esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methods
### Analysis  
The Pearson correlation coefficient (PCC) was calculated between SIF and NDVI [43]. The relations between monthly SIF and NDVI were explored across different land cover types and in different seasons. We selected a correlation coefficient that passed the significance level of \(p<0.05\). For two time series, \(x\) and \(y\), the PCC was calculated as follows:  
\[\text{PCC}=\frac{\sum_{i=1}^{n}{{{({x_{i}}-\overline{x})}\left({{y_{i}}- \overline{y}}\right)}}}{\sqrt{\sum_{i=1}^{n}{{{({x_{i}}-\overline{x})}^{2}} \sqrt{\sum_{i=1}^{n}{{{({y_{i}}-\overline{y})}^{2}}}}}}} \tag{1}\]  
where \(x_{i}\) and \(y_{i}\) denote the \(i\)-th value in the two corresponding time series (monthly NDVI and SIF across different land-cover types and in different seasons). The PCC values range from -1 to 1. A PCC value greater than 0 indicates a positive linear correlation, a PCC value less than 0 indicates a negative linear correlation, and a PCC value equal to 0 indicates no linear correlation.  
Next, we calculated the PCC values between monthly SIF (NDVI) and GPP. The temporal and spatial differences between SIF or NDVI and GPP were analyzed across different land-cover types and in different seasons to reveal the applicability of SIF and NDVI for monitoring GPP.  
Finally, polynomial fitting (univariate quadratic function) was used to reveal the relationship between SIF (NDVI) and the per-unit grain yield. The coefficient of determination (R\({}^{2}\)) was used to represent the abilities of the analyzed vegetation indicators to explain the measured changes in grain yield. Next, considering that China experienced relatively large-scale drought events during the 2009-2010 period, we compared the percentage changes in the SIF, NDVI, and per-unit grain yield values from 2009-2010 with the mean annual values recorded from 2001-2019 to reveal the abilities of NDVI and SIF to monitor grain-yield changes induced by drought. In addition, based on previous studies [5; 29; 35], in this study, spring, summer, autumn, and the growing season were defined as the periods from March to May, from June to August, from September to November, and from March to October, respectively.
## 2 Results and Discussion
### Anti-Biofilm Activity
Based on the most favorable parameters obtained for dioscin (the lowest MIC, the highest anti-biofilm activity in static conditions, and a relatively low stimulation of morphological transition into coccoids), we decided to extend the panel of studies on dioscin with the verification of its anti-biofilm activity against _H. pylori_ J99 under flow conditions. For this purpose, we used an automatic system, namely Bioflux 1000, generating the flow of culture medium and enabling observation of microbes in real time [53]. Although this equipment has previously been used to determine the antibacterial and/or anti-biofilm activity of antibiotics and newly tested compounds against different bacterial genera [54; 55; 56], our team is the first to apply it to _H. pylori_ (previously [57] and hereby). Analysis of time-lapse microscopy recordings of bacteria exposed to dioscin allowed us to confirm the concentration-dependent anti-biofilm effect of this compound (Figure 4, Figure 5 and Figure S2). After a 24-h incubation of _H. pylori_ under the above flow conditions, we noticed a significant reduction in the amount of biofilm formed (4.72 \(\pm\) 0.14% vs. 0.38 \(\pm\) 0.05% of the capillary coverage in the control and MIC-exposed samples, respectively) (Figures 4 and 5). Moreover, we observed a decrease in the green/red fluorescence ratio of dioscin-treated bacterial cells (9.59 \(\pm\) 1.17 vs. 2.26 \(\pm\) 0.11 for the control and MIC-treated samples, respectively) (Figures 4 and 5).
