esa-sceva/satcom-chunk-collection huggingface dataset:
# Predictive Energy-Aware Routing Solution for Industrial IoT Evaluated on a WSN Hardware Platform
## 5 PEAR Solution: Hardware Integration and Results Evaluation
### Scenarios Description  
#### 5.2.1 Energy Profiles  
The energy profiles are represented as the total number of Tx/Rx data packets (DPDU) per minute. Correspondingly, when a reference battery capacity is given, the energy profile can be represented as the expected network lifetime expressed in days, months or years. Moreover, the energy profile can be converted to mAh or mWh considering that 7.534 mAh/149.132 nWh and 41.425 nAh/271.24 nWh is required for a DPDU transmission and reception.
## 2 Flexibility Index: Ramping Capability Shortage Probability
### System Ramping Capability (SRC), Ramping Capability Requirement (RCR)  
The ramping capability (RC) of a power generation unit is defined as the ability to change in a power generation of a unit over a given period. The aggregated RC can be thought of as the system's ability to respond to load-generated imbalance due to uncertainties. Flexibility relies on the secured RC. The system RC (\(SRC\)) is the total RC in the system and can be formulated as:  
\[SRC_{t}=\sum_{i\in I}A_{i,t-\Delta t}O_{i,t-\Delta t}\text{min}(P_{max,i}-P_{i,t- \Delta t},rr_{i}\Delta t) \tag{1}\]  
Here, \(I\) means a set of generators. \(A_{i,t-\Delta t}\) is a random variable representing the availability of generator \(i\) at time \(t-\Delta t\). \(O_{i,t-\Delta t}\) is a variable representing whether generator \(i\) is online at time \(t-\Delta t\). _min(\(t\))_ is a function selecting a smaller value in the brackets. \(P_{max,i}\) is a maximum output of generator \(i\). \(P_{i,t-\Delta t}\) is the output of generator \(i\) at time \(t-\Delta t\). \(Rr_{i}\) is the ramp rate of generator \(i\). \(\Delta t\) is the minimum interval between operating points. The \(SRC_{t}\) represents the amount of RC the system can supply during time \(\Delta t\), considering the uncertainty. The larger the \(SRC\), the greater the flexibility. However, the uncertainty of the system can reduce the RC availability, which directly relates to the parameter \(A_{i,t-\Delta t}\), which indicates whether the unit fails from \(t-\Delta t\) to \(t\). The values of \(O_{i,t-\Delta t}\) and \(P_{i,t-\Delta t}\) are determined by the generation schedule.  
The RC requirement (\(RCR\)) can be expressed as follows:  
\[RCR_{t}=NLFE_{t}+FNL_{t}-\sum_{i\in I}A_{i,t-\Delta t}O_{i,t-\Delta t}P_{i,t- \Delta t} \tag{2}\]  
where  
\[NLFE_{t}=LFE_{t}-VGFE_{t} \tag{3}\]  
\[FNL_{t}=FL_{t}-FVG_{t} \tag{4}\]  
Here, \(NLFE_{t}\) is a random variable representing net load forecast error at time \(t\). \(FNL_{t}\) is a forecast net load at time \(t\). \(LFE_{t}\) is a random variable representing load-forecast error at time \(t\). \(VGFE_{t}\) is a random variable representing variable generation forecast error at time \(t\). \(FL_{t}\) is a forecasted load at time \(t\). \(FVG_{t}\) is the forecast variable generation at time \(t\). The \(RCR_{t}\) indicates the amount of RC required to manage unexpected net load fluctuations and unit failures from \(t-\Delta t\) to \(t\). The random variables \(NLFE_{t}\), \(LFE_{t}\), and \(VGFE_{t}\) represent the forecast errors.
# Analysis of Hydrological Characteristics of Blue Nile Basin, Nishe Watershed
## 2 Materials and Methods
### Data Collection and Analysis
#### 2.5.2 Soil Data  
Soil is also one of the SWAT inputs and the soil database describes the surface and sub-surface of the watershed. The soil properties play an important role in hydrological modeling [29]. All information of the soil is associated to describe the physical and chemical properties. Eutric cambisols, haptic alisols, haptic aerosols, Rhodic nitosols, chromic luvisols, eutric vertisols, water, eutric leptosols, dystric vertisols are the soil types of the study area (Figure 3a). The dominant soil types of the study is haptic alisols and rhodic nitosols, respectively.  
Figure 3: \(Cont\).  
Figure 2: The digital elevation model (**a**) and sub-basin representation of the study area (**b**).  
