esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Texture Information on Crop Classification with Machine Learning and UAV Images
## 3 Results and Discussion
### Time-Series Analysis of Normalized Difference Vegetation Index for Selection of Optimal UAV Image  
Spectral characteristics of crops depend on crop type and health conditions, but different crops may exhibit similar spectral response [35; 50]. Accordingly, time-series images acquired during growth cycles of crops are often used to examine how well these images account for temporal variations of spectral response. For example, if temporal patterns in spectral responses of crops in the study area are significantly different, classification based on multi-temporal images can achieve satisfactory classification accuracy. Conversely, discrimination of crops with similar temporal variations of spectral responses may be difficult, even when multi-temporal images are used.  
Figure 9 shows temporal variations in the average of normalized difference vegetation index (NDVI) values at pixels belonging to each crop. NDVI is a standardized index that quantifies greenness by using the difference in reflectance between NIR and red bands [51]. The average NDVI value of highland Kimchi cabbage was significantly lower than other crops on 12 July, and peaked in late August. In late July, cabbage had the highest NDVI value, followed by potatoes. The difference in average NDVI values between highland Kimchi cabbage and cabbage was not great in the August image (Figure 9), which led to difficulty in discerning the two crops. Although the difference was greater on 27 July, as shown in Figure 9, the lowest NDVI value of highland Kimchi may have resulted in the confusion with fallow and other small vegetation in the classification result using the 27 July  
Figure 9: Temporal profiles of average normalized difference vegetation index (NDVI) values for three crops.  
image. If only one UAV image should be acquired, the image needs to be acquired when the vegetation vitality of the crop of interest reaches its maximum. Since high-hand Kimchi cabbage reached its maximum NDVI value in the 25 August image, we selected that image as the optimal single image. Actually, the classification accuracy using either the 12 July or the 27 July image was either similar to or lower than that using the August image. Despite the risk of misclassification using only the August image, similar spectral responses of different crops highlight the necessity of using additional information such as texture features, as applied in this study. Since the time to reach the maximum peak in NDVI may differ every year depending on weather conditions, however, the selection of the most appropriate acquisition date should be made by considering conditions and types of crops. Therefore, more extensive experiments should be carried out in other areas with different crop types. In addition, if phenological characteristics can be estimated from the entire time-series image set [52; 53], a single-image acquisition date can be determined more optimally.
## 2 Materials and Methods  
### Substrate and Inoculum  
A mixture of acetate, propionate, and butyrate was used as a substrate to evaluate the toxicity of PAM. Surplus activated sludge (SS) from Kuryanovo wastewater treatment facilities (Moscow, Russia) and simulated organic fraction of municipal solid waste (s-OFMSW) were used as substrates. Dry pet food (Chappi, Mars Petcare, Russia) was used to simulate the organic fraction of the MSW. Similar to OFMSW, dry pet food has a high content of easily degradable organic matter; it differs from OFMSW in its more homogeneous composition, which is important for laboratory experiments. Total solids (TS) content in surplus activated sludge and s-OFMSW was 13.4 \(\pm\) 0.1 and 905.5 \(\pm\) 0.2 g/kg, respectively. Volatile solids (VS) content was 8.0 \(\pm\) 0.2 and 846.8 \(\pm\) 0.9 g/kg for surplus activated sludge and s-OFMSW, respectively. Thickened thermophilically digested sewage sludge from Kuryanovo wastewater treatment facilities was used as inoculum. TS and VS content in thickened thermophilically digested SS was 48.6 \(\pm\) 0.1 and 20.5 \(\pm\) 0.1 g kg, respectively.  
The cationic polyacrylamide flocculant Praestol 650 (PAM) (Ashland, Germany), which is widely used in wastewater treatment plants in Russia, was used as a flocculant.
