esa-sceva/satcom-chunk-collection huggingface dataset:
# A Deep Learning Framework for Enhanced Detection of Polymorphic Ransomware
## 2 Related Work
### Current Detection and Prevention Methods
#### 2.3.2 Gaps in Ransomware Prediction and Detection  
Truly effective ransomware prediction demands a synergistic blend of operational and situational data. Operational data, gleaned from ransomware processes, must be contextualized by situational intelligence, encompassing the system's security posture and real-time state. This fusion enables models to adapt to the evolving and dynamic nature of ransomware. Alarmingly, a significant portion of current models remain tethered to historical data alone, rendering them woefully inadequate against sophisticated, contextually aware attacks. Advanced approaches include machine learning and deep learning models. For example, GANs generate new malware samples to train detection engines on unseen variants [30]. Models like MalDeepNet and MalGAN predict malware behavior trends and create adversarial samples to enhance black-box detection systems [31; 32]. Supervised learning models [33] and regression-based neural networks [58] predict ransomware behavior by analyzing past attack data, but their lack of situational awareness reduces their effectiveness for adaptive threats. Likewise, lightweight models like those designed for IoT devices incorporate context ontologies for feature extraction [40], which reduces computational complexity. However, these models often block behavioral data, which is critical for understanding ransomware's adaptive strategies. Similarly, deep learning approaches like LSTM provide early malware predictions on Android devices by analyzing data from active sessions [42], but limited input data disrupts accuracy for long-term predictions. Addressing the inherent limitations of current predictive models necessitates a paradigm shift: the fusion of operational data with comprehensive situational and threat intelligence. This integration is not merely an enhancement; it is a critical imperative, enriching the model's knowledge base and empowering it to dynamically adapt to ransomware's rapidly evolving behaviors and diverse environmental influences. Table 1 provides a critical overview of existing ransomware behavior prediction studies, dissecting their problem statements, proposed solutions, methodologies, tools, and inherent limitations.
## 4 Results and Analysis
### Structural Model Assessment
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Relationship** & **Path Coefficient** & \(t\)**-Value** & **95\% CIs (Bias-Corrected)** & **Results** \\ \hline IDL \(\rightarrow\) DTE \(\rightarrow\) IN & 0.147 ** & 2.819 & [0.054, 0.255] & Supported \\ IDL \(\rightarrow\) DTE \(\rightarrow\) PS & 0.153 ** & 3.039 & [0.058, 0.257] & Supported \\ ISM \(\rightarrow\) DTE \(\rightarrow\) IN & \(-\)0.009 & 0.233 & [\(-\)0.090, 0.066] & Not supported \\ ISM \(\rightarrow\) DTE \(\rightarrow\) PS & \(-\)0.010 & 0.231 & [\(-\)0.089, 0.073] & Not supported \\ DIPI \(\rightarrow\) DTE \(\rightarrow\) IN & \(-\)0.020 & 0.675 & [\(-\)0.082, 0.038] & Not supported \\ DIPI \(\rightarrow\) DTE \(\rightarrow\) PS & \(-\)0.021 & 0.667 & [\(-\)0.085, 0.040] & Not supported \\ RES \(\rightarrow\) DTE \(\rightarrow\) IN & 0.112 ** & 3.013 & [0.037, 0.183] & Supported \\ RES \(\rightarrow\) DTE \(\rightarrow\) PS & 0.117 ** & 2.917 & [0.034, 0.192] & Supported \\ COL \(\rightarrow\) DTE \(\rightarrow\) IN & 0.131 *** & 4.304 & [0.076, 0.194] & Supported \\ COL \(\rightarrow\) DTE \(\rightarrow\) PS & 0.137 *** & 4.743 & [0.082, 0.192] & Supported \\ \multicolumn{4}{c}{Stoner-Geisser Q\({}^{2}\)} & R\({}^{2}\) & GOF & \\ Digital Transformation Enablers & 0.346 & 0.381 & 0.363 & \\ Innovation & 0.276 & 0.211 & 0.241 & \\ Problem-Solving & 0.242 & 0.231 & 0.236 & \\ SRMR & & 0.070 & & \\ \hline \hline \end{tabular}  
* Notes: IDL: Information and data literacy; ISM: Information security management; DIPI: Digital piracy; RES: Responsibility; DTE: Digital transformation enablers; IN: Innovation; COL: Collaboration; PS: Problem-solving *** \(p\) < 0.01; Source: Authors.  
