esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Coating Deposition and Temperature Measurements  
A composite coating exhibiting an appropriate biological response and acceptable stoichiometry was created by modifying the Ti:Cu ratio, target voltage, and additional process parameters. Therefore, we selected them to study the effects of the applied negative bias voltage on the structure and biological properties of the Cu-TiO\({}_{2}\) coatings.  
For magnetron sputtering, a Ti target (\(\mathcal{O}\)71 and purity 99.95 at%) with two copper wires (purity 99.98 at%, \(\mathcal{O}\)1.5 cross-section) implanted in the top sputtering area was employed. The component ratio for the entire cathode was calculated as 1250:1 = Ti:Cu. In this way, it was possible to ensure a mixed Ti-Cu co-sputtering of the mosaic target material. The permanent stage rotation at 0.33 rpm ensured the coatings were consistent in composition since all samples travelled through the same plasma area.  
A base pressure of 3 \(\times\) 10\({}^{-2}\) Pa was reached when the cubic vacuum chamber with walls that were cooled by water was evacuated. The samples were subjected to a glow discharge cleaning process (Figure 1a) for 15 min at an increasing target voltage of up to 800 V and a working pressure of 2.5 Pa in a clean argon environment to increase the adherence of the oxide coating. Subsequently, a thin metallic layer was deposited for 10 min in the Ar atmosphere at a working pressure of 2.5 \(\times\) 10\({}^{-1}\) Pa and a target current of 1 A (450 V). The oxide coatings were deposited for 240 min in a pure O\({}_{2}\) environment with a target current of 1.3 A (500 V) and a working pressure of 2.5 \(\times\) 10\({}^{-1}\) Pa (Figure 1b). The substrate bias was set to \(-\)25, \(-\)50, \(-\)75, and \(-\)100 V, for each set of samples, with the cathode being 50 mm away from the substrate at all times. After much testing, the previously specified technological settings were the most suitable.  
Temperatures of the substrate were continuously observed. Any substrate heating resulted from the energetic particles because the stage was not heated. A thermocouple sensor was employed to study the heat processes on substrates (Figure 1c). The plate was placed at a junction of a chromel-alummedromouple, where the conductor diameter was 0.3 mm.  
During the process, polished Ti6 Al4V samples for adhesion, hardness, and wettability studies, as well as pure Ti foils for cross-section observations, were coated.
# DMLU-Net: A Hybrid Neural Network for Water Body Extraction from Remote Sensing Images
## 3 Experiments
### Datasets
#### 3.1.1 GID Dataset  
The GID (Gaofen Image Dataset) is a large-scale high-resolution remote sensing land cover dataset developed from China's Gaofen-2 satellite imagery. Comprising 150 high-resolution Gaofen-2 satellite images, it spans a geographical area exceeding 50,000 square kilometers. The image size is 7200 \(\times\) 6800 pixels, and the spatial resolution is 0.8 m. It includes five land cover categories, namely, construction, farmland, forest, grassland, and water area, with the water body category containing lakes, ponds, rivers, paddy fields, etc. Only the water body category was retained, with the other categories designated as the background. The images were cropped to 512 \(\times\) 512 image blocks, and images without water body information and images with water body information greater than 95% were removed. Finally, 5502 images were generated. The cropped images were subjected to image enhancement in the form of random rotation, Gaussian blur, and random adjustments of brightness and contrast, and they were randomly divided into training and test sets at a ratio of 8:2, with a random seed set to 42. Several images and their labels obtained from this dataset are presented in Figure 4. Among these labels, black represents non-water parts, with a label of 0, and white represents water parts, with a label of 1.
## 5 Post Analysis and Discussion
It was observed that some false positives occur in the presence of a rapid increase of voltage which did not cross the threshold voltage of 7.5 V.  
Since the data was labeled up to 10 min in advance before AE, it was possible to apply the model to these samples and analyse the lead time, see Figure 8. About 50% of all true positives are predicted 3 min in advance, while 35% of all true positives have a lead time of 1 min.
