esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Influence of Physical Properties of NS on Communication, Navigation, and TT&C Integration
### Atmospheric Loss in NS
#### 3.3.2 Cloud Attenuation in NS  
According to the Rayleigh scattering approximation, the water particles in clouds or fog are very small. Below 200 GHz, the attenuation rates of clouds and fog can be expressed as follows [77],  
\[\gamma_{c}=k_{1}M\ \ (\mathrm{dB/km}) \tag{8}\]  
where \(k_{1}\) is the coefficient of loss rate ((dB/km)/(g/m\({}^{3}\))), its value is related to temperature and communication frequency, and the value range is between 0.02 and 0.8 when the signal frequency is 10-40 GHz and the temperature is \(-8^{\circ}\) to \(20^{\circ}\). \(M\) is the liquid water content (g/m\({}^{3}\)), typical values of \(M\) are 0.05 g/m\({}^{3}\) and 0.5 g/m\({}^{3}\) for medium fog with 300 m visibility and fog with 50 m visibility. Then, the corresponding oblique path loss can be expressed as follows [77],  
\[A_{\mathrm{C}}=\left\{\begin{array}{ll}\gamma_{c}\frac{H_{ fog}-H_{0}}{\sin\theta}&(\theta\geq 5^{\circ})\\ \gamma_{c}\frac{2(H_{fg}-H_{0})}{\sqrt{\sin^{2}\theta+\frac{2(H_{fg}-H_{0})}{ \mathrm{Re}}+\sin  
where \(H_{\rm fgg}\) is the height of cloud (km); the meanings of other parameters are the same as in Equation (2). Figure 6 shows the simulation results of cloud attenuation in NS under different conditions.  
As can be seen from Figure 6, with the increase in frequency, the loss of clouds and fog gradually increases, but with the decrease in the antenna elevation angle, the loss also gradually increases. In addition, relatively speaking, under the condition of moderate fog, the loss of clouds and fog at a low temperature of \(-8\)\({}^{\circ}\)C is significantly greater than that at a normal temperature of 20 \({}^{\circ}\)C. Taking the elevation angle of 60\({}^{\circ}\) as an example, the loss ranges of clouds and fog in the two situations are 0.00 dB-0.002 dB and 0.222-0.831 dB, respectively.
# A Seabed Real-Time Sensing System for In-Situ Long-Term Multi-Parameter Observation Applications
###### Abstract  
Aiming at the real-time observation requirements in marine science and ocean engineering, based on underwater acoustic communication and satellite communication technology, a seabed real-time sensing system for in-situ long-term multi-parameter observation applications (SRSS/ILMO) is proposed. It consists of a seabed observation system, a sea surface relay transmission buoy, and a remote monitoring system. The system communication link is implemented by underwater acoustic communication and satellite communication. The seabed observation system adopts the \"ARM + FPGA\" architecture to meet the low power consumption, scalability, and versatility design requirements. As a long-term unattended system, a two-stage anti-crash mechanism, an automatic system fault isolation design, dual-medium data storage, and improved Modbus protocol are adopted to meet the system reliability requirements. Through the remote monitoring system, users can configure the system working mode, sensor parameters and acquire observation data on demand. The seabed observation system can realize the observation of different fields by carrying different sensors such as those based on marine engineering geology, chemistry, biology, and environment. Carrying resistivity and pore pressure sensors, the SRSS/ILMO powered by seawater batteries was used for a seabed engineering geology observation. The preliminary test results based on harbor environment show the effectiveness of the developed system.  
