esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background and Related Work
### Visibility Calculation in a GIS  
Certain applications benefit from GNSS shadows, for example, [31,32] predict what satellites should not be visible from different locations and then use these shadows to calculate receiver position during a survey. However, the goal of this paper is to assist the surveyor in avoiding GNSS shadows when planning the survey, particularly for mobile surveys. In this study, a GIS has been applied to help with calculating satellite visibility.  
Using a GIS to model line of sight/pathing is not a novel concept, as this capability has been used for many applications including archaeology [37], least-cost path analysis [38] and intercepting solar radiation [39]. One method is to employ the ArcMap line-of sight (LOS) tool. The LOS tool requires that you specify two points on a line, the first and last vertex. One is the observer, one is the target and then visibility is calculated through ray tracing (a method recently applied in a study for satellite visibility using Google Earth [40]). Calculating GNSS shadows over a wide area requires each satellite to be modelled and the line of sight calculated for every possible surveyor position in the area. In [18], the authors designed a system to encompass the whole GNSS satellite range, used airborne LiDAR to create a 3D city model of a city and the 3D Analyst Line of Sight tool to calculate visibility. In their work on satellite visibility, [24] also applied the 3D Analyst Line of Sight tool. In [35], the authors applied the r.obstruction and r.planning.static modules in GRASS for their work on GNSS planning.  
To improve on this, the final novel element of the proposed methodology is to apply the viewshed algorithm from ESRI ArcMap, thereby negating the requirement for each observer/target point to be specified. A viewshed calculates each target cell in a raster that can be seen from multiple observation locations, which in in this case are the satellites. Each pixel in the output image then stores a value of how many observer points can be seen from that position. In [33], the authors highlighted that the primary restriction of the ArcMap viewshed tool that prevents it from being used for satellite visibility prediction is that the observer points (satellites) cannot be thousands of kilometres from the test area and in a different coordinate system. The novel methodology proposed in this paper of using a pseudo satellite position created from the true bearing and azimuth enables the circumvention of these restrictions.
# A Constrained Coding-Aware Routing Scheme in Wireless Ad-Hoc Networks
## 4 Performance Evaluation
### Results from a Random Topology  
The other scenario is a random topology, which is a more complex network. We constructed a random topology with 30 nodes in 100 \(\times\) 100 m\({}^{2}\) areas, as shown in Figure 13, and the numbers of neighbors in each node were 1-6. Moreover, we compared the performance of CCAR, DCAR, and AODV + COPE under the random topology and evaluated the total throughput, average end-to-end delay, and energy consumption under different numbers of flows from 4-16.
## 6 Discussion  
### Comparison between the Minimum Oil Film Thickness and the Asperity Contact Percentage  
While the engine is operating, the lubricating oil separates the bearing shell and journal to prevent dry solid friction. The lubricating oil negates the heat generated by the fluid friction work. When asperity contact occurs, the bearing friction work increases sharply and results in a temperature increase in the oil film, which causes a temperature increase and a viscosity decrease in the lubricating oil. Furthermore, the bearing capacity and the minimum oil film thickness decrease, which eventually leads to bearing wear failure. Therefore, asperity contact significantly influences the lubrication state of the bearing.  
Figure 5: Axis orbits of tests and simulation calculations at different loads: (**a**) Test and calculation results at 20 MPa; (**b**) Test and calculation results at 40 MPa; (**c**) Test and calculation results at 60 MPa; (**d**) Comparison of calculation and test results under different loads.  
In Figure 5(a), the calculation results show that asperity contact occurred between the bearing shell and journal within a crank angle range of 120\({}^{\circ}\)-350\({}^{\circ}\), which corresponds to the minimum oil film thickness of less than 5 \(\upmu\)m. With a decrease in the minimum oil film thickness, the asperity contact percentage increases, which leads to a sharp increase in contact pressure and a rapid increase in friction power consumption, as demonstrated in Figure 5(b).  
As shown in Figure 6(a), the total pressure is approximately 40 MPa before asperity contact occurs. According to Figure 6(b), the total pressure is approximately 70 MPa when asperity contact occurs. As Figure 6(c) illustrates, the total pressure approaches 80 MPa when the asperity contact percentage reaches its maximum. It is thus shown that with the occurrence of asperity contact, the working condition of the bearing worsens. This indicates that the bearing is in the mixed lubrication state.  
