esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Hazard Evaluation and Strain Dynamics in the
## 3 Methodology and Implementation
### Gutenberg-Richter Law
#### 3.2.1 Seismic Hazard Parameters
[PERSON] [103] stated that in a study conducted across 11 different seismic regions worldwide, including the Americas, Alaska, New Zealand, and Japan, the \(b\)-values ranged between 0.75 and 0.85. Additionally, various suggestions containing different values based on the \(a\) and \(b\) parameters have been made for different continents, regions, time intervals, and specific seismic sources [104; 105; 106; 107; 108; 109]. Traditionally, parameters of the G-R calculated using the maximum likelihood (ML) estimation method based on past earthquakes with a certain sample size. Unlike the maximum likelihood method, which requires large datasets for accurate parameter estimation, hazard parameters can also be estimated using linear or nonlinear least squares regression (LSR). Generally, these two methods are the most commonly used for parameter estimation [111; 112]. In LSR, which is essentially a curve fitting technique, the \(b\)-value is typically estimated by applying linear regression to the logarithm of the histogram. This method performs effectively in various situations, such as assessing the likelihood of large-magnitude earthquakes [113; 114]. Therefore, in this study, the estimation of \(a\) and \(b\) parameters according to the G-R law was carried out using the nonlinear least squares method. Gutenberg-Richter frequency-magnitude relation is applied in the sources on and around SFZ, which were identified from recent seismotectonic understanding of the region. In this study, earthquakes without moment magnitudes were converted into moment magnitudes using the coefficients obtained from orthogonal regression, and the \(a\) and \(b\) parameters were estimated using a catalog from over 4500 earthquakes with M \(\geq\) 3 (Figure 5). The obtained results for the \(a\)- and \(b\)-values are 6.33 and 0.92, respectively. These results indicate high seismic activity in the region based on the \(a\)-value, while the \(b\)-value suggests that moderate to large earthquakes occur regularly.
# Wire-Grid and Sparse MoM Antennas: Past Evolution, Present Implementation, and Future Possibilities
## 3 Optimization
### Special Cases  
As optical metasurfaces become more and more common, the fields of their application are becoming more and more complex. In [208], an adaptive genetic algorithm (AGA) was presented as a powerful method of evolutionary optimization capable of solving complex design problems and considering a large number of parameters in the field of optical metasurfaces. To demonstrate the advantages of the AGA method over traditional design methods, the authors solved several problems of interest to the optical community. For example, the binary plasmonic reflection matrix was successfully optimized for beam control applications. The authors showed that large datasets generated by GAs can be used as data for next-generation computational algorithms such as machine learning and deep learning. A new methodology was presented to directly design planar directional antennas [209]. Surface shaping was achieved by adding/removing hexagonal cells; this made the optimization efficient and free of manufacturing tolerances. The design is based on GA combined with an MoM-based electromagnetic solver. The proposed method opens the way for designing planar antennas with very high gain at high frequencies.
## 3 Results
### In Silico Analysis
#### 3.3.2 Alignment Analysis  
The amino acid sequence of LCYB from _D. salina_ (accession number: ACA34344.1) was compared with those of known \(\beta\)-lycopene cyclase homologous proteins (Figure 4). _D. salina_ LCYB showed high similarity with many \(\beta\)-lycopene cyclases from other microalgae, e.g., 68% identity with that from _Haematococcus lacustris_ (AAO64977.1), 65% with that from _Chlamydomonas eustigma_ (GAX83195.1). In contrast, less identity was observed with plant LCYB (50% with that from _Arabidopsis thaliana  
\begin{table}
\begin{tabular}{c c c c} \hline
**Enzyme** & **Position** & **Group** & **Score** \\ \hline \multirow{2}{*}{**CGP**} & K308 & WLLGM **AKPE** & 0.79 \\  & K262 & AVSEA **FKEG** & 0.68 \\ \hline \multirow{2}{*}{**LCYB**} & K271 & HARKL **VKYQ** & QFNP & 0.93 \\ \cline{2-4}  & 124–128 & QRQPAADL**LV**V**G**S**G**S**G**S**G**A & 0.70 \\ \cline{2-4}  & 149–153 & AGGFSV**C**V**D**LD**PY**APMIP** & 0.62 \\ \hline \end{tabular}
\end{table}
Table 2: Putative SUMO (small ubiquitin-related modifier) interacting motifs in CGP (carotene globule protein) and LCYB (lycopene \(\beta\)-cyclase), showing scores of predicted sites (bold) and the lysine residue (underlined) implicated in interaction with SUMO.  
