esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Q-Learning-Based CCM-SPS Resource Allocation Scheme Proposed for JCS Sidelink
### Collision Detection Mechanism  
The full-duplex (FD) transceiver calculates the echo power as a condition for collision detection. The total received power of the FD receiver can be expressed as:  
\[P_{r}^{FD}=P_{R}+P_{n}+I_{t,i} \tag{15}\]  
where \(P_{R}\) represents the power of the reflected echo signal. Assume only the closest signal to the transmission vehicle is considered. Since the CAM signal contains distance information, \(P_{R}\) can be calculated through path loss using the distance of CAM messages. \(P_{n}\) is the Gaussian white noise power. \(I_{t,i}\) represents the interference of other transmitting vehicles to VUE \(i\) in the same time slot \(t\) according to (6).  
By calculating the difference between the received power \(P_{r}^{FD}\) and the echo power \(P_{R}\) from the nearest vehicle, while also accounting for the noise power \(P_{n}\), the interference power strength \(I_{t,i}\) can be effectively estimated.  
Then, the collision detection rules for collisions in the JCS sidelink are given as follows:  
\[\begin{cases}P_{r}^{FD}-P_{R}-P_{n}>P_{\Delta},&COL=1\\ P_{r}^{FD}-P_{R}-P_{n}<P_{\Delta},&COL=0\end{cases} \tag{16}\]The threshold is used to determine whether the interference impact exceeds the critical power. If \(COL=0\), this indicates no resource collision; if \(COL=1\), this indicates a resource collision.
###### Abstract  
This study presents a methodology to detect and monitor surface water with Sentinel-1 Synthetic Aperture Radar (SAR) data within Cambodia and the Vietnamese Mekong Delta. It is based on a neural network classification trained on Landsat-8 optical data. Sensitivity tests are carried out to optimize the performance of the classification and assess the retrieval accuracy. Predicted SAR surface water maps are compared to reference Landsat-8 surface water maps, showing a true positive water detection of \(\sim\)90% at 30 m spatial resolution. Predicted SAR surface water maps are also compared to floodability maps derived from high spatial resolution topography data. Results show high consistency between the two independent maps with 98% of SAR-derived surface water located in areas with a high probability of inundation. Finally, all available Sentinel-1 SAR observations over the Mekong Delta in 2015 are processed and the derived surface water maps are compared to corresponding MODIS/Terra-derived surface water maps at 500 m spatial resolution. Temporal correlation between these two products is very high (99%) with very close water surface extents during the dry season when cloud contamination is low. This study highlights the applicability of the Sentinel-1 SAR data for surface water monitoring, especially in a tropical region where cloud cover can be very high during the rainy seasons.  
SAR; Sentinel-1; surface water monitoring; neural network; Mekong Delta; Landsat-8; MODIS 2017
## 6 Metric
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multicolumn{2}{c}{**RNN**} & \multicolumn{2}{c}{**RNN-WGN**} & \multicolumn{2}{c}{**LSTM**} & \multicolumn{2}{c}{**LSTM-WGN**} \\ \cline{3-10}  & **1 Layer** & **2 Layers** & **1 Layer** & **2 Layers** & **1 Layer** & **2 Layers** & **1 Layer** & **2 Layers** \\ \hline \multirow{3}{*}{ECG5000} & Accuracy & 0.935 & 0.932 & **0.939** & **0.939** & 0.935 & 0.933 & 0.930 & 0.925 \\  & LOE & 192.791 & 2446.467 & 29.486 & 52.538 & 56.822 & 86.378 & **25.096** & 63.990 \\ \hline \multirow{3}{*}{Coffee} & Accuracy & 0.964 & 0.964 & 0.964 & 0.964 & **1.000** & **1.000** & **1.000** & **1.000** \\  & LOE & 35.237 & 68.573 & 67.513 & 27.677 & 35.013 & 64.383 & 86.321 & **25.374** \\ \hline \multirow{3}{*}{Computers} & Accuracy & 0.600 & 0.524 & 0.556 & 0.572 & 0.652 & 0.600 & 0.624 & **0.660** \\  & LOE & 18.627 & 27.378 & 27.817 & 23.865 & 3.876 & **0.543** & 20.221 & 26.314 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Best metric on training and development sets on UCR. Bolded values are for best performance. