(z)))] \tag{3}\]  
Figure 1: Architecture of GAN and BiGAN. (**a**) GAN. (**b**) BiGAN.  
However, in the early training Equation (3) cannot provide enough gradient for the learning of \(G\). In practice, [PERSON] proposed an alternative generator loss function:  
\[\mathcal{L}_{G}=-\mathbb{E}_{z\sim p_{Z}}[\log(D(G(z)))] \tag{4}\]  
By alternating training \(G\) and \(D\), when the algorithm converges, the generated distribution of \(G\) coincides with the real data distribution, i.e., \(p_{G}=p_{X}\), and \(D\) will not be able to distinguish whether the sample is real or fake, i.e., \(D(G(z))=0.5\).  
Compared with the traditional generative model, GAN is simpler to construct and more straightforward to train. However, the original GAN model can only utilize unlabeled data to perform unsupervised learning tasks such as generating fake data. It lacks the ability to perform supervised learning such as classification. For that reason, we have to modify the architecture of GAN for fault diagnosis.
## 3 Estimation of Model Parameters
### MLE with Marine Predators Algorithm
The prey perform Levy flights and updates based on: \(\mathbf{s}_{i}=R_{\mathbb{L}}\otimes\left(\mathbf{Elite}_{k}^{i}-R_{\mathbb{L}} \otimes\mathbf{\gamma}_{k}^{i}\right)\), \(\mathbf{\gamma}_{k}^{i}=\mathbf{\gamma}_{k}^{i}+P*\mathbf{R}\otimes\mathbf{s}_{i},i=1,\ldots,n/2\) The predators perform Brownian motion and updates based on: \(\mathbf{s}_{i}=R_{\mathbb{B}}\otimes\left(\mathbf{Elite}_{k}^{i}-R_{\mathbb{B}} \otimes\mathbf{\gamma}_{k}^{i}\right)\), \(\mathbf{\gamma}_{k}^{i}=\mathbf{Elite}_{k}^{i}+P\cdot CF\otimes\mathbf{s}_{i},i=\frac{n}{ 2}+1,\ldots,n\) Or elseif\(Iter>2*max\_iter/3\) \(\mathbf{s}_{i}=\mathbf{R}_{\mathbb{L}}\otimes\left(\mathbf{Elite}_{k}^{i}-\mathbf{R}_{\mathbb{L }}\otimes\mathbf{\gamma}_{k}^{i}\right)\), \(\mathbf{\gamma}_{k}^{i}=\mathbf{Elite}_{k}^{i}+P*CF\otimes\mathbf{s}_{i},i=1,\ldots,n\) End (if) 3. Accomplish memory saving and \(\mathbf{Elite}_{k}\) update 4. Applying Fish Aggregating Devices (FADs) effect and update \(Prey_{k}\) based on: \(\mathbf{\gamma}_{k}^{i}= \begin{cases}\mathbf{\gamma}_{k}^{i}+CF[lb+R\otimes(ub-lb)] \otimes\mathbf{U},r\leq FADs\\ \mathbf{\gamma}_{k}^{i}+[FADs(1-r)+r]\mathbf{(\gamma}_{k}^{i-1}-\mathbf{\gamma}_{k}^{i2}),r>FADs\end{cases} \) End While
```  
**Algorithm 1** Pseudocode of MPA  
In the pseudocode mentioned above, \(P,\mathbf{R},\mathbf{R}_{\mathbb{B}},\mathbf{R}_{\mathbb{L}},CF,\)\(FADs\), \(\mathbf{U},\)\(r,r_{1}\), and \(r_{2}\) are the algorithm parameters. Their structure and value-assigning methods were based on recommendations in the literature [43]. Appendix A provides supplementary explanations for some key calculation details in the above pseudocode. Moreover, to test the effectiveness of the MPA in solving the problem presented in this study, a comparative analysis will be conducted in the case study. The performance of the MPA will be compared to two other optimization techniques: the genetic algorithm (GA), which is a widely-used heuristic algorithm, and the gradient descent algorithm (GD), which is a popular gradient-based method.