esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistical Analysis  
To evaluate the accuracy of the models we preformed a multivariate data analysis using the Unscrambler software (version 10.4, CAMO, ASA, Oslo, Norway), and, for the descriptive analysis, the means and standard deviations (SD) of CP and NDF reference values were calculated.  
A PLSR algorithm was used to obtain the calibration model to predict pasture CP and NDF. PLSR is a calibration technique widely used in studies involving absorbance, as it avoids the problem of very high intercorrelation between different absorbance bands [40]. The entire spectra were used, ranging from 1093 nm to 1670 nm. First derivative and SNV pre-processing transformations were used prior to calibration modelling but they did not improve the model performance in comparison to raw spectra. So, the raw spectra data was used to build the calibration models. For the model development, an internal calibration (cross-validation) was preformed using Kernel algorithm with 20 segments. Outliers were excluded by PLSR. Warning limits were predefined for the detection of potential outliers. The criteria used to detect outliers was based on the F-Residuals and Hotelling's T2 statistics, calculated with an error of 0.05 and with 95% of confidence, respectively. Model performance was evaluated through coefficient of determination (R\({}^{2}\)), the root-mean-square error (RMSE), residual predictive deviation (RPD) and average difference between predicted and actual values (Bias). The R\({}^{2}\) is an excellent indicator of robustness and model accuracy [37]. Although R\({}^{2}\) is often the main statistical indicator used to assess the accuracy of the calibration model, according to [PERSON] and [PERSON] [41] it is not the best indicator for this purpose, as it depends on the range. The RPD value is directly related to the quality of the calibration model, and the higher its value, the better the model [42]. This index corresponds to the ratio between the standard deviation (SD) of the reference values of laboratory determinations and the RMSE [28; 37]. Finally, the bias indicator is related to the difference between the calibration and validation predicted and measured values [28; 31]. The smaller the bias, the more accurate the model.  
The RMSE (Equation (1)), as the designation indicates, is an error, which should have its absolute value as low as possible. The RPD (Equation (2)) is an index of model quality, which should be as high as possible.  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}\left(E_{i}-M_{i}\right)^{2}}{n}} \tag{1}\]  
\[RPD=\frac{SD}{RMSE} \tag{2}\]  
where \(n\) is the number of observations, \(E_{i}\) are the estimated values and \(M_{i}\) are the observed values.  
The software for multivariate data analysis was \"UnscramblerX\" (version 10.5.1), with a confidence level of 95% (\(p<0.05\)).
# Lightweight and Security-Enhanced Key Agreement Protocol Using PUF for IoD Environments
###### Abstract  
With the increasing demand for drones in diverse tasks, the Internet of Drones (IoD) has recently emerged as a significant technology in academia and industry. The IoD environment enables various services, such as traffic and environmental monitoring, disaster situation management, and military operations. However, IoD communication is vulnerable to security threats due to the exchange of sensitive information over insecure public channels. Moreover, public key-based cryptographic schemes are impractical for communication with resource-constrained drones due to their limited computational capability and resource capacity. Therefore, a secure and lightweight key agreement scheme must be developed while considering the characteristics of the IoD environment. In 2024, [PERSON] proposed a secure key agreement protocol for securing the IoD environment. However, [PERSON]'s protocol suffers from high computational overhead due to its reliance on elliptic curve cryptography and is vulnerable to drone and mobile user impersonation attacks and session key disclosure attacks by eavesdropping on public-channel messages. Therefore, this work proposes a lightweight and security-enhanced key agreement scheme for the IoD environment to address the limitations of [PERSON]'s protocol. The proposed protocol employs a physical unclonable function and simple cryptographic operations (XOR and hash functions) to achieve high security and efficiency. This work demonstrates the security of the proposed protocol using informal security analysis. This work also conducted formal security analysis using the Real-or-Random (RoR) model, Burrows-Abadi-Needham (BAN) logic, and Automated Verification of Internet Security Protocols and Applications (AVISPA) simulation to verify the proposed protocol's session key security, mutual authentication ability, and resistance to replay and MITM attacks, respectively. Furthermore, this work demonstrates that the proposed protocol offers better performance and security by comparing the computational and communication costs and security features with those of relevant protocols.  
