esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A Proofs of Theorems  
_Appendix A.1. Proof of Theorem 1 (Optimal Fusion Convergence)_  
1. **Mutual Information Maximization:** Define the variational lower bound \[I(F_{fused};F_{HR},F_{LR}) \geq\mathbb{E}\Bigg{[}\log\frac{q(F_{fused}|F_{HR},F_{LR})}{p( F_{fused})}\Bigg{]}\] (1) \[\propto\sum_{s}A_{s}\cdot\mathbb{E}[KL(q_{s}||p_{s})]-\mu\Big{\|} F_{fused}\Big{\|}_{2}^{2}\] (2) where \(q_{s}\) are variational distributions and \(p_{s}\) are prior distributions.
2. **Lagrangian Optimization:** Enforce \(\sum_{s}A_{s}=1\) via Lagrangian multipliers \[\mathcal{L}=I(F_{fused};\cdot)-\lambda_{det}\mathcal{L}_{det}-\eta\left( \sum_{s}A_{s}-1\right)\] (3)
3. **Gradient Analysis:** The partial derivative with respect to \(A_{s}\) is \[\frac{\partial\mathcal{L}}{\partial A_{s}} =\Bigg{(}\frac{\partial I}{\partial F_{fused}}-\lambda_{det} \frac{\partial\mathcal{L}_{det}}{\partial F_{fused}}\Bigg{)}\frac{\partial F _{fused}}{\partial A_{s}}-\eta\] (4) \[=[\text{Mutual Information Gradient}-\text{Detection Gradient}] \frac{\partial F}{\partial A_{s}}-\eta\] (5)
4. **Optimality Condition:** Setting the gradient to zero yields \[\frac{\partial I}{\partial F_{fused}}=\lambda_{det}\frac{\partial\mathcal{ L}_{det}}{\partial F_{fused}}\] (6) Substituting back gives the theorem condition \[\frac{\partial A_{s}}{\partial l}=\lambda\Bigg{[}\frac{\partial\mathcal{L}_{det}}{ \partial F_{fused}}\frac{\partial F_{fused}}{\partial A_{s}}\Bigg{]}\] (7) where \(l\) represents the illumination level (in lux).
5. **Convergence Verification:** The Lyapunov function \(V=\sum_{s}(A_{s}-A_{s}^{*})^{2}\) satisfies \[\frac{dV}{dl}=-2\sum_{s}(A_{s}-A_{s}^{*})\frac{\partial A_{s}}{\partial l}\leq 0\] (8)where \(A_{s}^{*}\) represents the optimal attention weight values. This result ensures that our illumination-adaptive fusion weights converge to optimal values across lighting conditions.
## 3 Application
### Artificial Intelligence Model Results
#### 3.2.3 Gaussian Process Results  
Using the Gaussian process method, the hyper-parameters were searched, and the prepared training datasets were trained. After analyzing, the results show that the Gaussian process model can provide the predictive performance with _VAF_ of 90.4209, \(R^{2}\) of 0.9051 and _RMSE_ of 0.1798 for training datasets and _VAF_ of 86.7895, \(R^{2}\) of 0.8900 and _RMSE_ of 0.1733 for testing datasets, and the actual _BRZT_ and predicted _BRZT_ were plotted in Figure 6.  
Figure 4: Performance of the multiple linear regression (MLR) model.  
Figure 5: Performance of the optimal artificial neural network (ANN) model.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **PA 1 (\%)** & **SP 2 (\%)** & **EO 3 (\%)** & **EC 4 (\%)** & **OA 5 (\%)** \\ \hline SRI [11] & 75.40 & **92.98** & 24.60 & **7.02** & 83.28 \\ NSVDI [18] & **94.29** & 81.52 & **5.71** & 18.48 & 88.56 \\ MC3 [20] & 86.28 & 73.06 & 13.72 & 26.94 & 80.35 \\ SI [14] & 62.64 & **97.03** & 37.36 & **2.97** & 78.07 \\ SDI [19] & **96.18** & 86.16 & **3.82** & 13.84 & **91.69** \\ MPSI6  & **95.78** & **94.77** & **4.22** & **5.23** & **95.33** \\ \hline \hline \end{tabular}  
* **PA:** Producer’s Accuracy; **25P**: Specificity; **3 EO**: Omitted Error; **4 EC**: Committed Error; **5 OA**: Overall Accuracy;
* **6 MPSI:** The proposed shadow detection method.  