# On the Feasibility of Interhemispheric Patch Detection Using Ground-Based GNSS Measurements
## 3 Test Period  
In order to evaluate the applicability of relative STEC values, we have analyzed the period of 17-19 March 2015, involving the main and recovery phases of the St. Patrick's Day storm. The selected event was the first extremely strong storm of 24 solar cycle, and is sometimes classified as a super storm. As reported in [44], its source was coronal mass ejection (CME) associated with a C9.1 flare and series of radio bursts. The interplanetary CME arrived at Earth at 4:48 UT, and caused a sudden storm commencement (SSC), which had positive signature exceeding 60 nT that were clearly observed in SYM-H variations (Figure 3). The main phase of the storm can be divided into two intervals of strongly negative IMF. The first of them caused a rapid decrease of the SYM-H index to \(-\)101 nT (\(\sim\)9:30 UT). After a short-lasted period of positive IMF Bz, the following reorientation initiated the second phase of the storm. This part was related to the magnetic cloud field, and dipped the SYM-H index to  
Figure 2: Distributions of GNSS permanent stations in both hemispheres (as seen from geomagnetic poles). Stations that were located over 75\({}^{\circ}\) of altitude-adjusted corrected geomagnetic (AACGM) latitude were depicted with red dots.  
\(-\)234 nT (\(\sim\)22:50 UT) [44; 45]. During both of these phases, the auroral electrojet (AE) index was at the elevated levels with maxima at \(\sim\)9:00 and \(\sim\)13:30 UT, respectively. Furthermore, the increased particle precipitation resulted in an equatorward expansion of the auroral oval to \(\sim\)60\({}^{\circ}\) and \(\sim\)50\({}^{\circ}\) of the geomagnetic latitude on the dayside and nightside, respectively [46]. In the next days, one could observe the extensively elongated recovery phase, which lasted until 25 March 2015.  
Due to the extreme conditions, this storm has been an object of intensive studies covering all of the regions of the ionosphere. In specific, in [47], the authors used multi-instrumental observations (ground-based and satellite GNSS, ionosondes, altimetry data, and in situ plasma density) to analyze the response of the ionosphere on a global scale. These results demonstrated the positive effect of the storm, which occurred during its entire main phase. They also revealed the significant depletion of ground-based VTEC on 18 March 2015. This pattern for days 17-18 March 2015 was confirmed in [48; 49], showing the variations of global/regional electron content (GEC, REC) and regional TEC mapping, respectively. Furthermore [48] demonstrated the interhemispheric asymmetry of VTEC with larger values in the southern part. With regard to high latitudes, the studies reported the growth of scintillations associated with the occurrence of polar patches and auroral blobs during the main phase of the storm [50; 51]. The plasma source of these structures (SED) was observed at the equatorward edge of the ionospheric through TEC fluctuation maps as well [46].
## 2 Related Work
### SNR Evaluation  
Typical SNR evaluation methods include the maximum likelihood (ML) ratio-based SNR evaluation algorithm [13], Second-order and Four-order Moments (M2M4) algorithm [15], etc. In recent years, [PERSON] and [PERSON] proposed the Adaptive Spectrum Sensing (ASS) algorithm [19]. [PERSON] et al. proposed an algorithmic model that fuses convolution neural network (CNN) and long short-term memory (LSTM) network to improve the SNR evaluation accuracy. The algorithms introduced above are described next, respectively.  
In the case of accessible auxiliary data, the maximum likelihood estimation method is the most satisfactory. Its basic premise is to obtain the joint probability density function with the received channel based on the probability density function of the noise. It is a typical DA SNR estimation algorithm. The advantage of this estimation method is that the estimation is accurate. The result is close to the actual value under high SNR conditions. However, the disadvantages are: the computational effort is enormous, a large deviation occurs when the SNR is low, and carrier synchronization is necessary.  