Internet of Drones (IoD); physical unclonable function (PUF); key agreement; security +
Footnote †: journal: _Mathematics_  
_Article_  
Lightweight and Security-Enhanced Using PUF for IoD Environments
## 7 Qualitative Benefits of Wireless Interconnects: Flexibility, Quick Design and Time to Market  
Besides the quantitative benefits discussed so far, there are some qualitative benefits of adopting wireless interconnections for chip-to-chip communications. Primary among the qualitative benefits is the fact that there is no need for the layout of physical interconnects, neither metallic nor optical fibers. This implies that the designs will be more flexible and modular. Therefore, systems can be designed in a plug-and-play manner where the interconnect routing and placement will not impose additional constraints on the system design. Eliminating interconnect place-and-route procedures for large and complex systems can result in significant reduction in design, test, and verification times. Chiplets equipped with wireless transceivers can be just integrated on a physical platform without the need for physical interconnect layout, thus simplifying the design and integration process. This will reduce time-to-market for multi-chip systems, potentially reducing cost.  
Due to the absence of physical layouts of interconnects the integration is possible between chips or components on a flexible substrate such as a polymer [95]. Bio-medical devices, smart watches and augmented vision devices are a few examples where wireless interconnects can eliminate the constraints of rigid platforms such as FR4 boards and interposers. This is suitable for wearable electronic gadgets where ICs on a flexible substrate can have relative motion between them. However, evaluation of the communication links in these specific systems are needed to enable their adoption in this case.
## 2 Analysis and Derivation of the Inclination Angle of the Sliding Surface Behind the Shaft Wall
### Comparative Validation and Analysis of Influencing Factors  
Figure 3a illustrates the variation of the sliding surface inclination angle with the radius-to-height ratio under the given conditions and compares the calculation results of this study with the Coulomb theory values and the results reported by [PERSON] et al. (2022).  
As shown in the figure, compared with the plane retaining wall, the sliding surface inclination angle in the circular shaft is significantly influenced by the radius-to-height ratio. When the radius-to-height ratio is less than 15, the sliding surface inclination angle in the circular shaft is greater than that predicted by [PERSON]'s theory, and the smaller the radius-to-height ratio, the more pronounced the difference between the two. In addition, the sliding surface inclination angle decreases as the radius-to-height ratio increases and gradually approaches the Coulomb solution. Furthermore, a larger wall-soil friction angle results in a smaller sliding surface inclination angle for the same radius-to-height ratio. Finally, the calculation results of this study show good agreement with the values reported by [PERSON] et al. (2022), thereby verifying the rationality of the proposed calculation method.  
Figure 3b further illustrates the relationship between the sliding surface inclination angle and soil cohesion. As shown in the figure, with an increase in soil cohesion, the sliding surface inclination angle also increases, but the rate of increase gradually slows down. Moreover, the smaller the internal friction angle of the soil, the more significant the effect of soil cohesion. This is because, as soil cohesion increases, it becomes necessary to generate  
Figure 3: Comparison and validation of the sliding surface inclination angle and analysis of Influencing factors. (**a**) Influence of radius-to-height ratio \(r_{0}/H\) on the sliding surface inclination angle \(\beta\)([PERSON] et al., 2022); (**b**) influence of soil cohesion \(c\) on the sliding surface inclination angle \(\beta\).  
sufficient shear stress in the soil with higher cohesion to satisfy the limit equilibrium condition, thereby initiating active failure. This, in turn, leads to an increase in the sliding surface inclination angle.
## 5 Simulation Results and Discussions
### Channel Selection Strategies of Users and the Jammer  
As an example, Figure 7 shows the time-frequency diagram after the ICADCSA algorithm converging in the first stage (4800 to 4850 ms), where User 1 and User 3 were influenced by co-channel interference. In Figure 7, the Y-axis represents the channels that users and the jammer can choose (from Channel 1 to 4), while the X-axis represents the simulation time. For better description, we used u1, u2, u3 and \(J\) to denote the channel selection of User 1, User 2, User 3 and the jammer, respectively. The red square denotes the jamming channel, and the yellow square, black square and light pink square represent the channel selection of User 1, User 2 and User 3, respectively. The mixed color square illustrates that either more than two users choose the same channel or users and the jammer choose the same channel in one certain slot. During the first stage, users were under the threat of a malicious jammer and co-channel interference. Thus, User 1 and User 3 adopted multi-agent Q-learning and took joint channel selection, whereas User 3 employed single Q-learning as it was not influenced by co-channel interference. As is shown in Figure 7, the users' channel selections avoided the vast majority of jamming channels. Moreover, User 1 and User 3 avoided being influenced by co-channel interference, as they selected different channels in each time slot. In addition, although there existed some overlapping areas between User 2's channels and other users' channels, the communication of User 2 would not be influenced by co-channel interference as its received co-interference signal energy was lower than the threshold. In a word, the time-frequency diagram shows that the proposed ICADCSA algorithm was effective.