\end{table} 
Table 1: Shadow detection accuracy data for test image A in Figure 5a. Figure 7: Test image C. (**a**) The original image. (**b**) The reference shadow image. (**c**) Shadow image by the SRI-based algorithm [11]. (**d**) Shadow image by the NSVDI-based algorithm [18]. (**e**) Shadow image by the MC3-based algorithm [20]. (**f**) Shadow image by the SI-based algorithm [14]. (**g**) Shadow image by the SDI-based algorithm [19]. (**h**) Shadow image by the developed MPSI-based approach. Practically, in real-time or approximate real-time shadow detection applications for VHR multispectral remote sensing images, timesaving algorithms draw more attention from most end-users, given equivalent shadow detection results. Therefore, the time consumption is an important metric that is usually considered while designing and selecting shadow detection algorithms. Table 4 summarizes the computational time required for detecting shadows in test images A-C, shown in Figures 5a, 6a and 7a, using the proposed MPSI-based shadow detection approach and five other investigated comparable shadow detection algorithms. From the last row of Table 4, the proposed MPSI-based shadow detection approach was verified using a timesaving algorithm. The significant reason for this finding is that the proposed shadow index (MPSI) is very simple.
# Analysis of Pre-Driver and Last-Stage Power--Ground-Induced Jitter at Different PVT Corners
## 4 PSIJ Sensitivity Study of Two-Stage Driver
### Amplitude Sensitivity of PSIJ Transfer Function  
The amplitudes of distinct PSV noises of the driver's IO-stage circuit are independently swept at \(f=70\) MHz. Figures 13 and 14 show the amplitude-dependent PSIJ of the last stage (i.e., as shown in Figure 8b setup) and the pre-driver stage (i.e., as shown in Figure 8b setup), respectively. Generally, the total IO buffer PSIJ and the separate jitter of the driver's IO stages increase as the amplitude of the PSV increases.  
Figure 12: Parametric plot of the driver’s jitter TF as the last-stage frequency is swept for different sinusoidal tone excitation of the pre-driver stage.  
Figure 13: Last-stage parametric plot of the IO buffer amplitude-dependent jitter TF as the last-stage amplitude is swept for different \(V_{dd}(t)\) supply noise.  
By comparing the Figures 13 and 14 results, the PSIJ TF has higher sensitivity to the amplitude of the supply voltage noise of the pre-driver than the last stage. This is mainly due the fact that the pre-driver stage is composed of three inverter circuits powered by a \(V_{DD}\) supply. For instance, the sensitivity of the pre-driver and last stages are \(S_{P}\approx 0.7\) ps mV\({}^{-1}\) (i.e., Figure 14) and \(S_{L}\approx 0.12\) ps mV\({}^{-1}\) (i.e., Figure 13).
## 4 Results
### Simulated Dual-Doppler Lidar Measurement Scenarios
However, it shows smaller absolute quantities than the single elevation scan and also has a larger spread in the count of measurements over the grid. For the single elevation scan, the average height offset from hub height is linearly increasing from west to radially outward. The volume scan approaches the hub height more convincingly, i.e., the average absolute offset of the measurements is much lower, namely 1.6 m instead of 6.8 m. However, due to the heterogeneous pattern, some local peaks and sharp discontinuities are present. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case** & **Lidar Selection** & **Scanning Scenario** & **Continuity Correction** & \(MAE\) **(m-s\({}^{-1}\))** \\ \hline
1 & \(1+2\) & volume & on & 0.171 \\
2 & \(\Delta\chi\approx 60^{\circ}\) & volume & off & 0.175 \\
3 & & single elevation & on & 0.156 \\
4 & & single elevation & off & 0.158 \\ \hline
5 & \(1+3\) & volume & on & 0.3  
The large absolute errors on both the absolute velocity and the wind direction estimated by the volume scan to the far east of the measurement domain (see Figure 6b,e) are possibly caused by an average height difference of 8 m locally (see Figure 7c). The error in the wind direction directly east of the wind turbine in case of the single elevation scan (see Figure 6f) is most probably caused by the average height difference of 10 m locally (see Figure 7d). Alternatively, wake-induced \(v\)-components combined with the spatial averaging applied in the algorithm could also be the reason of the observed absolute errors in the wind direction.