The M2M4 SNR estimation method is a self-adaptive algorithm that uses the relationship between the variance and peakedness of a signal to perform SNR evaluation. It is a moment-based SNR estimator proposed in modulated communication signals. This algorithm solves the problem of estimating the SNR of complex sinusoidal signals with deterministic but unknown phases under additive Gaussian noise interference and is an NDA estimation algorithm. Its advantages include simple calculation and insensitivity to carrier phase deviation. Since it is a cumulative volume algorithm, it has the feature that the evaluated results are better with increased data volume. However, the estimation error of this method for the SNR ratio is positively correlated with the SNR ratio and modulation order.  
The ASS algorithm evaluates the SNR by estimating the noise and signal power separately. A significance probability level constant representing the fluctuating characteristics of additive Gaussian white noise is set. The frequency domain samples of the received signal are divided into low- and high-frequency bands. The band with lower average energy is selected, and the selected part is again divided into high- and low-frequency bands until the significance probability level of this band is less than the set constant. This method results in the estimated received signal power. The noise power is estimated oppositely, requiring the significance probability level of the last obtained band to be greater than the constant. Finally, the two are calculated using the SNR formula to obtain the estimated value.  
Finally, the CNN-LSTM algorithm is analyzed. The segmented signal is extracted using a CNN-LSTM network to obtain feature vectors. Then the features are fused by a fully connected layer, and the SNR value is calculated. This method uses one-dimensional signal data for estimation and has superior performance, but the complexity is relatively high. The comparison of the related SNR evaluation methods are concluded in Table 1.
## 3 Rolling-MFO-GM (1,1) Model
### Rolling-GM (1,1)  
The procedures of rolling-GM (1,1) are much more complicated than those of GM (1,1). Since the rolling mechanism focuses on utilizing recent data to predict future electricity demand, which can exhibit the latest development of economic and society, it can enhance the accuracy of forecasting results to a great extent [33; 40].  
It is assumed that \(p\) data elements are used as the input sequence of GM (1,1), and \(q\) data elements are to be forecasted by GM (1,1) in each rolling course. The procedures of rolling-GM (1,1) can be summarized below.  
Step 1: sequence \(\left\{x^{(0)}(1),x^{(0)}(2),\cdots,x^{(0)}(p)\right\}\) is initially utilized as the input sequence of GM (1,1), and then data elements \(\left\{x^{(0)}(p+1),x^{(0)}(p+2),\cdots,x^{(0)}(p+q)\right\}\) can be predicted.  
Step 2: As the rolling mechanism focused on updating data with the most recent ones, GM (1,1) needs to be rebuilt with \(p\) new actual data elements. In order to forecast the data elements \(\left\{\overset{\chi}{x}^{(0)}(p+q+1),\overset{\chi}{x}^{(0)}(p+q+2),\cdots, \overset{\chi}{x}^{(0)}(p+2q)\right\}\), the sequence \(\left\{x^{(0)}(1),x^{(0)}(2),\cdots,x^{(0)}(p)\right\}\) need to be replaced by the latest \(p\) data elements \(\left\{x^{(0)}(q+1),x^{(0)}(q+2),\cdots,x^{(0)}(q+p)\right\}\), which would be employed to rebuild GM (1,1).  
Step 3: Repeat Step 2 until all the data elements that need to be forecasted are obtained.  
The forecasting procedure of rolling-GM (1,1) is illustrated in Figure 5.
## 3 Smart Metering in Germany
### Overall Architecture and Requirements  
There are three basic functionalities provided by iMSys: firstly, the reception, processing, and transmission of meter data to authorized entities; secondly, the visualization of meter data for consumers and service technicians; and thirdly, communication interfaces for the implementation of value-added services, such as smart grid applications like demand response. Demand response usually requires forwarding of control and switching instructions to DERs. As DERs often do not implement the required interfaces to communicate within the SMGW infrastructure, additional local communication units need to be integrated. These units can be systems like control boxes, which only forward and translate messages between different protocols, or automated EMS, which optimize the usage of energy. Using a security-by-design approach, the BSI comprehensively defined restrictions that need to be satisfied by each iMSys, and especially each SMGW. Since the presented prototype falls under the category of value-added services, we focus on the technical regulations relevant for such services.  