(AAA81880.1)) and very low identity with that from cyanobacteria (33% with LYBC from _Acaryochloris marina_ (WP_012166204.1). The sequence alignment also revealed that \(D\). _salina_ LCYB exhibited N-terminal and C-terminal insertions when compared to plant and cyanobacteria LCYB in addition to an internal insertion of 15 residues corresponding to the \(\alpha\)6 helix in the generated 3D model. Several conserved regions in the protein sequences already identified in other lycopene cyclases from bacteria and plants were also found in \(D\). _salina_ LCYB [18]. Amongst these conserved sequences, PTFLYAM and FLEETSL, highlighted in red in Figure 4, were involved in the formation of \(\beta\)12 and \(\beta\)13 strands. To further understand the role of these conserved regions in the enzyme activity, we proposed further investigation of the 3D model.
# Privacy-Preserving Authentication Based on PUF for VANETs
## 6 Performance Evaluation  
Since initialization, vehicle registration, RSU registration, and path planning in our scheme are all one-time operations that require less computation and communication overhead throughout the entire implementation process, this section focuses solely on  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scheme** & **Authentication and Integrity** & **Physical Protection** & **Anonymity** & **Traceability** \\ \hline
[19] & ✓ & ✓ & \(\times\) & \(\times\) \\
[20] & ✓ & ✓ & ✓ & \(\times\) \\
[21] & ✓ & \(\times\) & ✓ & ✓ \\
[22] & ✓ & \(\times\) & ✓ & \(\times\) \\ Ours & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular} \(\gamma\): Implemented; \(\times\): Not implemented.  
\end{table}
Table 1: Security comparison.  
comparing the computational and communication overhead incurred during the authentication phase.
## 5 Measurement Tools and Methods  
As a means of measuring the power quality, the recorders of power quality \"Parma RK3.02\" [24] were used. These devices comply with the requirements of IEC 61000-4-30:2008\"Electric energy. Electromagnetic compatibility of technical equipment. Power quality measurement methods\" [25] and CSA IEC 61000-4-7-2017 \"Electromagnetic compatibility (EMC)\" [26].  
The current was measured using power quality analyzers FLUKE-435 [27]. The devices were connected by flexible current clamps with a transformation coefficient of 3000:1, and for voltage measuring clamps with a transformation coefficient of 1:1.  
Taking into account the requirements of Russian Standard GOST R 32144-2013 \"Electric energy. Electromagnetic compatibility of technical equipment. Power quality limits in the public power supply systems\" [28] (complies with the European regional standard EN 50160:2010 \"Voltage characteristics of electricity supplied by public distribution network\" [29]), compliance with the standards of the following voltage PQI were investigated:  
* Frequency deviation;
* Slow voltage changes (over-deviation and under-deviation);
* Coefficient of voltage unbalance by the zero and reverse sequence;
* Voltage total harmonic distortion (THD);
* Coefficient of the 2 nd to 40 th harmonic components of the voltage;
* Coefficient and duration of voltage swell;
* Depth and duration of the voltage dip.  
Furthermore, according to the requirements of standards [28; 29], the length of the time interval of measurements was assumed to be seven days, and the total number of measured intervals at the measuring points was five. The number of measured intervals is determined by the need to cover all operating modes of the GTPS.
