esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of High Water Levels in Lake Baikal on Rare Plant Species in the Coastal Zone
###### Abstract  
This paper presents an assessment of potential losses and damage costs to rare coastal plant species of Lake Baikal (UNESCO World Heritage Site) as a result of inundation at high water levels. The lake's ecosystem is characterized by an exceptional diversity of rare and endemic animal and plant species. The construction of a hydroelectric power plant caused an increase in the water level of Lake Baikal, resulting in the inundation of low-lying coastal areas, the destruction of the coastline, alterations to the hydrological regime, etc. However, there are practically no works devoted to water-level modeling and the assessment of its impact on riparian vegetation, including rare species. We conducted fieldwork to determine the abundance of four vulnerable species and identified inundation zones at different high water levels on the basis of digital elevation models based on aerial photography data. The analysis revealed that at the maximum level of inundation, the number of plant species affected would total 5164, amounting to a financial loss of biodiversity estimated at 3098.4 thousand rubles. To mitigate the projected losses, it is imperative to implement measures that restrict water-level fluctuations above the 457.00 m threshold. The absence of flora as an object of state environmental monitoring, which is not specified in the regulatory legal document, must be rectified in a timely manner.  
Russia; reservoir; hydropower plant; endemics; inundation; damage cost
## 5 Conclusions  
This study presented a lock-in thermography evaluation process to quantitatively investigate the effect of thinning depth on specimens simulating nuclear power CLPs with various thinning rates. The results were analyzed by combining heat wave signals and image processing and showed that LIT can be applied for fast and accurate assessment of CLP thinning detection and depth assessment in nuclear power plants. The possibility of detecting defects was compared through Otsu binarization processing, and the estimation of defect depth according to the thinning rate was evaluated. In particular, it was found that the phase angle according to the temperature difference in lock-in thermography plays an important role in evaluating thickness thinning.  
In future research, we plan to improve reliability of actual defect shapes and ratios by reducing transient heat transfer effects through research on advanced noise removal algorithms based on thermal characteristics. Additionally, research will be conducted to examine complex structures such as actual nuclear power plant CLPs in an occlusive environment and practical complementary detection technologies.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Defect ID**} & \multicolumn{3}{c}{**Pixels**} \\ \cline{2-4}  & **Real Pixel** & **Estimated Pixel** & **Error (\%)** \\ \hline B1 & 1995 & & \(-\)63.75 \\ B2 & 3179 & & \(-\)42.24 \\ B3 & 3795 & & \(-\)31.05 \\ C1 & 5589 & & 1.54 \\ C2 & 6153 & & 11.79 \\ C3 & 6427 & & 16.77 \\ \hline \end{tabular}
\end{table}
Table 5: Calculation of the number pixels and error for defect depth in binarization image.  
**Author Contributions:** Conceptualization, W.K., Y.C. and S.L.; data curation, Y.C.; formal analysis, Y.C. and S.L.; funding acquisition, W.K.; investigation, W.K., Y.C., S.L. and C.K.; methodology, Y.C., S.L. and C.K.; project administration, W.K.; resources, W.K.; software, Y.C., S.L. and C.K.; supervision, W.K.; validation, W.K. and Y.C.; visualization, S.L.; writing--original draft, Y.C. and S.L.; writing--review and editing, C.K. and W.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Research Foundation of Korea (NRF-2022R1A2C1011413), funded by the Korean Government, Ministry of Education, Science and Technology (MEST) and the project for Industry-University-Research Collaboration. R&D was funded by the Korea Ministry of SMEs and Startups in 2023 (RS-2023-00224854).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Due to the nature of this research, participants of this study did not agree for their data to be shared publicly and thus are only available upon reasonable request.  