2.5.3. Land Use Land Change (LULC) and Slope Data  
The LULC data are the most significant factor influencing different processes in the hydrological characteristics of the catchment [30]. The land use/land cover map of 30 m spatial resolution for the year 2018 was used for the Nashe watershed. The characteristics that can be affected might be surface runoff, erosion, groundwater system, and evapotranspiration. The LULC classification map shows the spatial extent of different LULC categories of the study watershed (Figure 3b) and its areal coverage is shown in Table 1. The topographic parameters of the study watershed, such as terrain slope, channel slope or reach length was also derived from the DEM. (Figure 3c) and Table 2 shows the spatial and areal extent slope classification of Nashe watershed respectively.
## 3 Results
### Composition of VOCs  
A total of 89 compounds were detected across all tested strains, but different strains produced different types of VOCs. Some VOCs were unique to a specific strain, but others were produced by several strains. The number and abundance of VOCs produced by each strain were also different. The number of VOCs released by an individual strain ranged from 7 to 33. The number of VOCs released by strains No. 3, No. 26, No. 34, No. 35, No. 50, No. 62, No. 146, No. 2-18, and No. 2-60 were 7, 17, 21, 30, 10, 29, 10, 18, and 33, respectively (Table 1). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**VOCs**} & **3** & **26** & **34** & **35** & **50** & **62** & **146** & **2-18** & **2-60** \\ \cline{2-9}  & & & & (\(\times\)**10\({}^{8}\) Ab \(\times\) s)** & & & \\ \hline (E)-1-Methyl-2-(prop-1-en-1-yl)disulfane & 0.14 & & & & & & \\ beta.-Phenylethyl butyrate & 0.15 & & & & & & \\
11-Dodecen-2-one & & & & & & 2.34 & \\
2-methyl-1-Butanol & & 1.93 & & & & & & \\
3-methyl-1-butanol & 3.12 & 4.76 & 1.16 & 0.11 & 0.51 & & 0.18 \\
1-Butanol, 3-methyl-, acetate & & 0.17 & 0.12 & & & & 0.22 \\
1-Octadecene & & & & & & & 0.47 \\
1-Octanol & & & & & & & 0.30 \\
7-methyl-1-Octene & & & & & & & \\
1-Pentadecene & & 0.26 & & & & & \\
2-Chloropropionic acid, hexadecyl ester & 0.93 & & & & & & \\
5-heptyldihydro-2(3H)-Furanone & & & 0.13 & & 0.42 & & \\
2,4,6-Cyclobutapterien-1-one & & & & & & & 1.23 \\
2,4-Chloripamentane & 0.43 & & & & & & 0.26 & \\
6-methyl-2-4-Heptendence & & & & & & & 0.26 & \\
3,7,11-trimethyl-2,6,10-Dodecateri-1-ol & 1.82 & & & & & & & \\
2-Decanone & & 0.12 & 0.12 & 0.55 & & 0.63 & & 0.49 & 0.30 \\
2-Dodecanone & & & & 0.25 & & & & 1.45 & \\
2-ethyl-1-Hexanol & 0.20 & & & 0.18 & 0.68 & 0.14 & 0.30 & 0.68 & 0.97 \\
2-Heptendence & & 0.18 & 0.25 & 1.99 & & 1.67 & & 0.27 & 0.38 \\
3-methyl-2-Heptanone & & & & & & 0.24 & & \\
5-methyl-2-Heptendence & & & & & & 3.84 & & 7.12 & 0.50 \\
6-methyl-2-Heptanone & & & & 1.12 & & 2.75 & & 1.36 & 1.48 \\
2-Hexadecanone & & & & & & & 0.12 & \\
2-Hexanone & & & & & & 0.12 & & \\
3,4-dimethyl-2-Hexanone & & & & & & & 0.13 \\
5-methyl-2-Hexanone & & & & & & 3.86 & & 0.52 & 0.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Composition of VOCs produced by different bacterium and their peak area on mass spectrum. Figure 1: (**A**): Inhibition rate of different bacteria against _Rhizctonia solani_. The inhibition rate was measured with the following formula: Inhibition rate = (The colony diameter of control _R. solani_ - The colony diameter of treated _R. solani_) The colony diameter of control _R. solani_ \(\times\) 100%. (**B**): Pictures of _R. solani_ treated with volatile organic compounds (VOCs) from different bacteria. ck: _R. solani_ treated with uninoculated beef extract peptone medium. There were 15 compounds unique to strain No. 2-60, six unique to No. 35, seven to No. 62, six to No. 34, five to No. 2-18, eight to No. 26, six to No. 50, two to No. 146, and
# Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants
## 3 Materials and Methods
### Preprocessing & Post-Processing  
#### 3.3.1 eGRID Data Preparation  
EPA NO\({}_{2}\) data was collected in an hourly format for the whole year and averaged by day. In order to scale the data, it was divided by 1 \(\times\) 10\({}^{5}\) and rounded to five decimals.  
