esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Results and Discussions
### Quantitative Time-Frequency Analysis of Water Molecule  
Based on the derived optimal wavelet parameters, the QTF analysis of the water molecule vibration is performed with CMOR wavelet transform in this part, and the results are shown in Figure 3. The frequency of each target vibration mode is assigned as \(f_{k}\). Moreover, the intensity of \(f_{k}\) over time is given by the CMOR wavelet transform of H-atom trajectories. One cosine signal is constructed to represent the vibration mode with a frequency nearest to \(f_{k}\). The frequency of this cosine signal is \(f_{n}\).  
Obviously, it can be found from Figure 3 that in the initial stage of the FPMD simulation (\(t<0.2T_{0}\)), the water molecule vibrations are mainly dominated by the O-H bending, which is accompanied by a slight symmetric stretching vibration. However, the intensity of the O-H asymmetric stretching vibration significantly increases when \(t>0.5T_{0}\), as shown in Figure 3c. It indicates that there is an uphill energy relaxation in the water molecule vibration; namely, the lower frequency vibration mode can excite the higher one. Experimentally, 2D-IR and pump-probe measurements reveal that strong couplings of water molecular vibration allow bend excitation to drive stretching moti  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Wavenumber (cm \({}^{-1}\))** & \(\delta f\) **(cm \({}^{-1}\))** & \(\delta f\) **(cm \({}^{-1}\))** & \(f_{b}\) & \(f_{c}\) & \(\Delta f\) **(cm \({}^{-1}\))** & \(\delta t\) **(10 \({}^{-10}\) s)** \\ \hline
1560.6 & 2150.5 & 100.0 & 6.0 & 3.0 & 113 & 0.00667 \\
3711.1 & 108.2 & 70.0 & 20.0 & 6.0 & 74 & 0.01024 \\
3819.3 & 108.2 & 60.0 & 24.0 & 7.0 & 59 & 0.01271 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Optimal wavelet parameters for water molecule vibration.  
Figure 2: The CMOR wavelet transform of cosine signal (O-H bending vibration): (**a**) The 3D time-frequency analysis, (**b**) The intensity variation over time for \(f_{k}\), (**c**) Intensity of various frequencies at \(0.5T_{0}\) (\(\Delta f=113\) cm\({}^{-1}\)).  
downhill energy relaxation pathway that form the bend to low-frequency modes [35; 36]. Therefore, our result agrees well with the experimental result, which in turn further confirms the feasibility and reliability of our selection method for the optimal CMOR wavelet parameters.
# Integrated Radar and Communications Waveform Design Based on Multi-Symbol OFDM
## 6 Discussion  
To further characterize the IRC waveform obtained by the proposed algorithm, we will specifically discuss the radar and communication performance of the IRC waveform here, where in addition to the PSLR performance mentioned above, the radar SNR, sidelobes in the low Doppler domain, and communication BER are mainly discussed, which are also very typical performance metrics in IRC systems. The IRC waveforms to be analyzed are generated at \(\alpha=5\). The solid-state power amplifier (SSPA) with [PERSON]'s model [40] is considered.  
The IRC waveform is passed through an SSPA. The input/output relationship of the SSPA is modeled by [40]  
\[\widetilde{G}[s(q)]=\frac{s(q)}{\left(1+\left(|s(q)|/A_{sat}\right)^{2b} \right)^{\frac{1}{2b}}} \tag{49}\]  
where \(A_{sat}\) is defined as a saturation level, and \(b\) determines the output sharpness parameter. In this paper, to compare the BER performance of the IRC systems with that obtained from traditional communication systems, we define the input communication power back-off (ICPBO) as  
\[\text{ICPBO}=\frac{A_{sat}^{2}}{P_{in}} \tag{50}\]  
Figure 7: PMEPR and PSLR performance with different weights \(\alpha\).  
where \(P_{in}\) is the input communication average power of SSPA. In our simulations, the SSPA parameters are \(b=3\) and \(A_{sat}=11.3\) dB compared to the average signal power before optimization.
