esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Multicast Routing Approaches in DTN
### Geographical Multicast Routing in DTN  
Geographic routing exploits geographic position information to make message forwarding decisions. The source is either aware of the destinations' location or selects appropriate relays based on their geographic position. The ferry or data mule move from one region to another region to forward messages. For example, patrols and workers carry handheld devices around a deployed area according to a scheduled route to provide periodic network connectivity. Ferry-based routing approaches are hence scenario-specific, since the route must be defined before implementation. Location-based routing approaches are based on node location to forward data and require minimal network information. The nodes are equipped with navigation systems to collect information about the location of destinations and neighbors during multicast sessions.
# An Approach to Predicting Sediment Microbial Fuel Cell Performance in Shallow and Deep Water
## 1 Introduction  
Microbial fuel cells (MFCs) work by providing electrogenic bacteria, e.g., _Geobacter_ spp., in anaerobic sediments with an electron acceptor (initially a conductive anode) that stimulates metabolism of organic matter [1; 2; 3; 4; 5; 6; 7]. The buried anode is connected via control circuitry to a cathode exposed to oxygen in the overlying water. During metabolism, bacteria release hydrogen ions into the sediment and transfer electrons extra-cellularly to the anode, which eventually reduces dissolved oxygen at the cathode, forming water. The open circuit voltage is approximately 0.8 V [5; 7]. The voltage between electrodes is operationally kept at 0.4 V with a potentiostat. The current is chiefly limited by the rate of microbial metabolism at the anode. Work at SPAWAR Systems Center Pacific, a Navy laboratory in San Diego, involves fuel cell design and testing, applications to low power sensors [8; 9; 10; 11; 12], and studies of important environmental parameters that affect fuel cell performance.  
Earlier work in shallow sediments of San Diego Bay [13] showed that the most important environmental parameters that control fuel cell power output in San Diego Bay were total organic carbon (TOC) in the sediment and seasonal water temperature. Parameters that were dismissed as non-limiting were dissolved oxygen levels, light level, and initial sediment bacterial populations. Other ancillary but important parameters were found to be anode exposure to oxygen via bioturbation of the sediment, and fouling of the suspended cathode. Anode size was found to be an important non-environmental design parameter in predicting power output. Power density, the power output per anode surface area (typically mW/m\({}^{2}\)), was observed to decrease as the anode size increased, presumably due to resistive losses in the anode [10; 11; 13; 14]. With carbon fabric or brushes, the anode material commonly used in laboratory and field tests, MFC power is not linearly scalable with anode size. Factors influencing microbial fuel cell performance are clearly complex. Herein we show anapproach for predicting sediment microbial fuel cell performance based on two tractable environmental features: sediment TOC and water temperature. We separate performance characteristics into those of shallow (<100 m) and deep (\(\geq\)1000 m) sediments.
# SSKM_DP: Differential Privacy Data Publishing Method via SFLA-Kohonen Network
## 3 Definitions
### Kohonen Network
#### 3.2.2 Maximum Information Coefficient  
The Pearson coefficient, Spearman coefficient, mutual information (MI), and k-nearest distance (KNN) are often used to measure the degree of correlation between two attributes. However, the Pearson coefficient cannot measure nonlinear and non-functional relations. Although the Spearman coefficient can be applied to simple monotone nonlinear relationships, its statistical efficiency is low. The mutual information has weak computing power for continuous variables, has low accuracy, and cannot compare the calculation results of different data. KNN needs to calculate the distance between each sample and all sample points to obtain its k nearest neighbors, which requires a large amount of calculation. Maximal Information Coefficient (MIC) [30] is a new method to measure the correlation between variables based on mutual information and meshing proposed by [PERSON] et al. in 2011, which can overcome the shortcomings of the above methods. It captures the linear, nonlinear, and non-functional relations among attributes more accurately and has  
Figure 2: Schematic diagram of the SFLA algorithm.  
the advantages of universality, balance, and low computational complexity. The pairs of common coefficients are shown in Table 2.  