## 3 Results  
### Comparison and Analysis of Time-Frequency Maps  
The original Morlet wavelet's direct current component is not zero and does not satisfy the wavelet admissibility condition. The results are as follows: (1) the time-frequency map is blurred and unclear, and (2) the different frequency components interfere with each other, resulting in aliasing or crossing of energy distribution in time-frequency diagram.  
Figure 6 is the time-frequency diagram of the original Morlet wavelet transform for four signals (\(\omega_{0}=\pi,\ \beta=1.82\)). Figure 7 is the time-frequency diagram of the Morlet wavelet transform for four signals (\(\omega_{0}=3\pi/2,\ \beta=1.82\)). Figure 8 is a time-frequency diagram of the modified Morlet wavelet transform (\(\omega_{0}=2\pi,\ \beta=1.82\)).  
Figure 5: Ten types of vibration signals.  
Figure 6: Time–frequency map obtained after original Morlet wavelet transform, with central frequency \(\omega_{0}=\pi\).  
Figure 7: Time–frequency map obtained by improved Morlet wavelet transform, with central frequency \(\omega_{0}=3\pi/2\).  
In Figure 6, it is apparent that the original Morlet wavelet does not satisfy the admissibility condition, resulting in fuzzy features, aliasing, and crossing in the time-frequency map. Figure 7 has the same effect as Figure 6, and aliasing still exists in the time-frequency diagram. Figure 8 shows the time-frequency map after the improved Morlet wavelet, which might satisfy the admissibility condition and improve the feature ambiguity and overlap in the time-frequency map.  
According to the above analysis, the improved Morlet wavelet can reduce the aliasing effect and improve the time-frequency map's resolution. However, in Figure 8b,c, it is apparent that there remains a small amount of feature aliasing because the modified Morlet wavelet only possibly satisfies the admissibility condition. To further separate and deepen features, the time-frequency maps can be input into the residual network. By using residual networks, deeper nonlinear transformations and feature extraction can be utilized in time-frequency map processing. This enables more advanced feature separation and enhancement on the time-frequency map to better capture the time-frequency characteristics of the signal.
## 2 MRP Models and Algorithm Description  
### Principle of MRP Method  
When the polarization state of a bipolarized signal changes rapidly enough due to the birefringence phenomenon of the fiber, the transmission of the bipolarized optical signal in the fiber can be represented by a generalized Manakov expression of the NLSE [41; 42]:  
\[\frac{\partial E_{x,y}}{\partial z}=-\frac{\alpha}{2}E_{x,y}-\frac{\beta_{2}}{2} j\frac{\partial^{2}E_{x,y}}{\partial t^{2}}+\frac{\beta_{3}}{6}\frac{\partial^{3}E_{x,y} }{\partial t^{3}}+j\frac{8}{9}\gamma(\left|E_{x,y}\right|^{2}+\left|E_{y,x} \right|^{2})E_{x,y} \tag{1}\]  
where \(E_{x,y}\) is the optical field of \(x\) or \(y\) polarization signal, \(\beta_{2}\) and \(\beta_{3}\) are second-order and third-order dispersion coefficients, respectively, \(\alpha\) is the fiber loss, \(\gamma\)= \(2\pi n_{2}/\lambda A_{eff}\) is the nonlinear parameter, and \(z\) is the fiber length. Here, we use normalization to represent the optical field in fibers, using:  
\[E(z,t)=U(z,t)e^{-\int_{0}^{z}\frac{\alpha(z^{\prime})}{2}dz^{\prime}} \tag{2}\]  
Then, we assume that the attenuation of the fiber is a constant \(\alpha\), and the inline amplifier after each span of fiber completely compensates for the fiber link loss, i.e., \(\alpha(z)=\alpha\big{(}z-L_{sp}\left|z/L_{sp}\right|\big{)}\). Where \(L_{sp}\) is the single span length of the optical fiber, \(\left|\cdot\right|\) is the remainder operator. Therefore, substitute Equation (2) into Equation (1), we update the representation of the Manakov formula. \[\frac{\partial U_{x,y}}{\partial z}=-\frac{\beta_{2}}{2}j\frac{\partial^{2}U_ {x,y}}{\partial t^{2}}+\frac{\beta_{3}}{6}\frac{\partial^{3}U_{x,y}}{\partial t ^{3}}+j\frac{8}{9}\gamma e^{-az}(\left|U_{x,y}\right|^{2}+\left|U_{y,x}\right| ^{2})U_{x,y} \tag{3}\]  
Assuming that the solution of the equation is a non-singular problem, i.e., small perturbations do not alter the properties of the equation solution, consider the solution in the conventional perturbation form [18; 43]:  
\[U(z,t)=U^{(0)}(z,t)+\gamma U^{(1)}(z,t)+\gamma^{2}U^{(2)}(z,t)+o(\gamma^{2}) \tag{4}\]  
In order to avoid high computational complexity, we do not consider nonlinear perturbation terms above second order.
