esa-sceva/satcom-chunk-collection huggingface dataset:
# Rain Monitoring with Polarimetric GNSS Signals: Ground-Based Experimental Research
## 2 Experiments and Data
### Data Processing Algorithm  
#### 2.3.1 Observation Equation  
Observation from our GPS receiver is pseudo-range and carrier phase. However, data from horizontally and vertically polarized (H-pol, V-pol) signals are observed independently by the dual-polarized antenna and tracked separately by the receiver. The polarimetric phase shift is the carrier phase difference between H-pol and V-pol phase. The observation equation of carrier phase can be expressed as [24]  
\[\phi(t)=\rho(t)+\rho_{atm}(t)+\rho_{hyd}(t)+\rho_{ion}(t)+m(t)+d+C(t)+b \tag{3}\]  
where \(\rho\) is the geometry distance between GPS satellite and the receiver, which is equal for H-pol and V-pol signals; \(\rho_{atm}\) represents the phase delay induced by neutral atmosphere (the same for H-pol and V-pol signals); \(\rho_{hyd}\) denotes the phase delay due to hydrometeors along the transmission path; \(\rho_{ion}\) is the ionospheric delay; \(m\) denotes local multipath effect on each signal; \(d\) represents the hardware effect; \(C\) refers to the clock drifts and errors of the satellite; the term \(b\) denotes phase ambiguity, which is an arbitrary initial constant for each signal; \(t\) is the time.  
Therefore, the carrier phase difference is  
\[\Delta\phi(t)=\phi_{H}(t)-\phi_{V}(t)=\rho_{hyd}^{H}(t)-\rho_{hyd}^{V}(t)+ \Delta m+\Delta d+\Delta b \tag{4}\]  
Figure 2: Sky view of Global Positioning System (GPS) satellites related to the experimental site (31.6\({}^{\circ}\)N, 119\({}^{\circ}\)E) at 21:54 on 3 June 2015 and receiving area of the dual-polarized antenna (enclosed by red dashed line).  
where \(\Delta m=m^{H}-m^{V}\), \(\Delta d=d^{H}-d^{V}\), \(\Delta b=b^{H}-b^{V}\).  
Based on Equation (4), the geometry distance and effect of neutral atmosphere, ionosphere, clock drifts and errors can be eliminated. However, those problems such as hardware effect, phase ambiguity and multipath effect should be further solved.
## 4 Efficient Intersection Solver  
The problem of intersection is summarized as follows: for an AMS (a convex polyhedron) and a ray (starting from the origin) in \(3D\) space, find the intersection point of the ray onto the AMS boundary. In our specific problem, the ray direction is defined by a vertex of the RMS. The difficult step is to find which facet the ray intersects with. Once the facet is found, the intersection point can be solved as a ray tracing problem [15].  
An example of the problem is shown in Figure 2(a). The problem can be simplified by rotating the original coordinate system representing the moment space and correspondingly a dimension reduction. Given the query direction of the ray, a coordinate transformation by rotation can be applied such that the ray aligns with the \(x\)-axis in the transformed coordinate, as shown in Figure 2(b). By doing so, the complexity of the problem is immediately reduced, due to the facts that:After rotation, only facets in the positive-\(x\) half (rotated) space can be the intersection facet, meaning the number of search facets is reduced by approximately half.  
The intersection facet contains the origin in the rotated \(y\)-\(z\) plane, meaning the 3 vertices of the facet can only be of one of the following 3 cases:  
* lie in 3 different quadrants of the \(y\)-\(z\) plane,
* only lie in quadrants 1 and 3 in the \(y\)-\(z\) plane,
* only lie in quadrants 2 and 4 in the \(y\)-\(z\) plane.  
This further reduces the number of possible candidate facets.  
The fixation for the intersection facet reduces to a \(2D\) \"point-in-triangle\" problem--the three vertices of the intersection facet must enclose origin in the \(y\)-\(z\) plane.  
Once the facet is found, the final solution can be found by ray tracing.  
Accordingly, a process diagram to solve for the intersection point is given in Figure 4. Note that, although the loop to search for the facet is inevitable, the calculation in each search iteration is minor compared to solving a set of linear equations. The following sections in this chapter briefly introduce the aforementioned steps.  
Figure 3: Comparison between the (**a**) original problem and (**b**) the rotated problem. To calculate the intersection, the priority task is to locate the intersection facet. In the rotated space as of (**b**), the query direction coincides with the \(x\)-axis. Therefore, the intersection facet can be located by solving a 2D “point-in-triangle” problem, that is, the facet which contains origin is the facet to solve for.
