esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
A\({}_{\text{VNDVI}}\) (Accumulative Visible Normalized Difference Vegetation Index), a new type of derivative parameters of NDVI, was set up by improving the computational formulas and importing the spectral information of visible bands after analyzing the construction idea of NDVI and its derivative parameters. Then, the characteristic values of VNDVI (Visible NDVI) were calculated by applying a combinational method of sensitive bands of visible bands. The study carried out the fitting analysis between NDVI, VNDVI, A\({}_{\text{VNDVI}}\), and LAI (Leaf Area Index). Several conclusions are obtained according to data analysis. Firstly, all of the determination coefficients between NDVI, VNDVI, A\({}_{\text{VNDVI}}\), and LAI of rapeseed can reach or exceed 0.83. The distribution of their RMSE values ranges from 0.4 to 0.5 and absolute values of RE vary from 0.9% to 2.1%. Secondly, the inversion sensitivity S\({}_{\text{V}}\) of VNDVI and LAI ranges from 0.7 to 1.9 relative to NDVI, and the inversion sensitivity S\({}_{\text{A}}\) of A\({}_{\text{VNDVI}}\) decreases in varying degrees with the promotion of capacity of resisting disturbance accordingly. Its value varies from 0.1 to 0.9. Thirdly, the values of S\({}_{\text{A}}\) remain stable between 0.1 and 0.3 with the increase of NDVI. Applying the inversion model of A\({}_{\text{VNDVI}}\) will be a considerable scheme when faced with a complex environment and many interfering factors.  
hyperspectral NDVI; derivative parameters; rapeseed; leaf area index Article
# Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants
## 4 Experiments and Results
### Improved ML Models
#### 4.3.4 XGBoost Results

Table 9: _Cont._  
Figure 27: XGBoost model fit over actual EPA NO\({}_{2}\) values for training/testing sets throughout the year using a single power plant (Alabama) dataset. Figure 28: XGBoost actual vs. predicted EPA NO\({}_{2}\) correlation plot using TROPOMI and date features for the study’s first scenario. Figure 30: XGBoost model fit over averaged EPA NO\({}_{2}\) values for training/testing sets throughout the year for all coal-fired power plants second scenario. Figure 29: XGBoost actual vs. predicted EPA NO\({}_{2}\) correlation plot using TROPOMI and date features for the study’s second scenario. The impact of having either daily or weekly MERRA-2 data are seen in Experiments #4 through #8 (Table 9). Using weekly MERRA-2 data, the correlation between MERRA-2 and EPA NO\({}_{2}\) improved slightly. When compared to the monthly MERRA-2 experiments, which performed significantly better, this model did not benefit from including the different MERRA-2 weekly and daily datasets. The study's first scenario is represented by XGBoost experiments #1, #2, and #3 (Table 9). Different feature sets produced a significant variance in MSE and error rate. Experiment #2 performed considerably better due to the inclusion of monthly MERRA-2, date features, and TROPOMI NO\({}_{2}\). These predictors helped experiment #2 estimate EPA NO\({}_{2}\) values 27.76% better than experiment #1 and 3.05% better than experiment #3. The second scenario findings are listed in Experiment #9 (Table 9), with the model correctly predicting 82.07% based on the error rate.
## 2 Methodology
### Refined Multiscale Symbolic Diversity Entropy  
Single-scale entropy feature contains limited information, and it is difficult to distinguish the differences in the dynamical complexity of vibration systems. The coarse-granulation process enables the time series to be segmented at multiple scales. By calculating the entropy value of the time series at each scale, multi-scale entropy can be obtained. As a result, the fault information can be greatly enriched, and the identifiability of fault features can be effectively improved. The calculation steps of refined multiscale symbolic diversity entropy (RMSDE) are as follows:  
1. The original time series is multi-scale segmented by using the coarse-graining procedure. If the scale factor is \(\tau\), then the original time series is partitioned into \(\tau\) multiscale time series with the following procedure: \[M_{j}(\tau)=\frac{1}{\tau}\sum_{i=(j-1)\tau+1}^{j\tau}x_{i},1\leq j\leq N/\tau\] (7) where \(x\) denotes the original signal sequence. The value of \(\tau\) is a constant positive integer. When \(\tau=1\), it means that the original sequence has not been multi-scale segmented. Figure 2 gives the procedure of coarse-graining of the time series when \(\tau\) is 2 and 3, respectively.