Figure 6: Calculation results at 3000 r/min with 60 MPa (peak load): **(a)** Minimum oil film thickness and asperity contact percentage; **(b)** Friction power consumption and asperity contact pressure.  
Figure 7: Specific analysis results for the test bearing simulation at 3000 r/min with 60 MPa (peak load). **(a)** Total pressure at 110.07 deg; **(b)** total pressure at 120.07 deg; and **(c)** total pressure at 250.07 deg.  
Therefore, the asperity contact percentage indicates whether the bearing is in the mixed lubrication state. The occurrence of any asperity contact percentage should be avoided in the lubrication design as much as possible.
# Non-Uniform Fusion Tree Generation in a Dynamic Multi-Sensor System
###### Abstract  
This paper addresses the proposal that the number of processed air tracks of a two-tier fusion process can be increased by applying a balanced fusion tree which can balance tracks across local fusion nodes. Every fusion cycle, a fusion process combines duplicate tracks from multiple radars and creates a single integrated air picture (SIAP). The two-tier fusion process divides the fusion process into local and global. The results of the local fusion process, executed at local fusion nodes, are used in the global fusion process. This hierarchical structure can be modeled as a fusion tree: each radar, local fusion node, and the central server is a leaf, internode, and the root, respectively. This paper presents a non-uniform fusion tree generation (NU-FTG) algorithm based on clustering approach. In the NU-FTG, radars with higher scores get more chances to become local fusion nodes. The score of a radar is in proportion to the number of tracks of the radar and its neighbors. All radars execute the NU-FTG independently with the information of their neighbors. Any prior information, such as the appropriate number of local fusion nodes, predefined tree structure, or position of radars, is not required. The NU-FTG is evaluated in the OPNET (Optimized Network Engineering Tool), network simulator. Simulation results show that the NU-FTG performs better than existing clustering methods.  
air surveillance system; track fusion; multi-sensor tracking; two-tier fusion process; fusion tree; distributed information processing +
Footnote †: journal: Sensors
## 3 Relevant Background
### Basic Architecture of a TSN  
As mentioned previously, the TSN is a new extension of the conventional Ethernet network, although both systems' physical components are the same. Precisely, the TSN communication system consists of several end systems (ESs) (information sources and destinations), switches (SWs), and physical links that connect the ESs and SWs through the full-duplex communication technique, as depicted in Figure 1. The network model is an undirected graph \(G(\mathcal{P},\mathcal{V})\), where \(\mathcal{P}=\{\downarrow_{1},\ \ldots,\ \uparrow_{k}\}\) represents the data links set, with \(\mathcal{V}=ES\cup SW\), \(ES=\{ES_{1},\ \ldots,ES_{n}\}\), and \(SW=\{SW_{1},\ \ldots,\ SW_{m}\}\). Each dataflow link \(i\) corresponds to \(\updownarrow_{i}=[\mathcal{V}_{a},\mathcal{V}_{b}]\) where \(\mathcal{V}_{a}\) and \(\mathcal{V}_{b}\in\mathcal{V}\). Each flow must pass through one or more links to move between source and destination, which correspond to the route or path \(r_{i}\in\Re\), where \(\Re\) is the set of an ordered sequence of data stream links that connect a single ES to one (unicast) or more (multicast) ESs. Here, we consider the rate of the egressed flows from a node \(h\) is denoted as \(C_{h}\).
# Multi-formalism Models for Performance Engineering
## 3 Queueing Networks Techniques
### Approximate Computing with Finite Capacity Region
#### 3.2.1 Description of the Problem  
A car navigation system designed to meet the needs of the users of a smart city must be adaptive to the highly variable conditions set by traffic, municipality constraints, and other unexpected events. Typically, a pipeline of various stages is used. The first one is the _alternative route planning_ that consists of the identification of \(k\) alternative paths from source to destination. This step is referred to as the _K-shortest paths with limited overlap_[30] problem, since the alternative paths should overlap less than a given threshold. The shortest path in terms of distance or average traveling time might not be the optimal one, as several other parameters may have a significant influence on the computation of the best solution. Examples of conditions that must be considered are: (i) traffic status; (ii) layout of the road map; (iii) municipality constraints; (iv) transient work in progress; (v) car accidents; (vi) severe climate conditions; an (vii) other unexpected events. Consequently, the computation times required by the algorithms are highly variable not only because of the different numerical algorithms implemented but also for those other variables considered.  