The scale applied to this feature is indicated by the dataset label \"EPA NO\({}_{2}\)/100000\". This feature was then merged with the other data sources listed below for each day of 2019. The number of EPA NO\({}_{2}\) values merged in the dataset for a power plant location was determined by our TROPOMI NO\({}_{2}\) available data. Certain power plants in the dataset had less than a full year of data due to TROPOMI NO\({}_{2}\) QA filtering, resulting in gaps.
## 6 Conclusions  
According to the characteristics of the satellite sensor, the existing methods are difficult to remove the stripe noise. In this paper, we propose an algorithm for combining adaptive moment matching with multi-level variation to remove stripes and adopt piecewise linear least squares fitting to recover details.  
Firstly, the adaptive moment matching is used to remove most of the stripe noise, which can avoid the interference of serious stripe noises when the multi-level variational model is used for destriping and to maintain image details. Then the multi-level variation method is used to remove the remaining stripes, and finally, the least squares fitting is used to recover the line details of the image. The experiment proves that the method has a good stripe removal effect on OHS.  
However, the current processing time is relatively long, so in the later stage, we should consider using other constraints to restore image details and reduce the number of iterations. In addition, our current algorithm cannot handle the oblique stripes. In the next stage, we consider destriping the oblique image. Last, maintaining or restoring vertical details is still a big challenge, and we will continue to consider better restoration methods or eliminate the effect of vertical details when removing stripes.  
Figure 34: CHRIS corrected data. (**a**) Corrected image of band 1; (**b**) corrected image of band 4; (**c**) corrected image of band 7; (**d**) corrected image of band 10; (**e**) corrected image of band 13; (**f**) corrected image of band 16.  
Figure 32: Hyperion corrected data. (**a**) Corrected image of band 93; (**b**) corrected image of band 95; (**c**) corrected image of band 97; (**d**) corrected image of band 99; (**e**) corrected image of band 101.  
**Author Contributions:** [PERSON] carried out the empirical studies, the literature review and drafted the manuscript. [PERSON] and [PERSON] helped to draft and review the manuscript, and communicated with the editor of the journal. All of the authors read and approved the final manuscript.  
**Funding:** This research is supported by the National Natural Science Foundation of China: [Grant Number 41371434] and the National Natural Science Foundation of China: [Grant Number 61562049].  
**Acknowledgments:** The authors would like to thank the editors and anonymous reviewers for their valuable comments, which helped improve this manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion  
### Fourier Transform Infrared Spectroscopy (FTIR)  
The FTIR spectrum of organo-modified montmorillonite (O-MMT) was recorded in the 4000-500 cm\({}^{-1}\) range as shown in Figure 2a. There were several peaks corresponding to the respective functional groups in O-MMT. The stretching band at 3618 cm\({}^{-1}\) and 3420 cm\({}^{-1}\) indicated the presence of a hydroxyl group (-OH). The peak at 1640 cm\({}^{-1}\) confirmed the bending in-plane vibrations of the OH group. The peaks at 2923 cm\({}^{-1}\) and 2843 cm\({}^{-1}\) presented the symmetric and nonsymmetric stretching vibrations of a methylene group (-CH\({}_{2}\)), while that at 1479 cm\({}^{-1}\) was for the methyl group (-CH\({}_{3}\)). A small peak at 911 cm\({}^{-1}\) showed the in-plane stretching vibrations of the Si-O bonds (Tetrahedral silica layers). All the peaks were confirmed by the literature [21; 22; 23].  
Figure 2b represents the FTIR of pure TPU (TPU-1) and its composites with mmT (TPU -6). Both spectra showed the characteristic peaks of polyurethane. The peak at 3320 cm\({}^{-1}\) was associated with the hydrogen-bonded -NH group. The peaks at 2923 cm\({}^{-1}\) and 2853 cm\({}^{-1}\) belonged to the nonsymmetric and symmetric stretching vibrations of the -CH groups, respectively. C=O was confirmed from the peak position at 1730 cm\({}^{-1}\) (stretching vibrations of free C=O) and 1702 cm\({}^{-1}\) (hydrogen bonded C=O). The peaks at 1126 cm\({}^{-1}\) and 1050 cm\({}^{-1}\) were due to the stretching vibrations of the O-Si-O group. All these peaks were confirmed by the literature [24; 25]. In the above composites, the polymer chains were well incorporated and intercalated into the OMMT galleries; however, the peak positions for the neat polymer were identical to those of the composites, indicating that the chemical structure of the polymer was not affected by adding filler [26].