The specific definition of MIC is described as follows:  
**Definition 3** (Maximum information coefficient).: _Given order to the data \(D\), \(X=\{x_{i},i=1,2,\ldots,n\}\) and \(Y=\{y_{i},i=1,2,\ldots,n\}\) are the two variables in \(D\), \(x_{i}\) and \(y_{i}\), respectively, according to the value of a mesh of \(\mathsf{a}\times\mathsf{b}\). There are many kinds of \(\mathsf{a}\times\mathsf{b}\) meshing, respectively, used to calculate the mutual information of each grid under different division \(I(X:Y)\), selecting different divisions under the maximum mutual information of \(Max(I(X:Y))\). The largest information coefficient is defined as shown in (7)._  
\[MIC(X:Y)=\max_{a\times b\leq B}\frac{Max(I(X:Y))}{\log_{2}\min(a,b)} \tag{7}\]  
_In the formula, \(B\) is the upper limit of the \(\mathsf{a}\times\mathsf{b}\) grid, generally \(n^{0.6}\)._  
In this paper, the maximum information coefficient was used to measure the correlation between sensitive attributes and between sensitive attributes and non-sensitive attributes in the data. The greater the value of MIC, the stronger the correlation between attributes; conversely, the smaller the value of MIC, the weaker the correlation between attributes.
# Estimation of Chlorophyll Content in Wheat Based on Optimal Spectral Index
## 1 Introduction
Scholars have extensively used various machine learning algorithms and methods to conduct research on the quantitative monitoring of crop attributes, achieving remarkable results. Therefore, the choice of appropriate spectral pretreatment methods, vegetation index methods, and machine learning methods is crucial for inverting the chlorophyll content in crop leaves using spectroscopy. Therefore, it is particularly important to choose appropriate spectral pretreatment methods, vegetation index methods, and machine learning methods to invert the chlorophyll content in crop leaves by spectroscopy. The analysis of the dynamic changes in chlorophyll content and vegetation index at different growth stages can directly or indirectly reflect the growth status of wheat. In this study, the chlorophyll content in wheat at four different growth stages, namely the flag-picking stage, flowering stage, grain-filling stage, and maturity stage, was taken as the research object. A five-point smoothing pretreatment was performed on the original hyperspectral reflectance. The correlation matrix method was used to screen out the optimal combination bands of chlorophyll content and five vegetation indices in the range of 400-1000 nm, and the chlorophyll content in wheat was estimated by polynomial regression, random forest, decision tree, and artificial neural network algorithm. This study explored the optimal vegetation index combination and the best estimation model at different growth stages, providing a theoretical basis and technical support for real-time monitoring of wheat growth and development.
## 2 Materials and Methods
### Sample Analysis  
#### 2.3.1 L*a*b* Value Determination  
Color measurements were performed using a CM-600 d from Konica Minolta Sensing, Inc. (Marunouchi, Japan), with an \(\varnothing\) 11 mm illumination area and a calibration cap CM-A177. Before measurements, samples of both treatments were taken from 4 \({}^{\circ}\)C storage and immediately cut, and freshly cut surfaces were used to determine luminosity coordinates (L*), red-green coordinates (a*), and yellow-blue coordinates (b*). The illuminant used was a D65 and the observer angle was 10\({}^{\circ}\). Reflection spectra were acquired between 400 and 700 nm with reflectance values every 10 nm. The software used was Spectra Magic NX (Konica Minolta Sensing, Inc., Marunouchi, Japan, [[https://www.konicaminolta.eu/eu-en/hardware/measuring-instruments/colour-measurement/software-colour-appearance/spectramagic-nx](https://www.konicaminolta.eu/eu-en/hardware/measuring-instruments/colour-measurement/software-colour-appearance/spectramagic-nx)]([https://www.konicaminolta.eu/eu-en/hardware/measuring-instruments/colour-measurement/software-colour-appearance/spectramagic-nx](https://www.konicaminolta.eu/eu-en/hardware/measuring-instruments/colour-measurement/software-colour-appearance/spectramagic-nx))).
## 2 Materials and Methods
### Data Processing
#### 2.3.2 Evaluation Model  
The _Zhou Guangsheng-Zhang Xinshi_ (_ZGS_) model [32] connects vegetation productivity with temperature and precipitation, which is conducive to studying the interaction between climate change and vegetation. The simulated productivity model is closer to the actual [33; 34; 35], therefore the simulated results are usually taken as the actual values. Therefore, the accuracy of the values obtained by the ZGS model is verified with our estimated results. The results are then compared and verified to verify the accuracy of the MOD17A3-_NEP_ data. The water heat balance vegetation net primary productivity model uses the precipitation and net radiation data obtained from the land surface to obtain the regional potential vegetation net primary productivity, i.e.,:  
\[NPP=RDI\cdot\frac{R\cdot R_{n}\left(R^{2}+{R_{n}}^{2}+R\cdot R_{n}\right)}{(R+ R_{n})\cdot\left(R^{2}+{R_{n}}^{2}\right)}\cdot Exp\left(-\sqrt{9.87+6.25 RDI}\right) \tag{3}\]  
where _RDI_ is radiation dryness; \(R_{n}\) is the annual net radiation (mm); \(R\) is annual precipitation (mm); \(NPP\) is the net primary productivity of vegetation (\(10^{2}\) g\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\)).  