To be effective, the _mean response time_ R (i.e., the time spent by a request in execution in the _fork/join region_ plus the time spent at the _Fork_ waiting to enter the region) of this step for the computation of the best route must be \(\leq 3\) s. To achieve this performance goal, sub-optimal solutions are computed considering only the results of the first \(k\) algorithms of N that completed their executions. The model is used to investigate the impact on the best route computation time of the variability of the execution times of the N algorithms and of the subset size \(k\).
# On Reliable and Efficient Data Gathering Based Routing in Underwater Wireless Sensor Networks
## 11 Simulation Results and Discussions
### Non Optimized and Energy Optimized Protocols
#### 11.3.1 Number of Alive Nodes and Network Energy Consumption  
In RE-AEDG, the defined sink trajectories and higher number of GWs lead to prolonged network lifetime. Initially both versions of U-LVRP protocol show less difference in energy consumption but with the passage of time the difference gets higher. This is due to the fact that initially nodes find their neighbors in their vicinity. However, with the passage of time the number of nodes in the network decreases. Ultimately, increased transmission distance between the neighboring nodes results on higher energy consumption of the sender node. On the other hand, for optimum RE-AEDG and AEDG, nodes create links by updating the number of predecessors already attached with the  
Figure 11: Energy consumption in delay optimized and non optimized protocols.  
Figure 10: Death rate of nodes in delay optimized and non optimized protocols.  
neighbor under consideration. This check reduces the packet reforwarding burden on the nodes. Eventually, the energy consumption is balanced in the network. Avoidance of intercommunication between the GWs leads to enhanced stability period as shown in Figures 12 and 13. In RE-AEDG-Eopt, the lowest energy consumption depicts that the cost being payed for achieving higher throughput rate could be minimized through controlled optimization process.
# A CNN-MPSK Demodulation Architecture with Ultra-Light Weight and Low-Complexity for Communications
## 3 The Proposed CNN-MPSK Architecture
### Parameter Distribution of CNN-MPSK  
The CNN-MPSK network architecture is illustrated in Table 1. The network is rather straightforward. Table 1 shows the type of operation, shape of the input and output, and the number of parameters in the operation. The network takes the \(1\times n\times 1\) single-channel data sequence as input. The convolution uses one \(1\times 3\) kernel and requires three weights and a bias. The \(1\times n\times 1\) data are processed by activation. Then, the data are pooled down by a factor of \(2\), yielding a \(1\times n/2\times 1\) output. Following this, the full connection module converts the \(1\times n/2\times 1\) input to a \(1\times M\) output with a \(M\times n/2\) kernel. Note that the activation as well as pooling has no parameters because there is nothing to learn. Thus, the total number of parameters is \(M\times n/2+M+4\), which includes \(M\times n/2+3\) weights and \(1+M\) biases. In particular, for BPSK, the value of \(M\) is equal to \(1\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type of** & **Input Shape** & **Output Shape** & **Size of Kernel** & **Parameters** \\ \hline convolution & \(1\times n\times 1\) & \(1\times n\times 1\) & \(1\times 3\) & 4 \\ activation & \(1\times n\times 1\) & \(1\times n\times 1\) & 0 & 0 \\ pooling & \(1\times n\times 1\) & \(1\times n/2\times 1\) & 0 & 0 \\ full connection & \(1\times n/2\times 1\) & \(1\times M\) & \(M\times n/2\) & \(M\times n/2+M\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The summary of CNN-MPSK structure.  
Figure 5: The operation of the full connection.  
Comparison between CNN-MPSK and Coherent Demodulation in Terms of Performance and Computational Complexity
## 1 Introduction
### Organization  
The remainder of this paper is structured as follows: Section 2 outlines our system model. In Section 3, we formulate the proposed optimization problem as a Markov decision process (MDP). Section 4 is our proposed solution based on PFL with an attention mechanism to minimize the UAVs' power consumption. Section 5 discusses the results and investigates the variations in UAV power consumption and optimal UAV trajectory prediction. Lastly, Section 6 concludes the paper.
# A Delay-Aware and Reliable Data Aggregation for Cyber-Physical Sensing
## 3 The System Model and Problem Statement
### The Network Delay Model  
End-to-end (E2E) delay is defined as the time from a packet's first transmission until its successful arrival at the sink. Let \(\Gamma_{i}\) denote the end-to-end delay from node \(i\) to sink. We define the network delay as the maximum time required for all nodes in the network finishes its single transmission to the sink. Let \(D\) denote the network delay.  