## 4 Methodology Evaluation
### System Configuration  
Figure 2 shows the schematic implementation of the system under study in the software RSCAD of RTDS. The system consists of two synchronous Generators G1 and G2, and their control systems (Governor PIDGOV, Automatic Voltage Regulator (AVR) EXST1, and Power System Stabilizer (PSS) STAB2A) [35, 36], two three-phase transformers, two transmission lines, and an electrical load modeled as a constant impedance. The parameters of the test system can be consulted in the Appendix A.  
Figure 3 presents the experimental setup of the system in RTDS. The measured three-phase voltages of buses 1, 2, 3, and 5 simulated in RTDS are sent to the PMUs. The estimated synchrophasors are sent via Ethernet in the IEEE protocol C37.118. The sample rate of synchrophasors is 60 cycles/s in protection mode. A GPS satellite clock SEL-2404 is used to timestamp the calculated phasors. All the phasors are received by a computer for further analysis.
## 2 Materials and Methods
### Determination of Total Phenolic Content (TPC)  
Total phenolic content (TPC) was measured using the Folin-Ciocalteu method as described by [PERSON] et al. [23] with slight modifications. Samples of dried materials (air- or freeze-dried) were extracted with distilled water (sample to water ratio: 1:20), sonicated for 10 min, mixed by vortex for 2 min, and centrifuged for 5 min at 8000 rpm (5804 Centrifuge, Eppendorf, Hamburg, Germany). Then, diluted fruit extract (250 \(\upmu\)L) was mixed with 2.5 mL of [PERSON]'s reagent (diluted 10-times in water). The mixture was vortexed,  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Sample** & **Fruit** & **Latin Name** & **Type of Drying** & **Fineness** & **Producer** \\ \hline S1 & barberry & _Berberis vulgaris_ & dried & whole fruit & EkoHerba \\ S2 & blackthorn & _Prunus spinosa_ & dried & whole fruit & Rafex \\ S3 & blackthorn & _Prunus spinosa_ & dried & whole fruit & EkoHerba \\ S4 & chokeberry & _Aronia melanocarpa_ & lyophilized & powder & Premium Rosa \\ S5 & chokeberry & _Aronia melanocarpa_ & dried & powder & Rafex \\ S6 & chokeberry & _Aronia melanocarpa_ & lyophilized & whole fruit & Rafex \\ S7 & chokeberry & _Aronia melanocarpa_ & dried & whole fruit & Kawon \\ S8 & elderberry & _Sambus nigra_ & lyophilized & powder & Premium Rosa \\ S9 & elderberry & _Sambus nigra_ & lyophilized & whole fruit & Rafex \\ S10 & elderberry & _Sambus nigra_ & dried & whole fruit & Rafex \\ S11 & elderberry & _Sambusus nigra_ & dried & whole fruit & Kawon \\ S12 & hawthorn & _Crataegus monogyna_ & dried & whole fruit & Rafex \\ S13 & hawthorn & _Crataegus monogyna_ & dried & whole fruit & Kawon \\ S14 & hawthorn & _Crataegus monogyna_ & dried & whole fruit & Dary Natury \\ S15 & hawthorn & _Crataegus monogyna_ & dried & whole fruit & EkoHerba \\ S16 & lingonberry & _Vacinium vitis-idae_ & lyophilized & powder & Premium Rosa \\ S17 & lingonberry & _Vacinium vitis-idae_ & dried & whole fruit & Rafex \\ S18 & rowanberry & _Sorbus aucuraria_ & dried & whole fruit & Rafex \\ S19 & rowanberry & _Sorbus aucuraria_ & dried & whole fruit & Dary Natury \\ S20 & rowanberry & _Sorbus aucuraria_ & dried & whole fruit & FloS \\ S21 & rowanberry & _Sorbus aucuraria_ & dried & whole fruit & EkoHerba \\ S22 & sea-buckthorn & _Hippphae rhamnides_ & lyophilized & powder & Premium Rosa \\ S23 & sea-buckthorn & _Hippphae rhamnides_ & dried & whole fruit & Rafex \\ S24 & sea-buckthorn & _Hippphae rhamnides_ & dried & whole fruit & Farmvit \\ \hline \end{tabular}
\end{table}
Table 1: The studied materials.  
and absorbance was measured after 20 min in a quartz cuvette at 750 nm using a UV-visible spectrophotometer (Dynamica Halo DB-20S, Berkshire, UK). The gallic acid (GAE) standard curve (5-250 mg/L) was used, and TPC was expressed as milligram gallic acid equivalent per gram of sample (mg GAE/g dw). Determinations were carried out in two replicates for each fruit sample and every spectrophotometric measurement was triplicated.
