esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Current Status of Research
### Air-To-Ground Optical Communications
These results show that UAV laser communication technology has significantly progressed in terms of miniaturization and high efficiency [6]. A physical diagram of the device is shown in Figure 5.  
Figure 4: Schematic diagram of uncrewed aerial vehicle and ground-end optical communication. In 2018, Prof. [PERSON] of Xi'an University of Technology and his research team conducted experiments on near-ground coherent optical communication, focusing on automatic control, polarization measurement and control, adaptive optics, and other technologies of the APT system. The experiments set the communication link distances to 600 m, 1.3 km, 10.2 km, and 100 km and used communication light with a 1550 nm wavelength. The experiments used the BPSK-modulated outlier detection method with a communication rate of 100 Mbps and successfully transmitted high-definition videos and images. These experiments are significant for developing coherent optical communication technology and applying it in near-earth communication environments [7; 8]. In 2019, the Thirty-fourth Research Institute of China Electronics Technology Group Corporation developed an airborne laser transmission technology for fast lift-off base station UAVs, realizing a communication distance of 5 km. which shows that the technology enables the UAV end and the ground end to automatically scan and quickly capture to complete tracking and targeting, accomplishing the establishment of a communication link within 8 s to meet the rate requirement. In addition, the institute conducted an extensive file transmission test, and the UAV-side and ground-side access points achieved an average transmission rate of 99.79 Mb/s, which enabled large files to be transmitted in seconds. The success of this technology demonstrates the strong potential of UAV laser communications for rapid deployment and high-speed transmission [9]. In 2020, [PERSON] et al. designed a high-precision targeting and tracking system for on-orbit laser communication. The communication model is shown in Figure 6. The system adopts the form of coarse and fine two-stage aiming, and the total weight of the terminal is 1.8 kg. The timing error of the system was less than 10 \(\upmu\)rad in the simulated external interference environment test [10]. Figure 5: Physical image of unmanned aerial vehicle’s laser communication equipment. Figure 6: Unmanned communication model. In 2023, [PERSON]. proposed a joint optimization algorithm based on UAV dynamic trajectory and transmit power. The communication model is shown in Figure 7, and the link transmission rate was analyzed under different visibility conditions. The results show that the FSO and RF links reach full-speed transmission when the visibility is 1.7 km. The proposed algorithm shows a 447.5% improvement in the average communication rate compared to the average communication rate of a fixed relay device and a 32.5% improvement in the average communication rate compared to the average communication rate of a link with a non-powered optimization algorithm [11].
# Sound Localization Framework for Construction Site Monitoring
## 3 Literature Review  
### Monitoring Approaches Suitable for Construction Sites  
#### 3.1.1 Active Localization Approaches  
RFID is one of the most widely used localization methods. RFID-based localization systems are highly accessible as they are easy to process RFID data at low costs [10]. Furthermore, they are used in closed environments for material status updates and supply network management [2; 11]. However, RFID tags are more suitable for updating data and have limited use at construction sites for localization [12; 13].  
Bluetooth technology involves low-bandwidth wireless frequency signals used for communication between devices. Beacon-based localization systems, where a beacon is a mobile tag that transmits Bluetooth signals, estimate locations by measuring distances based on the signal strength recorded by the receiver [14]. While these systems have a wide application range and are easy to install, tags can be easily lost or damaged at construction sites, while systems can face problems such as weak Bluetooth signals and repetitive receiver installations [15].  
Furthermore, there are localization methods that use GPS and IMU sensors. GPS-based localization methods calculate the location of mobile devices by using data connections with satellites or base stations. GPS provides state-of-the-art localization when used outdoors, especially when utilizing unencrypted military satellite signals. However, GPS is difficult to use indoors [16]. IMU sensors measure the movement of an internal gyroscope to calculate the movement path of an object and measure the distances. Furthermore, they can operate  
Figure 1: Research process.  
without an external connection. However, the movement paths are calculated relative to the starting point, and acceleration errors quickly accumulate into larger position errors if no additional correction is applied [17].  
Despite the high accuracy and easy accessibility of sensor-based localization methods, they have several limitations when applied to construction sites. Sensors attached to materials and equipment can be lost or damaged easily during construction. Furthermore, considering that several tags are used at a construction site, the number of added receivers is significantly large, which increases costs and causes management difficulties.