seabed observation; buoy; real-time; underwater acoustic communication; satellite communication; remote monitoring; sensing +
Footnote †: journal: Sensors
## 4 Analysis of Proposed FP-AFSRM
### Comparison of Magnetostatic Analysis Results and Discussion
Table 3 presents the \(T_{avg}\), \(T_{rip}\), and \(F_{avg}\) values obtained from analyses of the left stator as a \"single-stator and single-rotor FP-AFSRM\" at various current levels. Figure 10: (**a**) Torque ripple and (**b**) average force at 10 amps of single-stator and single-rotor FP-AFSRM. Figure 9: (**a**) Torque and (**b**) force curves of the single-stator and single-rotor FP-AFSRM. As seen in Table 4, when the proposed model with a double-stator and single-rotor structure operates as a single-stator and single-rotor, the torque ripple varies between 34.82% and 51.48%. The average torque ranges from 5.13 Nm to 20.84 Nm, while the axial force varies between 956.8 N and 1825.2 N. Although the proposed double-sided rotor and stator FP-AFSRM has twin factors and a single rotor in its design, the two stators are never energized simultaneously. One stator acts as the main stator, while the other functions as an auxiliary stator. When the torque curve produced by the main stator begins to decline, the auxiliary stator is positioned to generate high torque. In the range where the torque curve that is produced by the main stator starts to decline, the auxiliary stator is in a position to produce high torque. At this point, the main stator disengages, and the auxiliary stator, which will produce high torque, enters transmission. Although the proposed motor has a twin stator single rotor structure, it operates in the logic of a single stator and single rotor. Although the proposed model has a single-stator and single-rotor operating logic, it is seen that the torque ripple varies between 4.40% and 10.34%. The average torque value varies between 6.63 Nm and 28.52 Nm and the axial force varies between 713.7 and 1503.6 N. In the overall comparison, the proposed model achieved an increase in the average output torque ranging from 29.23% to 36.85%, and a smoother torque curve with reduced ripple was obtained. Additionally, the axial forces showed an improvement of 17.61% to 25.4%. The flux distribution variation with respect to the force curve for the proposed double-sided rotor and stator FP-AFSRM at 10 amperes is shown in Figure 11.  
When examining the flux distributions of the proposed model shown in Figure 11, as also indicated in the graph provided in Figure 6, the flux distribution is only on the main stator and rotor when the left stator is in transmission. Similarly, when the right stator is in transmission, the flux distribution is only on the auxiliary stator and rotor.
###### Abstract
However, defining a plant community, as a relatively uniform collection of plant species distinguishable from its neighboring patches [59], is not straightforward and researchers have attempted different systems for the organization of vegetation types with the support of different criteria such as bicclimate [60; 61; 62], ecosystem [63], physiognomy [64; 65], phytosociological association [66; 67], and dominant species [68]. Out of these criteria, bioclimatic, ecosystem, and physiognomic criteria are too broad and are only applicable for classifying vegetation types at coarse scales; whereas criteria of dominant species may be impractical for large-scale applications, especially in areas with mixed vegetation, and the characteristic species-based phytosociological association system may be inaccurately detected by remote sensing-based applications, particularly with high-resolution images. More recently, the Genus-Physiognomy-Ecosystem (GPE) classification system was developed by [PERSON] [69] for community-level vegetation classification at a large scale using high-spatial-resolution remote sensing images. In this system, community-level vegetation types are defined with the inference of dominant genus, physiognomy, and shared ecological conditions (Ecosystem). For example, Quercus Evergreen Broadleaf Forests, Quercus Deciduous Broadleaf Forests, Quercus Shrub, Alpine Herb, Coastal Shrub, Wetland Herb, etc. In the current research, the GPE classification system has been redefined as the Dominant Genus-Physiognomy-Ecological (DGPE) classification system and the resulting plant (ecological) community units are denoted as DGPE communities. Though previous study revealed the potential of the DGPE system for the classification of plant ecological communities at a regional scale using Landsat 8 multi-temporal images, it is still necessary to evaluate effectiveness of the DGPE system for seamless and operational mapping at a country level. In addition, from the previous study [69], the regional distribution characteristics of the community-level vegetation types and regional variations of the satellite data were understood. Unlike the broad categories of land cover types, the community-level vegetation types found in one prefecture may not be found in other prefectures. The satellite signals are affected by topographic and climatic variations across regions. The insights gained from the previous study drive the prefecture-wise mapping of plant ecological communities in the current research. Whereas the previous study was merely limited to the classification of plant ecological communities at a regional scale, the current research implements seamless and operational mapping countrywide by adopting the classification system developed in the previous study. The major objective of this research is to produce DGPE community maps including land cover and agricultural types (water, barren, built-up, solar panels, upland field, paddy field, cultivated pasture, and orchard) at 10 m spatial resolution at a country scale by harnessing Sentinel-2 multi-temporal images.