**Conflicts of Interest:** Author [PERSON] was employed by the company enesG. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Information and Communication Technologies and Agricultural Production: New Evidence from Africa
###### Abstract  
While information and communication technologies (ICT) have proven to be useful in boosting agricultural production and productivity, regardless of the geographical location, much of the discussion on ICT and their impact focus on the global north, with deficient literature on the global south. The limited account of the global south shows mixed conclusions on the impact of information and communication technologies on agricultural production, with most studies focusing on crop production, as a proxy for agricultural production, leaving out livestock production. Animated by this concern, this article explores the impact of ICTs on agricultural production (crop and livestock) in Africa using panel data from 32 African countries and the panel autoregressive distributed lag model as the estimation technique. We find that individuals using internet significantly increased crop production in the long run. Specifically, a percentage increase in internet patronage increases crop production by 0.071% but significantly decreases the livestock production index, both in the short and long run. Mobile phone subscriptions had a significant negative impact on crop production in the long run but had a significant positive impact on livestock production in the long run. Fixed phone subscriptions significantly increased crop production in the long run but significantly decreased livestock production index in the long run. The findings show bidirectional causality between crop production and internet patronage, livestock production and individuals using internet, crop production and mobile cellular subscription, crop production and net national income, and rural population and both crop and livestock production. We recommend that governments in Africa increase funding investment in digital technologies to foster increased agricultural production while addressing structural challenges that constrain increased access to digital agricultural technologies. It might be useful if governments in sub-Saharan Africa (SSA) incentivize the telecommunication companies to extend digital coverage to rural areas through tax rebates and holidays to encourage rural inclusion in the digital space to bridge the digital divide.  
Keywords:individuals using the internet; mobile phone subscriptions; fixed telephone subscriptions; net national income per capita; credit to the private sector; rural population; crop production; livestock production; panel ARDL +
Footnote †: journal: Applied Sciences
## 1 Introduction
Currently, the integration of nonlinear dynamic analysis methods with signal decomposition algorithms in signal feature extraction techniques is widely applied in fault diagnosis [12; 13], biomedicine [14; 15; 16], geophysics [17; 18], underwater acoustic signal processing [19; 20; 21], and other fields. Permutation entropy (PE), as a nonlinear dynamic analysis method, serves as a tool for describing the complexity of time series. This method focuses solely on information within the time series, making it computationally simple, resistant to noise, and highly robust [22]. In traditional research on vessel noise radiation signal processing, EEMD and its analysis of energy differences between high- and low-frequency signals are used to extract signal features. Still, the complexity of time series, namely entropy values, is not considered [23]. In fault diagnosis and electroencephalography (EEG) medicine, modal decomposition and its extended methods combined with entropy values have been widely confirmed. Reverse Dispersion Entropy (RDE), as a nonlinear dynamic analysis method, combines with EWT to decompose complex non-stationary signals into empirical wavelet functions with compact support spectra [24]. In addition, methods combining modal decomposition with sample entropy, approximate entropy, and others have also been proven effective [25; 26]. However, few research methods combine modal decomposition with entropy values for the feature extraction of vessel shaft frequency electric field signals. Therefore, targeted feature extraction for vessel target shaft frequency electric field signals is significant for target detection and recognition. Most vessel shaft frequency electric field signal feature extraction is based on the modal decomposition of the time-domain signal, and the problems of signal modal aliasing and noise signal residuals in modal decomposition have yet to be effectively solved. In addition, a single entropy calculation can only reflect the complexity of the time-domain signal and fails to extract the frequency-domain features effectively. Therefore, considering the problem of joint time- and frequency-domain feature extraction, this paper proposes research on a feature extraction method for the vessel electric field based on the maximum energy arrangement entropy of the Intrinsic Mode Function (IMF) of EMD. Aiming at the problems of mode aliasing and noise signal residuals, firstly, by establishing a test system to simulate the shaft frequency signal, the collected non-smooth random shaft frequency signal is decomposed into a set of IMFs by using EMD to analyze the signal characteristics from the time-domain aspect. Secondly, on this basis, the energy size of each group of signal components is calculated, and the largest energy component is selected as a characteristic parameter to find its PE value and realize multi-target electric field signal differentiation. Finally, the method is verified by actual marine vessel data, confirming that the method can better distinguish E-field signals of different vessel shaft frequencies.
