esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Standardization of 5G Network Slicing Energy Efficiency KPIs
### Improving Energy Efficiency in the RAN Part of 5G Network Slices
#### 5.4.3 Possibilities for Energy Savings of Sliced 5G RAN in the Frequency Domain  
Possibilities for improving the energy efficiency of 5G sliced RAN also exist in the frequency domain. Two approaches are seen as possibly being able to increase the energy savings of 5G networks in the frequency domain. The first one is based on completely turning off the 5G signal carrier, while the second one is based on reducing the bandwidth of the carrier by turning off some subcarriers. Considering the extension of the 5G operating frequency spectrum up to 6 GHz and the possibility of using the millimeter wave spectrum, the concept based on scheduling radiofrequency spectrum resources to be shared among NSs in the frequency domain to improve 5G energy efficiency gains new significance.  
This is particularly evident in areas where there is an overlap between the signals of 4G and 5G networks [44]. In situations where traffic in the 5G network is very low, users can be transferred to the 4G network by turning off the 5G carrier through the \(\chi 2\) interface (Figure 6). Once control mechanisms detect an increase in these users' activity or an increase in traffic demands, the carrier in the 5G network is reestablished through control mechanisms to accommodate these users and their traffic requirements [45].  
The presented approach, which is already implemented in new generations of 5G BSs, can be extended to 5G network slicing to improve 5G networks' energy efficiency. In Figure 17, two approaches to the allocation of the frequency spectrum to specific NSs are presented. In Figure 17a, the carrier frequencies and available spectrum (bandwidths) are statically allocated through the VNF configuration of the BS to the specific NS. In the case when there is no data traffic in specific NSs, the frequency carrier of that NS can be switched off and thus operate in energy-saving mode. In Figure 17b, the dynamic spectrum (bandwidth) allocation of specific frequency carriers to a corresponding NS is performed in accordance with the overall frequency spectrum availability and current bandwidth needs of the NS. In traffic situations when an NS requires a lower bandwidth, the allocation of a reduced channel bandwidth through the reduction in used subcarriers can contribute to the energy savings of the BSs serving that NS(s).  
Figure 17: Allocation of the frequency spectrum to specific NSs: (**a**) permanently; (**b**) based on the scheduling of frequency spectrum resources to be shared among NSs.
# A Geostationary Instrument Simulator for Aerosol Observing System Simulation Experiments
## 2 Methodology and Data
### TOA Radiance Simulations
#### 2.4.1 Generating Sub-Grid Cloud Pixels  
The 1 km cloud sub-pixels were generated from the G5 NR simulation using a statistical model of sub-grid column moisture variability. The approach, described in [PERSON] et al. [40], is to use a parameterized probability density function (PDF) of total water content for each model layer, and a Gaussian copula to vertically correlate the layer PDFs. The parameterized PDF used for the sub-grid moisture variability was a skewed triangle PDF, which is characterized by three parameters: a lower bound, an upper bound, and a mode. For the Gaussian copula, we used a correlation matrix with a fixed vertical decorrelation scale of 100 hPa, and a multiplicative flow-dependent correlation in total water. The details of the calculation of the PDF parameters are described in [PERSON] [41]. Once the correlation matrix and PDF are specified, the Gaussian copula correlated ranks of each grid-column layer are generated and then inverted with each layer's skewed triangle PDF. The result is profiles of total moisture content for the sub-grid pixels that sample the layer PDFs and have the specified vertical correlations. The transformation of layer total sub-grid moisture content to vapor, liquid water, and ice assumes the vapor is capped at the GEOS-5 saturation vapor content and that the excess moisture is condensate, split between the phases using an ice fraction linear in temperature over the \(-35^{\circ}\) to \(0^{\circ}\)C range.  
The sub-grid condensate moisture columns are subsequently clumped to give horizontal spatial coherence by applying a horizontal Guassian copula to the condensed water path to each layer [42]. The clumping algorithm generates sub-grid clouds with a reasonable horizontal structure, rather than being randomly distributed. This is important for testing cloud masking strategies that rely on spatial variance tests for identifying partially cloudy pixels.  
Vertical profiles of effective radii for the sub-grid liquid water and ice are taken from the GEOS-5 simulation, which are based on the grid-column temperature and pressure profiles. Finally, sub-grid liquid water and ice content is combined with the GEOS-5 diagnostic effective radii to generate vertical profiles of sub-grid liquid water and ice optical thickness, which are used as inputs to the radiative transfer model.