## 2 State of the Art
The developed estimator shows sufficient performance in steady-state regimes but improvements are required for transient cycles of engine loading. An ANN is employed in [57] to estimate emissions of \(CO_{2}\), NOx, and PM of a Common-Rail Diesel Injection (CRDI) engine. It was shown that increasing the number of hidden layers and neurons causes over-fitting and decreases the generalization of the model. In the work [58], to solve the engine optimization problem, a multi-layer perception (MLP) neural network followed by multi-objective optimization including a non-dominated sorting genetic algorithm II (NSGA-II) and strength Pareto evolutionary algorithm (SPEA2) were used. This study allowed the authors to decide which algorithm is preferable for optimizing engine emissions and fuel consumption. As an alternative to complicated physics-based models, a multidimensional data-driven approach is proposed in [59] to estimate NOx emissions. Using Deep Neural Networks (DNNs), separate models were developed: engine-out NOx and tailpipe NOx emissions. Two datasets were used from the onboard diagnostic system, namely, an engine dynamometer and a chassis dynamometer. Both the cold/hot Federal Test Procedure (FTP) and the Ramped Mode Cycle (RMC) were applied. The authors proved that high precision of the DNN models (R\({}^{2}\) = 0.92-0.95 up to 0.99) can be achieved by utilizing minimal engine and exhaust gas after-treatment parameters. Another factor affecting diesel engine emissions in articulated heavy-duty underground loaders is the depth of the mine. In [60], engine emissions were determined by a portable gas analyser at various depths from the surface (up to 7000 feet below sea level). Based on the measurement results, the authors concluded that carbon monoxide (CO) and diesel particulate matter (DPM) emissions decrease with depth because of the higher air density and air/fuel ratio for the same parameter set in the vehicle Electronic Control Unit (ECU). Instead, NOx emissions increase with depth. The authors related this to the effect of pressure on in-cylinder NO formation. The influence of ambient air temperature and humidity on NOx emissions is investigated in [61], which noted that an increase in intake air humidity (in the range of 31-80%) causes a 3-14% reduction in the NOx emissions at a constant temperature of 26 \({}^{\circ}\)C. The influence of intake air temperature on engine torque and emissions is analysed in [62]. They obtained accurate regression models (RMSE 72.38 and accuracy 99.2%) and discovered that the ambient temperature in the range 5-30 \({}^{\circ}\)C has a great influence on both the torque and the prediction of NOx. Nevertheless, since the temperature and humidity at a certain depth and geological conditions of underground mines are approximately constant, these factors can be neglected in the prediction model.
# RCS and Aero Surfaces Control Allocation Research on RLV's Re-Entry Phase
## 3 Control Allocation Scheme Design  
The control allocation scheme is mainly about how to allocate the virtual control command to RCS and AS, namely to decide RCS's thrusters firing command, and the deflection angles of each aero surfaces so that the real control command is as close as possible to the virtual one provided by the controller. In this section, there are three kinds of allocation schemes proposed, namely proportional allocation, daisy-chaining allocation, and multi-object based optimal allocation. In the proportional and daisy-chaining allocation schemes, there are two steps--first, use the corresponding algorithm to allocate between the RCS and the AS; during the secondary allocation, 0-1 integer programmingalgorithm is proposed for the firing command allocation among RCS's thrusters, and the daisy-chaining algorithm is used for the body flap and the elevator. Unlike these two schemes, the multi-object allocation algorithm can deal with the RCS's thrusters and aero surfaces at the same time, while achieving the optimal comprehensive performance, by choosing the optimal solution to the multiple objective functions.
## 3 Results and Discussion
### Optimal Wavelength Selection  
SPA, _Bw_, random frog, and CARS were used in this study to select the optimal wavelengths. To conduct SPA, the number of wavelengths to be selected was set as 5-30, and 15 optimal wavelengths were selected. The peaks and valleys of the wavelength-_Bw_ plot of the PLSR model were selected as the optimal wavelengths, and 21 optimal wavelengths were selected. To conduct random frog, according to [13], 10,000 iterations were sufficient. In all, 24 optimal wavelengths were selected by random frog. To conduct CARS, the number of iterations was set as 10,000, the maximum components were set to 20, and the ratio of samples used in each Monte Carlo sampling procedure was set as 0.9. In all, 26 optimal wavelengths were selected by CARS. The selected optimal wavelengths are shown in Figure 3 and Table 2.  
