esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Response of Vegetation Coverage to LULC Change  
#### 3.3.1 Response Distribution of Vegetation Coverage to LULC Changes  
The NDVI changes from 2000 to 2020 show that the vegetation coverage change in the coastal region of the Guangdong Province generally had positive values (improving), especially in the mountainous and hilly areas in the west and north, and the Pearl River Delta in the middle had a larger negative value (deterioration) (Figure 7a). Corresponding to the deterioration of vegetation coverage, the LULC change in the Pearl River Delta from 2000 to 2020 was characterized by the mutual transfer of cultivated land, built-up land and water bodies, as well as the mutual transfer with other LULCs (Figure 7b). The LULC types in regions with improved vegetation coverage in the north and west were mainly forest land, with relatively few changes.  
The response of the NDVI changes to LULC changes indicates that in almost all LULC transfers and NDVI changes, both improvement and deterioration coexist (Figure 7c). The transfer of cultivated land to other LULC types resulted in NDVI changes ranging from \(-0.484\) to \(0.428\) (mean \(-0.041\) to \(0.115\)). The NDVI change caused by the transfer of forest land to other LULC types was the most significant, reaching \(-0.54\)-\(0.684\) (mean \(0.015\)-\(0.115\)), while the NDVI change caused by the transfer of cultivated land to built-up land was higher than that of all other LULC transfers. The transfer of grassland to other LULC types resulted in NDVI changes ranging from \(-0.392\) to \(0.46\) (mean \(0.006\) to \(0.138\)). The transfer of water bodies to other LULC types resulted in NDVI changes ranging from \(-0.508\) to \(0.656\) (mean \(-0.053\) to \(0.095\)). The transfer of built-up land to other LULC types resulted in NDVI changes ranging from \(-0.504\) to \(0.588\) (mean \(-0.01\) to \(0.104\)), and the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **2000–2005** & **2005–2010** & **2010–2015** & **2015–2020** & **2000–2020** \\ \hline LULC & CL & \(-1.06\) & \(-0.329\) & \(-0.231\) & \(-0.046\) & \(-0.407\) \\ types & FL & \(-0.105\) & \(-0.055\) & \(-0.178\) & \(-0.09\) & \(-0.106\) \\  & GL & \(-0.773\) & \(-0.322\) & \(0.599\) & \(-0.635\) & \(-0.284\) \\  & WB & \(-0.412\) & \(-0.271\) & \(-0.457\) & \(0.187\) & \(-0.236\) \\  & BL & 5.638 & 1.536 & 1.426 & 0.592 & 2.612 \\  & UL & \(-1.25\) & 0 & 0 & \(-1.333\) & \(-0.625\) \\ \hline CD & & 2.614 & 0.979 & 0.904 & 97.887 & 106.306 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dynamic degree of LULC change (%).
## 2 Method
### The Classical SCR Systems  
#### 2.3.1 CNN-MFCC SCR System  
The block diagram of the CNN-MFCC model [39; 40; 41], which is the CNN model with MFCC features, is shown in Figure 1B. MFCC is a well-known feature extraction method [54] that has many successful applications in acoustic signal processing tasks [8; 26; 55; 56]. This study applied the MFCC method to extract the acoustic features for the CNN model, as one SCR system used. The speech signals of dysarthric speech (\(x_{i}\)) were collected and labeled. The procedure for MFCC included six steps: pre-emphasis, windowing, fast Fourier transform, Mel-scale filter bank, nonlinear transformation, and discrete cosine transform. The high-frequency power of the speech signal declines during translation, which causes loss of high-frequency information. First, pre-emphasis is applied to compensate for the high-frequency signal [57]. Then, the frame-blocking unit is used to obtain the small frames from the input speech information (\(s[n]\)). Next, the Hamming windows method is used to alleviate the issue of side lobe plummets between each frame. Then, fast Fourier transform is applied to obtain the frequency response of each frame for spectral analysis; meanwhile, the Triangular Bandpass Filters (TBF) [58]  
Figure 1: Three well-known speech command recognition (SCR) systems: (**A**) convolution neural network with Mel-frequency cepstral coefficient (CNN–MFCC), (**B**) convolution neural network with a phonetic posteriorgram (CNN–PPG) and (**C**) automatic speech recognition (ASR)-based.  
are used to integrate the frequency compositions from a Mel-filter band into one-energy intensity. Finally, the MFCC features (\(c_{l}[n]\)) are obtained based on the discrete cosine transform. Note that the librosa library [59] was used to obtain the \(X_{i}^{MFCC}\) in this study; 120-dimensional MFCC features (40-dimensional original MFCC + 40-dimensional velocity + 40-dimensional acceleration features) were used, because this setting provided suitable performance in previous studies [57; 60; 61].  