# Nonlinear Effect and MAI Impact on SAC-OCDMA System Based on 2D Multi-Diagonal Code and Laser Array
## 4 SAC-OCDMA System Performance Analysis
The nonlinear coefficient is defined as [30]:  
\[\gamma=\frac{2\pi n_{2}}{\lambda A_{eff}} \tag{15}\]  
where \(n_{2}\) and \(A_{eff}\) are defined as the nonlinear index fiber (2.6 \(\times 10^{-20}\) m\({}^{2}\)/W) and the fiber mode effective area, respectively. The effective area is defined as a quantitative measure of the area which a fiber mode effectively covers in the transverse dimensions. The fiber mode effective area is mathematically described as [31]:  
\[A_{eff}=\frac{\left(\iint|A(x,y)|^{2}dxdy\right)^{2}}{\iint|A(x,y)|^{4}dxdy} \tag{16}\]  
where \(A(x,y)\) is defined as the distribution of the optical field on \(x\) and \(y\) axis. The group delay term \(\beta_{1}\) is defined as follows:  
\[\beta_{1}=\frac{1}{v_{g}}=\frac{n_{g}}{c} \tag{17}\]  
where \(v_{g}\) and \(n_{g}\) defined as group velocity and group index. In the wavelength domain, the GVD parameters \(D\) and \(S\) are defined as follows:  
\[D=\frac{\partial\beta_{1}}{\partial\lambda}\text{ and }S=\frac{\partial D}{ \partial\lambda} \tag{18}\]  
However, in the frequency domain, the GVD parameters \(\beta_{2}\) and \(\beta_{3}\) are defined as follows:  
\[\beta_{2}=\frac{\partial\beta_{1}}{\partial\omega}\text{ and }\beta_{3}=\frac{ \partial\beta_{2}}{\partial w} \tag{19}\]  
where \(w\) is defined as the angular frequency of the optical field.
## 2 Materials and Methods
### Methods
#### 2.2.3 Evaluation of the RScan Algorithm  
Evaluating the clustering outcomes achieved through optimal hyperparameters is crucial for verifying the accuracy and effectiveness of the active region mining results. The RScan algorithm, an unsupervised lattice density clustering algorithm, cannot be assessed directly using external metrics due to the absence of actual labeling or categorical information for comparison. Consequently, internal evaluation metrics such as the silhouette coefficient and Davies-Bouldin Index are employed to gauge the compactness, separation, and overall distribution of clusters.  
The silhouette coefficient is utilized to measure both the cohesion and separation of the clustering results. For each sample point \((\mathrm{x})\) its silhouette coefficient \(\mathrm{s(x)}\) is computed. The average of these silhouette coefficients across all sample points is then calculated to represent the overall clustering result's silhouette coefficient.  
The Davies-Bouldin Index (DBI) evaluates the validity and separateness of the clustering results. It is determined based on the distance between clusters and the cohesion within each cluster. The DBI calculates the quality of clusters by assessing the ratio of the average distance between clusters to the cohesion within those clusters. The calculation is as follows:  
\[\mathrm{s(x)}=\frac{\mathrm{b(x)-a(x)}}{\mathrm{max}\{a(x),b(x)\}} \tag{1}\]  
Silhouette \(\mathrm{Coefficient}=\mathrm{mean(s(x))}\) (2)  
\[\mathrm{DBI(i)}=\frac{\mathrm{cohesion(i)+cohesion(j)}}{\mathrm{separation (i,j)}} \tag{3}\]  
\[\mathrm{DBI}{=}\mathrm{mean(DBI(i))} \tag{4}\]  
where \(\mathrm{a(x)}\) is the average distance of sample point \(\mathrm{x}\) from all other sample points in the same cluster (cohesion) and \(\mathrm{b(x)}\) is the average distance between sample point \(\mathrm{x}\) and all sample points in different clusters in the nearest neighbor (separation).  