# Experimental Study of a Signal Modulation Method to Improve eLORAN Data Channel Communications
## 2 Materials and Methods
### Additional Pulse On-Off Modulation  
In this article, an additional pulse on-off modulation method is proposed. Without changing the existing eLORAN system, eight pulses are added after the existing pulse set, and the presence or absence of pulses represents 1 and 0 in binary, similar to On-Off Key modulation. APM pattern is given in Figure 5.  
As the pulse group is added after each main station, the GRI is the same, and there is no need to change the function of receiving signals. The pulse group takes the place of a navigation substation. Td is set and adjusted according to needs, the time delay is appropriately increased between the main station and the pulse group, the continuous working time of the transmitter is reduced, the cooling time of the transmitter is ensured, and the growth rate of the thermal load of the power equipment of the transmitting equipment is reduced.  
APM pulse group starting position Td: In addition to the existing modulation signal 10 ms, there is a 50 ms gap for APM. However, considering the influence of sky waves, a 5 ms guard band is added before and after the existing system pulse, and at the same time, to avoid cross-interference from interfering with the existing pulse group and the APM pulse group at the same time, we considered selecting 31 ms as the APM pulse start time. Therefore, in this article, Td is set to 31 ms and Tg is 1 ms.  
The first pulse of the eight additional pulses is the marking pulse, which is in the constant state (convenient for receiver detection); the second through eighth pulses of the additional pulses are in two states: presence or absence, corresponding to the binary data, with presence = \"1\" and absence = \"0\", that is, each additional pulse set can represent 7-bit information.  
Since each group of APM modulation corresponds to 7-bit binary code, and the Eurofix modulation of the main pulse group also corresponds to 7-bit binary code, the APM modulation uses the same frame structure and coding format.  
One frame contains 30 GRI, the message information is 56 bits, the CRC (70, 56) cyclic redundancy code is 14 bits, and the RS (127, 107) error correction code is 140 bits, a total of 210 bits [29; 30].  
Figure 4: Ninth pulse modulation.  
Figure 5: APM pattern.  
CRC is designed to be 14 bits, so the information code polynomial \(M(x)\) and generator polynomial \(G(x)\) are, respectively,  
\[\mathrm{M(x)=a_{55}x^{55}+a_{54}x^{54}+a_{53}x^{53}+\cdots+a_{1}x^{1}+a_{0}x^{0}} \tag{2}\]  
\[\mathrm{G(x)=x^{14}+x^{13}+x^{7}+x^{5}+x^{4}+x} \tag{3}\]  
The RS code is designed to be 140 bits, so the primitive polynomial \(P(x)\) and the code generator polynomial \(g(x)\) are, respectively,  
\[\mathrm{P(x)=x^{7}+x^{3}+1} \tag{4}\]  
\[\mathrm{g(x)=(x-a)\Big{(}x-a^{2}\Big{)}\Big{(}x-a^{3}\Big{)}\cdots\Big{(}x-a^{ 20}\Big{)}} \tag{5}\]
# A Multi-Level Optimization Method for Elastic Constants Identification of Composite Laminates
## 4 Elastic Constants Identification of Composite Laminate
### Strain Analysis of Symmetric Composite Laminate under Axial Load
For instance, the commonly used least square type objective function was established as the weighted sum of (\(\varepsilon_{x}^{*}-\varepsilon_{x}\))\({}^{2}\) and (\(\varepsilon_{y}^{*}-\varepsilon_{y}\))\({}^{2}\). Through a detailed numerical study, it has been shown that, if the given or measured strains are exact, i.e., contain no noise, the use of the above least square type objective function, comprised of two given strains in the optimization based elastic constants identification, can produce excellent results. However, for practical consideration, uncertainties involved in the measurement of strains during testing are inevitable. If there is some noise in the measured strains (\(\varepsilon_{x}^{*}\) and \(\varepsilon_{y}^{*}\)), the identified elastic constants might deviate from their true values. Therefore, it was desirable to adopt a strain constraint to lower the dominance of the measured axial or lateral strain and regulate the variations of the theoretical axial and lateral strains so that the noise effects could be minimized. Before proceeding to the elastic constants identification, it is noted that, for a 0\({}^{\circ}\) or 90\({}^{\circ}\) layer with given elastic constants, the theoretically predicted normal strain in 45\({}^{\circ}\) direction, \(\varepsilon_{45}\), could be calculated directly from the theoretical axial and lateral strains of the layer as  
\[\varepsilon_{45}=\frac{\varepsilon_{x}+\varepsilon_{y}}{2} \tag{18}\]  
Herein, the given or measured normal strain in 45\({}^{\circ}\) direction, \(\varepsilon_{45}^{*}\), and the calculated \(\varepsilon_{45}\), was used to establish the strain constraint. The role of the strain constraint was to regulate the motions of the design variables so that the theoretical normal strain in 45\({}^{\circ}\) direction, calculated from Equation (18), was forced to have a good match with the given normal strain in 45\({}^{\circ}\) direction. Therefore, the given strains (\(\varepsilon_{x}^{*}\), \(\varepsilon_{y}^{*}\) and \(\varepsilon_{45}^{*}\)), together with the theoretical strains (\(\varepsilon_{x}\), \(\varepsilon_{y}\) and \(\varepsilon_{45}\)), were used in the proposed MLOM to identify the elastic constants.