# On the Development of a Neural Network Architecture for Magnetometer-Based UXO Classification
## 5 Results
### Ablation Studies  
To quantify the impact of individual architectural components on the overall model performance, we conducted a series of ablation studies.  
First, we analyzed the influence of the meta-features. The results, presented in Table 6, demonstrate that incorporating metadata improves the balanced accuracy by approximately 1 percentage point. These features provide crucial context that aids the network in interpreting the raw magnetometer signals. For instance, the sensor's height above the seabed directly correlates with the signal amplitude, which decreases rapidly with increasing distance. Similarly, the sensor's orientation (cardinal direction) provides contextual information that can help the model better characterize the shape of magnetic anomalies.  
Second, we investigated the effect of input data dimensionality. Our baseline model used only the scalar magnitude of the magnetic field. A key question was to determine the performance loss resulting from this simplification. We compared the baseline against a model trained on all four magnetic field components (X, Y, Z, and M). As shown in Table 7, providing the full vector data improves the balanced accuracy by over 2.3 percentage points (from 84.78% to 87.10%). This finding suggests that, when available, using the complete magnetic field vector from sensors like fluxgates [26] is significantly more effective.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type** & **Accuracy** &
\begin{tabular}{c} **Balanced** \\ **Accuracy** \\ \end{tabular} & \(\mathbf{F1_{macro}}\) & **Hiden dim.** & **MF dim.** & **FC** \\ \hline No Meta & 86.80 \(\pm\) 0.98 & 83.98 \(\pm\) 1.55 & 83.87 \(\pm\) 1.42 & [32,128,512] & 0 & 100 \\ \hline With Meta & 87.72 \(\pm\) 1.32 & 84.78 \(\pm\) 1.45 & 84.89 \(\pm\) 1.61 & [32,128,512] & 4 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The impact of meta-features on neural network performance. “Hidden Dim.” refers to the number of filters in the convolutional blocks. “MF Dim.” indicates the number of neurons in the metadata processing layer, where 0 signifies that no metadata was used. “FC” is the number of neurons in the final fully-connected layer.  
The final ablation experiment analyzed the contribution of the skip connections (SC) and element-wise batch normalization (BN) layers. The results, presented in Table 8, reveal a dramatic decrease in performance when both mechanisms are removed. The balanced accuracy and \(F1_{score}\) drop by over 13 percentage points. This significant degradation highlights the critical role of these components. The additive nature of the skip connections, combined with the regularizing effect of batch normalization, effectively mitigates the vanishing/exploding gradient problem and stabilizes the training of deep networks.
## 2 Materials and Methods  
### Experimental Site Situation and Biochar Characterization  
The experiment was conducted at Yangling Experimental Station (108\({}^{\circ}\)04\({}^{\prime}\) E, 34\({}^{\circ}\)17\({}^{\prime}\) N), Institute of Water-saving Agriculture in Arid Areas of China, which is located at an altitude of 506 m. The meteorological data were obtained based on a 30-year average from the Yangling Weather Station, which is about 200 m away from the experimental field. At the experimental site, the annual average temperature is 13.0 \({}^{\circ}\)C, the annual number of sunshine hours is 2196 h, and the frost-free period is 220 days. The precipitation is generally low with an uneven distribution in this region. The annual average precipitation was about 600 mm, the annual average evaporation was about 933.2 mm, and the average precipitation during the growth period of winter wheat was about 200 mm. The detailed precipitation and air temperature data during the experiment were also obtained from this weather station, as shown in Figure 1. The soil in the experimental field is Lou soil, which belongs to Eum-Orthric Anthrosols. Before the experiment, the basic properties of the soil in the top 0-20 cm layer were as follows: pH = 7.59, bulk density = 1.25 g cm\({}^{-3}\), soil organic matter = 13.22 g kg\({}^{-1}\), total nitrogen = 0.94 g kg\({}^{-1}\), available nitrogen (nitrate nitrogen and ammonium nitrogen) = 48.62 mg kg\({}^{-1}\), available phosphorus = 11.10 mg kg\({}^{-1}\), and available potassium = 110.69 mg kg\({}^{-1}\).  