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multicolumn{2}{c}{**RNN**} & \multicolumn{2}{c}{**RNN-WGN**} & \multicolumn{2}{c}{**LSTM**} & \multicolumn{2}{c}{**LSTM-WGN**} \\ \cline{2-9}  & **1 Layer** & **2 Layers** & **1 Layer** & **2 Layers** & **1 Layer** & **2 Layers** & **1 Layer** & **2 Layers** \\ \hline PPL & 383.20 & 972.38 & 144.97 & 141.02 & 125.27 & 293.15 & 118.58 & **110.89** \\ LOE & **37.77** & 104.59 & 58.05 & 54.52 & 53.14 & 55.62 & 55.15 & 54.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Best metric on training and development sets on Peen Treebank. Bolded values are for best performance. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multicolumn{2}{c}{**RNN**} & \multicolumn{2}{c}{**RNN-WGN**} & \multicolumn{2}{c}{**LSTM**} & \multicolumn{2}{c}{**LSTM-WGN**} \\ \cline{2-9}  & **1 Layer** & **2 Layers** & **1 Layer** & **2 Layers** & **1 Layer** & **2 Layers** & **1 Layer** & **2 Layers** \\ \hline \multirow{3}{*}{len = 24} & MAE & 0.806 & 0.943 & 0.978 & 0.886 & 0.802 & 0.866 & **0.778** & 0.902 \\  & LOE & 34.62 & 81.15 & **8.28** & 13.85 & 23.78 & 32.58 & 15.29 & 24.00 \\ \hline \multirow{3}{*}{len = 96} & MAE & 0.635 & 0.733 & **0.592** & 0.678 & 0.681 & 0.685 & 0.704 & 0.790 \\  & LOE & 9.94 & 17.58 & **6.38** & 11.83 & 20.03 & 27.33 & 14.63 & 21.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Best metric on training and development sets on ETTm1. Bolded values are for best performance. of the WGN approach is an effective method for optimizing model performance and achieving optimal values in different settings. It can be observed that the model with WGN exhibits markedly elevated accuracy and markedly reduced LOE in the ECG5000 dataset. In the Coffee dataset, the two-layer LSTM-WGN model demonstrates the optimal LOE, although the accuracy is not significantly enhanced. In the Computer dataset, the two-layer LSTM-WGN model attains the highest accuracy.
## 4 Experimental Setup and Result Analysis
### Comparative Analysis
Moreover, test results on high-resolution and complex surveillance scenarios indicate that the proposed method exhibits stronger robustness and adaptability in restoring structural consistency and edge continuity compared to other baseline approaches. These advantages significantly enhance both the visual quality of the corrected images and their practical usability in real-world applications. Experimental results demonstrate that the proposed multi-scale feature modeling mechanism, TPS control point prediction strategy, and optical flow-guided constraint collectively exhibit significant advantages in the geometric correction of high-resolution distorted images. Compared with existing methods, the proposed model achieves higher PSNR and SSIM scores on standard benchmark datasets, indicating superior image restoration quality. In terms of processing efficiency, the proposed algorithm delivers faster inference speed while maintaining excellent correction performance. For instance, on distorted images with a resolution of 640 \(\times\) 640 pixels, the model achieves a substantially lower  
Figure 19: Visual comparison of rectification results across different algorithms. average inference time compared to representative methods such as RecRecNet and MOWA, thereby fully meeting the real-time requirements of practical applications.