Following the network delay model in [25], assume that there are \(h\) hops from the source node \(i\) to the sink. Let \(P_{i}\) denote one routing path from node \(i\) to the sink, where \(P_{i}=\{v_{0}^{i},\,v_{1}^{i},\,\ldots,\,v_{h}^{i}\}\), \(v_{h}^{i}\) denotes the node whose distance to the sink is \(h\) hops in the routing path of node \(i\). The delay at each hop caused by transmission and aggregation processing are denoted by \(\tau_{i}^{T}=[\tau_{0},\,\tau_{1},\,\ldots,\,\tau_{h}]\). Therefore, the transport E2E delay of packet transmitted to sink from node \(i\) is \(\Gamma_{i}=\sum\limits_{k=0}^{h}\tau_{k}\). For the distributed in-network aggregation we have the equation \(D=\max\{\Gamma_{i}\}\)\((i=1,2,3,...)\).
## 6 Results and Discussion  
The burst spread spectrum based on synchronous overhead has some problems, such as low transmission efficiency, long space exposure time and decline in anti-interception performance. So, the optimal design of the burst spread spectrum is carried out. With \(P_{acq}\) better than 0.9999 and SNR of 3 dB before despreading as the design constraint, the design method of burst spread spectrum without synchronization overhead is studied using the BPSK modulation method. According to the relevant background project situation, the data length of 128 symbols can meet the needs of submarine and UAV burst transmission. Therefore, this paper assumes that the design requirements of burst spread spectrum are shown in Table 1:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Design Requirements** & **Parameters** \\ \hline \(E_{h}/N_{0}\) & 3 dB \\ data length & \(16\) bytes, 128 symbols \\ spread spectrum ratio & 128 \\ encoding mode & LDPC (1/2) \\ symbol rate & 32 ksps \\ modulation mode & BPSK \\ demodulation mode & coherent demodulation \\ signal duration & 4 ms \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design requirements.  
In addition, \(\alpha=0.3\), and demodulation deterioration is not considered temporarily, while only the carrier frequency difference and code synchronization error are considered, which are both \(1/4\), then \(d=1.87\) dB.
# Distributed Localization with Complemented RSS and AOA Measurements: Theory and Methods
## 3 Existing Methods
### Second-Order Cone Programming (SOCP) Method  
The authors in [61] studied the problem of distributed target localization based on integrating RSS (measured at all nodes) and AOA measurements (measured at anchor nodes exclusively) and proposed an algorithm based on convex optimization, namely SOCP. The main idea of the algorithm is based on deriving a relaxed version of the original problem by applying a sequence of second-order cone constraint relaxations of the form \(\|\mathbf{A}\mathbf{x}+\mathbf{b}\|\leq\mathbf{c}^{T}\mathbf{x}+d\), where \(\mathbf{A}\in\mathbb{R}^{k\times n}\) and \(\mathbf{x}\in\mathbb{R}^{n}\) is the optimization variable. This second order cone constraint is the same as requiring the affine function \((\mathbf{A}\mathbf{x}+\mathbf{b},\mathbf{c}^{T}\mathbf{x}+d)\) to lie in the second-order cone in \(\mathbb{R}^{k+1}\)[62], which is illustrated in Figure 6. Once the relaxed, convex, problem is derived, it can be readily solved by off-the-shelf solvers, such as CVX [63], a MATLAB software for disciplined convex programming. In [61], it was assumed that network coloring scheme [40] was achievable in order to establish a working hierarchy between targets and avoid message collisions.  
Although convex problems are easily solved Today, their solution is sub-optimal due to problem relaxation, which might increase the set of feasible solutions, as illustrated in Figure 7. Therefore, the quality of their solutions depends on the tightness of the applied relaxations since, otherwise, one may end up with a solution from the expanded set that might be considerably far away from the true one.