The biochar used in the experiment was purchased from Zedi Agricultural Science and Technology Company (Jiangsu, China). It was produced with the anaerobic pyrolysis of equal weight rice straw and rice husk at 500 \({}^{\circ}\)C for 2 h. The properties of biochar were as follows: pH = 9.27, total N = 6.5 g kg\({}^{-1}\), total C = 205.6 g kg\({}^{-1}\), total P = 11.34 g kg\({}^{-1}\), total K = 17.42 g kg\({}^{-1}\), cation exchange capacity = 16.2 cmol kg\({}^{-1}\), specific surface area = 29.6 m\({}^{2}\) g\({}^{-1}\), and bulk density = 0.35 g cm\({}^{-3}\).
## 3 Results and Discussion
### Screening for Characteristic Compounds in the Fermentation of Yellow Wine  
PCA was used to examine and evaluate the volatile compounds of the samples from various fermentation times [1]. The results of the score plots (Figure 3A) show that the samples in each group are close to each other, proving a good reproducibility of the results. In addition, F2 and F3 are clearly dispersed, proving that there is a clear difference in volatiles between the two groups. Expectedly, there is no discernible change between F0 and F1. F4 is, surprisingly, less distinct from these two groups. The comparison shows that F4 has a fairly homogenous material type since it reduces a lot of volatile chemicals. As a result, F4 differs from F0 and F1 less in terms of material type. The amount of alcohols and acids that F4 gathered, combined with the relative content, must have affected the flavor of the yellow wine. The distribution of volatile chemicals in the various groups is displayed by the bioplot (Figure 3B).  
Figure 3: The score of PCA results at different fermentation periods: (**A**) the bioplot of PCA; (**B**) OPLS-DA diagram of 36 volatiles; (**C**) the red marked dots are the volatile compounds that have changed significantly during the fermentation process of yellow wine. The farther away from the origin, the greater the difference. The \(p\)-value of characteristic compounds (**D**).  
The differential compounds in the fermentation process were screened using the OPLS-DA in conjunction with the Variable Importance in Projection (VIP) [36]. An OPLS-DA model with good fitting parameters (R\({}^{2}\)X = 0.986, R\({}^{2}\)Y = 0.996, Q\({}^{2}\) = 0.99) was used in this work to screen for signature compounds that contribute to changes in yellow wine scent during the fermentation process. VIP calculated the contribution of each volatile substance to the classification. Substances with VIP \(>\) 1 were typically thought to play an essential influence in sample classification. In the OPLS-DA model, 13 volatiles were finally screened (VIP \(>\) 1, \(p\) \(<\) 0.01) (Figure 3C,D), including: nonanal (H2), decanal (H3), 1-hexanol (H6), [R,R]-2,3-butanediol (H7), 1-phenylethanol (H9), phenylethyl alcohol (H10), ethyl tetradecanoic (H14), ethyl hexadecanoic (H15), ethyl acetate (H24), 2,4-di-tert-butylphenol (H27), acetic acid (H28), beneicosane (H30), and 1-chloropentane (H31). Among these, phenylethyl alcohol and acetic acid have been identified in previous studies as important aroma compounds in yellow wine [11].