Formula (3) can be further sorted as follows:  
\[NPP=RDI^{2}\cdot\frac{R\cdot\left(1+RDI+RDI^{2}\right)}{(1+RDI)\cdot\left(1+RDI ^{2}\right)}\cdot Exp\left(-\sqrt{9.87+6.25 RDI}\right) \tag{4}\]  
It is not suitable for predicting the net primary productivity of a terrestrial ecosystem under the condition of global change due to the fact that the calculation of annual radiation obtained from the land surface requires climate variables, such as solar radiation at the top of the atmosphere, possible sunshine hours, actual sunshine hours, reflectivity, air temperature, air humidity, altitude, and dimension. It is found that there is a highly significant correlation between possible evapotranspiration rate (_PER_) and annual radiation dryness (_RDI_) through analysis, i.e.,:  
\[RDI=\left(0.629+0.237 PER-0.00313 PER^{2}\right)^{2} \tag{5}\]  
The correlation coefficient is 0.90. Among them, _PER_ is obtained by using the relationship between Holdridge's possible evapotranspiration rate and biological temperature and precipitation:  
\[PER=\frac{PET}{R}=BT\cdot\frac{58.93}{R} \tag{6}\]  
where _PET_ is the possible evapotranspiration (mm), _BT_ is the annual average biological temperature (\({}^{\circ}\)C).  
\[BT=\sum\frac{t}{365}\text{or}=\sum\frac{T}{12} \tag{7}\]  
where \(t\) is the daily average temperature of <30 \({}^{\circ}\)C and >0 \({}^{\circ}\)C, and \(T\) is the monthly average temperature of <30 \({}^{\circ}\)C and >0 \({}^{\circ}\)C.  
The _NPP_ value of the observation station can be obtained from Formulas (4)-(7), and the _NEP_ value can be converted by combining the \(R_{H}\) value of the observation station.
## 2 Results and Discussion  
Twenty-two samples of _Calypogeia azurea_ from Poland (Table 1) were analysed for volatile specialized metabolites in the study. Supplementary Tables S1-S7 show the percentage of compounds detected present in liverwort cells. A total of 73 compounds were detected, 42 of which were identified. Depending on the extraction method used, the content of the identified VOCs differed. The study compares four extraction methods: Three (preparative) methods, which used different types of solvents with different polarities (HD, SLE, and MAE), and the non-preparative SPME method. The most common isolation method is SLE, and the extraction efficiency and activity are highly dependent on the type of solvent used. The polarity of the extraction solvent strongly influences the compounds present in the test sample. Therefore, extraction solvents are selected as they are critical to the complex sample matrix. The extraction solvent system is generally selected according to the purpose of extraction, the polarity of the components concerned, the polarity of the undesirable components, the total cost, safety, and environmental concerns [28]. Essential oils, which were prepared with different solvent methods, took different colours. The colour of the oil produced by the HD was slightly blue to purple. The compounds 1,4-dimethylazulene (**53**) (the bold numbers in the brackets refer to the compounds in Tables S1-S7) were responsible for these colours. Colourless solutions were formed during the maceration from n-hexane and turned dark green on extraction with methanol. Methanol is a very good extractant for chlorophylls, especially for resistant vascular plants and algae [29]. In the case of MAE, the extract obtained took the form of a dark brown liquid, regardless of the solvent used.  
The main component of the _Calypogeia azurea_ liverwort is 1,4-dimethylazulene (**53**). This VOC belongs to sesquiterpene hydrocarbons. The relative content of this compound during HD ranged from 16.64% (for n-hexane) to 19.49% (for n-xylene), depending on the solvent used to collect the extract. For this sesquiterpene, the best extraction method is SLE for 24 h n-hexane as the solvent. The relative content of 1,4-dimethylazulene (**53**) for this extraction method is 59.62%. MAE obtained only 1.37-0.21% of this sesquiterpene (Figure 1).  