2. Based on the procedure of RSDE calculation in the previous section, calculating RSDE of \(\big{[}M_{j}(1),M_{j}(2),\cdots,M_{j}(\tau_{\max})\big{]}\) with different scales \([1,2,\cdots,\tau_{\max}]\), RMSD can be written as follows: \[RMSDE(X,\tau,m,c,\epsilon)=RSDE\big{(}M_{j}(\tau),m,c,\epsilon\big{)}\] (8) For rolling bearings with different health states, the separability of multi-scale entropy is better than that of single-scale entropy. Due to the introduction of scale factor \(\tau\), RMSDE can more intuitively separate the different fault types of rolling bearings.
## 8 Routing Protocols of Multi-UAV Systems
### Topology-Based Routing Protocols
#### 8.1.3 Reactive  
Reactive protocols [85; 86; 23; 87] are optimized versions of proactive protocols that aim to minimize the bandwidth consumption. This sub-category of protocols is based on on-demand routing protocols, because routes are created only when necessary. If there are no data to share between two UAVs, then there is no need to calculate the route. This approach results in the efficient use of the bandwidth and reduced overhead. The most commonly used reactive protocols are Dynamic Source Routing (DSR) [85], Ad Hod On-demand Vector (AODV) [86], and Time-Slotted On-Demand (TSOD) [87].  
DSR [85] is a dynamic source-routing protocol whose routing is initiated by the source UAV that wants to send a message. The complete route to transmit a message is indicated in the routing table of the source UAV. However, it is not possible to send messages if the route to the destination UAV is not registered in the header of the source UAV. The AODV protocol [86] leverages this limitation; it is the most used routing protocol in both VANET and FANET. Before sending a message, the source UAV checks if the routing path already exists. If so, it uses the existing route. Otherwise, it triggers a process to compute the route to the destination UAV.  
On the other hand, a risk of collision between packets can occur when there are enough exchanges in the network. TSOD [86] allows us to avoid collisions; it is an extended version of AODV that introduces a time slot routing protocol. Each source UAV has a time slot allocated to it for sending messages, and they can only send messages during this slot. This protocol reduces significantly the lost packets and minimizes network bottlenecks.
## 3 Results and Discussion
For a velocity of 2600 mm/min, the results exhibited an initial increase in the signal due to bad quality, saturating the signal as a cut loss was obtained. Figure 5 shows the tests corresponding to the 10 mm-thick aluminum plate and oxygen as the assist gas. The peaks correspond to regular defects in the tests with a focal position of \(-2\) mm (Figure 5a) or 12 bars (Figure 5c), and an increase in the mean level for bad lateral quality for \(-4\) mm, lower laser powers and gas pressures, or increasing cutting velocity. Figure 4: IR monitoring mean levels (gain 2) obtained during the laser cutting of aluminum of 10 mm with nitrogen, comparing the ideal cutting conditions (black line) with variations in: (**a**) focal position; (**b**) laser power; (**c**) gas pressure; and (**d**) velocity. (**e**) Optical images of cut samples with the ideal cutting parameters (top), with a focal position of \(-2.8\) mm (center) and with a focal position of \(-3.2\) mm (bottom). Figure 5: IR monitoring mean levels (gain 2) obtained during the laser cutting of aluminum of 10 mm with oxygen, comparing the ideal cutting conditions (black line) with variations in: (**a**) focal position; (**b**) laser power; (**c**) gas pressure; and (**d**) velocity. (**e**) Optical images of cut samples with the ideal cutting parameters (top), with a focal position of \(-2\) mm (center) and with a focal position of \(-4\) mm (bottom). Figure 6: VIS (gain 4) and IR (gain 2) monitoring mean levels obtained during the laser cutting of aluminum of 5 mm with nitrogen, with: (**a**) focal position of \(-0.6\) mm; (**b**) velocity of 7500 mm/min. However, differences were encountered between the monitoring of both spectra, because not all the defects were correctly detected in the VIS spectrum by the monitoring system. For the 5 mm-thick aluminum plate, generated defects when varying focal, velocity and gas pressure tests were not detected by the photodiode in VIS spectrum even at the maximum gain (gain 4, 0.15 V), whereas those same defects provoked an increase in the signal in the IR spectrum (Figure 6). Isolated defects were displayed as regular peaks in the IR monitoring signal. In contrast, a general deterioration of the lateral cut quality was observed with a general increase in the IR mean level. The failure in defect detection for VIS spectrum was attributed to the low signal obtained, which was due to the combination of the intrinsic characteristics of the specific case (emissivity of the material, thickness, assist gas). In the other studied cases, all the defects were detected in both spectra. Thus, in the present study, it was found that it was more convenient to use the IR spectrum for monitoring and ensuring the laser cut quality. In addition, a system with a higher amplification of the VIS monitoring signal would allow better detection of defects in cases of thinner plates and/or materials with low emissivity.
