esa-sceva/satcom-chunk-collection huggingface dataset:
# The Chromosome Number and rDNA Loci Evolution in _Onobrychis_ (Fabaceae)
## 3 Discussion
The genera such as _Trifolium_, _Iris_ and _Citrullus_ show high interspecific polymorphisms in rDNA loci chromosomal patterns [76; 77; 78]. On the other hand, such taxa as _Chenopodium_ and _Daucus_ may exhibit little or no variation in this respect [18; 79]. Usually, 35S rDNA loci tend to be more variable [21], but in analysed _Onobrychis_, chromosomal distribution of both rDNA loci seems equally polymorphic. The repositioning of rDNA loci might indicate chromosome rearrangements such as translocation or inversion, or result from transposon-mediated transposition events [80; 81; 82; 83]. The rDNA loci consist of arrays of evolutionarily conserved repeats and mechanisms based on recombination (e.g., unequal recombination, illegitimate recombination) may play a role in the evolution of high variability in rDNA patterns even among closely related species [84; 85]. A chromosome bearing both types of rRNA gene loci was present in karyotypes of three species from subclade Ia. The evolution of these species was accompanied by duplication of 5S or/and 35S rDNA loci. Thus the most plausible explanation of the new pattern seems to be the insertion of a complementary rDNA array into chromosomes which already possess 35S or 5S rDNA locus. In karyotypes of species with multiple rDNA loci, the localisation of 5S and 35S rDNA sites in one chromosome was relatively often observed (nearly 53% of analysed species), while in species with a single locus of 5S and 35S rDNA,this pattern was rare (approximately 13% [74]). The insight into the rDNA organisation in the diploid ancestors is a prerequisite to hypothesising the evolution of rDNA in their derived polyploids. Although the origin of tetraploid _Onobrychis_ is unknown, eliminating at least one 35S rDNA loci might be suggested in _O. subacaulis_, a tetraploid with only one locus of these genes. All analysed tetraploids from subgenus _Onobrychis_ showed the same pattern of rDNA loci distribution, thus supporting the hypothesis of their close relationship (one or both common or close ancestors). However, rDNA loci in polyploids often undergo various reorganisations. Thus in many cases, the polyploidy does not reveal the expected additive patterns of the putative parental taxa. This phenomenon could be observed as the loss, gain or repositioning of rDNA loci [11; 86; 87; 88; 81].
# High-Resolution, Broad-Range Detection Setup for Polarimetric Optical Fiber Sensors
## 1 Introduction
The measurement range can be extended without degrading resolution by using a Soleil-Babinet compensator [34] or by introducing a step optical delay line in front of the birefringent wedge or the Wollaston prism and replacing the linear detector array with an area detector array [39]. Such modifications increase the cost and complexity of the detection setup, especially through the use of a custom-made, precision step optical delay line. In this paper, an alternative method for detecting the optical path difference is presented. Obviating the need for the step optical delay line, it uses a simple common-path interferometer and an area detector array. Phase measurement is performed in a manner similar to the Vernier scale, offering a broad measurement range and a high measurement resolution and making the presented method another application of the Vernier principle in the field of optical fiber sensors [40; 41; 42].
## 2 Materials and Methods
### Research Methods  
#### 2.3.1 Trend Analysis Method  
To investigate the spatial changes in vegetation cover within the Shanxi section of the Yellow River Basin from 2001 to 2020, a univariate linear trend analysis method was employed. This involved calculating trends for each pixel in the NDVI data on an annual basis [29]. The results provide insights into the spatiotemporal characteristics of vegetation cover changes across the entire study area [30]. The formula for this analysis is as follows:  
\[slope=\frac{n\times\frac{\overset{n}{i}i\times NDVII_{i}-\overset{n}{i}i\times \overset{n}{\underset{i=1}{\sum}}NDVI_{i}}{n\times\overset{n}{i=1}i^{2}-\left( \overset{n}{\underset{i=1}{\sum}}i\right)^{2}} \tag{1}\]  
where _slope_ represents the slope of the NDVI change trend for each pixel, where a positive slope (_slope_ > 0) indicates a growth trend and a negative slope (_slope_ < 0) indicates a degradation trend; \(n\) denotes the number of years during the study period, \(n\) = 20 in this study; and _NDVI_ is the _NDVI_ value of the _ith_ year of the cell. The significance of the change trend is determined using the F-test method, with the significance level indicating  
Figure 1: Geographical location and altitude of study area.  