# Performance Optimization of Grounding System for Multi-Voltage Electrical Installation
## 2 Principles of Grounding Systems
### Purpose of Grounding Systems
#### 2.1.2 Ensuring System Stability and Operational Reliability  
Grounding systems stabilize the electrical system by placing certain values on an electrical reference point of the system voltage. Without proper references, voltages may vary widely up or down and lead to equipment malfunctions or, in worse cases, failures. Grounding ensures that during normal operations, all parts of the system maintain consistent potential with respect to the earth, further reducing overvoltage risk and aiding in ensuring that protective devices will operate reliably.  
The increase in potential, \(V_{rise}\), at any point during a fault, depends both on the grounding system impedance as well as the fault current. The rise in potential across a connection at the site of a fault can be expressed as.  
\[V_{rise}=I_{fault}\times Z_{ground} \tag{2}\]  
where \(Z_{ground}\) is the impedance of the grounding system. System stability is highly dependent on ensuring that the impedance is minimized, particularly in high-voltage systems like 10 kV and 35 kV, where high-fault currents can cause significant potential rises.
# Voice Transformation Using Two-Level Dynamic Warping and Neural Networks
## 3 Voice Transformation Process
### Two-Level Dynamic Warping
The metric distance, \(\text{dist}(s_{1}(k_{1}),s_{2}(k_{2}))\), used looks only at the magnitude of the spectral information, so  
\[\text{dist}(s_{1}(k_{1}),s_{2}(k_{2}))=(|\,|s_{1}(k_{1})|-|s_{2}(k_{2})|\,|). \tag{4}\]  
When there are low energy speech segments (such as from unvoiced speech) which have no particular spectral information to match, the distance \(d(\mathbf{s_{1}},\mathbf{s_{2}})\) is generally noninformative. In order to downplay this effect the small energy segments, the minimimum energy \(E_{\text{min}}=\text{min}(\|\mathbf{s_{1}}\|^{2},\|\mathbf{s_{2}}\|^{2})\), is used to scale the distance, as follows. At the end of frequency warping process, the metric distance between spectral vectors to be used in (2) is computed as \(d(\mathbf{s_{1}},\mathbf{s_{2}})=E_{min}\,d_{F}(K,K)\). The DFW on each feature vector produces a sequence of indices \(\mathbf{a}_{F}=(a_{F}(1),a_{F}(2),\ldots,\,a_{F}(N_{F}))\) and \(\mathbf{b}_{F}=(b_{F}(1),b_{F}(2),\ldots,b_{F}(N_{F}))\). \(\mathbf{a}_{F}\) and \(\mathbf{b}_{F}\) are called the spectral warping function paths. Here \(N_{F}\) is the length of the spectral warping paths, which may be different from segment to segment. The temporally aligned feature vector of speaker \(\mathbf{s_{1}}_{TA}\) is transformed to spectrally match the target spectrum information of speaker \(\mathbf{s_{2}}\) by creating a modified, spectrally-aligned (SA), feature vector according to  
\[\hat{s}_{1,TF}(a_{F}(i))=s_{1,TA}(b_{F}(i)),i=1,2,\ldots,N_{F}. \tag{5}\]  
This SA map drags spectral components of source vectors to produce transformed voice spectra. Each time-aligned and spectrally-aligned segment is inverse Fourier transformed to produce the time signal for segment \(\ell\), \(\hat{\mathbf{r}}_{2}(\ell,:)\). This time signal is windowed (we used a Hamming window of length 512 with 16-ms overlapping when the sample rate was 16,000 samples/sec) and the overlapping segments are added together. The combination of the inner and outer warping processes applied to speech signal is portrayed in Figure 3. Starting at the bottom of the diagram, a speech signal is split into different overlapping segments. Spectral features are computed for each segment. These spectral feature vectors are passed through DTW for time alignment, and at every stage of DTW, the metric between vectors is computed using DFW. The method for doing two-level DW to achieve VT is given in Algorithm 1. \[\mathcal{S}_{i}=\{\mathbf{s}_{i}(1,\cdot),\mathbf{s}_{i}(2,\cdot),\ldots, \mathbf{s}_{i}(L_{i},\cdot)\},\qquad i=1,2. \tag{1}\]0 Inner/outer dynamic warping.
