esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy and Spectrally Efficient Modulation Scheme for IoT Applications
## 3 OSP-MSK Performance Analysis
### OSP-MSK ABER Performance
#### 3.1.2 Nakagami-M Fading Channel  
Using the same procedure, the ABER in the case of the Nakagami-m fading channel can be expressed as follows:  
\[ABER_{Nak}=\underbrace{\frac{m^{m}}{\overline{\Psi}^{m}}\;\Gamma(m)}\int_{0} ^{\infty}erfc\Big{(}\sqrt{\Psi}\;\Big{)}\;\Psi^{m-1}\;e^{-\frac{m}{\overline{ \Psi}}}d\mathcal{V}}_{I_{3}}-\underbrace{\frac{1}{4}\frac{m^{m}}{\overline{ \Psi}^{m}\;\Gamma(m)}\int_{0}^{\infty}\;erfc^{2}\Big{(}\sqrt{\Psi}\;\Big{)}\; \Psi^{m-1}\;e^{-\frac{m}{\overline{\Psi}}}d\mathcal{V}}_{I_{4}}. \tag{40}\]  
Integral \(I_{3}\) in Equation (40) is determined in detail in Appendix B, and it can be expressed as follows:  
\[I_{3}=\left\{\begin{array}{c}1-U\Big{(}\frac{\overline{\Psi}}{m}\Big{)} \sum\limits_{k=0}^{m-1}\Bigg{(}\begin{array}{c}2k\\ k\end{array}\Bigg{)}\Bigg{(}\frac{1-\mathfrak{U}^{2}\Big{(}\frac{\overline{ \Psi}}{m}\Big{)}}{4}\Bigg{)}^{k}\;\text{when}\;m\;\text{is}\;integer\\ \\ \frac{\sqrt{\frac{\overline{\Psi}}{m}}\;\Gamma(m+\frac{5}{\overline{\Psi}})}{ \sqrt{\pi}\;\Big{(}1+\frac{5}{\overline{\Psi}}\Big{)}^{\frac{m+1}{2}}\;\Gamma (m+1)}2F_{1}\bigg{(}1,m+0.5;m+1;\frac{1}{1+\frac{5}{\overline{\Psi}}}\bigg{)} \;\text{when}\;m\;\text{is}\;non\;integer,\end{array}\right. \tag{41}\]  
where \(\mathfrak{U}\Big{(}\frac{\overline{\Psi}}{m}\Big{)}\) can be expressed as follows:  
\[\mathfrak{U}\bigg{(}\frac{\overline{\Psi}}{m}\bigg{)}=\sqrt{\frac{\overline{ \Psi}}{\overline{\Psi}}}{1+\frac{\overline{\Psi}}{m}}. \tag{42}\]Integral \(I_{4}\) in Equation (40) is in the form of the integral \(A\) in Equation (35), but by substituting \(k_{1}=m\) and \(k_{2}=\frac{m}{\Psi}\). Therefore, it can be determined as in Equation (36) as follows:  
\[I_{4}=\frac{\mathbb{Z}}{4}-\frac{\mathbb{Z}}{\pi}\sum_{j=0}^{m-1}\frac{\left( \frac{m}{\Psi}\right)^{j}}{2j+1}2F_{1}\bigg{(}j+\frac{1}{2},j+1;j+\frac{3}{2}; -\left(\frac{m}{\Psi}\ +1\right)\bigg{)}, \tag{43}\]  
where \(\mathbb{Z}\) is given as follows:  
\[\mathbb{Z}=\frac{(m-1)!}{\Gamma(m)}. \tag{44}\]  
Substituting Equations (41) and (43) into Equation (40), the ABER over the Nakagami-m fading channel can be expressed as follows:  
\[ABER_{Nak}=\left\{\begin{array}{cc}1-\text{i}\left(\frac{\pi}{\Psi}\right) \sum\limits_{k=0}^{m-1}\binom{2k}{k}\left(\frac{1-\text{i}^{2}\left(\frac{\pi }{2}\right)}{4}\right)^{k}+\frac{\pi}{4}-\frac{\pi}{4}\sum\limits_{j=0}^{m-1} \binom{\left(\frac{\pi}{2}\right)^{j}}{2j+1}2F_{1}\Big{(}j+\frac{1}{2},j+1;j+ \frac{3}{2};-\left(\frac{\pi}{\Psi}+1\right)\Big{)}\\ \text{when $m$ is integer}\end{array}\right. \tag{45}\]
# Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data
## 4 Results
### Classification Results
In addition, a confidence interval was calculated for each of the accuracy values [70]. For the pixel-based classification, the largest confidence interval lies at 0.38% for dry land producer accuracy. For object-based classification, the largest confidence interval is 0.37% for the same class and accuracy. In addition to the classification of the validation extent, the classification for the entire study area was generated based on the same time series characteristics and the corresponding threshold values for the classes TOW and TFV. Figure 12a shows the pixel-based classification and Figure 12b the object-based classification. A difference between both classifications can be observed. In particular, the Dry Land areas of the pixel-based classification appear to be noisier in comparison to the Dry Land areas of the object-based classification and show slightly higher overestimation for all areas, especially for Dry Land. Both figures show that the disregard of the TFV areas would result in an enormous underestimation of the flood extent. Figure 11: Accuracy assessment results for pixel- and object-based classification, which are generated by means of the time series approach.