For each cluster \(\mathrm{i}\), \(\mathrm{DBI(i)}\) is calculated with its nearest neighboring cluster \(\mathrm{j}\). Subsequently, the average DBI(i) across all values, DBI(i), is computed; cohesion is defined as the average distance between all sample points in a cluster \(\mathrm{i}\); and separation is the average distance between cluster \(\mathrm{i}\) and other clusters.
## 3 Current Techniques for OP Study
### Bone Histomorphometry  
Bone histomorphometry is a histological investigation technique, developed in the late 1950s, providing quantitative information on bone microarchitecture as well as on bone remodelling and metabolism. It is used in clinics for the diagnosis of bone-related diseases and to monitor the effect of new drugs on bone [50; 51]. The analysis is performed on a cylinder of bone (5-8 mm in diameter) harvested from the iliac crest of the patient. The biopsy is then fixed, embedded in methyl methacrylate and cut into thin 5-10 \(\upmu\)m slices. A final staining step allows a clear identification of the bone structural components and cells under a light microscope [8]. The bone section images are acquired through a camera and analysed manually or automatically, with specific software to perform quantitative analyses on structural and remodelling parameters. These parameters were classified for the first time by [PERSON] et al. in 1987, following the request of the American Society for Bone and Mineral Research, in order to develop a unified system of terminology [52].  
The dynamic analysis of bone remodelling can also be carried out through the administration, several days before the bone biopsy, of a labelling agent (i.e., tetracycline) able to bind to hydroxyapatite at the sites of new bone formation [8; 51]. Thanks to this procedure, histomorphometry enables the collection of information about the bone turnover through a single analysis of a bone sample. This result cannot be obtained with any of the other imaging techniques on ex vivo bone samples.  
Even if bone histomorphometry remains the current gold standard for the study of bone microstructure, metabolism and remodelling, it presents some disadvantages. Firstly, it is an invasive technique with associated potential complications, such as pain and infection risk in the harvesting area. Moreover, sample processing and analysis are extremely time consuming and require a high level of specialisation [8].  
Even though histomorphometry can identify high turnover and low bone volume in osteoporoptic patients [8], as visible in Figure 8, OP-induced alterations of the bone structure organisation are less detectable, considering that the analysis is performed on 2D samples. Information about the 3D organisation of bone can be extrapolated through the application of stereological algorithms that allows the estimation of the third dimension by interference. These formulae, however, require several assumptions hardly satisfied in bone (for example, isotropy) [53].  
Considering the limitations of this analysis, other imaging techniques have been considered to better investigate the 3D bone microstructure as well as for the diagnosis of OP, i.e., magnetic resonance and computed tomography.
# The Effectiveness of Unimodal and Multimodal Warnings on Drivers' Response Time: A Meta-Analysis
## 3 Results
### Comparisons Between Unimodal and Multimodal Warnings  
#### 3.3.1 Unimodal Versus Bimodal Warnings  
As the previous results showed, only auditory and tactile warning conditions significantly improved drivers' performance as compared to the no warning condition; therefore,the current meta-analysis included only the auditory and tactile warning conditions as the representative samples of unimodal warnings for the comparison between unimodal and bimodal warnings. A total of six analyses were conducted, and unimodal warning conditions were set as group 1. Results of the two comparisons (i.e., auditory vs. auditory-tactile, and tactile vs. auditory-tactile) indicated that the response time was significantly shorter in bimodal warning conditions than unimodal conditions (see Figure 7a-c). Pooled Hedges' \(g\) and 95% CI of the six comparisons are presented in Table 2. The results of the publication bias are shown in the funnel plots in Figure 8a-f.
# On-Line Tendency Control of Dissolved Oxygen Concentration during Aerobic Fed-Batch Fermentations
## 2 Materials and Methods
### DO Control Model  
#### 2.3.1 DO Tendency Control Model  
The model used in this method is described by the equation  
\[Y_{t}=a+bt+\varepsilon_{t} \tag{1}\]  
where \(Y_{t}\) is the DO, \(b\) is the slope, which indicates the present tendency variation of DO over the time of fermentation (\(t\)), \(a\) is the intercept, \(t\) is the time, where the initial time is defined as minute 1, and \(\varepsilon_{t}\) represents the random errors [20].  