## Research Base  
References in this cluster were mostly published ten years ago (2008-2013), when researchers started to concern themselves with VANETs. There were many challenges on VANET in 2008, and research bloomed in all aspects.  
Considering route planning and safety applications, [PERSON] et al. discuss inter-vehicle communication (IVC) protocols and the demand for bidirectional coupling of network simulation and road traffic microsimulation for IVC protocols. They try to improve IVC and protocol evaluation by developing a hybrid simulation framework composed of the network simulation OMNeT++ and the road traffic simulator SUMO, called Veins(vehicles in network simulation) [45]. [PERSON] et al. study highway traffic regimes and propose a distributed vehicular broadcast (DV-CAST) protocol for VANETs, which relies on local topology information to handle broadcast messages and operate in all kinds of traffic regimes [49]. [PERSON] et al. focus on the broadcast storm problem (including redundancy, contention, and packet collisions), and present enhanced message dissemination based on roadmaps (eMDR), which could reduce the notification time [88]. [PERSON]. (2009) introduces a framework that helps generate the vehicular mobility model and illustrates approaches for the development of those models and their interactions with network simulators [89].  
Except for the above studies, researchers are interested in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. Results of a field testing campaign confirm the influence of the urban environment, including trees, heavy vehicles, and terrain elevation, on V2I communication and provide consideration for urban RSU deployment [47]. [PERSON] et al. conducted a review study of VANETs, including V2V and V2I communication, in terms of applications, resources, and the environment [48]. The IEEE also develops a system architecture that allows wireless access in vehicular environments [48], and [PERSON] et al. introduce the associated standards in their tutorial literature [90].
## 4 Results
### n-Alkanes and Isoprenoids, Light Hydrocarbons, and Diamondoids
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Well** & \multicolumn{4}{c}{**Chemical Composition (\%)**} & \multicolumn{4}{c}{**Dryness Index**} \\  & **C\({}_{1}\)** & **C\({}_{2}\)** & **C\({}_{3}\)** & **iC\({}_{4}\)** & **C\({}_{4}\)** & **iC\({}_{5}\)** & **C\({}_{5}\)** & **C\({}_{6+}\)** & **(C\({}_{1}\)/C\({}_{1-5}\)) (\%)** \\ \hline S1-3 & 83.65 & 5.99 & 2.66 & 0.58 & 1.01 & 0.38 & 0.45 & 0.33 & 88.31 \\ S  
shown in Table 3, the ratios of Pr/n-C\({}_{17}\), Ph/n-C\({}_{18}\), and Pr/Ph for the studied oil samples are 0.31-0.36, 0.35-0.42, and 0.98-1.10, respectively. As shown in Figure 3, the distribution patterns of the C\({}_{5}\)-C\({}_{7}\) hydrocarbons of the studied oils are remarkably similar, with n-alkanes being the main component, ranging from 35.68 to 43.21%, with an average of 40.99%. In general, molecular indicators of C\({}_{6}\)-C\({}_{7}\) hydrocarbons (n-hexane, n-heptane, and their isomers) have been consistently used in petroleum geochemistry over the years, as they have been documented to be influenced by the parent material's origin, thermal maturity, and alteration processes in the reservoir [38; 39; 40]. The C\({}_{6}\)-C\({}_{7}\) hydrocarbons of the studied reservoir oils are highly abundant in C\({}_{5}\)-C\({}_{7}\) LHs in the range of 70.15-78.10 %, averaging 75.97% (Figure 3b). In the total C\({}_{6}\)-C\({}_{7}\) range of hydrocarbons, the relative contents of alkanes, cycloalkanes, and aromatics are in the ranges of 71.73-78.38%, 14.00-19.15%, and 7.09-11.93%, with averages of 75.76%, 15.56%, and 8.68%, respectively (Figure 3b). Given the high thermal stability of diamondoids, their isomerization indicators have been effectively used to indicate thermal maturity and the extent of oil cracking [41; 42]. Generally, the methyl-adamantane index (MAI = 1-MA/(1- + 2-MA)) and methyl-diamantane index (MDI = 4-MD/(1- + 3-+ 4-MD)) are useful maturity indicators for highly mature light oils [43; 44]. Here, the calculated MAI and MDI ratios of the studied oils vary from 0.68 to 0.74 and from 0.45 to 0.50, respectively (Table 3).