## 4 Discussion
### Performance of Retrieval Indexes  
The I\({}_{3}\) index (blue-to-green ratio) scores highest, appearing nine times in the top-performing models. The superior performance of the I\({}_{3}\) index has been consistently recognized in comparative studies across varying trophic levels, including low trophic [18; 19; 23; 25; 86] and hypereutrophic [33] conditions. Of the nine instances, two are for Sentinel-2 and Landsat 8 each, four for Landsat 7, and one for Landsat 5. The second-most-frequent retrieval index among the 28 models is the I\({}_{\text{18}}\) index (also known as BRG and KIVU), with five appearances, three of which are for Sentinel-2 with average RMSLE and R\({}^{2}\) values of 0.30 and 0.39, respectively. In third position, we find I\({}_{\text{24}}\), with two instances each for Landsat 8 and 7. Aside from I\({}_{\text{24}}\), other retrieval indexes with formulations similar to FLH--such as I\({}_{\text{23}}\) (CI), I\({}_{\text{25}}\) (MCI), and I\({}_{\text{26}}\) (MPH), which are more commonly used with low-spatial-resolution satellites with enhanced fluorescence detection capabilities like MERIS, MODIS, and OLCI [87]--were absent from the top-performing models. Possibly, this reflects their poor performance in low-turbidity offshore waters, compared to turbid coastal waters [88]. The I\({}_{4}\) (NIR-to-green ratio), I\({}_{6}\) (blue-to-red ratio), I\({}_{7}\) (green-to-red ratio), and I\({}_{12}\) (NDGRI) indexes each appear twice, while I\({}_{1}\) (UB-to-blue) appears only once.  
Importantly, 18 of the 27 originally retained retrieval indexes from the literature are not in the top-performing models, including I\({}_{8}\) (RE-to-red ratio) that forms the basis of the equations used in [17]. Our findings further suggest poor performance of I\({}_{10}\) and I\({}_{11}\) (NIR-to-red ratio), I\({}_{16}\) and I\({}_{17}\) (NDVI), and I\({}_{27}\) (SABI) for Chl-_a_ concentration retrieval in low-turbidity waters. While some previous studies reached similar conclusions (e.g., [20]), others have reported that the use of the NIR-to-red ratio can be effective in Case 2 (turbid) waters where high concentrations of CDOM and suspended sediments complicate the retrieval of Chl-_a_ concentrations [22; 23; 32; 86]. Nonetheless, simple two-band ratios appear to perform better than more complex three-band ratios, while the sole four-band index considered is not among the top performers. One advantage of two-band ratios lies in their reduced dependency on the accuracy of atmospheric correction, leading to more robust inversion capabilities within water colour algorithms [23; 29]. Overall, our results support ACOLITE paired with I\({}_{3}\) as yielding the best-performing retrieval combinations.
# Gear Wear Process Monitoring Using a Sideband Estimator Based on Modulation Signal Bispectrum
## 1 Introduction
[16] introduced MSB, which is a significant improvement of the CB in that it enhanced CB to apply the modulation signal efficiently. The results showed reliable fault diagnosis and revealed that random noise can be suppressed effectively, being much better than that of the power spectrum (PS) and CB for diagnosing different seeded faults, such as tooth breakage and shaft misalignment [16]. MSB-SE--an extension of MSB analysis--was developed recently, and is used to achieve a simple representation for the complicated signal contents [17], which allows the small sidebands be estimated with a good degree of accuracy and hence produce more accurate and consistent diagnostic results [17; 18; 19; 20]. Inspired by the works presented in [17; 19], where MSB was successfully used for identifying both the presence and magnitude of the seeded faults, MSB method would be used to evaluate the natural gear wear monitoring and severity assessment--although it may be more challenging, as the wear process may induce vibration signature changes far weaker than tooth breakages. To examine the performance and extend the applications of modulation signal bispectrum-based sideband estimator (MSB-SE)-based vibration analysis technique to the monitoring of gearboxes, this paper investigates monitoring the deterioration process of an industrial multi-stage helical gearbox based on vibration measurements and MSB analysis. It applies MSB analysis to vibration signals measured progressively from a run-to-failure experiment to obtain accurate modulation sidebands characteristics, MSB-SE, and its corresponding coherence. A dimensionless indicator is proposed based on the peaks of MSB-SE slices, and its evolutionary behaviour with operating times are obtained, thereby implementing gear wear monitoring and severity evaluation. The paper has four sections. Section 2 presents the theoretical basis of gear wear detection based on the vibration signals analysis using MSB-SE. Section 3 describes the run-to-failure experimental setups for validating the proposedmethod. Section 4 shows the monitoring results in conjunction with critical interpretations, and finally, Section 5 is the conclusion.