## 7 Experimental Results
### Tasks Acceptance Ratio  
We simulated five tasks ten times with different period range and measured the percentage of the time tasks accepted. The same experiment has been performed and compared with existing approaches that we used in the case of utilization bound experiments. The result of the simulation has been shown in Figure 11. On average, the proposed approach accepts more tasks than all existing methods. With increasing tasks, the probability of acceptance drops, but the rate of decrease in the proposed approach is much less as compared to other approaches. For more than 25 tasks, all other approaches plummet towards zero, and the acceptance probability is very low. For a higher number of tasks, the \"No-threshold\" approach accepts a task after 1000 tasks, which is very minute and not an acceptable situation for most real-time systems. The optimal threshold approach allows more than 20% of the tasks even for a more broad range of 1 to 100 and thus indicates a significant gain over other similar approaches.  
Figure 10: Effect of CPU Utilization Bound on varying input task load for RM and EDF algorithms, given Period = 1–10, Execution Time Range = 1–5, Arrival Time Range = 1–5. (**a**) effect of CPU Utilization Bound on RM algorithm—Existing Approaches vs. Proposed Approach; (**b**) effect of CPU Utilization Bound on EDF algorithm—Existing Approaches vs. Proposed Approach.
# Quasi-All-Passive Thermal Control System Design and On-Orbit Validation of Luojia 1-01 Satellite
## 3 TCS Design Proposal
### Heat Insulation Design  
Heat insulation design is mainly implemented by two ways, one is to cover instruments with MLI made of polyester film, and the other one is to insert heat insulating pads between contact surfaces of instruments and mounting panels. The external surface of the satellite frame is covered with MLI and the solar panels are mounted to the satellite frame via heat insulated pads. For instruments vulnerable to temperature change like the camera and battery, a heat insulation method is introduced in order to undermine or to isolate them from the influence of other instruments. Instruments outside the satellite cabin also employ the two ways of heat insulation with the purpose of diminishing the influence on them from the by outside space thermal environment which is also beneficial to keep the satellite isothermal. The regions and instruments covered by MLI are listed in Table 8 and the instrument mounting surfaces using heat insulated pads are listed in Table 9.
goals, strategies, the feasibility of technology development, and the amount of funding required, as well as to evaluate the characteristics of the facilities and the site. The decommissioning stage involves decontamination, cutting, demolition, and radioactive waste management. In the final post-recovery stage, the site is restored, and site regulations are addressed. A nuclear decommissioning project is long-term and requires a minimum of several years to perform these tasks, with a total of 38 core infrastructure technologies needed. Before decommissioning, proper planning, assessment, and verification are needed to safely and successfully implement these technology-intensive decommissioning projects [4; 5]. In the decommissioning planning stage, it is vital to establish proper decommissioning procedures and to conduct a comprehensive assessment of the safety of the decommissioning process in order to select the optimal decommissioning process scenario. In particular, when decommissioning radioactive areas, the processes to be used and their safety should be assessed in advance to derive an optimal decommissioning process, thereby improving worker safety and decommissioning efficiency [6; 7].  
Currently, most of the safety assessments that have been performed for nuclear power plant decommissioning projects in developed countries have focused on the radiological risks involved [8]. According to the completion reports submitted for projects decommissioning nuclear power facilities in developed countries, the types of accidents that cause harm to workers during decommissioning are also associated with other hazards including occupational exposure, fires and explosions, handling of hazardous materials, and physical and electrical hazards [1; 9]. Radiological, physical, and chemical hazards account for 67.2%, 17.6%, and 14.8%, respectively, of the hazards encountered during decommissioning. Thus, both radiological and non-radiological hazards need to be considered when decommissioning nuclear facilities [2]. In order to protect workers and prevent accidents during the decommissioning of nuclear facilities, a proper decommissioning plan should thus be established that takes into account both radiological and non-radiological hazards. As nuclear decommissioning involves working close to radioactive structures, a risk management approach is needed to avoid both structural and job risks. The objective of this study was therefore to develop a structural risk assessment model that is suitable for both the decontamination and decommissioning of nuclear power plants. In general, the frequency and intensity of risks are considered in the assessment of risks. The frequency of risk can be recognized as an event in which a risk has occurred, and the intensity of risk can be given meaning as a depth of risk. This is assessed based on expert experience or judgment. The fuzzy theory proposed by [PERSON] (1965) has been applied to logically and scientifically reflect the uncertainty, ambiguity, and subjectivity of the analyst in risk assessment. Therefore, this study aims to quantify risk and systematically analyze the profile of possible risks [10].