In the already introduced MsbG, the competence to define technical requirements for the iMSys is delegated to the BSI. An iMSys is formed by the combination of an mME measuring data, and an SMGW processing and transmitting the data. Inside the SMGW, there is a separate device called security module which is responsible for all cryptographic functions. While the MsbG regulates the iMSys from a legal standpoint, the BSI translates the law into technical requirements that also include security aspects. Doing so, the BSI published two common criteria protection profiles [9; 10], and the technical guideline \"BSI TR-03109\" [28]. The technical guideline is composed of five related sub-guidelines (\"BSI TR-03109-x\") and several no-less important appendices and erratas to further specify and correct the sub-guidelines. Special emphasis is given to Part 1 [6] and Part 4 [29], and additional specifications of the certificate policy [30]. Part 1 of the guidelines describes the technical requirements that SMGWs need to fulfill. Part 4 and the certificate policy regulate the SM-PKI. In order to be used in the SMGW infrastructure, the SMGW and the security module need a certification by the BSI according to the respective protection profile. For the first generation of SMGWs it is not mandatory to implement all of the technical features listed in the TR-03109-1 [6] in order to get certified. Instead, an appendix to the technical guideline by the BSI bundles a selection of features in so-called \"device profiles\" which need to be present [31]. These device profiles are use specific. However, right now there is only a single device profile called \"SMGW_G1_BASIS\" [31]. A more detailed overview of the overall architecture and regulation in the SMGW ecosystem is provided in [26].
## 3 Results and Discussion
### Simulation of the Interaction between Five Peptides and Ester Compounds in Chi-Aroma Baijiu  
To investigate the interaction between peptides and VOCs, ethyl lactate and ethyl acetate were selected, which were key volatile components in Chi-aroma Baijiu (Figure 7A-F). A UV spectrophotometer was used to assess alterations in absorption. Peptide PA exhibited a peak absorption at 213 nm, reaching a maximum absorbance value of 0.538.  
After the introduction of ethyl acetate, the absorption peak moved to a wavelength of 224 nm, and the absorbance value was measured to be 0.642. The addition of ethyl lactate to PA caused a shift in the absorption peak to 223 nm, reaching a maximum absorbance  
Figure 6: PLS-DA analysis. (**A**) Score graph, (**B**) Model validation, (**C**) Hierarchical clustering analysis.  
Figure 7: Ultraviolet absorption spectrum of mixed peptides and esters. (**A**–**F**) represents the peptides Pro-Ala (PA), Pro-Val (PV), Gly-Leu (GL), Asp-Phe (DF), Pro-Leu (PL), and glutathione (GSH) mixed with ethyl acetate and ethyl lactate, respectively.  
value of 0.601. As for the peptides PV, GL, DF, PL, and GSH, it was observed that their maximum absorption peaks shifted to approximately 224 nm following their combination with ethyl acetate. All peptides showed a rise in absorbance values, except for PL.  
Upon mixing with ethyl lactate, the maximum absorption peaks shifted to 219 nm, 217 nm, 215 nm, 223 nm, and 223 nm, respectively. These findings indicate that the peptides and the two esters underwent polymerization, resulting in the creation of novel molecules, according to [PERSON]'s rule [47].The peptide bonds exhibit their highest level of absorption at around 210 nm [48], but the chromogenic groups and the amino acid residues of peptides can undergo alterations in various situations [49]. It is possible to ascertain the reaction between a small molecule drug and a peptide by analyzing the spectrum changes seen prior to and following their combination [50].