## 4 Conclusions  
A novel technology to predict anthocyanins, a micro-component in rice for single seed, was reported in our research in a rapid and non-destructive technique. The model's performance to predict anthocyanins in seeds was evaluated by comparing the model's performance based on the powder sample. NIR hyperspectral imaging combined with PLRS resulted in a prediction performance (\(R^{2}\)) of 0.75-0.90, with SEP ranging from 0.26 to 0.16 mg/g. The SNV pre-processing method significantly improves the model performances, denoted by the highest \(R^{2}\) of 0.85-0.95 and lowest SEP of 0.11-0.16 mg/g. This performance was comparable to the model developed based on the powder sample with \(R^{2}\) and SEP of 0.92-0.95 and 0.09-0.15 mg/g, respectively. A chemical image clearly confirmed the anthocyanins content in the powder sample. The anthocyanins prediction based on seed samples showed the potential of NIR hyperspectral imaging to predict anthocyanins in a single seed of rice.  
The HSI approach provides the potential for rapid analysis of a large numbers of seeds. Even though the single seed evaluation is not a substitute for the bulk analysis of anthocyanins, particularly in black rice, it can open up the possibility to develop an online chemical-based sorting machine for seeds. This machine has a potential application in the quality control of seed in the manufacturing industry as well as in the breeding program.  
Data curation, H.Z.A., M.A.F., M.P., and S.S.T.; formal analysis, H.Z.A., C.W., M.A.F., and M.-G.C.; software development, C.W. and M.P.; methodology and validation, R.E.M., W.-H.L., and K.-H.K.; and wrote the paper, H.Z.A. and B.-K.C. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a grant from the Next-generation BioGreen 21 Program (No. PJ01311303), Rural Development Administration, Republic of Korea.  
The authors declare no conflict of interest.
# Electric-Vehicle Routing Planning Based on the Law of Electric Energy Consumption
## 4 The Electric Vehicle Routing Problem with Time Windows Based on Driving Cycles (EVRPTW-DC)
### EVRPTW with Linear Energy Consumption (L-EVRPTW)  
Most of the previous works on EVRP assumed energy consumption to be a linear function of the traveled distance, which is less realistic compared to our proposed EVRPTW-DC. To conduct comparative analysis in a later section, here we present a brief introduction of EVRPTW with linear energy consumption, denoted as L-EVRPTW.  
Unlike EVRPTW-DC, L-EVRPTW has assumptions of constant speed during driving, and the energy consumption is linearly related to traveled distance only, without considering the impacts of speed and load. In addition to the notations in previous Table 2, \(v\) represents the constant speed of EVs, and \(\zeta\) is the energy consumption rate, which is also constant. The battery energy consumed between two nodes \(i\) and \(j\) with distance \(d_{ij}\) can then be calculated by the below formula:  
\[E_{ij}=\zeta\cdot d_{ij},\ \ \ \forall i,j\in N,k\in K \tag{65}\]  
The travel time is determined as:  
\[t_{ijk}=\frac{d_{ij}}{v},\ \forall i,j\in N,k\in K \tag{66}\]  
The remainder of the constraints and objective function of L-EVRPTW are the same as EVRPTW-DC.
## 1 Introduction
Plants with the C4 pathway of photosynthesis have developed a mechanism to minimize photo respiratory losses by building a higher concentration of CO\({}_{2}\) around Rubisco, and they exhibit better NUE than C3 plants [21; 22]. The introduction of C4-like traits into C3 plants, such as wheat and rice, therefore, has been a favored approach to improve the photosynthetic efficiency of C3 plants and enhance crop yield [21]. Additionally, studies have shown that _M. aquaticum_ is easy to harvest, and its high growth rate makes it a suitable candidate used in CWs for nitrogen-rich wastewater treatment [7; 9; 10]. Compared with other aquatic plants such as _Canna indica_, _Hydrocstyle verticillata_, and economic crops such as corn, rice, and soybeans, _M. aquaticum_ had a much higher crude protein and key amino acids content [4; 13; 23], which indicated that _M. aquaticum_ could be used as a suitable forage for livestock. In particular, although crude protein content is lower than that in duckweed, greater content of 10 essential amino acids in _M. aquaticum_ show that _M. aquaticum_ could contain more high-quality protein [4]. Previous studies also showed that _M. aquaticum_ could increase the activity of key enzymes involved in nitrogen metabolism such as GS, asparagine synthetase (AS), and nitrate reductase (NR) for ammonium assimilation [13; 24; 25]. For example, NR activity in _M. aquaticum_ leaves was significantly increased to accelerate the reduction of nitrate to ammonium for ammonium assimilation when treated with low concentrations of ammonium [13]. All of these studies indicated that _M. aquaticum_ possessed a highly efficient nitrogen metabolic network. However, few studies have investigated in detail the genetic regulation mechanism and key genes involved in such high NUE in _M. aquaticum_. In this study, it is hypothesized that the high NUE by _M. aquaticum_ depends on an interactive network regulating nitrogen, carbohydrate, transcription factors (TFs), and secondary metabolism. To verify this hypothesis, we grew _M. aquaticum_ at five concentrations of NH\({}_{4}\)Cl (0, 0.1, 1, 12, and 36 mmol/L) defined as LNC, L1, L2, L3, and L4 groups for 14 days. Plant biomass, ammonium content, and different kinds of amino acids were first determined. Since plant leaves serve as nitrogen-sinks during the vegetative stage [2], a transcriptomic analysis of leaf tissues was carried out to investigate the complex molecular responses to different concentrations of ammonium. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were used to identify the important biological processes in _M. aquaticum_ leaves. At last, the weighted gene correlation network analysis (WGCNA) was used to identify the most relevant gene modules related to NUE in _M. aquaticum_.
