esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### DNA-Binding Studies  
Electronic absorption titration is one of the easiest methods that helps to predict the type of binding mode of compounds with CT-DNA [41]. In this experiment, the concentration of the metal compounds has been maintained with the variation of the DNA concentration in the range of 2 \(\times\) 10\({}^{-6}\)-7 \(\times\) 10\({}^{-6}\) M.  
The free Schiff base ligand spectrum did not show any observed shift after the addition of the DNA nevertheless it showed a reduction in the absorption intensity (hyperchromic), suggesting a weak interaction between the ligand and DNA [42]. The intrinsic binding constant (K\({}_{\text{b}}\)) for the ligand with CT-DNA was 1 \(\times\) 10\({}^{6}\) M\({}^{-1}\).  
In general, for the metal complexes, the experiments showed two performances for the metal complexes with increasing CT-DNA concentration, hyperchromic with blue shift or hypochromic with redshift. The complexes of Zn(II) and Mn(II) showed a shift from the \(\lambda_{\text{max}}\) to more than 5 nm. This could indicate the change of the distance between the two helix structures in DNA [41], Figure S4a. The absorption spectra of Ni(II), Cu(II), and Co(II) complexes showed a hypochromic mode with a blue shift which indicated the change in the DNA confirmation or the damage that occurred to the DNA structure [37] Figure S4b.  
Figure 8: The 3D HOMO and LUMO plots of the ligand and the bivalence metal complexes.  
The binding constant (K\({}_{\text{b}}\)) for the complexes were presented in Table 3. Interestingly, all the complexes gave (k\({}_{\text{b}}\)) values ranging from 8 \(\times\) 10\({}^{5}\) to 3.33 \(\times\) 10\({}^{5}\). Comparing these values with Ru (III) complexes [43], we found that the values were assumed to be intercalation binding types. As observed from Table 3, the Ni(II) complex had the highest binding constant value, followed by the Zn(II) complex.
## 2 Preliminaries  
### Notations  
In this study, we adopt the following notations: \(R^{n}\) represents the \(n\)-dimensional Euclidean space; \(\mathbf{1}_{N}\) denotes the \(N\)-dimensional all-ones column vector; the set of \(n\times m\) real matrices is defined as \(R^{n\times m}\), where \(I_{n}\in R^{n\times n}\) denotes the identity matrix; \(\lambda_{max}(P)\) and \(\lambda_{min}(P)\) denote the maximal and minimum eigenvalues of matrix \(P\), respectively; \(Diag\{\cdot\}\) refers to the diagonal matrix; and \(\otimes\) is the Kronecker product. The Euclidean norm for vectors or the induced norm for matrices is defined as \(\|\cdot\|\).
## 2 Machine Translation Approaches
### Neural Machine Translation  
NMT, put simply, uses a single neural network to directly translate a source sentence to its intended target sentence [10]. Both SMT and NMT rely on a large corpora of pairs of sentences in the source language and their corresponding translated target sentences, but NMT implements continuous vector representations of linguistic units, unlike SMT, which implements discrete symbolic representations [11], i.e., propositions represented as discrete objects of varying sizes [12]. [PERSON] et al. used continuous representations for words and were successful in showing results that overcame the problem of scarcity in which the morphological, semantic, and syntactic properties of a language were represented [13].  
Although there are other language models in the literature such as recurrent neural networks (RNNs) and connectionist temporal classification (CTC) [14], this work focuses on discussing the encoder-decoder architecture in this section as it is the base model for most NLP sequence-to-sequence use-case models, especially in NMT [15]. It is also used in NMT systems, which will be discussed in Section 3. The encoder-decoder architecture is a standard method used to learn the conditional distribution of a sequence given another sequence. The encoder and decoder are trained together in order to maximize the conditional log-likelihood [13], and the encoder-decoder utilizes a series of recurrent neural network cells.  