## 3 Results
### Design of Experiments Outcome  
#### 3.3.1 Design of Experiment Outcome with aGCE  
The DoE procedure was applied to optimize the DPV parameters used for revealing the reduction peak of the DCF by-product. The sequence of experiments identified by the model was performed on three different days, running the series in a random way. All of the measurements were performed on a 0.02 mol L\({}^{-1}\) phosphate buffer solution, at pH 2.5, by successive additions of DCF and exploring the signals in the concentration range 3.0-30 \(\upmu\)g L\({}^{-1}\). The analytical response used for DoE was the mean current value obtained for a DCF solution of 12 \(\upmu\)g L\({}^{-1}\) (\(n\) = 3). The collected data were then processed with R software to estimate the model coefficients through Multiple Linear Regression (MLR) and generate the iso-response curves and response surfaces (Figure 5).  
A first analysis of the confidence intervals of the model coefficients (Figure 5a), calculated with a confidence level of 95%, suggested that the terms containing the SC were not significant. The highest currents are obtained with a high value of \(E_{\text{pulse}}\) and a low value of \(t_{\text{pulse}}\), as expected for DPV measurements (Figure 5b), but the maximum response was obtained for parameter values located at the border of the investigated ranges. Unfortunately, near these conditions, the precision of the measurement decreases. This is evident by observing the standard deviation values obtained for the different experiments conducted for the DoE, reported in Figure 6, and by the results obtained applying the DoE to the standard deviation values (Figure S3). Considering this issue, the experimental conditions  
Figure 4: EDS signals (5 keV) and weight percentage of the elements obtained analyzing the SPCE1 WE surface before the polishing procedure, after the polishing procedure, and after the electrochemical activation.  
Figure 3: FE-SEM images of the SPCE1’s WE before the polishing procedure (**a,c**), after the polishing procedure (**b,d**), and after the electrochemical activation (**e**). (**a,b**) 10 keV; FOV(Field Of View) = 1.35 \(\upmu\)m; (**c–e**) 10 keV; FOV = 17.00 \(\upmu\)m.  
that were chosen as optimal (see Table 2 and Figure S4) were a compromise between a high current value and a good precision of the measurement and which led to the achievement of satisfactory sensitivity and repeatability (red bar in Figure 6).  
Figure 6: Mean current intensity and the corresponding standard deviation calculated on the three measurement replicates of each DoE experiment conducted with the aGCE. The red bar identifies the measurement results obtained with optimized conditions.
## 4 Discussion
### Comparison of Technique Classification  
The classification outcomes of the striking techniques in this study are comparable to other projects, such as [PERSON] et al. [19] (overall mean accuracy of \(0.90\pm 0.12\) and \(0.87\pm 0.09\) for two different sensor placements), [PERSON] et al. [23] (approximately 86% accuracy), [PERSON] et al. [45] (91.1% accuracy for person-independent evaluation), and [PERSON] [21] (ranging from \(87.2\pm 5.4\)% to \(95.33\pm 2.51\)%).  
Importance of Divers Participant Groups  
However, the achieved accuracy in this study is slightly lower than the results reported by [PERSON] et al. [20] (98.68%). It is important to note that a direct comparison is not feasible since the researchers only classified three types of striking technique and both [PERSON] et al. [20] as well as [PERSON] et al. [19] did not evaluate the model using participants and data unknown to the model. It must be considered that using the same group of athletes on which the models were trained may introduce an optimistic bias and affect the model's performance on other athletes. Developing ML models requires establishing robustness and ensuring good generalization, which means accurate classifications for unseen data. Therefore, to realistically estimate the model's performance, evaluating it with new data  
Figure 5: Average accuracy (in percent) including standard deviation per participant group (Experienced: >5 years of training and competition athlete; Novice <4 years of training and no competition experience).  
becomes crucial. In this research, deliberate efforts were made to introduce diversity within the training group and maximize it within the testing group, enhancing the models' generalizability, while slightly lowering the accuracy measures. Furthermore, the study explored the classification of two techniques, backfist and ridge hand, not previously included in related research, which increased the models' susceptibility to errors due to the added data variation that needed to be distinguished. Despite these additional challenges, the implemented models demonstrated good generalization performance compared to the related projects.
## 7 Simulation Results
### Offset Calibration  
Figure 1 presents the results obtained by algorithm (4) based on the full consensus of the corrected offsets, and Figure 2 presents the results obtained by algorithm (10), (11) based on the introduction of one selected reference (node 1). The level of 0.15 was selected for \(\max_{t}|v_{t}^{i}|\) (this is much higher than it would be in reality, in order to present the main effects more visibly), with a step size of \(\delta=0.05\). In both figures, the estimates behave in accordance with Theorems 1 and 2. Namely, in Figure 1, we can easily recognize the large bias identified in (7). On the other hand, Figure 2 shows the effect of selecting a reference offset that is equal to zero: all the parameter trajectories show an obvious convergence to zero. It is possible to note that the convergence rate is somewhat slower and the variance is higher in the case of Figure 2 (keeping a lower number of parameters in mind, i.e., a lower number of degrees of freedom in (11)).