the confidence associated with the trend rather than the speed or magnitude of the change. The formula for calculating the F-test is as follows:  
\[F =U\times\frac{N-2}{Q}, \tag{2}\] \[U =\sum\limits_{i=1}^{n}{(\hat{y}_{i}-\overline{y})^{2}},\] (3) \[Q =\sum\limits_{i=1}^{n}{(y_{i}-\hat{y}_{i})^{2}} \tag{4}\]  
where \(U\) represents the sum of squared errors for the time series; \(Q\) signifies the sum of squared errors for the time series; \(y_{i}\) is the _NDVI_ value of the _ith_ year; \(\hat{y}_{i}\) is its regression value; \(\overline{y}\) is the average of the _NDVI_; \(n\) is the number of years of study. Based on the results of the \(F\)-test and trend analysis, this paper categorizes the trends in vegetation cover changes as follows: extremely significant improvement (_slope_ > 0, \(p\leq 0.01\)), significant improvement (_slope_ > 0, \(0.01\) < \(p\leq 0.05\)), insignificant improvement (_slope_ > 0, \(p\) > 0.05), insignificant degradation (_slope_ < 0, \(p\) > 0.05), significant degradation (_slope_ < 0, \(0.01\) < \(p\leq 0.05\)), and extremely significant degradation (_slope_ < 0, \(p\leq 0.01\)).
# Precise Orbit Determination of FY-3C with Calibration of Orbit Biases in BeiDou GEO Satellites
## 2 BeiDou Orbit Error Analysis and Mitigation  
### Assessment of BeiDou Precise Orbit Products  
Obviously, the precise orbit and clock products of the BeiDou constellation play an essential role in the accuracy of the BeiDou ranging observation models of POD using BDS measurements. There are several analysis centers (ACs) in the Multi-GNSS Experiment (MGEX) [20] project that provide BeiDou precise products. In our research we employed the GBM final products, which are computed using observations tracked by global ground stations from the International GNSS Service (IGS) [21] and MGEX network at Geo Forschungs Zentrum (GFZ) data processing and analysis center [22]. During July 2017 for BeiDou satellites the GBM products provide daily precise orbits every 5 min and clock offsets every 30 s. The daily static and kinematic PPP using only dual-frequency BDS observations with GBM products can accomplish average position errors of 2 cm and 17 cm, respectively [23]. Consequently, the GBM products are robust enough to conduct experiments for the POD of FY-3C only with BDS observations.  
DBDs of BeiDou orbits are introduced to evaluate the orbit quality. This is because the assessment method using orbit overlap differences is no longer applicable since only the middle day of the three-day arc length solution is published by GFZ. The missing satellite position at the last moment (also known as midnight) is obtained using high precision orbit prediction within a five minutes window. It is believed that the precision of propagated BeiDou satellite orbits can achieve several centimeters over such a short time interval.  
The statistical RMS of BeiDou orbit DBD series during July 2017 are listed in Table 1. The 3D RMS values of DBDs for GEO, IGSO, and MEO are within 1.66-8.36 m, 0.16-0.31 m, and 0.17-0.18 m, respectively. This shows that the orbit consistencies of BeiDou GEO are several times worse than those of IGSO and MEO.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**RMS of Orbit Day Boundary Discontinuities (m)**} \\ \cline{3-6}  & **Sat. ID** & **Along-Track** & **Cross-Track** & **Radial** & **3D** \\ \hline \multirow{5}{*}{GEO} & C01 & 1.10 & 1.24 & 0.09 & 1.66 \\  & C02 & 1.76 & 1.02 & 0.83 & 2.20 \\  & C03 & 1.59 & 1.23 & 1.16 & 2.32 \\  & C04 & 4.71 & 2.83 & 1.96 & 5.83 \\  & C05 & 7.87 & 0.88 & 2.67 & 8.36 \\ \hline \multirow{5}{*}{IGSO} & C06 & 0.14 & 0.07 & 0.10 & 0.19 \\  & C07 & 0.12 & 0.26 & 0.12 & 0.31 \\  & C08 & 0.09 & 0.06 & 0.11 & 0.16 \\  & C09 & 0.14 & 0.07 & 0.21 & 0.26 \\  & C10 & 0.08 & 0.11 & 0.20 & 0.25 \\  & C13 & 0.06 & 0.10 & 0.13 & 0.17 \\ \hline \multirow{5}{*}{MEO} & C11 & 0.12 & 0.07 & 0.12 & 0.18 \\  & C12 & 0.14 & 0.07 & 0.05 & 0.17 \\ \cline{1-1}  & C14 & 0.17 & 0.06 & 0.04 & 0.18 \\ \hline \hline \end{tabular}
\end{table}
Table 1: RMS of DBDs in along-track, cross-track, radial, and 3D for GBM orbits during July 2017.