A successful example can be seen in [16], where a transformer model that uses the attention mechanism to determine which parts of the input source text are of the most importance, to extract mutual dependencies between the input and the output, was implemented. The attention mechanism refers to a function that maps a query and a set of key-value pairs, represented as vectors, to an output, which is also represented as a vector [16]. To compute the output, first, a compatibility function of the query, along with the designated key, calculates a weight that is assigned to each of the values. The weighted sum of the values is then presented as the output [16].  
The architecture of the model is made up of an encoder and a decoder. The encoder is composed of six identical layers, where each layer has two sub-layers: a multi-head self-attention mechanism and a fully connected feed-forward network. A residual connection is then applied around each of the two sub-layers, which is followed by layer normalization. Similarly, the decoder is made up of six identical layers with a third sub-layer inserted that takes the output of the encoder and performs multi-head attention on it [16].
## 2 Material and Methods
### Data Analysis  
Results were analysed as a function of dietary PRSE level using regression analysis in Graphpad Prism (GraphPad Software, San Diego, CA, USA, V. 7.03). Data were tested against increasingly complex regression models, starting with the null hypothesis that  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pathogen Target** & **Primer Detail (5\({}^{\prime}\)-3\({}^{\prime}\))** & **DNA/RNA Genome** & **Reference** \\ \hline \multirow{3}{*}{HDV/HPV} & qF (4440)-CTA CTC CAA TGG AAA CTT CTG AGC & & \\  & qR (4571)-GGC ACT TCC TGT ATT TTT CCC G & DNA & [30] \\  & qProbe (4471)-TAC CGC CGC ACC CCA GCA GC & & \\ \hline \multirow{3}{*}{IHHNV/PstDV} & qF-CCT AAA AAA AAC AGT CCA GAA TAT GAC & & \\  & qR-TCA TCG TCA AGT TTA TTG ACA AGT TC & DNA & [31] \\  & qProbe-CTC CAA CAC TTA GTC AAA & & \\ \hline \multirow{3}{*}{Pir-A} & qF (Han)-TTG GAC TGT GGA ACC AAA CG & & \\  & qR (Han)-CCA CCC CAT TGG TAT TGA ATG & DNA & [32] \\  & qProbe (Han)-AGACAGCAAACATACACCTATCCCGGA & & \\ \hline \multirow{3}{*}{WSSV} & qF (OIE)-TGG TCC CGT CCT CAT CTC AG & & \\  & qR (OIE)-GCT GCC TTG CCG GAA ATT A & DNA & [33] \\  & qProbe (OIE)-AGC CAT GAA GAA TGC CGT CTA TCA CAC A & & \\ \hline \multirow{3}{*}{GAV} & qF-GGG ATC CTA ACA TCG TCA ACG T & & \\  & qR-AGT AGT ATG GAT TAC CCT GGT GCA T & RNA & [34] \\  & qProbe-TCA GCC GCT TCC GCT TCC AAC G & & \\ \hline \multirow{3}{*}{When-2} & qF-GGC TCT TTAGCC TGA CTT TAT CT & & \\  & qR-GCA GAG GAC AGG AAG TGA TTA & RNA & This publication \\  & qProbe-ACC TCA CTTG TCT GAG TTC TGC ACA & & \\ \hline \multirow{3}{*}{YHV-7} & qF-CAT CCA ACC TAT CGC CTA CA & & \\  & qR-TGT GAA GTC CAT GTC AAC GA & RNA & [35] \\  & qProbe-CAA CGA CAG ACA CCT CAT CCC TGA & & \\ \hline \multirow{3}{*}{Dicer-1} & qF-TGG TAC CAA AGT CAC CCA TTA G & & \\  & qR-ACC TTC CCA TCA ACA AGA CGT T & & \\ \cline{1-1}  & qProbe-AACC CAG AAA CAG CCA AAT & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Primer and Probe sequences used in this study.  