## 2 Materials and Methods
### Satellite Chlorophyll Fluorescence Data  
We used satellite SIF data retrievals from the Global Ozone Monitoring Experiment-2 (GOME-2) instrument on MetOp-A platform, which used channel 4 with the spectral resolution of 0.5 nm and near the wavelength of 740 nm far-red peak. The SIF at 740 nm was retrieved from a spectral range between 720 and 758 nm with an improved algorithm [21; 22; 47], which reduced the retrieval noise and sensitivity of the SIF retrieval to cloud contamination. Specifically, we applied the monthly gridded SIF product at 0.5\({}^{\circ}\) resolution from 2007 to 2016. The product has removed the data when the cloud fraction is greater than 0.5 for the cloud filtering. The GOME-2 instrument degraded significantly during its lifetime, particularly in the early stage of the record [48]. Therefore, we detrended SIF product to remove the degradation to allow focused analysis on interannual variability.  
Chlorophyll fluorescence yield is a sensitive indicator for the photochemical and biochemical processes of photosynthesis [49]. The fluorescence yield of the canopy and the fraction of the canopy emission escaping to the atmosphere can be indicated by SIF yield (SIF\({}_{\text{yield}}\)). SIF contained the amount of absorbed photosynthetically active radiation (APAR) and can be formulated as [24; 29; 30]:  
\[SIF=APAR\times\varepsilon_{f}\times\Omega_{c} \tag{1}\]  
\[SIF_{yield}=\frac{SIF}{APAR} \tag{2}\]  
\[APAR=EVI\times Radiation \tag{3}\]where \(\varepsilon_{f}\) is the actual fluorescence yield at the leaf level, and \(\Omega_{\mathrm{c}}\) is the probability of SIF photons escaping from the top of the canopy. \(\mathrm{SIF_{yield}}\) is defined as the actual fluorescence yield at the top of the canopy. \(\mathrm{SIF_{yield}}\), which is related to leaf biochemistry and canopy structure, removes the impact of APAR on SIF. This variable can indicate the dynamic process of SIF and photosynthetic efficiency of plants when the drought occurs [29]. Since the amount of incoming photosynthetically active radiation is proportional to the total downwelling shortwave radiation, the products of shortwave radiation and EVI were used to calculate APAR [50].
# Efficient and Robust Image Communication Techniques for 5G Applications in Smart Cities
## 5 Result Analysis and Discussion
Figure 12: _Cont._  
As illustrated by Figure 12a, to achieve a BER of \(10^{-4}\), in AWGN channel scenario, BPSK modulated FFT-OFDM necessitates SNR of 19 dB, but it reduces up to 8 dB for FFT-OFDM with MRC (1 Tx and 2 and 3 Rx). A similar observation can be made for the DCT-OFDM with MRC (1 Tx and 2 and 3 Rx), which requires 5.3 dB of SNR in comparison to 16 dB of SNR it requires without MRC. For QPSK modulation in AWGN scenario, FFT-OFDM requires 23.5 dB of SNR, which is more than 12 dB required in the case of FFT-OFDM with MRC. On the other hand, the same modulation system requires 22 dB and 10.5 dB of SNR to achieve the desired BER for DCT-OFDM without MRC and DCT-OFDM with MRC. Consequently, it is summarized that DCT-OFDM improves 1-3 dB of SNR over FFT-OFDM in AWGN and Rayleigh fading channel scenarios. It is further concluded that on employing the MRC schemes, significant improvement in SNR ranging in between 10-12 dB for both FFT-OFDM and DCT-OFDM has been reported in work. Figure 12: SNR vs. BER comparison of FFT-OFDM and DCT-OFDM for diverse Modulations over AWGN channel with MRC (nTx = 1 and nRx = 1, 2, 3), (**a**) BPSK (**b**) QPSK (**c**) 8-PSK (**d**) 16-PSK. Figure 13: _Cont._  
Figure 13: SNR vs. BER comparison of FFT-OFDM and DCT-OFDM for diverse Modulations over Rayleigh channel with MRC (nTX = 1 and nRx = 1, 2), (**a**) BPSK (**b**) QPSK (**c**) 8-PSK (**d**) 16-PSK. To emphasize that increase in BER will lead to a decrease in data rate, as depicted in Figures 12 and 13, as we go on increasing the modulation level to elevate the data rate the BER also keeps on increasing. This increase of BER will have adverse effects on the quality of the image being received at the receiver end, which can only be compensated by an increase in SNR. As the increase in SNR will result in increased signal strength at the receiver end, which will further augment the signal demodulation process at the receiver side. The position of constellation points for the received symbols at the input of demodulator will move to the ideal position on the constellation diagram, which will result in correct decision making at the output of the demodulator, and BER will decrease. However, in the modern-day systems, we cannot increase the SNR that easily as it may lead to increase size and cost of the equipment. These constraints will restrict the system operation to lower the modulation level, which will result in a reduction in data rate. Hence, the reduction in BER will allow us to switch to higher modulation levels and achieving higher data rates by keeping the SNR requirement in desirable limits and making the proposed system a suitable contender for 5G applications in smart cities. Moreover, the reliable mode of image communication will efficiently address the current need of diverse user drive application such as real-time monitoring of roads, smart homes, smart industries, crop disease monitoring, crop growth monitoring, surface defect detection, etc. [38; 39].