## 1 Introduction  
The vector optical field with spatially-variant states of polarization (SoP) has been extensively studied because of the fundamental interest and novel characteristics it offers [1; 2; 3; 4; 5; 6], such as focusing engineering [7; 8], micro-particle manipulation [9; 10], vectorial nonlinear optical collapse [11; 12], and the optical spin-orbital angular momentum conversion in a highly non-paraxial vector beam [13]. For a highly non-paraxial vector beam, the evanescent wave [14; 15] dominates the near field and the longitudinal component plays an important role in the evolution of the vector optical field. The quantitative knowledge of the near-field properties of an optical field is required in many applications ranging from nanophotonics to optical imaging [16; 17; 18; 19]. Recently, the characteristics of the evanescent wave for a vector optical field with radial polarization and azimuthal polarization have been reported [20; 21; 22]. These works indicate that the longitudinal component of both the evanescent wave and the propagating wave can be manipulated by the initial SoP alignments. The study regarding the full vector structure of a highly non-paraxial vector beam has found many novel phenomena and applications. On the other hand, the three-dimensional (3D) vector structure of a highly non-paraxial vector beam may result in a 3D vector structure of the optical spin angular momentum flux density (SAM-FD) which is associated with the circular polarization [23; 24]. In particular, the transverse SAM-FD with a spin axis orthogonal to the propagation direction offers a promising range of applications spanning from nanophotonics and plasmonics to biophotonics.  
In this paper, the radially-variant vector beam (RVVB) is split into the propagating wave and evanescent wave parts by using the angular spectrum method. A full vectorial description for the transverse and longitudinal components of a RVVB is presented. The profile of the longitudinal component is different from that of the transverse component. Both the longitudinal and transverse component profiles of the RVVB are closely related to the initial SoP arrangement. The coherent superposition of the longitudinal and transverse polarization components leads to the 3D SAM-FD profile. In particular, the longitudinal component dominates the near field in a highly non-paraxial RVVB. These results indicate that the 3D SAM-FD of a highly non-paraxial RVVB can be manipulated by the initial SoP in the beam cross-section.
# Intelligent Communication in Wireless Sensor Networks
## 3 The Proposed Strategy  
Our strategy ensures better gathering and management of information in a network; it takes into consideration the energy of each sensor node to make better network presentation. Due to the fact that a great deal of the detected information could be redundant or not useful, a minimization of information gathering could be an effective method to save the energy of the sensor node and extend the lifetime of the network.  
About this work, we propose a technique of smart communication that collects information and processes intelligently. Accordingly [1]: transmitting a quantity of data in a single message is less energy-costly than transmitting the same quantity in several short messages. Therefore, because of the density of the network, and high-energy consumption at the data transmission level, instead of having each node send its data to the sink, the network has been broken down into sectors.  
Our work is organized as follows: We insert in each node a program to build an agent in the network, which processes the information at the local level and judges their usefulness in order to neglect any unnecessary or redundant information. Moreover, the SN cooperates with neighboring nodes to transmit data.  
In the beginning, we grouped the network into sectors, in addition, we proposed a mobile agent at the level of each sector, to concatenate data processed by the SN, to send them in a single message by means of a well-defined strategy.  