# A Scene Graph Similarity-Based Remote Sensing Image Retrieval Algorithm
## 3 Remote Sensing Image Retrieval Algorithms Based on Scene Graph Similarity
### Similarity Calculation of the Remote Sensing Image of the Fusion Scene Map  
#### 3.4.1 Feature Extraction Module of the Scene Image  
A graph feature extraction module typically involves node embedding, which is often implemented using graph convolutional networks (GCNs) [41]. Traditional GCNs only consider the node features and graph structure, ignoring the edge information. To solve this limitation, we introduced a GCN with an edge feature (EGCN) [42]. The structure of the graph convolutional module composed of the EGCN is illustrated in Figure 10. In the EGCN, the input to the first layer included node features X0 of size N \(\times\) NF, with N being the node number in the graph and NF being the feature number per node. In addition, edge features E0 of size N \(\times\) N \(\times\) NP were included, where NP denoted the feature number per edge, and Xi and E represented the outputs of the i-th layer of the EGCN. Unlike traditional GCNs where the same adjacency matrix is used as the input in each layer, the EGCN replaced the adjacency matrix with an edge feature matrix. This allowed the neural network to learn using both node and edge features. In the EGCN, the edge features from the output of the previous layer were applied as the input to the next layer, enabling dynamic adjustments at each layer. This contrasts with traditional GCN modules where the same adjacency matrix is input to each GCN layer without incorporating edge-specific information.
# Future Directions for Electric Propulsion Research
## 1 Introduction  
The use of electric propulsion (EP) for space applications is currently undergoing a rapid expansion. There are hundreds of operational spacecraft employing EP technologies with industry projections showing that nearly half of all commercial launches in the next decade will have a form of electric propulsion. In light of their widespread use, the thruster types that have fueled this expansion--moderately-powered (1-20 kW) Hall effect, electrothermal, and ion thrusters--arguably have now achieved \"mature\" operational status. Given that the previous decades of electric propulsion research have been directed at the proliferation of these thrusters, we must decide where to aim continued efforts now that this goal has been achieved. What are the remaining challenges related to the operation of mature technologies, and which new technologies should be explored, if any?  
The goal of this article is to outline possible future directions of the field of electric propulsion by discussing the challenges faced by modern technologies. We begin with a brief overview of the concept of the \"s-curve\" to represent technology development cycles and to illustrate the different types of challenges the field currently faces. We next consider the research questions related to electric propulsion systems at different power levels. We conclude by summarizing the common challenges for EP devices and then reviewing strategies for overcoming these issues and quickening the pace of development for more immature technologies.
# On Fuzzy and Case-Based Dynamic Software Development Process Modeling and Simulation Approach
## 1 Introduction
However, SDP simulation is insufficient and lacks both theoretical and practical components. Current BP modeling and simulation techniques are suitable for the modeling and simulation of a static BP and can precisely predict which activities to execute and in what order. The bulk of present tools and techniques demand extremely tight BP specification, and unanticipated BP activity sequences cannot be included during BP execution [1]. Consequently, the main aim of this paper is to propose an approach to fuzzy and case-handling-based dynamic SDP modeling and simulation. For the sake of clarity, the Agile SDP management methodology is chosen for research in this paper. The main scientific contributions of this paper are as follows:  
1. A new, fuzzy, case-handling-based dynamic SDP modeling and simulation approach is proposed, which allows us to predict the SDP in terms of its resources, task execution time, and sequence of tasks. 2. The proposed approach to fuzzy and case-handling-based dynamic SDP modeling and simulation is applicable for: (1) dynamic SDP through application of the case-handling approach; (2) understanding the impact of the HF through fuzzification; and (3) using an adaptive neuro fuzzy inference system (ANFIS) to achieve more precise simulation results. 3. The proposed fuzzy and case-handling-based dynamic SDP modeling and simulation approach is implemented as an automatic SDP simulation, employing both the CMMN model and ANFIS. 4. The proposed fuzzy and case-handling-based dynamic SDP modeling and simulation approach was applied to a particular SDP management method: Agile. The novelty of the current research is the combination of fuzzy inference applying ANFIS with case-based process modeling using CMMN into one simulation model. This study contributes to SDP research by: investigating BP modeling approaches suitable for the SDP; adopting CMMN for dynamic and knowledge-intensive BP; and analyzing the impact of HF on SDP execution. Moreover, the results of this study allow the authors to precisely predict possible deviation and to reduce possible risks in the SDP. This paper is intended to assist researchers and practitioners working on software implementation projects. The practical utility of this paper is that it familiarizes researchers and practitioners with the key properties of the SDP, enabling their application in order to enhance the success of software implementation projects. This results in a more complete picture of the SDP during the simulation process. The rest of the paper is organized as follows: Section 2 describes preliminaries; Section 3 reviews the literature on SDP; Section 4 describes the approach of this research; Section 5 presents a case study and the results of an experimental study by using SDP modeling and simulation; and, finally, Sections 6 and 7 conclude the paper.