# Lithium Battery SOH Estimation Based on Manifold Learning and LightGBM
## 5 Instance Validation
### Feature Extraction
#### 5.1.1 Capacity Degradation Characteristics
Taking the fifth discharge cycle as an example, the schematic representation of each parameter in its capacity increment curve is shown in Figure 7.  
Figure 6: Discharge IC curves. (**a**) Original discharge IC curve; (**b**) discharge IC curve after filtering. Figure 7: Schematic diagram of the characteristic parameters in the IC curve. As shown in Figure 7, the feature parameters selected for extraction in this paper include the peak height, the position of the crest (the voltage value corresponding to the horizontal axis at the peak), the area contained in the crest, and the slope of the left and right sides of each peak. Among them, the peak height and the position of each peak are the peak at the highest point in the IC curve and its corresponding voltage value. For the left and right slope of the peak, we need to determine the voltage point on the left and right side. From the IC curves under different cycles in Figure 7, we can see that the curve rises sharply at about 3.3 V, indicating that it is greatly influenced by the discharge voltage at this time, so the voltage on the left side of the wave peak is determined to take the value of 3.3 V; the voltage point on the right side is calculated by calculating the position where the first trough appears and taking it as the voltage on the right side of the wave peak. The area contained in the wave crest is calculated as the area enclosed by the IC curve and the voltage points on the left and right side. In summary, the characteristic parameters in the MVF and discharge IC curves are selected as the first type of capacity degradation characteristics in this paper.
## 4 Conclusions  
Fe-CMB produced from the pyrolysis of cattle manure for generating biochar was used as an adsorbent to remove Sb(V) from aqueous solution. The adsorption kinetics and model analysis indicated that the rate of Sb(V) adsorbed was mainly controlled by chemical adsorption. The Langmuir model fit the data better than the Freundlich model, indicating that Sb(V) was adsorbed onto Fe-CMB via a monolayer. The maximum adsorption capacity of Fe-CMB for Sb(V) was 58.3 mg/g, which is higher than that of the other adsorbents reported in the literature. In this study, the adsorption of Sb(V) onto Fe-CMB was exothermic and spontaneous. Increase in initial solution pH decreased Sb(V) adsorption because of the less positive surface charge of adsorbents and competition with hydroxyl ions. However, the decrease in Sb(V) adsorption with increasing pH was unremarkable because of the low final pH observed, even for high initial pH conditions. The presence of PO4\({}^{3-}\) significantly inhibited Sb(V) adsorption due to the strong affinity of PO4\({}^{3-}\) towards Fe present on the surface of Fe-CMB. Hence, Fe-CMB can effectively remove Sb(V) even under alkaline conditions, and it can also be a potential adsorbent for other oxyanions in aqueous solutions.  
Figure 8: Sb(V) adsorption by Fe-CMB in the presence of 1 and 10 mM chloride (Cl\({}^{-}\)), sulfate (SO4\({}^{2-}\)), nitrate (NO3\({}^{-}\)), bicarbonate (HCO3\({}^{-}\)), and phosphate (PO4\({}^{3-}\)).  
**Author Contributions:** S.-J.P.: conceptualization, writing--original draft preparation, writing--review and editing, supervision; Y.-J.L.: methodology; J.-K.K.: validation, data curation, visualization; J.-C.L.: methodology; C.-G.L.: writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available upon request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Oil and Water Binding Capacity  
The particle size of non-extruded flour was shown to have no significant effect on the water binding capacity (WBC) and the oil absorption capacity (OAC). All samples of extruded rice flour showed significantly higher water absorption compared to the control samples (Table 3), which is also the result of a higher degree of starch damage (Table S1). According to the literature, the finest flour with a higher level of damaged starch granules had high water absorption than intact starch granules [50] and absorbed more water probably due to its higher surface-to-volume ratio. On the contrary, flours with a particle size \(>\)132-200 \(\upmu\)m were characterized by the highest water absorption, especially at higher process temperatures (120 \({}^{\circ}\)C), which may be due to the increased amount of gelatinized starch. These results are in line with those of [PERSON] et al. [51] who found that the water absorption of rice flour increased with increasing extrusion temperature. Furthermore, [PERSON] et al. [45], in their studies on extruded cassava starch, proved that the higher the extrusion temperature, the higher the water absorption was observed. In addition, the beneficial effect is also the higher water retention capacity of extruded flour, which could also affect the delay of water migration from the crumb to the outer part and thus decrease the staling rate [52].  
The extrusion process did not significantly change the oil absorption capacity of the flours. In all the samples, OAC remained at a similar level. [PERSON] et al. [53] showed an increase in the oil binding ability of rice flour after heat treatment at 120 \({}^{\circ}\)C for 120 min. The authors suggested that structures of rice glutenin could be changed by heat treatment at 120 \({}^{\circ}\)C and that smaller subunits of rice glutenin after heat treatment could cause deterioration in breadmaking properties, e.g., resulting in a smaller specific volume of bread.