# GAN-LSTM Joint Network Applied to Seismic Array Noise Signal Recognition
###### Abstract  
The purpose of seismic data processing in nuclear explosion monitoring is to accurately and reliably detect seismic or explosion events from complex ambient noises. Accurate detection and identification of seismic phases are of great significance to the detection and parameter estimation of seismic events. In seismic phase identification, discriminating between noise signals and real seismic signals is essential. Accurate identification of noise signals helps reduce false detections, improves the accuracy of automatic bulletins, and relieves the workload of analysts. At the same time, in seismic exploration, the prime objective in data processing is also to enhance the signal and suppress the noises. In this study, we combined a generative adversarial network (GAN) with a long short-term memory network (LSTM) to discriminate between noise and phases in seismic waveforms recorded by the International Monitoring System (IMS) array MKAR. First, using the beamforming data of the array as the input, we obtained the signal features of seismic phases through the learning of the GAN discriminator network. Then, we input these features and trained the joint network on mixed seismic phase and noise data, and successfully classified seismic phases and noise signals with a recall of 95.28% and 97.64%, respectively. Based on this model, we established a real-time data processing method, then validated the effectiveness of this method with real 2019 data of MKAR. We also verified whether improved noise signal identification improves the quality of phase association and event detection.  
GAN; LSTM; seismic array; noise signal recognition +
Footnote †: journal: Applied Sciences
## 3 GPS and AI in Agriculture  
GPS is a satellite-based navigation system, which uses a geographic coordinate system to give us the accurate location of a GPS module [6]. The geographic coordinates system consists of the longitudes and the latitudes. Every location on Earth can be specified using a set of numbers. The longitudes specify the horizontal position (east-west) and the latitudes specify the vertical position (north-south). Both combined pinpoint the exact location on the Earth. GPS is utilized to accurately measure the location of agriculture machinery working in the field. With the GPS coordinates at every instant of work done, we can keep track of the movement of the agriculture machinery. The most feasible way to record the GPS data these days is using smart IoT devices including modern smartphones. Modern smartphones contain a built-in GPS module that could be utilized for data collection purpose.  
AI has some very convenient use-cases that are revolutionizing the world [3]. It became a very powerful tool, which can be helpful to companies standout in the competition with their rivals, accomplishing tasks with ease and speed. The use of AI techniques surely reduces the chances of making errors as well as a great help in making critical decisions [21]. AI is making machines smart enough that they could mimic the cognitive functions of the human brain which includes decision making, learning and problem-solving.  
In agriculture, besides seeds, fertilizers and water, agricultural productivity is also dependent on on-farm labour productivity, which in turn is dependent on whether the farmers have access to tools, equipment and machinery to carry out farm operations efficiently. Mechanization, therefore, plays an important part in improving agriculture productivity. Farm mechanization has an enormous potential to leverage the efforts of farmers and boost crop productivity. By finding the precise work area of agriculture machinery, we can efficiently allocate our resources to increase productivity even further.  
In this paper, we discuss a use case where we detect the boundary points of the GPS data that we collect to record the movements of agriculture machinery. By getting the boundary points, we can determine the work area of machinery using AI algorithms.
# The Simulation Analysis of Cutting Characteristics and Chip Formation Mechanism in Gear Skiving
###### Abstract  
Gear skiving is an efficient and high-precision gear processing method, particularly advantageous for internal gears and coupling gears. However, the main limitation of its application is the rapid wear of skiving tools. This paper combines simulation analysis and numerical calculations to reveal the essential characteristics of the gear skiving process and the fundamental causes of tool wear. Firstly, it researches the distribution of cutting loads on the tool edges through the kinematic simulation analysis of gear skiving. Then, it analyzes the changing rule of working angle, clearance angle, tool inclination, and cutting speed and reveals the cutting state and chip flow patterns during the skiving process through numerical simulation calculations of gear skiving. Finally, it investigates the chip formation mechanism and the stress variation rule on the tool edges through finite element simulation of gear skiving. These analyses and calculations provide optimization guidance and methods for improving the cutting performance and the service life of skiving tools.  
gear skiving; cutting characteristics; chip formation; cutting simulation; tool wear Article
# Maximum Transmit Power for UE in an LTE Small Cell Uplink
## 3 Network and Propagation Model
### HetNet Deployment with Small Cells near Macro Cell eNodeB
#### 3.3.2 Interference  
The interference \(I_{\text{small}}\) offered by one interfering ring at a distance of \(2R\) from the central small cell eNodeB \(x\) can be given using Equation (9) as:  
\[\begin{array}{c}I_{\text{small}}=2\pi\int_{R}^{2R}\rho_{ue}p_{\text{o\_small }}\text{A}^{-\alpha_{\text{small}}}(2R-r)^{\alpha_{\text{small}}\eta}\text{A}r ^{-\eta}rdr\\ +2\pi\int_{2R}^{3R}\rho_{ue}p_{\text{o\_small}}\text{A}^{-\alpha_{\text{small}} }(-2R+r)^{\alpha_{\text{small}}\eta}\text{A}r^{-\eta}rdr\end{array} \tag{21}\]  
The macro cell interference is similar to that considered in Section 3.2. The total HetNet interference using Equations (18) and (21) is given as:  
\[I_{\text{HetNet}}=I_{\text{macro}}+I_{\text{small}} \tag{22}\]  
Compared to homogenous small cell deployment scenario, the interference is expected to be lower as there is only one interfering ring of small cells in this case.