# Quantum Circuit Implementation of Multi-Dimensional Non-Linear Lattice Models
## 6 Extension to Two- and Three-Dimensional Lattice Models  
In the two non-linear lattice models considered so far, i.e., the modified D1Q3 model as well as the D2Q4 model for the quasi one-dimensional Burgers equation, the non-linearity in the equilibrium distribution used in collision term was of the form \(u^{2}\). LBM models for flow problems in two- or three-dimensional space also contain squared-velocity terms in the respective equilibrium distribution functions. As shown previously in Equation (4), the equilibrium distribution function for 2D and 3D LBM models can generally be written as,  
\[f_{a}^{eq}=\rho w_{a}\Big{[}1+\frac{3}{c^{2}}\mathbf{e}_{a}\cdot\mathbf{V}+ \frac{9}{2c^{4}}\big{(}\mathbf{e}_{a}\cdot\mathbf{V}\big{)}^{2}-\frac{3}{2c^{ 2}}\mathbf{V}\cdot\mathbf{V}\Big{]} \tag{19}\]  
with \(a\in[0,8]\) for the D2Q9 model and \(a\in[0,26]\) for the D3Q27 LBM model. This means that for each of the discrete-velocity directions, a term \(-3(u^{2}+v^{2})/2c^{2}\) will result in 2D models, and similarly, \(-3(u^{2}+v^{2}+w^{2})/2c^{2}\), for 3D LBM models. In the present work, the extension to multi-dimensional non-linear problems is considered with a particular focus on constructing the non-linear equilibrium distribution function:  
* Floating-point arithmetic is used to represent the squared-velocity terms in the equilibrium distribution function;
* As a first step, the focus is on computing \(u^{2}+v^{2}\) for two-dimensional problems, where \(u\) and \(v\) represent the Cartesian velocity components in the \(x\)- and \(y\)-directions scaled by a suitable reference velocity (e.g., lattice velocity in LBM);
* The construction of quantum circuits for the 'full' equilibrium distribution function and the resulting collision term is left for future publications;
* A further motivation for the current focus on evaluating \(u^{2}+v^{2}\) is the obvious and direct connection to evaluating kinetic energy in a wider range of lattice models beyond the LBM.  
As mentioned in Section 1, the quantum circuits developed are aimed at near-future quantum computers with a relatively small number of 'logical' qubits with a level of fault tolerance facilitating the considered circuit depths. A well-considered trade-off between circuit width and circuit depth is, therefore, essential and will be discussed in the next section.
# Numerical Simulation-Based Study of Controlled Particle Deposition Technology for Wafer Surfaces
## 2 Materials and Methods  
### Numerical Simulation Methods  
#### 2.1.1 Modeling and Parameters  
As shown in Figure 1, the model grid and deposition schematic of the chamber are presented. The deposition chamber has a bottom diameter of 200 mm and a height of 120 mm, making it compatible with the standard 50 mm wafer size. In this study, particle motion and deposition distribution on the wafer wall were analyzed by modifying the chamber structure and adjusting particle properties. To achieve uniform deposition, the effects of nozzle diameter, nozzle-to-wafer distance, chamber volume, rotational speed, and particle diameter were investigated.  
Table 1 summarizes the physical properties and parameter values varied in the simulation, with their effects on deposition evaluated and discussed in the subsequent results section using the modified geometrical model as the experimental reference.