# Applications of GNSS-RO to Numerical Weather Prediction and Tropical Cyclone Forecast
## 3 Research and Applications of RO to TC Forecast
### Status of TC Forecast  
NWP is one of the primary methods for predicting the TC; however, it is hard to improve the accuracy. The authors of [49] investigated the predictions of TC using global NWP models, including Climate Change Canada's Global Environmental Multiscale Model (CMC), European Center's Medium-Range Weather Forecast Global Model (ECMWF), American National Center's Environmental Prediction Global Forecast System (GFS), and Met Office Global Model (UKMET), based on three verification metrics defined in [50], namely, the hit, false alarm, and miss. Here, the metric hit means a successful prediction whose location is within 5\({}^{\circ}\) against the target TC and whose time is within 24 h against the target TC; the metric miss means a failed prediction; the false alarm indicates a successful TC forecast modeling, but with incorrect time and track. As shown in Figure 10, all the institutes performed suboptimally, as most of the predictions fell in the low critical success index portion with a low detection probability, which is far from the perfect forecast.  
Figure 9: Planned RO data amount from 2020 to 2030 from all missions. The shadow area represents the estimates of possible RO data; the bold lines indicate three primary missions, namely, COSMIC, FY-3, and MetOp [12].
## 3 Experimental Section
### Synthesis
#### 3.2.5 General Procedure for Thioether **(5)**
123-124 \({}^{\circ}\)C; \({}^{1}\)H-NMR (DMSO-\(d_{6}\), 400 MHz), \(\delta\): 4.46 (s, 2H, SCH\({}_{2}\)), 5.09 (s, 2H, OCH\({}_{2}\)), 6.88 (d, \(J\) = 6.8 Hz, 2H, Ph), 7.27 (d, \(J\) = 8.0 Hz, 2H, Ph), 7.28-7.33 (m, 3H, Ph), 7.46-7.58 (m, 5H, Ph). ESI-MS: 478 [M + H]\({}^{+}\); Elemental analysis for C\({}_{22}\)H\({}_{16}\)Cl\({}_{3}\)N\({}_{3}\)OS: found C 55.22, H 3.07, N 8.51; calcd. C, 55.42; H, 3.38; N, 8.81. 4-(((5-(4-chlorophenylophenoxyoxy)methyl)-4-phenyl-4_H_-1,2,4-triazole0-3-yl)thio)methyl)benzonitrile **5h** yield 79%, m.p. 123-124 \({}^{\circ}\)C; \({}^{1}\)H-NMR (DMSO-\(d_{6}\), 400 MHz), \(\delta\): 4.46 (s, 2H, SCH\({}_{2}\)), 5.09 (s, 2H, OCH\({}_{2}\)), 6.88 (d, \(J\) = 6.8 Hz, 2H, Ph), 7.27 (d, \(J\) = 6.8 Hz, 2H, Ph), 7.34-7.74 (m, 9H). ESI-MS: 434 [M + H]\({}^{+}\); Elemental analysis for C\({}_{23}\)H\({}_{17}\)ClN\({}_{4}\)OS: found C 63.88, H 3.90, N 13.04; calcd. C, 63.81; H, 3.96; N, 12.94. 3-((3-chlorobenzyl)thio)-5-((4-chlorophenoxy)methyl)-4-phenyl-4_H_-1,2,4-triazoleole **5i** yield 77%, m.p. 108-109 \({}^{\circ}\)C; \({}^{1}\)H-NMR (DMSO-40 MHz), 8: 4.37 (s, 2H, SCH\({}_{2}\)), 5.09 (s, 2H, OCH\({}_{2}\)), 6.88 (d, \(J\) = 8.7 Hz, 2H, Ph), 7.27 (d, \(J\) = 8.7 Hz, 2H, Ph), 7.33-7.35 (m, 3H, Ph), 7.51-7.62 (m, 5H, Ph). ESI-MS: 443 [M + H]\({}^{+}\); Elemental analysis for C\({}_{22}\)H\({}_{1}\)Cl\({}_{2}\)N\({}_{3}\)OS: found C 59.54, H 3.97, N 9.45; calcd. C, 59.73; H, 3.87; N, 9.50. 3-((4-chlorophenoxy)methyl)-5-((2-fluorobenzyl)thio)-4-phenyl-4_H_-1,2,4-triazoleole **5j** yield 83%, m.p. 110-111 \({}^{\circ}\)C; \({}^{1}\)H-NMR (DMSO-40 MHz), 8: 4.37 (s, 2H, SCH\({}_{2}\)), 5.09 (s, 2H, OCH\({}_{2}\)), 6.88 (d, \(J\) = 7.8 Hz, 2H, Ph), 7.10-7.52 (m, 11H, Ph). ESI-MS: 427 [M + H]\({}^{+}\); Elemental analysis for C\({}_{22}\)H\({}_{1}\)ClFN\({}_{3}\)OS: found C 62.89, H 3.88, N 9.86; calcd. C, 62.04; H, 4.02; N, 9.87. 3-((4-(tert-butyl)benzyl)thio)-5-((4-chlorophenoxy)methyl)-4-phenyl-4_H_-1,2,4-triazoleole **5k** yield 81%, m.p. 62-63 \({}^{\circ}\)C; \({}^{1}\)H-NMR (DMSO-406, 400 MHz), 8: 1.25(s, 9H, Bu), 4.33 (s, 2H, SCH\({}_{2}\)), 5.09 (s, 2H, OCH\({}_{2}\)), 6.88(d, \(J\) = 8.6 Hz, 2H, Ph), 7.17(d, \(J\) = 8.0 Hz, 2H, Ph), 7.24-7.29 (m, 5H, Ph), 7.48-7.52 (m, 4H, Ph). ESI-MS: 465 [M + H]\({}^{+}\); Elemental analysis for C\({}_{26}\)H\({}_{26}\)ClN\({}_{3}\)OS: found C 67.13, H 5.75, N 9.23; calcd. C, 67.30; H, 5.65; N, 9.06.