Once the base station has designated the SN, it sends each sector a MA, to aggregate the data, with a list of well-defined SN. The list of SN is organized according to a route algorithm; the MA circulates between these nodes. For a neighboring sensor to cooperate with its SN, it must respond to two behaviors. The first is that a sensor cooperates with other sensors in the network as much as possible in order to save the transmission of information. Indeed, the reduction of cooperation may result in the loss of certain appropriate information. The sensor consumes energy in its cooperation, but it reduces the probability of losing important information. The second behavior is that a node agrees to cooperate with the network sensors that are in the itinerary to the sink. Indeed, the sensor consumes some energy in its cooperation, but it guarantees the transmission of the packets. During the itinerary of a mobile agent, if the energy of the next SN is not sufficient to transmit the mobile agent, this sensor transmits this information to an intermediate node between itself and the next SN in the list.  
Our sector approach based on a multi-agent system is divided into three phases: the first phase is to fix the SN for satisfying the interest according to the direct diffusion, the second is to divide the network into sectors, and the last phase is to determine the mobile agent route in each sector to collect information.
## 2 Methodology
### Experimental Procedures
#### 2.2.2 Sample Preparation and Characterization  
The commercial Phoenix 3D printer manufactured by 3D Systems and equipped with a fiber laser of 300 W maximum power (laser wavelength = 1060 nm, spot size = 70 \(\upmu\)m) was used for D-PBSLP of SiC samples. SiC samples with a size of 10 \(\times\) 10 \(\times\) 8 mm\({}^{3}\) were printed to study the effect of the scanning strategies on the D-PBSLP of SiC, while samples of 10 \(\times\) 10 \(\times\) 4 mm\({}^{3}\) were printed for sintering speed investigation. Different scanning strategies available in the Phoenix 3D printer, such as concentric in-out, hexagonal, linear, and zigzag (Figure 7), were investigated in this study. The zigzag strategy is inclined with 45\({}^{\circ}\). The hexagon size used in the hexagonal strategy is 700 \(\upmu\)m, and the scanning pattern inside the hexagon is inclined in a zigzag manner at 45\({}^{\circ}\) and repeats randomly until scanning the powder layer. Argon inert gas was used to prevent the oxidation of the powder during the printing process. It is worth mentioning that the commercial Phoenix 3D printer is equipped with a system for powder layer compaction, and 100% compaction was used for all samples.  
Figure 6: SEM of SiC powder [34].  
Figure 7: Scanning strategies used in this study: (**a**) Concentric in-out, (**b**) Hexagonal, (**c**) Linear and (**d**) Inclined Zigzag.  
Different characterization methods such as relative density measurements, 3D microscopic analysis, SEM imaging, and XRD analysis were used for the evaluation of the printed samples. Relative density was measured by the Archimedes method. The quality of the sample surfaces was checked using a 3D microscope (LEICA DM 4000 M). Bruker D8 was used for XRD analysis and JEOL JEM 6060 LV was used for SEM images.
## 2 Preliminary Knowledge
### OFDM with Index Modulation
\(Y_{g}(\gamma)\) is the \(\gamma\)th received signal after CP removal and DFT. More details for ML decoding in OFDM-IM can be found in [22]. The searching space of the ML detection per bit is given by the order of \(O((\binom{n}{k}+M^{k})\)[23]. Hence, the optimal ML detector has high complexity. To achieve near-optimal performance, two low-complex detectors have been proposed, including the low-complex ML and the a posteriori probability detection methods [24]. In this paper, an energy-based detection method is proposed in order to find the correct phase rotation factor used for PAPR reduction, and the ML detection is then utilized to find the transmitted symbols.
## 1 Introduction  
Generally speaking, ontologies play a double role in information technologies as a key structure for both database interoperability and information retrieval. In geographic information science, any request against the Internet is enriched not only by using ontologies, but also gazetteers, which can be loosely defined as dictionaries of place names or toponyms. Imagine somebody looking for information about the Italian city of Venice on the Internet. If he only speaks English, only English documents will be retrieved, whereas many documents are written in other languages, Italian in this case.  