Next, the obtained \(c_{l}[n]\) and label target were used as the input and output of the CNN model, respectively. The CNN [48; 49; 50] model was applied in the CNN-MFCC SCR, for which the detailed structure is shown in Appendix C (Table A3). Next, the \(X_{i}^{MFCC}\) and relative label information were used as the input and output of the CNN model to learn the suitable parameters to identify the speech command of dysarthric patients. Note that the training approach for the CNN model is like the description in Equations (1)-(6) in Section 2.2. Finally, the trained CNN-MFCC model was used to predict the input command (\(x_{i}^{\prime}\)) of dysarthric patients in the application phase.
## 3 Results and Discussion
### Biocompatibility Study  
Any chemotherapeutic research aims to obtain bioactive compounds that can selectively reduce or destroy cancerous cells or microbes with negligible side effects on the host cells [34]. This principle is known as selective toxicity and is the main goal for chemotherapeutic treatment [35]. There are different methods to assay for the cytotoxicity effect of compounds, among which 3-(4,5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolzolium bromide (MTT) and resazurin-based reducing assays are the most commonly used. Therefore, in the present study, a resazurin-based reducing assay was employed. The lactate dehydrogenase (LDH) assay is commonly employed to evaluate tissue or cellular damage, which can be monitored in the extracellular medium and is a good biomarker for cellular toxicity [36; 37].  
In the present study, the cytotoxic effect of the crude extracts (chloroform, ethyl acetate and ethanol) was evaluated. The percentage cell viability was \(55\pm 1.88\), \(52.31\pm 6.31\) and \(50.14\pm 7.72\) for chloroform, ethyl acetate and ethanol extracts at \(50\)\(\mathrm{\SIUnitSymbolMicro mL}^{-1}\), respectively (Figure 1). From the results, it was observed that the cell viability in all the extract-treated cells was more than 50% and this indicates that the extracts were not cytotoxic (moderate) because values below 50% are considered cytotoxic to the cells. Although the cell viability was almost half of the initial numbers, the extracts may be considered as safe for human use. Similar to our present findings, [PERSON] and [PERSON] [38] recorded that the organic extracts (chloroform, ethyl acetate and ethanol) of _Pongamia pinnata_ ([PERSON] had no cytotoxic effect against _P. falciparum_ (3D7 strain) and _P. berghei_ (ANKA). [PERSON] et al. [27] reported that the chloroform extract of _Dendrobium crepidatum_ inhibited the growth of 81.49% of HeLa cells at \(800\)\(\mathrm{\SIUnitSymbolMicro mL}^{-1}\). [PERSON] et al. [39] reported that the ethanolic extracts (flowers, stems and leaves) of _Stachytarpheta jamaicensis_ ([PERSON] [PERSON] had no toxic effect on HeLa cells. On the contrary, [PERSON] et al. [40] reported the cytotoxic effect of ethyl acetate, chloroform and ethanol extracts of _Lavandula coronopifolia_ against MDA-MB-321 cells and in the same vein, the crude leaves ethanol extract of _Lasiosibphon crioccephalus_ exhibited a high toxicity effect against HeLa cells [41]. In vitro cytotoxicity studies revealed that ethanol extracts of leaves of _Tamilnadia uliginosa_ exhibited toxic effects on HeLa cells [42]. [PERSON] and [PERSON] [43] also documented that the ethyl acetate of stem bark extracts from _Pycranthus angolensis_ showed a significant cytotoxic effect against HeLa cells.
## 4 Experiments and Results
### Results and Analysis of MSTAR Experiments  
#### 4.3.1 Bayesian Hyperparameter Optimization Results  
The Bayesian method was used to optimize the four hyperparameters of MFN: epoch, learning rate (lr), warmup and decay (wd) hyperparameters, and batch size. The results of the Bayesian optimization in SOC and EOC experiments are shown in Table 2. The following experiment utilized the optimal hyperparameter set to classify SAR images.
## 3 Results and Discussion  
The results of the voltage stability analysis of the practical system are investigated in this section. The Korean power system applies the algorithm proposed herein to verify its operation in real time. The Korean power system, which contains multiple buses, is reduced to a single bus equivalent system; hence, the TE circuit can be composed of TE impedances. Using that circuit, the VSM index is computed, and the margin is calculated and applied with the load modeling. The case study demonstrates that the circuit can predict a voltage collapse in the Korean power grid.  