The selected wavelength 975.01 nm, 981.72 nm, and 995.15 nm were attributed to the water absorption [15; 16; 17], the wavelength near 1020 nm (1018.65 nm) was assigned to protein [18]. The wavelength 1028.72 nm was assigned to a combination of C-H stretching first overtone and C-H deformation second overtone of CH3 [19], the wavelengths between 1150 nm and 1214 nm were assigned to the second overtones of C-H stretching [20; 21]; the wavelengths between 1408 nm and 1462 nm were attributed to the water absorption [21]; the wavelengths between 1230 nm and 1400 nm were corresponded to C-H second overtone [22]; the wavelengths between 1460 nm and 1600 nm were attributed to the first overtone of O-H stretching variations [23]; the wavelengths between 1600 nm and 1800 nm were attributed to the first overtone of C-H stretching [24].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Number** & **Wavelength (nm)** \\ \hline \multirow{2}{*}{SPA} & \multirow{2}{*}{15} & 1581.51, 1611.96, 1483.46, 1318.09, 1345.0601, 1281.01, 1372.05, 1213.65, 1159.8108, \\  & & 1112.72, 1554.45, 1436.1801, 1237.22, 975.01, 1055.59 \\ \hline \multirow{2}{*}{_Bw_} & \multirow{2}{*}{21} & 975.01, 995.15, 1018.65, 1028.72, 1052.23, 1075.75, 1106, 1173.26, 1210.29, 1233.85, \\  & & 1281.01, 1314.72, 1348.4399, 1388.92, 1422.67, 1449.6801, 1480.08, 1507.12, 1537.54, \\  & & 1578.13, 1611.96 \\ \hline \multirow{2}{*}{Random frog} & \multirow{2}{*}{24} & 1581.51, 1503.74, 1524.02, 1605.2, 1500.36, 1473.33, 1469.95, 1554.45, 1507.12, \\  & & 1608.58, 1534.16, 1584.89, 975.01, 1520.64, 1527.4, 1601.8101, 1476.71, 1530.78, \\  & & 1496.98, 1210.29, 1240.692, 1449.6801, 1466.5699, 1446.3101 \\ \hline \multirow{2}{*}{CARS} & \multirow{2}{*}{26} & 975.01, 981.72, 1156.4399, 1163.9, 1206.92, 1230.49, 1233.85, 1247.33, 1250.6899, \\  & & 1257.4301, 1311.35, 1338.3199, 1449.6801, 1463.1899, 1466.5699, 1469.95, 1500.36, \\ \cline{1-1}  & & 1503.74, 1524.02, 1527.4, 1530.78, 1534.16, 1554.45, 1581.51, 1605.2, 1608.58 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of optimal wavelengths and optimal wavelengths selected by SPA, _Bw_, random frog, and CARS.  
Figure 3: Optimal wavelengths selected by different methods.
## 4 Discussion
More important, recent studies [56; 57] have shown that near-infrared radiation such as SIF\({}_{761}\) also contains information on canopy structure such that a directional effect may lead to uncertainties in retrieving SIF signals. Also, SIF signals measured in this study are only part of total SIF emission, while measurements of GPP and \(T\) contain contributions from the whole canopy. SIF-IWUE relationships derived from total canopy SIF emission should be more robust, especially under high LAI values. To reduce these uncertainties, a process-based radiation transfer model can be used to separate physiologically and directionally induced changes in SIF emission [58; 59]. Finally, it is important to note that one important assumption in developing SIF-WUEs models is that transpiration flux dominates ET. Therefore, SIF-IWUE correlations would be weaker in wetlands or irrigated lands where nontranspiration flux may be significant.
# A Deadline-Aware Scheduling and Forwarding Scheme in Wireless Sensor Networks
## 7 Conclusion and Future Work  
There are a number of applications of WSNs that require low energy consumption and a specific packet delay, e.g., forest fire detection or intruder targeting. This paper has addressed the problems of maximizing the duty cycle interval in a duty-cycled WSN, such that packets are forwarded to the sink within a delay bound with a given probability of success. A novel algorithm, namely DASF, was proposed, which enables the operator to adjust the duty cycle interval to meet the different delay requirements and evaluate the needed sources in a duty-cycled WSN. In addition, our algorithm makes a forwarding decision based on local information and does not require time synchronization between nodes.  
In DASF, to maximize the value of the duty cycle interval, we evaluated the E2E delay's distribution as a function of duty cycle interval and other network parameters, such as the number of nodes, the total area, the communication range of a node, and so forth. Therefore, we can adjust the duty cycle interval to satisfy a given delay requirement. Moreover, DASF also allows the operator to predict the needed sources, such as the total number of nodes for a certain area in a WSN. The simulation results show that our work can adjust the duty cycle interval with a variety of requirement changes to satisfy the required DCSR; and DASF can also reach a high PDR and lower average E2E delay compared to other algorithms.  