## 5 Routing over the Hierarchical Virtual Topology
### Path Recovery after Node Failures
#### Recovery of a Zeroth-Tier Virtual Long-Distance Link  
When recovering a zeroth-tier virtual long-distance link, the flooding range of control packets can be restricted to a first-tier module. Therefore, a zeroth-tier virtual long-distance link can be reassigned to an alternative shortest physical path with low overhead.  
When a node assigned as a relay node in a zeroth-tier virtual long-distance link \((s,d)\) detects the failure of the forwarding node along \((s,d)\), it sends an error detection packet to source node \(s\) of the virtual long-distance link, via the reverse path of \((s,d)\). After node \(s\) receives the error detection packet, it carries out the same route discovery method mentioned in Section 5.3.1. The difference from that method alone is that the table to be updated is the long-link table.
# Principles and Characteristics of Different EDM Processes in Machining Tool and Die Steels
## 4 Research in EDM of Tool Steel
### Effect of EDM Process on the Surface Integrity
Electrodes of copper and graphite and dielectric fluids of kerosene and de-ionized water were used. The authors found that the workpiece surface had a high carbon concentration in the hydrocarbon-based dielectric fluid for both electrode materials. The residual stress on the machined surface increased as the non-homogeneities within the white layer increased. Figure 10 shows the cross-sectional micrographs of machined surfaces. A thin featureless layer was distinguished on the surface and between the overlapped recast layers (Figure 10a,b). The white layer remained featureless even at piled sections (Figure 10c,d). [PERSON] [104] utilized the atomic force microscopy technique to analyze the surface morphology, SR, and micro-cracks of AISI D2 tool steel machined using the EDM process. Figure 10: Cross-sectional micrographs of electric discharge machined surfaces (**a**) using kerosene dielectric and graphite electrode, (**b**) using kerosene dielectric and copper electrode, (**c**) using de-ionized water dielectric and graphite electrode, and (**d**) using de-ionized water dielectric and copper electrode [184]. Earlier studies also evaluated the surface integrity and stresses in machined surfaces of tool steel after EDM processes. For example, in one study [199], the effect of EDM process parameters on the 3D surface topography of tool steel was investigated, while another study [168] reported the crystallographic and metallurgical properties of the white layer in the EDM of Bohler W300 ferritic steel. In another investigation [145], the residual stress in SKD11 material was measured. The effect of the machining parameters of EDM on the surface characteristics and machining damage for AISI D2 tool steel material [105] was studied. Other works studied the surface modifications of tool steel during the EDM process for AISI H13 and AISID6 [131; 200]. Some studies [124] also investigated the effect of process parameters on the fatigue life of AISI D6 tool steel. Changes in the chemical composition of re-solidified layers of the electrodes and the workpieces during the EDM of T215 Cr12 die steel were also been studied [91]. Many researches focused on the resulting residual stresses in the machined surface, such as Reference [182].