# Rice Secondary Metabolites: Structures, Roles, Biosynthesis, and Metabolic Regulation
## 5 Conclusions and Future Perspectives  
This review focuses on the elucidation of the structures, biological functions, biosynthesis, and metabolic regulation of rice secondary metabolites carried out during the past 50 years. Some metabolites (i.e., diterpenoid phytoalexins) and their metabolic pathways are unique to rice [141]. Some minor or new rice metabolites should be identified by using new techniques, such as chemoinformatics [202], metabolomics [7, 18, 34], and compound prediction based on biosynthetic gene clusters [9]. Furthermore, the biological activities of many isolated metabolites (i.e., phenolic acids, flavonoids, and terpenoids) need to be systematically evaluated.  
The physiological functions of some rice secondary metabolites remain unknown. Commonly, the development of null mutants and transgenic over-expression lines is enabling the critical examination of biological functions. Phytoalexin-related gene over-expression generally results in increased resistance against pathogens in genetically modified plants [203, 204].  
The biosynthetic regulation of rice secondary metabolites is very complicated. Concentrated efforts have revealed the relevant factors and signaling pathways that are involved in the regulation of phytoalexin production in rice. It is still unknown, however, how transcription factors regulate phytoalexin biosynthetic genes in concert. Further research investigating the molecular mechanisms of the transcriptional regulation of phytoalexin biosynthetic genes and revealing how upstream signals activate each transcription factor in the signaling cascade is essential. Rice metabolic regulation is certain to far exceed the complexity of the biosynthetic pathways [11, 17, 205].  
In addition, exogenous genes such as stilbene synthase (STS) gene were successfully transferred from other plant species to rice to improve its resistance against rice blast disease [206]. The importatance of some secondary metabolites, such as 2-acetyl-1-pyrroline (**248**) [14], phytoalexins [204], and momilactones [207], has been emphasized in agriculture and in the medicinal and food industries. These will be important fields of application for rice secondary metabolites.  
**Author Contributions:** [PERSON] and [PERSON] performed bibliographic research, drafted, and corrected the manuscript. [PERSON] and [PERSON] retrieved the literature, participated in the discussions, and supported manuscript corrections. [PERSON] reviewed the manuscript and helped to revise it. [PERSON] conceived the idea, designed the review structure, supervised manuscript drafting, and revised the manuscript. All authors read and approved the final manuscript.  
**Funding:** This work was co-financed by grants from the National Key R&D Program of China (2017 YFD0201105) and the National Natural Science Foundation of China (31471729).  