The characteristic of the Korean power system is that most of the load is concentrated in the metropolitan area in the northern part. Approximately 42% of the total loads are massed in this area. Only 20% of the total generations exist in this high-cost area. Further, most generations are gathered in the nonmetropolitan area located in the southern part. Therefore, the power flows from the south to north. Hence, a large amount of active power is transmitted through the interface lines connecting the metropolitan region and its neighboring region. The loading must be constrained by the thermal, voltage, and stability limits to operate the system securely because heavy power flowing into the region  
Figure 11: PV curves with the ZIP load.  
in the normal state can cause system insecurity if severe contingencies occur. In the Korean power system, the interface flow limit operation is considered under a fixed load demand condition [48]. Under these geographical circumstances, six major lines are constructed in the metropolitan area to supply large quantities of power. The six interface lines constitute the most important lines in the Korean power system. Figure 12 shows the diagram of the Korean power grid.  
Figure 12 shows the characteristics of the Korean power system that consists of a major transmission line supplying most of the active power required for the metropolitan area. Power system operators in Korea are concerned that the heavy active power flow into the metropolitan area may intensify, and that the constraint of the interface flow will be a significant factor that results in congestion. Therefore, investigating both sides of those six lines is the best solution for monitoring the voltage stability in the Korean power system. For the case study, it is assumed that PMUs are installed on every receiving-end bus and sending-end bus of six major transmission lines. The voltages and currents are measured in the phasor waveform from every PMU, and the active power and reactive power are calculated.
# The Role of Electron Trajectories in XUV-Initiated High-Harmonic Generation
## 5 Conclusions and Outlook  
In summary, we resolve the intensity dependence of XUV-initiated HHG, demonstrating that it plays an important role in tuning the central frequencies of the emitted harmonics. The multidimensionality of the measurement, where we control the XUV-IR delay and therefore the instantaneous intensity, allows us to isolate the role of both the short and long trajectories and resolve their sub-cycle dependences. The agreement between the experimental results and the theoretical calculations provides an important validation of the underlying mechanism--indeed XUV-initiated HHG can be described via a semiclassical three-step model, reflecting its close analogy with tunneling-driven HHG.  
Looking ahead, XUV-initiated HHG has the potential to be a powerful spectroscopic method for ultrafast dynamics. It lifts the limitations of tunneling-driven HHG, not only by decoupling ionization and recollision, but also giving access to inner-shell electrons within the framework of HHG spectroscopy [39; 40; 41; 42]. The observation of long trajectories and the ability to isolate them from short trajectories extends the range of the attosecond clock provided by the travel time of the electrons.  
Using mid-infrared driving pulses, the range of the clock might span up to ten femtoseconds, sufficient to observe Auger decay processes.  
[PERSON] conceived and supervised the study; [PERSON] designed the experimental setup; [PERSON] and [PERSON] built the experimental setup, performed the experiments and analyzed the data; [PERSON] performed the theoretical calculations; [PERSON] supported the operation of the laser system; All authors discussed the results and contributed to the final manuscript.  
[PERSON] is the incumbent of the [PERSON] Professorial Chair. [PERSON] acknowledges the Minerva Foundation, the Israeli Science Foundation, the Crown Center of Photonics, and the European Research Council for financial support. [PERSON] acknowledges financial support from the Minerva Foundation and the Koshland Foundation.  
We acknowledge helpful discussions with [PERSON], [PERSON], and [PERSON].  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CCTSM & Coulomb-corrected three-step model \\ FWHM & Full-width at half maximum \\ H & Harmonic \\ HHG & High-harmonic generation \\ IR & Infrared \\ RABBIT & Reconstruction of attosecond beating by interference of two-photon transitions \\ TDSE & Time-dependent Schrodinger equation \\ XFROG & Cross-correlation frequency resolved optical gating \\ XUV & Extreme ultraviolet \\ \end{tabular}
# IAACaaS: IoT Application-Scoped Access Control as a Service +
## 4 IoT Application-Scoped Access Control as a Service
### Access Control Policies  
Following the XACML terminology, policies are composed by a set of rules (as well as other items that are out of the scope of this paper). Rules are in turn made of a target (e.g., the resource), an effect (e.g., allow/deny) and a condition [22]. In our approach, rules are used to implement permissions, in which the target is an action (e.g., an HTTP verb) plus a resource of the _Service_. Of course, more complex policies may be also defined, provided that they are supported by the policy-description language. _Roles_ are in turn sets of _Permissions_ that serve as a container so that the _Service Admin_ can assign more than one permission at once.  