# Bayesian Modeling of Traffic Accident Rates in Poland Based on Weather Conditions
## 3 Results
### Posterior Predictive Checks
#### 3.1.2 Model 2  
The predictive distribution of Model 2, as presented in Figure 15, demonstrates improvements over Model 1, with better alignment between observed and predicted frequencies across the accident count spectrum. The scatter plot shows tighter clustering around the identity line, particularly for counties with moderate accident counts, suggesting enhanced predictive performance.  
Following the same procedure, we evaluated the performance of the multifactorial model on predicting yearly accident rates. The results, presented in Figure 16, show a similarly strong correlation between observed and predicted values, with a slightly tighter clustering of points around the identity line compared to Model 1. The performance evaluation yielded a coefficient of determination of \(R^{2}=0.833\), indicating a marginal but positive improvement over the socioeconomic-only model. This suggests that, while socioeconomic factors are the primary drivers of variance in accident rates, the inclusion of weather-related variables provides additional explanatory power, resulting in a more accurate and comprehensive model.  
Model 2, as presented in Figure 17, maintains the stable intercept estimate while adding explanatory power through additional predictors. The socioeconomic factors retain their directional effects from Model 1, with passenger car ownership continuing to show the strongest positive relationship with accident counts, followed by population density, while road density maintains its negative association. The inclusion of weather predictors (mean temperature, mean humidity, and total precipitation) enhances the model's capacity to account for regional variations in traffic accidents, particularly in counties with distinctive climatic characteristics that may influence accident patterns beyond the core socioeconomic factors.  
Figure 15: Model 2 posterior predictive checks: (**a**) Comparison of observed accident frequency distribution (blue) versus model-predicted distribution (red), showing similar alignment compared to Model 1. (**b**) Scatter plot of observed versus predicted yearly accident counts with the identity line (red dashed). The coefficient of determination (\(R^{2}=0.813\)) indicates a slightly higher model fit compared to Model 1.
## 4 Proposed Scheme
### Integration into Distributed Applications
#### 4.7.2 IoT and Device Authentication  
In an IoT network, devices can use the proposed scheme to authenticate themselves to other devices or a central server. For example,  
* The IoT device generates the message \(M\): the message may contain data about sensor performance, device status, or other relevant information.
* The message \(M\) is signed using the private key \(K_{priv}\): the device applies the scheme described above to produce the signature \(\sigma\).
* The message and signature are sent to the server: the server validates the signature using the public key \(K_{pub}\), guaranteeing that the message comes from an authorized device.  
The benefits of using this scheme in distributed applications are evident in several ways. Immutability is one of the main advantages, since validated signatures are permanently stored in the blockchain. This property guarantees data integrity, preventing any possibility of subsequent modification, which gives them high reliability for critical applications.  
Confidentiality is ensured through the flexibility offered by the \(T\) parameter, which allows the level of anonymity to be adjusted. Thus, IoT devices or parties involved in transactions can benefit from the adequate protection of their identity, reducing the risks of exposing sensitive data, like in collected data storage in databases used in [34; 35].  
In addition, blockchain improves the auditability of the system, keeping a verifiable history of all transactions and signatures. This facilitates subsequent inspection and contributes to the transparency of the entire process, which is particularly valuable in the context of strict regulations or subsequent investigations.  
The scheme also stands out for its scalability, which is designed to work efficiently in distributed systems. The simplicity of cryptographic verification operations allows it to be adapted to large networks and high transaction volumes, without compromising performance or security.
## 2 The Method for Self-Healing Efficiency Estimation
### Adaptive Filter Preparation  
A special filter in the wavenumber-wavenumber domain is designed. It allows for the extraction of information about local Lamb wave changes, which may be used for damage identification and assessment.  
Figure 1: Proposed nondestructive self-healing efficiency estimation method diagram.  
The adaptive filter is prepared only once for the whole process. As reference data, pristine condition sample measurements may be used. However, this method works effectively also when current state data is used for filter preparation, which was presented in [19; 20]. A step-by-step description is given in the following subsections.