\end{table} 
Table 5: Results of testing the research model: indirect effects. recorded values for the coefficient of determination of the explained variance (R squared) indicator showed that for the 38.1 percent of digital transformation enablers, 21.1 percent of innovations, and 23.1 percent of problem-solving capabilities, all indicate a high level of explanatory power in the proposed structural model. Using the square root of the product of communality and \(\mathbb{R}^{2}\), the goodness-of-fit (GOF) was manually computed for all dependent and intermediate latent variables. The GOF values for digital transformation, innovation, and problem-solving capabilities was calculated as 0.363, 0.241, and 0.236, respectively, and generally meet the acceptable range of 0-1.
## Appendix A  
The technical input parameter for STPP is categorized in to six parameters and presented in Table 1, while the financial parameters used for the SAM simulation presented in Table 1 are fixed by the SAM or taken from other research works. Therefore, the total tower cost and the total receiver cost are calculated by using Equations (A1) and (A2) [27; 94; 95; 96].  
Total Tower Cost \(=\) Fixed Tower Cost \(\times\) eTower cost scaling exponent\(\times\)(Tower height\(-\frac{Tower height}{2}+\frac{Hidistant height}{2})\)Receiver Cost \(=\) Receiver \(Reference\) Cost \(\times\)\(\left(\frac{Receiver\ Area}{Receiver\ Reference\ Are}\right)\)Receiver Cost Scaling Exponent  
The LCOE plays a crucial role in determining the viability of a power plant. Therefore, in the case of the SAM software, it calculates both LCOEs (nominal and real). The major difference between both LCOEs is that the nominal LCOE is calculated when the project is for a short-term period analysis, which also uses a present dollar rate; on the other hand, the real LCOE is used for a long-term period analysis and uses a constant dollar rate. In the present study, the authors opted for the real LCOE because the analysis was considered for a long-term period, i.e., 25 years. Therefore, the equation for the real LCOE is presented in Equation (A3) [15; 47; 59; 80; 81; 82; 83; 84; 85; 86; 87; 88].  
\[LCOE\ (real)=\frac{-c_{0}-\frac{\sum_{n=1}^{N}C_{n}}{\left(1+d_{nominal}\right) ^{n}}}{\frac{\sum_{n=1}^{N}Q_{n}}{\left(1+d_{real}\right)^{n}}}\] (A3)  
where \(C_{0}\) denotes the project's equity investment; \(C_{n}\) is the total annual cost of the project in number of years; N is the period of analysis; \(Q_{n}\) is the total electricity produced by the plant in N number of years; \(d_{nominal}\) is the nominal discount rate ($); \(d_{real}\) is the real discount rate ($).  
Other important financial parameters are NPV and NCC in relation to the economics of a CSP plant project. NPV indicates the absolute value of profitability of CSP Projects [27; 47]. The NPV [47] and NCC [27; 79] are calculated according to Equations (A4) and (A5), respectively.  
\[NPV=\sum_{n=0}^{N}\frac{C}{\left(1+D\right)^{n}}\] (A4)  
\(NCC=\) Working Capital Reserving Funding \(+\) Financial costs and Fees\(+\)  
Total Installed Cost \(-\) Total Based incentives
## 2 Results  
### Cloning and Functional Validation of cDNAs Encoding a Second DXS Isoform, CMS, CMK and HDR  
Cloning of cDNAs encoding the missing MEP pathway genes (CMS, CMK, HDR) was initiated in the late 2000s before the massive release of _C. roseus_ transcriptomic data. Partial cDNAs encoding each enzyme were thus amplified from a _C. roseus_ cDNA mixture using degenerate primers. The corresponding full-length coding sequences were subsequently isolated from an orientated _C. roseus_ cell cDNA library with specific primers. A similar approach was also used to isolate the second isoform of DXS and phylogenetic studies revealed that both isoforms belong to clade II DXS and were thus named DXS2.1 (initially named DXS) and DXS2.2 (Figure S1; [22; 24]). Analysis of the neopublished _C. roseus_ transcriptomic data confirmed the existence of all the cloned coding sequences. The deduced amino acid sequences of _C. roseus_, CMS, CMK, HDR and DXS2.2, display a high degree of identity with the corresponding _A. thaliana_ orthologs as shown in Table 1. In addition, DXS2.2 exhibits 69% amino acid identity with the previously cloned DXS/DXS2.1. Putative transit peptides (TP) were identified at the N-terminal end of newly characterized enzymes as well as in previously identifiedones (Table 1). Depending on the software used for the predictions, either plastid- or mitochondrion-TP were predicted, even though TargetP predictions were always in favor of a plastid targeting. Functional validations of DXS2.2, CMS, CMK and HDR were achieved by performing complementation assays in mutant _E. coli_ strains, in which a disruption of the corresponding gene results in a lethal phenotype [42; 43]. TP-truncated versions of the four proteins were thus expressed in their respective _E. coli_ mutant strains using the pQE-30 plasmid. We found that each TP-truncated protein was able to complement the corresponding deficiency of _E. coli_ mutants, therefore validating the biochemical activity of the cloned _C. roseus_ DXS2.2, CMS, CMK and HDR enzymes (Figure 2A,B). The coding sequences of each enzyme were deposited to Genbank (Table 1).