## 3 Distortions Induced by Low Cost
### Nonlinear Impairments  
#### 3.2.1 Nonlinear Devices: Level-Dependent Noise and Level-Dependent Skew  
The low cost of devices not only causes the bandwidth limitation but also produces nonlinear distortions like amplitude-dependent noise and level-dependent skew. An example for APD or PD with the optical amplifier is depicted in Figure 4, where different power levels of the PAM4 signal have different distributions of dominant noise [63]. The probability of distributions of regular equally-spaced PAM4 signal after OA + PD and APD receivers are illustrated in Figure 4a,b, respectively. The peaks of probability density represent different noise variances for different levels. From Figure 4c we can notice that high power levels suffer larger noise interference and more symbols cross the decision thresholds, which is consistent with the probability of distributions. Note that the distribution of noise depends on which impairment is dominant, so it is diversified. For instance, the noise of two middle levels could be larger than the noise of two marginal levels considering the nonlinearity of the modulation curve.  
Figure 4: (**a**) The nonlinear probability density distributions of received PAM4 signal for (**a**) OA + PD, and (**b**) APD. (**c**) The waveform of received PAM4 signal after APD (reprinted from [63] with permission from authors).  
Figure 3: (**a**) Four-level pulse amplitude modulation (PAM4) signaling tolerance on dispersion in terms of optical signal-to-noise ratio (OSNR) penalty (reprinted from [62] with permission from authors). Eye diagrams for 56 Gbps PAM4 signal after (**b**) 0 km and (**c**) 20 km transmission.  
The level-dependent skew induced by the nonlinear chirp characteristic of modulators can produce significant penalties due to the differences in optimum sampling time for the different level of the eye. As shown in Figure 5a,b, the simulated PAM4 signal without any skew and the simulated 50 Gb/s PAM4 signal using spatial-temporal rate equation modeling [64]. An obvious amplitude-dependent eye can be observed, and hence this inevitably introduces timing impairments. The measured eye diagrams for different signal rates and bias currents of the laser are presented in Figure 5c-f. These figures indicate that the phenomenon of level-dependent skew is more serious under the condition of high-speed and low power consumption, which severely degrades the transmission performance.
## 5 Result and Discussion
### Protocol Comparisons: Topology Control (TC) Messages Sent Metric  
The topology control message contains a portrait of the quantity of TC messages transmitted by all of the network's nodes. The topology control (TC) messages are forwarded by the MPR nodes, changing the topology to the one that is best for routing at that precise moment. The more MPR nodes chosen by the network, the more topology control messages are forwarded by those nodes. The TC messages for standard OLSR, the proposed OLSR, and AODV are portrayed in Figure 8 and values are shown in Table 5. Compared to standard OLSR of [16] and AODV [26], the proposed OLSR sends a significantly higher number of TC messages which yields better performance.  
Figure 6: Comparison of Standard OLSR vs. Proposed OLSR vs. AODV Protocol: Load.  
Figure 7: Comparison of Standard OLSR vs. Proposed OLSR vs. AODV Protocol: Packet Delivery.
## 2 Rescue Process of TPM for SRP
### Selecting the Water Landing Site Based on the Uncertainty Health Weight of PDTs  
After obtaining \(C[cluster]\), i.e., the location of the optional water landing site for the amphibious aircraft, it is important to choose the order of clustering points for the SRP of the multiple clustering centers. In this study, a method was proposed to calculate the uncertainty health weight based on the survival probability of the PDTs.  
The survival time of the PDTs varied significantly because of the uncertainty of the health conditions of the PDTs. The maximum survival time of the PDTs after falling into the water can be given by:  
\[T^{i}_{max}=\sigma_{i}\text{-}5.75\text{-}\exp(0.1\text{-}wt) \tag{5}\]  
where \(T^{i}_{max}\) is the maximum survival time of the ith PDT, \(\sigma_{i}\) is the correction factor, whichdepends on the uncertainties of the PDTs, and \(wt\) is the water temperature at the current position.  