# Low-Cost Solution for Kinematic Mapping Using Spherical Camera and GNSS
## 2 Materials and Methods
### Processing Methods
#### 2.6.5 Accuracy Evaluation  
For the calculations of relative accuracy and absolute accuracy, the formula of standard deviation for a single coordinate (1), for a plane (2), and for space (3) was used.  
\[StDev=\sqrt{\frac{\sum_{i=1}^{n}v_{Xi}^{2}+v_{Yi}^{2}}{n-1}},\]  
\(\Delta x_{i}\) _represents the value of the \(i-th\ point\) for the given coordinate \((X,Y,Z)\), \(\Delta\underline{x}\) is the mean value for the given coordinate \((X,Y,Z)\); \(n\) is the number of points_  
\[StDev_{XY} = \sqrt{\ \frac{\sum_{i=1}^{n}v_{Xi}^{2}+v_{Yi}^{2}}{n-1}}\]  
\[StDev_{XYZ} = \sqrt{\ \frac{\sum_{i=1}^{n}v_{Xi}^{2}+v_{Yi}^{2}+v_{Zi}^{2}}{n-1}}\]
## 2 Background
### IoMT Context
#### 2.3.2 IoMT Applications  
With rapid technological advancement, the various IoMT applications are exponentially increasing. Hundreds of applications are available for IoMT systems. These applications can be categorized as shown in Figure 2 [4]:  
* Monitoring applications use pervasive computing to remotely monitor a patient's health for prevention purposes [20]. Examples of widespread applications under this category include monitoring oxygen levels, blood pressure, asthma, electrocardiogram (ECG), glucose, etc.
* Diagnostic applications mainly use the semantics explained in electronic healthcare records to diagnose diseases. The effectiveness of these applications is highly dependent upon the quality of data collected through the IoMT devices and the predefined observations in the electronic healthcare records.
* Therapeutic applications involve remote interventions, which lead to many challenges according to the level of intervention. Remote surgery is an example of such applications. Therapeutic IoMT applications are not currently popular because they need advanced technologies and experts from different fields to implement them.
* Rehabilitation applications are mainly used to identify the patients' problems and help them to regain the functions needed in everyday life. An example of rehabilitation applications is the stroke rehabilitation system.
## 3 Methodology
### Preprocessing Raw Data
#### 3.1.3 Artifact Rejection and Reconstruction  
Filtered signal artifacts were addressed using the artifact subspace reconstruction (ASR) method. This process involved three steps: (1) extracting reference data from the raw EEG signals, (2) determining thresholds to identify artifact components, and (3) rejecting the artifact components while reconstructing the remaining signals [30]. The ASR method enables the rejection of artifacts while reconstructing the signals based on physiological activity, thereby enhancing the quality and reliability of the subsequent analyses. The rejected portions of the signal are marked in red in Figure 2, which highlights the artifact segments excluded from the cleaned signals (blue segments).