## 4 Phase Shift Optimization
### Received Power Maximization  
The optimal phase shift of RIS should be designed to align the phases of direct and cascaded links [8]. Firstly, the \(K\) RISs without HWI; then, the optimal phase shift for the \((m,n)\)-th element of the \(i\)-th RIS can be designed as \(\phi_{i_{m},i_{n}}(t)=2\pi f_{c}\big{(}\tau_{0}(t)-\tau_{i_{m},i_{n}}(t)\big{)}\leq 0\). However, in practical scenarios, the feasible phase range is \([0,2\pi)\), so we have  
\[\phi_{i_{m},i_{n}}(t)=2\pi f_{c}\big{(}\tau_{0}(t)-\tau_{i_{m},i_{n}}(t)\big{)} +2\pi k_{i_{m},i_{n}}(t), \tag{29}\]  
where \(k_{i_{m},i_{n}}(t)\) is the additional full carrier signal period delay in the \(m,n\)-th element of the \(i\)-th RIS at time \(t\)[14], which affects the received power. Therefore, maximizing the received power and minimizing the delay spread simultaneously can be transformed into the multi-objective problem as  
\[\text{(P1)}:\;\max_{\forall(i_{m},i_{n}):k_{i_{m},i_{n}}(t)}\;\bigg{[}P_{R}(t ),-T(t)\bigg{]}, \tag{30}\]  
where \(P_{R}(t)=|y^{\text{HWI}}(t)-w|^{2}\). We will solve P1 in the following section.
## 4 Results and Discussions
### Sensitivity Analysis  
#### 4.1.1 Cost Rate of Exergy Destruction  
The effect of the evaporating pressure on the costs of exergy destruction for the components in the three configurations evaluated is shown in Figure 4. For heat exchange equipment, especially evaporators, the cost rate of exergy destruction shows a significant decrease as evaporating pressure increases, which is due to the decrease in heat transfer irreversibilities. This phenomenon suggests the existence of an optimal operating condition in terms of evaporating pressure in order not to incur high-pressure ratios without an increase in the energy generated by the system. The cost rate of exergy destruction for evaporators decreases in a small range from 0 MPa to 1 MPa and is maintained with an asymptotic tendency for pressures up to 4 MPa.  
The increasing turbine efficiency is reflected in greater net power generated, as well as an increase in the overall exergetic efficiency of the system. However, these variations are such that the net effect is a decrease or constant behavior on the cost rate of exergy destruction for all components of the SORC and DORC configuration as shown in Figure 4d,f respectively. A percentage decrease of 80.6% in the cost of exergy destruction for the RORC configuration was presented for the turbine, as the efficiency of the turbine increased occasionally. This is mainly due to a considerable decrease in the destroyed exergy of the turbine, which constitutes about 18.1% of the total destroyed exergy of the RORC configuration. This trend is the same for the three configurations studied.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Component**} & \multicolumn{6}{c}{**Configurations of WHR Systems Based on ORC**} \\ \cline{2-7}  & \multicolumn{2}{c}{**SORC**} & \multicolumn{2}{c}{**RORC**} & \multicolumn{2}{c}{**DORC**} \\ \cline{2-7}  & \(\mathbf{r_{K}}\) & \(\mathbf{f_{K}}\) & \(\mathbf{r_{K}}\) & \(\mathbf{f_{K}}\) & \(\mathbf{r_{K}}\) & \(\mathbf{f_{K}}\) \\ \hline ITC1 & 0.44 & 0.53 & 0.35 & 0.91 & 0.68 & 0.99 \\ B1 & 34.54 & 0.84 & 23 & 0.87 & 5.87 & 0.99 \\ ITC2 & 0.48 & 0.54 & 0.35 & 0.64 & 3.55 & 0.99 \\ T1 & 1.50 & 0.89 & 1.55 & 0.90 & 0.51 & 0.99 \\ ITC3 & 1.41 & 0.18 & 0.93 & 0.41 & 0.08 & 0.99 \\ B2 & 3.13 & 0.91 & 2.89 & 0.90 & 2.63 & 0.99 \\ RC & - & - & 0.78 & 0.56 & - & - \\ T2 & - & - & - & - & 0.59 & 0.99 \\ B3 & - & - & - & - & 2.17 & 0.99 \\ ITC4 & - & - & - & - & 0.34 & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Relative cost difference and exergoeconomic factor by a system component.