PRSE had no effect on the outcome (horizontal line). Models were checked with inbuilt options for normality of residuals using the D'Agnostino and Pearson omnibus test, for curve deviation from data points using the replicates test, and for homoscedasticity. Models that failed one or more of these checks were rejected in favour of more complex models. Models with an adjusted R\({}^{2}\) < 0.2 were rejected in favour of simpler models.
# Assessment of Affordable Real-Time PPP Solutions for Transportation Applications
## 2 Materials and Methods
### Kinematic RT-PPP Campaign Setup  
To explore the accuracy of our proposed affordable multi-GNSS RT-PPP scenario for vehicular applications, a kinematic survey campaign was executed in a suburban environment in New Aswan City, Egypt, on day of the year (DOY) 60 in 2024. In our kinematic trial, we used three Trimble R4s geodetic receivers; two were mounted on the vehicle with the cost-effective ZED-F9P device (Figure 1a), while the other was located at the base station (Figure 1b). Figure 2 illustrates the vehicular trajectory layout in which the multi-GNSS datasets were collected using the ZED-F9P module over a period of 1 h with 1 s observation intervals. The number of tracked GNSS satellites and the position dilution of precision (PDOP) values are plotted in Figure 3. The tracked satellite number significantly increases when the combined GPS/GLONASS/Galileo is used, reaching about 20 satellites on average; meanwhile, the PDOP is dramatically improved.  
Figure 1: Base and rover receiver setup.  
Figure 3: _Cont_.  
Figure 2: Layout of vehicle’s path.  
The datasets were then processed employing a variety of processing strategies, including RT-PPP and RTK using GPS, GPS/GLONASS, GPS/Galileo, and GPS/GLONASS/Galileo observations. Although our ZED-F9P is a dual-frequency multi-GNSS device, only single-frequency BeiDou observations were gathered; as a result, we exclude BeiDou observations from our proposed solutions. To consider the orbit and clock errors, the instantaneous BKG, CNES, and WHU SSR correction products were applied [51]; conversely, quad constellation broadcast ephemeris (i.e., BRDM) products [52] were adopted for the RTK processing model.  
To develop a reference solution scenario, GNSS measurements were collected through two geodetic-grade Trimble R4s rover devices synchronous with one base receiver located at a reference station; hence, the datasets are processed using the traditional differential solution. The attained RT-PPP and RTK positioning solutions from the ZED-F9P module (i.e., \(\mathrm{E_{u}}\) and \(\mathrm{N_{u}}\)) were compared with the differential kinematic GNSS position estimates from the two geodetic receivers utilizing the configuration and formula provided in Figure 4. The RT-PPP, RTK, and differential processing solutions were obtained through the Net-Diff GNSS V. 1.13 software package [53]. Topospheric delay was estimated using the Saastamoinen tropospheric model [54] and dry/wet Vienna mapping functions (VMF1) [55]. In addition, a Kalman filter was adopted for the parameter estimation procedure. It should be pointed out that the ambiguity float solution was adopted in our processing strategies. A summary of each processing strategy is provided in Table 1; in addition, a flowchart illustrates the processing and evaluation scheme in Figure 5. It should be mentioned that the computed coordinates are part of the projected WGS84/UTM36N system.  
Figure 4: Reference and estimated receiver setup.  
Figure 3: The number of observed satellites and their PDOP values.
## 4 Simulation and Results
### Simulation Constraints  
In the simulation, the environment was set up as follows: We created 10 pages of original data \(u[k]\). Each page was a 1 \(\times\) 1,440,000 bit block and was converted into a modulated signal \(a[j,k]\) with a size of 1200 \(\times\) 1200. We used the first page to estimate the GPR target and equalizer during the training process. The remaining pages were used to test the model. The signal stream is described in Section 3. We defined the channel SNR as 10 log\({}_{10}(1/\sigma^{2})\), where \(\sigma^{2}\) is the additive white Gaussian noise power. AD is 3 Tb/in\({}^{2}\) (i.e., \(T_{x}=T_{x}=\) 14.5 nm) [13]. Table 3 shows the physical parameters of the simulation setup.  