# Structural Particularities, Prediction, and Synthesis Methods in High-Entropy Alloys
## 2 Structural Particularities of HEAs, Prediction and Estimation Concepts
### Microstructural Stability
#### 2.2.3 Slow Diffusion Effect  
Specialized studies have shown that the diffusion process, which takes place in alloys with high entropy, is slow [1; 8]. This statement is explained by the fact that nanocrystals and amorphous phases are formed during solidification and are based on qualitative interpretations of microstructural stability during cooling [12]. The first study to measure the retarded diffusion capability of high-entropy alloys was produced by [PERSON] et al. [15]. The authors used a simplified quasi-chemical model to investigate atomic diffusion in an ideal single-phase HEA (CoCrFeMnNi). Results were compared to similar phase (FCC) metal structures and concluded that diffusion is more sluggish in HEAs than in conventional alloys. [PERSON] [16] studied the formation of vacancies in high-entropy alloys and compared the diffusion coefficients for elements in pure metals, stainless steels, and HEAs. The study concluded that the order of diffusion rates in the three types of alloy systems is: HEA < stainless steels < pure metals. However, later studies performed by various research teams could not verify the initial statements and obtained diffusion behavior similar to conventional alloys [17; 18]. Most of the studies related to either typical FCC or stable single-phase solid solution structures, but various compositions have been analyzed in the HEA or newly developed CCA systems and data may be considerably different. Due to the complex nature of the alloys, accurate measurements of the diffusion coefficients are difficult to obtain and more work is needed in the future to validate the original assumptions.
# FFT-Based Scan-Matching for SLAM Applications with Low-Cost Laser Range Finders
## 3 FFT-Based Scan-Matching
### Solution of One-Dimensional Fast Fourier Transform (1D FFT)  
Considering two functions \(f_{1}(x)\) and \(f_{2}(x)\) with the following relationship  
\[f_{2}(x)=f_{1}(x-x_{0}) \tag{8}\]  
where \(x_{0}\) is the shift between the two functions. Take the FFT of both sides of (8) and use related theorems to give  
\[F_{2}(u)=F_{1}(u)e^{-\beta 2\pi ux_{0}} \tag{9}\]  
Taking the cross-power spectrum of (9):  
\[\frac{F_{1}(u)F_{2}^{*}(u)}{\left|F_{1}(u)F_{2}^{*}(u)\right|}=e^{-\beta 2\pi ux_{0}} \tag{10}\]  
where \(F^{*}\) is the conjugation of \(F\) and \(|\bullet|\) is magnitude. Taking the inverse FFT of both sides of (10) gives an impulse function. The translation parameter \(x_{0}\) can be derived from the position of the magnitude of the impulse function [33].  
Similarly, 2D FFT function relation can be written in this way:  
\[f_{2}(x,y)=f_{1}(x-x_{0},y-y_{0}) \tag{11}\]  
where \(f_{1}(x,y)\), \(f_{2}(x,y)\) are two 2D functions and \(x_{0}\), \(y_{0}\) are displacements between them. We can also calculate the cross-power spectrum of (6):  
\[\frac{F_{1}(u,v)F_{2}^{*}(u,v)}{\left|F_{1}(u,v)F_{2}^{*}(u,v)\right|}=e^{- \beta 2\pi(ux_{0}+vy_{0})} \tag{12}\]
## 2 Materials and Methods
### Eddy Covariance Measurements  
The cluster of EC systems was installed in six native prairie pastures to collect CO\({}_{2}\), water vapor (H\({}_{2}\)O), and surface energy fluxes. The EC systems are composed of an open path infrared gas analyzer (LI-7500 RS, LI-COR Inc., Lincoln, NE, USA) and a 3D sonic anemometer (CSAT3, Campbell Scientific Inc., Logan, UT, USA), mounted at 2.5 m tall from the ground surface in all sites. The EC systems were deployed in each field, with enough fetch lengths of several hundred meters in all directions (Figure S1). An Oklahoma Mesonet station, which is a part of more than 110 Mesonet stations across Oklahoma [30], is located in P13, and all pastures were within a few kilometers of the Mesonet station. Rainfalldata for this study were obtained from this El Reno Mesonet station ([[http://mesonet.org/](http://mesonet.org/)]([http://mesonet.org/](http://mesonet.org/)) (accessed on 9 August 2021)). The 30-year mean values of daily, monthly, and annual air temperature and rainfall were computed for the 1981-2010 period to compare with those for the study periods.  