**Acknowledgments:** This work was supported by the 2015 Research Fund of University of Ulsan.  
**Author Contributions:** [PERSON] and [PERSON] developed the network protocols and algorithms. The experimental simulations and analysis of this work were conducted by [PERSON] and [PERSON]. [PERSON] performed the analysis of this work. All authors have written and approved the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 6 Simulation Results  
A simulation was performed to compare the proposed PRM method with the existing SEF and PSM ones. A performance criterion is balanced energy consumption and success rate of data transmission. We also analyze the detection power of proposed method. In the simulation, the network consists of 1,000 nodes spread over a territory whose size is 100 \(\times\)120 m. The nodes are randomly deployed in the territory and the base station is placed at the end of the territory. Each sensor node takes 16.56 \(\upmu\)J/12.5 \(\upmu\)J to transmit/receive a byte, and each MAC generation consumes 15 \(\upmu\)J. The size of original report and of MAC is 24 and 1 bytes, respectively. There are 1,000 keys in the key pool, which is divided into 10 partitions.  
Figure 3 shows a filtering rate for false reports with a security weight in case that the number of forged MACs per a report is 1, 4, 10 and 16. For the same network topology, routing paths on SEF, PSM and PRM are established, respectively. Then we generate false reports in the network. Assigned keys in each node are randomly generated with various seed values from 0 to 9. We calculate an average dropping rate for the false report.  
In the figure, the proposed method is better than SEF but less efficient than PSM in terms of dropping ratio. The figure illustrates a similar performance for PRM and PSM. In PSM, each node chooses a super-node by considering information of keys on incoming path from the base station. In PRM, each node only has partition information of nodes within five hop counts, so the performance of the proposed method is a little less efficient than PSM.  
Figure 4 shows an average number of traveled nodes to filter the false report. The reports are generated with 1, 4, 10, 16 forged MACs. The number of traveled nodes in the original SEF approach is the highest since routing paths are chosen with consideration of only hop counts. Though PSM detects the false reports earlier than PRM, the performance gap is acceptable.  
Figure 5 shows the number of alive node that can send a data to next node or base station in SEF, PSM and PRM. We generate false reports that include eight forged MACs and inject the report into the network.  
Figure 4: Average number of traveled nodes for filtering false reports with the security value (\(\alpha\)).  
Figure 3: Ratio of filtered false reports with the security value (\(\alpha\)).  
When the routing paths are established, each node considers hop counts or hop counts and partition information of keys. Therefore, the path would make the most secure paths in heavy traffic so that the nodes along the paths would consume more energy resources. Also, the super-node that has high in-degree (_i.e._, many sub-nodes) would consume more energy resources. That is, the communication traffic of the super-node is more than others that have a low in-degree and the node should be consumes much energy than others. In other hand, PRM considers communication traffic when a sub-node selects a super-node. For these reasons, the network life time of PRM is better than that of either SEF and PSM.
# Electronic and Magnetic Properties of Bulk and Monolayer CrSi\({}_{2}\): A First-Principle Study
## 3 Results and Discussion
### Electronic Structure
Figure 2: Phonon dispersion curves along the high-symmetry directions in the Brillouin zone of **(a)** bulk CrSi\({}_{2}\), **(b)** monolayer CrSi\({}_{2}\). structure of TMDs are greatly affected by the crystal structure [20]. [PERSON] et al. [42] have explored the origin of the high metallicity on MoSi\({}_{2}\) nanofilms in detail. We can elucidate the physics mechanism of why bulk CrSi\({}_{2}\) is a semiconductor, whilst monolayer CrSi\({}_{2}\) is metallic using [PERSON]'s theory. Both surface and confinement effects contribute to the high sensitivity of the metallicity on nanofilms type, explaining the reason why monolayer has a metallic character. To further investigate the physical mechanism of magnetism, which may be dependent on the dimension of materials, we calculated the total density of states (DOS) and partial density of states (PDOS) of bulk and monolayer CrSi\({}_{2}\) systems, as depicted in Figure 4. Total DOS and PDOS of the bulk CrSi\({}_{2}\) system