# An Accurate Jacobian Matrix with Exact Zoeppritz for Elastic Moduli of Dry Rock
###### Abstract  
Seismic velocities are related to the solid matrices and the pore fluids. The bulk and shear moduli of dry rock are the primary parameters to characterize solid matrices. Amplitude variation with offset (AVO) or amplitude variation with incidence angle (AVA) is the most used inversion method to discriminate lithology in hydrocarbon reservoirs. The bulk and shear moduli of dry rock, however, cannot be inverted directly using seismic data and the conventional AVO/AVA inversions. The most important step to accurately invert these dry rock parameters is to derive the Jacobian matrix. The combination of exact Zoeppritz and Biot-Gassmann equations makes it possible to directly calculate the partial derivatives of seismic reflectivities (PP-and PS-waves) with respect to dry rock moduli. During this research, we successfully derive the accurate partial derivatives of the exact Zoeppritz equations with respect to bulk and shear moduli of dry rock. The characteristics of these partial derivatives are investigated in the numerical examples. Additionally, we compare the partial derivatives using this proposed algorithm with the classical [PERSON] and [PERSON] approximations. The results show that this derived Jacobian matrix is more accurate and versatile. It can be used further in the conventional AVO/AVA inversions to invert bulk and shear moduli of dry rock directly.  
exact Zoeppritz equations; dry rock; Biot-Gassmann equations; Jacobian matrix; accurate solution +
Footnote †: journal: Applied Physics Letters
###### Abstract  
The distinctive features of acoustic communication channel-like high propagation delay, multi-path fading, quick attenuation of acoustic signal, etc. limit the utilization of underwater wireless sensor networks (UWSNs). The immutable selection of forwarder node leads to dramatic death of node resulting in imbalanced energy depletion and void hole creation. To reduce the probability of void occurrence and imbalance energy dissipation, in this paper, we propose mobility assisted geo-opportunistic routing paradigm based on interference avoidance for UWSNs. The network volume is divided into logical small cubes to reduce the interference and to make more informed routing decisions for efficient energy consumption. Additionally, an optimal number of forwarder nodes is elected from each cube based on its proximity with respect to the destination to avoid void occurrence. Moreover, the data packets are recovered from void regions with the help of mobile sinks which also reduce the data traffic on intermediate nodes. Extensive simulations are performed to verify that our proposed work maximizes the network lifetime and packet delivery ratio.  
opportunistic routing; potential neighbor number; communication void; local maxima; energy consumption; packet delivery; latency; depth adjustment 2018 18062; doi:10.3390/s18041062
# Hydrological Drought Frequency Analysis in Water Management Using Univariate Distributions
## 2 Methodology
### Probability Distributions
#### 2.1.2 Pearson III (PE3)  
The Pearson III represents a generalized form of the two-parameter gamma distribution and a particular case of the four-parameter gamma distribution [18; 20]. It represents the parent distribution in Romania in statistical analysis in hydrology [30; 31].  
\[f(x)=\frac{(x-\gamma)^{\alpha-1}}{\beta^{\alpha}\cdot\Gamma(\alpha)}\cdot \exp\biggl{(}-\frac{x-\gamma}{\beta}\biggr{)}=\frac{1}{\beta}\cdot dgamma \biggl{(}\frac{x-\gamma}{\beta},\alpha\biggr{)} \tag{8}\]  
\[F(x)=1-\int\limits_{\gamma}^{x}f(x)dx=1-\frac{1}{\beta\cdot\Gamma(\alpha)}\cdot \int\limits_{\gamma}^{x}\biggl{(}\frac{x-\gamma}{\beta}\biggr{)}^{\alpha-1} \cdot\exp\biggl{(}-\frac{x-\gamma}{\beta}\biggr{)}dx=\frac{\Gamma\Bigl{(} \alpha,\frac{x-\gamma}{\beta}\Bigr{)}}{\Gamma(\alpha)} \tag{9}\]  
\[x(p)=\gamma+\beta\cdot qgamma(1-p,\alpha) \tag{10}\]  
where \(\alpha,\beta,\gamma\) are the shape, the scale, and the position parameters, respectively, and \(x\) can take any values of range \(\gamma<x<\infty\) if \(\beta>0\), or \(-\infty<x<\gamma\) if \(\beta<0\) and \(\alpha>0\); \(\mu,\sigma\) represent the mean (expected value) and standard deviation. If \(\beta<0\) (negative skewness), then the first argument of the inverse of the distribution function gamma, \(\Gamma^{-1}(1-p;\alpha)\) becomes \(\Gamma^{-1}(p;\alpha)\).  