The sum of sesquiterpene hydrocarbons produced is the highest for this research material and ranges from 13.73% to 78.07% of the total essential oil content, depending on the extraction method used. However, for the SPME method, the relative content of 1,4-dimethylazulene (**53**) is 42.67% of all detected VOCs present in liverwort cells. Another sesquiterpene compound, which is also a characteristic chemical compound for this species of liverwort, is anastreptene (**18**). The relative content of this compound ranges from 5.98% for MAE using methanol as a solvent to 15.74% for HD using m-xylene to collect the extract.
## 2 Materials and Methods
The bulk density (\(\rho_{\text{b}}\)) of the granules was determined according to the ISO 18754 standard [43] with the following equation:  
\[\rho_{\text{b}}=\text{m}_{1}\times\rho_{\text{l}}/(\text{m}_{3}-\text{m}_{2}), \tag{1}\]  
where m\({}_{1}\), m\({}_{2}\), m\({}_{3}\), and \(\rho_{\text{l}}\) are the weight of a dried specimen (one particle), weight of a specimen submerged in water, weight of a specimen saturated with water, and the density of water at 25 \({}^{\circ}\)C (-1.0 g/cm\({}^{3}\)), respectively. The porosities of the specimens were calculated based on the bulk densities of the specimens and the theoretical density of diatomic (\(\rho_{\text{th}}\): 2.30 g/cm\({}^{3}\)), according to the following equation [44]:  
\[\text{Porosity}\left(\%\right)=(1-\rho_{\text{b}}/\rho_{\text{th}})\times 100 \tag{2}\]  
The pore size distribution was assessed by means of mercury porosimetry (Autopore IV 9510, Micromerities, Norcross, GA, USA). N\({}_{2}\) adsorption/desorption isotherms at the liquid nitrogen temperature (77 K) were measured utilizing a Belsorp-max II system (MicrotracEBL Corp., Osaka, Japan). The specific surface areas were calculated from the linear part of the adsorption data using the Brunauer-Emmett-Teller (BET) method. All the specimens were degassed at 150 \({}^{\circ}\)C under vacuum conditions for 8 h to ensure the detection of the pressure gradient. The total pore volume was determined at a relative pressure level of p/p\({}_{0}\) = 0.99 [6]. The fracture force (F\({}_{\text{B}}\)), which represents the maximum load applied to break an individual granule [3], was measured using a compression tester machine (DU-B004, DUE tester CO., Gyeonggi, Republic of Korea). The compressive strengths of the granules were calculated from F\({}_{\text{B}}\) values considering the dimensions of the tested granules (radius: R) [3]:  
\[\text{Compressive strength}=\text{F}_{\text{B}}/\pi\text{R}^{2} \tag{3}\]  
The phase analysis of the granules was conducted using an X-ray diffractometer (D/Max 2500 V/PC, Rigaku Corporation, Akishima, Japan) with Cu K\(\alpha\) radiation generated at 40 kV and 200 mA and a scanning speed of 5\({}^{\circ}\)/min. Cross-sectioned microstructures and surface morphologies of the particles were observed with a scanning electron microscope (SEM, JSM-5800, JEOL, Tokyo, Japan).
# Minimum Frequency and Voltage Stability Constrained Unit Commitment for AC/DC Transmission Systems
## 2 HVDC Transmission System Model
### Minimum Frequency Limit Constraints  
1. Primary frequency regulation capacity for HVDC N-1 fault  
As we all know, HVDC's rated capacity is much larger than thermal generators. It means that if any HVDC blocked, larger power deficiency and low frequency would emerge in the power system. Therefore, enough primary frequency regulation capacity for HVDC's N-1 fault is critical to keep frequency within limit. Constraints (10) and (11) describe that when the largest capacity HVDC blocked, the power grid's frequency would drop less than \(\Delta f_{\text{max}}\), meeting the operation requirement.  