## 6 System Evaluation and Model Prediction Performance
### EKF Model Effectiveness Evaluation  
The experiment conducted in this study aimed to evaluate the effectiveness of the EKF model in estimating the SoH of electric vehicle batteries. Using an 60 Ah lithium battery installed in a BMW i3 electric vehicle (German Marque, Shah Alam, Malaysia) as the subject, we applied the EKF to generate a SoH change curve over time, as illustrated as Figure 10. The x axis of the graph denotes the duration of battery usage in days, while the y axis represents the SoH of the battery. The graph reveals fluctuations in SoH, indicating that, under normal operating conditions, the EKF predicts a decline in the battery's SoH to 80% after 7810 days of usage. Subsequently, when the SoH falls  
Figure 9: Implemented user dashboard interface.  
below 80%, the degradation accelerates, with the SoH plummeting to below 30% after approximately 12,300 days. The SoH of the battery exhibits a rapid decrease from 91% to 80% within the initial period, possibly due to factors such as aggressive driving, extreme temperatures, or fast charging. Subsequently, from day 7810 until day 12,300, the SoH shows an exponential downward trend, decreasing by less than 30%, which could be attributed to aging effects and prolonged usage. Potential causes for the abrupt change in SoH observed in Figure 10 include unexpected high discharge rates or deep discharges beyond typical operational parameters, both of which can lead to significant and sudden capacity degradation. Furthermore, sensor inaccuracies or calibration issues might result in sudden drops in the measured SoH.  
Figure 10(a),b illustrate the application of EKF simulation to estimate the state of health (SoH) change distribution across 20 lithium batteries used in electric vehicles. Figure 10(a) presents the distribution map of SoH, while Figure 10(b) displays the kernel density estimation (KDE) of SoH. The simulation outcomes reveal that the EKF, as proposed in this study, predominantly estimates SoH values above 90% for electric vehicle lithium batteries, with additional values clustered within the range of 20-85%. These results align with the anticipated performance characteristics of the majority of batteries employed in electric vehicles. Our observations indicate that the model and method proposed in this study effectively estimate the SoH of electric vehicle batteries.
## 4 Discussion
### Assessment of LAI and Pigment Estimations Accuracy  
Even though the site LAI is very low (mean LAI is 0.8 m\({}^{2}\)/m\({}^{2}\) and mean field points' LAI is 0.88 m\({}^{2}\)/m\({}^{2}\)), LAI estimations were encouraging. The LAI heterogeneity within the Tonzi site was captured, as for a given date, points from both low and high LAI parts of the site were correctly estimated. Seasonal variations were also visible, as LAI variations from summer to fall are clearly followed. Indeed, in Figure 9, blue and dark green points come from the same part of the site (see site map, Figure 1) but are from fall and summer, respectively. Estimation RMSE (0.22 m\({}^{2}\)/m\({}^{2}\)) is remarkably low, and nRMSE (RMSE divided by the mean field points' LAI) is 0.25, quite similar to what can be obtained over a higher-LAI site: When estimating LAI over a Mediterranean grassland with a mean LAI of 2.87 m\({}^{2}\)/m\({}^{2}\), [PERSON] et al. [67] obtained nRMSEs of 0.22 and 0.18 using statistical and RTM-inversion approaches, respectively.  
C\({}_{ab}\) estimation performances were in line with previous studies. Foliar chlorophylls content estimations (RMSE = 5.21 \(\upmu\)g/cm\({}^{2}\), \(R^{2}\) = 0.73) are as good as what could be obtained over higher-LAI coniferous and deciduous sites in various studies (RMSE = 4.34 \(\upmu\)g/cm\({}^{2}\), \(R^{2}\) = 0.47 [68]; RMSE = 4.2 \(\upmu\)g/cm\({}^{2}\)[69]; RMSE = 8.1 \(\upmu\)g/cm\({}^{2}\), \(R^{2}\) = 0.40 [29]). Carotenoid estimations also performed well with a low RMSE and high R\({}^{2}\) (RMSE = 1.34 \(\upmu\)g/cm\({}^{2}\), R\({}^{2}\) = 0.59). This is similar to the values obtained by [PERSON] et al. [57] using high-resolution imagery (50 cm) over vineyards (RMSE below 1.3 \(\upmu\)g/cm\({}^{2}\) and R\({}^{2}\) of at most 0.46 when using the SAILH and the FLIGHT radiative transfer models).  