## 2 Methodology and System Specifications
### Methods and Equipment for Soil Measurements
The 10 HS device provides \(\pm\)3% accuracy in most soil conditions and \(\pm\)2% accuracy with soil-specific calibration. The sensor's operating temperature is 0 \({}^{\circ}\)C to +50 \({}^{\circ}\)C and the measurement range in soil is 0 to 0.570 m\({}^{3}\)/m\({}^{3}\) for VWC. The sensing volume of influence is approximately 1300 mL. The 10 HS has an excitation voltage ranging from 3 to 15 V DC and has to be applied for more than 10 ms for accurate readings, while its rated amperage consumption is less than 15 mA at 3 V. The output of the instrument varies between approximately 0.3 V and 1.25 V, according to the volumetric water content of the soil in which it is buried. The physical connection with the measuring circuit is achieved via a 3.5 mm stereo plug connector that the 10 HS device has. * The Theta Probe ML2 sensor from Delta-T Devices determines volumetric water content by applying a 100 MHz wave and responding to changes in the apparent dielectric constant. ML2 has a compact, cylindrical shape that consists of the cable, the sensor body, and the sensing head, which has three sharp steel rods arranged in a circle around a fourth central rod. This creates a cylindrical zone of measurements, and they can be made close to the soil surface. The rods can be easily replaced and are highly resistant to corrosion and harsh field conditions. The operation frequency of 100 MHz has the advantage of minimizing the effect of ionic conductivity. The ML2 sensor has a VWC range from 0 to 1 m\({}^{3}\)/m\({}^{3}\), and its accuracy is \(\pm\)2% (0.02 m\({}^{3}\)m\({}^{-3}\)) after calibration in a specific soil type or \(\pm\)5% (0.05 m\({}^{3}\) m\({}^{-3}\)) using the supplied soil calibration. Its power requirements range from 5 V to 5 V and 19 mA (23 mA maximum) in operative mode. The ML2 should remain active practically for at least 2 s in order to provide accurate analog readings (1 to 5 s, according to the manufacturer). The output of the ML2 instrument ranges from 0 V to 1.5 V DC, but practically it does not exceed 1 V for generalized mineral soils of up to 0.5 m\({}^{3}\)m\({}^{-3}\) volumetric soil moisture. The physical connection of the ML2 device with the proposed measuring circuit is achieved via a 3.5 mm stereo plug connector that has to be soldered at the end of its signal cable.
# Traffic-Aware Optimization of Task Offloading and Content
## 5 Experimental Evaluation
### Comparative Experiments
#### 5.2.2 Comparison with Change in CPU Cycles  
Figure 4(a) clearly illustrates the significant advantage of TOCC over LOCAL and TFO when the number of CPU cycles per task increases by a factor, and TOCC slightly loses to F_NSGA-III. In terms of the average execution time, TOCC achieves a 54% improvement over LOCAL, a 53% improvement over TFO, and a 46% reduction over F_NSGA-III. In Figure 4(b), as the number of CPU cycles per task increases, TOCC achieves the lowest energy consumption compared to LOCAL, TFO, and F_NSGA-III. In terms of total energy  
Figure 4: Comparison of average execution time and total energy consumption with the change in content types.  
consumption, TOCC is 78% higher than LOCAL, 93% higher than TFO, and 94% higher than F_NSGA-III. This is because the strategy in TOCC can better balance the average execution time and energy consumption and does not make one party dominant.