**Acknowledgments:** We acknowledge some authors of cited papers for providing their figures, and [PERSON] for his assistance in collecting literature.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
The access road to the powerhouse's hydraulic system's facilities in Guaramales (Azuay, Ecuador) presents a medium-sized landslide. Geophysical tests were conducted in the initial research stage, combining electrical and seismic methods. A vertical electrical sounding (VES) and horizontal to vertical spectral ratio (HVSR) survey campaign have been taken as a reference for the analysis of the landslide area. The distribution of these test points has been at three different levels along the landslide where the access road crosses it, trying to cover the area's most extensive possible length and width. In the area, we find the geology dominated by the presence of schists, altered to different degrees and presenting blocks of material with a lower degree of alteration within colluvium formed by a clayed matrix and coarse material of the exact nature. There is also observed runoff water and groundwater in the area. The results obtained through SEV tests have allowed for defining the separation zone of the mobilized or sliding materials compared to the fixed or immobile ones (potentially, the sliding surface was marked). Using the HVSR technique, the natural vibration frequencies of the ground associated with the sliding mass (separation of seismic impedances between a two-layer model: mobile and fixed) have been determined. Previous authors proposed an empirical relationship establishing the exponential relationship, already proposed by previous authors, between sediment thickness and natural frequencies. It has been possible to determine the depth of the position of the loosely compacted sediment zone (and probably moving or mobilized) compared to that of compact materials (immobile) and thereby define the potential rupture surface. Guaramales (Ecuador); landslide; HVSR; VES; rupture surface +
Footnote †: journal: Applied Sciences  
0203 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)) 4.0/). 0000-0002-0002-0002-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-00000-00000-0000  
logging, or inclinometer tests) as a direct source of an information database. However, the transportation and location of drill machines and their application processes in that high-slope or soft-soil areas (sometimes both) involve expensive economic outlays. Also expensive is the use of UAV photogrammetry or A-DInSAR interferometric methods [3]. It is hard work, and most are challenging to perform, even create or increment mass movements. Geophysical surveys are a low-cost tool and an easy way to perform deep investigations and get valuable information in a short time that can be correlated with geological or geotechnical parameters [3; 4].
# Regional Seismicity of the Northeastern Tibetan Plateau Revealed by Crustal Magnetic Anomalies
## 7 Curie Point Depths
### Distribution of Curie Point Depths  
The CPDs in the study area range between 18 and 42 km, with an average of 30 km (Figure 12). This result agrees well with the CPDs obtained from the aeromagnetic surveys. Stable continental regions have a thick magnetic crust, while tectonically active regions are dominated by a thin magnetic crust. The CPDs are deep in the Tarim, Ordos, Qaidam, and Sichuan basins (32-42 km). The deepest CPD is 42 km in the Ordos Basin. The shallowest CPDs (18-30 km) are in the NETP (except for the Qaidam Basin). The shallowest CPD of 18 km is in the Qiangtang Block.  
Figure 11: Example of power spectrum analysis for one subregion and linear fitting to estimate (a) \(Z_{4}\) and (b) \(Z_{0}\). For this subregion, the centroid depth \(Z_{0}\), the depth with respect to the top of \(Z_{4}\), and CPD \(Z_{0}\) are 17.91 \(\pm\) 0.98, 7.59 \(\pm\) 0.01, and 28.23 \(\pm\) 2.00 km, respectively.  
Figure 12: Map of the Curie surface of the NETP and adjacent areas. The red points indicate surface heat flow. For black lines, please see Figure 1. The CPDs in the study area are 18–42 km, with an average of 30 km.  
The NETP (except for the Qaidam Basin) is characterized by a shallow CPD due to tectonism. This is consistent with low-velocity layers being ubiquitous in the crust [37] and high heat flows [38]. The high temperature in the NETP crust weakens the crustal magnetism and results in a shallow Curie surface. The Qilian orogenic belt has a shallow Curie surface (CPD = 22-27 km). The crust beneath the orogen is hot and is characterized by intense magmatism, low S-wave velocity anomalies [39], and high heat flow (average = 68 mW/m\({}^{2}\)) [40]. The Qinling orogenic belt was formed from a continental collision between the North China and South China blocks, and it contains many collision-extrusion-related and extensional detachment structures [41], as well as obvious low-seismic-velocity anomalies [42], which correspond to a shallow CPD (22-27 km). Strong earthquakes (Figure 1) and low S-wave velocity anomalies characterize the Songpan-Ganzi Plateau and Bayan Har Block [42], beneath which the CPD is also shallow (24-27 km).
# A New Mobility Model for Multi-UAVs Reconnaissance Based on Partitioned Zone
## 4 Performance Evaluation  
We evaluated performance comparison of the Random Waypoint model, the Pheromone Repels model, and the proposed model for the UAV model in Table 1. Let us note that we used the parameters in Table 1 since other simulation results show similar tendency for other UAV parameters. Table 4 shows other parameters used in simulations. Ten UAVs reconnoitered in the area of 30 km \(\times\) 30 km for 2 or 3 h. We divided the scanning area into 15\(\times\)15 zones. We implemented a reconnaissance model simulator using C++ language, and measured the average performance by repeating 20 times in each case.  