A factor that could explain some estimation errors (and specifically the underestimation of the C\({}_{ab}\) summer 2013 pink point) is the assumption that the biochemistry measured on one tree can be considered representative of (i) the biochemistry of leaves that are sensed by AVIRIS-C (i.e., mostly top of canopy leaves) and (ii) the leaf biochemical content of all the trees within a pixel. While to address (i), attention was given to only collecting leaves on the sunlit part of the crown (see Section 2.2.2), it is not possible to confirm (ii) with the available field data, and a leap of faith must be made. There is therefore always an uncertainty concerning how representative validation data are with regards to their associated pixels.
## 3 Mobility-Aware Duty Cycling
### Network Layer  
This study does not seek to test routing protocols. However, a routing protocol is required in order to ensure final delivery is to the MSN. Otherwise, MAC layer frames would simply be sent in bursts, with the MSN behaving as any other node in the network would. Given this, the optimized link-state routing protocol [39] (OLSR) is utilised in this study. This is an unconventional approach as OLSR is not usually used in a WSN environment. However. OLSR is a resource-heavy protocol which places a great load on the network, especially in terms of energy consumption. For our purposes this proved to be of benefit and resulted in accelerated tests, requiring a lower simulation time in order to acquire the desired results.
## 4 Results and Discussion
### Comparative Analysis of Results Using Statistical Methods and Selection of the Final Model
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Pairs for Comparison**} \\ \cline{2-4}
**RReliefF—Pareto 80/20 Rule** & \begin{tabular}{c} **Backward Selection via the** \\ **Recursive Feature** \\ **Elimination—ReliefF** \\ \end{tabular} & \
\begin{tabular}{c} **Backward Selection via the** \\ **Recursive Feature** \\ **Elimination—ReliefF** \\ \end{tabular} &
\begin{tabular}{c} **Backward Selection via the** \\ **Recursive Feature** \\ **Elimination—Pareto 80/20 Rule** \\ \end{tabular} \\ \hline Z & \(-3.077\) & \(-7.848\) & \(-18.727\) \\ Asymp. Sig. (2-tailed) & 0.002 & 0.000 & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Wilcoxon signed-rank post hoc test results. Figure 13: Normality tests of delay prediction results: (**a**) RRelieff algorithm; (**b**) Backward selection via the recursive feature elimination algorithm; (**c**) Pareto 80/20 rule. applies to all combinations, it is concluded that there are statistically significant differences among the delay prediction results for all three pairs of approaches to optimizing the input set of variables. Based on the presented results, the k-NN model whose number of inputs is optimized to four by the algorithm Backward selection via the recursive feature elimination is chosen as the final model. Figure 14 shows the prediction results of the dependent variable \(\mathrm{D_{EtoE}}\) using this model based on the data from the test set. The diagram also shows the regression line equation that explains the linear relationship between the actual and the predicted \(\mathrm{D_{EtoE}}\) values. State-of-the-art methods and techniques in network delay prediction are based mainly on machine learning and deep learning. In particular, Graph Neural Networks (GNN) stand out, which are adapted for processing data structured in the form of graphs. Other popular state-of-the-art techniques include Autoregressive Integrated Moving Average (ARIMA), LSTM, Gated Recurrent Unit (GRU), RNN, Convolutional Neural Network (CNN) models. Some of the most important advantages that distinguish the final selected k-NN model in this research with the mentioned state-of-the-art technique are the simplicity of interpretation and application, it maintains good performance with small data sets, it is adaptable to changes in the data set and it does not require time stationarity data.