## 5 Wireless Technologies Roadmap and Future Trends for MG
### MGMS Structure Development  
New wireless technologies should consider communication specification requirements of MG application such as bandwidth, latency, and security. To provide a mechanism for applying wireless in MG, we require a definition of unified information exchange and standard. The unified model should be flexible to apply in the different control structures of MG depending on the extent, application, and geographical location of MG. As discussed before, MG can be centralized or decentralized. One of the promising strategies in distributed control of MG is MAS, which facilitates control of MG and assists the SG concept approach in reality by accommodating self-healing characteristics for the power grid [7; 58; 59; 60; 61]. In the MAS environment, every element of MG can interact and cooperate with other elements intelligently to achieve power system global objectives, as can be seen in Figure 4. In this case, although there is no \"one size fits all\" approach, robust technologies such as 5G can be deployed in two control architectures. Central control of MG can derive active power and reactive power of load information and control generation portion of each power resource in MG by applying 5G communication and cloud computing at the edge. It is also possible for 5G employment in decentralized MG control based on droop control power, which needs real-time reference control signals, large distance coverage, and security [62]. As well as the independent grid characteristic of MG, there are new aspects in the smartization of MG, making it an integrated part of the public grid, which is the main hypothesis of this paper. MG as an AS of the main grid is one of the aspects, which will be an incentive for penetration extension of MG as it provides profit for owners. Such aspects require the contribution of technologies in MG such as V2G, smart inverter, etc. Wireless technology is a good candidate as a communication infrastructure of these technologies [63]. AS provision of the main grid, including frequency regulation, voltage control, and black-start aid by MG needs its communication characteristics, especially time constraints, which can be met by 5G mm-Wave characteristics offering fast, reliable, and robust interacting [64; 65; 66; 67].
# Development and Testing of a Two-UAV Communication Relay System
## 3 Hardware Platform
### Mission Control Subsystem  
The control of key onboard functions: communication relay, flight status monitoring, and mission control, is centrally managed by the mission control subsystem, which UAV \"Mom\" and \"Son\" both carry onboard. The onboard mission control subsystem is mainly in charge of the following functions:  
* Executing radio communication frame processing and relay control.
* Decoding commands contained in frames, and translating the commands into low-level instructions to the UAV's flight controller.
* Monitoring UAV flight status, and reporting to the GCS on a regular basis.
* Handling ad hoc functions that specific missions necessitate, for example, image processing, dynamics flight route planning, smart target identification, etc.  
Figure 6: TDM process flow chart.  
Figure 7: Frequency hopping of telemetries.  
The above-mentioned functions require the UAVs to be equipped with an on-board computer with adequate processing power. In this research, Raspberry Pi 2 Module B (Rpi 2B) was chosen as the onboard computer for both UAVs because of its relatively small size, strong processing power, and abundant IO port. Figure 8 shows the on-board computer module, with Rpi 2B mounted on top.  
The onboard computer needs to frequently exchange data with and acquire information from other onboard devices: radio telemetries, the flight controller, the GPS module, and various onboard sensors. The onboard computer communicates with the peripheral devices through USB ports. Rpi 2B readily supports USB protocol and has four USB ports available. Most peripheral devices, however, utilize a low-level communication method named Universal Asynchronous Receiver and Transmitter (UART). Therefore, a protocol conversion module based on CP2102 microchip was wired between the USB ports of Rpi 2B and the peripheral devices, which handles the conversion between the USB protocol and the UART protocol. Figure 9 shows the hardware connection diagrams of the onboard computer module with other subsystems. The Rpi 2B of \"Son\" was connected to two USB devices which are T2-2 and the flight controller. The Rpi 2B of \"Mom\" was connected to three USB devices including T1-2, T2-1, and the flight controller.  
The onboard computer module is powered by the UAV's main battery and is switched on when the UAV is powered up. After power-up, the onboard computer module then automatically executes  
Figure 8: Onboard computer module.  
Figure 9: Hardware connection diagram of (**a**) UAV “Son” and (**b**) UAV “Mom”.  
a self-initialization program, which configures necessary system parameters and starts up a set of software programs. The programs then run on the onboard computer module and execute functions such as communication relay control and mission control. Details about software structure and realization of the communication relay subsystem will be discussed in Section 4.