# End-to-End Object Detection with Enhanced Positive Sample Filter
## 2 Related Work  
### Object Detection  
Object detection is one of the most challenging tasks in computer vision, and has witnessed great progress in recent years [19; 20; 21; 22; 23]. Existing CNN-based object detectors can be roughly divided into anchor-based and anchor-free detectors. Anchor-based detectors [1; 2; 4; 24; 25; 26] utilize a set of anchor boxes with different sizes and aspect ratios to predict object localizations and categories, where anchor boxes can be viewed as training samples. These anchor boxes are classified as positives or negatives via the classification task and regressed offsets to obtain the refined bounding box localizations. They can be further classified into two-stage and one-stage frameworks according to architecture designs. Two-stage detectors [1] mostly generate coarse region proposals at the first stage and then pass cropped proposal features to a downstream network at the second stage to obtain refined object localizations and categories. On the other hand, one-stage detectors [2; 4; 24] directly predict object categories and localizations based on multi-scale features. They are usually more computationally efficient than two-stage ones while achieving a competitive performance on mainstream benchmarks. However, performances of these anchor-based detectors are sensitive to hyper-parameters, e.g., sizes, aspect ratios and numbers of anchor boxes. This limits the generalization ability of models since detection tasks in different scenes may require different settings. Additionally, the training efficiency is influenced by the large computation complexity involved with the calculation of intersection-over-union (IoU) between anchor boxes and GT bounding boxes during the label assignment step. Moreover, anchor-based detectors are too difficult to deploy in actual industry applications. To this end, some works [3; 27; 28; 29] attempt to discard pre-defined anchors in fully convolutional one-stage pipelines. Most of them utilize pixel-wise prediction of object localization and categories, where center points or points inside GT regions are viewed as positive training samples with extra predicted offsets to obtain target bounding boxes. The most representative anchor-free detector might be FCOS [3], which is widely employed in industry.  
Nevertheless, all the above detectors produce redundant detection results, which makes NMS a necessary post-processing step for duplicate removal. NMS hinders the pipeline from end-to-end training, which limits the generalization capacity. For industrial deployment, the new trend is to employ an end-to-end detection pipeline.
# Fuzzy Neural Network with Ordered Fuzzy Numbers for Life Quality Technologies
## 1 Introduction  
At the present time, many IT solutions are being implemented with the aim of improving the quality of social life. These solutions for steering processes and data gathering are used to facilitate decision making or even making the decisions instead of humans, but at a significantly faster rate. It contributes to the reduction of electric power consumption and better use of resources, and, above all, allows users to save time. The foregoing solutions are implemented under the concept of the Internet of Things. For instance, having connected home devices, smart homes are then constructed. As a result, from an economical point of view, money is saved; from the psychological perspective, people have better life satisfaction. The main contribution of this paper is the proposal of the solution to the scientific problem. These days, artificial intelligence, especially deep artificial neural networks, are used in many tasks. Unfortunately, these solutions cannot always be implemented due to their requirements in terms of processor power or RAM. The authors of this paper hypothesize that an artificial neural network can be constructed by means of fuzzy logic, i.e., ordered fuzzy numbers (OFN) in its neurons. In this paper, a short introduction to OFN is presented, with the examples of their applications, such as the detection of attacks on computer networks, anticipation of server load, management of multiplexing connections, or forecasting transmission error rate. The results of these studies led to the conclusion that OFN contributes to faster responses of developed algorithms, and thus improves their quality. Moreover, the increase in the quality of solutions impacts the quality of users' lives positively. Therefore, we conducted research on our hypothesis, which is based on acomparison of the implemented network with an ordinary one. Today, millions of computers with an infinite variety of programs are being used in everyday life, which means the occurrence of numerous lines of code with potential mistakes. These mistakes may generate two kinds of issues, including working and security problems. First, stopping the work of computers may result in preventing the completion of tasks performed by people. The second issue is connected with security. Some mistakes in the code may allow hackers to gain access to the system without permission and take over the data. That is why devices and programs should be monitored to verify the proper operation. When we consider the variety of devices, including small ones with low computational power, it is necessary to have some algorithms for simple access and control. These small systems provide no possibility for installing solutions that require high computational power and lots of memory. To solve the problem of security and access that affects the user quality of life, the authors propose a fuzzy neural network with ordered fuzzy numbers, which does not require high computational power and can be used to detect and predict problems with the devices and programs.