The channel coefficient without the TMR effect (\(\Delta_{off}=0\)) and media noise (\(\Delta_{x}=\Delta_{z}=0\)) is given as follows:  
\[\mathbf{H}=\begin{bmatrix}0.0824&0.3876&0.0824\\ 0.2125&1&0.2125\\ 0.0824&0.3876&0.0824\end{bmatrix}. \tag{22}\]
# Chaos Based Cryptographic Pseudo-Random Number Generator Template with Dynamic State Change +
## 3 Generator Implementation and Testing
### Throughput Test Results  
The throughput analysis was done on a Lenovo 920-13 IKB convertible, with an Intel Core i7-8550U processor having a maximum frequency of 4 GHz, 16 GB Dual-Channel SODIMM DDR4 at 2400 MHz, running Linux Mint 19.2 and using g++ version 7.4.0. The results are shown in Figure 9.  
From the three existing algorithms, ChaCha8 is the fastest when compiled with Intel AVX extensions. This feature allows the processor to generate 32 bytes of random data at once using the 256-bit wide XMM registers. To test this, the generator function was called for each output byte (instead of generating an entire 64-byte block at a time), and as a result, the performance plummeted from 390 MB/s to 12.5 MB/s. The Mersenne Twister implementation, as well as any chaos based pRNG, can not make use of SIMD extensions, as the computation is recursive and as such, inherently sequential. As for the Adaptive Chirikov Map, because it computes four \(sin\) values for each random bit, it is significantly slower than all others, being able to output only 410 kB of random data each second.  
Analyzing several of the proposed configurations, it can be seen that the performance of the generator depends strongly on the selected entropy builder and the parameter change interval. However, compared to the non-CSPRNG Mersenne Twister, there is a significant performance improvement when using \(E_{1}\) as an entropy builder. The best performance is given, as expected, by the generalized Henon Map without any entropy builder, but that configuration has a low percentage of tests passed (\(\approx\)50%). A good trade-off between tests passed and speed is the Henon3D with the \(E_{1}\) builder and 35 iterations between parameter changes. It provides an almost 300% increase of performance over the Mersenne Twister with only a 10% decrease in percentage of seeds with no test fails. A safer approach would be to use either \(E_{2}\) with a change interval of 10 or \(E_{1}\), \(E_{2}\) with a change interval of 15, which pass the same amount of tests as Mersenne Twister but with about 30% increase in throughput. Compared to AVX-accelerated ChaCha8, the performance needs to be improved. This is an important target for future work and might be achieved by using several chaotic maps in parallel.  
Figure 8: Monte Carlo analysis of seeds and failed tests for several generator configurations.
## 4 Discussion  
### Spatiotemporal Dynamics in GPP and GPP-LUE Models' Performance Reliability  
The combined of vegetation indices and climate factors have been often used to simulate seasonal and annual GPP dynamics [17]. The vegetation phenology APAR at canopy level and APAR at chlorophyll were highly suggested to separate GPP estimates [6;44]. The distinctions  
Figure 9: Spatial correlations between GPP and LST during 2001–2020. (**a**) MOD17A2H, (**b**) GPP-VPMCanopy, (**c**) GPP-VPMChl, and (**d**) GPP-CASA.  
between APAR canopy and APAR chlorophyll reactions and the PAR absorbed photosynthetic components were considered as fundamental components and main datasets to run the employed LUE models (Section 2.3.2). Given that, the ecosystems characterized by high vegetation cover (high EVI and NDVI values) indicated high GPP values [8; 9; 10; 19]. This was supported by the results of this study where high values of GPP were found in the lower plateau of the Democratic Republic of Congo (DRC) and the areas closer to Indian Ocean coastlines, while the non-vegetated areas such as high mountain regions and the Kalahari Desert were characterized with lower GPP values (Figure 4).  