## 3. PCFMIS Algorithm
### Improvements on Parallel Algorithm  
#### 3.3.1 Optimized Compression in CFMIS  
Stage 3 is the central step of the CFMIS algorithm, and it also takes most of the time in CFMIS. The subsequence processing of Stage 3 must find all subtrees of a compression tree to determine whether each of them is frequent. In fact, it consumes up to 70% of the  
Figure 2: An example of weight tree dividing.  
execution time in this stage. Optimizing Stage 3 can greatly reduce time consumption of the algorithm, particularly when the data size is large. In this section, CFMIS is refined by optimizing Stage 3.  
According to Property 2, for any edges \((x,y)\in A_{k}\), all the induced subtrees of \(\gamma(x,y)\) containing \((x,y)\) can be found in the group \(k\), so frequent induced subtrees of \(\gamma(x,y)\) can also be found. For other edges \((p,q)\
otin A_{k}\), \((p,q)\in A_{f}\), all the induced subtrees of \(\gamma(p,q)\) containing \((p,q)\) can be found in the group \(f\). For the reasons above, we only need to find all the frequent induced subtrees of \(\gamma(x,y)\) which contain \((x,y)\) instead of all the frequent induced subtrees in group \(k\). Other frequent induced subtrees could be found in other groups. If a CTS in group \(k\) does not contain \((x,y)\), this CTS does not have to match with the CTSs following it. That is, this improvement in Stage 3 avoids finding all subtrees of a compression tree to determine whether each of them is frequent. The improvement in compression in groups makes the running time shorter. The improved parallel algorithm is called PCFMIS2. Although the time has been reduced, the load on slave nodes is not balanced.
# A Multi-Dimension Spatial Method for Topology Awareness and Multipath Generating
## 7 Conclusions  
Multipath diversity significantly impacts multipath transmission quality; however, multipath diversity is difficult to guarantee on overlay networks thanks to the inaccurate topology awareness of underlay networks and the multipath generating methods considering little about underlay diversity. In this paper, we proposed a multi-dimension spatial method to improve topology awareness accuracy and multipath diversity. The topology view generated with network positioning methods was decomposed into intra and inter autonomous system dimensions. Based on the multi-dimension topology view, we designed some spatial mechanisms to generate multiple paths. Numerous simulations demonstrated that the proposed method improved topology awareness accuracy and multipath diversity.  
The multi-dimensional spatial method could also be further considered in other networks that have similar features of existing bearing networks--for example, devices are deployed in the cascade manner, and the multi-dimensional organization approach could simplify the complexity.  
**Author Contributions:** Conceptualization, Y.G., W.L., W.Z., Y.Z., H.L. and S.Z.; Data Curation, Y.G.; Formal Analysis, Y.G.; Investigation, Y.G., Y.Z., H.L. and S.Z.; Methodology, Y.G., W.L. and W.Z.; Supervision, W.L. and W.Z.; Validation, Y.G.; Writing--Original Draft Preparation, Y.G.; Writing--Review & Editing, Y.G., W.L. and W.Z..  
**Funding:** This work was supported by the National Key Research and Development Program of China Grant No. 2018 YFB1702000, the National Natural Science Foundation of China Grant No. 61671141 and the Liaoning Provincial Natural Science Foundation of China Grant No. 20180551007.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 The Proposed QoS-Aware Routing  
Figure 2 shows the components and their interconnections in our proposed data-centric multiobjective QoS-aware routing protocol, DMQoS. The DMQoS consists of five modules: the dynamic packet  
Figure 1: Body Sensor Network (BSN).  
classifier, delay control, reliability control, energy-aware geographic routing and QoS-aware queuing and scheduling modules. The details of these modules are presented in the following subsections.
## 3 Pseudolite-Based Positioning System for Legacy GNSS Receivers
### System Configuration
#### 3.1.4 User Terminal  
The user terminal includes a general legacy GPS receiver, a data communication module, and a post-calculation module. The GPS receiver calculates a position using pseudolite signals. However, this _a priori_ position is not correct, because the simulated GPS signal includes additional delays due to the use of pseudolites while the receiver does not know the existence of the pseudolites. The post-calculation module re-processes this false output of the GPS receiver using information about GPS satellite positions and pseudolite positions. The detailed algorithm of the post-calculation module will be described later. Figure 4 illustrates the structure of the user terminal. A smartphone is a good example of a user terminal, as it includes a GPS receiver and data link. A platform to adapt the post-calculation algorithm is also prepared.