## 5 Transfer Hydrogenation of Other Organic Molecules in Glycerol  
In a recent patented work [59], a method for the preparation of primary alcohols by transfer hydrogenation of carboxylic acids in glycerol has been disclosed (Scheme 11). The reactions, which were performed at 140-150 \({}^{\circ}\)C in the presence of inexpensive CoCl\({}_{2}\)\(\cdot\)6H\({}_{2}\)O as catalyst and KOH, delivered the alcohols in a selective manner, and in excellent yields, after a very short reaction period. This new method represents a good alternative to the classical ones involving the use of molecular hydrogen or NaBH\({}_{4}\).  
**Scheme 11.** Transfer hydrogenation of carboxylic acids in glycerol.  
\(\begin{array}{c}\includegraphics[width=142.26378 pt]{Fig1}\end{array}\)  
\(\begin{array}{c}\includegraphics[width=142.26378 pt]{Fig2}\end{array}\)  
\(\begin{array}{c}\includegraphics[width=142.26378 pt]{Fig3}\end{array}\)  
\(\  
Finally, in the context of a broad study, glycerol was very recently evaluated as a potential hydrogen-donating solvent for the hydrogenolytic depolymerization of lignin, using nickel nanoparticles supported on the mesoporous silica AI-SBA-15 as catalyst under microwave irradiation [62]. After three hours of irradiation at 150 \({}^{\circ}\)C, _ca._ 25 wt% of the initial lignin was converted into the desired bio-oil (a mixture of the monomeric phenolic products syringol, vanillin, syringaldehyde and acetosyringone). This result was superior to that found using 2-propanol as the hydrogen source (_ca._ 2 wt% of bio-oil), but slightly poorer in comparison to that reached in formic acid (_ca._ 30 wt% of bio-oil), the best H-donor solvent found in the study. We must also note that, contrary to the case of formic acid, undesirable formation of char was also observed in glycerol (_ca._ 40 wt%).
## 7 Discussion
### InSAR-Detected Ground Motions in Ireland (AOI-1 and AOI-2)
This is visually shown by comparing the displacement time-series of one PS taken in the central part of the peat and the precipitation amount data of the nearest weather station (Figure 16). The relationship between wet/dry periods with uplift/subsidence is in this case evident. PS-InSAR is in this case able to record such small variations in the displacement trends only in the central part of the peat where the water table is closer to the surface (few cm to tens of cm), while only the general linear trends are registered moving toward the peat margins that are characterized by decreasing water table levels. Past studies have mainly used low-resolution approaches for long-term InSAR-based monitoring of peatlands [63]. Our study represents to our knowledge the first to gain consistent PS-InSAR results for surface motions in peatland, opening the possibility of a full-resolution approach to InSAR-based monitoring of such ecologically and environmentally sensitive areas. Ground-motions in AOI-2 are found in areas spatially associated with the development of the Tara mine and the TSF. The clearest motions occur along the minor road in Figure 17b, where subsidence rates of \(-1.5\) mm/year to \(-6.8\) mm/year occur over a distance of about 1 km. The largest displacement rates occur toward the center of this profile. We estimate the associated differential strain to be in the order of \(1.2\times 10^{-4}\) to \(5\times 10^{-5}\) (i.e., 0.12 mm/m-0.05 mm/m), values that are an order of magnitude lower than those empirically associated with even slight damage to surface structures (see Figure 218 of [71]). Such small subsidence values may reflect both the depth of mining in this area (\(\sim\)700-900 m below ground surface [46]) and the back-filling of slopes (mined-out areas) with tailings and cement adopted to minimize ground subsidence [72]. Subsidence rates of up to \(-17\) mm/year at the 22 m-high TSF embankment can be explained as the expected compaction and consolidation of the fine and coarse material constituting the dam by its self-weight following its 2015 vertical extension. Although requiring ground-truthing by precise-levelling data, as discussed in general above, the results obtained here for the Tara mine site demonstrate the potential of PS-InSAR for conducting monitoring activity of active or disused mining sites in Ireland.