The performance of four GPP-LUE models (CASA, VPM canopy, VPM Chl and MOD17) were highly consistent in their predictions of dynamics and magnitude changes in seasonal and annual GPPs based on their comparison with the field based GPP observations (GPP-EC) (Figure 5). According to the results, CASA and VPM Canopy models indicated more reliability to track the magnitude annual and seasonal GPP dynamics. As supported by the comparison of GPP-CASA and GPP-VPM canopy simulations against the field based GPP observations (GPP-EC), these two LUE models indicated strong positive relationships (R\({}^{2}\) = 0.88 and R\({}^{2}\) = 0.85) with GPP-EC (Figure 5).  
Although good performances of these three approaches, GPP-MOD17 was claimed by several researches to indicate a substantial lower estimate compared to other LUE models [16]. This was supported by the obtained lower correlation (R\({}^{2}\) = 0.59) between GPP-MOD17 and GPP-EC. Consequently, the analysis of simulation discrepancies RE < \(\pm\)10% and Lower BIAS values) of seasonal and inter-annual sum of predicted GPPs and GPP-EC and indicated GPP-CASA and GPP-VPM Chl were among the approaches characterized by more smaller RE and bias values (Figure 5b,c). However, despite the obtained imbalance in GPP simulation capabilities, these evaluated LUE models indicated reasonable estimates of GPP dynamics in the GLR-SSA.
## 5 Results
### Pointing Error Angle Simulation
If the orbit control is not performed when the satellite passes through the Earth station, the pointing error angle will become larger and larger, which will have an incalculable impact on the inter-satellite terahertz communication system, and even the communication will be interrupted. It can be seen from Figure 11 that the azimuth angle has little influence on the pointing error angle, and the pitch angle has a  
Figure 9: Schematic diagram of stk simulation scene. great influence on the pointing error angle. When the pitch angle is close to 90 degrees, the pointing error angle increases sharply. In addition, when the azimuth and pitch angles are equal in magnitude, the pointing error angle is the smallest.
# Spatial-Temporal Self-Focusing of Partially Coherent Pulsed Beams in Dispersive Medium
## 3 Spatial-Temporal Self-Focusing of PCP Beams in Dispersive Medium  
In this section, the propagation properties of the proposed PCP beams in dispersive medium are investigated. Water and air are chosen as two common examples of dispersive medium. The evolution behavior of the proposed PCP beams upon propagation is explored by detailed numerical simulations. In the following calculation, the pulses and medium parameters are chosen to be \(w_{0}=1\) cm, \(\sigma=1\) cm, \(T_{0}=6\) ps, \(T_{\mathrm{c}}=4\) ps, \(\omega_{0}=2.355\) rad/fs and \(\beta_{2}=24.88\) ps\({}^{2}\) km\({}^{-1}\) unless different values are specified.
## 5 Statistical Analysis of Residual Displacement Ratios
### Effect of Frequency Content  
The frequency content of mainshocks and aftershocks may exhibit differences due to variations in their source mechanisms (e.g., rupture complexity and scale), propagation-path effects (e.g., attenuation or scattering), and post-seismic stress redistribution. Site-specific amplification can further modify these spectral signatures. Empirical studies [25; 29; 56] have explicitly identified the significant difference in frequency content between the mainshock and the aftershock. However, the issue concerning the effect of such difference on residual displacements has not been examined. To this end, the definition of the mean period proposed by [PERSON] et al. [57] is adopted to quantify the frequency content of sequences. In accordance with the relation between the mean period of mainshock (\(T_{\text{m,ms}}\)) and aftershock (\(T_{\text{m,as}}\)), all the seismic sequences are divided into two subgroups: (a) the mean period of aftershock is longer than that of mainshock (\(T_{\text{m,as}}>T_{\text{m,ms}}\)), corresponding to 52 seismic sequences; (b) the mean period of aftershock is shorter than that of mainshock (\(T_{\text{m,as}}<T_{\text{m,ms}}\)), corresponding to 92 seismic sequences.  