# A Novel Satellite PRN Code Assignment Method Based on Improved RLF Algorithm
## 5 Discussion  
The traditional method of satellite PRN code assignment is suitable for GNSS constellations consisting of dozens of satellites. However, LEO constellations have huge satellite numbers compared with the GNSS. These constellations may operate as navigation systems and may broadcast navigation signals in the future, placing a huge burden on the navigation receiver. Therefore, a novel satellite PRN code assignment method needs to be proposed.  
As aforementioned, the novel method in which several satellites share the same PRN code was shown to be feasible and beneficial for the navigation receiver and PRN code design. For most LEO constellations, the storage resources and the average satellite acquisition time of the receiver will be between one-fifth and one-tenth of the previous values when this method is used. Even if the orbital altitude increases to 3000 km, the storage resources and the average satellite acquisition time of the receiver will be about one-third of the previous values. This method can also greatly reduce the design difficulty of PRN codes.  
For hybrid constellations, we suggest that the minimum number of PRN codes is calculated for each polar-orbiting or walker constellation separately at first. For constellations proposed by different companies, we also suggest that the minimum number of PRN codes is calculated for each constellation separately. This can avoid the mixing of signals from different satellites sharing the same PRN code as much as possible.  
In terms of algorithms, we believe that several algorithms can be used for obtaining the satellite PRN code assignment scheme, but in order to minimize the number of PRN codes as much as possible, the RLF algorithm was selected and improved. If there are new algorithms available for graph coloring in the future, we will try to apply them to the satellite PRN code assignment problem.  
It should be pointed out that the conclusions of this paper were obtained under the condition that the constellation configuration remains unchanged. However, the actual satellite is affected by various perturbation forces, and the orbital parameters of the satellite will shift and change periodically. Therefore, in future work, after making the satellite orbit more precise, it will be possible to obtain better satellite PRN code assignment results.
## 4 What Is NFV?
### How NFV Will Help Cellular Network  
The virtualization technique provides the capability to move the network functions onto virtualized environments, leading to numerous benefits for power consumption reductions in cellular communication systems. Hardware abstraction helps the operator to choose more energy-efficient hardware to consolidate network functions into fewer and more powerful servers. By running multiple virtual network functions on a single physical server, NFV makes server consolidation easier. Comparatively, this consolidation improves the utilization of hardware resources and reduces the power consumption of the network [17]. Based on the workload, NFV dynamically allots resources such as memory and processing power. This mechanism reduced power consumption, especially during times of low network activity or changes in the network traffic. Along with these, NFV also enables the use of low-power processors and hardware optimized for specific network functions [91].  
Figure 11 illustrates the power consumption in traditional network appliances used for deploying various network functions, as well as in a virtualized system. It has been shown that virtualization techniques can reduce power consumption by approximately 40 to 50%, depending on the dynamic placement of VNF chains [92]. An architecture for 5G networks that supports NFV was proposed by [PERSON] et al., and it was found that virtualization can result in energy savings of up to 38% [93]. Additionally, [PERSON] et al. [94] utilized Bell Lab's GWATT tool to quantify the energy savings achieved by implementing NFV technology in different parts of the cellular network. Their findings indicated that virtualizing the core and RAN can lead to reductions in power consumption by 22% and 17%, respectively. Furthermore, NFV plays a significant role in deploying edge computing resources closer to end-users at the network's periphery. This approach reduces the need for long-distance data transmission, thereby decreasing the power consumption associated with data transport.  
NFV can also be applied in RAN. With the introduction of C-RAN, computing resources are being deployed closer to user equipment, facilitating the execution of critical network functions and applications in a virtualized environment. Virtualized RAN leverages NFV principles by distributing virtualized baseband functions across multiple servers, thereby enhancing network efficiency and flexibility [95].
# Tracking Extended Targets: Novel TPMB Filter Driven by Model and Data Collaboration
## 2 Background  
In this section, we first introduce the state and trajectory parameters of targets, then model the multi-target extended model state parameters and measurements, and finally present the TPMB approximation of the PMBM posterior density.  
A finite spatial set is \(D\), which is denoted as \(\mathscr{F}(D)\), and the cardinality of its subset \(A\in\mathscr{F}(D)\) is \(|A|\). We use \(\uplus\) to denote the union of disjoint sets, \(\langle f,g\rangle\) to denote the inner product \(\int f(x)g(x)dx\), and the multi-target index is represented as \(f^{A}\). For certain real-valued functions, the product \(\prod\limits_{x\in A}f(x)\) with \(f^{\varnothing}=1\) is conventionally represented. Additionally, \(\delta_{x}[\cdot]\) and \(\delta_{x}(\cdot)\), respectively, denote the Kronecker delta centered at \(x\), and the Kronecker delta function. Here, \(x\) in \(\delta_{x}[\cdot]\) represents a subspace \(\mathscr{V}\) within the Cartesian product space, \(x\in\mathscr{X}^{\mathscr{V}}\).