\[(\sum_{i\in N_{G}}K_{G,i}\times u^{t}_{i}+K_{L})\times\Delta f_{\text{max}} \geq\overline{P}^{t}_{d},\,\,i\in T \tag{10}\]  
\[\overline{P}^{t}_{d}=\text{max}\Big{\{}P^{t}_{d,1},P^{t}_{d,2},\ldots P^{t}_{ d,N_{L}}\Big{\}} \tag{11}\]  
2. Security constraint after secondary frequency regulation  
When HVDC blocking fault happens, secondary frequency regulation would dispatch generators to re-achieve power balance at each bus, as shown in constraints (12) and (13). Meanwhile, the power's wide transfer would occur and result in transmission lines' power exceeding limits. To avoid the above situation, we introduce constraint (14) in the UC model.  
\[\sum_{i\in N_{m}}\left(P^{t}_{G,i}+\Delta P^{t}_{G,i,s}\right)\times u^{t}_{i} =P^{t}_{L,m}+V^{t}_{m,s}\sum_{n\in m}V^{t}_{n,s}(G_{mn}\cos\theta^{t}_{mn,s}+ B_{mn}\sin\theta^{t}_{mn,s}) \tag{12}\]  
\[\sum_{i\in N_{m}}\left(Q^{t}_{G,i}+\Delta Q^{t}_{G,i,s}\right)\times u^{t}_{i} =Q^{t}_{L,m}+V^{t}_{m,s}\sum_{n\in m}V^{t}_{n,s}(G_{mn}\sin\theta^{t}_{mn,s}- B_{mn}\cos\theta^{t}_{mn,s}) \tag{13}\]  
\[PL_{mn,\text{min}}\leq PL^{t}_{mm,s}\leq PL_{mn,\text{max}},\,\,\,\,mn\in N_{B },t\in T \tag{14}\]
# Performance Comparison of ANFIS Models by Input Space Partitioning Methods
## 4 Experimental Method and Result Analysis
### Database  
The CCPP database [52] is a database that was collected for six years from 2006 to 2011, when combined power plants were set to operate at full load. The database is made up of data sheet (.xls) files and has a total of five sheets. The size of one sheet is \(5\times 9568\). It has four input variables and one  
Figure 7: Context-based fuzzy C-means clustering method: (**a**) contexts generated from output space, and (**b**) center of cluster corresponding to each context.  
output variable. The input variables consist of the time average ambient temperature (AT), ambient pressure (AP), relative humidity (RH), and exhaust vacuum (V). The output variable represents the electrical energy (EP) generated per hour in a combined power plant. In this experiment, the data of sheet1 were used, and divided into the training data and verification data in the ratio of 50:50, by randomly selecting the data. To obtain a more uniform value, the data were normalized to values between 0 and 1 through a normalization process.  
The Auto-MPG database [53] was derived for different types of automobiles with vehicle fuel consumption. It is provided in the StatLib library maintained by Carnegie Mellon University. It was first used in the American Statistical Association Exposition in 1983 and is a benchmark database frequently used in predictive experiments. The data size is 392 \(\times\) 8, and it has seven input variables and one output variable. Input variables include the number of cylinders, displacement, horsepower, weight, acceleration, model year, and vehicle model name. In this experiment, six input variables were used except for the automobile model name. The output variable was the fuel consumption of the vehicle. The Auto-MPG data were also divided into learning and verification data in the ratio of 50:50, and the data were normalized to values between 0 and 1. Figure 8 shows the correlation of input variables of the CCPP data [54]. The \(x\)-axis in Figure 8 is composed of four plots, showing the time-averaged ambient temperature (AT), ambient pressure (AP), relative humidity (RH), and exhaust vacuum (V) from left to right. The \(y\)-axis consists of four plots, showing the average ambient temperature (AT), ambient pressure (AP), relative humidity (RH), and exhaust vacuum (V) over time in order from top to bottom.
## 5 Experiment
### Performance Evaluation
#### 5.4.3 Effect of Window Lengths on the Model Performance  
We evaluated the model performance with various window lengths on the five datasets, and the effect of window lengths on the model performance is shown in Figure 9. For window lengths 16 and 30, we applied two Conv1D hidden layers, while three layers were employed for the rest of the window lengths. It can be observed that there is not much difference in detection performance for different window lengths in the SMD and PSM. The reason is that most anomalies in SMD and PSM are large spikes, because it does not need a large window length for detection. Otherwise, mild and context anomalies are contained in the ASD, and the longer window length might help to trigger these types of anomalies. Specifically, MST-VAE achieved 0.9661 with window length 16 and 0.9766 with window length 100. However, for the water treatment datasets SWaT and WADI, MST-VAE works well with the window length of 30, and its performance tends to degrade when the window length increases.