After determining the average per minute values, the coefficients are calculated using least squares regression. Therefore, the estimates of the slope and intercept are described by:  
\[\hat{b}=\frac{\sum{(Y-\overline{Y})(t-\overline{t})}}{\sum{(t-\overline{t}) ^{2}}} \tag{2}\]\[\hat{a}=\overline{Y}-\hat{b}\overline{t} \tag{3}\]  
where \(\overline{Y}\) and \(\overline{t}\) are the arithmetical means of \(Y_{i}\) and \(t_{i}\), respectively.
## 2 Materials and Methods
### Biome-BGC Model and Its Validation
#### 2.6.2 Indirect Validation of the Biome-BGC Model  
The NPP product of MODIS has been used by many researchers, and many studies have reported on its accuracy and reliability [52; 53]. Researchers often use MODIS NPP products to directly validate the accuracy of NPP simulated by the Biome-BGC model [54]. In this study, NBP was selected as the output of the Biome-BGC model in order to explore the relationship between the topological characteristics of vegetation and its net carbon sequestration capacity in the YRB. However, there are hardly any spatially available data and products that can directly validate the NBP of vegetation ecosystems. The values of NPP are different from those of NBP, and its direct validation of NBP is not possible. However, NPP and NBP have large overlapping processes in their calculations, and they both describe two important mechanistic processes in vegetated ecosystems: photosynthesis and autotrophic respiration. Furthermore, the focus of this study was to explore the relationship between the spatial topology of vegetation nodes and their net carbon sequestration capacity. We put more emphasis on the reliability of the Biome-BGC model in simulating the spatial heterogeneity of productivity of different vegetation nodes. Therefore, we chose MODIS NPP as an indirect validation product of the NBP simulated by the Biome-BGC model. The objective of the indirect validation was to determine if the spatial variability trends of NBP at different vegetation nodes were close to the spatial variability trends of NPP at these nodes. We compared these two productivity results in a dual \(Y\)-axis coordinate system to see if the trends in variability of these two productivity results at different nodes were similar and to determine if this similarity was statistically significant through correlation analysis. The resolution of the NPP product of MODIS is 500 m, which was sufficient for the 1 km resolution ecological source of this study. We extracted NPP values for all vegetation nodes from the MODIS NPP products and used correlation analysis to determine the correlation between NPP and NBP. The above operations were finished in ARCGIS 10.4 and SPSS 24.0.
# Simulation of an Indoor Visible Light Communication System Using Optisystem
## 1 Introduction
The simulation considers LOS and NLOS propagation models with single and dual LEDs. Our simulation implements a direct path with a single LED as the LOS propagation model's transmitter. The effect of the incident and irradiance angles in LOS propagation is considered for further analysis. In the NLOS propagation model, simulation considers two cases: a single LED as transmitter and dual LEDs as transmitters. We tested our simulations by evaluating the signal shape on the transmitter and receiver sides using single and double LEDs in the NLOS propagation models. The impact of the incident and irradiance angles in the LOS propagation model and eye diagrams of LOS/NLOS models are evaluated for signal distortion analysis. * We estimated a bitrate value based on the theoretical analysis. We implemented a VLC system model with principles and mathematical formulations. We analyzed the impact of FOV on bitrate estimation from our NLOS single LED as a transmitter-based Optisystem simulation. * A VLC transmitter and receiver hardware designs are presented for real-time VLC implementation. We tested the VLC hardware system and verified its functional performance in LOS and NLOS conditions. The rest of the paper is organized as follows. Related work on the VLC system is discussed in Section 2. An Optisystem simulation-based VLC system is added in Section 3. Section 4 presents a VLC system model with principles and mathematical formulations. The VLC transmitter and receiver hardware designs are described in Section 5, and the simulation results are presented in Section 6. Finally, the paper concludes our VLC simulation in Section 7.