## 2 Materials  
### The Dataset  
After a satellite is put into orbit, the SNR performance of the remote sensing payloads can only be estimated on remote sensing images. In this study, the experimental data in Table 1 are all remote sensing imagery. The noisy image dataset is obtained by the superposition of the original signal and the white Gaussian noise. Thus, the noise variance levels are known. However, the texture information in an image increases the evaluation errors. After reducing texture information, white Gaussian noise could be learned by neural networks. Answering the question of how to reduce the interference of ground objects is the key to manufacturing the dataset.  
The imaging process of the ground objects captured by an remote sensor on its focal plane can be described as below:  
\[\textit{Image}=A\times(\textit{Landscape}\otimes\textit{PSF})\ (x\textit{,}y )\times\sum\limits\delta(x\textit{-}j\times\triangle x,y\textit{-}i\times \triangle y)+\textit{noise}, \tag{1}\]_Landscape_ is the ground objects, which is a continuous function of spatial variables (\(x\),\(y\)). Additionally, its value is directly proportional to the ground object irradiance entering a remote sensor. After convolution operation with the point spread function--_PSF_--Landscape is sampled by the focal plane CCDs with sampling intervals of \(\triangle x\) and \(\triangle y\).  
According to Equation (1), the approximately noise-free dataset at a specified ground sample resolution could be obtained after mean filtering and downsampling. Additionally, different levels of white Gaussian noise are added into the data to construct simulated data with known true-noise SDs. The data processing is summarized as follows: (1) 3 \(\times\) 3 mean filtering on the original data; (2) two (or more) instances of downsampling to obtain images without noise; (3) the division of images into small blocks with 32 \(\times\) 32 pixels; (4) the addition of white Gaussian noise to the data.  
In the cases of differences in dynamic ranges or imaging conditions for different remote sensors, the same noise levels make some data appear saturated and degraded. Therefore, the principle of adding noise is: the noise added into images could not exceed the signal power. Considering the principle, the maximum noise variance added into images was \(\sigma^{2}\) = 20.  
The input of the model is a small block with a size of 32 \(\times\) 32 pixels. Thus, images are also divided into 32 \(\times\) 32 pixels. Additionally, each image is randomly divided, and the number is 50% of the formatted division. In addition, some small blocks are rotated and mirrored to increase the robustness of the model and the expansion of the dataset. The information is shown in Table 2.
# Scheduling Sparse LEO Satellite Transmissions for Remote Water Level Monitoring
###### Abstract  
This paper explores the use of low earth orbit (LEO) satellite links in long-term monitoring of water levels across remote areas. Emerging sparse LEO satellite constellations maintain sporadic connection to the ground station, and transmissions need to be scheduled for satellite overfly periods. For remote sensing, the energy consumption optimization is critical, and we develop a learning approach for scheduling the transmission times from the sensors. Our online learning-based approach combines Monte Carlo and modified k-armed bandit approaches, to produce an inexpensive scheme that is applicable to scheduling any LEO satellite transmissions. We demonstrate its ability to adapt in three common scenarios, to save the transmission energy 20-fold, and provide the means to explore the parameters. The presented study is applicable to wide range of IoT applications in areas with no existing wireless coverages.  
Internet of Remote Things; sparse LEO satellite transmission; water-level monitoring 202323588110.3390/s2312588110.  
In remote regions such as Greenland or Antarctica, where sea level information is critical for climate monitoring [5; 6], field campaigns are expensive and it may be prohibitively expensive to maintain a dense network of sensors. Wireless connectivity should reduce the maintenance cost of remote sensor networks by reducing the frequency of expensive site visits to collect data or check the status of instruments. This paper focuses on a low-cost and energy-efficient wireless communication technique using low earth orbit (LEO) satellites that is suitable for remote water-level sensor networks, notably GNSS-IR ones, Figure 1.
## 3 Methodology
### Remote Sensing Index
#### 3.1.2 Ndvi  
The NDVI was calculated based on the reflectance of the near-infrared and red bands. The formula is as follows:  
\[\mathrm{NDVI}=\frac{\rho_{nir}-\rho_{red}}{\rho_{nir}+\rho_{red}} \tag{3}\]  
where \(\rho_{nir}\) and \(\rho_{red}\) are the reflectance from the near-infrared and red bands, respectively.  