Both _Permissions_, _Roles_ and their relationships are defined in the _PAP_.  
Note that this approach allows both a simpler RBAC scheme and a more complex, more flexible ABAC scheme. It all depends on how the _Permissions_ are defined when creating the XACML rules.
## 3 Results  
### Correlations  
The significant correlations between RPE, wellness parameters, and external load parameters are presented in Figure 1. All the correlations between the above parameters are presented in Table S1 in the Supplementary Files. Independent of the microcycle's duration, the results for RPE demonstrated high correlation with DIST (_p_ < 0.001, r = 0.523), moderate correlation with DUR (_p_ < 0.001, r = 0.301), ACC2 (_p_ < 0.001, r = 0.478), ACC3 (_p_ < 0.001, r = 0.161), DEC2 (_p_ < 0.001, r = 0.440), DIS4 (_p_ < 0.001, r = 0.302), DIS6 (_p_ < 0.001, r = 0.304), SPEF (_p_ < 0.001, r = 0.431), MAXS (_p_ < 0.001, r = 0.363), MPW4 (_p_ < 0.001, r = 0.449), MPW5 (_p_ < 0.001, r = 0.475), MPW6 (_p_ < 0.001, r = 0.429), and MPWEF (_p_ < 0.001, r = 0.360).  
The wellness responses had low correlations with EXLD indicators. SLP correlated with DUR (_p_ = 0.003, r = 0.103), ACC3 (_p_ = 0.035, r = -0.072), DIST (_p_ = 0.15, r = 0.083), DIS6 (_p_ = 0.16, r = 0.083), and MPW4 (_p_ = 0.039, r = 0.071). SRN with DUR (_p_ < 0.001, r = 0.124), ACC2 (_p_ < 0.001, r = 0.123), DIST (_p_ < 0.001, r = 0.182), DIS4 (_p_ < 0.001, r = 0.158), DIS5 (_p_ < 0.001, r = 0.150), DIS6 (_p_ < 0.001, r = 0.150), SPEF (_p_ < 0.001, r = 0.189), MAXS (_p_ = 0.005, r = 0.097), MPW4 (_p_ < 0.001, r = 0.212), MPW5 (_p_ < 0.001, r = 0.170), MPW6 (_p_ < 0.001, r = 0.170), and MPWEF (_p_ < 0.001, r = 0.132). FTG with DUR (_p_ < 0.001, r = 0.128),  
Figure 1: Correlation between RPE, RPE, RPEdur, SLP, SORENES, FTG, and EXLD parameters at all the microcyles. RPE—rate of perceived exertion; RPE—RPE \(\times\) training duration; SLP—sleep perceived responses; SRN—sorens perceived responses; FTG—fatigue perceived responses; DUR—training duration; ACm—accelerations 2–3 m/s\({}^{2}\); ACh—accelerations \(>3\) m/s\({}^{2}\); DCM—decelerations \(-2\)—\(-3\) m/s\({}^{2}\); DCM—accelerations \(<-3\) m/s\({}^{2}\); TD—total distance, SPm—distance 14.4–19.7 km/h; SPh—distance 19.7–25.2 km/h; SPh—distance 25.2 km/h; SPh—number of actions \(>19.7\) km/h; MSP—maximal speed; PWm—metabolic distance 15–25.5 w/kg; PWh—metabolic distance 25.5–50 w/kg; PWh—metabolic distance 350 w/kg; PWa—number of actions \(>25.5\) w/kg. * denotes significant correlation at level \(<0.05\); ** denotes significant correlation at level \(<0.01\); *** denotes significant correlation at level \(<0.01\); *** denotes significant correlation at level \(<0.001\).  