## 4 Results and Discussion
### Discussion  
The research results show that with the increase in watermark entropy, it is easier to obtain the high transparency of the method; however, with too high a complexity of the binary signature (over 16,384 bits), transparency is impossible to maintain. Meeting the robustness criterion is not possible with very low entropy of the watermark, especially when increasing the value of the parameter \(n\); however, in the case of very complex watermarks encoded using many shades of gray, it is also not possible to decode the watermark without errors, which is caused by errors during the rounding of decoded bit values to the nearest interval defining the specified binary symbol. The use of the parameter \(n=1\) allows these errors to be limited to 0. However, the coding of very complex binary signatures using 1 bit is very computationally expensive. With the increase in the resolution of the watermark, the time required for the training and evaluation of individual algorithms is significantly longer due to the longer time required for mapping and demapping binary signatures. Table 5 compares the training times of algorithms with 1-bit symbols for different lengths of binary signatures.  
To train the algorithm embedding 4096 bits, it was necessary to conduct the learning process for almost two days, while the training of the algorithm embedding the same number of bits when coding 4 bits per symbol lasted less than 20 h. It is necessary to find a compromise between the parameter \(n\), which is the number of bits used to encode one symbol, and the efficiency of the algorithm, which will enable the development of a method characterized by efficiency and relatively low computational complexity. Encoding a larger number of bits is important for practical reasons because it allows an increase in the information capacity, which allows the encoding of a large amount of data regarding, for example, the owner of the content or the creation of a larger number of unique binary signatures, enabling the recording of a large number of various types of content.
## 5 MATLAB Simulation Results
Figure 5: Plot of **1** for a constant velocity evader. Figure 6: The (**top**) figure depicts the pursuer’s speed with respect to time. The (**bottom**) figure depicts the distance between the evader and the pursuer with respect to time (constant velocity evader). As the second scenario, we set the evader's speed as \(v_{k}^{e}\) = 70 + 30 \(\times\) sin(\(T\)\(\times\)\(k\)\(\times\) 0.01) in m/s. This implies that the evader changes its speed. In addition, the evader maneuvers using \(u_{k}^{e}\) = 0.005 \(\times\) sin(\(T\)\(\times\)\(k\)/100). Figure 7 shows pursuer and evader trajectories. In Figure 8, \(\mathbf{I}\) = \(\frac{\mathbf{r}^{e}-\mathbf{r}^{p}}{\|\mathbf{r}^{e}-\mathbf{r}^{p}\|}\) is plotted. \(UnitX-proj\) and \(UnitY-proj\) indicate the projection of \(\mathbf{I}\) on the x-axis and y-axis respectively. In the motion camouflage state, \(\mathbf{I}\) converges. Hence, \(UnitX-proj\) and \(UnitY-proj\) also converge. Figure 8 depicts that a pursuit-evasion system converges to the motion camouflage state. But, the pursuer leaves the motion camouflage state as the distance between the evader and the pursuer is too small. This phenomenon can be explained using Theorem 3. Figure 8: Plot of \(\mathbf{I}\) for a maneuvering evader. The top figure in Figure 9 depicts the pursuer's speed with respect to time. See that in the terminal phase, the pursuer adjusts its speed so that it can capture the evader with low speed. The bottom figure in Figure 9 depicts the distance between the evader and the pursuer with respect to time. Even though the pursuer slows down in the terminal phase, the distance monotonically decreases as time passes.