Raw EC data were processed in the EddyPro software (LI-COR Inc., Lincoln, NE, USA), version 7.0.6 in express mode (i.e., default settings), to compute 30 min values of CO\({}_{2}\), H\({}_{2}\)O, and surface energy fluxes. We screened poor-quality data with quality flag 2 (bad-quality fluxes) and unreliable fluxes (beyond the ranges of \(-\)50 to 50 \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\) for NEE, \(-\)200 to 500 W m\({}^{-2}\) for sensible heat (H), and \(-\)200 to 800 W m\({}^{-2}\) for latent heat (LE) [31; 32; 33]. Additionally, statistical outliers (>3.5 times the standard deviation for every 14 days) were excluded [34].  
We used the REddyPro package from the Max Planck Institute for Biogeochemistry, Germany ([[https://www.bgc-jena.mpg.de/bgi/index.php/Services/REddyProWebRPackage](https://www.bgc-jena.mpg.de/bgi/index.php/Services/REddyProWebRPackage)]([https://www.bgc-jena.mpg.de/bgi/index.php/Services/REddyProWebRPackage](https://www.bgc-jena.mpg.de/bgi/index.php/Services/REddyProWebRPackage)) (accessed on 15 December 2021)) for filling gaps in eddy fluxes and meteorological variables and for partitioning NEE into gross primary production (GPP) and ecosystem respiration (ER) [35]. Gap-filling procedures in the REddyProc included look-up tables, mean diurnal course, and marginal distribution sampling [35]. For NEE partitioning, we employed a nighttime-based flux partitioning method [36] that uses an Arrhenius-type [37] temperature response function of nighttime NEE (=ER). Fluxes for low turbulent periods (u\({}^{*}\) < 0.2 m s\({}^{-1}\)) were also removed and gap filled in the REddyProc. Diurnal mean values of fluxes were determined using non-gap-filled best-quality fluxes (i.e., the quality flag of 0) only. Cumulative fluxes were derived using gap-filled flux data. Uncertainty estimates (due to gap filling) of cumulative ET and NEE were derived from standard deviations (SD) of the data points used to fill the gaps. A negative NEE indicated a net gain of CO\({}_{2}\) by the prairie pastures in this study.
# Assessing Groundwater Level with a Unified Seasonal Outlook and Hydrological Modeling Projection
## 3 Study Site and Modeling Configurations
### Modeling Configuration and Calibrations
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Events**} & \multicolumn{2}{c}{**Coefficient of Determination**} & \multicolumn{2}{c}{**Root-Mean-Square Error**} & \multicolumn{2}{c}{**Nash-Sutcliffe Model Efficiency**} \\  & & **(R2)** & **(RMSE)** & **Coefficient (CE)** \\ \hline \multirow{4}{*}{Surface water} & Soulik (2013) & 0.910 & 0.253 & 0.896 \\  & Kong-rey (2013) & 0.832 & 0.209 & 0.791 \\  & Soudelov (2015) & 0.817 & 0.479 & 0.803 \\  & Rainfall event (2015) & 0.840 & 0.176 & 0.807 \\ \hline \multirow{4}{*}{Groundwater} & Average & 0.850 & 0.279 & 0.824 \\  & 2013 (1 January-31 December) & 0.475 & 0.363 & 0.459 \\  & 2014 (1 January-31 March) & 0.763 & 0.108 & 0.670 \\  & 2014 (1 July-31 October) & 0.638 & 0.195 & 0.471 \\  & 2015 (1 April-31 October) & 0.550 & 0.460 & 0.885 \\ \hline \multicolumn{4}{c}{Average} & 0.607 & 0.282 & 0.621 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Error indicators for evaluating the performance of the surface water/groundwater model. Figure 5: Rainfall-induced flow patterns of surface water and groundwater during (**a**) 4 July to 17 July of 2013, and (**b**) 12 May to 27 May of 2015.