We measured the coverage rate and the averaged inter-visiting time. The coverage rate was defined by the rate of scanning the area in the whole area. The inter-visiting time was the time interval between two consecutive visits in a unit region. Thus, the average inter-visiting time of the set of visited unit regions, \(V_{R}\), for the simulation time \(T_{sim}\) is defined by Equation (3).  
\[T_{avg}=\frac{1}{|V_{R}|}\sum_{r\in V_{R}}\frac{T_{sim}}{N_{r}} \tag{3}\]  
where \(N_{r}\) is the number of visits in unit region \(r\). The lower inter-visiting time indicates that UAVs visit the unit region frequently. Thus, we used the average inter-visiting time as a measurement of how evenly UAVs reconnoiter the area.
# Estimation of Saprolite Thickness Needed to Remove _E. coli_ from Wastewater
## 4 Summary and Conclusions  
We developed and used an equation to estimate the thickness of saprolite needed to filter _E. coli_ from wastewater below septic drainlines at 12 field sites. The equation computed the minimum volume of pores \(\boldsymbol{<}\) 10 \(\upmu\)m, which were small enough to filter _E. coli_. The equation was derived from results of prior experiments with saprolite columns, which used simulated wastewater having an _E. coli_ concentration of 1 \(\times\) 10\({}^{5}\) CFU/100 mL. The saprolite textures evaluated from the 12 sites ranged from coarse sand to clay loam, with most being sandy loam. The estimated thicknesses of saprolite needed to filter _E. coli_ ranged from 35 to 113 cm with a mean of 58 cm. In general, the silt percentage was the best saprolite property to use to estimate in the field the thickness of saprolite required for safe wastewater disposal. Saprolite thickness estimates increased as silt percentage decreased. These estimates pertain to non-aggregated material such as saprolite and may not apply to aggregated (i.e., well structured) soils. The results of this study show that water retention measurements can be used for assessments of the thickness of porous materials needed to filter bacteria such as _E. coli_ from wastewater. More work in the area of wastewater treatment by saprolite is needed to verify the findings of this study under field conditions, particularly for the very sandy saprolites.  
[PERSON] contributed to project conceptualization, methodology selection, validation of data, formal data analysis, writing of the original draft, review and editing of the manuscript, project administration, and funding acquisition. [PERSON] contributed to project conceptualization, methodology selection, validation of data, writing of the original draft, review and editing of the manuscript, and funding acquisition. [PERSON] contributed to project conceptualization, writing of the original draft, review and editing of the manuscript, and funding acquisition. All authors have read and agreed to the published version of the manuscript. Funding for the research was supplied by the N.C. Department of Health and Human Services, Division of Public Health under Contract No. 00038125. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: All supporting data are included within the manuscript. Authors will supply clarification upon request.  
**Acknowledgments:** Statistical analyses were completed by [PERSON], and his contributions are greatly appreciated.  
**Conflicts of Interest:** The authors have no conflicts of interest.
# A New Rotor Position Measurement Method for Permanent Magnet Spherical Motors
## 3 Rotor Position Measurement for a PMSM  
### The Structure of the Measurement Device  
Figure 3 shows the structure of the RPM device for the PMSM, which consists of two parts: A high-speed camera and a LED LSGM. The LSGM was installed at the top of the rotor shaft. The distance between the bottom of the rotor shaft and the top surface of the LSGM is 90 mm, and the distance between the lens of the high-speed camera and the top of the LSGM is \(l\).  
Figure 1: Structure of the PMSM  
Figure 2: Three degree-of-freedom (DOF) motion of a permanent magnet spherical motor (PMSM): (**a**) Rotor shaft motion and the coordinate frames; (**b**) spinning motion; (**c**) tilting motion.  
Three LEDs were installed at the top surface of the LSGM, which were arranged in a straight line, as shown in Figure 4. The LED \(L_{c}\) is located at the center, the distance between \(L_{c}\) and \(L_{s}\) was 7 mm and the distance between \(L_{c}\) and \(L_{l}\) was 10 mm. The three optical feature points (OFPs) were identified through the different distances between them. The three LEDs are represented by \(L_{c}^{*}\), \(L_{s}^{*}\), and \(L_{l}^{*}\) in the image coordinate frame (ICF), respectively.  