# Speed-Gradient Adaptive Control for Parametrically Uncertain UAVs in Formation
## 2 Point-Mass Model of UAV Dynamics
### Design of Main Control Loop
In our case, the first minor \(\Delta_{1}=-\alpha_{x}<0\), and the second should satisfy the inequality  
\[\Delta_{2}=ma_{x}\lambda_{x}-0.25\Big{(}1-ma_{x}^{2}\Big{)}^{2}>0. \tag{34}\]  
We can represent inequality (34) in the following form:  
\[\lambda_{x}>\frac{1}{4 ma_{x}}\Big{(}ma_{x}^{2}-1\Big{)}^{2}. \tag{35}\]  
Obviously, with the known values of \(m\) and \(\alpha_{x}\), there is always a value of \(\lambda_{x}\) for which inequality (35) is satisfied. In a similar way, by introducing vectors \(\eta_{y}=[e_{y1}\,y_{y}]^{\rm T}\), \(\eta_{h}=[e_{h1}\,y_{h}]^{\rm T}\) and matrices  
\[H_{y}=\left[ \begin{array}{cc}-\alpha_{y}&0.5\big{(}1-ma_{y}^{2}\big{)}\\ 0.5\big{(}1-ma_{y}^{2}\big{)}&-m\lambda_{y}\end{array} \right] \tag{36}\]  
\[H_{h}=\left[ \begin{array}{cc}-\alpha_{h}&0.5\big{(}1-ma_{h}^{2}\big{)}\\ 0.5\big{(}1-ma_{h}^{2}\big{)}&-m\lambda_{h}\end{array} \right], \tag{37}\]  
it can be shown that  
\[\dot{V}_{y}=\eta_{y}{}^{\rm T}H_{y}\eta_{y}<0,\quad\dot{V}_{h}=\eta_{h}{}^{\rm T }H_{h}\eta_{h}<0, \tag{38}\]  
is negative definite if the conditions  
\[\lambda_{y}>\frac{1}{4 ma_{y}}\Big{(}ma_{y}^{2}-1\Big{)}^{2},\quad\lambda_{h}> \frac{1}{4 ma_{h}}\Big{(}ma_{h}^{2}-1\Big{)}^{2}. \tag{39}\]  
are satisfied. As a result, we obtain the inequality  
\[\dot{V}=\eta_{x}{}^{\rm T}H_{x}\eta_{x}+\eta_{y}{}^{\rm T}H_{y}\eta_{y}+\eta_{ h}{}^{\rm T}H_{h}\eta_{h}<0, \tag{40}\]  
It can be easily shown, cf. [49], that the above inequality implies the following one:  
\[\dot{V}<-\rho V, \tag{41}\]where \(\rho>0\) is a certain constant. From this, it follows that the system (21) and (22) with control (23) is exponentially stable and that control goal (19) is satisfied. This completes the proof.