# Bioactive Compounds from Herbal Medicine Targeting Multiple Myeloma
## 3 Flavonoids in Multiple Myeloma
### 3.12. _Formononetin_  
Formononetin (Figure 12), is an isoflavone, isolated from the roots of _Trifolium pratense_, _Astragalus membranacus_, _Glycyrrhiza glabra_, or _Pueraria lobate_[321].  
It has the ability to influence a number of carcinogenesis pathways in different malignant cells through various molecular mechanisms [322; 323; 324; 325; 326]. Formononetin has various pharmacological activities, such as anti-inflammatory, antiviral, antioxidant, neuroprotective activity, and wound healing [325; 327; 328; 329; 330].  
Formonentin inhibited NF-kB and activated AP-1 in MM cells, attenuated activation of PI3K/ AKT and MAPK pathways, decreased tumorigenic protein levels, induced apoptosis, potentiated the antitumor properties of bortezomib, and enhanced its apoptotic effects [321].  
Formononetin affected MM growth through the negative regulation of (STAT) 3/5 cascades mediated via oxidative stress. Formonentin inhibited STAT activation in MM cells, reduced the ability to bind STAT3 and STAT5 to DNA, as well as the nuclear pool of p-STAT3 and p-STAT5 in U266 cells. Formononetin inhibited the activation of upstream kinases involved in the STAT3 signaling cascade in MM cells, inhibited IL-6-induced STAT3-dependent reporter gene expression, decreased the expression of proteins involved in anti-apoptosis, angiogenesis, and proliferation (CDK2, CDK4 and Cyclin E), activated caspase-3 and induced cleavage PARP in U266 cells, negatively affected the cell cycle in MM cells, reduced the expression of cyclin D1 and cyclin B1 associated with cell cycle regulation, inhibited cell viability in MM cells, inhibited glutathione reductase expression in U266 cells, induced antitumor effects in the MM model xenograft, inhibited the growth of MM in vivo and inhibited the activation of STAT3 / 5 in tumor tissues, activated caspase-3 and induced cleavage of MM tissular PARP and reduced the expression of apoptotic proteins in tumor tissues [331].  
It is also thought to be involved in various signaling pathways, such as NF-kB, phosphatidylinositol 3--kinase/protein kinase B (PI3K/ AKT), and AP-1 [332; 333].
# Deep Learning Regressors of Surface Properties from Atomic Force Microscopy Nanoindentations
## 2 Methods
### AFM Experimental Data  
Surface properties of a given sample can be extracted through the analysis of AFM nanoindentation data. In this work, the Young's modulus and adhesion energy will be the main focus. To transform raw AFM data into force-indentation (F-I) curves, specific procedural steps must be followed. As depicted in Figure 1, while the probe approaches and withdraws from the sample, a photodetector records the voltage as a function of piezo position, \(z_{p}\) (vertical displacement of the cantilever).  
By knowing the system sensitivity, the voltage can be translated into cantilever displacement, \(z_{c}\). The tip-sample force is calculated with the cantilever stiffness, \(k\), applying [PERSON]'s law:  
\[F=k\cdot z_{c}. \tag{7}\]  
The indentation depth, \(\delta\), relates to the cantilever deflection, \(z_{p}\) position and the piezo position at contact point, \(z_{0}\), by  
\[\delta=(z_{p}-z_{0})-z_{c} \tag{8}\]  
We used available experimental AFM F-I data from primary non-epithelial vaginal cells, isolated from vaginal tissue harvested below the cervix from nulliparous LOXL1 knockout and wild-type mice (refer to [38] for detailed methodology). For all approach curves, the corresponding Young's modulus (apparent elastic modulus) had been obtained by Hertz contact model fitting. As for retraction curves, no surface properties were accessible from them, leading to the exclusive use of approach data for testing the developed machine learning models.  