The built-in function from Mathcad, \(qgamma(1-p,\alpha)=\gamma^{-1}((1-p)\cdot\Gamma(\alpha),\alpha)\), returns the inverse cumulative probability distribution for probability p, for the gamma distribution, where \(\gamma^{-1}\) is the inverse of the lower incomplete gamma function [32].
# High-Dimensional Data Analysis Using Parameter Free Algorithm Data Point Positioning Analysis
## 6 Results and Discussion  
In this section, the experimental outcomes of the proposed data point positioning analysis (DPPA) method are presented. Furthermore, four popular clustering methods (Mean Shift, K-means, affinity propagation, DBSCAN) are implemented to validate and compare the effectiveness of the proposed approach. The implemented methods are tested on higher-dimensional datasets to examine the ability of DPPA to find the clusters. The experimental results are illustrated as follows.
# Application of Compressive Sensing in the Presence of Noise for Transient Photometric Events
## 4 Numerical Results
### CS Analysis with Source Noise  
This set of simulations vary the amount of source noise, \(n_{b}\), added to the observed image for a single-lens microlensing event with \(\mu_{0}=0.1\) and \(\mu_{0}=0.01\). Simulations are performed using the model described in Equation (7).  
To characterize source noise or background noise, we use Gaussian noise, with mean 0 and varying standard deviation. By varying the standard deviation of the added Gaussian noise, we obtain the different SNRs for the observed image and noisy observed image. Figure 8 shows the CS reconstruction error with different amounts of added source noise to the observed image. A binomial measurement matrix with 25% CS measurements is used for this simulation.  
Figure 6: Rate of decay of coefficients for \(\mu_{0}=0.1\) and \(\mu_{0}=0.01\).  
Figure 7: Rate of decay of coefficients, zoomed in towards the higher magnification coefficients to view the difference between \(\mu_{0}=0.1\) and \(\mu_{0}=0.01\).  
In Figure 8, we can note that the % error is the lowest when the magnification peaks at \(t_{0}\), as shown in Figure 5. We also see the effect of the different levels of noise on the % error. Figure 9 shows the comparison between \(\mu_{0}=0.1\) and \(\mu_{0}=0.01\). As the SNR decreases, the % error difference between the two \(\mu_{0}\) also increases. For high-magnification events, where \(\mu_{0}\) is lower, sparsity is higher. Thus, the % error is lower in those circumstances, as noted in Figure 9. From Figures 8 and 9, it is evident that for low SNR images, CS reconstruction works better when the impact factor \(\mu_{0}=0.01\), as opposed to when \(\mu_{0}=0.1\). A summarizing result from this figure is that CS works well for high-magnification images, even with addition of noise, following the need for sparsity in signals for accurate CS reconstruction.  
Figure 8: Summary of the % error for an image with added Gaussian source noise for a single-lensed microlensing event with \(\mu_{0}=0.1\) and \(\mu_{0}=0.01\) for varying levels of Gaussian noise addition to the spatial region of interest.  
Figure 9: Average % error for an image with added Gaussian source noise for a single-lensed microlensing event with \(\mu_{0}=0.1\) and \(\mu_{0}=0.01\).
# Typhoon Track Prediction Based on Deep Learning
## 3 Experiment Results and Analysis  
To verify the accuracy of the model, the typhoon path data and selected meteorological variables data in the best path data set of typhoons in the South China Sea from 1949 to 2006 were used as the training set for the typhoon path data and selected meteorological variables data from 2007 to 2020 were used as the test set, and the typhoon path data and selected meteorological variables data were used as the validation set, and some experiments were conducted to predict the typhoon paths.