ACC2 (\(p\) = 0.002, r = 0.105), DIST (\(p\) \(<\) 0.001, r = 0.170), DIS4 (\(p\) = 0.004, r = 0.099), DIS5 (\(p\) = 0.003, r = 0.101), DIS6 (\(p\) \(<\) 0.001, r = 0.13), SPEF (\(p\) \(<\) 0.001, r = 0.133), MAXS (\(p\) = 0.039, r = 0.071), MPW4 (\(p\) \(<\) 0.001, r = 0.129), MPW5 (\(p\) \(<\) 0.001, r = 0.122), and MPW6 (\(p\) = 0.001, r = 0.111). Figure 2 presents the significant correlations of RPE and wellness parameters with performance parameters for MIC5. Similarly, Figure 3 shows the correlations of RPE and wellness parameters with
## 5 Discussion  
The results obtained from the optimization of control parameters using GA for the DAB in different control strategies (PI control, SMC control, and MPC control) have shown significant improvements in control performance and system stability. The optimized controllers demonstrate reduced voltage drop and increased phase and gain margins, indicating better transient and steady-state responses under sudden load decrease conditions. These findings are crucial in enhancing the overall performance of the DAB in various practical operating conditions.  
The optimized controllers' improved performance, stability, and reduced steady-state error hold significant implications for the broader field of DC-DC converters and power electronics. The enhanced control of the DAB's output voltage ensures reliable operation in practical scenarios with varying loads and disturbances. The application of GA in controller optimization can have a broad impact, not only in DABs but also in other power electronics systems, where precise control and stability are crucial.  
Although the GA-optimized controllers demonstrate significant improvements, it is essential to acknowledge potential limitations. The optimized controllers' performance may vary under extreme operating conditions or when facing highly nonlinear disturbances--for example, a shift in the direction of the DAB's work or the input and removal of energy storage modules. Further investigations and robustness testing should be conducted to identify such scenarios and ensure the optimized controllers' reliability in diverse situations. Meanwhile, the parameter tuning of GA can be challenging, requiring expertise and trial-and-error adjustments. Furthermore, the GA does not guarantee finding the global optimal solution, and may converge to local optima. Hence, in practical applications, it is essential to carefully tailor and refine the algorithm based on the problem's characteristics and requirements. Particular attention should be given to the design of the fitness function, as it significantly influences the algorithm's performance. By strategically addressing these concerns, one can enhance the effectiveness and efficiency of genetic algorithms for optimization tasks.
# Effect of Precision Nitrogen Fertilization of Grassland on Soil Microbial Structure
###### Abstract  
The synergistic application of advanced technologies enables precise determination of plant growth, health, and nutritional requirements. However, despite the widespread use of modern technologies, the microbial status of the soil is often neglected, even though it significantly impacts soil productivity. Soil microbial activity serves as a crucial indicator of site-specific soil conditions. This article presents efforts to explore the quantitative and qualitative relationships between identified actinomycetes and soil nitrate content, as well as their distribution within the soil profile. Field data analysis facilitated the assessment of nitrate concentrations and the evaluation of the quantitative and species composition of actinomycetes in the soil profile at depths ranging from 0.05 to 0.35 m. The highest nitrate concentration (22 mg/100 g of soil) and actinomycete abundance (1076 CFU/g of soil) were observed in the toposoil layer. Additionally, spatial correlations between these parameters were analyzed for each soil layer. The correlation coefficients were approximately \(-0.6\), indicating an inverse relationship. Areas with low nitrogen content corresponded to reduced microbial abundance within the soil profile, as supported by the spatial correlation data. These findings demonstrate the potential to predict actinomycete abundance in the soil profile based on nitrate content, offering valuable insights into soil health and productivity.  
soil microorganisms; spatial distribution; soil profile; nitrogen fertilization; grassland 2020
# A Comparative Analysis of Anomaly Detection Methods in IoT Networks: An Experimental Study
## 3 Methodology
### Comparative Analysis of Selected Machine Learning Methods
#### 3.4.2 Isolation Forest  
Isolation Forest [43] is an ML method that specializes in anomaly detection using two key features of anomalies namely rarity and dissimilarity from typical data patterns. The algorithm builds multiple decision trees (isolation trees), randomly selecting subsets of the training data and randomly selecting attributes to split the data at each node. For each observation, the algorithm calculates the average path length from the root of the tree to the leaf where that observation is located. Anomalies tend to have shorter paths because they are easier to isolate. The average path length for each observation is used to calculate a so-called 'anomaly point'. The lower the anomaly point is, the more likely it is that an observation is an anomaly.  
In Figure 3, we can see that the points marked as anomalies (red) are located in areas that are less densely covered by other data (white).Isolation Forest successfully identified these observations as outliers because they are easier to isolate from the main data. It  
Figure 2: Anomaly detection using RF (own elaboration).  
is worth mentioning that the algorithm is fast and efficient, especially for large datasets. It handles both global anomalies (outliers far from the main data) and local anomalies (outliers within clusters of data).