were fully symmetric, indicating that bulk CrSi\({}_{2}\) system cannot have a magnetic characteristic, in accordance with Figure 3a. In contrast, for the monolayer CrSi\({}_{2}\) system, both total DOS and PDOS of monolayer CrSi\({}_{2}\) system were asymmetric, manifesting that monolayer CrSi\({}_{2}\) system possesses a magnetic characteristic, in good agreement with Figure 3b. The degree of dissymmetry in PDOS of the Cr atom in monolayer CrSi\({}_{2}\) system was greater than the Si atom's, which is the reason why the Cr atom has a larger local magnetic moment as depicted in Table 1. In addition, the total density of state near Fermi level of the bulk CrSi\({}_{2}\) system mainly consists of Cr-3d orbital electron. The total density of state near Fermi level of the monolayer CrSi\({}_{2}\) system is mainly made up of Cr-3d orbital electron, with Cr-3p and Si-3p orbital electrons making limited contribution to the total density of state of the system. Moreover, the results also indicated that total magnetic moment (3.68 \(\upmu\)B) arose mainly from the spin-up Cr-3d states. [PERSON] [9] has investigated the origin of magnetic behavior in monolayer FeSi\({}_{2}\) and CoSi\({}_{2}\) by orbital coupling of atoms. The stronger orbital coupling between atoms may account for the quench of magnetism of the atom. It can be seen from Figure 4b that no noticeable coupling between p orbital of Si atom and d orbital of Cr atom is found around the Fermi level, which indicates that monolayer CrSi\({}_{2}\) has magnetic behavior. Figure 3: Band structure with spin-up and spin-down of (**a**) bulk and (**b**) monolayer CrSi\({}_{2}\).
## 3 Results and Discussion
### Measured Light Scattering Properties of Natural Particulate Assemblages
In contrast to \(\widetilde{b}_{bp}^{LISST}\), the \(DoLP_{p,max}^{LISST}\) data show no clear relational trend and very weak correlation with POC/SPM (\(R=0.31\)), indicating that the maximum degree of linear polarization does not provide a useful optical signature for the organic vs. inorganic content of particulate assemblages in our dataset (Figure 12c). A similar result with no correlation (\(R=0.07\)) is observed for \(DoLP_{p,max}^{LISST}\) vs. Chla/SPM, indicating that varying proportion of phytoplankton in total particulate assemblage has no discernible systematic effect on the maximum degree of linear polarization (Figure 12d). We also determined that there is no significant correlation between \(DoLP_{p,max}^{LISST}\) and \(\widetilde{b}_{bp}^{LISST}\) in our dataset (\(R=-0.22\)), as well as between \(\psi_{max}\) and POC/SPM or Chla/SPM (\(R=-0.09\) and \(0.06\), respectively). It is also of interest to note that the range of our \(DoLP_{p,max}^{LISST}\) data is generally consistent with the range of values reported in literature for natural seawater samples, although the reported range in some earlier studies extends to somewhat lower values, as low as about 0.4 [15; 39; 40; 41; 42; 79; 80]. The assessment of potential presence of systematic effects of particle size distribution (PSD) on \(\widetilde{b}_{bp}^{LISST}\) and \(DoLP_{p,max}^{LISST}\) is presented in Figure 13. In this assessment, we use two PSD metrics: the 90 th percentile diameter, \(D_{V}^{00}\), derived from the particle volume distribution, and the power function slope, \(\zeta\), derived from the particle number distribution. We also tested other percentile-based diameters such as the median \(D_{V}^{50}\) but no improvements in the examined relationships were observed. Figure 13a,b shows no trend in the data of \(\widetilde{b}_{bp}^{LISST}\) associated with variations in the particle size metrics, even though these metrics vary over a significant dynamic range. This is the case for the entire dataset as well as a subset of highly organic samples with POC/SPM > 0.2, which supports the interpretation of results presented in Figure 12b in terms of the role of refractive index. The data of \(DoLP_{p,max}^{LISST}\) vs. \(D_{V}^{00}\) show the potential for the presence of a relational trend (Figure 13c). Although the scatter in these data points is significant and correlation is weak (\(R=-0.47\)), the lowest values of \(DoLP_{p,max}^{LISST}\) tend to occur along with the highest values of \(D_{V}^{00}\). This result indicates that the decrease in the maximum degree of linear polarization tends to be associated with particulate assemblages exhibiting a higher proportion of large-sized particles. The potential usefulness of the relationship between the degree of linear polarization and particle size has been proposed for the first time in 1930 [81], and the trend observed in our data is consistent with those early results.