One of the key problems we are facing is that the majority of ontologies are developed in English with English concepts. Additionally, ontologies developed in other languages regroup not only different terminologies, but also different organizations of the content. In short, one has to consider two networks with totally different structures. Then, how does one match them?  
Indeed, in several applications, such as cross-border environmental planning, for instance along the Rhine river, several countries must be involved with different languages, so different documents and different ontologies. Beyond the interoperability framework [1], multilingualism in ontologies is a great challenge. Secondly, multilingualism must be the base of the fusion of different documents retrieved on the Internet.  
Consider two countries and their governments. One can have a king and another an elected president. Afterwards, one can have 10 ministers and several sub-ministers, whereas another has 50 ministers. When there is a delegation of three ministers visiting another country, since all of them have no identical titles and responsibilities, usually they say they meet their counterparts. The same observation can be made when considering two ontologies, each written with a different language: there are practically never strict equivalences, but rather homologies.  
Remember that ontologies can be defined as shared conceptualization in which several experts do agree [2]. For translations, since there are no authorities able to claim exact or good translations, we can assert that we must consider shared translations of concepts. The same consideration can be extended to place names. Let us call homology the relationship between similar concepts and similar toponyms in two different languages.  
Often, specialists or experts mastering both languages are asked to translate geographic ontologies manually. However, this can also be done automatically by using gazetteers, or, specifically, geometric characteristics and toponyms, in order to match geographic concepts. In other words, the main contribution of this paper is to use geographic properties to match multilingual ontological concepts via gazetteers.  
In this paper, after having examined the specificities of geographic ontologies, we will study toponyms and gazetteers. Finally, some inference rules of translating geographic concepts based on geographic properties of toponyms will be given.
# Optimal Progressive Pitch for OneWeb Constellation with Seamless Coverage
## 5 Conclusions  
This paper mainly analyzes the downlink interference from the OneWeb system with a highly elliptical beam to the GSO satellite system and proposes an interference analysis method. From the distribution of EPFD in the orbital plane, a single satellite inline interference model is proposed. The corresponding off-axis angle threshold (\(\mu\)th) can be obtained, which is the basis for designing the spatial isolation method. In addition to satisfying the angular isolation of Oneweb satellites, it is important to ensure seamless coverage of the constellation and to use genetic algorithms to maximize the average total satellite communication capacity. The design of the encoding and operator of the genetic algorithm effectively simplifies the solution of this problem.  
Figure 6: The comparisons of different schemes in terms of total capacity, minimum off-axis angle and coverage overlap: (**a**) total capacity of all active beams of the LEO satellite; (**b**) minimum off-axis angle in the minor axis of LEO satellite beams; (**c**) coverage overlap between adjacent LEO satellites.  
According to the simulation results, the progressive pitch strategy based on a genetic algorithm can effectively maximize the communication capacity of the LEO satellite under the conditions of interference avoidance and seamless coverage. The performance of the proposed genetic algorithm with the initial guess of brutal force scheme could approach the limit of the interference-only scheme.  
This paper presents an interference avoidance strategy designed to ensure seamless coverage of the LEO satellite constellation. However, the interference avoidance only includes changing the satellite pointing and turning off beams and does not study the interference avoidance strategy based on power control, which is a very effective way for interference avoidance. In future research, we will focus on interference avoidance strategies with power control, which will bring benefits to the system performance improvement.  
Conceptualization, C.Z. and L.S.; methodology, L.S.; software, C.Z.; validation, J.C., F.S. and H.W.; formal analysis, C.Z. and H.W.; investigation, C.Z. and J.C.; resources, G.L.; data curation, H.W. and F.S.; writing--original draft preparation, C.Z.; writing--review and editing, H.W.; visualization, F.S.; supervision, G.L.; project administration, G.L.; funding acquisition, G.L. and L.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National special support plan for high-level talents (No. WRJH19 DH01), National Science Foundation of China (No. U21A20443), and Shanghai Sailing Program (No. 19 YF1446400).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.