[PERSON] and [PERSON] (2016) proved that the CPDF and multivariate linear prediction methods are equivalent when the ground motions are zero-mean Gaussian processes. The CPDF method has also been widely investigated and extended to simulate nonstationary ground motions ([PERSON] and [PERSON], 2016; [PERSON], 2016; [PERSON], 2016). Most of these conditional simulating methods assume the Fourier coefficients of the ground motions to be Gaussian. The Gaussian model has been widely adopted in existing ground motion simulation methods. However, after comparing the SMART 1 array records with the ground motion random fields generated by the Gaussian model, we discovered that the Gaussian model underestimates the periodograms' amplitude variation of actual ground motions. The variation of periodograms' amplitude with the separation distance reflects the statistical features of the PSD and can be used directly in the ground motion simulation. Therefore, the periodogram was used instead of the FAS to describe the spectral variation. This study develops a modified Gaussian model to solve the problems mentioned above. Section 2 introduces the SMART 1 array and the records chosen for analysis. Section 3 investigates the periodograms' amplitude variation of the SMART 1 record and that of the Gaussian model. The power's variation is also studied to prove, in parallel, the conclusion drawn from the periodograms' amplitude variation. Section 4 covers the modified Gaussian model proposed by the authors and the main novelty presented in this article. This model involves a local power coefficient (LPC) in the conventional Gaussian model to amplify the periodograms' amplitude variation. The statistical model of the LPC is derived according to the actual earthquake records. Monte Carlo simulation results are presented to verify the modified Gaussian model.
# Improved Control of Forest Microgrids with Hybrid Complementary Energy Storage
## 2 Operation Mode and Coordination Control of Microgrid in Forest Area
### Coordination Control  
With no frequency and reactive power in the DC microgrid, the DC voltage fluctuation is mainly caused by the uncertainty of system input power [25]. As the DC bus voltage reflects the internal power state of the microgrid and is a sign of system stability, power-voltage regulation is the core of stability control, as well as energy management. This paper utilizes DC bus voltage as the basis for operation mode, switching of each unit to effectively simplify system control.  
In this method, the various units of the system are divided into a slack terminal, which is responsible for adjusting power, and power terminals, with constant output in different working modes, as is shown in Figure 2. They are not static, and switch with the change of the operating state of the system. The corresponding switching diagram of control mode is shown in Figure 3. For example, under free mode and emergency mode, the primary power regulating units of the system are the biomass power generation unit and the energy storage unit, respectively. In power limiting mode, the corresponding power control unit is the wind power system.  
In addition, the supercapacitor functions under the condition of rapid power fluctuation in free mode and emergency mode, while kinetic energy regulation of the wind turbine acts under large power disturbances in power limiting mode as well as load shedding mode.  
In the DC microgrid, as the voltage value at the DC bus of each unit makes very little difference, the working mode of each unit can be determined only by detecting the DC voltage at the outlet of each converter. Consequently, there is no need to establish a complex communication system by utilizing an operation mode-based sectional coordinated control strategy. With the characteristic of plug and play, the coordinated control method has the advantage of simplicity and reliability. In addition, it boasts the feature of self-adaption besides coordination of different units. Due to the fact that the operation mode of the system is divided based on DC voltage, and under each mode power regulation is conducted by the most suitable controllable unit, the stability of the islanded system can be ensured.
# Low Complexity DSP for High Speed Optical Access Networking +
## 1 Introduction
Such a PON has the capability of full C-band tunability with 100 GHz channel spacing, positioning it as an attractive solution for 40 Gb/s lane rate PONs, and hence, enabling PON links with >1.6 Tb/s link capacity (e.g., by using 48 WDM channels of 40 Gb/s transceivers over the whole C-band). Here, experimental demonstrations were performed using a single channel 40 Gb/s PAM-4 signal transmission over 20 km (30 km) SMF with a link power budget of 38 dB (30.7 dB), at a pre- forward error correction (FEC) bit-error-rate (BER) of 10\({}^{-3}\). In addition, when a simpler KP-4 FEC with a BER threshold of 2.2 \(\times\) 10\({}^{-4}\) was considered, a link power budget of 33 dB (27 dB) was achieved after a 20 km (30 km) SMF transmission.