The parameters of the high-speed camera are shown in Table 1.  
Figure 4: Arrangement of three LEDs on the top surface of light spot generation module (LSGM).  
Figure 3: Schematic diagram of rotor position measurement.  
Figure 5 shows the images of three OFPs photographed by the high-speed camera at different exposure times. It can be seen that, when the exposure time of the high-speed camera was 5000 us, only three OFPs in the image could be identified, which brought great convenience to the subsequent image processing and could be used to extract the coordinates from the three light spots in the ICF.
## 3 System Architecture and Methodology
### System Architecture  
The system architecture follows a modular design principle, implemented across three distinct layers of abstraction. Figures 1-3 illustrate the system's architecture from different perspectives, providing a comprehensive view of its operation.  
Figure 1 depicts the container architecture, revealing the internal structure of the steganography system. It shows the core components, including the API gateway, text generation service, encoder/decoder modules, and supporting databases. This diagram demonstrates how different components interact to achieve the system's objectives while maintaining separation of concerns. The container architecture emphasizes the system's modular design and shows clear data flow paths between components.  
Figure 2 illustrates the encoding process sequence, providing a detailed view of the message hiding workflow. This sequence diagram tracks the step-by-step process from initial message submission through text generation, synonym processing, and final stego  
Figure 1: DeepStego system architecture.  
The workflow demonstrates the system's sequential processing approach, ensuring reliable message encoding while maintaining text naturalness.  
Figure 3 illustrates a concrete example of DeepStego's encoding process. The message 'Hello' is first converted to its binary ASCII representation. The system then performs synonym substitution on the cover text, replacing words according to specific bit patterns. Each substitution ('weather \(\rightarrow\) climate', 'today \(\rightarrow\) now', etc.) encodes a portion of the binary message while preserving semantic coherence. The resulting stego text appears natural to human readers while carrying the hidden information '01001100', corresponding to 'Hello'. This example demonstrates how DeepStego achieves efficient information hiding without compromising text naturalness.
## 2 Fifth-Generation Telecommunications Technology and Architecture  
In order to better understand the security threats and challenges that face 5G technology, we will start by analyzing the 5G network technology and architecture in detail; 5G, the fifth generation of mobile networks, represents a substantial technological leap in telecommunications technology, laying the foundation for a hyper-connected world. Marking a significant departure from its predecessors, 5G's infrastructure has been designed to enable an array of new services, cater to increasingly dense traffic, support massive device connectivity, and offer high-speed data transfer with ultra-low latency. At the core of 5G technology are a set of new technologies and concepts, including advanced antenna techniques, beamforming, massive Multiple-Input Multiple-Output (MIMO), Network Function Virtualization (NFV), Software-Defined Networking (SDN), and Mobile Edge Computing (MEC), among others [11]. Together, these technologies enable 5G networks to offer unprecedented network performance in terms of speed, capacity, and flexibility. A key differentiating feature of 5G technology is its flexible architecture, allowing it to serve a broad spectrum of use cases, each with its specific requirements. Unlike the previous generations of mobile networks that were primarily designed for human-oriented communication, 5G is intended to support diverse application scenarios encompassing human-oriented and machine-oriented communications [12]. Furthermore, 5G is not simply an evolution of 4G LTE; it brings forth entirely new network paradigms such as dense networking and network slicing, making it possible to customize different network slices for specific use cases or services [13]. This degree of customization is a step towards providing a more personalized, efficient, and secure network experience. One of the most intriguing features of 5G is its spectrum flexibility. While previous mobile network generations primarily operated on sub-6 GHz bands, 5G extends the usable spectrum to higher frequency bands, including millimeter wave bands (up to 100 GHz). The use of these high-frequency bands opens up large amounts of spectrum, enabling higher data rates, reduced latency, and increased capacity [14]. Additionally, 5G networks utilize advanced modulation and coding schemes, as well as sophisticated multi-antenna techniques, to further enhance the data rate and network capacity. These enhancements not only improve the quality of service but also increase the spectral efficiency, ensuring that the network resources are used optimally [15]. While the technical intricacies of 5G networks are complex, the fundamental objective is to build a flexible, efficient, and secure network that can accommodate the increasing demand for connectivity in the modern digital age. An evolution of mobile network security concerns and technology for each generation from 1G to 6G is shown in Table 1.