# SDS: Scrumptious Dataflow Strategy for IoT Devices in Heterogeneous Network Environment
## 4 Performance Analysis
### Computing Round Trip Time  
The RTTP represents the amount of time it takes for a source device to send a request to the destination device and to receive acknowledgment. Usually, this technique is used to determine the health of a network connection. Analyzing the results shown in Figure 4, the overall response time of HSPA, CCN, DSR, and the proposed SDS protocols can be observed more scruptiously. It can be seen that the network devices are responding quickly and remain active. If these devices take longer, in contrast to the standard time, it means that there are some anomalies that hinder the packet movement as well as the network lifespan.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Number of networks & 4 \\ \hline Area & 500 \(\times\) 500 m\({}^{3}\)  
When compared to the rest of the protocols, the SDS takes substantially less time. In reality, our suggested technique uses LSA to identify the best link between the source and the following device. The chosen communication link carried the most energy, but the identical devices coupled with other protocols carried far less energy and were judged inoperable. It produces tangle-free routing and seamless network functioning.  
It can be observed that as the number of devices increased, i.e., to 20, the RTTP for CCN somehow became greater, but when approaching device 30, it decreased. This happened due to some unavoidable changes in the routing port, which sometimes blocks the traffic but soon releases. Although HSPA worked better than the other protocols in certain ways, since devices with modest distances were picked more frequently, the data reveal a fast delay when reaching device 50. For DSR, transmission appeared to be more crucial, reducing network lifetime and requiring additional resources to alter response time. It is worth noting that device responsiveness appears to be lower in CCN than in others. The source device continues to deliver data packets until the energy level falls below a certain threshold, putting a heavy pressure on the server. The SDS, on the other hand, changes the momentum during packet transmission and responds quickly. From device 10 to 50, the RTTP time remained less than all other protocols.
## 3 Results  
### Initial Measurements  
#### 3.1.1 Measurements in Eddy Current and Magnetic Experiments  
The results obtained from eddy current (EC) measurements are characterized by magnitude and phase shift (which can be easily converted to complex numbers--imaginary (_Im_) and real (_Re_) components). Figure 14 presents an example of EC measurement.  
The phase shift value only yields information about the rebar's position. Subsequent analyses are, therefore, focused on the magnitude.  
AMR sensors allow for the analysis of all three spatial components of magnetic induction (_B_). Figure 15 illustrates sample waveforms.  
Considering the results' symmetry and the measurements' time-consuming nature in both methods, the measurement range is limited. Only half of the waveform (to the rebar or from the rebar) is analyzed.  
The results shown in Figures 13 and 15 reveal that when the excitation moves along with the sensor (as in the EC method), the resulting waveform exhibits more dynamic variations compared to the magnetic method, where the excitation remains stationary. This characteristic is advantageous for assessing dense reinforced meshes. Rebars are typically spaced 10, 15, or 20 cm apart in a standard reinforcing mesh. When the distance between rebars is only 10 cm, there is a significant risk that the nearby bar will affect the shape factors related to lower amplitude percentages, such as \(d_{10}\) and \(d_{20}\). The smaller transducer can be utilized in such situations to solve this problem. However, this change will negatively impact the effective range of the method. Experiments show that even transducer T25 is  
Figure 14: Example eddy current measurements: (**a**) magnitude and phase shift vs. transducer position, (**b**) real (_Re_) and imaginary (_Im_) components vs. transducer position, (**c**) _Im_ vs. _Re_.  
Figure 15: Example magnetic measurements with the use of AMR sensor; (**a**) \(B_{\times}\), (**b**) \(B_{y}\), (**c**) \(B_{z}\).  
ineffective at evaluating rebars placed at distances higher than 60 mm from the transducer. Small transducers like T5 become ineffective if \(h\) exceeds 40 mm.  
The magnetic method utilizes a distinct approach from the EC method. A magnetized rebar can be detected from a considerable distance. However, the magnetic field of the magnetized rebar is significantly stronger (up to ten times [44]) than that of unmagnetized rebars, which reduces the influence of other elements in the reinforcing mesh. To further diminish the impact of neighboring rebars, all rebars in the measurement area can be demagnetized (remove residual magnetization) before testing with a strong electromagnet powered by alternating current. This procedure effectively reduces their influence and improves the effectiveness of the method.