Figure 1: Experimental setup to obtain AFM force-indentation curves. The dashed cantilever represents its original, undeformed configuration, \(z_{c}\) is the cantilever deflection and \(\delta\) is the indentation depth.  
The accuracy of each fitting from the original approach curve dataset was evaluated using the \(R^{2}\) metric (\(R^{2}=1-\sum_{i=1}^{n}(f_{i}-\hat{f}_{i})^{2}/\sum_{i=1}^{n}(f_{i}-\bar{f})^{2}\)) to compare the experimental force, \(f_{i}\), with the predicted force by the contact model, \(\hat{f}_{i}\), for each indentation value. Instances with an \(R^{2}<0.9\) were removed from the analysis, due to their likelihood of exhibiting experimental anomalies or simply not being suitable to be fitted with Hertzian theory. Some of these instances presented negative forces for high indentation values, while others even presented an unphysical negative Young's modulus resulting from the fitting procedure. Then, all the curves with a maximum indentation lower than 150 nm (the maximum indentation defined for our synthetic dataset) were dropped and force-indentation points were uniformly removed from each curve until they reached a final value of 50 points, thus enabling their application as inputs for the ML models. Following these restrictions, a total of 24,304 curves were obtained, with the apparent elastic modulus distribution being presented in Figure 1(a), while examples of individual F-I curves are shown in Figure 1(b).
## 10 Future Directions
### Trust and Incentive Mechanisms  
Developing robust trust management systems is vital for evaluating and verifying the credibility of participating clients. Trust mechanisms can help to maintain the integrity of FL systems by ensuring that only trustworthy participants contribute to the model training. Additionally, designing incentive mechanisms that encourage honest participation while deterring malicious behavior could enhance the overall security framework of FL systems.
# Dynamic Range Compression and the Semantic Descriptor Aggressive
## 4 Perceptual Listening Experiments
### Listening Experiment 2 Method
#### 4.2.2 Statistical Analysis of Experiment 2 Results  
A two-way repeated measures ANOVA was run to determine the effect of compression settings measured to have or not have distortion and the interaction effect of the three songs and compression settings on an aggressive sound quality. [PERSON]'s test of sphericity indicated that the assumptions of sphericity had been violated for the two-way interaction between the song and settings \(\chi^{2}(2)=71.82\), \(p=0.001\). Therefore, a Greenhouse-Geisser correction was applied (\(\varepsilon=0.578\)). [PERSON]'s test of sphericity indicated that the assumptions of sphericity had not been violated for the effect of the settings \(\chi^{2}(2)=7.45\), \(p=0.593\).  
Simple main effects were run and showed, again, that there was no statistically significant two-way interaction between the songs and settings on aggressive sound quality \(F\) (8,136) = 0.208, \(p=0.081\). There was, however, a statistically significant effect of settings on an aggressive sound quality, \(F\) (4,68) = 181.722, \(p<0.001\), suggesting that the settings used have a statistically significant effect. Post-hoc analysis with a Bonferroni adjustment showed the mean aggressive scores for all compressed settings were statistically significantly higher than the scores for no compression (\(p<0.001\)). The scores for both the 1176 settings were statistically different from one another (\(p<0.001\)), but the scores for both the software settings were not statistically different (\(p=0.57\)). This indicates that the faster timing behaviour of the SW All setting, which was emulating the timing curve of the 1176 in all-buttons, has little additional effect over the SW 4:1 setting in the creation of an aggressive vocal sonic signature. The scores for both the 1176 settings were statistically higher than the scores for both the software settings (\(p<0.001\)). This indicates that while a clean, fast-acting compressor can give a vocal a more aggressive sound quality than the uncompressed audio, compression settings that impart audible distortion are required for the most significant effect.  
Figure 6: Aggressive results from the second listening experiment.