This study proposes the uncertainty health weight of the PDTs based on the maximum survival time. In this way, the survival probability of the ith PDT at the moment, \(T\), after distress can be given by:  
\[P_{i}=\left\{\begin{array}{ll}\frac{T_{max}^{i}-T}{T_{max}},&0\leq T<T_{max}^{i }\\ 0,&T_{max}^{i}\leq T\end{array}\right. \tag{6}\]  
Figure 4: The results of clustering of the persons in distress by k-means*.  
Figure 3: The flowchart of k-means*.  
Next, the uncertainty health weight of each PDT can be given by:  
\[W_{i}=\frac{P_{i}}{\sum_{i=0}^{N\text{nut}-1}P_{i}}\, \tag{7}\]  
where \(W_{i}\) is the uncertainty health weight of the ith PDT, and \(Num\) is the total number of the PDTs.  
Then, the uncertainty health weight in the jth cluster of the \(C[cluster]\) clustering category result can be calculated as:  
\[W^{j}=\sum_{i=0}^{C_{num}^{j}-1}W_{i}^{j} \tag{8}\]  
where \(C_{num}^{j}\) is the number of the PDTs in the jth cluster of the \(C[cluster]\) clustering category result, and \(W_{i}^{j}\) is the uncertainty health weight of the ith PDT in the jth cluster. Finally, the optimal position can be chosen as the water landing site which is one with the maximum \(W^{j}\).
## 2 Materials and Methods
### Model Overview
#### 2.1.3 The MODIS GPP Algorithm (MODIS-GPP)  
The latest MODIS-GPP product (MOD17A2H, version 6) [70] is driven by daily MODIS land cover, fPAR, leaf area index (LAI), interpolated surface meteorology, and the global vegetated land surface [66]. The MODIS-GPP is calculated as:  
\[GPP\ =\ SW_{rad}\times 0.45\times fPAR\times\varepsilon_{g} \tag{3a}\] \[\varepsilon_{g}\ =\ \varepsilon_{max}\times f(Tmin)\times f(VPD)\] (3b) \[\text{f(Tmin)}\ =\ \left\{\begin{array}{cc}0&Tmin<Tinit_{min}\\ \frac{Tmin-Tmin_{min}}{Tmin_{max}-Tmin_{min}}&Tmin_{min}<Tmin<Tmin_{max}\\ 1&Tmin>Tmin_{max}\end{array}\right.\] (3c) \[\text{f(VPD)}\ =\ \left\{\begin{array}{cc}0&VPD>VPD_{max}\\ \frac{VPD_{min}-VPD}{VPD_{max}-VPD_{min}}&VPD_{min}<VPD<VPD_{max}\\ 1&VPD<VPD_{min}\end{array}\right. \tag{3d}\]  
where \(SW_{\text{rad}}\) represents the incident shortwave radiation and _f(Tmin)_ and _f(VPD)_ illustrate the minimum temperature stress and atmospheric moisture stress on GPP, respectively. \(Tmin_{\text{max}}\) is the daily minimum temperature at which \(\varepsilon_{\text{g}}=\varepsilon_{\text{max}}\), \(Tmin_{\text{min}}\) is the daily minimum temperature at which \(\varepsilon_{\text{g}}=\varepsilon_{\text{max}}\), and \(VPD_{min}\) is the daylight average VPD at which \(\varepsilon_{\text{g}}=\varepsilon_{\text{max}}\), and \(VPD_{min}\) is the daylight average VPD at which \(\varepsilon_{\text{g}}=0\). The values of \(\varepsilon_{\text{max}}\), \(Tmin_{\text{max}}\), \(Tmin_{\text{min}}\), \(VPD_{max}\), and \(VPD_{min}\) varied among land use types and were obtained from the biome properties lookup tables. \(SW_{\text{rad}}\), \(T_{\text{min}}\), and VPD were obtained from an updated version of the daily Global Modeling and Assimilation Office (GMAO) meteorological data, and fPAR were obtained from the MODIS LAI/fPAR products (MOD15A2).