The residual displacement ratios, \(C_{\text{resq}}\), corresponding to the two subgroups are compared in Figure 11, where two levels of post-yield stiffness ratios (\(r=0.0\) and \(0.1\)) are considered. As can be observed from Figure 11a, except for several short natural periods, seismic sequences with \(T_{\text{m,as}}>T_{\text{m,ms}}\) generally produce larger \(C_{\text{resq}}\) than those with \(T_{\text{m,as}}<T_{\text{m,ms}}\). This is largely attributed to the fact that the fundamental vibration period of structures will generally be elongated against strong mainshock, and \(T_{\text{m,as}}>T_{\text{m,ms}}\) greatly increases the possibility of the resonant response of damaged structures to aftershock. Such observation, however, does not hold true for high levels of post-yield stiffness ratio. As shown in Figure 11b, the \(C_{\text{resq}}\) has less to do with frequency contents for \(r=0.10\).  
Figure 11: _Cont._  
Figure 10: Residual displacement ratios for different duration: (**a**) \(r=0.00\); (**b**) \(r=0.03\); and (**c**) \(r=0.10\).
# ERS-1/2 and Sentinel-1 SAR Data Mining for Flood Hazard and Risk Assessment in Lima, Peru
## 3 Results
### Flooding
#### 3.1.1 Flooded Areas  
The F1 polygons concentrate mainly in the district of Lurigancho-Chosica. This is mainly due to the large residential and commercial areas that were flooded in mid-2017. In particular, Figure 8 shows the quarter in Carapongo lying on the right bank of the Rimac River, east of Av. Las Torres that runs along the bridge crossing the river not far from the Cascada de Huachipa. The RGB composite (Figure 8a) shows patterns of cyan color in correspondence with the area affected by the flooding event. Consequently, in that area the ratio map shows an increase of radar backscatter (Figure 8b). This outcome can be explained accounting that floodwater was not clear water, but rather contained solid material transported by the strength of the water flow. Field photographs published in the media show that, alongside wide flooded areas, there were several areas with debris accumulation, rubble, uprooted trees, damaged cars and heavy vehicles. This has also likely contributed to increase both the surface roughness and the soil moisture locally and, therefore, increase the radar backscatter.  
This interpretation is also corroborated by comparing two very-high resolution optical images taken on 22/02/2017 (pre-event; Figure 8c) and 21/03/2017 (post-event; Figure 8d) and accessed through Google Earth. It is evident that the roads of the whole quarter were particularly muddy due to inundation. Furthermore, the careful inspection of the photographs capturing the Carapongo area mentioned above suggests that the effect of double-bounce backscattering resulting from the combination of inundation depth and the local urban layout, typology, geometry of buildings and road networks, may have contributed to the observed increase of radar backscatter. As recalled in Section 2.2.3, this complies with the SAR imaging theory in flooded anthropogenic environments, where the inundation depth and the height of the non-flooded portion of the building are sufficient to create the dihedral conditions for backscattering [68,69].  
Figure 7: Distribution of the areas of the Rimac River basin where change patterns in the radar backscatter that are likely related with flooding were observed and mapped. Where visible, dashed lines indicate sections of rivers where no change patterns have been found (e.g., the narrow urban canyon downstream displayed in Figure 1f).  
It is worth noting that all the 15 F1 changes were found on alluvial deposits and within urban areas. Of these areas, those closest to the riverbank have a B-class slope (\(5^{\circ}<\beta\leq 10^{\circ}\)), while some of those that are more distant (500 m) are located on a D-class slope (\(15^{\circ}<\beta\leq 20^{\circ}\)). Therefore, the satellite evidence that these areas were flooded confirms the local susceptibility to this hazard, as expected for areas located in lowlands and the alluvial valley.