## 3 Results
### The Effect of the Duration of ES on Reference Gene Activity  
Part of this study was to determine if increasing the duration of the ES over five daily applications had an effect on the expression of the six potential reference genes in our study. The \(\Delta\Delta C_{T}\) method was used to calculate the fold changes, for which a \"gold standard\" reference gene should remain as close as possible to 1.0 under all experimental conditions versus the unstimulated control cells and another reference gene. Finding a reference gene pair is particularly advantageous as this corrects for any inaccurate RNA quantification steps and for differences in the efficiency of the reverse transcription process. Furthermore,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Gene** & **Reference** & **Product** & **Slope** & **Efficiency** & **y-Intercept** & **R\({}^{2}\)** \\
**Name** & **Reference** & **Length** & & **(\%)** & **y-Intercept** & **Value** \\ \hline PPIA & NM\_001300981.2 & 81 & \(-\)3.184 & 106.10 & 27.07 & 0.999 \\ \hline YWHAZ & NM\_001135699.2 & 110 & \(-\)3.250 & 103.09 & 26.45 & 0.999 \\ \hline \multirow{3}{*}{18S} & Universal Probe & & & & & \\  & Library, 18S & 114 & \(-\)3.184 & 106.10 & 15.67 & 0.999 \\  & probe 77 & & & & & \\ \hline B2M & NM\_004048.4 & 219 & \(-\)3.311 & 100.46 & 26.69 & 0.997 \\ \hline GAPDH & NM\_001357943.2 & 171 & \(-\)3.367 & 98.15 & 26.54 & 0.998 \\ \hline ACTB & NM\_001101.5 & 189 & \(-\)3.495 & 93.25 & 27.72 & 0.999 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Standard curve parameters and primer efficiencies of each potential reference gene.  
Figure 1: Standard curves of the threshold cycle value (\(C_{T}\)) versus the log of the total cDNA (ng) for the 6 potential reference genes considered in this study.  
it was necessary to assess if the fold changes after 60 m/d of ES were statistically different from that of the 30 m/d group or the control group. The fold changes of PPIA, 18S, and GAPDH were normalized to YWHAZ in unstimulated cells or in cells exposed to both durations of ES (Figure 3). The increases observed in 18S after 30 and 60 m/d were not significant, nor was the rise in expression of GAPDH after 60 m/d. For all three of the reference genes tested, there were no significant differences in expression between the ES durations of 30 m/d and 60 m/d. The clear similarity observed in response for both the 30 m/d and 60 m/d groups provided the rationale to continue focusing solely on the expression changes in cells stimulated for 60 m/d. It was also assumed that a reference gene exhibiting stable, consistent expression after 60 m/d of ES would be likely to also remain stable under a shorter period of ES of the same electric field strength.
## 4 Discussion  
In this work, the development of a method based on the use of neural networks for the detection and location of faults in the FiFi system of a ship has been presented.  
This neural network-based system uses the data collected from the sensors of the firefighting system to determine whether the series of pressure data corresponds to normal operation or to the possible rupture of a section of pipe in the system. In addition, the system makes it possible to determine the area of the vessel in which the corresponding breakage has occurred.  
To do this, the system has been trained using real data from a ship's FiFi system. Data of normal operation as well as breaks in the pipes of the system have been collected. These data have been labeled for use in training. The results obtained in the training of neural networks for detection and localization of anomalies have been satisfactory.  
Data from pressure sensors in the FiFi system must be collected at a higher sampling rate than normally used for system monitoring. However, it has been demonstrated that modifying the sampling rate is feasible. In the case of use considered, with a sampling frequency above 10 Hz, the results obtained were satisfactory, but a sampling rate above 20 Hz is recommended. After analyzing different alternatives in training, it can be affirmed that it is possible to detect and locate the anomaly with a sample size of 10 s.  
The data utilized in this study were processed as received from the ship's control system, with modifications made only to the sampling frequency and significant figures. Despite potential challenges, such as noisy data, varying sampling rates, or missing values, the inherent robustness of our neural network models allowed for effective fault detection and localization. It is noteworthy that the noise present in the signals did not pose significant issues during the fault detection and localization processes. This illustrates the practicality of our approach under realistic operational conditions and emphasizes the effectiveness of the methodology even with minimal preprocessing.  
Future work will focus on expanding the system by continuously collecting onboard data, enabling the system to learn from new fault scenarios and further improve its fault localization capabilities. Additionally, deploying this system on different ships may require retraining of the neural network to accommodate varying ship configurations and sensor setups, ensuring the model's adaptability to different operational conditions. The integration of hybrid models that combine analytical methods with neural networks will also be explored to enhance fault detection and localization in shipboard systems.  
The developed system will be implemented in the ship's digital twin, providing it with intelligence and allowing autonomous detection of faults. Although this methodology has been validated, employing this development on another ship could require retraining of the neural network. This is due to differences in ship configurations and sensor setups that may necessitate the model to adapt to new conditions and ensure accurate fault detection and localization.