# Large Scale Imaging by Fine Spatial Alignment of Multi-Scanning Data with Gel Cube Device
###### Abstract  
In vitro three-dimensional (3D) culturing is considered essential in many biological fields. However, the imaging of developed 3D formations is often difficult, especially if the size of the sample is relatively large. The \(z\)-resolution of fluorescent imaging is low using low magnification lenses (4\(\times\) and 10\(\times\)) due to large focal depths. This paper describes 3D culture platform enabling large scale 3D imaging by fine spatial alignment of the image dataset obtained from multiple directions. A gel cube device was employed to conduct the multi-scanning and then a self-fluorescent microstructure in a cubic frame allows us spatially align image dataset within a few pixels. By synthesizing data from multiple scans, the platform enables us to visualize millimeter-sized 3D sample structure and individual cellular actin filaments at the same time. Millimeter depth imaging of a developed bronchial tree was achieved with high \(z\)-resolution. The device, which is applicable to most microscopy systems, can enhance the image quality without modifying current systems.  
3D culture; 3D image; multi-directional imaging Article
## 3 Experimental Results
### Characterization of LNAPL Migration Rates
#### 3.1.3 The High-Permeability Lens Test Group  
When the LNAPL encountered a high-permeability lens, it created an obvious \"preferential flow path\" effect in the vertical direction [33]. As the LNAPL traversed the high-permeability lens, irregular finger-like flows were observed. This phenomenon was primarily due to the large particle size of the coarse sand. The accumulation of loose sand leads to significant internal pore size variation and greater microscopic inhomogeneity in pore size compared to pulverized clay. This resulted in the LNAPL advancing at a faster pace in certain areas, surpassing the average front velocity. From an overall perspective, the vertical diffusion appears to be greater, and the shape of the entire front is olive-like.  
The LNAPL's horizontal migration rate peaked at 0.625 cm/min before encountering the high-permeability lens at 40 cm, then declined as it entered this lens (Figure 5). The LNAPL's spatial redistribution occurred within the lens due to its high porosity, which facilitated rapid lateral migration. However, continuous leakage increased vertical gravity, weakening horizontal migration after reaching 33 cm into the lens. The vertical migration rate decreased because the capillary force of the host medium was stronger than that of the lens medium; thus, when the LNAPL moved from coarse sand to fine sand, it faced greater capillary resistance, slowing vertical diffusion. After the leak stopped, the LNAPL migration in the fine sand continued, but the vertical rate significantly decreased due to reduced infiltration pressure. The LNAPL front generally diffused outward from the center, with similar horizontal migration distances on both sides, reaching 33 cm in 300 min. The process had two phases: the first 300 min with a horizontal rate of 0.16 cm/min, exceeding the vertical rate of 0.12 cm/min, and a noticeable break in the oil front upon encountering the low-permeability clay lens due to reduced permeability. Some LNAPL infiltrated the lower part of this lens, while the majority bypassed it, converging below with a higher vertical rate than the horizontal's 0.16 cm/min. In the second phase (300-690 min), the LNAPL accumulated at the lens's lower part, with horizontal rates rebounding and vertical rates slowly decreasing as the distances exceeded 20 cm. Between 510 and 690 min, vertical migration rates slowly increased with more accumulation. Overall, horizontal diffusion exceeded vertical.