# Blockchain-Based Model for Incentivized Cyber Threat Intelligence Sharing
## 5 Conclusions  
A review of the related literature revealed that many authors have used incentives to either increase confidence in CTI data or to motivate the sharing of the data, but they have not pursued both goals together. We presented a model for sharing CTI data that includes both monetary incentives and reputation management. The combination of two incentives into a single model is the main novelty and contribution of our model. The monetary incentive stimulates the participation of the users in the CTI data-sharing process. The reputation incentive is important to ensure trust in the shared CTI data. The model was implemented on a private Hyperledger Fabric blockchain, enabling the sharing of sensitive CTI data. The unique feature of the Hyperledger Fabric blockchain (i.e., channel-based privacy) was utilized to implement the sharing of sensitive CTI data in accordance with the latest version of the Traffic Light Protocol. The CTI data are stored off-chain, as they can be large in volume. The hash of the CTI data is only stored on the chain. The key elements of the model to support the monetary incentive are a subscription fee and a reward score for the activity shown. The network's users play the role of either CTI data producer or CTI data consumer. CTI data consumers are encouraged to write a review for newly submitted CTI data. However, the quality of these reviews is not always satisfactory or manageable. Therefore, reviewing CTI data is a limiting factor in our model. Our next research direction involves the development of a method to automatically assess the quality of CTI data.  
Conceptualization, V.J. and A.V.; Methodology, V.J.; Software, D.B.; Validation, V.J., D.B. and B.M.; Formal Analysis, V.J.; Investigation, V.J.; Resources, V.J. and D.B.; Data Curation, V.J. and D.B.; Writing--Original Draft Preparation, V.J.; Writing--Review and Editing, B.M.; Visualization, B.M.; Supervision, A.V.; Project Administration, A.V.; Funding Acquisition, A.V. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Economic Revitalization and Resilience Enhancement Plan \"New Generation Lithuania\" as part of the execution of the \"Mission-driven Implementation of Science and Innovation Programmes\" project (No. 02-002-P-0001).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 4 Results and Analysis  
### Accuracy Validation of NPP  
This research compared simulated NPP values with data from eight flux sites during the same period. In addition, it compared NPP simulations for forests, shrubs, and grasslands in 2005-2010 with time series data from the Dinghu Mountain, Haibei, and Dangxiong sites. To further evaluate the NPP estimates for various vegetation types, this study included three NPP products (MOD17A3 HGF, GLO_PEM, and Chinanpp).  
The study results show that the simulated NPP values based on the CASA model are strongly correlated with the measured values at the stations (\(R^{2}>0.6\)) (Figure 4). Interestingly, the NPP estimated with \(k\)NDVI (\(R^{2}=0.74\)) had a better correlation than that estimated with NDVI (\(R^{2}=0.65\)). This finding suggests that the NPP values estimated using \(k\)NDVI were more accurate than those obtained using NDVI. Compared with NDVI, the NPP values estimated based on \(k\)NDVI were closer to the measured NPP values in both densely vegetated and sparsely vegetated sites (Figure 5), and more closely matched the phenological cycle measured at the flux stations, making this approach more suitable for estimating terrestrial vegetation NPP on a national scale.  
Figure 4: Model validation.  
The comparison revealed that the mean values of the three NPP products and NPP estimated by this study varied (Figure 6), with GLO-PEM NPP (1431.95 g C/m\({}^{2}\)) \(>\) NDVI NPP (1161.43 g C/m\({}^{2}\)) \(>\) kNDVI NPP (942.46 g C/m\({}^{2}\)) \(>\) MOD17A3 HGF NPP (864.17 g C/m\({}^{2}\)) \(>\) Chinnapp NPP (397.33 g C/m\({}^{2}\)). The authors of [39] demonstrated that the NPP values estimated by the GLO-PEM model were generally higher than the MOD17A3 HGF product NPP values and [40] produced the Chinnapp dataset, with the maximum light energy utilization determined as 0.55, while the maximum light energy utilization estimated by the other two products and in this study was defined according to each vegetation type (Table 1); thus, the NPP values of Chinnapp products were low. The NPP mean estimated by NDVI in this study was high compared with the NPP mean of the MOD17A3 HGF product, which was overestimated, while the NPP mean estimated by kNDVI was close to the NPP mean of MOD17A3 HGF, and both of them were in better concordance. Generally, the NPP values estimated using kNDVI in this study were reliable and could be used in subsequent analysis.  
Figure 5: NPP time series of (**a**) forest at Dinghushan station, (**b**) scrub at Haibei station, and (**c**) grassland at Dangxiong station from 2005 to 2010.