# A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication
## 5 Simulation and Experimental Results
### Pre-Trained Model Analysis  
It is always important that a pre-trained model perform well on the training data before testing on new or target data. The key importance remains its generalization capability from the training data to unseen environments and tasks. If a model generalizes poorly to the training data, it would imply that the learning process failed to capture the underlying patterns in the data; hence, there might be a possibility of failure in performance when dealing with the target data. Good performance on training data, when combined with good results on the validation data, is a positive indication that general features have been learned by the model and may generalize well on new, unseen data, becoming suitable for real-world applications.  
Figure (a)a plots the training performance of the pre-trained model, in which the accuracy increases and the loss decreases in a systematic way during training. The model's accuracy over the training set increases steadily and eventually well over 95%, indicating good learning. Much more importantly, the validation accuracy (plotted in red) is a measure of the model's performance over unseen data. The comparable training and validation accuracy tells us that the model is not overfitted and has learned features that generalize sufficiently well. In Figure (b)b, the confusion matrix of the pre-trained model shows its performance in predicting the channel conditions for validation data. Here, the model demonstrates high accuracy of 95% for S1 and 95.2% for S2, which is indicative of a robust model. The misclassification rates are also relatively low (5% for S1 and 4.8% for S2), suggesting that the model has learned to distinguish between the two conditions with reasonable accuracy. These results are important because they confirm the model's ability to perform well, not only on the training data but also on new data (validation data). This means the model has successfully learned the critical features of the problem and can generalize to different conditions, which is vital for deployment in real-world scenarios, especially when using transfer learning for target data.  
Figure 22: ROC under channel environment mismatches, SNR is 15 dB. **(a,b)** compare training and testing on same vs. different channel scenarios between NOF1 and BCH1.
## 3 Results and Discussion  
In order to compare the OMC rates and \(0v\beta\beta\)-decay amplitudes for \(J_{k}^{\pi}\) states in a meaningful way, we consider the following physical assumptions: both quantities depend on the energy (\(E_{k}(J_{k}^{\pi})\)), multipolarity (\(J^{\pi}\)), and nuclear-structure content of the \(J_{k}^{\pi}\) (virtual) states being populated in the process. In the case of OMC rates, phase-space factors contribute directly to OMC rates. For \(0v\beta\beta\)-decay NMEs, the dependence of neutrino potentials on energy affects the concerned decay amplitudes. For our analysis, we can assume the energy dependence to be a constant for all the states as their energy is \(\leq\)1 MeV. Therefore, our analysis simplifies, and we attribute the observed trends in OMC rates and \(0v\beta\beta\)-decay amplitudes to multipolarity (\(J^{\pi}\)) and nuclear-structure content of \(J_{k}^{\pi}\) states. We see effects of such dependencies in the computed \(0v\beta\beta\)-decay amplitudes presented in Tables 1 and 2 for the aforementioned computational scheme in Section 2.1. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(J^{\pi}\) & \(E_{\text{exc}}\) [keV] & \(\mathbf{M_{F}}\) & \(\mathbf{M_{CT}}\) & \(\mathbf{M  
Computed OMC rates for individual \(J^{\pi}_{k}\) states can be found in Reference [33]. In order to smooth out the variations of the OMC rates and \(0\
u\beta\beta\)-decay amplitudes from one individual \(J^{\pi}\) state to the other, we study the combined contribution to a given multipole \(J^{\pi}\), an effective strategy already implemented in Reference [32]. Another important consideration is that, given that the nuclear-structure calculations are not perfect, we only consider the trends within the same nuclear model. Therefore, for the purposes of this paper, we look at trends in pnQRPA (**qrpa-phen**) and NSM (**sm-phen**) results independently. In Table 3, the cumulative OMC rates OMC(\(J^{\pi}\)) and amplitude contributions to \(M^{(0x)}(J^{\pi})\) for multipole \(J^{\pi}\) are given, and are also plotted in Figures 1 and 2. In the figures, positive and negative multipoles are plotted separately for clarity, and the quantities are scaled appropriately for optimal comparison. As seen from the plots in Figures 1 and 2, regular variations between the quantities are observed. The variation of OMC rates (\(J^{\pi}\)) and \(M^{(0v)}(J^{\pi})\) appear to be roughly \"mirror reflections\" of each other, both in the context of pnQRPA and NSM. Further conclusions cannot be made given the limited number of states and multipolarity, but the results look promising, giving impetus to a larger-scale study involving a larger set of states for each multipolarity, and covering a larger range of multipolarities, possibly for the Gamow-Teller giant resonance region in the case of the pnQRPA.