MODIS Level 1B calibrated radiances data with a spatial resolution of 250 m (MYD02 QKM) for 2013 and 2014 were also downloaded from the NASA website at the above address and then pre-processed using a data processing chain, including radiometric, geometric, atmospheric corrections, and cloud detection and NDVI calculation [21].
## 4 Discussion
Note that the optimized classification accuracies of L1-MCCA, CCA and PSD are consistently higher than those without optimization. The findings indicate that L1-MCCA is better than CCA and PSD in the classification of SSVEP signals. In addition, the combination of L1-MCCA with SVM classifier significantly improved the accuracy of BCI classification. In further exploratory studies, we will try different classifiers or optimization algorithms, aiming to provide useful insights for the accuracy of BCI classification based on SSVEP. At present, there has been some research in other directions based on the L1-MCCA algorithm. A document proposes a sparse Bayesian learning L1-MCCA (SBMCCA), which improves the computational efficiency based on a certain classification accuracy [42]. In addition, [PERSON] et al. [45] proposed a new decision selector (DMS) that integrates classification decision combinations based on CCA's different frequency methods (including seven types of CCA, MsetCCA, L1-MCCA, et al.) into SSVEP. The results show that DMS-ECCA&TRCA obtained the best classification accuracy rate which reaches 98.3%. It shows that the combination of multiple techniques is also a research direction. One of the limitations in this study is the relatively small sample size and category of stimuli. In further studies, larger sample sizes and stimulus categories will be conducted to further confirm the current findings. The second limitation is that filtering short interval BCI scenarios will lead to edge effects and may affect the accuracy of decoding. In a further exploratory study, advanced data processing methods will be utilized to overcome this issue. An additional limitation is that different stimulations were presented separately and randomly rather than simultaneously, which was not exactly the same as the real BCI. Although there are certain limitations, this study still provides some insights into the field of BCI. In a further exploratory study, we are not going to stop at this stage but will adopt advanced technologies and data processing methods to make the L1-MCCA based SVM method more applicable clinically.
## 3 Results and Discussion
### Unconfined Compressive Strength (UCS) Test
#### 3.1.2 Effects of M-GGBS on UCS  
For the M-GGBS stabilizer, all specimens showed enhancement of their UCS values. Figures 5 and 6 presented the maximum UCS of the M-GGBS-stabilized soils at the MGGBS ratio of 1:3 when a 20% binder was used to stabilize the sulfate-bearing soil: 3327.33, 7125, and 12,115 kPa at 7, 28 and 90 days of curing, respectively. This improvement may have been because of the high content of highly reactive M, and because an excessive residual  
Figure 4: UCS of soils stabilized with 10% of L, M, and M-GGBS before and after soaking in the presence of sulfate: (**a**) after 7 days of curing, (**b**) after 28 days of curing, (**c**) after 90 days of curing.  
can negatively impact the strength of stabilized soils. Nevertheless, the UCS values of the stabilized samples before and after soaking indicated enhancement in the mechanical performance of the samples after wetting, except ratios of 3:1 and 1:1 when used to treat gypseous soil samples with 10% M-GGBS-stabilizer (see Figure 5); for example, the samples treated with 5M-5 GGBS deteriorated by 13.02%, 33.11% and 29.76% at 7, 28 and 90 days of curing, respectively. The UCS of the treated soil sample with 2.5M:17.5 GGBS was 11,275 kPa after soaking, which was significantly higher than that before soaking (914.23%) after 7 days of curing. After soaking, the 5M-15 GGBS ratio of 1:3 exhibited an optimum UCS value of 12,831 kPa, which was increased by 5.91% after 90 days of curing. However, the UCS values showed a decline at higher M-GGBS ratios. These findings agree with the report by [PERSON] et al. [2], where highly reactive M was found to have better activation efficiency with GGBS compared to low-reactive M; therefore, lower M-GGBS ratios are recommended to achieve higher UCS values.  
Figure 5: UCS of soils stabilized with 20% of L, M, and M-RHA before and after soaking in the presence of sulfate: (**a**) after 7 days of curing, (**b**) after 28 days of curing, (**c**) after 90 days of curing.
## 4 Results and Discussion  
Three particular test cases were selected from the available data, and the key parameters are listed in Table 1, including the projectile velocity from the experimental and computational fluid dynamics (CFD) results. Initial gas temperature was 293 K. However, in case 3, the system failed to capture the projectile velocity. The projectile velocity was measured at an optical window 3320 mm downstream of the launch tube exit. Since the pressure in the target chamber was usually set to a low pressure in the order of several kilo Pascal. It is assumed that the projectile speed has no significant attenuation.